esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Path Tracking Method Based on Multilayer Model Predictive Control
## 2 Controller Design  
The design of the controller consisted of three steps. First, we propose a framework for multilayer MPC, then, we design the first layer controllers and finally the second layer controller.
# A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
###### Abstract  
In contemporary computer vision, deep learning-based real-time single image super-resolution approaches have gained significant attention for their ability to enhance the resolution of images in real time. These approaches are interconnected with various other computer vision domains, including image segmentation and object detection. Numerous surveys have summarized the state of the image SR domain. However, there is no survey that specifically addresses real-time single image SR on IoT devices. Therefore, in this study, we aim to explore strategies, identify the technical challenges, and outline the future directions of SR research, with a special emphasis on real-time super-resolution techniques. We begin with an overview of the core concepts related to real-time SR, recent challenges, and algorithm classification and delve into potential application scenarios that merit attention. Additionally, we explore the challenges and identify promising research areas related to real-time SR specifically related to IoT devices, highlighting potential advancements, limitations, and opportunities for future innovation in this rapidly evolving field.  
Keywords:real-time image SR; image enhancement; real-time systems; single image super-resolution +
Footnote †: journal: Applied sciences
## 2 Materials and Methods
### Data and Methods  
#### 2.2.1 Landsat and HJ-1 A/B Time Series Remote Sensing Image Data  
Landsat series satellites operate on a near-polar sun-synchronous orbit, which allows the satellites to cover most of the Earth's surface in a way that they capture each area of the world at a constant local sun time from the same angle. The geometric accuracy of the Landsat images is high and their spectral information is stable. In this study, we download 26 scenes of Landsat imagery (path 127, row 39/40) from USGS GLOVIS portal [21]. These images are mainly acquired during summer (mostly around September) when the vegetation is abundant, with less clouds and good overall image quality. Data collection time ranges from 1999 to 2013 (Table 1). It is worth mentioning that since the Fuling District belongs to the mid-subtropical humid monsoon climate and has a long summer, the data in October is also used when there is a lack of data due to cloud and fog interference.  
The HJ-1 satellite is a Chinese satellite constellation for the monitoring and forecasting of the environment and disasters [17]. We selected three images (30 September 2009, 16 September 2010 and 17 August 2012) captured by the CCD1 sensor of the HJ-1 B satellite in this study.  
In order to ensure data consistency in each time series, the FLAASH atmospheric correction model is used to perform the radiation correction and the relative radiation normalization for the two time series remote sensing images, based on the pseudo invariant feature normalization method. Function of mask (Fmask) method [22] is applied thereafter to detect the cloud and cloud shadow of the Landsat time sequence and the strip of Landsat 7. The strips are repaired based on the multi-image local adaptive regression analysis model. As the HJ-1 A/B remote sensing image selected in this study  
Figure 1: Study area.  
have good image qualities, the method of threshold detection and manual correction is adopted for the detection of cloud and cloud shadow.
## 3 Spatio-Temporal Distribution Characteristics of PMGIM
### PMGIM Distribution Law of 182602 Working Face
#### 3.3.1 PMGIM Plane Distribution  
The kernel density estimation method centers on the location of specific element points and reflects the clustering state of elements in the current space by calculating the density value of each grid under varying search radii. This method is suitable for analyzing the planar distribution patterns of PMGIM, and the calculation formula is as follows [25]:  
\[P_{n}(x)=\frac{1}{n\pi R^{2}}\sum_{i=1}^{n}K_{i}[\left(1-\frac{\left(x-x_{i} \right)^{2}+\left(y-y_{i}\right)^{2}}{R^{2}}\right]^{2} \tag{9}\]  
where \(P_{n}(x)\) represents the kernel density value of the \(x\)-th microseismic event in the study area; \(R\) is the search radius; \(n\) is the number of all microseismic events within the search radius; \(K_{i}\) is the weighting function; \((x-x_{i})^{2}+(y-y_{i})^{2}\) is the square of the distance between two microseismic events at \((x,y)\) and \((x_{i},y_{i})\).  
