esa-sceva/satcom-chunk-collection huggingface dataset:
# LSANet: Lightweight Super Resolution via Large Separable Kernel Attention for Edge Remote Sensing
## 3 Method
### Residual Large Kernel Separable Feature Distillation Module (RLSKFDM)  
This paper proposes an enhanced feature distillation module termed the residual large kernel separable feature distillation module (RLSKFDM), with its architecture illustrated in Figure 2.  
This module builds upon the RFDB framework with key architectural improvements: replacing the original SRB module in RFDB with RLSKAB to introduce large receptive field feature learning, enabling the model to more effectively capture global contextual information and local details; substituting the CCA module in RFDB with RFEM to enhance feature extraction efficiency and channel correlation utilization. RFEM leverages an efficient channel attention mechanism to enhance focus on key features and optimizes feature representation during the distillation process. Through these designs, RLSKFDM significantly improves feature distillation quality while maintaining module lightness.  
Assuming the input feature is \(F_{in}\), the calculation process of the \(i\)-th RLSKFDM can be depicted as  
\[F_{RLASKB}^{i}=RLSKAB\left(F_{in}^{i}\right), \tag{6}\]  
Then, the intermediate features of multiple branches are distilled and fused using \(1\times 1\) convolution and feature concatenation operations:  
\[F_{\text{fuse}}^{i}=\text{Concat}\left(\phi_{1}\left(F_{RLSKAB}^{i}\right),\phi _{2}\left(F_{RLSKAB}^{i}\right),\phi_{3}\left(F_{RLSKAB}^{i}\right)\right), \tag{7}\]  
Here, \(F_{\text{fuse}}^{i}\) represents the fused features generated by the distillation and concatenation operations of \(F_{RLSKAB}^{i}\), and \(\phi\) represents the feature transformation of \(1\times 1\) convolution in the branch. The fused features are then further processed by RFEM:  
\[F_{\text{enhanced}}^{i}=RFEM\left(F_{\text{fuse}}^{i}\right), \tag{8}\]  
Here, \(F_{\text{enhanced}}^{i}\) represents the enhanced features after further processing by RFEM, and \(RFEM\) represents the enhancement mechanism of RFEM. Finally, the module obtains the output features via residual connections:  
\[F_{\text{out}}^{i}=F_{\text{enhanced}}^{i}+F_{\text{in}}^{i}, \tag{9}\]
## 2 Fundamentals of SAR for Antenna Sizing and Transmitting Power Consideration  
### Altitude of SAR Platform  
The optimal altitude range for SAR satellites depends on several factors, including mission objectives, imaging requirements, and technical considerations. The altitude of the satellite directly impacts the achievable spatial resolution of the SAR system. Higher altitudes result in coarser resolution due to the longer synthetic aperture length and larger ground footprint. Lower altitudes provide a finer spatial resolution, enabling the detection of smaller features on the ground. The altitude influences the width of the swath coverage, which is the area imaged by the SAR system in a single pass. Lower altitudes result in narrower swaths, while higher altitudes offer wider coverage. The altitude is considered for the required coverage area and revisit time for the intended applications. The altitude affects the received signal strength and signal-to-noise ratio (SNR) of the SAR system. Higher altitudes typically result in stronger backscattered signals due to a larger illuminated area on the ground. However, at higher altitudes, the received signal may also be affected by increased atmospheric attenuation [36; 37].  
Moreover, the altitude selection should consider the stability and maneuverability requirements of the satellite. Higher altitudes may offer more stable orbits with reduced orbital perturbations. The altitude of the SAR satellite affects the data volume and data transmission rate. The required downlink bandwidth and ground infrastructure should be considered when determining the altitude [38]. The optimal altitude range for SAR satellites is typically determined based on a trade-off between spatial resolution, coverage area, revisit time, signal strength, surface penetration, and mission-specific requirements. Additionally, factors such as regulatory requirements, collision avoidance with other satellites and space debris, and compatibility with other satellite constellations or Earth observation missions may also influence the selection of altitude. SAR satellites typically operate between 500 and 800 km in altitude. Radar parameters together with geometry define the radar range. Therefore, the platform altitude is another limit to the system's performance.  
