esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Encoder Layer: BiGRU Layer
In the text data processing of Chinese marine coral reef ecosystem studies, Named Entity Recognition (NER) is a key task aimed at identifying critical ecological entities from complex natural language texts, such as species names, locations, ecological phenomena, etc. Due to the characteristics of Chinese text, such as flexible word order and the lack of clear word boundary markers, this task is particularly challenging. Traditional unidirectional GRUs may not adequately capture the contextual information around entities, especially posterior information, which could affect the accuracy and completeness of entity recognition. By introducing the Bidirectional GRU (BiGRU) network, this study has optimized the Named Entity Recognition task for Chinese marine coral reef ecosystem texts. The BiGRU integrates forward and backward GRU units to capture contextual information from both before and after the entity at each time step. This structure is particularly effective in Chinese text processing, as it allows the model to consider both the modificatory information before the entity and the limiting information after the entity, thereby achieving a more comprehensive understanding of the entity. Taking the sentence \"In the coral reef ecosystem of the South Sea, the blue starfish exhibits extremely high adaptability\" as an example, the BiGRU can effectively utilize the information from both \"In the coral reef ecosystem of the South Sea\" and \"exhibits extremely high adaptability\" to enhance the recognition of the entity \"blue starfish\". In the Chinese context, this bidirectional analysis is especially important because the attributes and descriptions of an entity are often distributed on both sides of the entity. Therefore, by adopting BiGRUs, this study has not only improved the accuracy of entity recognition in Chinese marine coral reef ecosystem texts but also enhanced the model's versatility and flexibility in processing Chinese natural language. This demonstrates that BiGRUs have significant application value and potential in the task of Named Entity Recognition in the Chinese marine ecological field. The structure of the BiGRU is illustrated in Figure 5. The BiGRU (Bidirectional Gated Recurrent Unit) leverages both forward and backward GRUs for the extraction of contextual information features and weights; sums the outputs; and then passes them through a linear layer to map a d-dimensional vector to an m-dimensional vector, resulting in the final output label vector list of the BiGRU network, \(H=\{h_{1},h_{2},\ldots,h_{n}\}\), where \(H\in\mathbb{R}^{n\times m}\), with \(n\) being the length of the text sequence and \(m\) being the number of entity type labels. Figure 5: BiGRU structure.
# A Review of Modular Multilevel Converters for Stationary Applications
## 2 MMC Topologies
### Submodule Topologies
#### 2.1.2 Multilevel Submodule Topologies
The SCSM is able to ride through a DC-link short circuit by turning off all the IGBTs. A composite three-level submodule (CSM) was designed in [54] containing two capacitors, six power switches, and one diode, as presented in Figure 3j. The main feature of a CSM is that it offers alternative DC fault blocking solutions including all-blocked, partly blocked, or staged blocking. All IGBTs are set off in blocked mode to avoid over-current. In partly blocked mode, S3 and S5 are switched on to connect C1 and C2 in parallel, which balance the capacitor voltages effectively. Staged blocking is the combination of all-blocked and partly blocked. With DC fault blocking schemes, especially the latter two, a CSM is capable of addressing DC fault current together with capacitor unbalanced charging. A comparative summary of the aforementioned submodule topologies is presented in Table 1[25; 55; 56]. The comparison shows the performances of different submodule configurations associated with the number of sources and switching devices, output voltage levels, bipolar operation, DC fault blocking capability, power losses and cost, together with control complexity. The HBSM, due to its simple construction, has the lowest power losses and cost, which are related to the count of devices. However, it is not able to deal with bidirectional power flow and is sensitive to DC short failure. Other topologies, e.g., FBSM, CDSM, or HSM, have a bipolar operation and DC fault blocking capability at a price of increasing structural complexity. It is desired to carry out a submodule construction that is capable of bipolar operation, short circuit blocking, as well as symmetrical voltage levels at a minimum cost. The main challenge for the optimal submodule blocking topology is to make a tradeoff between the construction complexity and its performances. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Performance** & **HBSM** & **FBSM** & **SCSM** & **NPCSM** & **FCSM** & **CDSM** & **HSM** & **CCSM** \\ \hline No. of sources & 1 & 1 & 1 & 2 & 2 & 2 & 2 \\ No. of transistors & 2 & 4 & 3 & 4 & 4 & 5 & 6 & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between different SM topologies.
