esa-sceva/satcom-chunk-collection huggingface dataset:
# EL-MTSA: Stock Prediction Model Based on Ensemble Learning and Multimodal Time Series Analysis
## 4 Experimental Results and Discussion
### Evaluation Metrics  
To more accurately evaluate the prediction performance, we employ three metrics: the root mean squared error (RMSE), the mean absolute error (MAE), and the symmetric mean absolute percentage error (SMAPE).  
The root mean squared error (RMSE), also referred to as the residual standard deviation, is primarily used in regression analysis as a criterion for variable selection and model evaluation. It is calculated as follows:  
\[RMSE=\sqrt{MSE}=\frac{\sqrt{MSE}}{\sqrt{n}}=\sqrt{\sum\limits_{i=1}^{n}{(y_{i} -\hat{y}_{i})}} \tag{41}\]  
The mean absolute error (MAE) quantifies the average absolute difference between predicted values and actual values. It measures the deviation of predictions from the ground truth, where lower values indicate higher prediction accuracy. It is calculated as follows:  
\[MAE=\sum\limits_{i=1}^{n}{\frac{|\hat{y}_{i}-y_{i}|}{n}} \tag{42}\]  
The symmetric mean absolute percentage error (SMAPE) is an improved metric designed to address the limitations of the mean absolute percentage error (MAPE). It mitigates the issue of disproportionately large errors when the true values are small, providing a more balanced evaluation. It is calculated as follows:  
\[SMAPE=\frac{100\%}{n}\sum\limits_{i=1}^{n}{\frac{|\hat{y}_{i}-y_{i}|}{(|\hat{ y}_{i}|+|y_{i}|)^{2}}} \tag{43}\]  
In the above three equations, \(y_{i}\) is the actual value, \(\hat{y}_{i}\) is the predicted value, and \(n\) represents the total number of actual value and predicted value.
## 5 Experimental Tests
### Fuel Cell Emulation  
Firstly, the fuel cell emulation was validated by comparing the emulated curve with the real FCS-C300 fuel cell curve provided by Horizon Fuel Cell Technologies. As described previously, the control unit evaluates the current delivered by the power supply through its monitoring port and then, after calculating the corresponding voltage, sends a signal to the PS control voltage port. The PS translates this signal and sets the output voltage in accordance to it. Figure 7 shows the comparison between the emulated FC V-I curve and the real FC V-I curve. The real FC curve was obtained from the datasheet of the fuel cell. It can be stated that the two curves are very close; thus, the FC is properly emulated. This test was repeated at least twice at different time periods in order to verify the capability of the system to provide approximately the same FC curve. Repetition of the test showed that the system provides approximately the same FC curve, with a max deviation of around 0.9% in terms of the voltage difference between the test repetitions. However, the max deviation was recorded at low loads, where the V-I curve slope is greater, while for medium-high loads, the difference was reduced to around 0.6%.
## 2 Materials and Methods
### Loop-Closure Detection
#### 2.4.2 Candidate Frame Detection  
This section will discuss the specific process of finding candidate frames. We adopt a multi-layer detection method to achieve efficient and accurate detection. In the first layer of detection, although there are two feature datasets, \(S_{c}\) and \(S_{r}\), composed of column-aggregation and row-aggregation features, the primary goal is preliminary filtering to quickly narrow the detection range. It does not require precise results, so selecting one feature dataset is sufficient. We use Manhattan distance to select \(k\) feature vectors with the smallest distance from the feature set \(S_{r}\), obtaining a candidate feature set \(C_{r}\) (\(\#C_{r}=k\)). The second layer of detection will perform refined screening among the k-selected frames. This includes feature distribution judgment and more accurate similarity calculations. The screening process will comprehensively use aggregation features \(\mathbf{v}_{r}\) and \(\mathbf{v}_{c}\) from each segmented region to calculate similarity.  
