esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Constellation Construction
### Payload  
HN-1 01, specifically designed for ocean observation, is equipped with a dual-line wide array camera and AIS. It operates in various modes, including single-line array imaging, double-line array imaging, and double-line array detection. This versatility enables it to detect and retrieve ship targets. In single-line array imaging mode, the output is a remote sensing image acquired in low-power mode. In the double-line array imaging mode, the output is two remote sensing images. In double-line array detection mode, automatic ship detection is determined by image matching/comparison of two images with a temporal resolution of 5 s. Targets can be detected, and slice images of ship targets could be the main output.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Satellite** & **Spatial Resolution (m)** & **Swath Width (km)** & **Spectral Range** \\ \hline HN-01 01 & 5 & 110 & 0.77–0.89 \(\upmu\)m \\ HN-01 02 & 1.5 & 12 (push-scan) & 0.4–0.7 \(\upmu\)m \\ WC-01 01/02 & 5 & 115 & 0.45–0.90 \(\upmu\)m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical specifications of HN-01 01/02 and WC-01 01/02 satellites.  
Figure 4: On-orbit status of the WC-1 01/02 satellite.  
HN-1 02 carries a main payload (a Bayer RGB push-sweep video integrated high-resolution optical camera) and a secondary payload (an AIS receiver). The high-resolution camera employs a Ritchey-Chretien optical system and a correction mirror, offering push-scan and video imaging working modes. The push-scan mode allows continuous imaging, while the video imaging mode captures 20 images per second with automatic exposure.  
WC-1 01 and WC-1 02 feature a main payload (an area array CMOS optical camera) and a secondary payload (an AIS receiver). The CMOS optical camera supports panchromatic and multi-spectral push-sweep working modes, utilizing a refractive system and partition coating for imaging.  
Partial images collected by these satellites are shown in Figure 5.
## 3 Demand-Response Aggregator Frameworks in Future US Electricity Markets
### Competition in the Prosumer-Aggregator Frameworks
However, many system operators currently prohibit prosumers from registering with multiple DR aggregators, a fact that makes this competition type inappropriate. An aggregator may choose how to design the prosumer-aggregator mechanism; therefore, a Stackelberg game would be preferred because it can provide closed-form strategies for game analysis in the full-information setting (see Table 7 for various aggregator applications formulated as Stackelberg/bi-level optimization programs). No estimation or learning would be required for this analysis. In most designs, no direct competition is assumed between aggregators, but rather through the market. \begin{table}
\begin{tabular}{l l l l} \hline
**Description of Bi-Level Program** & **Service/Market** & **Approach** & **Paper** \\ \hline Large consumers, significant wind & Energy, reserves & Stochastic, MPEC & [190] \\ Electric vehicle aggregator & Flexible ramping & Robust optimization & [191] \\ DAM and RTM prosumer aggregator & Energy, reserve, flexible ramping & Robust, risk-averse & [192] \\ Batteries and flexible load & Energy, flexible ramping gen. & Co-optimization, MILP & [193] \\ Battery swap aggregator & Economic, peak shaving & RL, MILP & [194] \\ Large-scale electric vehicles & Frequency regulation & Piece-wise linear & [195] \\ Strategy evaluation of DER aggregator & Economic, wholesale and local & Stochastic, MPEC & [196] \\ Large-scale biogas and DR & Energy, DR & Non-linear, MPEC & [197] \\ Ground source heat pump aggregator & Peak shaving, valley filling, DR & Optimal control & [198] \\ Specialized load aggregator & Balancing, frequency, reserves & Custom algorithm & [199] \\ Combined heat and power & Energy, balance & Stochastic, big-M, MPEC & [200] \\ Mix load aggregator & Energy, flexibility, local & Robust, cutting planes & [201] \\ DSO and battery-generator aggregator & DR & \(\epsilon\)-constraint, KKT & [202] \\ Residential consumer aggregator & DR & Stochastic, MPEC & [203] \\ Bidirectional solved uni-directionally & DR & Convex optimization & [204] \\ Battery-generator aggregator & DR & MINLP & [205] \\ \hline \end{tabular}
\end{table} 
Table 7: Aggregator applications formulated as Stackelberg games (also known as bi-level programs). Application areas include different aggregator or prosumer types, as well as different market types where such games are observed between aggregators and prosumers. The existing literature considers either the aggregator's or the prosumer's cost optimization, but not both. Simultaneous cost optimization would require an underlying competition assumption and would have to be modeled with game theory (open research question about competition in Section 3.7). Such a problem would be very complex and potentially computationally challenging in a centralized manner, especially if device model constraints and energy/demand forecasts are considered for the distributed load (see distributed load mix in Section 2.2). However, few efforts in the game-theoretical direction appear in the literature and are analyzed next.
