esa-sceva/satcom-chunk-collection huggingface dataset:
# Use of Machine Learning and Deep Learning to Predict the Outcomes of Major League Baseball Matches
## 2 Literature Review
### DCNN-Related Literature  
In recent years, CNNs have been widely used in various fields; they can process data of various formats, such as 1D data (signals and sequences), 2D data (images), and three-dimensional (3D) data (videos), with good results. 1 DCNNs have been used in many fields in recent years. [PERSON] and [PERSON] [15] proposed two deep learning frameworks, 1 DCNN and 1 DCNN + long short-term memory (LSTM) for diagnosing unipolar depression, and the highest accuracy rate achieved was 98.32%. Their results showed that the proposed deep learning framework could successfully diagnose depression automatically. [PERSON] et al. [13] proposed a deep learning framework for crop yield prediction using a CNN-recurrent neural network (RNN); they combined their proposed framework with other popular methods such as the random forest algorithm, deep fully connected neural network method, and least absolute shrinkage and selection to predict the yields of corn and soybeans in the United States from 2016 to 2018. Their results showed that the CNN-RNN model outperformed the other prediction methods. They used a 1 DCNN to capture weather characteristics and soil data and used LSTM in the RNN to capture changes in crop yields over time due to genetic modification.  
[PERSON] and [PERSON] [14] proposed two 1 DCNN deep learning algorithms--a 1D single CNN (1 DS) and a 1D multiple CNN (1 DM)--to predict the wind direction and speed from German and Dutch wind data sets. The 1 DS predicts the main wind speed and direction using 1D time-series wind data, whereas the 1 DM uses multiple views of time-series wind data to improve the accuracy of the 1 DS. The total accuracies in predicting wind speed and wind direction were 95.2% and 95.1% for the 1 DS and 98.8% and 99.7% for the 1 DM, respectively. [PERSON] et al. [16] proposed an effective regression model based on a generative CNN to predict movie ratings. The model also included a solution to overcome the problem of insufficient training samples. The experimental results obtained from using IMDb data indicated that their proposed model outperformed a set of baselines and state-of-the-art approaches; their model had a 9.3% lower mean square error and a 7.6% higher hit ratio. The results can provide a reference for investors, movie studios, and interested users.  
All of the aforementioned studies employed a 1 DCNN for various applications, and all achieved favorable prediction results. However, the 1 DCNN has not been used for predicting outcomes in sports. Therefore, after data on baseball variables were obtained, a 1 DCNN was used in the present study to establish a model for predicting the outcomes of MLB matches.
## 3 Results
### Technical Analysis
#### 3.2.6 Implementation (Efficacy Proof)  
To verify the functionality of the control systems proposed in their papers, the researchers employed two strategies. The first strategy is based on simulations and implies the use of a dedicated simulation environment to model the controlled process together with the control system using predefined blocks or blocks and functions designed and developed by the researchers. The most popular software used to perform simulations is MATLAB, which has specialized components like Simulink and Fuzzy Logic Toolbox [118]. Other software found rarely in the papers included DIgSILENT, dSPACE, C++, and PSCAD/EMTP. The second strategy used to show the efficacy and efficiency of the proposed control methodology is physical experimental implementation. When approaching the experimental assessment, the researchers used down-scaled and equivalent power system models, signal generators, qualified measurement apparatus, dedicated sensors and transducers, and specialized microcontrollers.  
In the analyzed papers, we found studies that used simulations and papers that utilized both simulations and laboratory (real-time) experiments. Figure 8 graphically shows the yearly distribution of implementation strategies employed in the surveyed articles. The chart's columns underline the preference for simulations over hardware laboratory experimentations, as software implementations represent more than 80% of the total number of 135 implementation methods used in the analyzed papers.  
Table 9 enumerates and briefly describes the 10 chosen papers from the final set that clearly explain the implementation procedure and the obtained results. Another four papers had detailed information about the implementation process, but they are not included in the table below as they are already described in Table 8 [118], Table 7 [119], Table 6 [76], and Table 4 [120].  
Figure 8: Yearly distribution of published papers concerning the proposed control systems’ implementation and testing. Software and hardware—simulations using MATLAB and laboratory implementation; Other software—PSCAD/EMTP, dSPACE, C++, DIgSILENT; MATLAB—Simulink, SimPowerSystems, Fuzzy Logic Toolbox.