\[score=\frac{\sum_{i}^{n}r_{\mathbf{v}
# Internal Interface Diversification as a Security Measure in Sensor Networks +
## 1 Introduction
First, based on our previous conference paper [7], we present a study on diversifiable interfaces in IoT operating systems. This study has been extended here considerably with a discussion of 10 additional operating systems. Second, we offer two proof-of-concept implementations for the IoT environment that demonstrate the feasibility of diversification. These solutions were first introduced in [8]. In this paper, we provide more implementation details and an extended discussion of these solutions in the context of sensor networks. Based on our experiments, we evaluate the feasibility, strengths and drawbacks of interface diversification in the IoT environment. We also discuss attack scenarios that are mitigated by interface diversification in sensor networks. The rest of the paper is structured as follows. Section 2 introduces interface diversification and related concepts. We also introduce some common attack scenarios in sensor networks that we aim to combat with interface diversification. Section 3 reviews related work in the field of diversification research. Section 4 explains how the operating system interface diversification can be applied to IoT operating systems and presents a review of diversifiable interfaces in 20 commonly used IoT operating systems. Section 5 presents our proof-of-concept implementations for two IoT operating systems. We provide practical implementations for memory layout shuffling in Thingsee OS and symbol diversification in Raspbian operating systems. Furthermore, experiments with these solutions are conducted to show the feasibility of interface diversification in the IoT environment. Section 6 discusses interface diversification as a security measure in sensor networks and reviews its strengths and weaknesses. Finally, Section 7 concludes the paper.
# On the Use of Structured Prior Models for Bayesian Compressive Sensing of Modulated Signals
## 2 Proposed Method
### Structured Spike and Slab for Modulated Signals
#### 2.4.4 Weak Assumptions on In-Block Symmetry and Inter-Block Similarity  
Another concern about models (6) and (7) is that the perfect symmetry assumption about the support around the carrier harmonics may be somehow altered due to the sensor transfer function or phase modulations. In that case, we can further relax this assumption by letting either the local mixing value in the slab component or the selection variable in the spike component (or both of them) take different values. This yields the following model  
\[\forall b\in\{1,\,\ldots,\,B\}\forall q\in\{1,\,\ldots,\,Q\} \forall j\in\{1,-1\} \tag{8}\] \[x_{j,q,b} \sim(1-z_{j,q,b})\delta_{0}+z_{j,q,b}\mathcal{CN}(0,\lambda_{j,q,b})\] \[z_{j,q,b} \sim\mathcal{B}(\phi(g_{q}))\] \[\mathbf{g} \sim\mathcal{N}(\mathbf{m},\mathbf{R})\]  
Model (8) is very close to the univariate model (A2); the only difference is that a structured prior about the support is implicitly assumed through the latent variable \(\mathbf{g}\). Similarly to (6) and (7), we can set \(\lambda_{j,q,b}=\gamma_{q}v_{b}\) to further upgrade the symmetry of the spectrum.
## 3 Game Analysis
### Game I: OP and Sinks
#### 3.1.1 WSN Subscription Game  
This stage is played once the OP has fixed its price \(p\). Sinks equilibrium is solved using the unified framework provided by Population Games described in [48]. This framework is useful for study strategic interactions between agents with certain properties that our model satisfy. Furthermore, the analysis is easily extensible from static to dynamic games, which will allow us to obtain more realistic conclusions in future studies. The equilibrium reached is a Nash equilibrium.  
Population Game  
* _Strategies:_\(S=\{0,1\}\), where \(0\) means not to subscribe to the OP and \(1\) means to subscribe to the OP.
* _Social State:_\(X_{s}=\{x_{0},x_{1}\},x_{0}+x_{1}=1\). Sinks distribution between not being served and OP.
* _Payoffs:_\(F_{s}(x_{0},x_{1})=\{F_{s0}(X),F_{s1}(X)\}=\{0,U_{s}(3)\}\), where \(F_{s_{0}}(X)\) is the utility of the users choosing the strategy of not to subscribe to the OP and \(F_{s1}(X)\) is the utility of the users choosing the strategy of subscribe to the OP.  
Figure 3: Description of the games stages.  
Pure Best Response  
The first step for solve the population game is to obtain the pure strategies that are optimal at each social state \(X_{s}\).  
\[b(X_{s})\equiv\underset{i\in S}{\text{argmax}}\,F_{si}(X_{s})=\begin{cases}i=1& \text{if}\quad F_{s1}(X_{s})\geq F_{s0}(X_{s})\iff x_{1}\leq\frac{c-p}{c\tau Nr }\\ i=0&\text{if}\quad F_{s0}(X_{s})\geq F_{s1}(X_{s})\iff x_{1}\geq\frac{c-p}{c \tau Nr}\end{cases}\quad. \tag{9}\]  
Mixed Best Response  
Once we have obtained the pure best responses, we can extend the results to include the optimal mixed strategies.  
