esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
During the last few decades, free space optical (FSO) systems attracted very significant research and commercial interest, due to the plenty of advantages they can offer and the development margins they show. The high data rate transmission and security level they offer, along with the relatively low operational cost and the license free installation, are some reasons why the FSO links are part of the modern communication networks, i.e., 5G/5G+ [1,2].  
On the other hand, the performance, the reliability, and the availability of the FSO systems, strongly depend on the atmospheric and weather conditions as the laser beam propagates through the atmosphere. An important factor that affects the performance of FSO links is attenuation due to absorption and scattering, which might cause serious performance degradation or even outages, in cases of dense fog or rain [3, 4, 5, 6]. Furthermore, another factor that deteriorates the performance of the FSO links is the atmospheric turbulence that causes the scintillation effect [7, 8, 9, 10, 11, 12]. All these factors were extensively studied in a theoretical way and various channel models were extracted in order to estimate the main metrics of a communication system, such as the outage probability, the bit error rate, etc. [13, 14, 15, 16, 17].  
During the last years, various experimental FSO links were deployed in order to validate the accuracy of many theoretical models, and at the same time, extract empirical models for the attenuation estimation of optical power, in specific cases [18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]. In this work, a novel empirical experimental model for the attenuation coefficient was extracted using a multiple linear regression method for the maritime environment during night-time, as a function of the atmospheric temperature, the relative humidity, and the wind speed. Various regression techniques are already used in order to extract empirical models in FSO communications [29; 30; 31]. The presence of sea water below the FSO link creates weak to strong turbulence conditions, due to water vaporization and high attenuation due to high levels of humidity. Therefore, it is important to extract a model that predicts the communications' quality of an FSO system, in an environment with such a great impact on its performance. Its accuracy was validated by comparing the estimated attenuation coefficient results versus the real ones, which were obtained from measurements.  
The remainder of this work is organized as follows. In Section 2, the theoretical background is mentioned, while in Section 3, the experimental setup is presented. Next, in Section 4, the experimental model is derived and its results are presented. Finally, the conclusions of this work are presented in Section 5.
# A Review of Blast Loading in the Urban Environment
## 2 Current Knowledge
### Alternative Protective Methods  
[PERSON] and [PERSON] [81] explore alternative solutions regarding blast protection that are not easily identifiable as protective methods. They focussed their research on studying the pressure-reducing effects of Bamboo, Barberry, Thuja and Yew trees. The experimental set-up consisted of a 5 kg TNT equivalent explosive at a 5 m stand-off distance from the various plants. This mass was chosen as it was within the range of an explosive mass that could be carried inconspicuously by an individual. Overpressure time histories were measured by pressure sensors (type 113B28, PCB Piezotronics) with a sampling rate of 1 MHz. A tri-axis arena was set up to allow for the simultaneous testing of multiple plant types.  
The results showed that peak overpressures were in good agreement; however, the impulse values did not correspond to the expected values. This was attributed to the shape of the pressure sensor housing. It was determined that the pressure sensor used meant that the impulse migration effects of plants could not be assessed further. Significant pressure reductions were seen in the areas behind the Thuja and Yew trees at 39% and 45%, respectively. It was noted that little biomass was removed from these plants due to the blasts, indicating that continued protection is possible from repeated blast events. In contrast, the Bamboo and Barberry plants achieved an initial pressure reduction of 30% each, but a large proportion of the biomass was removed, thus reducing the protective properties from repeated blasts. It was concluded that all plants tested offered some form of protection compared to an open-air scenario, but needle-type plants proved to be the better form of natural defence.  
It has been shown that single obstacles [82] and an array of multi-scale (pre-fractal) obstacles [83] can lead to considerable blast wave attenuation through shock disruption and vortex shedding. Up to 14% and 26% reductions in loading were seen for the single and multiple obstacles, respectively. For a more comprehensive review of blast-obstacle interaction, the reader is directed to [PERSON] et al. [84].