The spacecraft velocity of the orbiting platform is a consequence of this altitude and directly affects the SAR equation. Global coverage leads to the design of the orbit as a polar one and the high-power consumption of the active system mostly results in a Sun-synchronous orbit (SSO) [38; 39].
## 4 Results
### Experimental Data and Preprocessing  
We adopted the NLPCC dataset from the NLPCC2014 and NLPCC2017 emotion category tasks. The dataset had more than 3 million Weibo sentences collected with sentiment labels, including eight sentiment categories: anger (7.9%), aversion (11.9%), satisfaction (11.4%), happiness (19.1%), sadness (11.7%), fear (1.5%), surprise (3.3%), and neutrality (33.2%).  
Since there are no large-scale ready-made emotional session data, we built a session dataset (STC) with emotion labels on the basis of the NLPCC dataset [8]. Specifically, we first removed the data noise and data containing the fear and surprise emotion categories (which provided only a small amount of data), then used a Bi-LSTM model to train the emotion classifier on the NLPCC dataset, using this classifier to perform emotion classification. Finally, we used the classifier to annotate the STC dataset in order to obtain a conversation dataset with emotion tags.  
When constructing the STC dataset, we chose a Bi-LSTM model as the emotion classifier for the following reasons. According to the research of [PERSON] et al. [13], compared with other classifiers, the Bi-LSTM classifier has the best accuracy. The accuracy of the emotion classifier is shown in Table 2. To better train and test our model, we divided the STC dataset into training/validation/testing sets at a ratio of 9:0.5:0.5, respectively. Specific STC statistics are shown in Table 3.
# Analytical Model for Enhancing the Adoptability of Continuous Descent Approach at Airports
## 4 Model Development
### Adopting Queueing Theory to Our Model
#### 4.2.2 Capacity of the Stacking Space for Aircraft Arrivals
To attain this operationally, the runway, as a critical element in ATM and airports operations, is assumed to have an arrival capacity that is at least as large as the \(AAR\). The maximum runway arrival hourly capacity is calculated by dividing the average aircraft ground speed, \(GS\), in knots, crossing the runway threshold by the longitudinal separation distance, \(d\), in nautical miles, required between successive arrivals, as follows:  
\[RwyCap=\frac{GS}{d} \tag{20}\]Observe above that the stacking space capacity, \(k\), was determined above based on the minimum allowable separation distances between a pair of aircrafts of a similar weight class. Thus, the value of \(k\) represents a bound on the capacity of aircrafts that may safely fit within the stacking space. Consequently, an upper bound of \(k\) should be determined based on the worst-case sequencing of aircrafts within the stacking space. That worst case can be determined by assuming a sequence in which the lightest aircraft scheduled to land that day is followed by the heaviest aircraft scheduled that day followed by the lightest aircraft scheduled and so forth. Following these calculations, the air traffic controller may be provided with the lower and upper bounds of the stacking space capacity under the assumption that all aircrafts arriving during that time period use CDA. When a particular aircraft arrives during the period, the controller may compare the number of aircrafts presently in the system versus the lower and upper bounds. If the number in the system is less than the lower bound, then that is a favorable indicator that the arriving aircraft may be admitted using CDA. If the number is above the upper bound, then that indicates that CDA should be denied. If the number of aircrafts within the stack is as much as the lower bound or greater and yet below the upper bound, then the controller will need to consider other factors in reaching a decision on whether CDA is safe for that arriving aircraft. Even if the number in the system is below the lower bound, those other factors may need to be considered by the controller to ensure there is sufficient separation distance between the arriving aircraft and the aircraft it follows most closely within the stacking space. Providing the controller with lower and upper bounds on stacking space is an optional tactic. Doing so depends on whether it would be too much additional information for the, often busy, controller to absorb.
