esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works  
In this section, we provide a concise overview of the existing literature on forecasting sales of household appliances. ML and DL technologies have proven to be effective in addressing related forecasting challenges.  
As for ML models, [PERSON] et al. [12] implemented three different ML models to achieve the precise household appliance sales forecasting. In [13], the authors proposed a novel clustering-based forecasting model that combines a clustering approach and machine learning method for computing the retailing sales forecasting. In this model, K-means, self-organizing map, and growing hierarchical self-organizing map were used as clustering techniques, while extreme learning machine (ELM) and support vector regression (SVR) were used as ML techniques. In [14], the authors used efficient ML techniques for household appliance sales prediction. All the above studies can be accelerated by utilizing big data as a tool for predictive analysis in household appliance sales forecasting.  
In recent years, some works have employed DL models for sales forecasting. In [10], the authors used convolutional neural networks (CNNs) to learn effective features from the historical sales data automatically and predict household appliance sales. In order to exploit the temporal correlation among the historical sales data, the authors in [15] utilized LSTM for sales prediction in the E-commerce realm. This algorithm focuses on the household products with fluctuating demands over short periods. To further improve the predictive accuracy of sales forecasting, [PERSON] et al. [16] designed a novel algorithm that combines LSTM with particle swarm optimization (PSO). In [17], the authors proposed a novel hybrid prediction model based on the autoregressive integrated moving average model (ARIMA) and recurrent neural network (RNN) model. [PERSON] et al. [18] cascaded CNN and LSTM networks to predict future market sales. In [19], a CNN-LSTM-Attention model was introduced to address the limitations of traditional LSTM architectures. Table 1 summarizes some studies related to the topic of sales forecasting in this paper. For time series forecasting, [PERSON] et al. [20] considered a Transformer architecture and multiple features in the input. [PERSON] at al. [21] proposed a CNN model combined with a bidirectional LSTM. [PERSON] et al. [22] proposed a graph convolutional neural network based on WaveNet to perform wind speed forecasting.  
In summary, most of the previous works [10; 12; 13; 14; 15; 16; 17; 18; 19] assumed that there are sufficient household appliance sales data for predicting model training. Unlike these existing works, we consider the scenario of forecasting household appliance sales in small towns, which lack sufficient sales data. We will employ TL technology to transfer the sales knowledge learned from big cities to small towns and make a precise prediction for household appliance sales.
## 1 Introduction
Recent research introduced mechanisms, including coarse-to-fine [28] and adaptive step-size [29], to constrain the scanning range, thereby speeding up the convergence and enhancing robustness. However, the above methods are only designed for the ideal NMFP imaging condition. Low-quality fundus images caused by non-ideal imaging conditions are common (around 21%), such as stray light, motion blur, and nonhomogeneous brightness [30; 31]. These cases degrade the performance of traditional sharpness evaluations and require additional customization for focus windows. In addition, poor imaging environments exacerbate curve noise, which makes search strategies susceptible to local extrema and decelerates the scanning process. To solve the above problems, a fast and robust IBAF method is introduced for enhanced autofocus in NMFP. First, an adaptive window was designed using imaging prior knowledge, grayscale features, grid screening, and morphological operations to improve the performance of sharpness evaluation when irrelevant image contents interfere. Then, the exponentially weighted moving average (EWMA) was integrated into the traditional hill-climbing method to optimize the search path in real-time, which improves the ability to suppress local extrema and reduces axial sampling. In simulation analyses and clinical studies involving 40 volunteers, the proposed method performed well under non-ideal imaging conditions such as pupil misalignment, eyelash occlusion, and nystagmus, which are difficult to resolve using existing IBAF methods.
# Review and Evaluation of MAC Protocols for Satellite IoT Systems Using Nanoatellites
## 2 Overview of Space and Communications Systems
### Communication Satellite Systems
Generally, stations on the ground require a large antenna along with a high transmission power to establish effective links. Satellites serving broadband communications usually operate in the Ka, Ku, L, and C bands. * Voice services: Using small devices such as satellite telephones, these satellite systems offer voice connectivity on almost any part of the planet. * Signaling services: In this area, some of the highlight services are the reception of automatic identification system (AIS) and automatic dependent surveillance broadcast (ADS-B) signals, which can track the path of vessels and aircraft, respectively. * Sensor data collection: These satellite systems offer services at low data transfer rates, which allow data to be retrieved from small sensors placed on the ground. Table 1 provides a list of some of the commercial constellations providing communication services in different orbits.