# Performance Improvement of an NMP Mini Segway Using Sample and Hold Inputs
## 4 Dual-Loop Control Architecture
### SHI Parameter Selection  
For \(\delta\in(0,1]\) and \(p\in(0,1]\), the zero locations of the \(K_{ci}\) stabilized MS system can be calculated, and the SHI feasible region is shown with the shaded area in Figure 4, which is the common area formed by the sampling period and duty cycle resulting in an MP DT system for the closed-loop system with the inner-loop controller. Note that the feasible region was calculated by an exhaustive search of \(\delta\) and \(p\) that resulted in an MP system.  
The black horizontal line on the top of Figure 4 represents the feasible sampling periods of ZOH, a special case of SPSHI with \(p=1\) (100% duty-cycle). The smallest feasible sampling period of ZOH is as large as 0.75 s. This sampling period can be reduced down to 0.1184 s using the SPSHI with an extremely small duty cycle. For example, if a sampling period of 0.2 s is selected, a wide range of duty cycles can be chosen between 0 and 0.6. Note that small sampling period is very important for improving closed-loop system performance using the outer-loop controller.  
The normalized system bandwidths are calculated, and they are 1.3 Hz and 33.9 Hz for cart displacement and cart pitch angle, respectively. In order to ensure that the outer-loop controller sample frequency to is adequate to cover system dynamics for both outputs, the required sampling frequency is at least two times faster than the system dynamics [3], that is, 67.8 Hz for the outer-loop controller. In this case, it is impossible to have the outer-loop controller cover system dynamics for both outputs. Consequently, for the same reason, only the cart displacement output is considered since it requires a controller with the lowest sample frequency of 2.6 Hz [3].  
Therefore, a small sampling period within the feasible region shown in Figure 4 is preferred. Due to the experimental validation result in [13], a small duty cycle leads to better performance. As a result, for the SPSHI, the outer-loop sampling period is chosen to be \(\delta=0.2\) s, marked with the dashed line in Figure 4, and the implementation duty cycle is chosen as \(p=0.2\) (20% duty-cycle).  
Figure 4: The feasible sampling period (in \(s\)) of ZOH that results in an MP DT system and the \(p\)-\(\delta\) feasible regions of SPSHI.
## 2 Fault Identification Method for Deep Integration
### The Cause of Fault Propagation
After corrected by the system state of navigation filter, INS output the navigation solution. Combining navigation solution and ephemeris data, carrier frequency, code frequency and code phase of the \(i\)th satellite signal can be predicted. In [33], their expressions are defined as follows:  
\[\Delta\textbf{X}=\left[\Delta\textbf{R}^{e},\Delta\textbf{V}^{e},\Delta\textbf{q} ^{e},\Delta\textbf{W}^{b},\Delta\textbf{A}^{b},\Delta\textbf{C}\right]^{\text{T}}, \tag{1}\]1  
\[\Delta\textbf{X}=\left[\Delta\textbf{R}^{e},\Delta\textbf{V}^{e},\Delta\textbf{q} ^{e},\Delta\textbf{W}^{b},\Delta\textbf{A}^{b},\Delta\textbf{C}\right]^{\text{T}}, \tag{1}\]2  
\[\Delta\textbf{X}=\left[\Delta\textbf{R}^{e},\Delta\textbf{V}^{e},\Delta\textbf{q} ^{e},\Delta\textbf{W}^{b},\Delta\textbf{A}^{b},\Delta\textbf{C}\right]^{\text{T}}, \tag{1}\]3  
where \(f_{\text{IF}}\) is the intermediate frequency, \(f_{\text{sa}}\) is the frequency of satellite signal, \(f_{\text{code}}\) is the frequency of pseudo code, \(\big{[}\dot{x}_{i},\ \dot{y}_{i},\ \dot{z}_{i}\big{]}\) is the velocity vector of the \(i\)th satellite, and \(\big{[}\dot{x},\ \dot{y},\ \dot{z}\big{]}\) is the velocity vector of receiver. \([\delta x_{i},\delta y_{i},\delta z_{i}]\) and \([\delta x,\delta y,\delta z]\) are the position variations of the \(i\)th satellite and the receiver from time \(k\) to \(k+1\), respectively. \(\Delta t_{b}\) is the clock bias variation of receiver from time \(k\) to \(k+1\), and \(t_{d}\) is the clock drift error of receiver. \(\big{[}\alpha_{x,i},\alpha_{y,i},\alpha_{z,i}\big{]}\) is the unit vector in the line-of-sight direction from receiver antenna to the \(i\)th satellite. When an abnormality occurs in \(\mathbf{Z}_{k}^{\text{Nav}}\), the value of \(\Delta\mathbf{X}\) is contaminated according to Equation (8). After corrected by \(\Delta\mathbf{X}\), INS output a wrong navigation solution. According to Equations (10)-(12), the navigation solution is shared by all visible satellites. Then, the wrong navigation solution leads to deviations in the predicted receiving parameters of all satellite signals. Thus, no matter which satellite is abnormal, its errors will spread to other receiving channels, spoiling the reception of satellite signals. Moreover, the errors will spread to each other continually. The navigation system eventually loses lock on all satellite signals with the accumulation of errors.