\[B(X_{s})\equiv\left\{\left[z_{0}+z_{1}=1;z_{i}\in R_{+}\right]:z_{i}>0\Rightarrow i \in b(X_{s})\right\}=\begin{cases}z_{0}=0,z_{1}=1&\text{if}\quad x_{1}\leq \frac{c-p}{c\tau Nr}\\ z_{0}>0,z_{1}>0&\text{if}\quad x_{1}=\frac{c-p}{c\tau Nr}\\ z_{0}=1,z_{1}=0&\text{if}\quad x_{1}\geq\frac{c-p}{c\tau Nr}\end{cases}\quad. \tag{10}\]  
At this point social state \(x\in X_{s}\) is a Nash equilibrium of the game \(F_{s}\) if all the agents chooses a best response to \(x\in X_{s}\):  
\[NE(F_{s})\equiv\left\{x\in X_{s}:x\in B(X_{s})\right\}=\begin{cases}(0,1)& \text{if}\quad p\leq c\left(1-\tau Nr\right)\\ (1-\frac{c-p}{c\tau Nr},\frac{c-p}{c\tau Nr})&\text{if}\quad c\left(1-\tau Nr \right)\leq p\leq c\\ (1,0)&\text{if}\quad p\geq c\end{cases}\quad. \tag{11}\]
###### Abstract  
Soil moisture (SM) is an important component of the Earth's surface water balance and by extension the energy balance, regulating the land surface temperature (LST) and evapotranspiration (ET). Nowadays, there are two missions dedicated to monitoring the Earth's surface SM using L-band radiometers: ESA's Soil Moisture and Ocean Salinity (SMOS) and NASA's Soil Moisture Active Passive (SMAP). LST is remotely sensed using thermal infrared (TIR) sensors on-board satellites, such as NASA's Terra/Aqua MODIS or ESA & EUMETSAT's MSG SEVIRI. This study provides an assessment of SM and LST dynamics at daily and seasonal scales, using 4 years (2011-2014) of in situ and satellite observations over the central part of the river Duero basin in Spain. Specifically, the agreement of instantaneous SM with a variety of LST-derived parameters is analyzed to better understand the fundamental link of the SM-LST relationship through ET and thermal inertia. Ground-based SM and LST measurements from the REMEDHUS network are compared to SMOS SM and MODIS LST spaceborne observations. ET is obtained from the HidroMORE regional hydrological model. At the daily scale, a strong anticorrelation is observed between in situ SM and maximum LST (R \(\approx-0.6\) to \(-0.8\)), and between SMOS SM and MODIS LST Terra/Aqua day (R \(\approx-\) 0.7). At the seasonal scale, results show a stronger anticorrelation in autumn, spring and summer (in situ R \(\approx-0.5\) to \(-0.7\); satellite R \(\approx-0.4\) to \(-0.7\)) indicating SM-LST coupling, than in winter (in situ R \(\approx+0.3\); satellite R \(\approx-0.3\)) indicating SM-LST decoupling. These different behaviors evidence changes from water-limited to energy-limited moisture flux across seasons, which are confirmed by the observed ET evolution. In water-limited periods, SM is extracted from the soil through ET until critical SM is reached. A method to estimate the soil critical SM is proposed. For REMEDHUS, the critical SM is estimated to be \(\sim\)0.12 m\({}^{3}\)/m\({}^{3}\), stable over the study period and consistent between in situ and satellite observations. A better understanding of the SM-LST link could not only help improving the representation of LST in current hydrological and climate prediction models, but also refining SM retrieval or microwave-optical disaggregation algorithms, related to ET and vegetation status.  
evapotranspiration; correlation; water-limited; energy-limited; coupling; disaggregation; critical soil moisture; REMEDHUS; SMOS; MODIS
## 4 Simulation Validation
### Intelligent Cruise Control of Single Following Vehicle  
The cooperative control of the intelligent connected vehicle was developed by the adaptive cruise system (ACC). With the integration of the ACC and the LKS, some automotive companies developed and launched the traffic jam assistance system (TJA), also called ICC system. The particle model is compared with the FWIDSV model under the same control parameters, and a more realistic dynamic response verifies the effectiveness of the reference vector field control, tire force allocator, and actuator control layer.  
As can be seen from parts (a) and (b) of Figure 11, the performance of the FWIDSV is worse than the ideal particle dynamics model, including the overshoot and tracking convergence speed and longitudinal spacing convergence speed while following a leading vehicle. This is understandable because of the nonlinear characteristics of the FWIDSV. It must also be mentioned that the error of lateral path tracking and spacing flowing still tend to zero, which proves the effectiveness of the controller.  
