esa-sceva/satcom-chunk-collection huggingface dataset:
# A Feature Fusion Model with Data Augmentation for Speech Emotion Recognition
## 3 Proposed Methods
### Feature Fusion  
In many tasks, fusing features of different scales is an important means to improve the performance of the model. Relying on a single feature-extraction method, the effect is often not satisfactory. Due to the particularity of speech signal processing, the methods of feature extraction are different, and the feature granularity and focus are also different, such as frequency domain and time-domain features, frame level, global statistical features, and so on. Because of different feature sources, the focus of feature representation of emotional information is also different. The network model using a single feature input may bring the problem of insufficient use of the emotional information of the original speech signal.  
To solve the above problems, this section integrates the emotion classification model based on the Multi-Head Attention (MHA-CRNN) mechanism proposed above with the global statistical features extracted by the stage feature selection method of the popular machine-learning method LightGBM, to improve the speech emotion recognition effect of the overall model, as shown in Figure 1.  
The purpose of the feature fusion model is to combine the advantages of different features to complement each other. In this paper, two-dimensional Log-Mel spectrum and one-dimensional global statistical features are used. The two-dimensional Log-Mel spectrum takes continuous frames as units, extracts the spectrum characteristics of each frame of audio, and represents the spectrum space information of a whole sentence. One  
\begin{table}
\begin{tabular}{c} \hline
**Input Log-Mel Spectrogram [Num\_Frames, 64]** \\ \hline Conv2D 3 \(\times\) 3@64 ReLu \\ MaxPooling2D 2 \(\times\) 2 \\ Conv2D 3 \(\times\) 3@128 ReLu \\ MaxPooling2D 2 \(\times\) 2 \\ Dropout (0.25) \\ MaxPooling2D 3 \(\times\) 3@256 ReLu)*2 \\ MaxPooling2D 2 \(\times\) 2 \\ Dropout (0.25) \\ (Conv2D 3 \(\times\) 3@512 ReLu)*2 \\ MaxPooling2D 2 \(\times\) 2 \\ Multi-Head Attention 8 \(\times\) 512 \\ Bi-LSTM(2 \(\times\) 128) \\ Dense 256 \\ \hline \end{tabular}
\end{table}
Table 1: Phase feature selection method based on LightGBM.  
-dimensional global statistical features, which are the prosodic features and sound quality features of each frame of audio in a sentence, complement each other.  
One of the most commonly used methods of feature fusion is splicing or addition. For example, in ResNet and FPN, the method of adding elements is used for feature fusion, while in DenseNet, the method of splicing is used for feature fusion [45]. Aiming at the characteristics that the two features complement each other, this paper adopts the method of splicing the global statistical features through linear layer mapping and the output of MHA-CRNN, a deep-learning model based on multi-head attention mechanism. To make the input fusion features have the same contribution to the final classification as the output of MHA-CRNN in this step, the number of linear layer units after fusion features is 256, which is the same as the dimensions of the Bi-LSTM output vector.
# Biosynthetic Pathways of Hormones in Plants
## 1 Introduction
\begin{table}
\begin{tabular}{c c} \hline
**Phytohormone Class** & **Occurrence and Site of Biosynthesis** \\ \hline Abscisic acid & Roots, mature leaves, particularly in response to water stress, seeds \\ \hline Auxins & Leaf primordia, young leaves, developing seeds \\ \hline Brassinosteroids & Leaves, shoots, roots, fruits, seeds, pollen \\ \hline Cytokins & Root tips, developing seeds, leaves, stem, flowers, siliques, fruits, shoot meristem \\ \hline Ethylene & Tissues undergoing ripening ( fruits), roots, shoots, particularly in response to stress \\ \hline Gibberellins & Young tissues of the shoot, developing seeds \\ \hline Jasmonates & Leaves, roots \\ \hline Melatonin & Leaves, stems, roots, fruits, seeds \\ \hline Polyamines & Most tissues, particularly in response to stress, in tissues undergoing senescence or ripening \\ \hline Salicylic acid & Leaves, particularly in response to pathogenic attack \\ \hline Strigolactones & Roots, shoots \\ \hline \end{tabular}
\end{table} 
Table 1: The localization of hormones in plants [7; 8]. Figure 1: Precursors of phytohormone biosynthesis. assortment of new tools and methods has been developed, resulting in the discovery of phytohormone substrates, intermediates, and final products. The biosynthetic pathways of plant hormones have been elucidated, and extensive research has been carried out into the genes found in the plant genome, encoding the enzymes that catalyze the various stages of phytohormone synthesis [1; 2]. This review aims to outline the biosynthetic pathways of non-peptide plant hormones (Figures 1 and S1).
