esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
We propose a correction method (based on dark pixels) to reduce the dark current noise of a QWIP, analyze the dark current correction model, and introduce the fabrication method for a dark pixels QWIP system. Training data were experimentally collected. The feature data of dark pixels and imaging pixels were obtained after processing. RNN was used to fit the model of the two types of feature data. The target data were collected for validation. The mean response of the processed dark pixels was input into the model, the output was obtained as the dark level correction value and then corrected for the target data. After correction, noise was reduced by 49.02% on average and the effect was significant. This method reduces the effect of dark current noise caused by QWIP temperature fluctuations and only needs to collect training data once to correct all subsequent imaging data; this supports continued use of a QWIP in LWIR remote sensing detection for complex thermal environments.  
Conceptualization, H.D. and J.X.; methodology, H.D.; software, Z.Y.; validation, J.X.; formal analysis, H.D.; investigation, H.D. and M.C.; resources, M.C.; data curation, H.D.; writing--original draft preparation, H.D.; writing--review and editing, J.X. and D.S.; supervision, Z.Y.; funding acquisition, D.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Major Program of National Natural Science Foundation of China, grant number 42192582, and the National Key Research and Development Program of China, grant number 2016 YFB0500400.  
Not applicable.  
The authors declare no conflict of interest.
###### Abstract  
Single-cell RNA sequencing (scRNA-seq) has become a powerful technique to investigate cellular heterogeneity and complexity in various fields by revealing the gene expression status of individual cells. Despite the undeniable benefits of scRNA-seq, it is not immune to its inherent limitations, such as sparsity and noise, which would hinder downstream analysis. In this paper, we introduce scCGImpute, a model-based approach for addressing the challenges of sparsity in scRNA-seq data through imputation. After identifying possible dropouts using mixed models, scCGImpute takes advantage of the cellular similarity in the same subpopulation to impute and then uses random forest regression to obtain the final imputation. scCGImpute only imputes the likely dropouts without changing the non-dropout data and can use information from the similarity of cells and genetic correlation simultaneously. Experiments on simulation data and real data were made, respectively, to evaluate the performance of scCGImpute in terms of gene expression recovery and clustering analysis. The results demonstrated that scCGImpute can effectively restore gene expression and improve the identification of cell types.  
scRNA-seq; sparsity; imputation +
Footnote †: journal: Applied Sciences  
Acticle  
scCGImpute: An Imputation Method for Single-Cell RNA Sequencing Data Based on Similarities between Cells and Relationships among Genes
# Extending the Performance of Hybrid NoCs beyond the Limitations of Network Heterogeneity
## 5 Conclusions  
In this paper, an efficient three-stage pipelined adaptive VC router with reduced low-load latency is proposed to improve the performance of emerging high performance communication fabric that promises to satisfy the communication dynamics of modern CMP design. The proposed router architecture has a cost-effective dual datapath design that can minimize packet delay under both low loads and high loads. A fast bypass datapath is proposed to alleviate the performance degradation due to multi-hops along the long horizontal wires of the state-of-the art hybrid NoC architectures under low-load conditions. Furthermore, a deadlock-free adaptive routing algorithm is proposed to avoid congested paths when the NoC is heavily loaded with traffic. Emerging high-performance and low-cost NoC architectures, such as inhomogeneous 3D NoCs, mm-wave- and surface wave-based hybrid wired-wireless NoCs, suffer from reduced performance due to the heterogeneity in hop count and wire delays between the present communication fabric. Thus, the proposed dual datapath router is designed to extend the performance of the 2D routers in such hybrid NoC. Cycle-accurate experiments show that the proposed router can significantly reduce applications' execution time (e.g., by an average of 16.9% compared to the low-latency router SWIFT). Furthermore, the performance effect of replacing conventional 2D routers with the proposed router architecture in inhomogeneous 3D NoCs, as well as mm-wave and surface wave hybrid wired-wireless NoCs is evaluated by cycle-accurate simulations. The experimental results show significant reductions in the average packet delay compared to existing high-performance inhomogeneous 3D NoCs and SmallWorld-enabled hybrid wired-wireless NoCs, even when efficient adaptive routing is used. Future work includes a reliability model for the proposed 2D router to address the TSV yield and failure issues in 3D NoCs, as well as the QoS-aware run-time adaptive routing algorithm and NoC architecture for optimized communication in WiNoCs.  