esa-sceva/satcom-chunk-collection huggingface dataset:
# A Discrete Hidden Markov Model for SMS Spam Detection
## 3 The Discrete HMM for SMS Spam Detection
### Observation Probability Distribution  
We calculate the probability of each observation state, i.e., each word in \(W\), appearing in ham and spam SMS sets. The probability distribution is depicted in Figure 3. The word order in \(W\) is fixed for easy comparison, as shown in Figure 3.  
The higher the frequency of a certain word, the higher the probability in the distribution. As the word order in \(W\) is fixed in Figure 3, we can compare the probability of each word in ham and spam sets visibly. It is found that:  
* As some words only have probability in a single dataset and their probability is equal to zero in another dataset, this indicates that these words only appear in the spam messages set or ham messages set;
* As the probabilities of many words are quite different in different datasets, it is referred that these words appear in both sets with much different word frequencies;
* Only a very small portion of them appear in both sets evenly.  
It is true that the BoW also took advantage of this information to design term weights algorithms. However, the TF algorithm does not work well in the SMS scenario, because of the shortage of term occurrence.  
In this paper, we first calculate two observation state distributions in spam and ham subsets. The two distributions are combined together to form the initial value of the HMM observation probability distribution matrix, \(B=\left(b_{ij}\right)_{2\times n}\).
## 5 Advanced Intelligent Frame Generation Algorithm  
As described in the system model in Section 2 of this paper, for the RTFG module, when the frame generation waiting time for the delay-sensitive queue reaches the threshold \(T_{h}\), the data packets arriving during this period are encapsulated into frames. On the contrary, if the number of packets in the delay-sensitive queue has filled the MPDU packet area before the threshold value \(T_{h}\) arrives, it is also encapsulated into a frame. For the HEFG module, if the packet size of the non-delay sensitive queue reaches the maximum length \(L_{h}\) of an MPDU packet area, it is encapsulated into a frame without filling the idle packet. The pseudo-code of the advanced intelligent framing generation algorithm in Advanced Orbiting Systems designed for differentiated QoS requirements is as shown in Algorithm 2. ```
0:\(\mathcal{D}\leftarrow\mathcal{D}\cup\{\pi_{old}(a_{k}\mid s_{k})\}\)  Initialize network parameters \(\theta\), and buffer \(\mathcal{D}\) for each episode do  Initialize PPO hyperparameters for each Delay Sensitive Queue do  Construct the state space \(s_{k}\) for current time slot  According to \(s_{k}\), the action \(a_{k}\) is selected by \(\pi_{old}\), and save action probability \(P(\pi_{old}(a_{k}\mid s_{k}))\) if Execute \(a_{k}\), \(r_{t}(\theta)>1\)then  Abandon the threshold value result derived from this action  Break  Execute \(a_{k}\), \(s_{k}\gets s_{k+1}\), obtain the reward function  Store the current dataset \(\{s_{k},a_{k},P(\pi_{old}(a_{k}\mid s_{k})),r_{k}\}\) in buffer \(\mathcal{D}\) if\(a_{k}\
eq a_{1}\) (not the first action) then for\(n_{update}\)do  if time step reaches \(update\_timestep\)then  Calculate objective function \(\kappa^{\text{CLIP}}(\theta)\) using Equation (18), update PPO  policy loss function  Calculate the mean squared error using Equations (20) and (21), update the critic network endif if action space is sufficient & time step = action std decay freq (action standard deviation decay frequency) then  Update action standard deviation according to Equation \(\sigma_{t+1}=\max(\sigma_{t}-\text{Y},\sigma_{\text{min}})\) endif  Complete a series of updates of the agent endfor  Calculate cumulative reward \(sum(r_{k})\) and store it endif if the amount of data in buffer \(\mathcal{D}\) exceeds maximum capacity \(>K\)then  Update network parameters \(\theta_{\text{old}}\leftarrow\theta\), clear buffer \(\mathcal{D}\) endif \(s_{k}\gets s_{k+1}\) endif endfor endfor
```  
**Algorithm 2** The advanced intelligent frame generation algorithm described in this section, including the modeling of action, reward, and policy structure, is proposed by the authors as an original design for the specific requirements of AOS.
## 3 System Model
### SDN-Based Space Information Network Architecture
#### 3.1.2 Network Control Architecture  
Based on the structure of SDN, the control architecture of the SIN is divided into three layers: application layer, control layer, and infrastructure layer [26]. The top layer is the application layer, which refers to a series of space tasks such as emergency communication and deep space exploration completed by the SIN. At the bottom is the infrastructure layer, which refers to satellites in different orbits, stratospheric vehicles, gateway base stations, and so on. In the middle is the control layer, which is composed of network controllers and the SIN resource management and scheduling center. The hierarchical and domain-based control structure of the SIN is shown in Figure 2.  