In Figure 12a, the ideal vehicle accelerations determined in the reference vector field are listed as discrete points. The acceleration is mainly distributed above, below, and the center of the friction circle, which is determined by \(\varepsilon\), \(\mathbf{v_{0}}\), and the specific working conditions. When \(\mid\mathbf{\mathrm{e}}\mid\leq\mathbf{v_{0}}\), using \(\varepsilon\) as much as possible will make the vehicle less prone to stability. Figure 12b shows the performance of the FWIDSV under tire force distribution and control layer control and it demonstrates that, when the initial error \(\mathbf{\mathrm{e}}\) is larger, the larger tire force is used to track the path and longitudinal spacing. However, after the error is reduced, the use of tire force is stabilized at around zero. In summary, the reference vector field and the FWIDSV integrated controller can guarantee the stable and reliable tracking of the path and the preceding vehicle by FWIDSV under this condition.  
Figure 11: Motion of different model: (**a**) The path comparison of different models; and, (**b**) The following error comparison of different models.  
To verify the control effect of the actuator control layer, the desired accelerations of FWIDSV and the actual acceleration are compared. It can be seen from the Figure 12b that, through the direct integrated control of the FWIDSV wheel angle and torque, the actual acceleration response of the vehicle conforms to the changing trend of the desired acceleration. However, due to reasons, such as reasonable simplification or difficulty in modeling, there is a reasonable deviation between the two. The main reason is that in the calculation of the ideal acceleration, factors, such as resistance, are not considered. To more intuitively display the control process of the execution layer, the torque and steering angle of the FWIDSV are as shown in Figure 13.
## 2 Background and Previous Research
### Benchmarking with Text Mining  
One of the applications of text mining and processing corresponds to the benchmarking or comparison of different attributes, products, or services of companies. For instance, the pioneering research proposed in [7] determines which areas are critical within companies in the UK retail sector. These areas were obtained by applying the LDA algorithm, sentiment analysis, and comparison of the main keywords of each topic with a theoretical review (performed manually) of the main factors of each area, which is similar to the CIER survey performed in Colombia [12].  
On the other hand, ref. [13] details the use of a new method to analyze the perception of customers regarding the major features of the products of some cell phone brands. The methodology is described as MSAS--Microblog Sentiment Analysis System--and involves the definition of the main metrics to compare and process the tweets, the definition of the polarity, and the use of visualization tools to compare which attributes are more important.  
Another piece of research that applies benchmarking in the field of satisfaction and service levels is explained in [14]. Here, a methodology is proposed to determine the level of detraction and potential promotion of three mobile network operators in South Africa. It highlights the use of different processing and analysis techniques, such as graphical methods to find trends, the definition of correlations based on timelines, and cluster definition of geo-referenced data. Additionally, topic modeling methods such as the LDA algorithm are applied to detect and classify the main topics obtained from mobile users.  
One of the critical variables in the applications of text mining in benchmarks corresponds to the enrichment of the dictionaries to be used, as mentioned in [15]. This study proposes a model of predictability for followers of the Twitter pages of telecommunications companies in India after the entry of a new competitor to the market. When comparing the projections made with actual industry data, it is found that much of the success of the development is due to the enrichment of the dictionaries implemented.  
In [16], lexicon enrichment in benchmarks also becomes highly relevant; an accuracy level of 73% is reached due to the implementation of a Naive Bayes-based model to identify improvement opportunities from customer feedback in the private transport sector.
## 2 Adaptive Filtering Systems for Active Control  
Conventionally, digital filters have been applied for modeling dynamic responses to any type of given inputs in engineering structures and acoustic platforms. They apply numerical processes in the discrete time domain for a certain signal to obtain a desired signal shape, and they are broadly used in electronics, computer science, and mechanical engineering. The finite impulse response (FIR) filter is extensively used to represent system responses because of its inherent simplicity, stability, and ease of implementation. In addition, digital filters notably applicable in adaptive structural design and control. Adaptive digital signal processing is applied to update digital filter coefficients automatically in order to create a desired signal from a reference signal and error based on recursive algorithms. Digital filters are thus commonly used in active noise and vibration isolation, system identification, and inverse modeling. An adaptive digital filtering system was designed for active control of the unwanted vibration of continuous structures with a new adaptive filtering algorithm.