## 3 Results and Discussion
### Evolution of Electrochemical Impedance Spectroscopy (EIS)  
The EIS results (Nyquist plot) for steel bars with or without corrosion inhibitors at different immersion times (5 days, 30 days, and 60 days) are observed in Figures 3-5. The diameter of the capacitive loop in the Nyquist plots represent the corrosion degree of steel bars [37]. It is evident that the capacitive loop of the reference group reduces over time, which reflects the corrosion caused by chloride occurred on the steel bar surface without the corrosion inhibitor protection.  
The group of multi-component corrosion inhibitors displays a larger diameter capacitive loop than other groups for the early corrosion stage (5 d). In contrast, steel bars with the addition of calcium nitrite have a smaller capacitive loop diameter than the multi-component corrosion inhibitors. The development of the incomplete passive film on steel bars is due to the lack of protection effect from organic or inorganic corrosion inhibitors.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multicolumn{3}{c}{**OCP (V vs. SCE)**} \\ \cline{2-4}  & \(5\) d & \(30\) d & \(60\) d \\ \hline Ref. & \(-0.23\) & \(-0.22\) & \(0.12\) \\ Calcium nitrite & \(-0.28\) & \(-0.31\) & \(-0.38\) \\ Multi-component corrosion inhibitor & \(-0.37\) & \(-0.36\) & \(-0.38\) \\ Organic corrosion inhibitor & \(-0.22\) & \(-0.32\) & \(-0.33\) \\ Inorganic corrosion inhibitor & \(-0.39\) & \(-0.4\) & \(-0.45\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: OCP values of steel bars immersed in different corrosion inhibitors.  
Figure 4: EIS curves of steel bars immersed in SCP solutions with different corrosion inhibitors at 30 days.  
Figure 3: EIS curves of steel bars immersed in SCP solutions with different corrosion inhibitors at 5 days.  
The EIS results of steel bars after soaking for 30 days and 60 days with different corrosion inhibitors are shown in Figures 4 and 5. After being immersed for 30 days, the multi-component corrosion inhibitor exhibits excellent corrosion resistance, which is primarily characterized by a large diameter capacitive loop. Simultaneously, calcium nitrite and organic corrosion inhibitors are involved in the formation of passive film. Nevertheless, the capacitive loop of those two groups becomes smaller than the multi-component corrosion inhibitors. It may be attributed to passive film degradation and steel bar corrosion caused by chloride. After 60 days soaking, the low-frequency capacitive reactance diameter of the inorganic corrosion inhibitor group decreases sharply. The capacitive reactance diameter of the multi-component corrosion inhibitor is larger than that of the reference group, as evidenced by the results shown in Section 3.1.
## 6 System Development
### Crdt
#### 6.3.8 Snowflakes Keys  
A potential fix for the idempotency issue is to introduce some unique identifier that can be used to decide if a certain operation needs to be performed or not. There are multiple approaches to generate unique keys for data entries (UUIDs, global key providers, randomly composed keys, etc.) but many of them require a look-up table of some sort to extract information about the entity they represent, e.g., checking timing aspects, creation dates, identifying the node handling the request, etc.  
In the context of the proposed system design, doing look-up operations might be expensive in terms of resources and can introduce delays in the propagation of results to the end user.  
Snowflake identifiers are a special kind of uniquely generated IDs that incorporate the functionality of query support, are unique across the system, and are capable of ordering. In general, in a modern distributed application there is a designated service that handles the creation of IDs based on business requirements. In the context of removing the duplicate events emitted upon a document interaction, the Snowflake ID should contain the following:  
1. A timestamp for further time-based operations and checks on past events;
2. A way to identify the original creator of the message;
3. An ordinal to keep track of incremental messages.  
As a representation, a Snowflake ID can be encoded in any format: binary, base64, base10, etc. The simplest approach is to start from a binary representation and assign several bits for each component of the ID. Once the ID structure is complete, the binary data can be encoded in another format to obtain a smaller-size identifier.  
