esa-sceva/satcom-chunk-collection huggingface dataset:
_Energies_**2012**, \(5\), 4824-4843; doi:10.3390/en5114824  
_Article_  
**Distributed Semantic Architecture for Smart Grids**  
**[PERSON]**\({}^{1,\ast}\)**, [PERSON]**\({}^{2}\) **, [PERSON]**\({}^{3}\)**, [PERSON]**\({}^{1}\)**, [PERSON]**\({}^{1}\)  
**and [PERSON]**\({}^{4}\)  
\({}^{1}\) Deusto Institute of Technology, University of Deusto, Avenida de las Universidades 24, Vizcaya, 48007 Bilbao, Spain; E-Mails: [EMAIL_ADDRESS] (C.E.B.); [EMAIL_ADDRESS] (A.P.)  
\({}^{2}\) Department of Computing Sciences, Umea University, Umea SE-901 87, Sweden;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{3}\) Electrical Engineering Department, Metropolitan Autonomous University, Delegacin Talapan, Mexico City 09340, Mexico; E-Mail: [EMAIL_ADDRESS]  
\({}^{4}\) Indra Systems Inc., 35 Avenida De Bruselas, Alcobendas, Madrid 28108, Spain;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +34-943-326-600 (ext. 5607).  
_Received: 9 October 2012; in revised form: 1 November 2012 / Accepted: 14 November 2012 / Published: 20 November 2012_  
**Abstract:** The smart grid revolution demands a huge effort in redesigning and enhancing current power networks, as well as integrating emerging scenarios such as distributed generation, renewable energies or the electric vehicle. This novel situation will cause a huge flood of data that can only be handled, processed and exploited in real-time with the help of cutting-edge ICT (Information and Communication Technologies). We present here a new architecture that, contrary to the previous centralised and static model, distributes the intelligence all over the grid by means of individual intelligent nodes controlling a number of electric assets. The nodes own a profile of the standard smart grid ontology stored in the knowledge base with the inferred information about their environment in RDF triples. Since the system does not have a central registry or a service directory, the connectivity emerges from the view of the world semantically encoded by each individual intelligent node (_i.e._, profile + inferred information). We have described a use-case both with and without real-time requirements to illustrate and validate this novel approach.  
**Keywords:** smart grids; architecture; semantic; distributed control
## 3 Results
### Basin-Scale Error Intercomparison
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Minimum (\%)**} & **Mean (\%)** & **Maximum (\%)** & **SD (\%)** \\ \hline \multirow{3}{*}{**Yellow River**} & hourly & IMERG & \(-\)40.5 & 4.4 & 236.2 & 28.7 \\  & 3-hourly & IMERG & \(-\)40.3 & 3.7 & 149.0 & 26.1 \\  & & 3B42V7 & \(-\)33.1 & 5.8 & 192.9 & 24.2 \\ \hline \multirow{3}{*}{**Yangtze River**} & hourly & IMERG & \(-\)46.9 & 12.0 & 118.2 & 21.7 \\  & 3-hourly & IMERG & \(-\)46.8 & 10.8 & 118.2 & 20.4 \\  & & 3B42V7 & \(-\)35.4 & 13.8 & 129.3 & 19.4 \\ \hline \multirow{3}{*}{**Brahmaputra**} & hourly & IMERG & \(-\)77.8 & 35.6 & 422.6 & 49.9 \\  & 3-hourly & IMERG & \(-\)77.7 & 35.6 & 410.8 & 49.5 \\  & & 3B42V7 & \(-\)41.0 & 92.6 & 340.3 & 59.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of Relative Bias (RB) features (i.e., minimum, mean and maximum) and corresponding Standard Deviation (SD) for IMERG and 3B42V7 rainfall estimates at hourly and 3-hourly timescales in the Yellow River, Yangtze River and Brahmaputra basins in the warm season of 2014 over the Tibetan Plateau (TP). Figure 5: Variation of area mean daily rainfall for each river basin, i.e., **(a)** Yellow River; **(b)** Yangtze River and **(c)** Brahmaputra, in the warm season of 2014. The black line denotes gauge observations; the red one stands for the IMERG estimates; and the blue one is the 3B42V7 products.