# Oncogenic EFNA4 Amplification Promotes Lung Adenocarcinoma Lymph Node Metastasis
## 3 Results  
### EFNA4 mRNA Up-Regulation and CNV Amplification in Human Tumors  
Lymph node metastasis is common in patients with NSCLC. In order to find new targets for effective treatment of metastatic lung cancer, we examined data from multiple cancer types from TCGA project and screened gene with high specific expression in metastatic tumors through cancer genome studies. First, GISTIC analyses [33] were performed in lung cancer to identify genomic regions that are significantly gained, and nine statistically significant \"peak\" regions of amplification in lung cancer were identified (Figure S1A). Chromosome region 1q21.3 was found to be frequently amplified in lung cancer, especially in metastatic lung cancer. The analysis of the RNA-Seq data reveals six significantly up-regulated genes (fold change > 3, \(p\)-value \(<\) 0.001) in 1q21.3 between TCGA lung tumor samples and normal tissues (Figure S1B). By [PERSON]'s exact analysis, overexpression of _EFNA4_ had a strong correlation with lymph node metastasis in lung tumor (Figure 1A). As shown in Figure 1B, EFNA4 is highly expressed in multiple tumor types, especially in LUAD, compared with paired normal tissues. Then, we assessed _EFNA4_ copy number variation (CNV) in patients and found that _EFNA4_ is amplified in many tumor types (Figure 1C). Immunohistochemical data from the Human Protein Atlas (HPA) database also showed that EFNA4 was highly expressed in the tumor (Figure 1D). Thus, we speculated that _EFNA4_ is a novel lung-cancer-metastasis-associated gene.  
Figure 1: _EFNA4_ expression and copy number variation (CNV) status in human cancers. (**A**) The relationship between the expression level of 6 upregulated genes at 1q21.3 and the stage of lymph node metastasis. Gene expression data and related clinical information of patients were obtained from TCGA project. \(p\) values were determined using [PERSON]’s exact test; (**B**) _EFNA4_ mRNA level in various types of human tumors compared with normal tissues. Gene expression data were obtained from TCGA project. The box shows median value and 25 th and 75 th percentiles; (**C**) _EFNA4_ copy number values (GISTIC scores) obtained from GISTIC2 software in cancers are shown based on TCGA database. GISTIC2 CNV value 0 means normal copy number. The box shows median value and 25 th and 75 th percentiles; (**D**) IHC images of the EFNA4 in LUAD and normal tissues obtained from the HPA database. The positive cells were quantified by ImageJ. \(p\)-values are represented as ns (not significant), \(**>\) 0.01, ***\(p\) \(<\) 0.001 and ***\(p\) \(<\) 0.0001.
# Correction Compensation and Adaptive Cost Aggregation for Deep Laparoscopic Stereo Matching
## 2 Related Work
### Learning-Based Stereo Matching
Overall, deep learning techniques and large datasets have greatly advanced stereo matching algorithms when applied to natural images such as outdoor highway scenes. Among these algorithms, supervised ones have shown better performance and are even capable of handling challenges in registration, such as specular highlights, large textureless regions and correction errors. Relatively speaking, due to the lack of the strong supervisory signal, i.e., disparity regression loss, self-supervised models still face challenges in dealing with these difficulties.
