esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Key Technologies in Trust Management for WSNs
### Trust Evaluation Methods
#### Selective Re-evaluation Method  
In indirect trust evaluation, [PERSON] [2] used the following strategy to avoid false recommendations from malicious nodes. The recommending node sends the risk assessment  
Figure 8: Function plot of the forgetting curve.  
value of the evaluated node to the evaluating node. Based on the received assessment value, the evaluating node decides whether to implement stricter evaluations on the evaluated node. The risk assessment value is not included in the trust value calculation. It is calculated based on all malicious behavioral activities of the evaluated node in the network, including packet loss, data deviation, and energy consumption.  
Indirect trust evaluation methods can be used to defend the network against attacks such as bad/good mouthing. In this type of attack, the attacking node generates and propagates false recommendations across the WSN. While it may be hard to determine whether a recommendation is truthful, recommendations from nodes with 'good' trust values should be more reliable. This way, the trust value of the recommending node can be used to assign a weighting to the recommendation [5]. To address the risk of a previously trustworthy node becoming compromised and starting to spread false trust information, receiving nodes compare the recommendations they receive. In the case of a significant deviation, a recommendation may be declared untrue [10]; the recommender's trust value will be decreased accordingly.  
Indirect trust evaluation requires the sharing of trust information between nodes, which increases communication costs. As it should not affect normal data communication and should ensure that trust values are updated in a timely manner, it is suitable for the early stages of establishing the trust system. During stable operation, propagating trust information when there are significant changes in the trust values is a feasible approach.
## 3 Results and Discussion
### Magnetic Nanoparticles Enable MSC Spheroid Culture  
The effect of MSC cell seeding density with regard to resultant spheroid size was assessed (Figure 5). Three different seeding densities were used; 6 \(\times\) 10\({}^{3}\), 1 \(\times\) 10\({}^{4}\), and 2 \(\times\) 10\({}^{4}\) cells/mL, with spheroid diameter measured after 24 h in culture medium under 280 mT magnetic field, based on the green fluorescent magnetic nanoparticles within the cells. There was no significant difference (\(p\) > 0.005) in the mean areas of spheroids (Figure 5D); however, interestingly, spheroid number increased with seeding density, suggesting that the cells have a finite rate of spheroid formation within 24 h. Further characterisation via scanning electron microscopy indicates the close relationship between cells within the spheroid. MSCs become compacted when compared with the monolayer culture, where cell diameter is \(>\)50 \(\upmu\)m, but here MSCs do not exceed 10 \(\upmu\)m and are tightly bound to neighbouring cells (Figure 5E). Tunnelling electron microscopy allows the internal structures of the MSC to be probed. Once internalised, mNPs are retained inside endosomes within the cell cytoplasm (Figure 5F), but do not enter the nucleus and are not toxic to the cell (as shown in previous publications [12; 18; 38]).
# Multi-Objective Optimization of Production Objectives Based on Surrogate Model
## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Applied Model**} & \multirow{2}{*}{**Algorithm**} & \multicolumn{4}{c}{**Factors/Input Parameters in**} & \multicolumn{4}{c}{**Resposes/Production Objectives in**} \\  & & \multicolumn{4}{c}{**Design Space**} & \multicolumn{4}{c}{**Criterion Space**} \\ \cline{3-10}  & & **A** & **B** & **C** & **D** & **Flow Time** & **Costs Per Part Unit** & **Number of Products** & **Machine Utilization** \\ \hline \multirow{3}{*}{FCD model} & Numerical optimization & \multirow{3}{*}{6} & \multirow{3}{*}{2} & \multirow{3}{*}{26} & \multirow{3}{*}{11} & \multirow{3}{*}{433.20} & \multirow{3}{*}{10.21} & \multirow{3}{*}{274.49} & \multirow{3}{*}{70.98} \\  & Multi-response method & & & & & & & & \\ (Design-Expert\({}^{\text{\textregistered}}\)) & & & & & & & & \\ \(D=0.631\) & & & & & & & & \\ Simulation (Witness) & & & & & & & & & \\ \hline \hline \end{tabular} \({}^{1}\) by Stat-Ease, Minneapolis, MN, USA.  