# LoRaWAN Network for Fire Monitoring in Rural Environments
## 1 Introduction
However, LoRa is gaining popularity and the availability of low-cost LoRa nodes and gateways is increasing, leading to the increment in its attraction for the development of applications that require the wireless coverage of large areas with difficult access. Additionally, compared to SigFox, which is the closest technology, LoRa can reach a higher Data Transfer Rate. Considering the importance of early forest fire detection and the advantages of using LoRa in WSN-based forest fire detection systems, this paper presents a network for estimating fire risk on an area by using the 30-30-30 rule. This rule permits determining the level of fire risk in an area considering the values of relative humidity, wind speed, and temperature. A high fire risk implies having a temperature greater than 30 \({}^{\circ}\)C, a relative humidity lower than 30%, wind speed greater than 30 km/h and absence of precipitation in the last 30 days. Additionally, it can also work as a fire detection system for the cases where intentional (or due to natural causes) fires were generated. The system is comprised of CO\({}_{2}\), temperature, humidity and, wind speed sensors that monitor the environment connected to an Arduino and Dragino LoRa Shield module. LoRa is used to send the information from different nodes located at the mountain to the gateway, where the data is sent to the TTN server to be processed by the application server and to graphically be shown on a website. The system was tested in a real environment. The tests are focused on the network performance, measuring the Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR) and, the operation of the sensors. The rest of the paper is organized as follows. Section 2 presents the Related Work on other existing forest fire detection systems. An overview of LoRa and LoRaWAN is provided in Section 3. Section 4 presents the system description. The configuration of the TTN server and the design of the application server are detailed in Section 5. Tests and results are shown in Section 6. Lastly, the conclusion and future work are presented in Section 7.
# Mambo-DQN: Adaptively Tunes Visual SLAM Parameters Based on Historical Observation DQN
## 3 Method  
### Problem Summary  
As shown in Figure 1, we reformulate the parameter adaptation task of visual SLAM as a reinforcement learning problem that involves the interaction between the agent and the environment, employing ORB-SLAM3 as the environment and designating the agent as the action decision module. This approach allows us to derive the optimal parameter combinations through the dynamic interplay between the agent and the environment. The agent is composed of an improved DQN network. The ORB-SLAM3 system calculates the pose based on the parameters given by the agent and the corresponding video frames.  
We formulate the visual SLAM parameter adaptation task as a Markov decision process (MDP), represented by the tuple \(M=(S,A,P,R,\gamma)\). In \(M\), \(s_{t}\in S\), where \(s_{t}\) represents the state, which is composed of feature maps of video frames. \(a_{t}\in A\), where  
Figure 1: Mamba-DQN SLAM framework.  
represents the action defined in DRL, consisting of system parameters. \(r_{t}\) represents the reward, which is obtained by calculating the absolute trajectory error between the true pose and the system-predicted pose.  
When the agent executes the current action \(a_{t}\) with probability \(P\), the state \(s_{t}\) will be replaced with the next frame \(s_{t+1}\) of the continuous sequence of video frames in time. An observation consisting of \((s_{t},a_{t},r_{t},s_{t+1})\) is called \(Observation_{t}\). By combining historical observations, all observation trajectories \(T=(s_{0},a_{0},r_{0},s_{1},a_{1},r_{1},\ldots,s_{t},a_{t},r_{t})\) can be obtained. As an action decision maker, the task of an agent is to find a control strategy \(\pi(s,a)\) in the historical observation trajectory \(T\) that maximizes the sum of long-term rewards: \(R_{\pi}^{*}\).  