## 3 Modified PROS with Local Search Mechanism
### Analysis of the Modified Algorithms
: Let \(P_{j}^{Q}\) be the probability that a _tame_ solution is found by sampling using the quadratic distribution on the \(j\)-th decision variable in the \((t+1)\)-th iteration, given the best solution vector in the \(t\)-th iteration. That is,  
\[P_{j}^{Q}=P\{x_{j}^{(t+1)}\in S_{j}|\mathbf{x}_{-j}^{(t)},x_{j}^{(t+1)}\sim \mathcal{Q}(a_{j},b_{j},x_{j}^{(t)})\}.\]  
For case 1, \(x_{j}^{(t)}<x_{j}^{*}\):  
\[P_{j}^{Q}=\int_{x_{j}^{l}}^{x_{j}^{r}}f_{Q}(x)dx\]  
\[=\int_{x_{j}^{l}}^{x_{j}^{r}}\frac{3(x-b_{j})^{2}}{(b_{j}-a_{j})(x_{j}^{(t)}-b _{j})^{2}}dx\]  
\[=\frac{l_{j}}{b_{j}-a_{j}}\frac{(l_{j}+v_{j})(l_{j}+2v_{j})+v_{j}^{2}}{(u_{j}+ l_{j}+v_{j})^{2}}.\]  
Similarly, for case 2, \(x_{j}^{(t)}>x_{j}^{*}\):  
\[P_{j}^{Q}=\int_{x_{j}^{l}}^{x_{j}^{r}}f_{Q}(x)dx\]  
\[=\int_{x_{j}^{l}}^{x_{j}^{r}}\frac{3(x-a_{j})^{2}}{(b_{j}-a_{j})(x_{j}^{(t)}-a _{j})^{2}}dx\]  
\[=\frac{l_{j}}{b_{j}-a_{j}}\frac{(q_{j}+l_{j})(2q_{j}+l_{j})+q_{j}^{2}}{(q_{j}+ l_{j}+r_{j})^{2}}.\]  
**Corollary 4**. : _The probability of tame convergence of QROS is higher than or equal to that of TROS and PROS for all convex functions._  
Proof. : For convex functions, \(u_{j}=0\) (for case 1) and \(r_{j}=0\) (for case 2). For case 1, \(x_{j}^{(t)}<x^{*}\):  
\[p_{j}^{Q}=\frac{l_{j}}{b_{j}-a_{j}}\frac{(l_{j}+v_{j})(l_{j}+2v_{j})+v_{j}^{2}} {(u_{j}+l_{j}+v_{j})^{2}}\]  
\[=\underbrace{P_{j}^{\text{L}I}\Big{(}1+\frac{v_{j}}{l_{j}+v_{j}}\Big{.}}_{P_{ j}^{\text{T}}}+\frac{v_{j}^{2}}{(l_{j}+v_{j})^{2}}\Big{)}\]  
\[\therefore \begin{cases}>P_{j}^{\text{T}}>P_{j}^{\text{L}I}&,\,v_{j}>0\\ =P_{j}^{\text{T}}=P_{j}^{\text{L}I}&,\,v_{j}=0\end{cases} .\]For case 2, \(x_{j}^{(t)}>x^{*}\):  
\[P_{j}^{Q}=\frac{l_{j}}{b_{j}-a_{j}}\frac{(q_{j}+l_{j})(2q_{j}+l_{j})+q_{j}^{2}}{(q _{j}+l_{j}+r_{j})^{2}}\]  
\[=\underbrace{P_{j}^{LI}\Big{(}1+\frac{q_{j}}{q_{j}+l_{j}}\Big{.}}_{p_{j}^{T}}+ \frac{q_{j}^{2}}{(q_{j}+l_{j})^{2}}\Big{)}\]  
\[\therefore\left\{ \begin{array}{ll}>P_{j}^{T}>P_{j}^{LI}&,q_{j}>0\\ =P_{j}^{T}=P_{j}^{LI}&,q_{j}=0\end{array} \right.\]  
Therefore, for convex functions, \(P_{j}^{Q}\) can never be less than \(P_{j}^{T}\) and \(P_{j}^{LI}\). The probability density function of distributions \(\mathcal{U}\), \(\mathcal{T}\) and \(\mathcal{Q}\) are characterized by a degree-0, degree-1 and degree-2 polynomials, respectively. From Corollary 4, the increment in the probablity of \(tame\) convergence diminishes as the degree of the polynomial increases. We expect sampling using an even higher degree distribution (e.g., cubic distribution with the mode narrower than \(\mathcal{Q}\) and \(\mathcal{T}\)) would give higher probability of \(tame\) convergence, but the gain diminishes as the degree grows.