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] proposed, implemented the router architecture and perform experiments on them; [PERSON] extended the router architecture to hybrid 3D and Wireless NoCs; [PERSON], [PERSON] and [PERSON] analysed the data, contributed to the formulation of Surface-Wave WiNoC theories and experimental setup; [PERSON] and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Technology and Tools for 3D Modelling  
Authoritative databases and Volunteered Geographic Information (VGI) systems-i.e., OpenStreetMap (OSM), Flickr, and Wikiloc - allow for the collection of available data relating to the 3D features of the urban system. In particular, VGI enriches knowledge at an urban scale through user-generated content (i.e., buildings, infrastructures, facilities, and points of interest). Planning and managing UES within an urban decision-making environment require upgraded tools to monitor the state of services by identifying potential and critical levels, prioritising actions, and geo-locating optimal solutions rapidly and effectively.  
Notwithstanding some limitations, by dealing with the incompleteness over broad zones and geometric heterogeneity, VGI frequently provides more accurate data than authoritative sources [67]. Many authors, indeed, have remarked upon the completeness and semantic accuracy of the OSM data set, through its comparison with authoritative databases [68; 69; 70]. One of the main advantages of using these types of data (e.g., those derived from Flickr, Panoramio, or Instagram) may concern the coverage of zones with scarce or limited availability of official information, due to financial and governmental restrictions [71].  
Regarding the control system for user contributions, statistical sampling methods that allocate points in a grid system have been used to limit the uncertainty, to acknowledge locations in which more in-depth data are needed, and to understand which type of information has been requested [72; 73].  
Furthermore, LiDAR technology provides relevant 3D information to improve the knowledge regarding the built and natural capitals of a city, in terms of accurate elevation-based values for buildings, vegetation, and other surfaces, at one metre-to-ground resolution.  
According to [PERSON] [74], LiDAR is one of the data-acquiring methods commonly used for 3D urban modelling with geospatial technology. It employs laser rays to measure the position of a point in space, by recording first-pulse and last-pulse return rays, depending on the different properties of the absorption and reflection of laser beams of the detected object [74; 75].  
Digital Terrain Models (DTMs) and Digital Surface Models (DSMs) are the primary outcomes that the system produces from object heights. A DTM is a mathematical grid model of the Earth's surface, in which each pixel has a unique elevation value, whereas a DSM is a mathematical grid model that includes the elevation values of off-ground objects (e.g., buildings and vegetation) [76]. Finally, the object heights can be found by subtracting the DSM from the DTM, combining the opportunities of the two tools and processing the elevation data necessary for spatial analyses.
# Coordinated Control Strategy of CU-MTDC under Abnormal Conditions Considering Power Supply Security
## 3 Proposed Coordinated Control Strategy of CU-MTDC under Abnormal Conditions
### Calculation of Active Power Reference Value  
In this section, calculation principles for active power reference value are described as follows:  
1. The reference value should ensure the N-1 security criterion under normal conditions, which is \[P_{\textit{refn}}=\max\Bigl{\{}P_{\textit{refnL},i,j}\Bigr{\}}\] (1) where \(P_{\textit{refn}}\) is the active power reference value of converter station \(n\). \(P_{\textit{refnL},i,j}\) is the active power reference value of the \(n\) station to ensure the N-1 security after the AC line \(Li,j\) outage. Considering the requirement of security operation of the power grid, the security margin of \(P_{\textit{refnL},i,j}\) should be reserved. \[P_{\textit{refnL},i,j}=f_{p}\bigl{\{}(1-\varepsilon)S_{\textit{L},i,j}\bigr{\}}\] (2) where \(\varepsilon\) is the security margin of the transmission line. \(S_{\textit{L},i,j}\) is the maximum capacity of the \(Li,j\). According to (1) and (2), the CU-MTDC provides enough power supply security support to the AC system.
