esa-sceva/satcom-chunk-collection huggingface dataset:
# Future of IoT Networks: A Survey
## 3 Current Market and Research Trend of IoT
### Research Programs
#### 3.2.2 United States  
The National Intelligence Council (NIC) of the United States selected IoT as one of six technologies that has potential impacts on national interests through 2025 [31]. Specifically, industry giants such as Cisco and Qualcomm primarily focus on extending the current network infrastructure into the IoT and construct the hyper-connected infrastructure. Further, legacy sensor devices and radio frequencyidentification (RFID) are based on seamless connection. In terms of the government aspect, the National Institute for Standards and Technology (NIST) regards IoT as a basis technology for smart grid implementation. Moreover, the National Science Foundation has selected IoT as one of the technology areas in the Small Business Technology Transfer (STTR) program [32].
# A Sensor Data-Driven Decision Support System for Liquefied Petroleum Gas Suppliers
## 5 Consumption Forecasts
### Short-Term LPG Consumption Prediction  
Short-term LPG consumption prediction supports the every day operation of the LPG supplier visualizing the predicted gas level in every customer's tank. Therefore, it helps to decide where and when to deliver the gas, and it is performed for each tank in a 1-7 day horizon.  
It was assumed in this work that the analysis is performed on the tanks that are equipped with meters that can communicate with the supplier IT infrastructure sending gas level measurements. Due to regular sensor readings reporting the LPG level, it is easy to correctly react to and refuel the tank when the gas is scarce at a given location. However, from the economical point of view, sending a transport vehicle that refuels the tank as rarely as possible is the most profitable. This means that sending a tanker truck to a client location where there is still a sufficient fuel supply can be classified as mismanagement in most cases. In order to safely balance fuel availability, thorough predictions are required.  
Another motivation for short-term LPG consumption prediction is a situation when the number of transport vehicles necessary on a given day exceeds the capacity available for a supplier. It is then required to minimize the risk of delayed delivery to selected clients. Again, the evaluation of such a risk should be based on thorough predictions.  
Referring to the workflow presented in Section 4, the analysis starts by collecting the raw data for the decision support system. In the case of the short-term LPG consumption prediction, the raw data consist of:  
* measurements collected by telemetry such as time and the gas level in the tank,
* measurements collected by meteorological stations such as time and temperature,
* weather forecasts consisting of time and predicted temperature.  
Raw data are preprocessed and stored in a data repository. These data are then retrieved by a data scientist user who has a specific business task--creating predictive models. Therefore, the data scientist has to properly prepare data for the model, e.g., selecting attributes and generating derived variables. Next, the created models are tested and selected depending on their quality. As a result, the selected predictive model and a feature vector derived for this model are stored in the data repository.  
In the next step, a series of calculations are triggered according to the schedule defined. Therefore, forecasts or the recommendation algorithm can be invoked. This process is carried out in the shape of a pipe, where data exchange is carried out through the data repository. On the basis of the resulting predictions, the recommendation and visualization (see Section 5.1.2) are presented.
## 4 Augmented Performance Analysis
### Acquisition Performance with LEO Assisted
The probability density function (pdf) of the chi-square distribution is as follows:  
\[f_{n}(z)=\frac{z^{\frac{b}{2}-1}e^{-\frac{z}{2}}}{2^{\frac{b}{2}}\ \Gamma \Big{(}\frac{b}{2}\Big{)}} \tag{17}\]  
where \(\Gamma(\cdot)\) is the Gamma function, and \(k\) represents the degrees of freedom. Here, \(k\) is twice the non-coherent number. Assuming that the false alarm probability \(P_{fa}\) is 0.5%, the threshold \(V_{t}\) and detection probability can be calculated. Figure 11 gives the comparison of the signal detection probability of the two methods, where the coherent integration time is  
Figure 10: Frequency searching numbers according to the frequency range. set to 200 ms with two non-coherent integrations according to the analysis above. It can be seen from this figure that the detection probability can reach 91% at C/N\({}_{0}\) = 15 dB-Hz using the proposed method, which combines 200 ms coherent integration and two non-coherent integrations. However, the detection probability with 91% of the standalone receiver is at 22 dB-Hz. That means that the LEO-assisted acquisition has about 7 dB gain with regard to the detection probability.