To fix the idempotency issue on document event propagation, the common Twitter (X) Snowflake ID structure was used [67]:  
1. Where 1 reserved bit was set as 0.
2. Where 41 bits were used for UNIX epoch representation. With 41 bits, the maximum usable epoch is 241 which is suitable for 136 years from the starting date.
3. Where 10 bits were used for the running instance count. The system can support 210 = 1024 running nodes dispatching document editing-related messages.
4. Where 12 bits were used for the ordinal, allowing 4096 items to be generated on each epoch for each running instance.  
The total length of the Snowflake ID will be 64 bits. While the ordinal and epoch values can be generated, the worker ID must be extracted from the running microservice hosting the application. To provide a more contained and predictable system, each RTC microservice will receive an incremental worker ID. Alternatively, the task of assigning worker IDs can be abstracted in a standalone microservice.  
The newly created idempotency safe ID, an RTC instance can decide if the message originated from the same instance and thus avoid re-broadcasting it to all the consumers.
###### Abstract  
Network traffic classification is crucial for effective security management. However, the increasing prevalence of encrypted traffic and the confidentiality of protocol details have made this task more challenging. To address this issue, we propose a progressive, adaptive traffic classification method called SwiftSession, designed to achieve real-time and accurate classification. SwiftSession extracts statistical and sequential features from the first K packets of traffic. Statistical features capture overall characteristics, while sequential features reflect communication patterns. An initial classification is conducted based on the first K packets during the classification process. If the prediction meets the predefined probability threshold, processing stops; otherwise, additional packets are received. This progressive approach dynamically adjusts the required packets, enhancing classification efficiency. Experimental results show that traffic can be effectively classified by using only the initial K packets. Moreover, on most datasets, the classification time is reduced by more than 70%. Unlike existing methods, SwiftSession enhances the classification speed while ensuring classification accuracy.  
t +
Footnote †: journal:  
_Article_  
1
Footnote 1: School of Cyberspace Security, Hangzhou Dianzi University, Hangzhou 310018, China; [EMAIL_ADDRESS] (T.X.); [EMAIL_ADDRESS] (X.Q.); [EMAIL_ADDRESS] (H.Y.); [EMAIL_ADDRESS] (Z.S.)
## 2 Model Preparation and Testing Procedure
### Experimental Program
#### 2.2.2 Input Motions  
Two input motions (an artificial earthquake with a high intensity and wide bandwidth frequency excitation, and a historical earthquake called as Hachinohe earthquake with a long period and short duration (recorded at Tokachi Oki, Japan in 1968), as shown in Figure 3) were applied as input excitations in each test. Frequency components of these two input motions ranging from 1 to 6 Hz are covered fixed-base frequency (\(f_{n}\)) of S1 and S2 structure, which is approximately 2 Hz. The f\({}_{n}\) of the S3 structure is higher than the frequency range of both input motions. Input motions were filtered with allowable frequency range (40 Hz to 300 Hz at model scale) of the earthquake simulator. By normalizing input time series to a scale factor to obtain target peak acceleration (a\({}_{\text{peak}}\)), three different levels of input motions were applied as base excitation. Note that the suffix of H (a\({}_{\text{peak}}\) = -0.3 g), M (a\({}_{\text{peak}}\) = \(\sim\)0.2 g), and L (a\({}_{\text{peak}}\) = -0.1 g) in the name of each case indicated high, medium, and low intensity of the input motion, respectively.
## 9 Samples of the Results
### DDPG for Continuous Robotic Control  
[PERSON] et al. [3] proposed the DDPG algorithm to address challenges in continuous action domains, employing an actor-critic framework with deterministic policy gradients. The experimental evaluation covered various challenging physical control tasks simulated using MuJoCo, including classic problems, such as cartpole swing-up, pendulum balance, and locomotion with cheetah-like agents. The study involved training on both low-dimensional state descriptions (e.g., joint angles) and high-dimensional pixel inputs, showcasing DDPG's ability to learn policies \"end-to-end\". The experiments used consistent hyperparameters and network architectures across tasks, with reward functions tailored to encourage task-specific behaviors such as smooth locomotion or object manipulation. Policies were evaluated periodically without exploration noise to assess stability and convergence. Figure 17 presents performance curves for a variety of environments. This figure demonstrates the effectiveness of DDPG, highlighting the impact of using components like target networks and batch normalization. Notably, the inclusion of these components led to more stable learning and better overall performance, especially when training on pixel-based observations.  