Figure 7 illustrates the kernel density cloud map of microseismic events at the 182602 working face. During period 1, the core density of PMGIM events was centered on the Zhonglicun syncline axis, forming a \"V\"-shaped, two-center continuous pattern. The high-frequency core density zcenters \"A\" and \"B\" were positioned near the main grouting hole and the central region of the 182101 working face, respectively. As a result of continuous grouting, the range of core density center \"A\" in February 2018 expanded significantly. In period 2, the planar distribution of core densities for microseismic events exhibited phased changes. In March 2018, the nuclear density values displayed a two-center discontinuous pattern extending from the northwest to the southeast. By June 2018, the nuclear density values revealed a central discontinuous pattern oriented from the northwest to the southeast. Compared to March 2018, the density value of core \"A\" rose by 19.2%, while the density value of core \"B\" declined by 41.3%. By September 2018, the nuclear density values exhibited a discontinuous layout from northwest to southeast, with the two centers disappearing. Compared to June 2018, the core density value of center \"A\" decreased by 45.1%, and that of center \"B\" decreased by 41%. During period 2, there was virtually no microseismic event response in the 182602 working face, indicating effective sealing of the fractures.  
Figure 7: Nuclear density distribution of PMGIM events at 182602 working face.
## 2 Materials and Methods
### Test 3: Vertical Inclinometer Measurement and GG and CG Transducers  
In the following, Figure 13 shows the comparison of the strain measures obtained using CG and GG transducers with the strain arising from the derivative of the displacement obtained by the inclinometer cylinder. In the figures below, the transducer measures refer to two pickup phases, namely fiber 1 and fiber 2; subsequently, the two measures were averaged, resulting in the fiber average dataset. Furthermore, in the diagram, the strain obtained by the inclinometer displacement measure is drawn as well.  
Figure 11: Centerline deformation imposed at 6.5 cm.  
Figure 12: Strain diagram GG transducer.  
From the comparison of Figure 13, it can be seen that the strain obtained using the CG transducer presents fewer errors due to the quality of the used fiber and the transducer building technique compared to the GG transducer. The GG transducer, indeed, was realized with G.652 fiber that suffers a more significant loss of signal in the presence of relatively high curvatures. The CG transducer was made of G.657 fiber that presents less loss of signal in the presence of curvatures of the fiber. Moreover, the manufacturing process in the case of the GG transducer has suffered some little, but not negligible, localized curvatures of the fiber. Henceforth, the diagrams show recognizing that the CG transducer, equipped with ITU (International Telecommunication Union) G.657 fiber, furnishes a better strain map than the GG transducer equipped with G.652 fiber. The worst result, at the GG transducer, is obtained at a position near to the collar joining the cylinder segments, at a point about 1250 mm from the inclinometer casing top.
# A Deep Learning Approach for MIMO-NOMA Downlink Signal Detection
## 2 Fundamentals of MIMO-NOMA and the Deep Learning System
### Deep Learning Basics
The convolution layer, which is composed of multiple feature maps, can dramatically reduce the number of connections via the convolution operation. The pooling layers, also called down-sample layers, can further compress the local data and avoid the overfitting problem. Max-pooling and mean-pooling are the most common choices to extract the maximum and mean values, respectively, from the output of convolution data according to the pooling size. The RNN is another research hotspot in the field of NLP for its ability to memory the data. By establishing the relationship between current data and past data (and even future data), RNNs can deal with the situation where sequences at different slots have relations to each other. The basic structure of an  
Figure 5: Construction of the LeNet-5. RNN is shown in Figure 6. It can be seen that the information of previous data are summarized as state \(W_{k}^{(t)}\) to solve the output \(\hat{y}^{(t)}\) with the current input \(x^{(t)}\). The number of RNNs' output can differ from the input for various purposes, such as one input to many outputs for music generation, many inputs to one output for sentiment classification, and many inputs to many outputs for machine translation.