# An Anti-Interference Method Based on Energy Residual Searching in GNSS Positioning Applications
## 1 Introduction
Wavelet transform, while partially addressing this issue, suffers from high computational complexity and limited real-time capabilities. The Wigner-Ville distribution, on the other hand, has cross-term interference issues, complicating signal analysis. To overcome these limitations, this paper proposes an anti-interference method based on the fractional Fourier transform (FrFT). The FrFT provides flexible resolution capabilities, allowing it to better adapt to the characteristics of LFM interference and achieve effective suppression of the interfering signal. In this study, we innovatively propose an energy residual search method, which enhances interference detection and suppression on the basis of the FrFT, ensuring reliable GNSS receiver performance in complex electromagnetic environments. The subsequent chapters of this paper are organized as follows: Section 2 presents a review of related work; Section 3 discusses the GNSS received signal model;  
Figure 1: GNSS receivers are susceptible to RF interference. Section 4 describes the energy residual search-based anti-interference method; Section 5 presents the experimental results, including a comparison of detection performance among various algorithms; and Section 6 concludes the paper.
## 4 Genome Editing for Autosomal Dominant Retinitis Pigmentosa  
Amongst IRDs, RP is in itself a large and genetically heterogeneous group of disorders. Therefore, we focus here on the most frequent causative genes and associated mutations for the autosomal dominant forms, and discuss potential targeted therapeutic strategies.  
RP has a prevalence of 1 in 4000 individuals and is the most characteristic clinical representation of rod-cone dystrophies [53]. It is characterized by a \"tunnel vision\", resulting from progressive loss of peripheral vision. Central vision is usually maintained until the end stages of the disease [54]. RP can be transmitted with an autosomal dominant, recessive or X-linked mode of inheritance. Almost 90 genes have been associated with RP ([[https://sph.uth.edu/retnet/](https://sph.uth.edu/retnet/)]([https://sph.uth.edu/retnet/](https://sph.uth.edu/retnet/))), 29 of which cause adRP (Table 1). Among these, 6 genes (_NR2E3_, _NRL_, _RHO_, _RP1_, _RPE65_ and _BEST1_) cause both autosomal dominant and recessive forms. Furthermore, there is genetic overlap between RP and other retinopathies. In Figure 4, we can see that 6 genes are responsible for both autosomal dominant forms of RP and macular dystrophy or cone-rod dystrophy. We place a particular emphasis here on the 8 most prevalent causative genes of adRP, which are _RHO_, _PRPF31_, _RP1_, _PRPH2_, _IMPDH1_, _NR2E3_, _SNRPN200_ and _CRX_[55, 56].  
Figure 4: A Venn diagram of genes shared between different autosomal dominant IRD forms. Genes that cause RP are indicated in the yellow circle, genes that cause macular dystrophies are indicated in the blue circle, and genes that cause cone-rod dystrophies are indicated in the red circle. The genes _CRX_ and _SEMA4A_ cause both RP and cone-rod dystrophy, whereas the genes _BEST1_, _FSCN2_ and _GICA1B_ cause both RP and cone-rod dystrophy. _PRPH2_ is associated with all three forms. _PROMI_ causes both autosomal dominant macula dystrophy and cone-rod dystrophy but not adRP. The genes indicated in blue (_IMPDH1_, _CRX_ and _OTX2_) also cause autosomal dominant LCA.
## 2 Models and Methods
### Morphological and Kinematic Parameters of a Seagull's Wing
#### 2.2.1 Airfoil Shape  
According to the data from reference (2007), we take the common gull as the research subject, setting wing-span and average cruise speed as 1.1 m and 11.6 m/s, respectively.  
The moment of inertia can be obtained if the body of bird can be seen as a cylinder:  
\[I_{\mathrm{b}}=\frac{m}{12}(3R^{2}+{l_{\mathrm{b}}}^{2}) \tag{11}\]  
where \(R\) represents the radius of the body, \(l_{\mathrm{b}}\) represents the length of the body (which can be approximated to the length of root chord), and \(m\) is the total mass of the seagull.  
The ratio between the root chord and semispan of the seagull wing is as follows:  
\[c_{0}/(b/2)=0.388 \tag{12}\]  
The wing chord distribution can be expressed as:  
\[c=c_{0}[F_{OK}(\xi)+F_{corr}(\xi)] \tag{13}\]  
where \(\xi=2y/b\) represents the spanwise coordinate normalized by semispan, \(F_{corr}(\xi)=\sum\limits_{n=1}^{5}E_{N}(\xi^{n+2}-\xi^{8})\), \(F_{OK}(\xi)\)=1 for \(\xi\in[0,0.5]\) and \(F_{OK}(\xi)=4\xi(1-\xi)\) for \(\xi\in[0.5,1]\).  
Table 1 gives the wing chord distribution of a seagull according to the above formulation.  