## 5 Conclusions  
This article has presented a crypto processor design for elliptic curve PM computation over \(GF(2^{233})\) with an aim of reducing hardware resources. In this regard, a bit-serial finite field modular multiplier has been implemented, which is also employed for the computation of modular squares, modular multiplication, and modular inversion operations, minimizing a significant amount of hardware resources. Moreover, on Virtex-6 and Virtex-7 FPGA devices, we found that the decrease in clock frequency shows an increase in the computation time or latency. In addition, higher circuit frequencies consume more power. For \(GF(2^{233})\) on Virtex-7 FPGA, our crypto processor utilizes 391 slices, achieves a maximum frequency of 161 MHz, takes 4.45 ms latency, and consumes 77 mW power for one PM execution. Moreover, the proposed design uses less area and consumes less power than state-of-the-art designs. Our results demonstrate that the proposed crypto processor design is beneficial for applications that need low hardware area and less power for cryptographic computations.  
Conceptualization, M.R., S.S.J. and A.A.; methodology, A.R.A. and M.A.; software, D.J.A.; validation, M.R. and S.S.J.; formal analysis, A.A. and A.R.A.; investigation, M.R. and S.S.J.; resources, D.J.A.; data curation, A.A.; writing--original draft preparation, A.R.A. and A.A.; writing--review and editing, M.R., S.S.J. and M.A.; visualization, A.A.; supervision, A.A. and M.R. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deanship of Scientific Research at University of Tabuk for funding this work through Research no. S-0151-1443.  
Not applicable.  
The authors declare no conflict of interest.
# Graphene-Based Membranes for CO2/CH4 Separation: Key Challenges and Perspectives
## Mixed-Matrix Membranes
Similarly, in another work, a 5 wt% loading of GO nanosheets in a 4_A_\({}^{\prime}\)-oxydiphthalic anhydride-2,4,6-trimethyl-\(m\)-phenylenediamine (ODPA-TMPDA) polymer matrix was also able to produce improved tensile strength and Young's modulus by 17% and 3%, respectively [59]. Furthermore, in both works, dual fillers were exploited to create a synergistic effect in the separation performances of the membranes. The ability of GO nanosheets to elevate the mechanical properties of the mixed-matrix membranes, therefore, helps to modulate the drop in the mechanical properties caused by the second filler. As an illustration, 10 wt% ZIF-8 was added to the ODPA-TMPDA membrane and found to reduce the tensile strength and Young's modulus of the membrane by 11% and 8%, respectively. With the incorporation of 5 wt% GO nanosheets alongside 10 wt% loading of ZIF-8, the tensile strength and Young's modulus not only reverted to that of the unfilled ODPA-TMPDA membrane but also slightly improved by 9% and 1%, respectively [59]. Here, it is noteworthy to highlight that the optimal loading of GO nanosheets is polymer- and application-specific. There is no strong indication to suggest a ubiquitously optimal filler loading as evidenced by mixed-matrix membranes of graphene-based or other 2D-layered materials for a variety of applications [60; 61; 62]. However, one thing for sure is that as the loading of GO nanosheets gets higher, there runs a higher risk of defective membrane formation as a result of increased difficultiesin dispersing the GO nanosheets. This generally will lead to increased agglomeration of the GO fillers, in which the extent depends on the solvent systems used in the polymer dope solutions.
# A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning
## 2 Materials and Methods
### Feature Extraction
#### 2.4.2 Dilution of Precision for Positioning Satellites  
In GPS navigation and positioning, Dilution of Precision (DoP) is used to evaluate the influence of the spatial geometric distribution of visible satellites on positioning accuracy. DoP is an indicator of position quality. We predict the result of position accuracy according to the position relationship of each satellite and other satellites in the constellation. A small DoP value indicates a high probability of strong satellite geometric position and accuracy. We calculated the cumulative distribution probability of DoP values in the data collected in a week under complex indoor and outdoor scenarios, as shown in Figure 7.  
In rare cases, there are lower DoP values in an indoor environment. Although there is a certain amount of error in relying on GPS positioning in outdoor scenarios, the value of DoP is generally low. Since it takes a short time for us to collect each piece of data, and smartphones obtain the DoP value through a calculation process, there is a high probability that the DoP value in outdoor scenarios exceeds 10 in our statistical results. We extracted PDoP, HDoP andVDoP as features from \(\mathbf{S}\).
