esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Spatial-Temporal Variations of NPP in the Songnen Plain
#### 3.1.2 Monthly Variation Characteristics  
Based on the MOD17 data from 2001 to 2020, the spatial distribution of monthly mean NPP over a 20-year period in the Songnen Plain was studied (Figure 6). The results showed that there were significant differences in the spatial distribution of NPP from January to December. The NPP values for January and December are the lowest monthly values throughout the year, both being 0 gC\(\cdot\)m\({}^{-2}\)/month. From February, only the southwestern fringe of the Songnen Plain demonstrated a slight NPP growth. By March, the NPP values increased over a large area, with high values still concentrated in the southwestern part. A gradual transition from high NPP areas to the center and the northeast started in April. The high values in May and June were maintained in the central and northeastern margins. Significant increases are observed in July and August, exhibiting a trend of higher values in the northeast and lower values in the southwest. Among these, July reaches the highest NPP value for the entire year. The high-value areas shifted from north to south during September and November and finally returned to the southwest in November.  
To analyze the monthly NPP trends of various land cover types in the Songnen Plain from 2001 to 2020, the NPP values of different land covers in each month in this period and the variation value were measured. Figure 7 shows the variation trends of NPP for each land cover type. The peaks of all land types occurred in July, among which PAD reached the highest value of 26.61 gC\(\cdot\)m\({}^{-2}\)/month, 4.78 gC\(\cdot\)m\({}^{-2}\)/month higher than the lowest value of GRA in the same period. Although the trends of the variation characteristics were similar among the land classes, the monthly NPP increments vary significantly among different land cover types (Figure 8). The growth of FOR from January to June remained the highest, with the most prominent increase in May. It was only in June that a significant increase was seen in the other four land types, and all land types showed a significant increase in the volume of changes, all exceeding 7.5 gC\(\cdot\)m\({}^{-2}\)/month. In July, PAD and DRY continued to grow with significant growth. PAD reached the highest growth of 13.41 gC\(\cdot\)m\({}^{-2}\)/month for the year, and other land types sustained momentum with a slower growth trend than June. The number of changes in land types decreased from August to December, with all  
Figure 5: Spatial distribution diagram of NPP variation trend in the Songnen Plain.  
falling rapidly in September and October, among which DRY declined most significantly to \(-12.28\) gC-m\({}^{-2}\)/month in September.  
Figure 6: Spatial distribution diagram of monthly NPP in the Songnen Plain.  
Figure 7: The monthly NPP trends for different land cover types.
## 5 Discussion
### Clustering  
Clustering is a key strategy for optimising energy consumption and enhancing communication efficiency in IoT networks. In non-clustered IoT networks, individual nodes transmit data directly to a base station or central hub, leading to high energy expenditure as nodes engage in frequent, often long-range transmissions. This approach can quickly deplete battery life, especially in nodes located far from the base station, and may also overwhelm network resources, causing congestion and reduced network performance. By implementing clustering, IoT networks can reduce these demands and extend the operational lifetime of individual nodes and the network as a whole [46].  
In a clustered network, nodes are organised into clusters, with each cluster assigned a cluster head (CH). The CH is responsible for aggregating data collected from member nodes in the cluster and transmitting this aggregated data to the base station. This aggregation minimises the number of direct transmissions to the base station, significantly reducing energy consumption across the network. The centralised structure of clustering conserves energy by reducing the total number of long-distance data transmissions, allowing non-CH nodes to operate in low-power modes until it is time to transmit data to their CH.