2. Taking offshore wind power into account, the reference value should ensure the wind power is locally absorbed. Assuming N converters are interconnected with the CCUP grid. The maximum offshore wind power absorption can be expressed as \[\sum_{n=1}^{N}P_{\textit{refn}}=P_{\textit{w}}\] (3) where \(P_{\textit{w}}\) is the active power of wind farms. According to (1), (2) and (3), the active power reference value can be confirmed.
# Novel Calibration of MIESM and Reduction of CQI Feedback for Improved Fast Link Adaptation
## 2 Preliminaries
### MIESM-Based Fast Link Adaptation  
A user obtains the channel coefficient information from the orthogonal frequency division multiplexing (OFDM) symbols, determines a CQI suitable for the current channel through the MIESM-based FLA, and reports it to the BS. The goal of the FLA is to increase the throughput of the system while maintaining the target BLER for various channel conditions. The MIESM-based FLA process is described below [20].  
Firstly, the effective SNR (SNR\({}_{\text{eff}}\)), which is a constant representing the channel quality, is calculated from the instantaneous SNRs of the subcarriers based on the estimated values of the channel state information. The equation for this is given in Reference [11] as follows:  
\[\text{SNR}_{\text{eff}}\,=\,{f_{m}}^{-1}(\frac{1}{P}\sum_{p=1}^{P}f_{m}(\frac{ \text{SNR}_{p}}{\beta})), \tag{1}\]  
where \(P\) indicates the number of subcarriers, \(\text{SNR}_{p}\) is the instantaneous SNR of the \(p\)-th subcarrier, and \(\beta\) is a calibration factor to minimize the difference between the estimated effective BLER (\(\text{BLER}_{\text{eff}}\)) and the real BLER for the current channel in the calibration procedure. The function \(f_{m}\) (\(\cdot\)) is the bit-interleaved coded modulation (BICM) capacity curve which depends on the modulation order \(m\). This capacity is equivalent to the mutual information between channel input and output, which is the reason this process is called MIESM [21].  
Secondly, the calculated \(\text{SNR}_{\text{eff}}\) is mapped through the pre-calculated AWGN/BLER look-up table (LUT) to the \(\text{BLER}_{\text{eff}}\) value for each CQI. The AWGN/BLER LUT contains all the BLER information of every CQI under the AWGN channel over various SNR values. Then, we select the largest CQI such that the corresponding \(\text{BLER}_{\text{eff}}\) does not exceed the target BLER, which was set to 0.1 in this paper, and feeds the selected CQI back to the BS.
# 2D Gaze Estimation Based on Pupil-Glint Vector Using an Artificial Neural Network
## 3 Experimental System and Results
### Gaze Point Estimation
When a calibration process is accomplished, a model with \(8\times 8\) test markers is employed to validate the calculation accuracy of the gaze point. Figure 8a-d shows the gaze point estimated through the proposed method with/without considering error compensation, utilizing a four-, six-, nine-, or 16-marker calibration model, respectively. The cyan \"\(\bullet\)\" symbols represent actual reference gaze points on the monitor screen. The magenta \"+\" symbols represent gaze points estimated through the proposed method without considering error compensation. The blue \"*\" symbols represent gaze points estimated through the proposed method considering error compensation.