## 3 Results and Discussion
### Results and Discussion of Method Application
#### 3.1.1 Values of EFs in Liberec and Decin
As already stated, standard land-use and land-cover data are not detailed enough for the urban administrative territory level [32], and remote sensing data, which have recently become widely available, may be sufficient for carbon storage and possibly for estimating evapotranspiration but do not contain sufficient information on species and habitats for biodiversity assessment (although there is ongoing progress in this area; see ref. [105]). Therefore, the use of more detailed habitat mapping data using the lookup table approach seems to be the simplest way to obtain results on the distribution of EFs and biodiversity in urban administrative territories. The average value of evapotranspiration was 463 L-m\({}^{-2}\cdot\)year\({}^{-1}\) in Liberec and 502 L-m\({}^{-2}\cdot\)year\({}^{-1}\) in Decin. Comparing these values with the average value for the Czech Republic, 421.4 L-m\({}^{-2}\cdot\)year\({}^{-1}\) published by Sejak [99], they are higher by 9.9% and 19.1%, respectively. The amount of carbon production was 5.3 t-ha\({}^{-1}\cdot\)year\({}^{-1}\) in Liberec and 5.9 t-ha\({}^{-1}\cdot\)year\({}^{-1}\) in Decin, which exceeds the average value for the Czech Republic of 5.1 t-ha\({}^{-1}\cdot\)year\({}^{-1}\) published by Sejak [99] by 3.9% and 15.7%, respectively. The values of carbon production also significantly exceed the published values of average carbon sequestration in urban areas in various cities in Asia and North America, which ranged from 2.16 to 3.06 t-ha\({}^{-1}\cdot\)year\({}^{-1}\)[64; 106; 107]. The results clearly show that EFs related to climate change mitigation can be performed better in very heterogeneous urban administrative territories with urban forests, parks, and other green spaces than in common landscapes. In particular, peri-urban areas of cities located near (or within) protected landscape areas, as in the case of Liberec and Decin, can have a high value of EFs.
## 3 The Bike-Share Case  
### Business Context and Problem Illustration  
Docked bike-sharing systems, such as New York's Citi Bike [26], require customers to rent and return bikes at designated stations. The fixed nature of docked systems, compared to the flexibility of dockless models [49], can contribute to inventory imbalances that lead to outages, even when the overall bike supply is sufficient [50]. These outages--where bikes or docking slots are unavailable--limit both rentals and returns, potentially resulting in future stockouts. To manage these imbalances, providers may: (a) adjust starting inventory through overnight rebalancing, (b) monitor and rebalance during the day or (c) incentivise users to help redistribute inventory. While overnight adjustments (a) set a solid starting point, the variability of station demand can create imbalances throughout the day. Daytime rebalancing (b) is effective but may lead to negative externalities, such as vehicle emissions. Crowdsourcing (c), exemplified by Citi Bike's Bike Angels program, is appealing for its user engagement and environmental benefits [27]. Despite the potential of crowdsourced rebalancing, improper timing can limit its effectiveness.  
Our analysis of hourly bike utilisation at seven stations within a selected demand cluster (criteria detailed in Section 3.3) over a typical week shows that, while individual stations reach high utilisation (calculated as Docks Available/Capacity Available, with high utilisation defined at an 85% threshold), simultaneous stockout risks across multiple stations are infrequent; this indicates opportunities for balancing inventory among nearby stations to avoid stockouts. To illustrate this timing issue, consider station E 20 St and Park Ave, which ranked second in the number of return incentives offered during August 2023 (these return incentives are offered to users to \"give\" or return bikes to the station in question.) We defined incentives as reasonable if offered above 70% utilisation, missed if not initiated above 85%, and excessive if given below 70%. Figure 3, highlighting correctly timed incentives in green and errors in red, shows that misaligned incentives were more frequent than effective ones. Given the potential effectiveness of crowdsourcing strategies, we hypothesise that better-timed incentives could have helped prevent some stockouts.  