## 2 Distribution Network Flexibility Resources and Flexibility Evaluation Indicators
### Scenario Modeling Considering the Uncertainty of Wind Power and Photovoltaic Generation
#### 3.1.2 Conditional Deep Convolutional Generative Adversarial Networks  
Deep Convolutional Generative Adversarial Networks (DCGAN) use two convolutional networks to improve the structure of traditional GAN, which improves the stability of the network and accelerates the speed of convergence.  
In contrast to GAN, Conditional Generative Adversarial Networks (CGAN) integrate inputs characterized by conditional values within the generator and discriminator. This facilitates the generalization of data samples of the specified type.  
This paper presents a novel approach to scene generation that combines the strengths of the previously described methods. The proposed model employs a C-DCGAN to generate realistic images of wind power and PV. Its structure is illustrated in Figure 4.  
Specified labels y (e.g., time, weather, etc.) are added to both the generator and the discriminator to DG output scenarios under specific conditions. The objective function of C-DCGAN model is:  
\[\min_{G}\max_{D}V(D,G)=E_{x\sim P_{x}}[D(x|y)]-E_{z\sim P_{z}}[D(G(z|y))]- \lambda E[\|\,\
abla D(\ \sim)\|-1]^{2} \tag{19}\]  
where \(\lambda E[\ |\ |\ \bigtriangledown D(\sim)\ |\ |\ -1]^{2}\) is the penalty function representing the gradient of the loss function in D; \(\lambda\) represents the penalty coefficient.
# Multi-Target Detection Algorithm for Fusion Images Based on an Attention Mechanism
## 2 Related Work
DETR, developed by [PERSON] et al., formulates object detection as a set prediction task using an encoder-decoder transformer architecture, enabling the model to capture global context and reason about object relationships for direct bounding box prediction [17]. Compared to sequential attention modules like CBAM and BAM, the Global Attention Mechanism (GAM) proposed by [PERSON] et al. [18] captures both channel and spatial dependencies in a unified manner, enabling stronger global context modeling. This design enhances the representation of small or occluded targets, which is especially beneficial in infrared-visible fused imagery. Existing methods face key limitations: they lack attention mechanisms tailored to IR-VIS fused images, struggle with small-target detection due to scale and contrast variations, and perform poorly under noisy conditions. To address these issues, we propose a detection framework that integrates a global attention mechanism, adds a small-object detection layer, and refines the loss function to enhance accuracy and robustness. Table 1 summarizes the strengths and limitations of existing approaches: \begin{table}
\begin{tabular}{l l c} \hline \hline
**Method Type** & \multicolumn{2}{c}{**Current Capabilities**} & **Small-Target Detection** \\ \hline \multirow{3}{*}{Existing Methods} & Limited robustness in occlusion and noise; & \\  & No dedicated attention for fused modalities; & Limited \\  & Ineffective for small-target detection & \\ \hline \multirow{3}{*}{Proposed Method} & GAM-enhanced multi-scale representation; & \\  & Small-object detection layer integrated; & \\ \cline{1-1}  & Loss function optimized for fusion input & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of existing methods vs. proposed approach.
## 2 Motivation and Background Analysis
### Information-Centric Networking
#### 2.2.1 Types of ICN  
ICN is an umbrella concept, and there are several types of proposals (e.g., Content Centric Networking (CCN), Named Data Networking (NDN), Data Oriented Network Architecture (DONA), Publish-Subscribe Internet Technology (PURSUIT), Publish-Subscribe Internet Routing Paradigm (PSIRP), Scalable & Adaptive Internet soLutions (SAIL), Architecture and Design for the Future Internet (4 WARD), COntent Mediator architecture for content aware nETworks (COMET), CONVERGENCE, Mobility First) [6] continuously working under this umbrella concept. Among them, CCN [17] and NDN [18] are very familiar to the research arena, and recently both concepts have been merged and are working together. Although the core concept of all these types of ICN is the same, there are some little differences in routing, naming mechanism, name resolution handling, etc. mentioned in [6].