In the control layer, the single-domain controller collects the topological information of each node in the domain. When the intra-domain traffic arrives, the single-domain controller calculates the intra-domain links, and controls the nodes by downloading the flow table, so as to realize path building and service processing. The SIN resource management and scheduling center is responsible for the control and allocation of the whole-network resources. It obtains the domain topology resources from the single-domain controller and establishes the whole-network topology. When cross-domain service arrives, it is responsible for cross-domain path calculation to realize cross-domain service transmission. In addition, due to the heterogeneity of different inter-domain networks, the SIN resource management and scheduling center is also responsible for the unification of heterogeneous device interfaces to achieve cross-domain interconnection of heterogeneous devices.  
In the SIN management architecture based on SDN, north-direction agreement and south-direction agreement play an important role. North-direction agreement is a series of interfaces between application layer and control layer. There is no unified standard for its interface protocol. Therefore, the control layer provides many extensible application program interfaces (APIs) for different users in the application layer, and each API interface corresponds to a corresponding application; thus, the control architecture can implement a variety of application services. A typical south-direction agreement is OpenFlow [27], which is responsible for the interaction between the control layer and the underlying implementation switches to complete the forwarding of infrastructure layer data. In the OpenFlow protocol, an OpenFlow switch can connect multiple network controllers; however, at the same time, only one controller has control over it, and other controllers have read-only function. In the SDN-based SIN management architecture, all switches in each single domain can only be managed by its single-domain controller.
## 3 Results
### Ablation Experiments
#### 3.3.1 Effectiveness of Joint Streamlining  
Previous studies based on the human skeleton have usually been performed on the original skeleton connections. To verify the effectiveness of joint streamlining for gait recognition, we conducted experiments using the Kinect25 data. Out of the original 25 joints, the hand joints were averaged to obtain 19 body joints, which were then trained on the ST-GCN. According to the experimental results in Table 2, the accuracy of the model is not considerably affected and even slightly improved after the redundant hand joints are reduced from the original Kinect dataset. Hence, the proposed joint streamlining operation does not reduce the accuracy of the model for abnormal gait recognition. Therefore, the removal of the joints does not hinder the ability of the model to recognize abnormal gaits. Moreover, the number of floating-point operations per second (FLOPs) during model training was reduced by 24.08%. Thus, joint streamlining simplified the model and sped up the training process.  
Therefore, streamlining the hand joints is computationally helpful for abnormal gait recognition. In the subsequent experiments, the models were trained based on the streamlined data.  
Figure 5: Normal and pathological gaits.
# Adaptive Speech Separation Based on Beamforming and Frequency Domain-Independent Component Analysis
## 5 Discussions  
In the simulations and experiment, the proposed algorithm relied on ICA and DSB. The DSB algorithm can be replaced by the fixed beam coefficients with better spatial directivity or adaptive beamforming that can outperform the DSB-based approach in the low use ratio of ICA. The usage ratio of ICA to DSB in the algorithm is determined when sorting the second largest eigenvalue of all the frequency bins, rather than setting a threshold for the PCA.  
As the number of the sound sources increases, the performance obtained from ICA and DSB slightly varies. In practice, the longer FFT length, such as 2048 points, is recommended when the number of sampling is sufficient for iteration of ICA. Because longer FFT length means higher frequency resolution, it is easier to satisfy the assumption of sparsity of speech signals in the time-frequency domain.  
The switching mechanism in the proposed method is valid in different environments; however, the performance of separation is significantly degraded in smaller indoor environment because of the strong reverberation. Using the de-reverberation method, such as the generalized weighted prediction error (GWPE), as preprocessing is an effective approach to increase the upper limit of separation in a strong reverberation environment. However, the time spent is almost intolerable in real-time applications.  
Through the total running time of proposed method in this paper could be less than the length of the processed signal by controlling the proportion of ICA, the separation result is late to the original signal about 1 s in real-time applications. The signal cannot be processed by frame because the ICA needs supple samples to iterative converge. When the speaker is not moving and the environment is basically unchanged, iterating ICA by using stochastic gradient descent is a commonly available method after converging of the proposed method. In real-time application, processing the blocks which contain several frames is a feasible approach which considers the real-time and tracking properties of the proposed method and initialization of several blocks in the beginning is still required.