Figure 3: Analysis of suboptimal incentive timing at a Citi Bike station during August 2023, showcasing opportunities for improved inventory management through better-aligned incentives.  
**Conditional colouring formula:**  
COLOUR = [GREEN: if CU \(\geq\) HUT and RI \(>\) 0; RED: if CU \(\geq\) HUT and (RI = 0 or RI is null); RED: if CU < RHUT and RI \(>\) 0;  
GRAY otherwise]  
where CU = Average Capacity Utilisation = AVERAGE(DA/CA), RI = Sum of Return Incentives, HUT = High Utilisation Threshold, RHUT = Reasonably High Utilisation Threshold, DA = Docks Available, and CA = Capacity Available.
## 3 Results
(2019) modelled their laboratory results where WTR had adsorbed 40 g/Kg-WTR and predicted that less than 2% phosphorus would desorb from WTR when used in a field application. It is important to note that 556.31 g PO4\({}^{3-}\) removed does not include the impact of cartridges for the entire season, but only the days of sampling events, which implies that the actual PO4\({}^{3-}\) removal for the season was much higher than the measured values. Lowering the upstream PO4\({}^{3-}\) concentration could significantly impact HAB potential by limiting nutrient availability ([PERSON] et al., 2019). Figure 6: PO4\({}^{3-}\) levels immediately in the front and back of the gate during sampling events during 2020. (**a**) Gate 5, (**b**) Gate 6, (**c**) Gate 7, and (**d**) Gate 8. \({}^{\circ}\) denotes the date when WTR in cartridges were changed. Error bars are standard deviation of triplicate samples.
## 3 Results
### Dynamic Opportunistic Navigation Experiment  
Using the proposed NSWOA satellite selection algorithm and the multi-constellation LEO satellite dynamic opportunistic navigation models described in Equations (1) and (2), a positioning experiment was conducted with Starlink, Orbcom, and Iridium satellites, as shown in Figure 2. The receiver coordinates were estimated using the least squares method by minimizing the residuals between the measured and theoretical Doppler shifts, thereby obtaining the solution that best fits the observation data. As indicated by Equation (5), the primary factors influencing positioning error in this study are the Doppler measurement error and the DGDOP value of the selected satellite combination. Accordingly, a navigation threshold of DGDOP \(\leq\) 150 was applied, and the corresponding satellite selection results are shown in Figure 6. As illustrated, the NSWOA-based selection algorithm demonstrates high accuracy and strong autonomy under this constraint. The statistical data for the 2185 epochs of the algorithm are presented in Table 8. From Table 8, it can be observed that the DGDOP values obtained using the NSWOA are all below 150 for every epoch, indicating that the method meets the preset threshold requirements. Therefore, under reasonable threshold conditions, the proposed algorithm exhibits high accuracy, high automation, and broad applicability. The positioning results based on this satellite selection are shown in Figure 7.  
From Figures 6 and 7, it can be observed that the proposed NSWOA satellite selection algorithm strikes a balance between positioning accuracy and the number of selected satellites, resulting in a significant improvement in computational performance. With the positioning accuracy remaining constant at approximately \(2\sigma=260m\), the number of selected satellites decreased from an average of about 22 to fewer than 6. In terms of computation time, this represents approximately 54.96% of the time required for selecting all visible satellites.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**DGDOP** & **Less Than 130** & **Less Than 140** & **Less Than 150** & **Less Than 160** & **Less Than 170** & **Less Than 180** \\ \hline ALL & 99.82\% & 100\% & 100\% & 100\% & 100\% & 100\% \\ NSWOA & 3.34\% & 18.44\% & 64.26\% & 92.77\% & 99.36\% & 100\% \\ GWO & 3.39\% & 18.94\% & 64.90\% & 93.09\% & 99.45\% & 100\% \\ PSO & 3.07\% & 18.39\% & 63.71\% & 92.45\% & 99.36\% & 99.95\% \\ NSGAII & 3.71\% & 18.62\% & 65.22\% & 92.91\% & 99.45\% & 99.95\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: DGDOP result statistics for 2,185 epochs across different algorithms.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**DGDOP** & **Less Than 110** & **Less Than 120** & **Less Than 130** & **Less Than 140** & **Less Than 150** \\ \hline ALL & 91.21\% & 98.35\% & 99.81\% & 100\% & 100\% \\ NSWOA & 0.05\% & 0.23\% & 1.37\% & 23.80\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Statistical results of the algorithm.
