esa-sceva/satcom-chunk-collection huggingface dataset:
# A Stock Prediction Method Based on Heterogeneous Bidirectional LSTM
## 4 Experiment  
Since the model in this paper is an improvement based on BiLSTM, the main comparison model for He-BiLSTM is BiLSTM. The comparison involves aspects such as accuracy, robustness, generalization ability, and running speed between the two. In terms of robustness, the experiment compares the models at moments of stock price volatility, observing the models' adaptability to sudden changes through a graphical analysis. Additionally, the experiment also considers the ability to predict stock prices for multiple future days as a factor in judging the model's robustness. In terms of generalization ability, the same model is used in different financial datasets to evaluate the model's generalization capability.  
In the experiment, the data used were stock index data (S&P 500) and futures data (gold) to verify the performance of the model. For the S&P 500 data, 5000 data points from 26 January 2000 to 9 December 2019 were selected. For the gold data, 4960 data points from 25 January 2005 to 23 April 2024 were selected. The input vector dimensions are all four-dimensional, representing the opening price, highest price, lowest price, and closingprice. The time step is 10 days, using the data from the previous 10 days to predict the prices for the 11 th, 12 th, and 13 th days.  
In the data processing section, the dataset is first divided into a training set and a test set, with the training set consisting of 3000 entries and the test set consisting of 500 entries. After that, the data are checked for missing values, specifically looking for cases where the opening price is zero. Once the check is complete, the data are normalized. The formula for normalization is as follows:  
\[x_{t}^{*}=\frac{x_{t}^{\prime}-x_{t(min)}}{x_{t(max)}-x_{t(min)}}. \tag{56}\]  
Here, \(x_{t}^{\prime}\) represents the value of each element in vector, \(x_{t(min)}\) indicates the minimum value in each vector, and \(x_{t(max)}\) indicates the maximum value in each vector.  
The evaluation metrics for the model are mean squared error (MSE), mean absolute error (MAE), and the coefficient of determination (\(R^{2}\)). The smaller the values of MSE and MAE, the higher the model's predictive accuracy and performance; the closer \(R^{2}\) is to 1, the closer the model's predicted values are to the actual values. The calculation formulas for the three evaluation metrics are as follows:  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}(\hat{y}_{i}-y_{i})^{2} \tag{57}\]  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}|\hat{y}_{i}-y_{i}| \tag{58}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y})^{2}}{\sum_{i=1}^{n}(y_{i}-\hat{y} )^{2}}. \tag{59}\]
# Finite-Key Analysis of 1-Decoy Method Quantum Key Distribution with Intensity Fluctuation
## 3 Numerical Simulation
Figure 2: (Color online) Secret key rate vs. global attenuation for \(N_{Z}=10^{8}\) and \(N_{Z}=10^{9}\) with \(\delta_{\mu}=0\), 0.01, 0.05, 0.09. As can be seen from the figure, the stronger the intensity fluctuations, the more obvious the downward trend of the security key rate. In addition, the key rate is less affected by the intensity fluctuations during normal attenuation. However, when the global attenuation exceeds 60 dB, the security key rate decreases sharply, especially when \(N_{Z}=10^{8}\). In this part, we simulate the effect of the intensity fluctuations to the secret key rate of the 1-decoy state method with finite-length data sizes. The simulation results show that the secret key rate of the 1-decoy state method is sensitive to intensity fluctuations.
# A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors
## 4 Experiments and Results  
### Experimental Environment  
The experiments in our paper use the distributed machine learning system introduced in Section 3.3, which is deployed in a cluster of three nodes to simulate sensor nodes. The nodes are connected by Gigabit Ethernet, the operating system of the cluster is CentOS 7.0, and the configuration of the cluster node is 16 AMD (Processor 6136) with a main frequency of 2.4 GHz and 32 GB of memory.  
We use the MNIST handwritten digital font dataset [40] as our dataset. The training set contains 60,000 images and the test set contains 10,000 images. The training model is based on the classical LeNet-5 [40], which includes an input layer, an output layer, three convolutional layers, two pool layers, and a full connected layer. The batch size of the training model is configured to 64 and the maximum number of iterations is set to 10,000.  