## 5 Discussion
### Evolution of Research Themes and Technological Focus in VRU Wearable Safety Solutions
#### 5.1.1 Research Expansion from Specific User Groups to Diverse VRU Categories  
Early research on VRU safety wearable technology primarily focused on the basic mobility assistance needs of visually impaired individuals, as reflected in the formation of the \"#0 street crossing\" cluster. This strategic choice not only addressed the most urgent safety needs but also fully utilized the technological conditions available at that time [61, 79, 80]. With technological advancements and the release of the WHO road safety guidelines [27], the research vision began to strategically expand to encompass a broader range of VRU groups, including motorcyclists [54], scooter users [51], pedestrians [30], and cyclists [41]. This evolution reflects a fundamental shift in road safety research philosophy--from specialized solutions targeting specific populations toward the development of inclusive traffic safety systems, demonstrating an enhanced global awareness of VRU safety challenges. Nevertheless, this apparent progress toward inclusivity reveals research gaps, particularly the limited representation of wheelchair users and hearing-impaired individuals in the identified clusters. This uneven research coverage highlights the ongoing challenges in achieving truly comprehensive VRU safety solutions. Future research should prioritize these under-represented VRU groups to ensure equitable safety technology development.
# Time-Truncated Group Plan under a Weibull Distribution based on Neutrosophic Statistics
## 2 Design of the Proposed Plan using Neutrosophic Statistics
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{\(a\) = 0.5} & \multicolumn{4}{c}{\(a\) = 1.0} \\ \hline \(\beta\) & \(\mu_{N}/\mu_{N0}\) & \(gN\) & \(c_{N}\) & \(P_{dN}(p_{N1})\) & \(gN\) & \(c_{N}\) & \(P_{dN}(p_{N1})\) \\ \hline  & 2 & [156, 164] & [2, 3] & [0.902, 0.993] & [3, 5] & [2, 3] & [0.929, 0.958] \\  & 4 & [8, 23] & [1, 2] & [0.989, 1.000] & [1, 3] & [1, 3] & [0.983, 1.000] \\  & 6 & [20, 23] & [1, 2] & [0.994, 1.000] & [1, 3] & [1, 3] & [0.996, 1.000] \\  & 8 & [8, 23] & [1, 2] & [0.999, 1.000] & [1, 3] & [1, 2] & [0.999, 1.000] \\  & 10 & [9, 23] & [1, 2] & [1.000, 1.000] & [1, 3] & [1, 2] & [0.999, 1.000] \\  & 2 & * & * & * & [25, 27] & [3, 4] & [0.966, 0.983] \\  & 4 & [26, 37] & [1, 2] & [0.965, 0.999] & [2, 4] & [1, 2] & [0.966, 0.995] \\
0.1 & 6 & [18, 37] & [1, 2] & [0.995, 1.000] & [2, 4] & [1, 2] & [0.992, 1.000] \\  & 8 & [18, 37] & [1, 2] & [0.998, 1.000] & [2, 4] & [1, 2] & [0.997, 1.000] \\  & 10 & [33, 37] & [1, 2] & [0.999, 1.000] & [2, 4] & [1, 2] & [0.999, 1.000] \\  & 2 & * & * & * & [32, 34] & [0.957, 0.978] \\  & 4 & [27, 48] & [1, 2] & [0.964, 0.999] & [3, 5] & [1, 2] & [0.949, 0.994] \\
0.05 & 6 & [23, 48] & [1, 2] & [0.993, 1.000] & [3, 5] & [1, 2] & [0.988, 0.999] \\  & 8 & [45, 48] & [1, 2] & [0.995, 1.000] & [3, 5] & [1, 2] & [0.996, 1.000] \\  & 10 & [28, 48] & [1, 2] & [0.999, 1.000] & [3, 5] & [1, 2] & [0.998, 1.000] \\  & 2 & * & * & * & [49, 51] & [3, 4] & [0.935, 0.968] \\  & 4 & [27, 74] & [1, 2] & [0.964, 0.999] & [4, 6] & [1, 2] & [0.933, 0.992] \\
0.01 & 6 & [64, 74] & [1, 2] & [0.981, 1.000] & [4, 6] & [1, 2] & [0.984, 0.999] \\  & 8 & [71, 74] & [1, 2] & [0.993, 1.000] & [4, 6] & [1, 2] & [0.995, 1.000] \\  & 10 & [29, 74] & [1, 2] & [0.999, 1.000] & [4, 6] & [1, 2] & [0.998, 1.000] \\ \hline \hline \end{tabular}
*: Plan does not exist.  