## 3 Methodology
### Quantization  
Quantization, as introduced in Section 3.1.2, serves as a pivotal process to approximate continuous signal values or extensive discrete datasets into a finite and often condensed set of discrete values. In the domain of ship sensor data, this principle holds significant relevance for several reasons. Firstly, sensor measurement accuracy faces inherent limitations owing to environmental noise, technological constraints, or sensor precision, making the transmission of sensor readings with absolute accuracy unnecessary for the monitoring system. Instead, the system can maintain data precision to a specific decimal level. Moreover, ensuring sensor readings adhere to a predefined range is crucial, preventing the inclusion of outliers or erroneous values due to sensor anomalies. Effectively mapping floating-point sensor readings to a finite set of integers capitalizes on these properties. Quantization, as the initial step in the compression process, offers the system the capability to establish accurate thresholds consistently for all \(\mathbf{x}_{t}\in STH\)--a critical factor in aligning the algorithm with precise ship monitoring requirements. In essence, quantization strikes a balance between data precision and efficiency. It reduces data volume while maintaining a level of precision that precisely aligns with monitoring needs. Hence, we employed quantization to reduce ship sensor data volume while preserving the necessary precision. Fulfilling the demands of data precision and efficiency, the quantization step necessitates a configuration table. This table elucidates the practical significance of ship sensor data and defines the effective range required by the Beidou system. Practically, we prepared and maintained the configuration table in JSON format. Formally, the quantization step is described as follows:  
\[Q(\mathbf{x})_{i}=\text{round}(\frac{\mathbf{x}_{i}-\mathbf{L}_{d,i}}{10^{ \mathbf{a}_{i}}}) \tag{3}\]  
where round stands for the round function, which finds the closest integer of any real number.  
The post-quantization data \(\mathbf{y}_{t}\in\mathbb{Z}^{n}\) are defined as \(\mathbf{y}_{t}=Q(\mathbf{x}_{t})\). Each dimension of \(\mathbf{y}_{t}\) is limited to \(L_{i}\) possible values, a constraint arising from finite accuracy goals and specific upper and lower limits. Notably, \(L_{i}\) exhibits considerable variability across our dataset, ranging from 100 to 100,000. This substantial variation poses a challenge during subsequent encoding procedures.
## 2 Background
### Applied Optimization Methods
#### 3.4.2 Heuristics  
Genetic algorithms (GA) and related methods are the most popular choice for MOO, with a percentage of 26%, as shown in Figure 6. These methods are based on the classic GA that definesthe management strategies or interventions as genes and then simulates the crossover and mutation of genes to create solutions [80]. For a better performance, improvements are proposed for the classic algorithm to handle SOO targeting at the optimal solution [48; 81; 82] and MOO targeting at a preferred Pareto solution [83; 84; 85] or a set of Pareto solutions [86; 87; 88]. The GA and related methods are flexible, easily controlled, and can handle a variety of DM problems, including the complicated ones. Comparing with other heuristics, GAs often generate better solutions and have higher efficiency. These methods are recommended when DM problems are too complicated to be easily handled by deterministic methods. When analyzing large-sized problems, these methods may produce poor solutions.  
Neural networks (NN) and related methods are also applied in DM in IAM. They build a multi-layer network to analyze management outcomes and explore their relationships and their influence on the management plans. They can handle various objectives and constraints in terms of network inputs [89; 90]. NN can effectively handle different management outcomes, even when the relationship of management outcomes is fuzzy or unclear. They are often applied for small infrastructure asset networks and may not be efficient for large ones.  
Other heuristics are also introduced to assist management decisions for IAM, including Greedy heuristic [28; 91; 92], particle swarm optimization [93; 94], and simulated annealing [95].  
Heuristics, with 107 applications in the reviewed articles, are also a common choice for IAM. They are proposed as a general-purpose algorithmic framework, which largely increases their flexibility and robustness. Hence, they can handle a variety of DM problems, even the hard and complicated ones. Additionally, the understanding, application, and implementation of their algorithms is easy. However, the solution quality of heuristics is affected by a range of factors including problem size, constraint restriction, parameter calibration, etc. They may fail to obtain the optimal solution (SOO) and Pareto solutions (MOO), especially for the large-sized DM problems. Thus, heuristics are recommended when the problem cannot be easily solved by the deterministic methods. When applying a heuristic, several trials are suggested to confirm that the adopted algorithm calibration can produce the stable and good optimization result for the addressed DM problem.