## 3 Improvements in WPAN MAC
### Improved Backoff (IB) Algorithm  
In contrast to the BEB scheme, the IB scheme uses a small and fixed CW. In an IB scheme, nodes choose non-uniform geometrically increasing probability distribution (P) for picking a transmission slot in the contention window. Nodes which are executing the IB scheme pick a slot in the range of (1, CW) with a probability distribution P. Here, CW is the contention window and its value is fixed. We will present more information on CW in the later sections of this paper. Figure 5 shows the probability distribution P. The higher slot numbers have higher probability to get selected by nodes compared to lower slot numbers. In physical meaning we can explain this as: at the start node select a higher slot number for its CW by estimating large population of active nodes (n) and keep sensing the channel status. If no nodes transmit in the first or starting slots then each node adjusts its estimation of competing nodes by multiplicatively increasing its transmission probability for the next slot selection cycle. Every node keeps repeating the process of estimation of active nodes in every slot selection cycle and allows the competition to happen at geometrically-decreasing values of n all within the fixed contention window (CW).  
In contrast to the probability distribution P, in uniform distribution, as shown in Figure 5, all the contending nodes have the same probability of transmitting in a randomly chosen time slot. From Figure 5 we can conclude that when the population of competing nodes (n) is large, most of the nodes will choose medium to high slot numbers as their CW and a very few nodes will choose low slot numbers, hence, a collision-free transmission will take place in a low slot number. When n is medium, most nodes will choose higher slot numbers and a collision-free transmission will take place in a medium slot number. Similarly, when n is small, a collision free transmission will take place in a high slot number. Thus for any value of n, and for any fast change in n, a collision free transmission can take place. If only one node gets the chance to select the contention slot within the fixed CW, it will transmit in that slot. Other nodes will select new random contention slots for the next competition to access channel medium, regardless of the success or failure of transmission of the winner node. Here, it is noteworthy that the IB scheme does not use timer suspension like in IEEE 802.11 to save energy and reduce latency in case of a collision. Also, we do not need to consider the fairness issue of IB here as every node does not have to request a packet for PNC every time during CAP. For more details readers are referred to [11].  
Figure 5: Difference between uniform and truncated geometric distributions.
# A New Approach to Detect Hand-Drawn Dashed Lines in Engineering Sketches
## 4 Non-Solid Line Clustering
### Obtaining Candidate Dashes
The merit of each stroke (MeritDashLine[i]) is calculated by following the approach by [PERSON] et al., controlled by three parameters, as explained in [21]: _LineToMin_ defines the narrow tolerance band for optimal straight strokes, _LineToMax_ defines the wider tolerance band for acceptable straight strokes, and _LineSmoothPenalty_ reduces deviations to maintain stroke shape integrity. The merits reach a value of 1 if the shape of the stroke closely resembles a straight line (inside the narrow tolerance band) and 0 when there is a significant deviation beyond the relaxed band. Those merits are compared against the user-defined parameter _DashMin-MeritLine_ (which defaults at 0.25). Each dash is labeled as straight-enough or non-straight, and the chain must possess more than a minimum threshold of _MaxNonStraightDashes_, a percent of the minimum number of straight dashes (default is 40%). This filter, described in Algorithm 2, is used to avoid the acceptance of chained irregular strokes, while tolerating a moderate level of imperfection in the single dashes of a dashed line (Figure 6). The algorithm detects non-straight chains based on the evaluation of individual dash merits compared to a predefined threshold.
## 3 Results and Discussion
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & \(\mathbf{MI}\) & \(\mathbf{Q}^{\text{AB/F}}\) & \(\mathbf{Q}^{\text{LLSIM}}\) & **VIFF** \\ \hline LP & \(8.382\times 10^{-5}\) & \(2.396\times 10^{-4}\) & \(7.904\times 10^{-5}\) & \(1.257\times 10^{-5}\) \\ CVT & \(3.246\times 10^{-6}\) & \(5.494\times 10^{-12}\) & \(1.312\times 10^{-4}\) & \(2.392\times 10^{-11}\) \\ NSST-VGG & \(1.183\times 10^{-5}\) & \(7.662\times 10^{-11}\) & \(8.805\times 10^{-5}\) & \(1.238\times 10^{-7}\) \\ GFT & \(1.601\times 10^{-3}\) & \(1.510\times 10^{-11}\) & 0.0790 & \(1.346\times 10^{-12}\) \\ FusionGAN & 0.4428 & \(1.049\times 10^{-11}\) & 0.2528 & \(3.492\times 10^{-15}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: \(p\)-values of the proposed method compared to other methods in different metrics. Figure 7: Fusion results of the “Data-3” image set with different methods. (**a**) Morphology; (**b**) Phase; (**c**) LP; (**d**) CVT; (**e**) NSST-VGG; (**f**) GFT; (**g**) FusionGAN; (**h**) proposed.