\end{table} 
Table 9: Experiment results using the Desirability function multi-objective optimization method. optimization methods provide similar solutions in terms of the components of the production objectives vector within the specific transformation and weight vector. When comparing this, all solutions verified by simulation are very close to each other in objective space. The literature concerning metamodel-based solving MOOP [11; 16] indicates that the utilization of valid metamodels can shorten the computational time needed for optimization processes, namely in the optimization of expensive black-box functions. We observed a significant shortness of calculation time when applying both numerical approaches in contrast to simulation-based optimization, but the time for searching for MOOP solutions strongly depended on the parameters of the applied methods. According to the authors' best knowledge, there is no similar work that can be used for comparison conclusions due to the specifics of the underlying discrete-event driven simulation model. To discuss the limitations of the presented MOOP solving procedure based on surrogates, the three main following limitations should be mentioned:  
1. We assume that an adaptable simulation model of the production system must be built. 2. We assume that the controlled production system does not involve too fast inner parameters and structural changes. The inner parameters and structure can be flexible, but they need to be changed in hours, not in minutes. Even if the simulation model could reflect changes almost immediately, the steps of the designed MOOP procedure take some time to derive the surrogate model and consequently find the solutions. 3. We must consider that some of the production goals are too sensitive to the change of input parameters; therefore, the approximate solution is not satisfactory.
## 3 Results
### Clinical Parameter Estimation  
The performance of our developed segmentation method was also reflected in the computed clinical indices. These clinical indices are computed using the Simpsons method and the agreement between the ground truth and the same parameters computed using the automated segmentation results is reported using correlation statistical analysis by mapping the predicted volumes of the testing set onto the ground truth volumes of the training set. As illustrated in Table 6 the agreement between our method's prediction and ground truth is high, characterized by a [PERSON]'s correlation coefficient (rho) of 0.898 (\(p<0.01\)) for LV-EF, 0.723 for RV-EF (\(p<0.1\)) and 0.924 (\(p<0.01\)) for Myo-mass. There was a slight over-estimation in the RV blood-pool segmentation also reflected in the clinical parameters estimation.  
Figure 15: Reconstructions of a sample of input images when rearranging the spatial representation’s channels. Rearranging the channels results in reconstructing only left ventricle blood-pool or only right ventricle blood-pool only or all the ventricular structures.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & \multicolumn{2}{c}{**Reconstruction Quality**} \\ \cline{2-3}  & **CC (\%)** & **PSNR (dB)** \\  & **n = 288** & **n = 288** \\ \hline
**Model II:** GAN + REC & 0.912 & 27.32 \\
**Model III:** GAN + REC + DISENTANGLE (Proposed) & 0.934 & 28.89 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Image reconstruction assessment: correlation coefficient (CC) and PSNR comparison between reconstructed and input images based on 288 test sets.  
Figure 16 shows a graphical comparison between the clinical parameters estimated from the cardiac features segmented via C_q_SL and the same homologous parameters estimated from the ground truth manual segmentations for both healthy volunteers and patients featuring various cardiac conditions. As shown, the clinical parameters estimated using our automatically segmented features show no statistically significant difference from those estimated based on the ground truth, manually segmented features.
# MCIDN: Deblurring Network for Metal Corrosion Images
## 2 Related Works
### End-to-End Image Deblurring  
Recent advancements in CNNs have significantly improved image deblurring, outperforming traditional methods. DeepDeblur [38] introduced direct regression between blurred and sharp images using a multi-scale CNN, setting a new standard. Subsequent methods like the scale-recurrent network by [PERSON] et al. [41] and selective parameter sharing by [PERSON] et al. [42] further enhanced performance.  
As large models yield diminishing returns, [PERSON] et al. [15] proposed a pyramid-like hierarchical network for coarse-to-fine processing. [PERSON] et al. [43] improved feature exploration across network stages, while [PERSON] et al. [44] reduced computational complexity with a multi-input/output structure. However, the spatial invariance of convolutional operations still limits comprehensive contextual modeling.  
These methods primarily focused on spatial dimensions, often neglecting channel interaction with frequency. Our approach integrates spatial and frequency channels to enhance deblurring performance in blurry metal corrosion images.
## 2 State of the Art
### IoT-Based Energy Management in Smart Buildings  
IoT-based energy management systems in buildings rely on a network of distributed sensors to monitor environmental parameters such as temperature, humidity, air quality, occupancy, and energy usage. These sensors provide real-time data that is processed through edge computing or cloud-based platforms to optimize the operation of HVAC systems, lighting, and other energy-consuming devices [24]. For instance, smart HVAC systems utilize IoT-enabled CO2 and humidity sensors to implement DCV, dynamically adjusting airflow based on occupancy and indoor air quality metrics, which leads to significant reductions in energy consumption [10; 25]. Similarly, intelligent lighting systems integrate dimmable LED fixtures with occupancy sensors and daylight harvesting strategies, achieving energy savings of 38% to 47% compared to traditional lighting solutions [11]. Moreover, IoT supports hybrid occupancy detection models that combine PIR sensors, ultrasonic sensors, and computer vision techniques to enhance the accuracy of occupancy detection, thereby enabling more effective energy control strategies [26]. Predictive maintenance, another major application, helps reduce operational costs and extend equipment lifespan by identifying inefficiencies and failures before they occur [13].  
