esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Sensing Scene Classification and Explanation Using RSSCNet and LIME
## 3 Method
### LIME Model Explanation Kit  
Although a deep learning model can obtain quite good classification results, it is difficult to understand how the classification results are derived because of its black-box characteristics. How to interpret the reasoning mechanism of the deep-learning model has become an important topic of research. In recent years, among the deep-learning methods, LIME is a new evaluation method for the interpretability of the model [29], i.e., whether it is possible to understand the importance of the deep-learning model for the interpretability of the image in the subsequent classification and prediction. The problem with model interpretability is that it is difficult to define the decision boundary of the model in a way that humans can understand. As shown in Figure 7, LIME is a Python library that attempts to generate some local feature-circle super-pixels. This can be used to explain the principle on which the model is based, which is usually difficult to describe, and to help with understanding whether the basis on which the model applies its decisions is appropriate or not. Figure 7a shows that the most interesting super-pixel of the RSSCNet model contains an airplane; hence, it can be correctly classified by the RSSCNet model. Figure 7b depicts that there is no storage tank in the super-pixel unlike the RSSCNet model, thereby causing the RSSCNet model to make a misclassification.
# A Selective Dynamic Sampling Back-Propagation Approach for Handling the Two-Class Imbalance Problem
## 4 Dynamic Sampling Techniques to Train Artificial Neural Networks
### Method 2. Dynamic Over-Sampling  
In [19], a Dynamic Over-Sampling (DOS) technique to deal with the class imbalance problem was proposed. The main idea of DOS is to balance the MSE on the training stage (when a multi-class imbalanced dataset is used) through an over-sampling technique. Basically, the DOS method consists of two steps:  
1. _Before training_: The training dataset is balanced at 100% through an effective over-sampling technique. In this work, SMOTE [8] is utilized.
2. _During training_: The MSE by class \(E_{j}\) is used to determine the number of samples by class (or class ratio) in order to forward it to the ANN. The equation employed to obtain the class ratio is defined as: \[ratio_{j}=\frac{E_{max}}{E_{j}}\times\frac{Q_{j}}{Q_{max}};\text{ {for }}j=1,2,...,J\] (5) where \(J\) is the number of classes in the dataset and \(max\) identifies the largest majority class. Equation (5) allows balancing the MSE by class, reducing the impact of the class imbalance problem on the ANN.  
The DOS method only uses the necessary samples for dealing with the class imbalance problem and, in this way, to avoid getting a poor classifications performance as a result of training the ANN with imbalanced datasets.
###### Abstract  
Existing scanning laser three-dimensional (3D) imaging technology has slow measurement speed. In addition, the measurement accuracy of non-scanning laser 3D imaging technology based on area array detectors is limited by the resolution and response frequency of area array detectors. As a result, applications of laser 3D imaging technology are limited. This paper completed simulations and experiments of a non-scanning 3D imaging system with a single-pixel detector. The single-pixel detector can be used to achieve 3D imaging of a target by compressed sensing to overcome the shortcomings of the existing laser 3D imaging technology. First, the effects of different sampling rates, sparse transform bases, measurement matrices, and reconstruction algorithms on the measurement results were compared through simulation experiments. Second, a non-scanning 3D imaging experimental platform was designed and constructed. Finally, an experiment was performed to compare the effects of different sampling rates and reconstruction algorithms on the reconstruction effect of 3D imaging to obtain a 3D image with a resolution of \(8\times 8\). The simulation results show that the reconstruction effect of the Hadamard measurement matrix and the minimum total variation reconstruction algorithm performed well.  
s 3D imaging; compressed sensing; depth detection; single-pixel detector +
Footnote †: journal: Applied Physics Letters
# Development and Application of Intelligent Temperature Control System for Large Aqueduct
## 3 Engineering Application of Intelligent Temperature Control System for Large Aqueduct
### Simulation Calculation Module
#### 3.2.1 Tensile Strength of C50 Concrete  
C50 concrete has the highest strength among ordinary strength concrete, and its material properties such as strength and modulus of elasticity change with time in a significantly different way from other ordinary strength concrete, which is essential for the control of temperature stress in the structure of the aqueduct during the construction period. Equation (4) is an expression for the variation of the cube compressive strength of C50 concrete with time fitted by Zhao [13] based on experimental data. The mean, standard and design values of the axial tensile strength of concrete were obtained sequentially according to the cube compressive strength [14; 15; 16], as shown in Figure 4.  
\[f_{cu,t}=\frac{56.8t^{0.83}}{1.54+0.94t^{0.83}} \tag{4}\]  
\[f_{t,m}=0.395f_{cu,t}^{0.55} \tag{5}\]  
\[f_{tk}=0.88\kappa_{c2}f_{t,m}(1-1.645\delta) \tag{6}\]  
\[f_{t}=f_{tk}/\gamma_{c} \tag{7}\]  
where \(f_{cu,t}\) is the cubic compressive strength of concrete at age \(t\), \(\delta\) is the variation coefficient of concrete (0.11 for C50 concrete), \(\kappa_{c2}\) is the brittle reduction coefficient of concrete (0.97 for C50 concrete) and \(\gamma_{c}\) is the material subitem coefficient of concrete (taken as 1.40).  
