esa-sceva/satcom-chunk-collection huggingface dataset:
# MFMNet: Multi-Scale Feature Attention Mixture Network for Short-Term Load Forecasting
## 2 Methods
### Dual-Branch Attention Module  
In order to obtain a more powerful network and enable the network to give more important weight to important features, the dual-branch attention mechanism method is proposed in the residual unit, as shown in Figure 3, which enhances the network's learning ability of load characteristics from the channel level.  
Firstly, the global average pooling and the global maximum pooling layer are used to compress the feature image from the spatial perspective, and the two-dimensional channel is transferred to a real number, which represents the global receptive field to some extent, representing the response to the global information of the feature channel. Then it is sent into the full connection layer, one-dimensional convolution, and sigmoid function, respectively. After that, we multiply the two results with the original data and add them together to obtain the final output. The specific calculation formula is as follows:  
\[g_{1}=sigmoid(g^{1\times 1}(FC(M(g_{in})))), \tag{5}\]  
\[g_{2}=sigmoid(g^{1\times 1}(FC(A(g_{in})))), \tag{6}\]  
\[g_{out}=g_{1}\times g_{in}+g_{2}\times g_{in}, \tag{7}\]  
where \(g_{in}\) is the input feature, \(g_{out}\) is the output feature, \(M(\cdot)\) is global maximum pooling, \(A(\cdot)\) is global average pooling, \(FC(\cdot)\) is full connection layer, \(g^{1\times 1}\) a one-dimensional convolution with \(1\times 1\), \(sigmoid(\cdot)\) is the sigmoid function. The attention mechanism proposed will re-calibrate the rich features. The two-branch attention module can obtain the importance of each feature, and then enhance the useful information and suppress the information that is not important to the task according to the importance, which can also improve the ability of network load forecasting.
# Customer Churn Prediction in B2B Non-Contractual Business Settings Using Invoice Data
## 1 Introduction
### Aims of the Study  
The research presented in this paper aims to contribute to both the theoretical and empirical body of knowledge in the non-contractual B2B customer churn prediction domain. In particular, we explore: (i) whether it is possible to use a single common source of business data (i.e., invoice data) to devise predictive models capable of reliably identifying churners in real-world settings, (ii) the effects of using different amounts of historical data for devising features on the performance of resulting models, and (iii) whether using alternative churn definitions could yield models that perform well enough to serve as foundations for discussing new potential retention activities. We use a novel dataset coming from a domain not explored within previous studies, and thus aim to provide valuable insights to practitioners and academics researching this topic. Finally, by leveraging a recently proposed approach to training dataset creation and comparing it with the approach used traditionally, we aim to evaluate whether it generalizes to different case data.
## 3 Materials and Methods
### Datasets
#### 3.1.2 Remote Sensing Imagery  
Sentinel-2 is a remote sensing satellite system that comprises two satellites, Sentinel-2A and Sentinel-2B, which operate in a synchronized 180-degree orbit. These satellites  
Figure 2: Official slum reference map of Bandung City (2020), categorizing areas into light and medium slums based on governmental criteria. Reproduced with permission from Government of Bandung City, The Mayor of Bandung’s Decree Number: 648/Kep.1227-DPKP3/2020; published by the office of the Bandung City housing and settlement area, 2020 [20].  
are equipped with a Multispectral Instrument (MSI), a sophisticated sensor capable of capturing data across 13 spectral bands, spanning the visible, near-infrared, and short-wave infrared regions of the electromagnetic spectrum (European Space Agency, 2023). A significant benefit of the Sentinel-2 system is its impressive spatial resolution, achieving 10 m for the blue, green, and red bands, and 20 m for the near-infrared and shortwave infrared bands [21]. The imagery produced by the Sentinel-2A MSI provides a detailed representation of urban areas, characterized by high pixel resolution that enhances clarity. To maximize the effectiveness of this imagery, a smoothing process is employed, utilizing the original image from the Sentinel-2A MSI. This process is crucial for refining the visual quality of the built-up land observed, as the high pixel values in the Sentinel-2A MSI image facilitate a clearer depiction of urban landscapes.  