# A Review and Experimental Evaluation on Split Learning
## 3 Experimental Evaluation
### Experimental Results and Analyses  
In this subsection, we demonstrate the experimental results and answer the research questions accordingly (as listed in Table 4).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Research Question** & **Result** & **General Conclusion** \\ \hline How do different SL paradigms perform & & All SL paradigms can achieve a similar accuracy and are \\ when training in the IID setting? & & comparable with the centralized one in the IID setting. \\ \hline How do different SL paradigms perform & Figures 6 and 7 & In the non-IID setting, most SL paradigms can achieve a much \\ when training in the non-IID setting? & Tables 6 and 7 & lower accuracy to the centralized training, except SplitFed, \\  & Figure 8 & which is more robust to the non-IID data. \\ \hline Why does training in the non-IID setting & & In the non-IID setting, the local optimum of each client is \\ aggravate backward client decoupling? & & different, resulting in different behaviors of the model. \\ \hline What are the merits and disadvantages & & When training on IID data, SL can converge faster than FL. \\  & of SL compared to FedAvg? & \\ \hline What are the factors that affect & & When training on IID data, SL can converge faster than FL. \\  & the communication cost of SL? & When training on IID data, SL can surpass FedAvg. \\ \hline Can SL help clients improve their & & The choice of the cut layer, the SL paradigm, and the size of \\  & performance? & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Overview of research questions that we answer.  
In particular, our analyses were carried out along the following four dimensions.
* Comparison between SL paradigms in Table 3. We chose five SL paradigms based on categorization, including Vanilla, U-shaped, PSL, SGLR, and SplitFed. We provide results for the performance of the five SL paradigms when training on IID and non-IID data in Sections 3.2.1 and 3.2.2.
* Comparison with typical FL paradigms such as FedAvg [12]. We provide results and analyses in Section 3.2.3.
* Comparison and analysis of communication costs of the five SL paradigms. The results are presented in Section 3.2.4.
* Comparison with on-device local training. This part aims to figure out whether SL can improve the model performance by leveraging more data from different organizations. Detailed results are presented in Section 3.2.5.
# Similarity Measurement and Classification of Temporal Data Based on Double Mean Representation
###### Abstract  
Time series data typically exhibit high dimensionality and complexity, necessitating the use of specific approximation methods to perform computations on the data. The currently employed compression methods suffer from varying degrees of feature loss, leading to potential distortions in similarity measurement results. Considering the aforementioned challenges and concerns, this paper proposes a double mean representation method, SAX-DM (Symbolic Aggregate Approximation Based on Double Mean Representation), for time series data, along with a similarity measurement approach based on SAX-DM. Addressing the trade-off between compression ratio and accuracy in the improved SAX representation, SAX-DM utilizes the segment mean and the segment trend distance to represent corresponding segments of time series data. This method reduces the dimensionality of the original sequences while preserving the original features and trend information of the time series data, resulting in a unified representation of time series segments. Experimental results demonstrate that, under the same compression ratio, SAX-DM combined with its similarity measurement method achieves higher expression accuracy, balanced compression rate, and accuracy, compared to SAX-TD and SAX-BD, in over 80% of the UCR Time Series dataset. This approach improves the efficiency and precision of similarity calculation.  
time series; SAX; SAX-TD; SAX-BD; SAX-DM +
Footnote †: journal: _Algorithms_
## 3 Distributed Target Tracking Systems
### Phase I: Tasking Leader Selection  
When sensors are first deployed, they may apply the CAWT [23] to establish the cluster-based network architecture (Figure 2(left)). However, due to the target movement, the clusterhead may not be a proper  
Figure 1: Illustration of block diagram for the TCAT method.  