Despite these advancements, IoT-based solutions for energy management face several challenges [15]. A key issue is the lack of standardization and interoperability among heterogeneous IoT platforms, which complicates integration with existing building automation systems [14]. In addition, security and privacy concerns associated with IoT data transmission pose significant risks, requiring the implementation of robust encryption and authentication mechanisms [27; 28]. Another critical challenge is the need for efficient processing of the large volumes of data generated by IoT sensors in real time, which demands advanced computational paradigms such as edge/fog computing and federated learning [29; 30]. Furthermore, ensuring that sensors and embedded systems operate with low power consumption remains essential for sustainable and long-term deployment in smart building environments.
## 4 Experimental Results and Analyses  
### Experimental Setup and Evaluation Metrics  
The experimental environment, as summarized in Table 1, was established on a robust Linux-based system running Linux-4.19.36 + x86_64 with glibc2.28, leveraging the computational power of a Huawei Ascend 910B NPU. The implementation was carried out using Python 3.9.18, and our framework integrated LangChain-ChatChat alongside Fastchat to facilitate interactive functionalities. For text segmentation, we employed the ChineseRecursiveTextSplitter. Our system utilized the Internlm-20B-LoRA as the large language model, with the ms-bge@ascend serving as the embedding model. This configuration provided a stable and efficient platform for evaluating the performance of our intelligent question-answering system in the agricultural domain.  
The experimental environment, as summarized in Table 1, was established on a robust Linux-based system running Linux-4.19.36+ x86_64 with glibc2.28, leveraging the computational power of a Huawei Ascend 910B NPU. The implementation was carried out using Python 3.9.18, and our framework integrated LangChain-ChatChat alongside Fastchat to facilitate interactive functionalities. For text segmentation, we employed the ChineseRecursiveTextSplitter. Our system utilized the Internlm-20B-LoRA as the large language model, with the ms-bge@ascend serving as the embedding model. This configuration provided a stable and efficient platform for evaluating the performance of our intelligent question-answering system in the agricultural domain.  
This paper uses ROUGE, BERTScore, and FactCC to evaluate model performance. ROUGE (calculated using Equations (2) and (3)) measures the n-gram overlap between generated and reference answers, while BERTScore (computed via Equations (4)-(6)) assesses semantic similarity using a pre-trained BERT model to gauge answer accuracy and fluency. FactCC evaluates factual consistency by using a supervised binary classifier to determine whether the factual statements in the generated text align with authoritative data. Table 2 presents the performance of various models according to these metrics.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Configuration** \\ \hline OS & Linux-4.19.36 + x86\_64-with-glibc2.28 \\ NPU & Huawei Ascend 910B \\ Programming Language & Python 3.9.18 \\ LangChain-ChatChat version & 0.2.8 \\ Fastchat version & 0.2.33 \\ Splitter & ChineseRecursiveTextSplitter \\ LLM & Internlm-20B-LoRA \\ Embeddings model & ms-bge@ascend \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configuration information of experimental environment.
## 2 Materials and Methods
### Experimental Methods
#### 2.2.3 Microscopic Tests  
The microscopic structure of the clay soils was examined through the scanning electron microscopy (SEM) and the mercury intrusion porosimetry (MIP). The SEM test was concentrated on the geometrical appearance of the soil, while the MIP test was focused on the pore size distribution. Before carrying out each test, a freeze-drying technique was utilized to eliminate the water from the sample. During the freeze-drying process, each sample (around 2 g) was firstly placed in a freezer set at \(-80\)\({}^{\circ}\)C for an initial freezing period of 24 h. Subsequently, the frozen samples were transferred to a freeze dryer, where the chamber temperature was also maintained at \(-80\)\({}^{\circ}\)C, and a vacuum condition was applied. The drying phase then commenced, during which ice sublimated directly from the solid to the gaseous state without passing through the liquid phase. This sublimation process lasted approximately 48 h. Freezing the sample at \(-80\)\({}^{\circ}\)C minimized the formation of large ice crystals that could damage the soil fabric, thereby preserving the original arrangement of soil particles and aggregates, which has been verified by many scholars([PERSON], 1970; [PERSON], 1976; [PERSON] et al., 1999) [29; 30; 31]. And the 48 h freeze-drying period ensured complete dehydration of the sample.  