According to the chord distribution, we can obtain an aspect ratio AR = 6.52 and a taper ratio = 0.5, respectively, which will be used in a later section.  
The maximum camber coordinates can be represented as:  
\[z_{(\mathrm{c})\max}/c=0.14/(1+1.333\xi^{1.4}) \tag{14}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \(c_{1}\) & \(c_{2}\) & \(c_{3}\) & \(c_{4}\) & \(c_{5}\) & \(c_{6}\) & \(c_{7}\) & \(c_{8}\) & \(c_{9}\) & \(c_{10}\) \\ \hline
0.194 & 0.196 & 0.198 & 0.194 & 0.184 & 0.177 & 0.168 & 0.150 & 0.126 & 0.097 \\ \hline \end{tabular}
\end{table}
Table 1: The wing chord distribution of a seagull (mm).  
The mean camber line is expressed by:  
\[z_{(c)}(\eta)=z_{(c)\max}\eta(1-\eta)\sum\limits_{n=1}^{3}S_{n}(2\eta-1)^{n-1} \tag{15}\]  
where \(\eta=x/c\) is the normalized chordwise coordinate.  
The maximum thickness coordinate is represented as:  
\[z_{(t)\max}/c=0.1/(1+3.546\xi^{1.4}) \tag{16}\]  
The thickness distribution is given by:  
\[z_{(t)}(\eta)=z_{(t)\max}\sum\limits_{n=1}^{4}A_{n}(\eta^{n+1}-\sqrt{\eta}) \tag{17}\]  
The upper and lower surfaces of an airfoil are expressed as an addition and subtraction of the camber line and thickness distribution:  
\[\begin{split} z_{\mathrm{upper}}&=z_{(c)}+z_{(t)}\\ z_{\mathrm{lower}}&=z_{(c)}-z_{(t)}\end{split} \tag{18}\]  
The geometry of a wing section can finally be obtained after achieving the airfoil at each section, as shown in Figure 2. Here, we use 10 sections in the spanwise direction to form the wing.
# Application of DMLF in Pulse Ranging LiDAR System
## 2 Methods
### Analysis of the Differential-Optical-Path Approach  
In terms of stop-time moment, the traditional method of echo pulse can be described as Equation (5), which only uses an APD to receive the scattered light signal from the target and calculate the PPs of the echo pulse waveform. However, this method easily suffers from interference by noise and is only suitable for steep peak form.  
Unlike the traditional method, the DMLF is to add a beam splitter between the convergent lens 3 and beam splitter1, where \(d\) presents the axial shift in the APD2, which is determined by the pulse width and \(0<d<\sqrt{8\log(2)}\cdot c\tau\)[20]. The second APD receives the light signal derived from beam splitter2. The stop-time moment is obtained by calculating the differential of two APD signals and the differential echo pulse model can be denoted in the form of  
\[\left\{\begin{array}{c}S_{r1}(t)=B\cdot e^{(-\left(\frac{p(t-t_{g}-2R/c)}{ \tau}\right)^{2}+S_{n})}\\ S_{r2}(t)=B\cdot e^{(-\left(\frac{p(t-t_{g}-(2R-d)/c)}{\tau}\right)^{2}+S_{n}) }\\ S_{rd}(t)=S_{r1}(t)-S_{r2}(t)=B\cdot\left[e^{(-\left(\frac{p(t-t_{g}-2R/c)}{ \tau}\right)^{2}+where \(t_{rd}\) is the zero-crossing timing moment. Due to the determined position of APD1, the axial shift \(d\) and transmitted pulse delay \(t_{d}\) are constant. Therefore, Equation (7) demonstrates that the differential method can obtain the stop-time moment, similar to the traditional method, which also proves the feasibility of the DMLF.  
From the above analysis, the stop-time moment is successfully changed from PPs to PGZCPs of DMLF. Obviously, the change rates of amplitude per unit moment at the PGZCPs are higher than those at the PPs of the traditional method. Therefore, our proposed method has a higher sensitivity and more accurate timing moment.  
Using Equations (6) and (7), \(S_{rd}(t_{rd})=0\). Let \(f=\text{d}(S_{rd}(t))/\text{d}(t)|_{t=t_{d}+2R/c-d/2c^{\prime}}\), which is a function with the variable of \(d\). This means that the sensitivity of PGZCPs in DMLF is related to the axial shift \(d\). Therefore, we can obtain the optimal \(d\) corresponding to the maximum sensitivity by \(\text{d}(f)/\text{d}(d)=0\), yielding \(d=c\tau\). In order to quantitatively analyze the above theory, we carried out simulations for different axial shift \(d\), which is shown in Figure 2a. It can be seen that as the \(d\) increases, the slope of the differential signal near PGZCPs has a tendency to increase initially and then decrease. The largest slope at \(d=c\tau\), which is the optimal axial shift.