local controller in the neighborhood of the target. Thus, a cluster member may be a good control candidate and can be a leader for the tracking task. Denote a sensor with tracking responsibility as an active sensor. Otherwise, a sensor is marked as an inactive sensor. Suppose each sensor is an inactive sensor with the initial deployment. The tracking task is triggered when the target broadcasts a message of \(L_{id}\), where \(L_{id}\) is a leader ID with an initial value zero, which is used to inform the active sensors to compete for being a leader of the tracking task. Thus, when sensor \(i\) receives the message sent from the target, it will broadcast a \(Hello\) message and become an active sensor, which allows itself to estimate how many neighboring active sensors it has. A \(Hello\) message consists of (1) the sensor ID of the sending sensor, (2) the leader ID of the sending sensor, and (3) the cluster ID of the sending sensor. Therefore, the sensors update their neighboring information (_i.e._, a counter specifying how many neighboring active sensors it has detected) and decrease the random leader waiting time (LWT) through the received \(Hello\) message sent from neighboring active sensors.  
Assume the initial value of the waiting time of sensor \(i\), \(LWT_{i}^{(0)}\), is a sample from the distribution \(U(C,D^{\prime})\), where \(D^{\prime}=C+D\), \(C\) and \(D\) are positive numbers, and \(U(\cdot,\cdot)\) is a uniform distribution. The update formula for the random LWT is given by  
\[LWT_{i}^{(j+1)}=\alpha\cdot LWT_{i}^{(j)} \tag{1}\]  
where \(LWT_{i}^{(j)}\) is the random LWT of sensor \(i\) at time step \(j\), and \(0<\alpha<1\). Note that the setting of random LWT may depend on sensor residual energy level, target information, and measurement quality (e.g., the channel condition, the accuracy of positioning system). When the timer of sensor \(i\) expires, it then broadcasts a \(Leader\) message to claim that it is leader \(i\) (e.g., \(L_{id}=i\)) for the tracking task.
## 3 Results  
### Evaluation of Classification Performance  
Table 3 provides a summary of classification performance metrics calculated using a combined error matrix including all pseudo ground-truth points (derived from visual interpretation of the input ALOS-2 PALSAR-2 imagery combined with the field-based observation made in previous work [28], as outlined in Section 2.10) for all classes. The evaluation procedure demonstrated that RadWet-L performs consistently in mapping inundation in the Amazon Basin with an overall agreement of 97.0% (95 th Conf Int: 96.5 to 97.4%). RadWet-L performed particularly well in the classification of open water with an overall F1 score of 0.97 (Conf Int: 0.96 to 0.99). The main source of error related to false positives due to errors in the 'unsuitable' land cover mask (Sections 2.3.4 and 2.3.5), leading to the classification of relatively smooth, low backscatter targets such as grasslands as open water. There was very little confusion between open water and inundated forest. The overall F1 score for inundated forest was 0.90 (Conf Int: 0.88 to 0.91). The main source of error was the underestimation of inundation extent, indicated by a relatively high error of omission (15.8%). This omission tended to occur at the floodwater edge where the signal (an increase in backscatter due to inundation) is less pronounced, resulting in an underestimation in inundation extent.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Overall** & **95 th Confidence Interval** \\ \hline Overall (\%) & 97.0 & 96.52–97.40 \\ Kappa & 0.91 & 0.89–0.92 \\ Macro Average F1 Score & 0.95 & 0.94–0.96 \\ \hline Inundated Forest F1 Score & 0.90 & 0.88–0.91 \\ Inundated Forest Commission (\%) & 3.5 & 2.3–5.00 \\ Inundated Forest Omission (\%) & 15.8 & 13.2–18.4 \\ Inundated Forest Users Accuracy (\%) & 96.5 & 95.0–97.7 \\ Inundated Forest Producers Accuracy (\%) & 84.2 & 81.7–86.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of Classifier Performance.