The classification of slum areas in Bandung City using Sentinel-2A imagery was facilitated by the efficacy of several spectral indices, a consequence of the city's diverse land cover, which includes vegetation, buildings, and tributaries. The Normalized Difference Built-up Index (NDBI) has been identified as a reliable indicator of residential areas, while the Normalized Difference Vegetation Index (NDVI) has been shown to be a valid metric for assessing vegetation cover, particularly in contexts where vegetation is sparse, as is often the case in slum areas. The Normalized Difference Water Index (NDWI) and the Modified NDWI (MNDWI) have been demonstrated to be significant in the identification of water bodies in proximity to slum areas, with the MNDWI exhibiting superior accuracy in urban contexts. The Enhanced Vegetation Index (EVI) and Soil-Adjusted Vegetation Index (SAVI) have been utilized to differentiate between slum and non-slum areas, with the former being particularly effective in discerning these distinctions based on soil brightness. In conclusion, the Urban Index (UI) is employed to map urban areas and building density. The integration of these indices with machine learning algorithms has the potential to enhance the precision of slum classification, thereby facilitating urban planning initiatives.
# Evaluation of Land Subsidence during Groundwater Recovery
## 3 Methodology
### Theoretical Calculation  
Using the data provided by the DGR in 2012, the minimum groundwater level was equal to about 27 m from the ground surface in the Bangkok area. Using the monitoring data, [PERSON] and [PERSON] calculated the changes in pore-water pressure due to groundwater drawdown and groundwater recovery, as shown in Figure 3. The relationship between pore-water pressure and elevation was not linear in the first 20 m in both groundwater drawdown and recovery. The soil surface zone affected the groundwater level change [19]. Therefore, we focused on calculating the soil surface displacement by considering only 2 soil layers: the soft and stiff clay layers. Other soil layers were assumed to not have soil displacement during groundwater recovery. The calculation of the soil surface displacements related to the changing in the groundwater level by following the timing and groundwater level based on the monitoring data of the DGR, as evaluated by [PERSON] et al. [14]. The consolidation settlement equation, Equation (1), was used for the calculation in the case of overconsolidated clay because the maximum vertical effective stress was larger than the final vertical effective stress. Many parameters were calculated, such as the recompression index (\(C_{r}\)), initial void ratio (\(e_{0}\)), maximum vertical effective stress (\(\sigma^{\prime}_{vm}\)), final vertical effective stress (\(\sigma^{\prime}_{vf}\)), and thickness of the soil or drainage path. The surface displacement of the saturated soil, located below the groundwater level, was calculated with Equation (2) based on [PERSON]'s equation [30], while the surface displacement of the unsaturated soil, located above the groundwater level, was calculated with Equation (3), following [PERSON]'s equation [31]. The suction (\(S\)), pore air pressure (\(u_{a}\)), and pore water pressure (\(u_{w}\)) are the parameters in Equation (4). The parameters for the 8 boreholes were calculated by using the same equation.  
\[\Delta H=\frac{C_{r}}{1+e_{0}}\log\frac{\sigma^{\prime}_{vf}}{\sigma^{\prime} _{vm}}H_{0} \tag{1}\]  
\[\sigma^{\prime}=\sigma-u \tag{2}\]  
\[\sigma^{\prime}=[(\sigma-u_{a})+\chi(u_{a}-u_{w})]\quad(1>\chi>0) \tag{3}\]  
\[S=u_{a}-u_{w} \tag{4}\]
# Application of Tooth Gear Impact-Echo System for Repeated and Rapid Data Acquisition
###### Abstract  
Developments in air-coupled testing hardware in impact-echo (IE) tests have enabled new levels of scanning tests for concrete bridge decks. A tooth gear IE system has been developed using tooth gears as impactors and microelectromechanical systems (MEMS). Since the tooth gear moves and generates impacts itself, this system collects a large amount of test data across the field continuously. The contact duration of two different tooth gears is evaluated and the contact mechanism is compared to a conventional steel ball impactor by a high-speed camera. The data measurements were carried out on concrete slabs, where artificial delaminations were embedded at different depths. Based on our IE experiments, reducing the pitch or increasing the number of teeth was required to decrease the contact duration and generate the thickness mode frequency from deep delaminations. Rapidly obtained time domain data were transferred to the frequency-time domain using spectrograms to identify the dominant frequency band of the signal set. The results show that the developed system enabled us to acquire high-quality data during air-coupled IE tests and spectrogram analysis provided meaningful frequency information and verified its repeatability.  