## 2 Related Work
### Duty Cycling  
A typical problem in WSNs is energy waste due to idle listening. Duty cycling is a conventional method to solve this problem. To reduce idle listening, each sensor node usually is in the sleep state and periodically wakes up to detect communication demands.  
Different variants of duty cycling have been proposed [7]. In X-MAC [8], a sender node periodically transmits a short preamble carrying the receiver address until the receiver node wakes up, detects the preamble, and responds with an acknowledge frame (ACK). After receiving the ACK, the sender node transmits the data frame. Instead of using a preamble in X-MAC, in ContikiMAC [9] a sender node periodically transmits the data frame until it receives an ACK from the receiver or reaches the upper limit of retransmission.  
In these duty-cycling media access control (MAC) protocols, nodes are not synchronized to a common wake-up schedule, and a large communication delay tends to occur. This delay can be shortened by increasing the duty cycle (the percentage of active time) at the cost of more power consumption. In addition, repeated transmissions of a single message more than once at the sender node also lead to energy consumption.
# Generating Structurally Complete Stylish Chinese Font Based on Semi-Supervised Model
## 5 Conclusions  
In this paper, a reasonably efficient font generation method is proposed, which can directly transform fonts in the source domain into art fonts in the style of the target domain. By preserving the stroke encoding in the model, a semi-supervised scheme and an improved attention module are introduced to solve the problems of mode collapse, missing structures, and stroke errors that occur during the generation of Chinese fonts. By incorporating stroke encoding and the semi-supervised scheme, the model gains an improved ability to emphasize the local stroke information and global structural features of the characters. The addition of the attention module can further make the connection between the local stroke information and global structural features of the characters closer, thus making the generated character strokes more fluent and natural. The improved attention module better preserves the key pattern information of the stylish Chinese characters, thus improving the performance and reliability of the system. The reliability of our proposed model is validated across six representative font datasets. Through a comparative analysis with existing methods, including the Zi2 zi approach that relies on paired data, our model consistently outperforms them. Quantitative evaluations confirm its superiority in terms of performance. The generation result of our model reaches up to 93.8% for content recognition. The generation result of our model is the smallest at 0.0307 in terms of stroke  
Figure 10: Comparison between our method and the other three methods in generating fonts from _Regular Script_ to _Shu_.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Zi2 zi** & **CycleGAN** & **StrokeGAN** & **Ours** \\ \hline Content acc (\%). & 91.0 & 86.2 & 89.5 & **93.8** \\ Stroke error(\(\times 10^{-2}\)). & 4.89 & 6.83 & 5.94 & **3.01** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation of the performance of our method by comparison with several commonly used methods. The evaluation considered performance metrics such as content accuracy and stroke error.  
error. Moreover, the visual perception of the generated fonts clearly demonstrates their enhanced recognizability when compared to fonts generated by other models.  
Conceptualization, X.T. and F.Y.; Methodology, X.T. and F.Y.; Writing--original draft, X.T.; Writing--review & editing, X.T., F.Y. and F.T.; Visualization, X.T. and F.T.; Project administration, X.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Smart City Labeling: Case Studies on Matrix Testing  
### TAM (Technologies Analysis Matrix) Testing  
This project is part of a doctoral thesis in progress. We are now beginning to define the design of the matrix and the algorithms in more detail in order to obtain the best qualification process, with the requirements previously defined and explained in the preceding paragraphs. It may seem inappropriate to present in this article a model that still needs to be defined more clearly. However, we can show one of the analyzed technologies and why it is understood as the first step towards a good model for evaluating Smart Cities, applying different technologies and testing their usefulness and performance in real environments.  