# Improved RSSI Indoor Localization in IoT Systems with Machine Learning Algorithms
## 5 Results and Discussion
### F1-Score  
Figure 8 shows the relationship between the number of samples and the f1-score. By computing the harmonic mean of a classifier's precision and recall, the f1-score assimilates both into a single metric. It is mainly used to compare the effectiveness of two classifiers. As per the results, the f1-score also rapidly upsurged as the number of samples increased. According to the results, the kNN gave the highest f1-score, and the FNN offered the lowest value. Generally, the accuracy was suitable for a balanced dataset. However, the utilized dataset in this work was an imbalanced dataset, where we had an unequal number of data samples for four Zones A, B, C, and D. Since the dataset had an imbalance, the matrix f1-score was more suitable for evaluating this model.  
Figure 8: f1-score vs. various machine learning algorithms.  
Figure 7: Accuracy vs. various machine learning algorithms.
# Health Monitoring of Civil Infrastructures by Subspace System Identification Method:
###### Abstract  
Structural health monitoring (SHM) is the main contributor of the future's smart city to deal with the need for safety, lower maintenance costs, and reliable condition assessment of structures. Among the algorithms used for SHM to identify the system parameters of structures, subspace system identification (SSI) is a reliable method in the time-domain that takes advantages of using extended observability matrices. Considerable numbers of studies have specifically concentrated on practical applications of SSI in recent years. To the best of author's knowledge, no study has been undertaken to review and investigate the application of SSI in the monitoring of civil engineering structures. This paper aims to review studies that have used the SSI algorithm for the damage identification and modal analysis of structures. The fundamental focus is on data-driven and covariance-driven SSI algorithms. In this review, we consider the subspace algorithm to resolve the problem of a real-world application for SHM. With regard to performance, a comparison between SSI and other methods is provided in order to investigate its advantages and disadvantages. The applied methods of SHM in civil engineering structures are categorized into three classes, from simple one-dimensional (1D) to very complex structures, and the detectability of the SSI for different damage scenarios are reported. Finally, the available software incorporating SSI as their system identification technique are investigated.  
structural health monitoring (SHM); vibration-based damage detection; system identification; subspace system identification (SSI) +
Footnote †: journal: Applied Sciences
# On-Line Partial Discharge Monitoring System for Switchegears Based on the Detection of UHF Signals
## 6 Conclusions  
In this paper, an online PD monitoring system is developed based on the UHF method and IoT technology. The detector circuit and the data collector are designed to detect the PD signal accurately while reducing the cost. The system adopts a browser/server architecture design, which can check the insulation status of all switch cabinets without staff at the site, thus reducing labor cost. Through the circuit simulation, formula calculation, hardware design, and software development, the following conclusions are obtained:  
The designed detection circuit can widen the original GHz-level ultra-high frequency signal to about MHz-level, and the data collector is designed based on the low-power STM32F769, which can use the sampling rate of 2.4 MHz to retain the main characteristics of the original signal. In this situation the signal is detected, which reduces the cost of system monitoring.  
Data transmission is accurate, and the software runs stably. The insulation status of the switchgear can be viewed in the browser.  
Conceptualization, X.Y.; methodology, X.Y. and C.Z.; software, C.Z.; validation, C.Z. and C.C.; formal analysis, X.Y.; data curation, C.C. and L.B.; writing--original draft preparation, X.Y.; writing--review and editing, W.Z.; project administration, X.Y.; funding acquisition, X.Y. and W.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Key Research and Development Projects of Shaanxi Province, grant numbers 2022 GY-210 and 2023-YBGY-256; this research was funded by Shaanxi Education Department Special Program for Serving Localities, grant number 2022 JC057; this research was funded by Xi'an University and Institution Talent Service Enterprise Project, grant number 23 GXFW0089; this research was funded by Xi'an Post and Telecommunication University Graduate Student Innovation and Entrepreneurship Fund Program, grant number CXJJDL2022008.  
Not applicable.  
The authors declare no conflict of interest.