The MIP test was performed with consideration of optimum water content \(w_{\text{opt}}\) = 27.5% as well as its dry side \(w_{\text{d}}\) = 19% and wet side \(w_{\text{w}}\) = 35% using Autopore IV 9510 porosimeter (Micrometrics Corporate, Georgia, America). The mercury intrusion process was carried out by progressively increasing the mercury pressure, during which mercury infiltrated gradually into the large pores at low pressure and into the small pores at high pressure. At a given pressure, the total volume of the infiltrated pores was measured and the corresponding pore diameter was calculated following the method used by [PERSON] et al. [32].  
Regarding SEM test, three remolding water contents (\(w\) = 19%, 27.5% and 35%) were considered. During the test, the apparatus of SU8020 (Hitachi Corporate, Tokyo, Japan) was run at a voltage of 5 kV with a magnification of 5000\(\times\). Prior to SEM observation, aurum was sprayed onto the soil surface to form a thin layer, with which the electric conductivity of the soil was enhanced, and thus the imaging clarity was improved.
## 3 Results and Discussion
### Municipal Landfill with No Liner  
The remediated municipal landfill site is located in northern part of Poland, in the Kaszuby Lakeland mesoregion. The geological structure of the site consists of quaternary glaciation deposits. Morphologically, it is morainic plateau created during Pomeranian Vistula glaciation. The area of plateau is folded with depressions of gully-shaped lakes.  
The study site was closed in 1998/1999 and located at the area where a new development plan for a road construction was established. The cubic capacity of the landfill exceeds 0.6 mln m\({}^{3}\) (municipal waste, debris, and soil), and the thickness reaches 10 m. During reclamation works, the landfill body was capped with a 0.7-1.5 m clay layer. The site investigation comprised geotechnical tests, to determine the soil and waste mechanical parameters for future construction plans, and an environmental survey, aimed at a soil-water quality assessment. The geotechnical investigation included electric-resistivity tests to investigate the thickness of waste disposed on the landfill and to determine the depth of the impermeable soil layers within the subsoil.  
Based on geophysical tests, the geological structure (from the top) consists of a sandy layer with a thickness from 8.3 to 19.7 m, layered with clayey deposits. The hydraulic conductivity of the sand layers falls in a range of 1.7 \(\times\) 10\({}^{-5}\) to 5.5 \(\times\) 10\({}^{-3}\) m/s.  
The quantity interpretation of the results allowed the precise determination of the resistivity characteristics, which are presented in Tables 3 and  
The thickness of the waste investigated by geoelectrical tests could be biased due to an error, caused by the fact that, below the anthropogenic material, there is also a highly contaminated deposit of sand. It is worth highlighting that the geophysical tests were also compared and correlated with geological boreholes (10 drillings down to 12 m) and geotechnical static cone penetration tests (seven CPT tests down to 12 m). The CPT tests confirmed the presence of sandy soils with a friction ratio (\(R_{f}\)) of less than 2.2%, whereas the waste body \(R_{f}\) was between 3.2% and 4.8%.
# Quality Assessment by Region and Land Cover of Sharpening Approaches Applied to GF-2 Imagery
## 4 Discussion
### Impact of Pansharpening Methods in Different Land Covers
Regarding the EN and AG values (image quality analysis), from the viewpoint of increasing the image information and the texture of the pansharpened image, the PCA method added the largest amount of information and detail to the pansharpened images for most land covers, with the exception of the salt pan. The mIHS pansharpening method added the smallest amount of information and detail to the pansharpened images, except for the salt pan (where the CN method added the smallest amount). For all four pansharpening methods, compared with the other land covers, more information and detail were injected into the pansharpened images for the canal, forest, and sea water. The reasons for this large increase should be investigated in the future. Overall, it is evident that the behavior of the pansharpening methods was different among the regions, indicating differences in the pansharpening quality over different land covers. For most land covers, the mIHS pansharpening method produced better results than the GS, CN, and PCA methods by preserving the colors of the original MS image. This may be due to the different performances of the four pansharpening methods. In this study, the CN and GS methods essentially used the average values of all four bands to simulate the PAN band, which may have increased the correlation of the pansharpened images. The PCA method replaced the first component with the matched PAN band before the inverse principal component transformation, which may have resulted in the loss of spectral information, including from the original image. However, the mIHS method sharpened the four MS bands by iteratively repeating the sharpening process using the different band combinations and the spectral responses of a given sensor, which made the pansharpened images close to the original bands. For all four pansharpening methods, after pansharpening, the spatial autocorrelation of the forest, winter wheat, and sea water changed by >90.0%, and more information and detail were injected into the canal, forest, and sea water areas of the pansharpened images. This requires further study.