## 2 Problem Description
### Classification by RNNs  
Given a set of training examples \([(x(1),\hat{y}(1)),(x(2),\hat{y}(2)),...,(x(\text{T}),\hat{y}(\text{T}))]^{(n)}\), where \(x(t)\in\mathbb{R}\)\({}^{1\times 1}\) (\(1\leq t\leq\text{T}\)) is the input vector at the \(t\)-th time-step, \(\hat{y}(t)\in\mathbb{R}\)\({}^{\text{K}\times 1}\) (\(1\leq t\leq\text{T}\)) is the expected output vector, and the superscript \(n\) (\(1\leq n\leq\text{N}\)) is the order of training sets. The error between the real output value \(y(t)\) and the expected output value \(\hat{y}(t)\) can be described by error function \(L(\theta)\),  
\[L(\theta)=\sum\limits_{n=1}^{\text{N}}\sum\limits_{t=1}^{\text{T}}D_{t}(y(t)^{ (n)}-\hat{y}(t)^{(n)}) \tag{3}\]  
Figure 1: Unfolding a recurrent neural network structure proposed by [PERSON] (Elman-RNNs) in time by creating a copy of the model at each time-step; arrows indicate the flow of information inside the network.  
where \(D_{t}(\cdot)\) is a distance measure, such as Euclidean distance [9] and Cross-Entropy [26]. The parameters \(\theta\) (mainly in \(\mathbf{W}^{\text{HH}}\), \(\mathbf{W}^{\text{OH}}\)) can be estimated by minimizing the error between \(y(t)\) and \(\hat{y}(t)\). When using logistic sigmoid function in the output layer, the value of output unit \(y_{k}\) can be interpreted as the probability that input sequence \(X\) belongs to the corresponding the \(k\)-th class, that is:  
\[p(C_{k}|\mathbf{X})=y_{k} \tag{4}\]  
For classification problem with K > 2 classes, it requires K output neurons to indicate the classification result. Each value of output neurons is limited to (0,1), to interpret the corresponding class probabilities, and the sum of which equals 1, by normalizing the output activations with the Softmax function [27],  
\[p(C_{k}|X)=y_{k}=\frac{e^{a_{k}}}{\sum_{i=1}^{\text{K}}e^{a_{i}}} \tag{5}\]  
To reduce the parameters of subsequent network training, the logistic regression vector is represented to binary label vector by a threshold. A 1-of-K coding scheme represents the target class as a binary vector \(d(t)=[d_{1},d_{2},...,d_{\text{K}}]=[0,...,1_{k},0]_{1\times\text{K}}\), with all elements equal to zero except the \(k\)-th, which equals one, corresponding to the correct class \(C_{k}\). So, the conditional probability between judgement \(d(t)\) and input \(x(t)\) is shown as:  
\[p(d(t)|x(t))=\prod_{i=1}^{\text{K}}y_{i}(t)^{d_{i}(t)}=y_{k}(t) \tag{6}\]  
When using Cross-Entropy error function for classification tasks, \(L(\theta)\) can be rewritten as,  
\[L(\theta,t)=-\sum_{k=1}^{\text{K}}d_{k}\ln y_{k}(t) \tag{7}\]  
As mentioned above, using binary vector \(d(t)\), Equation (7) can be simplified as,  
\[L(\theta,t)=-\ln y_{k}(t) \tag{8}\]
## 5 Energy-Efficient Routing Protocols (E2 RPs) for UWSNs
### Reinforcement Learning Energy-Efficient Routing Protocols (RL-Based E2 RPs)
#### 5.4.5 Depth-Based Energy-Efficient Routing Protocols (D-b-E2 RPs)  
A routing protocols based on depth are a typical routing technique for UWSNs. In this type of E2 RPs, the forwarder communicating node is selected on the basis of the communicating node's depth. A hierarchical approach is used in the selecting the depth in network topology. When the depth level was less than the Tx anchor node present position. The Tx anchor node will select the forwarder node. This implies that, a communicating node which is near to the network sink node is the forwarder node, which was selected. In this part, E2 RPs for UWSNs are discussed, which are based on depth.
## 3 Results and Discussion
### Performance of Enhancement Ephemeris Monitoring
#### 3.2.1 Ephemeris Extrapolation  
The downcast ephemeris data of the medium-circular orbit satellite C27 in BDS-3 from 2020 were selected for analysis.  