# Avoiding Obstacles via Missile Real-Time Inference by Reinforcement Learning
## 4 Results and Discussions  
### Simulation Results  
#### 4.1.1 Simulation Results of Scenario A  
In scenario A, the obstacle avoidance guidance law shows a mission success rate of 0.96 in 10,000 Monte Carlo simulations. As it aims to operate with limited information, some episodes seem to fail and not be able to determine proper behavior with the given sparse information. This problem will be addressed in more detail in the next section.  
The test set of the environment in Figure 10 has a total of 50 obstacles. The initial look angle is randomly selected between \(-\pi/2\) to \(\pi/2\). Figure 10 shows several missile trajectories reaching the target while avoiding obstacles. Each missile initially moves in a direction where an obstacle is not detected; then, it tends to move closer to the obstacles and avoid them if it detects an obstacle nearby. Since the agent has no memory, the agent should decide its action only using the state at the moment and it seems to decide the direction not to encounter another obstacle soon. In some episodes, the agent seems to take advantage of the fact that there is no obstacle behind the target. The missile moves to the rear of the target, and hits the target from the rear.  
The episode in Figure 11 has four times more dense obstacles than the one in Figure 10. However, it can be seen from Figures 11 and 12 that the proposed guidance law avoids all obstacles and hits the target.  
While the missile initially moves in the target direction, terrain features are detected from the bottom on the right at about 1 s, and the missile tries to avoid this by giving the y-axis positive direction acceleration. After that, the mountain on the left is detected at about 5 s, giving negative y-axis acceleration to overcome it at about 10 s, and applying negative acceleration to avoid the mountain left being detected again at around 12 s. Finally, the missile overcomes every topological feature and strikes the target.
# Non-Orthogonal eMBB-URLLC Radio Access for Cloud Radio Access Networks with Analog Fronthauling
## 5 Non-Orthogonal Multiple Access (NOMA)  
In NOMA, URLLC UEs transmit in the same minislot where the packet is generated, and hence the access latency is minimal and limited to \(L_{U}=1\) minislot. However, the URLLC signals mutually interfere with the eMBB transmission, which spans the whole time-frequency resource plane. Due to URLLC latency constraints, the eMBB signals necessarily need to be treated as noise while decoding URLLC packets at the ENs. On the contrary, several strategies can be adapted in order to deal with the interfering URLLC signal. Beside puncturing, considered for 5G NR standardization [33; 34], this work considers two other techniques, namely Treating Interference as Noise (TIN) and Successive Interference Cancellation (SIC), as detailed in the rest of this section.
###### Abstract  
The Middle Route of the South-North Water Diversion Project has changed the water resources pattern in China. As advanced equipment for the country, it is responsible for the water supply \"lifeline\" of Beijing, Tianjin, Hebei, Henan, etc. Ensuring its safe operation is a top priority to promote social stability and coordinated economic development between the North and the South. Used persistent scatterer interferometric synthetic aperture radar (PS-InSAR) technology to monitor the deformation of the high fill characteristic canal in Wenzhuang Village, Ye County, during the period from October 2016 to June 2017 for the South-North Water Diversion Project showed that there was significant deformation on the 1 km-long slope of the east bank of the canal, with the maximum deformation volume reaching 36 mm. Through the comparison and verification with the second order leveling data, there are more than 87% of the root mean square error of both less than \(\pm\)2 mm. The correlation coefficient is 0.96, and the two were highly consistent in deformation trends and values. Through the vertical and cross-sectional analysis of the canal's east bank and four key monitoring sections, it was found that the east bank of the canal presents overall uneven subsidence, and the closer the canal is to the water, the greater the canal deformation, and vice versa. Further comparison of the PS-InSAR deformation results of the canal from October 2016 to February 2018 proves that this technology cannot only monitor the subsidence range and rate of the South-North Water Diversion canal but also accurately identify the subsidence sequence of the east and west banks. It can provide reliable technical support for the safety monitoring and disaster prevention of the South-North Water Diversion canal characterized by high fill and deep excavation.  