Figure 10: A distributed machine learning system based on Caffe and supports Dynamic Synchronous Parallel Strategy (DSP).
# A Comprehensive Survey for Deep-Learning-Based
## 4 Abnormality Detection with Images over Long Range  
There are many forms of satellite data. For example, we can obtain positioning data through GNSS satellites, and through remote sensing satellites, we can obtain optical and SAR images. These data can provide strong support for the abnormality detection of a power grid. In this review, we will classify different types of satellite data and introduce the related work for each type of satellite data in power grid abnormality detection.
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 10 Routing and Energy Allocation in Rechargeable WSNs with Multiple Sources and Destinations  
### System Model and Design Objectives and Considerations  
In [5], a rechargeable WSN whose batteries' replenishment profile is unknown a priori is considered. Aggregate utility functions for the sensors are to be maximized with low complexity. It was shown that the problem can be formulated as a standard convex optimization problem with energy and routing constraints. However, the solution requires centralized control and full knowledge of replenishment profiles in the future, which may not be available in practice. Therefore, a low-complexity heuristic solution was developed that is asymptotically optimal and can be approximated by a distributed algorithm. The following are the main elements of the system model in [5]:  
1. Time slotted system with finite number of slots was considered,
2. the battery of each sensor is assumed to have an infinite rechargeable capacity,
3. multiple sensing sources and multiple destination nodes are considered,
4. utility function that reflects the \"satisfaction\" of the node is associated with each source node when it transmits at an average data rate \(\xi^{s}\left(t\right)\) that is equal to the aggregate amount of data from that source to a particular destination over all time slots averaged over the duration of the frame. It is defined generally to be concave monotonically increasing in the average data rate of the source node.
###### Abstract  
For several decades, operational retrievals from spaceborne hyperspectral infrared sounders have been dominated by stochastic approaches where many ambiguities are pervasive. One major drawback of such methods is their reliance on treating error as definitive information to the retrieval scheme. To overcome this drawback and obtain consistently unambiguous retrievals, we applied another approach from the class of deterministic inverse methods, namely regularized total least squares (RTLS). As a case study, simultaneous simulated retrieval of ozone (O\({}_{3}\)) profile and surface temperature (ST) for two different instruments, Cross-track Infrared Sounder (CrIS) and Tropospheric Emission Spectrometer (TES), are considered. To gain further confidence in our approach for real-world situations, a set of ozonesonde profile data are also used in this study. The role of simulation-based comparative assessment of algorithms before application on remotely sensed measurements is pivotal. Under identical simulation settings, RTLS results are compared to those of stochastic optimal estimation method (OEM), a very popular method for hyperspectral retrievals despite its aforementioned fundamental drawback. Different tweaking of error covariances for improving the OEM results, used commonly in operations, are also investigated under a simulated environment. Although this work is an extension of our previous work for H\({}_{2}\)O profile retrievals, several new concepts are introduced in this study: (a) the information content analysis using sub-space analysis to understand ill-posed inversion in depth; (b) comparison of different sensors for same gas profile retrieval under identical conditions; (c) extended capability for simultaneous retrievals using two classes of variables; (d) additional stabilizer of Laplacian second derivative operator; and (e) the representation of results using a new metric called \"information gain\". Our findings highlight issues with OEM, such as loss of information as compared to a priori knowledge after using measurements. On the other hand, RTLS can produce \"information gain\" of \(\sim\)40-50% deterministically from the same set of measurements.  
ozone profile retrieval; deterministic inverse; regularized total least square; Tropospheric Emission Spectrometer (TES); Cross-track Infrared Sounder (CrIS); surface temperature; optimal estimation method (OEM) 2018109403390/rs10070994
## 1 Introduction  
Silicon carbide crystal (SiC) crystals are used to product semiconductor devices under higher-temperature, higher-frequency, and higher-power conditions [1]. Compared with silicon, silicon carbide crystal has better performance on chemical and thermal properties, such as higher thermal conductivity, chemical stability, radiation stability, and mechanical stability [2]. The physical vapor transport (PVT) method is the most successful and common method to grow SiC crystals at pressure \(\leq 50\) mbar, increasing crystal growth rate [3; 4; 5]. In this method, graphite crucible is heated by induction coils and the temperature in the furnace reaches more than 2300 K. The temperature distribution is fatal to SiC growth process, which decides the SiC growth speed and the product quality. However, it is difficult to measure temperature in a confined silicon carbide crystal growth furnace at both high temperature and low pressure, and so far, the infrared thermometry method is adopted to monitor temperature through a gap cooler hole. The detailed study of temperature distribution in the furnace is beneficial to the modification of the furnace structure and operation system.  