## 5 Simulation Validation and Discussion
### Analysis of Simulation Results  
Simulation results of implementing the proposed method are compared with that of problem case scenarios representing drawbacks of connecting existing IMD and GPT problem situations. The operability of IMD and GPT are compared separately.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Index** & **Value** \\ \hline Distributed Generation (transformerless PV) & \\ \hline Rated Power of DG & 9 [kW] \\ Voltage of DC side & 620 [V] \\ Voltage of AC side & 380 [V] (Line to Line) \\ Parasitic Capacitance & 900 [nF] \\ System Insulation Resistance & 500 [k\(\Omega\)] \\ \hline \hline \multicolumn{2}{c}{Insulation Monitoring Device} \\ \hline Auxiliary Signal Source & \(\pm\)50 [V] (Pulse Injection) \\ Resistance of Measuring Circuit & 2 [k\(\Omega\)] \\ Resistance of Coupling Circuit & 360 [k\(\Omega\)] \\ \hline \hline \multicolumn{2}{c}{Grounded Potential Transformer} \\ \hline Rated Voltage of GPT & \(380/\sqrt{3}:\ 110/\sqrt{3}:\ 110/\sqrt{3}\) [V] \\ Current Limit Resistor & 433 [\(\Omega\)] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation configuration and system parameters.  
simulation results demonstrate proper measurement of insulation resistance by securing the operability of the IMD through the implementation of the proposed method.
# Novel Methods of AI-Based Gait Analysis in Post-Stroke Patients
## 1 Introduction
### Specificity of Post-Stroke Gait Analysis  
Patients with gait impairment require dedicated solutions for the following reasons:  
* the significant prevalence of stroke (it is among the three most common causes of long-term disability, despite preventive measures implemented to reduce mortality) [23; 24; 25; 26];
* disorders occurring in both motor (including motor control) and sensory areas, often associated with other conditions and secondary lesions (including in long-term rehabilitation);
* disorders of both gait and balance functions;
* varying areas and degrees of damage and deficits which require a personalized approach to patient improvement;
* the return of lower limb function is an important goal for patients and their families as it significantly increases independence and motivation for further work;
* the risk of repeat strokes and an associated continuing need for prevention;
* the lengthy duration and multistage nature of post-stroke rehabilitation;
* the search for more effective methods of stroke rehabilitation than those currently in use is ongoing because it is not always possible to return a patient to full function, but only to the maximum performance level achievable for them, whereas improved methods of gait analysis may enable better comparative assessments and the selection of rehabilitation methods better suited to the particular condition [27].  
To date, there have been many attempts to classify the gaits of post-stroke patients. Where attempts have been made, however, most of these have involved subjective observations [28].  
The main reported gait disturbances in post-stroke people include the following:  
* weakness of muscle strength;
* disturbances in the regulation of muscle tone;
* disorders of superficial sensation;
* disorders of deep sensation.  
These abnormalities affect gait disturbances and manifest themselves in altered gait parameters, e.g., gait cycle length, stride length, and gait speed.
## 4 Experiments with a Group of Low-Cost UUVs
### Results  
Figure 5 shows the distances among three BlueROV-1 drones in case of using collision avoidance terms compared to those in none of the collision avoidance term.  
Figure 5: Distance among three BlueROV-1 in two experimental cases.  
We first examine the formation control without a collision avoidance term. It can be observed that the minimum distance among the UUV 2 and the UUV 3 is \(d_{23}=2\) m (see Figure 5a). That means there is a collision between the two UUVs. We then examine the formation control when applying the collision avoidance term. Figure 5b shows that the minimum distance between robots is and \(d_{23}\approx 4\) m. This guarantees no collision when the parameters selected are \(D_{out}=7\) m, \(d_{in}=4\) m, and \(\sigma_{ij}=3\).  