Figure 4: Tensile strength of C50 concrete.
# On Noise Modeling of Capacitive Feedback Transimpedance Amplifiers in CMOS
## 4 Discussion
The BiCMOS-based implementation also demonstrates constant input noise current density over a wider frequency range for the two configurations with the largest gain when compared to the  
Figure 11: The noise model from [PERSON] applied for our set of circuit parameters (dashed lines) and our developed complete analytical noise model for the same set of parameters (solid lines). CMOS-based implementation. Although all configurations demonstrate the noise level for the base gain configuration with \(Z_{\text{T}}=10\) k\(\Omega\) below \(2.0\) pA/\(\sqrt{\text{Hz}}\), further work is still needed on the development of accurate models for capacitive feedback TIA with cascode.
# Service-Driven Dynamic Beam Hopping with Resource Allocation for LEO Satellites
## 4 Simulation Results and Analysis
### Performance of the Applied Algorithm
When the throughput allocated by the system can meet the high traffic demand in the scenario, the situation of data packet loss will also improve. Figure 8 presents the change of the business data packet loss rate during the training process of the algorithm. It can be clearly observed that during the 2000-episode training process, the business data packet loss rate gradually decreases and tends to be stable. Eventually, the business data packet loss rate stably remains at about 23%. The result demonstrates that as training progresses, the algorithm can continuously optimize the resource allocation strategy, effectively reduce the business data packet loss rate, improve the stability and reliability of the system in handling traffic demands, and thus guarantee user satisfaction and system performance. Figure 9 shows the convergence process of the total system power consumption during the training of the algorithm, where the total power consumption refers to the average  
Figure 8: Episode packet loss rate under optimization weight \(\alpha=0.5\). Figure 7: The episode user satisfaction under the optimization weight \(\alpha=0.5\). value of the sum of the power consumption of all service beams used for data transmission. It can be seen from the figure that the total system power consumption eventually converges to around 18 W. In terms of principle, according to the Shannon formula, under the conditions of effectively avoiding interference and given noise, the downlink throughput of beam-positioned users has a logarithmic functional relationship with the transmit power. When the throughput allocated by the system can meet the relatively high traffic demands within the scenario, the power demand per time slot will also be optimized accordingly. Since the number of beam hopping time slots set in this paper is sufficient, business data can be dispersed for transmission in more time slots. The above method of transmission greatly reduces the power demand per time slot, thereby giving the algorithm significant advantages in energy-saving performance. During the training process, compared with the initial stage, the total system power consumption tends to be stable in the second half, and the energy-saving effect of the algorithm remains consistently good, which fully demonstrates that the algorithm framework can withstand environmental changes and achieve efficient energy saving while ensuring service performance. Throughout the iterative process, the algorithm always tends to maintain a relatively low power consumption level, showing good energy-saving characteristics.
## 1 Introduction
This research makes the following key contributions: (1) Introduces a mechanism that utilizes both primary and secondary links simultaneously, addressing the inefficiency of idle backup links and ensuring cost-effective resource utilization; (2) Focuses on managing two distinct types of traffic: data traffic for branch-to-branch communication and internet traffic for external services; (3) Employs an overlay network for secure data communication and an underlay network for internet traffic, enhancing security and optimizing network resource use; (4) Develops a route-mapping mechanism to manage both data and internet traffic effectively. This mechanism supports load sharing and link failover on the preferred link according to specific needs, ensuring optimal network performance; (5) Compares EIGRP and OSPF based on key network parameters such as delay, jitter, packet loss, and failover convergence time in an IPv6 environment. This comparison provides network engineers and administrators with the information needed to select the most appropriate routing protocol to enhance their routing infrastructure for effective traffic distribution. The remainder of this paper is structured as follows: Section 2 provides an overview of routing protocols; Section 3 presents a literature review; Section 4 discusses load sharing and link-failover systems; Section 5 highlights the importance of IPv6 in networking; Section 6 details the methodology and performance metrics; Section 7 presents the results and discussion; and Section 8 concludes the paper with a summary and suggestions for future work.