For the C27 satellite, the difference between the extrapolated ephemeris of the previous navigation message and the orbit position calculated from the latest navigation message in the X, Y, and Z directions are shown in Figure 14.  
Through the histograms of the difference in the distance in each direction, it can be seen that the error approximately conforms to the 0-mean Gaussian distribution. Gaussian fitting was performed, and the fitting results in each direction are shown in Figure 15. The statistical results of the comparison of the uplink injection message and the message on-orbit to be broadcast show that the last ephemeris set are in good agreement with the latest ephemeris, and 99% of the differences in the three-dimensional position are less than 0.2 m.  
The statistical characteristics of the variation of the difference in the distances over time are shown in Table 1. This analysis of the characteristics of the differences in the ephemeris are very close to the normal distribution.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & & **X Direction (m)** & **Y Direction (m)** & **Z Direction (m)** \\ \hline \multirow{3}{*}{C27} & Variance & 0.046 & 0.046 & 0.048 \\ \cline{2-5}  & Skewness & \(-\)0.03 & \(-\)0.04 & 0.06 \\ \cline{2-5}  & Kurtosis & 2.80 & 2.84 & 2.98 \\ \hline \end{tabular}
\end{table}
Table 1: Statistical properties of the difference in the distance in different directions.  
Figure 14: Differences in the position of the BDS-3 C27 satellite in the (**a**) X, (**b**) Y, and (**c**) Z directions.
## 4 Experiment Results and Discussion
### Experimental Parameters
#### 4.2.2 Normal FL and FAT Parameters  
The same hyperparameter configuration was applied for both normal FL and FAT in our study, as illustrated in Table 2. Normal FL involved the absence of adversarial examples during the local training process of the client, and this global model did not ensure its robustness. Conversely, the FAT approach incorporated adversarial samples into the training data with the goal of enhancing the robustness of the global model. In this study, the predetermined number of communication rounds was established at 30. Numerous experiments have shown that increasing the number of communication rounds did not significantly improve the accuracy of the global model. We opted for FedAvg as our aggregation method, a widely recognized approach that has proven effective in confirming the utility of our proposed methodologies.  
During the initial five communication rounds, all clients were requested to train the local model for a maximum of 35 epochs. Subsequently, the local epochs for the remaining communication rounds were reduced to 15. After five rounds, it was observed that increasing the number of epochs beyond 15 did not result in improved accuracy of local models. Also, this approach reduced the overall duration of the FL training process. The batch size for the client was set at 16 due to the client's limited local data within the actual application. Additionally, since the input image dimensions were significantly larger than those of datasets such as MNIST and CIFAR, a larger batch size would result in out-of-memory (OOM) problems. The early stopping threshold was established at 7 epochs. This was to ensure the training process terminated if the loss value did not decrease within that timeframe. Furthermore, it was sufficient to achieve the optimal local gradient for every client and reduce power consumption during training. The remaining hyperparameters aligned with the process of training natural models.
# A Novel MEMS Capacitor with a Side Wall for High Tuning Ranges
## 2 A Novel MEMS Capacitor  
Figure 1 shows the schematic diagram of the novel MEMS capacitor and this has not been optimized yet. Figure 1 is the ideal side wall. The cantilever beam's length of the novel MEMS capacitor is 260 \(\upmu\)m and the air gap is 1.25 \(\upmu\)m. Its stiffness changes in accordance with the cantilever beam's deflection when it comes into contact with the side wall when dc voltages are applied. In other words, we applied the side wall to the gradual stiffness increase by using a change in the cantilever's effective length.  
The location of the stiffness variation is described by the following equation [9].  
\[k=\frac{3 EI}{L^{3}}=\frac{Ebh^{3}}{4L^{3}} \tag{1}\]  
Carrier beams of number \(\infty\) can be assumed for the side wall, as shown in Figure 2. If vertices of the carrier beams are drawn, that is a profile of the side wall.  
Figure 1: A schematic diagram of the novel MEMS capacitor.  