\[R_{\pi}^{*}=\sum_{i=0}^{t}\gamma^{i}r(s_{i},a_{i}) \tag{1}\]  
Given an action in MDP, there is an action-value function \(Q^{\pi}(S,A)\), and the optimal control strategy that maximizes the expected reward is transformed into the generation of the optimal value action function, as shown in Equation (2):  
\[Q^{\pi}(S,A)=E_{\pi}\left[\sum_{i=0}^{t}\gamma^{i}r(s_{i},a_{i})\right] \tag{2}\]
## 3 Materials and Methods
### System Architecture  
In this research, we propose two architectures for the semantic segmentation of clouds: CloudRCNN (auxiliary loss) and CloudRCNN (feature fusion). Both architectures are based on MaskRCNN, and we add an auxiliary branch to enhance model performance. Figure 2 shows the system architecture of CloudRCNN (auxiliary loss) and CloudRCNN (feature fusion). For both architectures, satellite images are fed into MaskRCNN for semantic segmentation. The C4 layer feature map of ResNet is used as the input for the auxiliary branch. For CloudRCNN (auxiliary loss), as shown in Figure 2, the auxiliary branch is a deconvolution decoder, and the feature map obtained by the decoder will be used to supervise the foreground and the background information. For CloudRCNN (feature fusion), as shown in Figure 2, the auxiliary branch consists of a deconvolution decoder and a feature extraction network based on ResNet and FPN [38]. The feature map obtained by this branch will be used for feature fusion in the form of element-wise multiplication with the feature map of the backbone network of MaskRCNN.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Training Set (9477 Samples)** & \\ \hline
**Category** & **Sample** & **Percentage** \\ \hline Fish pattern & 2219 & 23.41\% \\ \hline Flower pattern & 1881 & 19.94\% \\ \hline Gravel pattern & 2341 & 24.70\% \\ \hline Sugar pattern & 3036 & 32.04\% \\ \hline
**Testing Set (2359 Samples)** & \\ \hline
**Category** & **Sample** & **Percentage** \\ \hline Fish pattern & 2219 & 23.82\% \\ \hline Flower pattern & 1881 & 20.52\% \\ \hline Gravel pattern & 2341 & 25.36\% \\ \hline Sugar pattern & 3036 & 30.31\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset basics.  
Figure 2: _Cont_.
## 3 Autonomous Navigation of a Mobile Robot
### Localization  
Localization is the procedure for finding where an autonomous robot is situated in the environment. It is very important to find the place from which a robot can start its mission [70]. Various methods have been derived for the localization process. This process can be achieved with the help of a GPS sensor. However, the low cost of the GPS sensor has errors when implemented. Algorithms of localization have been organized with the help of sensors of proprioception such as stereo and monovision sensors and LiDAR (2D and 3D), which gives higher accuracy. Bayes filters, such as Kalman filters, particle filters, unscented Kalman filters, extended Kalman filters, etc., are being applied by fusing information gathered from different sources to estimate the localization [71].
## 3 Initial Survey Results and Discussion
### Common Causes of Asset Failure and Implications for Material Substitution  
Firstly, respondents were asked about the leading causes of asset failure affecting listed and historical structures within NR. The responses were analysed and categorised into overarching failure mechanisms, as visualised in Figure 2.  
The results indicate that the most significant contributors to asset deterioration are insufficient and irregular maintenance throughout the asset's lifecycle and damage caused by water ingress and weathering, both of which frequently lead to structural failure.  
These findings align with the literature [39], highlighting historical assets' vulnerability to degradation processes such as corrosion and erosion due to the materials used in their original construction. Additionally, inadequate maintenance of drainage systems and protective coatings was identified as a key factor, supporting previous studies, such as one that suggested replacing station canopy daggerboards of timber with FRP to mitigate weather-related deterioration [24].  
Comparing these responses with participants' views on material changes, there is a clear correlation between the elements most affected by weathering, degradation, and failure and their architectural and aesthetic significance. This shows the importance of preserving these components to ensure both structural integrity and compliance with legal protection requirements.  
The RHT commissioned a report on using fibre-reinforced plastic (FRP) to replace station fascia daggerboards, which has significant advantages over traditional wooden elements prone to material failure. Building on this, the survey included a question seeking suggestions on which elements of a listed asset would benefit from replacement with contemporary materials.  
Figure 2: Participants’ response to the survey to establish the leading type of asset failure of historical assets.