Figure 17: Performance curves for a selection of domains using variants of DPG: original DPG algorithm (minibatch NFQCA) with batch normalization (light grey), with target network (dark gray), with target networks and batch normalization (green), with target networks from pixel-only inputs (blue). The addition of target networks and batch normalization significantly enhances stability and performance (Adapted from [3]).
## 6 Numerical Simulation Analysis of the Dual-Electrode Excitation Method
### Analysis of Influencing Factors on the Distribution of the Magnetic Induction Intensity Formation
#### 6.4.1 Investigation of Factors Influencing Inter-Well Distance Measurements  
The range distance \(d\) is modified, and the numerical simulations are conducted at intervals of 1 m, 2 m, 5 m, 10 m, and 25 m, respectively.  
Figure 5a illustrates the varying impact of different ranging \(d\) on the casing current. Regardless of the changes in ranging distance, when the axes of the two wells are placed parallel to each other, the casing current amplitude demonstrates a symmetrical distribution and decreases according to a cosine characteristic. As the distance from the ranging point M increases, the current attenuation rate decreases. The maximum amplitude of the casing current is reached at ranging point M;  
Figure 5 illustrates the impact of different ranging distances \(d\) on the induced magnetic field intensity along the axis of the drilling well. The attenuation characteristics of \(B_{c}\) are similar to those of \(l_{c}\). The maximum values of \(B_{c}\) corresponding to different distances \(d\) are as follows: 680 nT, 245 nT, 50.7 nT, and 13.4 nT, successively. Additionally, the magnetic field intensity values at the measurement position (\(\text{z}^{\prime}=\pm\) 2.5 m) are as follows: 20,610 nT, 2160 nT, 98.2 nT, 7.62 nT, and 0.212 nT, successively.
## 2 Literature Review
### Short Text Classification Methods  
Short text classification has seen major developments with the implementation of various DL methods. LSTMs [11; 12] are widely used due to their excellent sequential modeling capabilities, particularly for processing word embeddings like SkipGram [13] and GloVe [14]. Recently, the transformer model [15] has gained prominence due to its attention mechanism, assisting modeling long-range dependencies effectively. Pre-trained models (PTMs) [16], which include methods like MLM [17], have additionally enhanced NLP by learning context-based word embeddings and language modeling from large datasets.  
Several CNN variants have been developed to address specific challenges in text processing and classification. For text classification and sentiment analysis, TextCNN [18], developed by [PERSON] et al., leveraged static word vectors from the Word2 Vec model to classify sentences effectively [19]. In the stock domain, [PERSON] applied a bidirectional LSTM network with an attention mechanism for stock price classification; this led to a reduction in the number of parameters while maintaining accuracy [20]. Similarly, a Bi-LSTM method that integrates CNN and word embeddings to assign emotion labels to psychiatric texts was developed, enhancing accuracy through the combination of CNN's feature extraction and LSTM's sequential dependency capture [21; 22]. These studies demonstrated the importance of building a classification model by taking into consideration the integration of effective feature extraction, such as variations of CNNs, and the importance of capturing sequential dependencies for enhancing the accuracy and robustness of text processing tasks.  
Advancements in embedding techniques have also played a crucial role in improving text processing tasks. Character embeddings have been demonstrated in two methods in the literature. Initially, [PERSON] introduced out-of-vocabulary (OOV) embedding techniques to expand vocabulary handling, especially for rare or unseen words, which is vital in tasks where vocabulary is large and constantly evolving [23]. Later, [PERSON] and [PERSON] demonstrated the effectiveness of character-level CNN approaches, which capture detailed linguistic nuances by using character-level information, thus improving tasks like parsing and tagging [24]. These developments highlight how CNN variants and hybrid models have been tailored to specific challenges in text classification, sentiment analysis, and other related tasks, showcasing their benefits in different contexts.
## 4 Experimental Results and Discussion  
We first tested our TSR system on a PC with the following configuration: Intel(r) Core (TM) i57200 CPU, 64 bits; 8 GB of RAM. Then, a second test is carried out on a Raspberry Pi 4 board Model B equipped with a 1.5 GHz ARM Cortex-A72 quad-core processor, 64 bits of CPU, and 8 GB of RAM.