With the development of computer technology, numerical simulation is becoming a powerful tool to analyze temperature fields [3; 6; 7; 8; 9], crystal growth processes [10; 11; 12], and thermal stresses [13] as the SiC grows. These parameters are meaningful to optimize the process of crystal growth. Both conduction and radiation are the main two heat transfer processes in 2-4 inches SiC crystal growth furnaces. The weaker convection heat transfer is always ignored. For radiation heat transfer, species in the growth chamber are not participated in radiation process, so the surface to surface (S2S) radiation model is used to calculate radiation heat flux [2; 14; 15; 16]. The axisymmetric assumption is adopted in the majority of numerical simulation research, so the model is two-dimensional, the effect of non-axisymmetric coils on electromagnetic field and temperature field are always missed artificially. Moreover, some graphite crucible surfaces cannot see other surfaces because of the nontransparent crystal. Therefore, some methods should be developed to check whether radiation surfaces are visible to other surfaces or not, ensuring the accurate calculation of radiation heat flux.  
Herein, a three-dimensional numerical simulation algorithm was developed based on cylindrical coordinate system and C programing language to study the effects of axisymmetric model and spiral coils on electromagnetic and temperature fields at coil current \(I=1250\) A and current frequency \(F=16\) kHz. The least distance method is proposed to check visibility of radiation facing to others, and a parallel LU decomposition algorithm for radiation view factor matrix based on graphic processing unit technology is developed to accelerate the solution of surface to surface radiation heat transfer.
## 4 Systematic Experimental Process and Results Analysis
### Experimental Results  
#### 4.4.1 Comparison of Experimental Results of Multimodal Text Recognition  
Table 2 shows the comparison of the detection speed effect of the network model on the video dataset before and after improvement. From Table 2, it can be seen that compared to the model before improvement, the average recognition speed of the model for text in video increased from 19.2 frames per second before to 25.3 frames per second, an improvement of 31.77%.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test Dataset** & **Network Model** &
\begin{tabular}{c} **Average Detection Speed** \\ **(Frames/s)** \\ \end{tabular} \\ \hline Video data collected by crawler & Model before improvement & 19.2 \\ Video data collected by crawler & Improved model & 25.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the effect of model detection speed before and after improvement.  
Figure 9: Public opinion lab dataset.  
Figure 10 shows the comparison of the accuracy, recall, and F1 value effects of the network model on the image and video datasets before and after the improvement. As shown in Figure 10, compared to the model before improvement, the detection effect is slightly improved after adding the GTC method and the sinusoidal loss function scheme for rotation recognition. The \(F1\) value of the text detection effect on the image dataset ICDAR 2015 is improved by 12.58%, and the \(F1\) value of the text detection effect in the video is improved by 10.78%.  
Taking a single image from the ICDAR 2015 dataset, Figure 7, as an example, Table 3 shows the comparison of the text detection effect in the image before and after the improvement. The results in Table 3 show that the improved model can not only recognize more \"MACREME\" text but also improve the confidence of recognition because the improved model is slightly slower than the premodified model in terms of detection time. Therefore, the recognition ability of the improved model has improved.
## 5 Lightweight Clear Channel Assessment (LW-CCA)
### Dynamic RSSI Check Time in LW-CCA  
This section describes the performance of LW-CCA according to the description of the previous sections. As mentioned, LW-CCA performance is based on RSSI measurements. In this method, for a dynamic check, the radio check time works according to the models 1 and 2 (Section 5.2). Every node has two tasks in receiving RSSIs: i.e., receiving RSSI based on model 1, and re-evaluating RSSI based on the model 2 and its classification.  