South-North Water Diversion Project; time-series InSAR; deformation monitoring; high fill 2023
# Saliency Guided DNL-Yolo for Optical Remote Sensing Images for Off-Shore Ship Detection
## 3 Experiments
### Datasets  
We verify the performance of the above method on the South China Sea dataset. This dataset is produced from images returned by the Gaofen-1 remote sensing satellite. The size of each image is 18,192 \(\times\) 18,000 pixels, and the spatial resolution is 2 m. The spectrum ranges from 450 to 900 nm. We selected some images in different scenes and cropped them to a size of 512 \(\times\) 512 pixels. As shown in Table 1, 2344 image blocks are converted into one-dimensional grayscale vectors as training samples for scene classification. In addition, the training samples for object detection contain more than 2000 image patches, and the ratio of positive and negative sample images is 1:2. Moreover, there are many types of ship targets in training samples, and the size of the target ships in 2-m resolution images varies from 8 pixels to 157 pixels. They are illustrated in Figure 5.  
Moreover, for image samples in scenes such as thick clouds, there are fewer target samples due to a large amount of cloud and fog occlusion. Therefore, we used the simulation method usually used for small object augmentation [43] to create simulation samples, which is shown in Figure 6. Ship samples of different types and sizes are cut out and randomly added to the background image. Then, the boundary is blurred by multi-size Gaussian filters so that the target and the background can be integrated better. After that, we re-screened the obtained simulation samples and removed the images with poor simulation results to obtain the final data set.  
\begin{table}
\begin{tabular}{l c c} \hline – & **Scene Classification** & **Object Detection** \\ \hline Data size & \(1\times 256\) & \(512\times 512\) \\ Data type & Gray vector & Panchromatic \\ \hline Total samples & 2344 & 2721 \\ Train samples & 2110 & 2448 \\ Test samples & 234 & 273 \\ \hline Original samples & – & 2245 \\ Augment samples & – & 476 \\ \hline \end{tabular}
\end{table}
Table 1: Dataset overview.  
Figure 5: Schematic diagram of training sample blocks. The data set includes various types and sizes of ship targets in different scenes, as well as static targets and dynamic targets. At the same time, simulation samples are added to make up for the lack of samples in some scenes.
## 3 Prefiltered Frequency-Domain Equalization  
The block diagram of the CPM receiver with PF-SCFDE is shown in Figure 3. As the matched filter output sequence \(\mathbf{r}\) is converted to the FD, it is filtered by the equalizer. The noise whitening filter in the traditional SCFED is replaced by a prefilter in the proposed scheme. In that case, the output of the equalizer consists of the signal sequence, the partially correlated noise, and the forward information. Following the minimum mean square error (MMSE) equalization, the sequence is passed on to the symbol detector. As the modulation index is \(1/2\), the symbol detector de-maps the sequence to generate sequence \(\mathbf{a}\). By analyzing the memorized soft information \(a_{n}\), the real part represents the information about the current moment, whereas the imaginary part represents the symbol of the previous moment. Thus, weighted fusion and symbol estimation are performed with LLR based on the information contained in \(a_{n}\). Finally, the estimated symbols are dedifferentiated, and a hard decision is taken to get the transmission information. Additionally, for other modulation indices, the separation of forward information can be achieved by using the phase of \(\hat{b}_{n}\).  
Figure 3: CPM receiver with PF-SCFDE.
## 3 Results
### Area 2  
The 200 and 400 MHz antennas were also used in Area 2 (Tracuateua), where the GPR prospection reached a depth of 7.5-8.5 m but, once again, with high noise levels at depths of below 1 m (Figure 7). Horizontal layers were identified near the surface, between 0 and 1 m (AB profile, Figure 7a). In this profile, a horizontal reflector was also identified at 6-6.5 m, which corresponds to the local water table, as confirmed by a well drilled on the property, in which brackish water was found at a depth of 6.0 m.  