## 2 Methods
### Objective Function for Uncertainty Reduction  
The Figure 3 illustrates the distribution of calorie values within our data sample and highlights the worst 30% of predictions on the distribution. The figure show which types of data the model finds difficult to identify. The results indicate that there are almost no worst predictions for data with a large number of samples. In contrast, for data with fewer samples, specifically those at the ends of the data distribution, the prediction performance is lower. This pattern suggests a link to epistemic uncertainty [33], which may arise from an insufficient amount of data available for effective learning. According to a study [33], such uncertain data can prevent the model's ability to be properly regularized to the observed data during training, thereby affecting its performance and reliability. To address this issue and maximize learning efficiency, we propose a loss function that focuses more on reliable data using an uncertainty criterion while having less interest in data with high uncertainty. The proposed the loss function is defined as follows in Equation (4).  
\[\mathcal{L}(\theta,y)=\frac{1}{n}\sum_{i=1}^{n}(\frac{\mid y_{i}-f_{\theta}( x_{i})\mid^{2}}{max[1,\alpha\cdot exp(U_{i})]}+\beta\cdot[log(U_{i})+1]) \tag{4}\]  
The proposed loss function is composed of two components. The first component is a residual term that calculates the difference between the actual value \(y_{i}\) and the predicted values \(f_{\theta}(x_{i})\). The uncertainty, expressed as \(exp(U_{i})\), is applied as a weight to adjust the model's strictness, allowing for the model to focus on data with low uncertainty during the training process. For instance, when uncertainty is high, the residual error is intentionally reduced below the actual value to promote weaker learning. Conversely, when uncertainty is low, the residual error reflects the actual value more closely, encouraging more focused learning. Additionally, there is a constant \(\alpha\) in the function to adjust its strictness, which can result in values less than 1.0. This issue is addressed by using the max function. The second component is regularization, which aims to decrease uncertainty during training and to improve the estimation performance. In this context, \(\beta\) is a constant with a small value (\(\leq\)0.1). As a result of that, the loss ensures that results with high uncertainty are considered less important during the learning process, while data with high confidence are learned more intensively by the model.  
Figure 3: Distribution of calorie value in the dataset, with the worst 30% of predictions highlighted. The model performs worse on data with fewer samples, particularly at the distribution extremes.
# Bounce: A High Performance Satellite-Based Blockchain System
## 8 Limitations and Future Work
### Functional Limitations  
While we have focused on the payment use case in the design of the accumulator, there are other use cases in which no accumulator is needed. For example, there could be a blockchain whose main purpose is to store individual documentation such as Wills and business agreements. In that case, the user might not only sign such a document, but also encrypt it to protect against privacy violations.  
Other higher level functionality such as contracts and distributed databases are of course possible. The _Bounce_ protocol is a layer one protocol, whose only purpose it is to make all such higher level functionalities faster.
# Reducing Soil Permeability Using Bacteria-Produced Biopolymer
## 3 Results and Discussion
### Effect of Type and Content of the Stabilising Agent  
Independently of the curing time and the hydraulic gradient, the results depicted in Figure 4 clearly show that the stabilisation of the soil with both types of XG induces a significant decrease in the coefficient of permeability. This behaviour is due to the development of some links and viscous hydrogels due to the contact of the XG with the water (i.e., hydration of the hydrogels) that fill part of the soil's voids [23; 30], inducing a decrease in the soil's permeability. The results also reveal that the commercial XG is more effective than the Lab XG to reduce the soil's permeability; in fact, the use of 0.33% of the LabXLG in the mixtures has much less of an effect than the use of 0.33% of the commercial XG and even with the use of 0.16% of the commercial XG, mainly for higher curing times. This greater effectiveness of the commercial XG is consistent with its greater purity when compared with the LabXLG. Indeed, it is important to state that the LabXLG used in the tests is composed of the lyophilized biomass formed by _S. maltophilia_ Faro439, containing the biopolymer and the lyophilized cells that were responsible for producing the biopolymer, consequently a low level of purity is obtained.  