One example of a good model for testing the Technologies Analysis Matrix (TAM) technology called \"LUIX intelligent lighting\". The background for its development was in asking the following question: Why should we illuminate public spaces at unnecessary times? Several studies have shown that more than 70% of the energy used for lighting is used to illuminate spaces without the presence of vehicles or people at night. LUIX has developed a technology based on presence detection and automatic regulation of public lighting, leading to major savings in energy and maintenance costs (Figure 6). This is achieved by increasing light intensity only when the presence of vehicles or people  
\begin{table}
\begin{tabular}{c c c} \hline
**Technology** & **Technology qualification** & **Area weighing** & **Final labelling** \\ \hline T XX 1 & TT1 & AW1 = (TT1m:TT1e:TT1q) \\ T XX 2 & TT2 & AW2 = (TT2m:TT2e:TT2q) \\ T XX 3 & TT3 & AW3 = (TT3m:TT3e:TT3q) \\ T XX 4 & TT4 & AW4 = (TT4m:TT4e:TT4q) \\ T XX 5 & TT5 & AW5 = (TT5m:TT5e:TT5q) \\ T XX n\(\ldots\) & TT n\(\ldots\) & AWn = (TTnm:TTne:TTnq) \\ \hline \end{tabular}
\end{table}
Table 3: Smart City final qualification Matrix (SCM).  
is detected. This technology also transforms the electric grid into a data network, a concept fully aligned with the strategy of Smart Cities [19].  
Finally, why is understood that this analyzed technology is a good example for Smart Cities:  
* Use of new technologies by turning the grid into a data network;
* No negative impact on people or the environment;
* No impact on the city's appearance, because it leverages existing infrastructure;
* Environmental sustainability: lower energy consumption and lower CO2 emissions;
* Economic sustainability: less spending on public electricity bills;
* Social sustainability: less light pollution for the neighbours.  
The score obtained by this technology on the matrix (4.29) reveals that could work properly in a Smart City.
# Opportunities and Challenges for Error Correction Scheme for Wireless Body Area Network--A Survey
## 2 Error Correction Scheme (ECS)
### Automatic Repeat Request (ARQ)  
In automatic repeat request (ARQ) [8], transmitter retransmits the packet if the receiver found errors. The error-detection at the receiver is usually performed by using error-detection techniques, such as parity checking and cyclic redundancy check (CRC). Until the receiver received the error-free packet, the retransmission process is continued. In this process, the transmitter applied the error-detecting code to the packet before transmission. Afterwards, the checksum is calculated at the receiver to find whether error has occurred or not. If the checksum is not equal to right value, the ARQ scheme send negative acknowledgement (NACK) to the transmitter while using the feedback channel and the transmitter resend packet. Otherwise, the receiver send positive acknowledge (ACK). In this scheme, the transmitter resends the packet if it does not received ACK packet within a pre-specified timeframe. The main downside of this scheme is the additional cost of retransmission. The retransmission is very common when channel quality is poor and rarely happens when the channel quality is good. ARQ scheme consist of error detection and re-transmission and it involves two bottom layers (Physical (PHY) layers and Media Access (MAC) Layer) of the Open Systems Interconnection (OSI) model.
## 3 Results and Discussion
### Foaming Ability and Foam Stability of GE  
Foaming ability and foam stability of the \(\beta\)-glucan from highland barley are very important for its further application in food industry. Thus, these properties at different temperatures and pHs as well as NaCl concentrations were investigated.  
As shown in Table 1, the foaming ability of GE displayed slight decline and foam stability decreased substantially with the temperature (\(p\) < 0.05). High temperature could reduce the viscosity of solution, thus accelerating the evaporation of liquid film of the foam. When the liquid film of the foam was thin enough, a burst occurred [16]. Foaming ability and foam stability were better under neutral condition than alkaline, and worst in acidic environments. It is likely that a strong base could make \(\beta\)-glucan particles on the solution interface dissolve into solution with changing the interface property of solution, and under acidic condition there were more undissolved \(\beta\)-glucan particles that broke the interface property of solution. The effect of NaCl concentration on foaming ability and foam stability of the GE was shown in Table 1. The addition of NaCl could enhance simultaneously the foaming ability and foam stability of GE. Likely, Na\({}^{+}\) could weaken the electrostatic repulsion between and within \(\beta\)-glucan molecules, and promote stronger intermolecular interaction thereby enlarging their steric configuration, which was very helpful for the formation and stability of foam.  