# A Domain-Independent Classification Model for Sentiment Analysis Using Neural Models
## 2 Related Works
\begin{table}
\begin{tabular}{c l l l} \hline \hline
**Paper** & **Dataset** & **Labels** & **Approach** \\ \hline
[7] & Hotel Reviews & Multi-labels & Supervised machine learning techniques using conditional random fields models for aspect detection sentiment analyzing. \\ \hline
[8] & Multiple datasets (Restaurant reviews, medical descriptions, Yelp) & Two labels & Unsupervised machine learning technique using probabilistic topic modeling approaches for sentiment content clustering. \\ \hline
[9] & Product reviews & Two labels & Supervised machine learning techniques using Markov logic for sentiment classification. \\ \hline
[10] & Hotel Reviews and MP3 & \multirow{2}{*}{5 star rating} & Unsupervised machine learning techniques for Latent Expect Rating Analysis Model. \\ \hline
[11] & Chinese product reviews & Two labels & A hierarchical clustering approach for product aspects extraction and clustering. \\ \hline
[12] & Companies employee & Ratings & A novel supervised joint topic model approach to select helpful reviews among a set of reviews. \\ \hline
[13] & Different products reviews & Ratings & A novel hybrid approach to implement aspect-level sentiment analysis that assigns sentiment labels to the reviews. \\ \hline
[14] & IMDb & Two labels & N-grams followed by a random forest classifier. \\ \hline
[15] & IMDb & Two labels & Bidirectional LSTM. \\ \hline
[16] & IMDb & Two labels & Maximum entropy classification combined with support vector machines using unigrams and bigrams. \\ \hline
[17] & IMDb & Two labels & Lexical filtering. \\ \hline
[18] & IMDb & Two labels & Context-Free Grammars (CFGs). \\ \hline
[19] & Movie Review & Two labels & Convolutional Neural Networks (CNN). \\ \hline
[20] & Movie Review & Two labels & Novel machine learning frame-work based on recursive autoencoders. \\ \hline
[21] & Movie Review & Two labels & Multiple classifiers—a hybrid approach. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary of the state-of-the-art approaches for sentiment analysis. Based on the previous works, this research has started to be one of the highlights for scientific contributions because: (a) it has different applications for recommender systems and fact checking systems, and (b) it contains several challenging research problems that motivate researchers to work and improve their works on them.
## 5 Results and Discussion
### Parametric Studies
#### 5.3.3 \(\mathrm{O_{2}/Coal}\) Ratio  
To further reduce temperature to match the value (750 \({}^{\circ}\)C or 1023 K) shown in Ref. [3] for high-pressure E-STR operating condition, the amount of oxygen is reduced by decreasing the oxygen/coal ratio from 0.92 in the base case to one half the value (0.46), which is noted as 50% \(\mathrm{O_{2}}\). It is understood that a significant reduction of oxygen will lead to poor carbon conversion rate and unsatisfactory gasification process. However, as an exploration study, it is interesting to see how a significantly reduced \(\mathrm{O_{2}/Coal}\) ratio would affect the gasification process under a high operating pressure condition. The results are compared in Table 10. It is shown that a reduction of 50% oxygen supply effectively reduces temperature to 1100 \(K\), which is close to the level of design value at 1023 K. However, the unreacted char increases to 80%, and the CO yield decreases to 20%. The centerline temperature distribution is compared with the base case in Figure 6.  
The case with 50% \(\mathrm{O_{2}}\) leads to a higher temperature at the 1 st stage due to less gaseous mass flow rate introduced. After the 2 nd stage injector, lower oxygen supply results in reduced combustion and lower temperature all the way to the gasifier exit, as shown in Figure 6. The distribution of CO and \(\mathrm{H_{2}}\) are compared in Figure 7. For the case of 50% \(\mathrm{O_{2}}\), the mole fractions of CO and \(\mathrm{H_{2}}\) increase faster in the beginning, but remain constant at 20% in the rest of the gasifier after the injection zone. In the base case, both CO and \(\mathrm{H_{2}}\) continuously increase to the position about 70% height of the E-STR reactor.  
Among the conducted parametric studies, the reduction of oxygen supply is the most effective way to lower the gasifier temperature. When the interior temperature is low enough, the effect of liquid volatiles/tar would be significant. The molar ratio of \(\mathrm{H_{2}/CO}\) is 0.663, 0.716, and 0.912 for the base case, the case of 70 atm, and the case of reduced oxygen, respectively. The results indicate that high pressure and low oxygen supply leads to a higher, more favorable \(\mathrm{H_{2}/CO}\) ratio for synthetic natural gas (SNG) production.  