The proposed method is illustrated in Figure 8, where nodes perform radio checks in two modes: listen and transmit mode. In the LW-CCA method, a reduction in Rx radio check time is considered. Each ContikiMAC node periodically checks the radio with CCA pairs. In fact, each CCA recognizes the validity and invalidity of radio activity by comparing the RSSI threshold. Based on Section 5.1, in LW-CCA each radio check uses the RSSI directly. LW-CCA nodes first assign a value of eight symbols (Model 2) to the status check RSSI. The value obtained is evaluated based on the classification in Section 5.3. In this case, two states are considered:  
**1/ RSSI is a member of rssi_null list:**  
If the RSSI value is a member of the rssi_null list, the node returns 1 to ContikiMAC and quickly go to sleep. In fact, the node does just eight-symbols RSSI status check.  
Figure 7: RSSIs classified as null.  
2/ RSSI is a member of the rssi_active List or it is a new RSSI:  
If the RSSI value is outside the rssi_null list, it is either a new value or a member of rssi_active. In both cases, the node re-evaluates the radio based on Model 1. In this case, after the RSSI check, the CCA check will be considered for validation and classification of RSSI. If CCA returns a value of 1, the RSSI value is detected as an idle listening and stored as a worthless value in the rssi_null list. Otherwise, ContikiMAC applies its timing based on Section 4 to check for false Wake Up or positive WakeUp. If the radio activity in the node results in a false WakeUp the RSSI value is stored in the rssi_null list and the node goes to sleep quickly based on the Fast-Sleep mechanism. If the RSSI results in positive WakeUp the node stays on the receiving mode.  
The proposed method actually uses the maximum value of the RSSI status check time when the RSSI value is detected either new or active. Therefore, in other cases, at least eight symbols are considered. LW-CCA thus reduces the amount of radio check-in false WakeUps and idle listening as much as possible to reduces the inefficient power consumption caused by radio activity in Rx mode. Also, using RSSI validation based on the CCA value makes the node performance reliable in the real environment as well.
## 4 Research Results  
### Influence of Rainwater on Measurement Using the UPV Testing Method  
Based on the results of measurements of ultrasound transmission rates in slabs, an analysis of variance (ANOVA) analysis was performed.  
Having a few independent samples (i.e., three different rain conditions), there was a need to compare their mean values. A pairwise comparison test could not be used; therefore, the analysis of variance (ANOVA) was employed. First, the Bartlett test of homogeneity of variances was conducted. If its \(p\)-value was larger than the significance level (\(\alpha\) = 0.05), we proceeded to test the null hypothesis H: \(\upmu_{1}=\upmu_{2}=\upmu_{3}\) against the alternative hypothesis A: \(\upmu_{i}\
eq\upmu_{j}\) for i, j = 1, 2, 3 and i \(\
eq\) j, using the F-statistics calculated as the ratio of mean squares, which describes variability within and between the samples. If the hypothesis H was rejected, the post hoc test--Tukey test--was used to compare the differences between the samples and to point out which pair of means was significantly different.
# Stochastic Safety Radius on UPGMA
## 2 Stochastic Safety Radius
```  
**Algorithm 1** UPGMA [15]  
Figure 2: Example for Algorithm 1. _A distance matrix computed from the tree shown in Figure 2 is_  
\[d=\left[ \begin{array}{c|cccc}&1&2&3&4\\ \hline 1&0&1.6&4&4\\ 2&1.6&0&4&4\\ 3&4&4&0&2.4\\ 4&4&4&2.4&0\\ \hline\end{array} \right]\]  
_Note that_  
* _For_ \(i=1,j=2,k=3\)_:_ \[\max\{d_{12},d_{13},d_{23}\}=4\] _and_ \[d_{13}=d_{23}=4.\]
* \(i=1,j=2,k=4\)_:_ \[\max\{d_{12},d_{14},d_{24}\}=4\] _and_ \[d_{14}=d_{24}=4.\]
* \(i=1,j=3,k=4\)_:_ \[\max\{d_{13},d_{14},d_{34}\}=4\] _and_ \[d_{13}=d_{14}=4.\]
* \(i=2,j=3,k=4\)_:_ \[\max\{d_{23},d_{24},d_{34}\}=4\] _and_ \[d_{23}=d_{24}=4.\]  
_Therefore, d satisfies Equation (3). Thus, this \(4\times 4\) matrix is an ultrametric. With UPGMA algorithm shown in Algorithm 1, we have_  