In this research study, when dc voltages (\(V_{dc}\)) are applied to the MEMS capacitor, electrical force (\(F_{e}\)) is generated according to the number of carrier beams.  
\[F_{e1}=\frac{1}{2}\frac{CV_{dc}^{2}}{(gap-y_{0})}=k_{1}y_{0}=\frac{Ebh^{3}}{4L_{ 0}{}^{3}}y_{0}\]  
\[F_{e2}=\frac{1}{2}\frac{CV_{dc}^{2}}{\{gap-(y_{0}+y_{1})\}}=k_{2}(y_{0}+y_{1})= \frac{Ebh^{3}}{4L1^{3}}(y_{0}+y_{1})\]  
\[F_{e3}=\frac{1}{2}\frac{CV_{dc}^{2}}{\{gap-(y_{0}+y_{1}+y_{2})\}}=k_{3}(y_{0}+y _{1}+y_{2})=\frac{Ebh^{3}}{4L_{2}{}^{3}}(y_{0}+y_{1}+y_{2})\]  
\[F_{en}=\frac{1}{2}\frac{CV_{dc}^{2}}{\{gap-(y_{0}+\ldots+y_{n})\}}=k_{n}(y_{0} +\ldots+y_{n})=\frac{Ebh^{3}}{4L_{n}{}^{3}}(y_{0}+\ldots+y_{n})\]  
Therefore, it can be represented by a single formula as follows.  
\[F_{en}=\frac{1}{2}\frac{CV_{dc}^{2}}{\{gap-(\int y_{n})\}}=k_{n}\int y_{n}= \frac{Ebh^{3}}{4(L-n\Delta L)^{3}}\int y_{n} \tag{2}\]  
\[L_{n}=L-n\Delta L,(n=0\ldots n)\]  
\[\Delta L=\frac{L}{N}\]  
In this manner, Equation (2), which is \(n\) order of the equation, indicates the profile of the ideal side wall. However, in this paper, we demonstrated that the side wall was assumed to be the number of four carrier beams, and we proceeded with the optimization for the side wall.
# Digital Twin-Driven Stability Optimization Framework for Large Underground Caverns
## 3 Practical Implementation and Case Study
### Digital Twin Application for Monitoring and Stability Assessment
#### 3.2.5 Actual Construction
The installation methodology offered several practical advantages on site. It was relatively simple and cost-effective, allowing for efficient deployment even under constrained working conditions. The ease of installation also made it feasible to reposition or adjust targets in response to changes in the excavation geometry, such as the presence of construction niches, cable galleries, or support system elements. These adaptive modifications ensured uninterrupted monitoring coverage throughout the construction process. The integration of geological mapping, excavation, support measures, and convergence equipment, which replicates the virtual environment in the physical world in the DT environment, facilitated a responsive and adaptive construction approach, enhancing safety, reducing uncertainties, and improving the long-term stability of the powerhouse cavern.
## 5 Discussion  
It can be recognized by comparing the different frost depth calculation methods in this study that all of them yield better accuracy when predicting frost depth in fine soil types such as clays and silts. Under the fine-soil scenario, the MTQ method matched the field data the best; however, it resulted in a considerable overestimation of the frost penetration for the case of the coarse-soil scenarios. This can be attributed to the fact that the method's accuracy strongly depends on material properties as model inputs. The segregation potential of the subgrade is crucial for determining the frost penetration depth, and the results can be highly biased if incorrect values are used. The authors of this research believe that availability of laboratory data regarding the materials could have improved the accuracy of the results for coarse materials.  
Both the MTO and TCGA methods are primarily empirical approaches. Comparing the two methods, MTO performed better than the TCCA model. The results from MTO analysis had better accuracy when predicting the frost depth of fine soils. As previously explained, the equation used by MTO to estimate frost penetration depth is based on an empirical method and was established through observations made of several highways across Ontario. Consequently, this model is more representative of the frost penetration through the soils that are present in the region [19]. Nevertheless, due to the empirical nature of both the MTO and TCCA models, the methods' applicability to various soil types may be limited since they do not account for specific soil properties. However, both are still very practical approaches for estimating frost penetration when no information is available about the soil properties.  
The climate change predictions for the next 40 years, considering emission scenario RCP8.5, show an increase of up to 5 \({}^{\circ}\)C in the annual mean temperature and a noticeable decrease in the freezing index for the cities evaluated in this research. The results from all the methods indicate that there will be a decrease in frost penetration depth due to climate change that ranges from 12% up to 66%. Frost have will most likely decrease in all areas not underlain by permafrost, with decline rates that range from 7% to 30%. Therefore, the increase in average planetary temperature defined by global climate change can be positive in areas not underlain by permafrost. The thaw penetration projection results show that the cities underlain by permafrost will suffer from an increase in seasonal active layer depth, since thawing depth will likely increase within a range of 41% to 57% in 2061. It should be emphasized that, although such increments might seem modest, soil temperatures in permafrost layers are already close to zero, and hence are highly vulnerable to thawing. In other words, it is important to note that temperature fluctuations around 0 \({}^{\circ}\)C--the typical freezing temperature for water--could increase the frequency of freeze-thaw cycles and potentially pose further issues with respect to bound pavement material durability.