Most of carbohydrates cannot perform great foaming ability due to strong hydrophilic property. Nevertheless, some polysaccharide can achieve it because of high molecular cross-linking degree and molecular weight. It was reported that oat containing blistery polysaccharide added into coffee could successfully improve the fine and smooth taste by promoting the formation of foam [10]. Additionally, in the production of beer, hydrophilic polysaccharide such as \(\beta\)-glucan from barley could be employed to improve the foam stability [17]. Meanwhile, \(\beta\)-glucanase exogenous enzymes are of common use for solving filterability issues caused by the addition of \(\beta\)-glucans in brewing [18].  
Based on the results of the processing characteristics study above, GE was considered to have a good processing performance, and it is promising to be applied in baked foods, dairy products, health-care products, beers, and so on.
# Enhanced K-Nearest Neighbor for Intelligent Fault Diagnosis of Rotating Machinery
## 2 Principal Knowledge
### Sparse Coding  
Sparse coding is inspired by the biological brain network, and its target is reconstructing the input vector. Its core assumption is that the input vector can be approximated by linear fitting via some of the existing base vectors. Suppose the input matrix is \(\mathbf{X}\), and \(x_{i}\) is the \(i\)-th target vector in it; \(\mathbf{V}\) is the dictionary matrix and \(v^{i}\) is the \(i\)-th column base; \(\mathbf{U}\) is coefficient matrix of \(\mathbf{X}\), where \(u^{i}\) is the \(i\)-th coefficient vector of \(x_{i}\), which reconstructs \(x_{i}\) from the dictionary matrix \(\mathbf{U}\). It is worth mentioning that the amount of base vector \(v^{i}\) in \(\mathbf{V}\) is much larger than the input dimension, which means the dictionary matrix is over-complete. Correspondingly, the objective function of sparse coding can be formed as Equation (4).  
\[\min_{u,v}\sum_{i=1}^{m}\left\|x_{i}-\sum_{j=1}^{k}u_{i,j}v_{j}\right\|_{F}^{2}+ \lambda\sum_{i=1}^{m}\sum_{j=1}^{k}|a_{i,j}| \tag{4}\]  
where the first term is the reconstruction term, and the second term is the regular term for coefficient matrix. It means that each input vector is only composed of the several bases in \(\mathbf{V}\); therefore, the coefficient matrix \(\mathbf{U}\) is bound to be sparse and encouraged to be sparse by minimizing the L1 norm of each coefficient vector here.
## 2 Methodology
### PolSAR Data Structure  
The quad-pol SAR receives target backscattering signals and measures the amplitudes and phases in terms of four combinations: HH, HV, VH and VV, where H represents horizontal mode and V represents vertical mode. A 2 \(\times\) 2 complex matrix \(\mathbf{S}\) that collects the scattering information can be obtained for each pixel, these complex numbers relate the incident and the scattered electric fields. The scattering matrix \(\mathbf{S}\) usually reads:  
\[\mathbf{S}=\left[\begin{array}{cc}S_{HH}&S_{HV}\\ S_{VH}&S_{VV}\end{array}\right], \tag{1}\]  
where \(S_{VH}\) denotes the scattering factor of vertical transmitting and horizontal receiving polarization, and the others have similar definitions.  