\end{table} 
Table 4: Optimal parameters of the proposed GASP under neutrosophic statistics when \(r_{N}=\) [4, 6] and \(\delta_{N}=\) [1.9, 2.1]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{\(a\) = 0.5} & \multicolumn{4}{c}{\(a\) = 1.0} \\ \hline \(\beta\) & \(\mu_{N}/\mu_{N0}\) & \(gN\) & \(c_{N}\) & \(P_{dN}(p_{N1})\) & \(gN\) & \(c_{N}\) & \(P_{dN}(p_{N1})\) \\ \hline  & 2 & * & * & * & * & * & * \\  & 4 & [6, 10] & [2, 3] & [0.932, 0.990] & [
## 4 Materials and Methodology
### Data Preparation and Preprocessing
#### 4.3.4 Extraction of Images Using HOG, LBP, and Saliency  
In the data preparation stage, following data localization and augmentation, several filters were applied to enhance the quality and relevance of the extracted features. The primary objective was to prepare the images for extracting parameters that would be used as input in the classification process. Various techniques, including data normalization, lighting correction, and noise removal, were employed to optimize feature extraction and ensure data consistency and reliability. Initially, the Histogram of Oriented Gradients method was utilized to obtain a detailed representation of the chicken meat images' characteristics. A function was implemented to convert the image to grayscale and compute the HOG, displaying both the original and grayscale images alongside the resulting HOG. In addition to HOG, the saliency map method was applied to highlight the most relevant regions in the images. This was achieved by converting the image to grayscale and computing the gradient using the Sobel operator. The saliency map was normalized to emphasize the most important areas of the image, showing the original image, grayscale image, and resulting saliency map. Lastly, Local Binary Pattern techniques were employed to extract additional image features. The calculations were performed using specific functions to compute the LBP of the grayscale image, displaying the original image, grayscale image, and resulting LBP image. In each case, the images were resized as necessary for processing, ensuring that features were consistently calculated across the entire dataset. These feature extraction methods provided a detailed and discriminative representation of the chicken meat images, facilitating their subsequent processing and analysis using machine learning techniques. As shown in Figures 9-12, the refined image processing involved applying LBP, HOG, and saliency filters to images belonging to different classes (C1, C2, C3, and C4).  
Figure 8: Images illustrating the differentiation of chicken parts after applying color filters.  
Figure 7: Images illustrating the differentiation of chicken parts before applying color filters.
# Ensemble Deep Learning on Time-Series Representation of Tweets for Rumor Detection in Social Media
## Methodology  
The structure of our proposed model is shown in Figure 1. The model takes Twitter conversations as an input, where each conversation is a stream of tweets that contains source-tweet and its corresponding reactions. In the data pre-processing stage, we parsed every tweet and extracted its creation timestamp value. Once all tweets were parsed, we generated time-series data for different time intervals and conducted data cleaning on it. We pre-processed the data by reducing time-series data sparsity, normalizing the data, and removing duplicate data samples. Then we fed that cleaned data as input to the ensemble model. The ensemble model has \(n\) base learners, which are \(n\) different neural networks that are represented as \(m_{1},m_{2},\cdots,m_{n}\), where each of them yields its individual prediction results (i.e., \(r_{1},r_{2},\cdots,r_{n}\)). Finally, we performed the majority-voting process on all the predictions of those base learners, i.e., summing up all the prediction results and deciding the final prediction result as 0 (non-rumor) if the total sum is less than \(\lfloor n/2\rfloor+1\) or as 1 (rumor) otherwise.
## 3 Data Processing
### Evaluation  
To train the CNN, k-fold cross validation is used. Typically, in machine and deep learning, \(k=10\) is chosen as in the present investigation. First, the dataset is randomly shuffled. Afterwards, the dataset is divided into ten equal-sized parts, where each part is used once for testing [22; 23]. The remaining data are used to develop the CNN-model, where eight parts are used for training and one part is used for validation. As consequence, the model is trained ten times. To evaluate the model accuracy, the mean result as well as the best and the worst iterations are considered. Cross validation ensures that all data points can be used for training and testing without resulting in overfitting [24]. Without a good evaluation, many models tend to classify the trained data perfectly but fail for new data. This is called the generalization problem, which is overcome by using cross validation.  