# Smart Energy Meters for Smart Grids, an Internet of Things Perspective
## 2 Sensing Techniques
### Gas Meters
The authors in [95] discuss the manufacturability of ultrasonic gas meters to reap the benefits of higher accuracy and greater range at lower cost due to the economies of scale. Another interesting implementation is reported in [96]; it uses a microprocessor embedded inside a simple diaphragm-based flowmeter to estimate the gas usage of individual appliances. One of the earliest fluidic flow meter implementations is reported in [97]. This review of gas meters is a sampling of different implementation techniques and gives a broad overview of the technologies mainly in use for domestic and industrial energy gas metrology; it does not cover industrial process flow metering technologies, which are considerably advanced and varied owing to the variation and stringent requirements of the process technologies. Again, the integrated gas and electricity meter has not been reported, to the best of authors' knowledge. This translates to the aggregation of the energy consumption data at higher levels, which adds complexity and restricts analysis of the finer resolutions of energy consumption.
# Flavour Volatiles of Fermented Vegetable and Fruit Substrates: A Review
## 3 Results
### Furans  
Furural, 2-ethyl-furan, 2-propyl-furan, 2-pentyl-furan, 2,5-dimethyl-furan, 2,4-dimethylfuran, trans-2-(2-pentyl)furan, 2,3-Dihydrobenzofuran, and acetyl-furan were the major furans identified in 13 studies under furans/aldehyde/others/heterocyclic compounds. Furfural was detected in 8 studies, where it was mainly classified as an aldehyde. Production of furfural is linked to Maillard reactions, and higher levels of furfural may have a negative impact on the flavour of fermented substrates. However, LAB fermentation reduces the amount of furfural, most likely as a result of the consumption of precursors such as amino acids and reducing sugars [24; 77; 78]. The furfural concentration was reduced after LAB fermentation in 4 studies: (1) In jujube juice fermented by a mixture of _L. plantarum_, _L. rhamnus_, and _S. thermophilus_, the furfural concentration was 1886 ng/g, compared to 3873 ng/g in the unfermented juice [47]; (2) in cherry juice fermented by four different _L. plantarum_ strains, the furfural concentration was 26.1, 36.8, 43.5, and 51.6 ng/mL, compared to 101.7 ng/mL in the unfermented juice [29]; (3) in apple juice fermented by either _L. rhamnus_, _L. casei_, or _L. acidophilus_, the furfural concentration was 95.1, 106.9, and 91.8 ng/g, respectively, compared to 114.2 ng/g in the unfermented juice [21]; and (4) in pomegranate juice fermented by different _L. plantarum_ strains, the furfural concentration was reduced compared to the concentration in the unfermented juice [24]. However, in jujube (Muzao and Hetain varieties) juice, the initial furfural concentration in the Muzao varietal juice was 2.6 \(\mu\)g/mL, which was increased after fermentation by different LAB to a range of 2.9-5.2 \(\mu\)g/mL. In the same study, in Hetain varietal juice, the initial furfural concentration of 4.2 \(\mu\)g/mL was increased after fermentation by LAB to a range of 5.1-5.8 \(\mu\)g/mL [20]. Further, in goji juice fermented by a mixed bacterial culture, the 2-pentyl furan concentration ranged from 233.8 to 422.5 ng/g, compared to 36.3 ng/g in the unfermented juice [25], and in watermelon juice fermented by either _L. plantarum_, _L. brevis_, _P. pentosaceous_, _L. casei_, or _L. rhamnus_, the 2-pentyl furan concentration was 96.7, 115, 117, 112, and 99 ng/mL, respectively, compared to 79.4 ng/mL in the unfermented juice [48]. However, the concentration of 2-pentyl furan was reduced in another 2 studies [18; 38].
# Towards the Quantum Internet: Satellite Control Plane Architectures and Protocol Design
## 2 Related Works
In this paper, in addition to defining a possible architecture, we propose a networking protocol for the generation of entanglement between two GSs through the use of multiple QSRs.