The target feature vector can be readily obtained by vectorizing the scattering matrix. Reciprocal backscattering assumption is commonly exploited, then \(S_{HV}\) is approximately equal to \(S_{VH}\) and the polarimetric scattering matrix can be rewritten as the Lexicographic scattering vector:  
\[\mathbf{h}=\left[\begin{array}{cc}S_{HH}&\sqrt{2}S_{HV}&S_{VV}\end{array} \right]^{T}, \tag{2}\]  
where the superscript \({}^{T}\) denotes the transpose of vector. The scale factor \(\sqrt{2}\) on \(S_{HV}\) is to ensure consistency in the span computation. Then, a polarimetric covariance matrix \(\mathbf{C}\) can be constructed as the following format:  
\[\mathbf{C}=\mathbf{h}\mathbf{h}^{*T}=\left[\begin{array}{cc}\left|S_{HH} \right|^{2}&\sqrt{2}S_{HH}S_{HV}^{*}&S_{HH}S_{VV}^{*}\\ \sqrt{2}S_{HV}S_{HH}^{*}&2\left|S_{HV}\right|^{2}&\sqrt{2}S_{HV}S_{VV}^{*}\\ S_{VV}S_{HH}^{*}&\sqrt{2}S_{VV}S_{HV}^{*}&\left|S_{VV}\right|^{2}\end{array} \right], \tag{3}\]  
Figure 1: Flowchart of the whole study method.  
where the superscript \({}^{*}\) denotes the conjugate of a complex number. Alternatively the Pauli-based scattering vector is defined as  
\[\mathbf{k}=\frac{1}{\sqrt{2}}\big{[}\begin{array}{cc}S_{HH}+S_{VV}&S_{HH}-S_{ VV}&2S_{HV}\end{array}\big{]}^{T}. \tag{4}\]  
By using vector \(\mathbf{k}\), a coherency matrix \(\mathbf{T}\) can be constructed as follows:  
\[\mathbf{T}=\frac{1}{M}\sum_{m=1}^{M}\mathbf{k}_{m}\mathbf{k}_{m}^{H}, \tag{5}\]  
where \(M\) indicates the number of looks. The coherency matrix \(\mathbf{T}\) is usually spatially averaged to reduce the inherent speckle noise in the SAR data. This preserves the phase information between the polarization channels.  
The covariance matrix \(\mathbf{C}\) has been proved to follow a complex Wishart distribution, while the coherency matrix \(\mathbf{T}\) contains the equivalent information of the same PolSAR data. They can be easily converted to each other by a bilinear transformation as follows  
\[\mathbf{T}=\mathbf{N}\mathbf{C}\mathbf{N}^{T}, \tag{6}\]  
where \(\mathbf{N}\) is a constant matrix:  
\[\mathbf{N}=\frac{1}{\sqrt{2}}\left[\begin{array}{ccc}1&0&0\\ 1&0&-1\\ 0&\sqrt{2}&0\end{array}\right]. \tag{7}\]
## 4 Description of Setup, Control Mode, and ROV Trajectories for Real Experiments  
The simulations described in the previous section allowed validation of the behavior of the V-shape system by comparing the different modes of cable control. This section describes the preparation for real experiments that took place in water tanks. This preparation includes the setup, control scheme, and two kinds of trajectories that were used to check the system behavior in real conditions. To ensure the ground truth of the movements of the cable, compliant buoy-ballast system, and ROV, an underwater motion capture system was implemented.
# Automatic Identification of Children with ADHD from EEG Brain Waves
###### Abstract  
EEG (electroencephalogram) signals could be used reliably to extract critical information regarding ADHD (attention deficit hyperactivity disorder), a childhood neurodevelopmental disorder. The early detection of ADHD is important to lessen the development of this disorder and reduce its long-term impact. This study aimed to develop a computer algorithm to identify children with ADHD automatically from the characteristic brain waves. An EEG machine learning pipeline is presented here, including signal preprocessing and data preparation steps, with thorough explanations and rationale. A large public dataset of 120 children was selected, containing large variability and minimal measurement bias in data collection and reproducible child-friendly visual attentional tasks. Unlike other studies, EEG linear features were extracted to train a Gaussian SVM-based model from only the first four sub-bands of EEG. This eliminates signals more than 30 Hz, thus reducing the computational load for model training while keeping mean accuracy of \(\sim\)94%. We also performed rigorous validation (obtained 93.2% and 94.2% accuracy, respectively, for holdout and 10-fold cross-validation) to ensure that the developed model is minimally impacted by bias and overfitting that commonly appear in the ML pipeline. These performance metrics indicate the ability to automatically identify children with ADHD from a local clinical setting and provide a baseline for further clinical evaluation and timely therapeutic attempts.  
ADHD; ANOVA; cross-validation; EEG; PCA; SVM Article