# An Effectively Finite-Tailed Updating for Multiple Object Tracking in Crowd Scenes  
[PERSON]  
1 MIT Key Laboratory of Pattern Analysis and Machine Intelligence, Collaborative Innovation Center of Novel Software Technology and Industrialization, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; [EMAIL_ADDRESS] (B.X.);  
1 [PERSON]_ADDRESS] (L.L.); [EMAIL_ADDRESS] (R.Q.)  
2 Shanghai Institute of Satellite Engineering, Shanghai Academy of Spaceflight Technology, Shanghai 210016, China; mingguang  
of the person with a red arrow changes from 363 to 217 when the object we are tracking is occluded.  
To address this issue, we propose an effective and flexible appearance update mechanism named finite-tailed updating (FTU). Except for the appearance template of the object in the previous frame, FTU also combines the accumulated appearance templates of the objects in earlier frames with the Re-ID feature of the object in the current frame. Specifically, for an input video, we firstly input the video frame into the backbone network to obtain the detected bounding box of each object in the current frames. Then, we use the Re-ID module and our proposed update mechanism to obtain the actual appearance template of each object in the current frame. Finally, a Hungarian algorithm [9] is used to match the objects in different frames to obtain their trajectories. The contributions of this paper are as follows:  
* We propose an effective and flexible appearance update mechanism, named finite-tailed updating (FTU), which combines the object's historical accumulated appearance templates in multiple earlier frames with its Re-ID feature in the current frame to improve the identification performance of the object in the current frame.
* We propose an effective MOT solution that can obtain better performance than the state-of-the-art methods. The experimental results have shown that our tracker outperforms the state-of-the-art methods on MOT Challenge Benchmark.  
The remainder of this paper is organized as follows. We discuss the related works in Section 2. We formulate the problem and describe the proposed method in detail in Section 3. The experimental results are presented in Section 4, and the conclusions, limitations, and future work are presented in Section 5.
## 5 Results and Discussion
### Speech Recognition Results  
The obtained results corresponding to speech recognition performance are presented in Table 4. It can be seen that the average WER values did not exceed 5% even in noisy conditions and the standard deviation values were below 1%. This suggests that speech recognition performance is not significantly affected by noise.
# Specific-Input LIME Explanations for Tabular Data Based on Deep Learning Models
## 1 Introduction  
Deep learning models have made significant advances in a variety of computer vision tasks, including image classification, object recognition, semantic segmentation, and ancillary tasks such as visual question answering and autonomous driving. Artificial intelligence (AI) is now widely used in our daily lives. To put this into perspective, the International Data Corporation (IDC) predicts that global investment in AI will rise to USD 154 billion by 2023. Meanwhile, according to Statista, global AI market revenues are expected to reach USD 1.8 trillion dollars in 2030 [1]. This will influence the next generation of digital business models and ecosystems, alongside immersive experiences, digital twins, event thinking, and continuous adaptive security [2].  
On the other hand, deep neural networks (DNNs) are difficult to understand because they behave like black boxes. Most researchers emphasise the framework and many internal parameters of the model when building deep neural network models, but if the model is incorrect, they are unable to make a correct interpretation of the model's output. Determining confidence in individual predictions is an important issue when using the model for decision making. For example, when applying machine learning to medical diagnosis or terrorism detection, predictions cannot be trusted because their impact can be catastrophic.  
Despite the apparent effectiveness of AI algorithms in terms of outcomes and predictions, there remains an opacity that makes it difficult to gain insight into their core operating mechanisms. As a result, users in healthcare, banking, and security are unable to trust the decisions made by the models. We need to create transparent models that show consumers how algorithms think. The creation of transparent models would benefit from an understanding of errors, debugging, and the discovery of potential biases in the training data.  
XAI advocates a move away from transparent artificial intelligence. The goal is to build more understandable models while maintaining high levels of performance, making the decisions of DNN models more transparent, understandable, and trustworthy to humans.  
In this paper, we propose specific-input LIME, a novel XAI method for interpreting deep learning models based on tabular data, by combining the specific-input procedure with the LIME method. The specific-input LIME method replaces some of the steps in LIME with feature importance and PDP. First, we obtain the basic interpretation of the data by simulating user behaviour. Second, we use feature importance and PDP to understand \"what\" features deep learning models focus on and how they affect the model's predictions. Analysis of the experimental results shows that our approach provides more detailed explanations than the LIME approach, and also compensates for the vulnerability of LIME to random sampling.