impact-echo; tooth gear impactor; contact duration; delamination; rapid scanning; non-destructive testing; concrete slab +
Footnote †: journal: Applied Sciences
## 3 System Framework Overview  
The proposed system framework consists of a PF facility and three main cloud-based components: the context-aware middleware cloud, the services cloud, and the social network cloud. The generic architecture and a data workflow overview of the system are illustrated in Figure 2. According to this, all system's functional elements cope with context, whereas the operation of context awareness is accomplished by the flow of the context in a cloud-based distributed middleware component which is the core of the system.  
In particular, sensor data, acquired by a Wireless Sensor and Actuator Network (WSAN) in a PF facility, are transmitted via a gateway as raw data to the context-aware middleware cloud where they are converted to context. These contextual data as well as the incoming rules provided by the services cloud (containing applications for the end user) are managed inside the middleware component to produce monitoring information, services and control actions (such as cultivation control). The context-aware middleware cloud responds back to the PF facility enabling the appropriate equipment to perform context-aware operations as well as back to the end user (farmers, agronomist engineers and agricultural products merchants) providing them with new context-aware services and monitoring information in order to take further assistive actions. In this sense the end user can operate the PA facilities remotely via the cloud services. A detailed overview of the system's components is given below while a layered diagram of the system's architecture is being illustrated in Figure 3. It should be noted that this system's architectural framework may apply to more than one PF agriculture facilities.  
Figure 3: Layered architecture diagram for a context-aware IoT agricultural system framework.  
Figure 2: Generic architecture and data workflow overview for a context-aware IoT agricultural system framework consisting of three major mobile cloud-based components.
## 3 Materials and Methods  
Overall steps of the proposed automatic face recognition (AFR-Conv) system are described in the subsequent paragraphs. Also, the steps are visually presented in Figure 1.  
The AFR-Conv system is an automated face recognition approach that combines advanced techniques for accurate recognition of human faces. It begins by initializing parameters such as ConvMixer blocks and AdaBoost iterations, utilizing pre-trained CNN models like ResNet-50, Inception-v3, and DenseNet-161 as backbone to the net-work. The pretrained CNN models are used to allow ConvMixer to extract relevant features. ConvMixer architecture is established with skip connections, and AdaBoost is employed with weak classifiers. ConvMixer models are iteratively trained, while AdaBoost refines  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Cited** & **Description** & **Techniques** & **Dataset** & **Results** \\ \hline \multirow{6}{*}{[13]} & \multirow{6}{*}{End-to-end FR network that is not directly impacted by face masking} & & \multicolumn{1}{c}{} & ACC:75.50\% (CASIA) \\  & & & CASIA, & 98.41\% (LFW) \\  & & & Masked LFW, & 86.15\% (CALFW) \\  & & & CALFW, CPLFW, & 79.42\% (CPLFW) \\  & & & Masked CFP-FF & 94.44\% (CPF-FF) \\  & & & FDDB, PASCAL Faces, & \\
[14] & severe occlusion and unconstrained pose variations & CNN & AFW, and WIDER & Recall: & 92.84\% (for FDDB) \\  & & & FACE & 92.84\% (for FDDB) \\
[16] & Proposed a mask-aware face recognition system & SVM & \multicolumn{1}{c}{} & ACC: \\  & & & ResNet-50 & 99.53\% \\  & & & ACC: & \\  & & & ResNet-50 & 99.64\% \\
[17] & combines deep and traditional machine learning. & SVM & \multicolumn{1}{c}{} & SMFD & 99.49\% \\  & & & LFW & 100\% \\  & & & ACC: & \\  & & & MS1 MV2 & 99.78\% \\
[18] & & & LResNet-50 & Masked LFW & 98.92\% \\  & & & Masked CFP-FF & 98.33\% \\  & & & Masked CFP-FP & 88.43\% \\  & & & & ACC: \\  & & & IJB-B & 94.2\% \\
[19] & & & LFW & 99.82\% \\  & & & CALFW & 95.45\% \\  & & & CPLFW & 92.08\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of affective states-related work.  