Figure 3: Set-up used for the permeability tests.  
The comparison of the use of XG with the use of Portland cement (the most usual binder for soil stabilisation) shows that the use of 0.33% commercial XG is more effective than the use of 1% cement, namely for a curing time of 7 and 14 days. Even for a curing time of 28 days, the effectiveness of 0.33% of commercial XG is similar to the use of 0.33% of cement. Thus, these results indicate that XG can be used to replace the use of cement, mainly in the short term (for instance in temporary works).  
Considering the efficiency of both biopolymers and that the production process of the commercial XG is associated with a complex industrial process with more environmental concerns (as discussed in Section 1) than the production of the Lab XG, the use of the Lab XG emerges as a viable alternative to reduce a soil's permeability.
# A Nested UNet Based on Multi-Scale Feature Extraction for Mixed Gaussian-Impulse Removal
## 1 Introduction
[17] proposed a non-local mean-based CNN (NM-CNN) method. This approach first detects the locations of outlier pixels using a median filter and replaces them with non-local mean values; subsequently, AWGN is removed using a CNN. [PERSON] et al. [18] introduced a generative adversarial network (GAN) that employs generators and discriminators for feature extraction. This method incorporates a joint loss function based on image prior and visual perceptual metrics, further enhancing image denoising performance. [PERSON] et al. [19] proposed a CNN architecture incorporating Conv, batch normalization (BN), and rectified linear units (ReLU) as basic components for mixed noise removal. In [20], a serial attention module-based CNN method (SACNN) is proposed, which employs a serial attention module to better preserve texture details. Overall, these deep learning-based methods have demonstrated significant improvements compared to traditional denoising methods. In summary, most existing methods for removing mixed noise do not fully utilize the local and global information of the image, resulting in the inaccurate modeling of complex noise during the denoising process. This leads to the deformation and distortion of the image structure, causing the loss of fine details. To address this issue, this paper proposes MSNUNet for mixed noise removal. The key contributions of MSNUNet can be summarized as follows:  
1. We propose a nested UNet architecture based on multi-scale feature extraction for mixed noise removal. In MSNUNet, we introduce MSU-Subnet for multi-scale feature extraction. These multi-scale features contain rich local and global features, which help the model estimate noise more accurately and improve its robustness. 2. We introduce MSCAM into the MSNUNet model to effectively aggregate multi-scale features. Additionally, MSCAM utilizes channel attention (CA) to enhance the extraction of important features, enabling the network to better preserve intricate textural details in images. 3. Our experimental results demonstrate that MSNUNet achieves superior performance in terms of quality metrics compared to state-of-the-art methods and generates visually satisfying denoised images. The remainder of this paper is organized as follows: Section 2 describes the related work. Section 3 presents the mixed noise model, while Section 4 introduces the proposed MSNUNet model. In Section 5, extensive experiments are conducted to evaluate the performance of MSNUNet, and the conclusions of the study are presented in Section 6.
## 3 Results and Discussion
### Physical and Mechanical Characterization  
The density and open porosity of the AA-WBA binders were determined to analyze the mechanical behavior because these properties are extremely linked. The bulk density (\(1.18\pm 0.1\) g-cm-3) and open porosity (\(38\pm 0.5\)%) results demonstrated the material formation with low density and porosity, probably due to the reaction between metallic aluminum and the alkaline activator to generate hydrogen gas [33; 34]. Figure 10 depicts the compressive strength and bulk density results where the same trend can be observed in both cases. A maximum value of compressive strength and bulk  
Figure 9: SEM images of AA-WBA binders. (**a**) 2, (**b**) 4, (**c**) 6, (**d**) 8M.  
density was obtained in the AA-WBA-6M sample, in congruence with selective chemical extraction results and the AA-WBA microstructure observed in SEM images (Sections 3.2 and 3.3, respectively).