# A Velocity-Aware Handover Trigger in Two-Tier Heterogeneous Networks
## 2 Related Work
### Velocity and Handover Performance  
In wireless communication networks, the estimation of the mobile users' velocity is an essential part of upgrading network execution. Subsequently, in recent years portable velocity estimation has been broadly considered in the literature [5; 6; 7; 8]. Handover count-based strategy [6] is firmly related to the velocity estimation approach, where the UE's mobility is evaluated utilizing the handover quantity achieved by the UE in a predefined time window. Existing Long-Term Evolution (LTE) and LTE-Advanced innovations utilize the handover-count strategy to classify the mobility condition of UE into three large classes: low, medium, and high mobility. Hypothetically, the sojourn time-based velocity estimation in [7] resulted more exact than handover count-based velocity estimation. The author appraised the UE velocity in light of sojourn time samples and small cell base station (SBS) density. However, this work used an unrealistic mobility system. Furthermore, indeterminate estimation in the limit of small cells was experienced. In [5], the radial velocity was gained through the most extreme Doppler spread estimation of the received signal in mobile communications, although it required an extensive perception interim and signal noise ratio (SNR) higher than 30 dB.  
Tracking velocity can fundamentally enhance handover algorithm execution by simultaneously decreasing both the handover quantity and the handover delay, as demonstrated in [9; 10]. The authors of [9] perceived the effect of the handover execution on communication quality, and they thus proposed a basic, effective, aware handover management scheme endeavoring to maintain longer service continuation with serving base stations (BSs) and decrease the handover rate and its related signaling. Using mathematical expressions, they demonstrated that the proposed handover method accomplished an impressive gain in terms of throughput. However, they supposed during handover execution that no information was transmitted. Taking into account that their numerical results for delay achievedhigh esteem, allowing communication and measuring packet loss through handover is attractive and will help produce the QoS achieved by their system. The authors of [10] demonstrated how low quality in handover execution could result in a severe interruption and call drops in communication, which remains a pressing issue yet to be solved. The authors thus examined a handover system in view of distance data for LTE high-velocity rail networks. The strategy for choosing a handover reference point depended on the situation given specific channel conditions. The outcomes demonstrated that a small region appropriate to triggering in the overlap area can be obtained, and handover execution can be expanded by diminishing the HOF probability and wiping out ping-pong (PP) handover.
# An Empirical Study on Ensemble of Segmentation Approaches
## 2 Materials and Methods
### Loss Functions  
Some loss functions tested for the design of the networks' ensemble will be presented in this section, including the loss functions suggested in [24] (Section 2.2.1) and the new ones here tested/proposed (Sections 2.2.2-2.7).  
Different loss functions influence the training phase and the performance of the model. In semantic segmentation tasks, pixel-wise-cross-entropy is one of the most widespread and adopted loss functions; this operates at the pixel level, verifying whether the predicted label of a given pixel coincides with the ground-truth. One of the main problems with this approach is the critical situation in which the dataset is unbalanced with respect to the labels, but it can be solved through the use of counterweights. The dice loss function [25]aims to verify the overlap between the predicted segmented images and the ground-truth. This approach, that we have also used in this work, is widespread in semantic segmentation.  
An exhaustive overview of image segmentation and loss functions is available in a recent survey [25].
# Proposal for the Evaluation of Eco-Efficient Concrete
## 5 Discussion and Study Limitations  
Two limitations of this study are a need to expand the assessment factors for durability performance (service life) and to develop an eco-efficiency assessment method for high-strength concrete (above 40 MPa).  
(1) Expansion of assessment factors for durability performance (service life)  
Among various deterioration phenomena, this study considers only neutralization as an assessment factor for durability performance. The assessment model for service life is based only on the neutralization of concrete, and the model fails to reflect durability performance for salt damage and freeze-thaw processes. Because the service life of concrete differs according to deterioration phenomena, differences may arise during eco-efficiency assessment of the same type of concrete. When assessment is conducted according to neutralization only, the service life of concrete is slightly reduced by the mixing of admixtures (GGBS). On the contrary, salt damage tends to show an increase in the service life of concrete due to the mixing of admixtures. Therefore, the measure of eco-efficiency will change according to differing service life values of concrete with the same mix design depending on the type of deterioration phenomenon. In future studies, the service life of concrete should be assessed according to various deterioration phenomena, such as neutralization and salt damage, simultaneously.  
(2) Development of an eco-efficiency assessment method for high-strength concrete (above 40 MPa)  
The equation for calculating service life applied herein for the assessment of durability showed mostly effective results for concrete with a water-binder ratio between 40% and 60%. The equation was inappropriate for high-strength concrete with a water-binder ratio below 40%, however.  
It is necessary to develop an equation that can calculate the service life of high-strength concrete against compound deterioration.  
Figure 9: Analysis of eco-efficiency by admixture ratio (60%).