The CD profile (Figure 7b) had a number of features that were similar to those of the AB profile (Figure 7a), including horizontal layers (0-1 m) and a water table at 5.5-6 m, as well as hyperbole-shaped reflectors at depths of between 1.75 m and 5 m, which would appear to indicate the presence of buried pipes.  
These profiles (Figure 7) are color-coded as in those of Figure 5, where the yellow shading indicates a zone of low amplitude due to the attenuation of the GPR signal. The other colors represent zones of high amplitude or reflection.  
The conductivity was higher in Area 1, with values ranging from 16 to 48 mS.m\({}^{-1}\) (Figure 8). These values are also consistent with the presence of a clayey subsoil [38]. These soils are of the yellow latosol type [65].  
The soil samples collected from Area 2 (Tracuateua) have a very high clay fraction (60-100%), in particular, at depths of below 1 m. The occurrence of organic matter was very similar to that observed in Area 1.  
Figure 7: The GPR profiles (200 MHz antenna with a 200 ns time window) recorded in Area 2 (Tracuateua): (**a**) AB; (**b**) CD.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Depth (m)** & **Lithology [60]** & **Wave Velocity (m/ns)** & **Dielectric Permittivity (\(\epsilon_{s}^{\prime}\))** \\ \hline
0–4.769 & Sandy silt, yellow & 0.092 & 10.63 \\
4.7–8.5 & Medium clay & 0.070 & 18.37 \\  & with pebbles & & \\ \hline \end{tabular}
\end{table}
Table 3: Dielectric permittivity (\(\varepsilon\)) and electromagnetic wave velocity (\(\
u\)) measured from the different layers of the subsoil in study Area 1 (CEANPA, Braganca).
## 4 Experimental Setup and Results Analysis  
### Datasets  
In experiments pertaining to drug-drug interactions, we employ four datasets: DrugBank, TwoSides, DrugComb, and DrugCombDB. Details regarding these four datasets can be found in Table 1, where Management context \(|C|\) refers to how a dataset is organized and maintained. Furthermore, the number of labels \(|Y|\) represents different types of interactions, such as drug interactions categorized into various classes like strong interactions, weak interactions, harmful interactions, benign interactions, and more. Understanding the label count helps in assessing the diversity and complexity of the dataset and determines its suitability for different types of analyses and models.  
DrugBank [6]: This compilation encompasses 1706 pharmaceuticals and 383,496 DDIs, exhibiting 86 broad sentence structures, with each delineating a distinct DDI variant. Training sets comprise 80% of the overarching data. Drugs correlate with their respective SMILES string portrayals [48]. Transitioning to graph format employs the freely available RDKit chemistry information library. Each medicinal entity is represented as a graph; connections symbolize edges, while elements denote nodes. Each element spans 55 dimensions, encapsulating the following: (1) elemental symbol (44, one-hot depiction), (2) elemental degree (count of neighboring elements), (3) implicit valence, (4) tally of unattached electrons, (5) hybridization type (5, one-hot depiction), (6) aromaticity status of the element, and (7) accumulated hydrogen count on said element. Every DDI duo is categorized as a positive instance, from which a negative instance is derived. Singular interactions are associated with each DDI tuple in this compilation.  
TwoSides [18]: Given by filtering the original TwoSides adverse drug data [1] from [18], this dataset contains 644 drugs and 499,582 DDI pairs. Contrary to the DrugBank collection, the interactions here are phenotypic in nature rather than being based on transcriptional dimensions. That is, the interactions here are just adverse effects, such as headaches, sore throats, and symptoms such as those in [18].  
DrugComb [49]: DrugComb aims to provide free access to standardized drug combination screening results. We collected and managed high-throughput drug combination screening datasets involving 4146 drugs tested on 288 cancer cell lines from 10 different tissues in 659,333 combinations using computational tools available on network servers.  
DrugCombDB [50]: DrugCombDB covers 191,391 dual drug combinations, 2956 unique drugs, and 112 cancer cell line samples.