# Mental Workload Assessment Using Machine Learning Techniques Based on EEG and Eye Tracking Data
## 3 Materials and Methods
### Brief Overview of Machine Learning Algorithms
#### 3.5.5 Gradient Boosting Machines (GBM)  
Friedman [64] introduced gradient boosting machines (GBMs) as an alternative method for implementing supervised machine learning techniques. The \"gbm\" model consists of three primary tuning parameters: \"ntree\" for the maximum number of trees, \"tree depth\" for the maximum number of interactions between independent values, and \"learning rate\" [65].
# Evaluation of the Ultimate Strength of the Ultra-High-Performance Fiber-Reinforced Concrete Beams
## 3 Test Results
### Moment-Rotation Relation and Crack Pattern  
Figure 4 shows the relationship between the load-resisting capacity and its associated deformation of the test specimen obtained through the experiment. Figure 5 showed the final failure condition of the test specimens. Since the shear span to depth ratios of the specimens differed, the load acting on the specimen was expressed by the moment, and the rotation angle expressed the deformation capacity. The rotation angle of each specimen was expressed by the value obtained by dividing the deflection occurring at the loading point by the distance from the support to the loading point.  
Figure 4: Test results: Moment-rotation curve.  
Figure 3: Material test results.  
The specimens were found to have flexural and shear failure modes depending on the shear span to depth ratio and the existence of steel fiber and shear reinforcements. U-S-F-6.6 was failed by crushing on the compression side, as shown in Figure 5a after typical bending behavior. Furthermore, it was confirmed that even in the moment-rotation angle relationship shown in Figure 4, a large deformation was experienced without losing the load-bearing capacity after the maximum load manifestation.  
For test specimens with shear span to depth ratio of 3.4, the mode of failure was determined depending on the steel fiber inclusion. Two specimens, U-0-0-3.4 and U-S-0-3.4, which used the 200-0 matrix, showed expanded diagonal shear crack and spalling  
Figure 5: Test results: Crack pattern of the test specimens (at failure).  
of web area, respectively, as shown in Figure 5b,c. As shown in Figure 5d, in the case of ultra-high strength concrete specimens reinforced with steel fiber, diagonal shear cracks were observed, but these cracks were not expanded, and the maximum load applied to the specimen was observed with crushing of concrete at compression fiber of beam center. Therefore, the failure pattern of this specimen was defined as flexural failure. Specimen U-S-F-3.4 also showed a similar crack pattern with U-0-F-3.4, as shown in Figure 5e. The test specimens using 100-F matrix without stirrups, H-0-F-3.4 failed with the expansion of diagonal crack. On the other hand, H-S-F-3.4 was failed with crushing of compression region at beam center. Therefore, it was confirmed that failure modes were changed by shear failure and flexural failure due to the installation of the stirrup. It was confirmed that the specimens with a shear span ratio of 2.0 were destroyed by the occurrence and diffusion of diagonal cracks connected between the loading point and the supporting point and the increase of the crack width. When the steel fiber was not reinforced, the fracture occurred due to the diffusion of the cracks in the diagonal direction. It is judged that the incorporation prevents the longitudinal destruction of the strut and causes an increase in strength. Table 5 summarizes the strength with the first yield of tensile flexural reinforcement and ultimate strength with corresponding rotational angle.