Figure 1: A systematic flow diagram of proposed AFR-Conv system to recognize human faces.  
predictions using sample weights. This pre-trained ConvMixer architecture extract features, and their weighted votes are combined by AdaBoost for label prediction. The algorithm's efficacy depends on ConvMixer block and AdaBoost iterations, leveraging pre-trained CNNs and preprocessing for robust face recognition that addresses occlusion and benefits from transfer learning.
## 4 System Model
### Markov Chain Model
\begin{array}{l}\sum\limits_{i=0}^{m}\sum\limits_{k=0}^{W_{i}-1}b_{i,k}= \sum\limits_{i=1}^{m}\sum\limits_{k=1}^{W_{i}-1}b_{i,k}+\sum\limits_{k=1}^{W_{0 }-1}b_{0,k}+\sum\limits_{i=0}^{m}b_{i,0}\\ =\sum\limits_{i=1}^{m}\sum\limits_{k=1}^{W_{i}-1}(\left\lfloor\frac{W_{i}-1-k }{r}\right\rfloor+1)b_{i,0}+\sum\limits_{k=1}^{W_{0}-1}(\left\lfloor\frac{W_{0 }-1-k}{r}\right\rfloor+1)b_{0,0}+\sum\limits_{i=0}^{m}b_{i,0}\\ =1\end{array}  \tag{11}\]  
If we have the exact values of \(W_{i}\), we can derive all the states as functions of \(p\) from equations derived above. Unlike in distributed coordinating function channel access analysis [40], not only STAs in state \(b_{i,0}\) but also STAs in state \(b_{i,k}\), where \(k\) is equal or smaller than \(r\), transmit their frames. Therefore, we can derive \(\tau\), the probability of transmission, as follows. \[\tau=\sum\limits_{i=0}^{m}\sum\limits_{k=0}^{r}b_{i,k} \tag{12}\]From the transmission probability, we can re-express the UORA collision probability \(p\) as follows. \[p=1-\left(1-\tau\right)^{n-1} \tag{13}\]  
Because every steady-state probability can be expressed as a function of \(p\), we can obtain exact steady-state probabilities if we have specific \(W_{i}\), \(r\), and \(n\) values.
## 2 Materials and Methods
### Representation of Basic Kelvin-Voigt Electrochemical Cell Models  
In the electrochemical analysis, impedance models are regularly represented in terms of complex variables as a function of the complex term j\(\omega\), explicitly, where \(\omega\) is the frequency in radians per second. In contrast, in control theory and electrical circuit analysis, another approach is to obtain the frequency-domain impedance model of ideal, linear time-invariant passive components in terms of s = \(\sigma\) + j\(\omega\), through Laplace-Transform. Thus, impedance models of Kelvin-Voigt (parallel) or Maxwell (series) elements can be represented as a function of s. For instance, the equivalent impedance of the Kelvin-Voigt RC parallel network (Figure 3), \(Z_{K\cdot V}(s)\), can be represented as a first-order, strictly proper transfer function of s:  
\[Z_{K-V}(s)=\frac{R}{sRC+1} \tag{1}\]  
The equivalent impedance, \(Z_{K\cdot V}(s)\), contains a product RC \(\equiv\tau\), which is the time constant, and R \(\equiv\) H, is the system gain when \(s\)\(\rightarrow\)0 and the root of the denominator (pole) is \(\left(-\frac{1}{\tau}\right)\):  
\[Z_{K-V}(s)=\frac{R}{sRC+1}\equiv\frac{H}{s\tau+1}\equiv\frac{\left(\frac{H}{ \tau}\right)}{s+\left(\frac{1}{\tau}\right)} \tag{2}\]  
Introducing a single resistor in series with the R-C parallel array (Figure 4) yields the Modified Kelvin-Voigt element.  
Figure 3: R-C parallel (Kelvin–Voigt type) model.  