However, the amount of condensed liquid volatiles/tar seems lower under current simulation without using catalysts. This may be due to the significantly reduced oxygen supply or the incorrect values being assigned to the condensation/re-vaporization temperatures. Further study will examine more closely these two parameters.  
Figure 7: Comparison of CO, \(\mathrm{H_{2}}\) and \(\mathrm{O_{2}}\) distributions with half oxygen-fed condition at 28 atm.
# GPU@SAT DevKit: Empowering Edge Computing Development Onboard Satellites in the Space-IoT Era
## 3 GPU@SAT DevKit
### GPU@SAT DevBoard and Embedded Software
Lastly, the capability to measure and report performance metrics (5) simplifies the testing and optimization of applications, making the development process more efficient and effective.
# Link Quality Estimation from Burstiness Distribution Metric in Industrial Wireless Sensor Networks
## 4 Evaluation
### Evaluating the Network with other Estimation Schemes  
In this examination, nine sensor nodes were deployed randomly, and the link quality between every two nodes was set randomly from 70% to 90%. Each sensor node generated 1000 packets periodically. The Time Slotted Channel Hopping (TSCH) MAC protocol was used with the Path Collision-aware Least Laxity First (PCLLF) [26] scheduling algorithm to guarantee that no collisions occurred in the network. We collected the packet reception rate of each sensor node at the sink.  
The resulting PRR data for each sensor node at the sink are presented in Figure 7, where the PRR of the proposed scheme almost achieved the target PRR with the estimated number of transmissions for each sensor node. It shows that the algorithm for calculating the number of transmissions worked well. Furthermore, the other schemes achieved a much lower PRR since the estimation of the number of transmissions by ETX and PRR was not very good. This is because ETX and PRR did not consider the burstiness that happens in the links.
# Kernel Point Convolution LSTM Networks for Radar Point Cloud Segmentation
###### Abstract  
State-of-the-art 3D object detection for autonomous driving is achieved by processing lidar sensor data with deep-learning methods. However, the detection quality of the state of the art is still far from enabling safe driving in all conditions. Additional sensor modalities need to be used to increase the confidence and robustness of the overall detection result. Researchers have recently explored radar data as an additional input source for universal 3D object detection. This paper proposes artificial neural network architectures to segment sparse radar point cloud data. Segmentation is an intermediate step towards radar object detection as a complementary concept to lidar object detection. Conceptually, we adapt Kernel Point Convolution (KPConv) layers for radar data. Additionally, we introduce a long short-term memory (LSTM) variant based on KPConv layers to make use of the information content in the time dimension of radar data. This is motivated by classical radar processing, where tracking of features over time is imperative to generate confident object proposals. We benchmark several variants of the network on the public nuScenes data set against a state-of-the-art pointnet-based approach. The performance of the networks is limited by the quality of the publicly available data. The radar data and radar-label quality is of great importance to the training and evaluation of machine learning models. Therefore, the advantages and disadvantages of the available data set, regarding its radar data, are discussed in detail. The need for a radar-focused data set for object detection is expressed. We assume that higher segmentation scores should be achievable with better-quality data for all models compared, and differences between the models should manifest more clearly. To facilitate research with additional radar data, the modular code for this research will be made available to the public.  
perception; deep learning; radar segmentation; radar point cloud; object detection +
Footnote †: journal: Applied Sciences
## 4 Detailed Design
### Low-Power Consumption  
The proposed smart home solution continuously measures user ADL, so the low-power consumption of wearable and tag devices should be considered. In this study, the stationary device is powered by a 12V/2A adapter, so it does not have battery restrictions. Therefore, we did not consider the low-power consumption of the stationary device. The following describes how to reduce the standby power of the wearable and tag devices.  