We further examine the distributed formation tracking control with collision in hazard environments. A group of UUVs start from a random configuration, reach a desired formation, and then follow a trajectory of the UUV-leader. During a move, a group of UUVs can change the shape of formation to cross a narrow area. Figure 6 shows the complete simulation scenario with three BlueROV-1. The distances among the UUVs are shown in Figure 6a, which converges to 10 m. It is clearly shown that the UUV follower is indeed tracking to the leader's position, and maintaining the desired distances with the same heading angle. The velocities of three BlueROV-1 are illustrated in Figure 6b-d, respectively. In addition, during the period from \(t=120\) s \(t=170\) s, it can be seen that the group of UUV changed the distance \(d_{ij}=6\) m to cross narrow areas. It should be noted that the velocity of the UUV is not smooth along the axes, but this result is still acceptable. The readers can find more details in Appendix A (see Figures 11 and 12).  
An example of the evolution of three BlueROV-1 in ROS/Gazebo is presented in Figure 7, where a visualization of BlueROV-1 dynamics is presented under the underwater hydrostatic and  
Figure 6: A complete simulation scenario with three BlueROV-1.  
hydrodynamic effects. Finally, we have also performed some experiments with a modified trajectory based on Algorithm 1 to avoid obstacles (see Figure A3 in Appendix A).
# Enhancing Image Dehazing with a Multi-DCP Approach with Adaptive Airlight and Gamma Correction
## 3 Preliminary  
[PERSON]'s image dehazing method [4] revolves around generating a DCP in a hazy image, estimating the transmission map, and then recovering it to produce a dehazed image. The DCP is computed by selecting the minimum value among the RGB channels for each pixel and further refining it locally using a patch size of \(15\times 15\). The brightest pixel within this dark channel image is the basis for estimating the airlight present in the scene. The transmission map \(t(x)\), indicative of the ratio of light reaching the observer from objects within the scene, is estimated as follows:  
\[t(x)=1-\omega\min_{c}\biggl{(}\min_{y\in\Omega(x)}\biggl{(}\frac{I_{c}(y)}{A_ {c}}\biggr{)}\biggr{)} \tag{1}\]  
where \(\omega\) is a preservation constant, \(I^{c}(y)\) is the hazy image of a color channel \(c\), \(A_{c}\) is the estimated airlight of \(c\), and \(\Omega(x)\) is the patch centered on pixel \(x\).
## 1 Introduction
algorithm proof of block and trade (PoBT) is proposed in [14] for BCIoT, which reduces computation time. In this study, the scalability problem for BCIoT applications is addressed. A very comprehensive study [15] for blockchain IoT explained most of the BCIoT problems by using a mindmap in which scalability is dependent on throughput, block size and transaction speed. This mindmap also tells that throughput for BCIoT can be achieved by proper use of a consensus algorithm. In [16], a strategical approach was used to build a consensus algorithm. It needs attention before the implementation of blockchain in the IoT field. Performance, limitations, and challenges were discussed based on more than one hundred studies for IoT, and protocols which were carefully reviewed before using them for BCIoT. In the IoT wireless sensor network (WSN), many sensors continuously collect vast amounts of data from the environment and send them to their central processing unit as shown in Figure 2. Blockchain has to validate these data more efficiently. So, several validations per second should be effective in BCIoT. The primary purpose of this study is to make a scalable consensus protocol for BCIoT. The rest of the paper is structured as follows: the related work is described in Section 2 highlighting the challenges and adopting the existing consensus algorithm for an IoT scenario. The blockchain overview is presented in Section 3 that illustrates the working, consensus, types, and some key terms of blockchain. A novel Consensus algorithm (CBoT) is presented in Section 4 by making suitable changes according to the case study. An analysis is carried out in Section 5 on the proposed algorithm by using flow charts, and results are concluded with limitations. After the analysis and results, conclusions and future work are presented in Section 6.