## 3 Methods
### Integration of DDPM and DP  
First, we handle (\(\epsilon_{ijk},\delta_{ijk}\))-Gaussian-LDP for a single fixed pixel (\(x_{ijk}\)) and extend this to handling all the pixels using the following composition theorem of DP:  
\[\epsilon=\sum_{i,j,k}\epsilon_{ijk}, \tag{13}\]  
\[\delta=\sum_{i,j,k}\delta_{ijk}, \tag{14}\]  
where \(\epsilon\) and \(\delta\) are the total privacy budgets.  
Because the deviations of perturbed images appear in DDPMs (1), we can write  
\[\sigma_{t}^{2}=\frac{\hat{\beta}_{t}}{\hat{\alpha}_{t}}. \tag{15}\]  
We can now connect the above equation with (12) as  
\[\sigma_{t}^{2}=\sigma^{2}. \tag{16}\]  
This indicates that we can compute the virtually infinite pairs of \(e\) and \(\delta\) if the noise scheduling (betas) of the DDPM and the sensitivity \(\Delta f_{ijk}\) are given.  
Note that we can obtain LDP-processed images by just picking intermediate images \(\mathbf{x}_{t}\) in the DDPM (the forward process), but those images do not preserve the class (head MR images in the present study) if the privacy budgets are small. To preserve the class even when the privacy budgets are small, we reversely apply the DDPM to the intermediate images as follows (the reverse process):  
\[\hat{\mathbf{x}}_{0}=\underbrace{\mathbf{f}_{\theta_{1}}\circ\mathbf{f}_{\theta_{2}}\circ \cdots\circ\mathbf{f}_{\theta_{t}}(\mathbf{x}_{t})}_{t\text{-steps}}. \tag{17}\]  
With this postprocessing, we can have images such that they (i) preserve the class, (ii) may be semantically different from original images, and (iii) have provable indistinguishability and therefore are practical for medical data sharing.
###### Abstract
However, the implementation of these DL models brings with it the correct and precise tuning and implementation of different hyperparameters (HPs), such as the number of hidden neurons, activation function, number of epochs, learning rate, optimizer, regularization technique, etc. Therefore, these HPs will determine the abstraction capability of a DL model. In this context, the present study has focused on the development of an LSTM-based model that is capable of forecasting the daily energy consumption of HVAC systems in buildings. Another important objective is to evaluate the proposed model with respect to different time lags, HP configurations, and to implement regularization techniques (such as dropout and early stopping). To identify the best configuration of the model in this scope, multiple experiments have been conducted to compare the performance of each configuration. Therefore, the developed research provides an insight into the strengths and weaknesses of applying some of the configurations analyzed and described in the present article. It should be noted that this research is part of the research project called \"Intelligent management system for optimizing energy consumption in building air conditioning\" [7; 30; 31; 32; 33]. We can summarize the contributions of this study as follows: (i) we analyze in depth real-world data on the energy consumption of the HVAC system in a complex building such as the Teatro Real; (ii) we deeply examine the past behavior of a chaotic time series (noisy data and sensor errors) to make a daily forecast of the energy consumption of the HVAC system; (iii) we apply a comprehensive methodological process that allows us to obtain a robust, generalizable and reliable model by tuning different parameters; (iv) we evaluate the advantages and disadvantages of applying regularization techniques in the context of HVAC systems; (v) we identify that the proposed model achieves reliable performance on the CVRMSE metric of 9.5% which is significantly lower than recommended by international agency guidelines; (vi) we determine the configuration of our model to achieve reliable and robust forecasts of HVAC energy consumption. The article is organized as follows. In Section 2, we present the related work regarding DL-based models aimed at forecasting energy consumption in HVAC systems, including motivation for the use of LSTM networks. In Section 3, we briefly describe how to address the problem in a particular real-world use case and how it has been addressed in this study. In Section 4, we detail the methodological procedure we carried out to build the LSTM-based DL model. Section 5 shows different configurations of the evaluated model. In Section 6, we analyze and discuss how the model configurations were performed. In addition, we identify the best configuration of the model regarding its time lags, HPs and regularization techniques. Finally, Section 7 provides some conclusions from the study carried out and presents future research work to be developed.