The impedance of the modified Kelvin-Voigt model, \(Z_{MK\cdot V}(s)\), results in a proper polynomial transfer function:  
\[Z_{MK-V}(s)=R_{0}+\frac{R_{1}}{sR_{1}C_{1}+1}=\frac{sR_{0}R_{1}C_{1}+(R_{0}+R_{ 1})}{sR_{1}C_{1}+1} \tag{3}\]  
which can be written in terms of gains and time constants:  
\[Z_{MK-V}(s)=R_{0}+\frac{R_{1}}{sR_{1}C_{1}+1}\equiv H_{0}+\frac{H_{1}}{s\tau+1 }\equiv H_{0}+\frac{\left(\frac{H_{1}}{\tau_{1}}\right)}{s+\left(\frac{1}{ \tau_{1}}\right)} \tag{4}\]
# Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method
## 2 State-of-the-Art in Non-Uniform and Low Latency Resampling
### Mathematical Implementations and Limitations  
The Whittaker-Shannon interpolation (WSI) provides perfect reconstruction of time-infinite uniform sampled signals by convolution with the sinc function if the band limit is less than the Nyquist frequency.  
\[\text{sinc}(t)=\frac{\sin\pi t}{\pi t}. \tag{1}\]  
The convolution is equivalent to an ideal 'brick-wall' low pass filter with a sharp cut at the Nyquist frequency.  
Unfortunately, such perfect reconstruction is only feasible for time-infinite signals. Truncating the signal results in reconstruction errors, especially at the margins of the available sampled time window. The WSI error can be reduced by multiplying the input samples with a window function, e.g., a Blackman Harris window, as suggested by [21].  
In the single-delay scenario, the signal is truncated at the current point of time, making it challenging to reconstruct this region of high interest in the signal. Therefore, new solutions are necessary for efficient resampling of non-uniform signals with low latency.  
Noise smoothing by frequency filtering cancels everything above the highest frequency of the useful signal \(f_{L}\). Signal reconstruction already limits the signal to the Nyquist frequency. For high \(f_{L}\), noise smoothing is not feasible or very limited and therefore not considered in this paper.
## 6 Numerical Results
### Measurement-Formation and Reconstruction of OTD Datasets with the Proposed Termination Rule
the shortcoming of CS reconstruction algorithm itself in processing non-strictly sparse signals, which is beyond the scope of this article. Note that, although we can acquire a higher recovery quality and a lower error rate (\(1-3^{-\Delta M}\) according to Proposition 1) with a higher \(\Delta M\) value, the measurement cost (constructed in the network) would also have a significant increase (because of the extra number of required measurements). So, on the whole, there is a tradeoff between the performance and the cost, and the specific network implementation target also should be considered to choose a proper value of \(\Delta M\).
# Numerical Analysis of Ultrasonic Multiple Scattering for Fine Dust Number Density Estimation
## 6 Conclusions  
This study investigates the application of ultrasonic multiple scattering features to estimate the number density (number of particles per unit area) of fine dust particles. We propose a number density estimation approach based on the independent scattering approximation theory. Based on the presented results, the following conclusions were drawn:  
1. Independent scattering approximation can be used to estimate the number density, especially when the volume fraction of fine dust particles is low.
2. The proposed ultrasonic wave data processing approach enables the estimation of the number density of fine dust particles with an average error of 43.4% in the frequency band 1-10 MHz (\(ka\leq 1\)) at a particle volume fraction of 1%.
3. Variation of fine particle shapes is a cause of uncertainty in number density estimation.  
Despite the potential to estimate the number density of fine dust particles in the air, the presented work has some limitations. The number density estimation performance varies across the studied frequency band, and the overall error in the number density estimation is still high (43.4%). In future work, we will further investigate how to improve the number density estimation performance and translate the proposed approach to an experimental system.  
**Author Contributions:** Designed the study, H.S. and H.C.; performed numerical simulations, analyzed the simulation data, and drafted the manuscript, H.S. and U.W.; acquired the funding, supervised the study, reviewed, and improved the manuscript draft, H.C. All authors have read and agreed to the published version of the manuscript.  
Figure 9: Variations of scattering cross-section under varying scatterer shapes: (**a**) six different particle shapes and (**b**) their corresponding total scattering cross-sections.  
Funding: This study was supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 20 CTAP-C151907-02).  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** 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.