* **Wearable device:** The LF signal was used for indoor location recognition in this study because of its low-power characteristics and excellent permeability to obstacles [36]. The wearable device containing an LF receiver is normally in sleep mode. If it receives an LF signal that matches the receiver's predefined pattern ID, it wakes up. Therefore, the wearable device can remain in sleep mode and facilitate low power consumption. The payload of the LF signal includes door information and position data. After the wearable device wakes up, it starts the process of indoor location recognition;
* **Tag device:** To consume minimal power for a battery-powered tag device, it is not appropriate that the internal sensors in the tag device are always enabled. Instead, the tag device should only be activated for detecting appliance when there is a user in the space where it is installed [37]. In the proposed device network, the stationary device transmits activation messages to all connected tag devices when a user with the wearable device enters the zone of ADL measurement. A tag device that receives this message enables its internal sensors so that it is ready to detect the usage event of an appliance to which it is attached. Similarly, when the user exits the space, the stationary device sends deactivation messages to the connected tag devices. Thus, a tag device usually disables built-in sensors and enables them only when a user is in that space.  
The sleep-wake up method using LF not only lowers power, but also reduces the interference caused by multiple users. A typical BLE communication method generates an advertising beacon for each peripheral device, and when multiple devices are operated, this method causes communication interference. The LF-BLE mixed communication method, including the concept of opportunistic computing, reduces the BLE beacon noise by recognizing the user's Activity Trigger Event. In addition, even when transmitting activity information through BLE, the activity collection devices record the execution time of the corresponding data when a communication failure occurs due to interference, and retransmits to ensure activity data collection and data accuracy.
# An Experience Transfer Approach for the Initial Data of Iterative Learning Control
## 2 Problem Description  
### Iterative Learning Control  
ILC aims at a specific controlled system with a repetitive working process. ILC uses its historical operation data to modify the control action continuously, so as to improve the control accuracy. The typical P-ILC algorithm is described as follows:  
\[\begin{array}{c}u_{i+1}(k)=u_{i}(k)+L*e_{i}(k)\\ e_{i}(k)=y_{d}(k)-y_{i}(k)\end{array} \tag{1}\]  
where \(i\) is the index of iterations; \(k\) is the time of discretization; \(u_{i+1}\) is the control input of the \((i+1)\)-th iteration; \(u_{i}\) is the control input of the \(i\)-th iteration; \(L\) is the learning gain and \(e_{i}\) is the error between the desired \(y_{d}\) and the output \(y_{i}\) in the \(i\)-th iteration process.  
It can be seen from the above that the control input \(u\) is adjusted iteratively on the iteration axis according to the input and output error of former iterations, which can be written as follows:  
\[u_{i+1}(k)=u_{0}(k)+L*\sum e_{i}(k), \tag{2}\]  
where \(u_{0}\)_(\(k\))_ is the initial control input of iterative learning. If the initial control input of the first iteration could be close to the control input of the final iteration, the turns of iterations required to achieve the desired control accuracy will be significantly reduced.
## 5 Experimental Framework and Results
### Case Study Analysis of CARAG
#### 5.1.2 Context-Aware Evidence Retrieval for Claim 59  
Using the thematic embedding generated for CARAG, we conducted evidence retrieval for Claim 59, incorporating it as part of the retrieval query. To enable a comparative evaluation, we implemented three different retrieval approaches: (1) retrieving only the annotated evidence from _FactVer_ as the ground truth evidence identified during dataset annotation; (2) applying the baseline RAG approach, which utilizes only the claim vector for evidence retrieval from the FAISS vectorized corpus (setting \(\alpha=1\) in Equation (2), as detailed in Section 4.2); and (3) using CARAG with a balanced combination of the claim vector and thematic embedding by setting \(\alpha=0.5\) in Equation (2).  
For each approach, we selected the top \(k=6\) evidence items, in alignment with our dataset distribution statistics (Section 3.2), which indicate that the majority of claims are supported by six pieces of evidence. Table 3 presents a side-by-side comparison of evidence retrieved by these three approaches for Claim 59.  
A key observation from the evidence comparison in Table 3 is the overlap between certain evidence items retrieved by RAG and CARAG (e.g., references to the car-making industry and Korean EV tax policies). This overlap underscores CARAG's effectiveness in capturing a broad context similar to RAG while offering enhanced thematic alignment to the claim's topic. CARAG further strengthens this retrieval by incorporating additional climate-specific evidence directly related to the selected claim, demonstrating its advantage in filtering relevant information from broader contextual data.