# Jointly Production and Correlated Maintenance Optimization for Parallel Leased Machines
## 6 Conclusions  
Based on an industrial case study, this paper deals with a new developed approach in the frame of group preventive maintenance policy for parallel leased machines. Production and maintenance plans are studied taking into account new constraints related to leasing machines which have to work during regular and overtime work in order to satisfy a random demand over a finite time horizon and considering a given service rate.  
Thus, to deal with this issue, and opting for a sequential resolution method, we start at first by proposing an approximate solution which minimizes the production costs under constraints related especially to service rate and equipment availability. Secondly, the obtained optimal production plan was considered as input data to perform the individual maintenance plan. To do this, we proposed an algorithm which allows organizing maintenance actions over the finite time horizon taking into account the degradation of each leased machine according to its production rate. A correlated group preventive maintenance policy based on the Center-of-Gravity approach is then developed and integrated to the algorithm in order to find a new optimal interval of group maintenance which allows the firm minimizing both maintenance and total costs. To further validate the model, a sensitivity analysis has been performed considering two case studies in which we vary the distribution of the random demand in order to study its impact in the developed approach.  
[PERSON] and [PERSON] conceived and designed the mathematical models; [PERSON] developed the approaches and the algorithms; [PERSON] and [PERSON] evaluated the results; [PERSON] supervised the work and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Case I** & **Case II** \\ \hline Production Cost (IPM) & \(1.1613\times 10^{7}\) mu & \(2.8739\times 10^{7}\) mu \\ Production Cost (GPM) & \(1.1614\times 10^{7}\) mu & \(2.8741\times 10^{7}\) mu \\ Individual Maintenance Cost & \(5179.42\) mu & \(5246.09\) mu \\ Group Maintenance Cost & \(2636.7\) mu & \(2593.3\) mu \\ Total Cost for IPM & \(1.16181\times 10^{7}\) mu & \(2.87441\times 10^{7}\) mu \\ Total Cost for GPM & \(1.16166\times 10^{7}\) mu & \(2.87435\times 10^{7}\) mu \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of the performance of the considered approaches for both cases.
# Bootstrap-CURE: A Novel Clustering Approach for Sensor Data--An Application to 3D Printing Industry
## 4 Contributions  
This paper presents three novel contributions:  
* A modification of the CURE approach consists of substituting the first phase of the original CURE approach by a bootstrap process that generates several small samples (\(S\) samples) from the original dataset and runs some clustering and super-classification processes to create the centroids that constitute the input of the second step of the CURE strategy. This contribution permits to scale up a hierarchical clustering process to large datasets and also reduces the CPU time drastically. Section 5.1.1 provides the details on it.
* As a consequence of using the _bootstrap-CURE_ strategy in real large dataset applications, a new challenge appears of developing an automatic criterion to cut the resulting dendrograms (\(S+1\)) in order to identify the number of clusters in such a way that the number of clusters manually proposed by an expert is properly approached (see Section 5.2).
* A third contribution is the proposal of an entire data science process that inserts _bootstrap-CURE_ with the automatic criterion to cut the dendrograms in a process, including the steps from the preprocessing to the interpretation-oriented tools. This automatically interprets the clusters emerging from this process, in line with the works in [89; 90] and with the emerging field of _explainable AI_[91]. The proposal is described in Section 5.  
Therefore, this work contributes to automatically profile the operation modes of 3D printers, providing an understandable description of profiles. This bridges the gap between obtaining the profiles with advanced clustering methods and connecting them with actionable knowledge, supporting decision making. The proposal is extremely useful in a real production process to better manage 3D printers, as it can automatically detect the operational modes of 3D printers by analyzing their sensor data and is also potentially linkable to an intelligent alert system, for example. Although the methodology presented is developed in the context of 3D printing, it is easily adaptable to other situations, where a machine is monitored through sensors and thus has a wide range of applications, contributing to the maintenance and follow up of machines in industrial settings as well as contributing to solving the open problem of real-time management of the machines.