# Full-Field Mode Shape Identification Based on Subpixel Edge Detection and Tracking
## 2 Theory and Methodology
### Identification of Full-Field Mode Shapes
#### 2.4.2 Automatic Identification of Physical Modes Using Hierarchical Cluster Analysis  
The stability diagram is typically used with the SSI-COV technique to determine the modal parameters. However, it requires to manually distinguish the false modes from all the identified modes to obtain the real modes. Therefore, the hierarchical cluster analysis is adopted to improve the efficiency and to automatically identify the physical (real) modes. Each modal parameter is regarded as a cluster, and the distance between each cluster is calculated. The two clusters with the smallest inter-cluster distance are merged into a new cluster, and the distances between clusters are recalculated and the merge process is repeated until the distances between all clusters are larger than the preset threshold. The inter-cluster distance is calculated as  
\[d(r,s)=\left|\frac{f_{r}-f_{s}}{f_{s}}\right|+(1-MAC_{r,s}), \tag{17}\]  
where \(f_{r}\) and \(f_{s}\) are the natural frequencies of \(r\)-order and \(s\)-order, and \(MAC_{r,s}\) represents the modal assurance criterion between the \(r\)-order and \(s\)-order mode shapes.
## 4 Experimental Results
### Two-Stream UDN
#### 4.2.2 Effectiveness of Adding Local Network  
Table 2 shows the LAMR of the two-stream UDN and its two sub-networks on the Caltech and ETH datasets. As can be seen from Table 2, the LAMR of the two-stream UDN network for pedestrian detection on the Caltech dataset is 37.66%.  
Compared to the Global network, the two-stream UDN improved the miss rate by 1.07%. If only considering local features, the detection result is 58.15%, because the local network is only sensitive to a partial region, and it is easy to misjudge round objects as persons. Combing the Global network and Local network together, the results of the two-stream UDN network are better than each single network. A similar trend is shown on the ETH data set,  
do not have head and shoulder features will obtain lower scores than that of the Global network. This illustrates that the extraction of head and shoulder features can be effectively enhanced by adding the Local network. Thus, the proposed two-stream UDN structure can improve the pedestrian detection results.
## 5 Conclusions  
Motion states of different damping systems are studied with the help of discontinuous dynamic analysis in this paper. The identification method based on relatively synthetic motion states at the interface is proposed by analyzing the coupling property of relative motion states at the interface. In addition, the large shaking table test of SSI damping system under the vibration of increasing dynamic load is done. The consistency of motion rules of different parts and the continuity of interface motion state for SSI system are investigated from linear to nonlinear development process.  
Theoretical and experimental studies show that the boundary of movement in discontinuous dynamics theory is critical for identifying classical damping. It is the easiest to produce at the interface of different materials or fracture surfaces. In addition, the coupling of relatively synthetic motion states at the interface is the direct criterion for the class of damping systems. The identification method of a classical damping system based on motion states is proposed in this paper. It can reflect dynamic characteristics of multi-material systems under the linear and nonlinear processes of dynamic load soundly. This can be a reference for selecting nonlinear dynamic analysis methods of complex structural systems and further optimization of structural vibration control.  
Conceptualization, Z.Z. and Y.L.; Shaking table tests, Z.Z., Y.Y. and Z.H.; Theoretical analysis, Y.L. and Z.Z.; Analysis of experimental data, Z.Z., Y.L., Z.H. and Y.Y.; Writing, Y.L. and Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 51478387 and Natural Science Basic Research Plan in Shaanxi Province of China, Grant number 2021 JQ-891.  
Not applicable.  
All data are available in the paper.  
Key Laboratory of Structural Engineering and Earthquake Resistance of Ministry of Education, Xi'an University of Architecture and Technology for the experimental site and equipment.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
Figure 17: Function distribution of relative motion states at the soil-pile interface.
## 4 Case Studies  
To test the validity and superiority of the proposed mechanism and model, case studies were carried out on a modified IEEE 24-bus power system. The model was built using the MATLAB/YALMIP platform. All program code was solved by the Gurobi solver on a workstation with an Intel Core i7-8700 3.20 GHz processor and 32 GB RAM.