## 5 Results
### Satellite Product Intercomparison
#### 5.1.3 Spatio-Temporal Consistency
The green, blue and orange lines correspond to optimal, target and threshold uncertainty levels, respectively. Computation over all LANDVAL sites for the December 2013–May 2014 period over good quality pixels according to the quality flags (Table 3) for AL-DH-VI (**a**,**d**), AL-DH-NII (**b**,**e**), and AL-DH-BB (**c**,**f**). The worse performance in the comparison PROBA-V SA v1.0 versus MCD43A3 C6 was found for the total spectrum (Figure 9c-f and Table 10), with a large positive bias of \(\sim\)12-13%. As observed for the NIR, PROBA-V tends to provide higher values than MODIS C6 for albedo values lower than 0.5 and the opposite trend for albedo values higher than 0.5 (i.e., snow cases). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**PROBA-V SA v1.0 vs. MCD43A3 C6 (2014)**} \\ \cline{2-7}  & **AL-DH-VI** & **AL-DH-NI** & **AL-DH-BB** & **AL-BH-VI** & **AL-BH-NI** & **AL-BH-BB** \\ \hline
**Median\_Err** & 0.011 & 0.025 & 0.027 & 0.012 & 0.029 & 0.031 \\
**(Median\_Err \%)** & (9.8\%) & (9\%) & (13.4\%) & (10.1\%) & (9.9\%) & (14.5\%) \\ \hline
**Bias** & 0.006 & 0.024 & 0.024 & 0.006 & 0.029 & 0.029 \\
**(Bias \%)** & (5.3\%) & (8.4\%) & (11.9\%) & (5.1\%) & (9.6\%) & (13.3\%) \\ \hline
**RMSD** & 0.048 & 0.039 & 0.044 & 0.048 & 0.045 & 0.048 \\
**(RMSD \%)** & (43.3\%) & (14\%) & (21.5\%) & (41.4\%) & (15.1\%) & (22.2\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Main performance statistics of C3S PROBA-V SA v1.0 versus MCD43A3 C6 broadband albedo products over all LANDVAL sites during the year 2014. The computation was performed over good quality pixels according to PROBA-V and MCD43A2 QFLAGS. Figure 9: Top: C3S PROBA-V SA v1.0 versus MCD43A3 C6 products scatter-plots, where the dashed lines correspond to the optimal, target and predefined threshold uncertainty levels around the 1:1 relation (continuous line). Bottom: Box-plots of the bias per bin albedo range, where red bars indicate the median values, blue boxes stretch from the 25 th percentile to the 75 th percentile of the data and whiskers include 99.3% of the coverage data (\(\pm\)2.7 \(\sigma\)). Outliers are not displayed. The green, blue and orange lines correspond to the optimal, target and threshold uncertainty levels, respectively. Computation over all LANDVAL sites during the year 2014 over good quality pixels according to the quality flags for AL-DH-VI (**a,d**), ALI-NI (**b,e**), and AL-DH-BB (**c,f**). * Compliance with user requirements  
The compliance matrix of C3S PROBA-V SA v1.0 versus the reference products (SPOT/VGT SA v1.0 and MCD43A3) with predefined uncertainty levels based on different user requirements is presented in Table 11.
# Parameter Optimization of Orbital TIG Welding on Stainless Steel Pipe
## 3 Results and Discussion
### Tension Test Results
The average UTS values were 237.3 MPa, 321.3 MPa, and 162.7 MPa, corresponding to \(\rm D_{w}\) of 0.8 mm, 1.0 mm, and 1.2 mm. An increase in the \(\rm D_{w}\) value from 0.8 mm to 1.0 mm led to a rise in tensile strength. However, increasing the \(\rm D_{w}\) further to 1.2 mm resulted in a reduction in UTS value. The low UTS value of the weld joint could be the filling wire diameter melting rate. This means that a larger diameter and a higher wire feeding speed could result in a poorer melting rate, and therefore the penetration depth was lower [28; 29; 30]. \(\rm D_{w}\) of 0.8 mm and 1.0 mm are generally better than \(\rm D_{w}\) of 1.2 mm.