* _For_ \(k=1\)_, we pick a pair of leaves_ \((1,2)\) _with_ \(d_{12}=1.6\)_. Set_ \(x_{1}\) _as the parent node of_ \((1,2)\)_. Assign the branch length from_ \(x_{1}\) _to_ \(1\) _as_ \(1.6/2=0.8\) _and assign the branch length from_ \(x_{1}\) _to_ \(2\) _as_ \(1.6/2=0.8\)_. Now we add_ \(x=(1,2)\) _as a leaf set_ \(X\)_. Thus we have_ \(X=\{x,3,4\}\) _with_  
\[d_{x3}=\frac{1}{2}(d_{13}+d_{23})=\frac{1}{2}(4+4)=4\]  
_and_  
\[d_{x4}=\frac{1}{2}(d_{14}+d_{24})=\frac{1}{2}(4+4)=4.\]
* _For_ \(k=2\)_, we pick a pair of leaves_ \((3,4)\) _with_ \(d_{34}=2.4\)_. Set_ \(x_{2}\) _as the parent node of_ \((3,4)\)_. Assign the branch length from_ \(x_{2}\) _to_ \(3\) _as_ \(2.4/2=1.2\) _and assign the branch length from_ \(x_{2}\) _to_ \(4\) _as_ \(2.4/2=1.2\)_. Now we add_ \(y=(3,4)\) _as a leaf set_ \(y\)_. Thus we have_ \(X=\{x,y\}\) _with_ \[d_{13}=d_{23}=4.\]0  
_After the for-loop, we record the branch length from the root to the leaf \(x\) and the leaf \(y\) as \(4/2=2\). From this, we can compute the branch length from the root to \(x_{1}\) by \(2-0.8=1.2\) and the branch length from the root to \(x_{2}\) by \(2-1.2=0.8\)._  
In this paper, we use UPGMA in order to investigate their stochastic safety radius and lower bounds for the probability for UPGMA to return the true tree topology if the input distance matrix is not ultrametric. Here we assume that the multivariate random variable \(\delta\) is defined as follows:  
\[d_{13}=d_{23}=4.\]1  
where \(\epsilon_{ij}\sim N(0,\sigma)\) are independently and identically distributed for fixed \(\sigma>0\) and for all \(i<j\in X\) and  
\[d_{13}=d_{23}=4.\]2  
for all \(i<j\in X\). **Remark 8**. : _In order to make it simple, we assume that the height of an equidistant tree \(T\) on leaves \(X\), which is the total branch length from each leaf \(i\in X\) to its root, is equal to \(1\)._
## 1 Introduction
Although such a general framework does not produce the prediction function that can be common to different systems, it can save the costs in developing diagnostic or prognostic functions by re-executing it. The most straightforward approaches for this purpose are the exhaustive grid search [32], which examines a subset of parameters with a constant interval, and the experience-based manual selection [33], where a human expert specifies the parameter values based on their experience. However, the former can be inefficient due to the expensive computational cost, and the latter is dependent on the expert's knowledge, which is not general to various datasets. In this regard, there is a pressing need to develop an efficient and data-independent approach, so we propose a new framework to develop a diagnostics and prognostics method based on an ensemble of genetic algorithms (GAs) that can be applied for both the fault-type classification and RUL prediction problems. Our framework handles four subtasks such as the data rebalancing, feature extraction, feature reduction, and machine-learning. Accordingly, our GA tries to select the optimal algorithm for each subtask and specify the optimal parameter values involved in the selected algorithm. In addition, the proposed method constructs an ensemble of the prediction models that are found by the GAs combined with various machine-learning methods. To verify the usefulness of our approach, we compared it to a traditional grid-search over three benchmark datasets of the fault-type classification (the steel plates faults and SECOM datasets) and the RUL prediction (NASA commercial modular aero-propulsion system simulation (C-MAPSS) dataset) problems. Our method showed a significantly better and more robust performance than the latter, with a practically acceptable running time. The remainder of this paper is organized as follows. Section 2 introduces the backgrounds on the diagnostics and prognostics problem and the performance evaluation metrics. Section 3 explains the details of our approach and Section 4 presents the experimental results along with discussion. Section 5 includes the concluding remarks and suggestions for future work.