The present investigation is a multi-class problem. The most common classifier metric is the accuracy (ACC). It represents the ratio of correctly classified samples to the whole dataset [25]:  
\[\text{ACC}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{TN}+\text{ FN}}. \tag{5}\]  
Therefore, the correctly classified samples of the positive class (TP) and the correctly classified samples of the negative class (TN) are set in relation to all sample points. This includes the correct classification as well as the false classification of the positive (FP) and negative (FN) class.  
This metric is useful if there is either a balanced class distribution or only the probability of a correct classification is important, independent of the classes. In the present case, a balanced class distribution is only given when differentiating in seven classes. But this might not be the case for each cross validation. In addition, further information about the ability to detect every single class is needed. Therefore, another metric, handling imbalanced datasets, should be used.  
A common solution for this is the balanced accuracy (BAC) [26; 27]. The BAC is the average over each class dependent recall:  
\[\text{BAC}=\frac{1}{N}\sum_{i=1}^{N}\text{recall}_{i}. \tag{6}\]  
Figure 3: Applied CNN architecture.  
The recall is the ratio of correctly classified samples of a defined class. Defining the considered class as positive and all others as negative, the class specific recall is the TP classified samples of a class in relation to all suggested class samples, which includes the TP but also the FN:  
\[\text{recall}_{i}=\frac{\text{TP}}{\text{TP}+\text{FN}}. \tag{7}\]
## 3 Results
### ET and Related Variables in Beijing Sponge City
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**District in** & **LAI** & **ET** & **GPP** \\
**Beijing Sponge City** & **(m\({}^{2}\) m\({}^{-2}\))** & **(mm d\({}^{-1}\))** & **(gC m\({}^{-2}\) d\({}^{-1}\))** \\ \hline Xicheng & 0.66 & 1.62 & 2.36 \\ Dongcheng & 0.70 & 1.60 & 2.44 \\ Shijingshan & 1.05 & 1.67 & 3.53 \\ Haidian & 0.93 & 1.64 & 3.10 \\ Chaoyang & 0.84 & 1.51 & 2.79 \\ Fengtai & 0.74 & 1.50 & 2.56 \\ Tongzhou & 0.86 & 1.66 & 3.01 \\ Overall Mean & 0.83 & 1.60 & 2.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The coohroological environment in different districts in Beijing Sponge City. Figure 6: Spatial sensitivity of ET to LAI for the Beijing Sponge City. (**a**) Spatial pattern of the fraction of Ec to ET in June 2018; (**b**) Change in the fraction of Ec to ET with LAI; (**c**) Spatial pattern of the ratio of ET to LAI in June 2018; (**d**) Change in ratio of ET to LAI with LAI. croplands in June 2018 in the Beijing Sponge City. The fraction of Ei/ET is small (\(\leq\)0.1) in most LCC types but in grasslands is 0.1-0.2 (Figure 7b). According to a previous study, the city impervious surface could contribute less than 20% of ET [4]. Surprisingly, the impervious surface evaporation (Eu) for the Beijing Sponge City contributes 0.2-0.3 fractions to the ET in June 2018 in most impervious areas (Figure 7c). Finally, all ET from lakes and rivers are contributed by water body evaporation Ew (Figure 7d). ET can be converted to latent heat flux (LE, in W m\({}^{-2}\)) and plays an important role in regulating surface energy balance (Figure 8). For June 2018 in Beijing, the surface receives about 260-270 W m\({}^{-2}\) of shortwave radiation, but about half is reflected in the atmosphere with the net radiation (Rn) for impervious surface less than 130 W m\({}^{-2}\). For mixed forests and grasslands, the Rn is about 140-150 W m\({}^{-2}\), and the croplands and water bodies have a higher Rn of 170-180 W m\({}^{-2}\) (Figure 8b). Lakes and rivers have the highest LE (\(>\)250 W m\({}^{-2}\)) but the smallest sensible heat flux (SH, \(<\)\(-\)60 W m\({}^{-2}\)). The SH for mixed forests, forest parks and croplands are relatively small (\(-\)20 to 20 W m\({}^{-2}\)), while both impervious surfaces and grasslands are high (40-60 W m\({}^{-2}\)) (Figure 8d). This result indicates that the high surface air temperature (reflected by SH) in the summer of Beijing's central city is mostly contributed by impervious surfaces and grasslands. Figure 7: The fraction of Es, Ei, Eu and Ew to ET in June 2018 for the Beijing Sponge City. (**a**) Spatial pattern of fraction Es/ET; (**b**) Spatial pattern of fraction Ei/ET; (**c**) Spatial pattern of fraction Eu/ET; (**d**) Spatial pattern of fraction Ew/ET.