# New Framework for Human Activity Recognition for Wearable Gait Rehabilitation Systems
## 2 Methodology of Work
### EMG Signal Analysis  
Dynamic electromyography (EMG) is a useful tool available to directly measure muscle activity [31]. Because the myoelectric signal well matches with the level of muscle activation, it can be a good indicator of what mechanical impact it has. EMG signals collected during gait can also be interpreted as an index of the magnitude of muscle activation. The linear  
Figure 2: The distribution of IMUs (black) and sEMG (red) sensors on human subjects in the utilized open-source dataset [27].  
envelope of the EMG signal seems to reflect the proportionate, or relative, amount of tension in muscle. These facts lead to the conclusion that technique and physiological factors play a crucial role in this relationship. Therefore, detecting alterations in the phasing, duration, or magnitude of muscle action to a pathological gait profile of an individual from such a complex EMG record is very hard to achieve. There is the multi-spike, random amplitude quality in EMG signals that makes it difficult for interpretation. This information gives a sense of the timing and intensity of muscle activity during a phase or of the whole gait cycle.  
There are two key points in capturing and recording the EMG signal. The first one is the signal-to-noise ratio, which is the ratio of energy in an EMG signal to the noise signal's energy. Normally, noise means electrical signals that are not part of the desired EMG signal. The other factor is the distortion of the signal, as the ratio of any given frequency component in an EMG signal should not be changed. All the muscle fiber action potentials from a single motor neuron together form what is called the motor unit action potential (MUAP). This is a distinctive, oscillating signal that can be detected using a non-invasive skin-surface electrode placed near the source or using an invasive electrode inserted on location into individual muscles. Equation (1) provides an overview of the structure and timing of electrical signals in muscles, especially EMG signals, as follows:  
\[x(n)=\Sigma_{m=0}^{N-1}h(m)\xi(n-m)+w(n), \tag{1}\]  
where \(x(n)\) is the modeled EMG signal, \(\xi(n)\) is the point processed representing the firing impulse, \(h(m)\) represents the MUAP, \(w(n)\) is the zero-mean additive white Gaussian noise and \(N\) is the number of motor unit firings.  
EMG signals are noisy due to the tissues they travel through and motion artifact inherent noise in electronic equipment, ambient noise and motion artifacts. Many methods have been suggested for computing transitions from muscle on- and off-timing. To detect a certain activity of one patient using an EMG signal, it is necessary to analyze the data that has been obtained. In this term, burst and silence moments in electromyography may be determined as shown in Figure 3. The burst part merged with the silence part forming a full signal.  
Figure 3: Time domain analysis of EMG signals by observation.
###### Abstract  
Periodic visual inspections are currently conducted to maintain the condition of railway bridges. These inspections rely on direct visual assessments by human inspectors, often requiring specialized equipment such as aerial ladders. However, this method is not only time-consuming and costly but also involves significant safety risks. Therefore, there is a growing need for a more efficient and reliable alternative to traditional visual inspections of railway bridges. In this study, we evaluated and compared the performance of damage detection using U-Net-based deep learning models on images captured by unmanned aerial vehicles (UAVs). The target damage types include cracks, concrete spalling and delamination, water leakage, exposed reinforcement, and paint peeling. To enable multi-class segmentation, the U-Net model was trained using three different loss functions: Cross-Entropy Loss, Focal Loss, and Intersection over Union (IoU) Loss. We compared these methods to determine their ability to distinguish actual structural damage from environmental factors and surface contamination, particularly under real-world site conditions. The results showed that the U-Net model trained with IoU Loss outperformed the others in terms of detection accuracy. When applied to field inspection scenarios, this approach demonstrates strong potential for objective and precise damage detection. Furthermore, the use of UAVs in the inspection process is expected to significantly reduce both time and cost in railway infrastructure maintenance. Future research will focus on extending the detection capabilities to additional damage types such as efflorescence and corrosion, aiming to ultimately replace manual visual inspections of railway bridge surfaces with deep-learning-based methods.  
U-Net; railway bridge; maintenance; drone; focal loss; IoU Loss; multi-class damage; site application