## 2 System Composition and Protocol Design
### Protocol Comparison  
A comparison of the CR-OFDMA with the conventional OFDMA and the DRA-OFDMA is shown in Figure 6. The right side of the figure shows the transmission process of the conventional OFDMA, and the green part is this protocol's effective use of spectrum resources. The middle shows the transmission process of the DRA-OFDMA, and the blue part shows the efficient use of the spectrum by the DRA-OFDMA compared to the traditional OFDMA. The left shows the CR-OFDMA in this paper. The CR-OFDMA has a more efficient spectrum utilization than the DRA-OFDMA with the extra red part. Among the three protocols, the CR-OFDMA has the highest utilization of spectrum resources.
# Multi-Tier Heterogeneous Beam Management for Future Indoor FSO Networks
## 4 Analysis Framework
Following this process, we find the optimal Tier 2 resolution and Tier 3 range in Equations (6) and (7), respectively. Given that \(\beta_{i+1}=1/\alpha_{i}\) for this specific problem formulation, we recognize the direct relationship between the optimal Tier 2 resolution and Tier 3 range. As such, the optimal value for \(\beta_{3}\) could be directly derived from knowledge of \(\alpha_{2}\) (or vice versa). However, the inverse relationship of the derived results in Equations (6) and (7) demonstrates that the general relationship holds and offers a sense of validation. Furthermore, the direct relationship implies that all relevant information \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Description** \\ \hline \(T_{s}\) & Maximum Search Time \\ \(T_{d}\) & Discovery Time (Tier 1) \\ \(t_{i}\) & Configuration Switching Time (Tier \(i\)) \\ \(n_{i}\) & Number of Configurations (Tier \(i\)) \\ \(\alpha_{i}\) & Tier \(i\) Resolution [configurations/m\({}^{2}\)] \\ \(\beta_{i}\) & Tier \(i\) Range [m\({}^{2}\)] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: MTH analysis parameters. can be defined by either \(\alpha_{2}\) or \(\beta_{3}\); however, each can be used to better visualize the relative performance impact under different operating conditions (as seen in Section 5). \[\underset{\alpha_{2}\in\mathbb{R}_{>0}}{\arg\min}(T_{s})=\sqrt{\frac{t_{3}\alpha_ {3}}{t_{2}\beta_{2}}} \tag{6}\]  
\[\underset{\beta_{3}\in\mathbb{R}_{>0}}{\arg\min}(T_{s})=\sqrt{\frac{t_{2}\beta_ {2}}{t_{3}\alpha_{3}}} \tag{7}\]  
Under the given assumptions, these results could be used to define the ideal mmWave (Tier 2) emission profile for a system with known FSO emission profile and FSO/mmWave configuration switching times along with a specified coverage area. However, these simplifying assumptions are obviously limited in terms of practical application since a more realistic system would account for configuration overlap and non-uniform coverage, feedback and pipelined scanning, and other protocol characteristics. Accordingly, these results are intended primarily for qualitative analysis in order to observe the relative impact of various system parameters.
# SMILE: Segmentation-Based Centroid Matching for Image Rectification via Aligning Epipolar Lines
## 2 Related Work
### Image Rectification  
Image rectification [25; 26] is particularly useful for aligning images captured by two cameras, such as stereo images, enabling effective 3D reconstruction and depth perception. Therefore, it is used as a useful preprocessing step in fields such as computer vision. Traditionally, image rectification involves aligning the epipolar lines using the parameters obtained from camera calibration. Through this process, the corresponding points in the two images are aligned on the same horizontal line, which greatly reduces computational complexity and minimizes errors, making stereo matching and depth estimation easier. However, with the advent of deep learning models, especially Convolutional Neural Networks (CNNs) [27] that are highly effective in image processing, new methods for image rectification based on deep learning have been proposed [28; 29; 30; 31; 32; 33; 34]. Despite the potential of these deep-learning-based methods, they require a substantial amount of data to train the models adequately and are prone to issues such as overfitting during the rectification process. Additionally, due to the nature of deep learning models, they demand significant computational resources and involve complex calculations, which can be more resource-intensive compared to traditional methods. Despite the challenges, deep learning models are used because they can better handle complex nonlinear distortions, have superior generalization capabilities in various environments and conditions, and show high adaptability to new data, making them potentially more effective than traditional methods. Table 1 summarizes the strengths and limitations of existing algorithms for correspondence point detection.