## 2 Related Work
### Hypergraph Neural Network
In meteorological contexts, this capability is critical: many atmospheric phenomena (e.g., frontal systems, monsoonal circulations, mesoscale convective complexes) involve simultaneous interactions of spatially dispersed regions. These inherently group-based dynamics cannot be fully captured by pairwise graphs. Hypergraphs address this by connecting meteorologically similar stations via hyperedges, enabling joint reasoning over entire weather patterns and modeling coordinated regional evolution. Compared to alternatives like simplicial complexes or global attention, hypergraphs provide a more interpretable and scalable balance of expressiveness and computational feasibility. Their feature-similarity-based construction is particularly adept at handling non-local, non-stationary patterns pervasive in meteorological data.
## 3 The LOBIN Prototype: A Practical Study Case
### System Validation and Tests
#### 3.2.4 Overall System Tests  
After debugging and tuning each subsystem separately, the performance of the whole system was tested in a real scenario by deploying a pilot scheme in the Cardiology Unit at La Paz Hospital (Madrid, Spain). For this client-side validation, each subsystem was set up following the design criteria and conclusions from previous tests. Figure 12 illustrates the coverage area and network infrastructure that was deployed for the pilot scheme.  
The tests carried out at the hospital facilities involved five patients (_i.e._, five WDADs and five LWTBs) and ran for 24 hours. Such tests were considered successful, since the results obtained results met the expected ones (and so the requirements of the system):  
* Percentage of packet losses was lower than the maximum acceptable value (_i.e._, 2%)
* Battery life was proved to be about 8-9 hours for WDADs and two days for LWTBs
* The location algorithm was tested to work properly
* Measured physiological parameters were normal and met expected values
* The quality of the real-time ECG signals was approved by hospital doctors  
Figure 12: Pilot scheme schematics.  
In addition, hospital personnel provided valuable feedback to improve the quality and usability of the system, such as:  
* The tightness of the smart shirt was pointed out as an issue by hospital personnel, since patients from Cardiology Unit often present high abdominal perimeter. Nevertheless, no further complains were received.
* Hospital staff use the GUI both to monitor the patients' status in real-time easily, sitting in front of a PC, and to look at the signals over a long period of time, taking advantage of the features provided by the tool. Some of these features, such as checking just a given period of time by selecting initial time and final time or the scroll, were incorporated into the GUI on the request of hospital personnel.
* The ECG display was modified following the feedback from the hospital personnel in order to fit the ECG representation they are used to handle (e.g., from commercial electrocardiographic devices).  
Figure 13 shows the developed GUI, where the health status of five patients can be monitored at a glance and the ECG of one of them is displayed. The tool also includes a Geographic Information System (GIS) to locate the patients in the hospital facilities plan.
## 3 Results and Discussion
### Energy Analysis  
The total electricity cost of a dehumidification system can be calculated by considering the dehumidification load and the required energy to drive the process air (i.e., for the fan). A simple process of a dehumidification system including the dehumidification and regeneration processes is represented by Figure 6.  
As shown in Figure 6, the energy resources for the regeneration process opens a contribution to renewable energy and industrial waste heat. The required total electricity can be reduced somehow by coupling the dehumidification system with the solar thermal energy to elevate the temperature of regeneration. The process inside the dehumidification system is altered continuously from adsorption and desorption. Within the study, as also mentioned in Section 4, the proper desorption temperature for the current system was 318 K. Therefore, the analysis of energy cost was based on the properties of the current system under 318 K.  
The total energy demand can be calculated as the total of electricity for dehumidifying the air (\(Q_{lat}\)), regenerating the material (\(Q_{sen}\)), and circulating the air by using a fan (\(W_{fun}\)). The total energy demand (\(E_{t}\)) is given as  
\[E_{t}=Q_{lat}+Q_{sen}+W_{fan} \tag{18}\]  
Figure 6: The simple process inside the desiccant dehumidification system.  
and  
\[W_{fan}=\frac{\dot{V}_{a}\Delta P_{air}}{\varepsilon_{fan}} \tag{19}\]  
where \(\dot{V}_{a}\), \(\Delta P_{air}\), and \(\varepsilon_{fan}\) denote the volume flow rate (m\({}^{3}\)/s), pressure drop (Pa), and efficiency of the fan (%), respectively. The assumption of the efficiency of the fan was considered at the value of 70% [43]. Then, the electricity of the fan was found to be 0.15 kW. On the basis of the experimental results, the total energy demand was found in the value of 0.624 kWh. The calculation of the cost mainly depends on the supply price of electricity in each country. In the case of the standalone dehumidification system, the current system consumed less energy when compared to conventional air conditioning. However, the initial cost for the desiccant dehumidification system might be higher than the conventional one. The benefit can cover this drawback in that the energy-saving from the standalone dehumidification system is able to lower the operational cost of the system [44].