esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Multi-Model Classification Technique for Sound Event Detection in Urban Environments
###### Abstract
In this article, the available real-life dataset (here, Tampere University (TAU) spatial sound events 2017) is divided into two sub-classes, with the first class consisting entirely of artificial sounds and the second class including natural sounds. Our proposed multi-model uses two joint features, MFCC and RASTA-PLP, instead of using a single feature for the two sub-classes. In addition, three machine learning algorithms (ANN, CRNN, and S-CRNN) are used with these features for the two sub-classes. Our obtained results show considerable improvements in both F1-score and ER compared to the baseline technique. The glossary/abbreviation section at the end of the article describes the acronyms and abbreviations used throughout in the article. The major contributions of this article are as follows:  
* Our proposed stacked multi-model is accurate when used to split and classify artificial and natural sounds, with a high F1-score when fed into the S-CRNN separately. * For overlapping sounds, that is, polyphonic sound events, CRNN works well compared to alternatives, with the overall ER improved to 0.11 and the F1-score by 10% on the evaluation dataset. Figure 1: A general illustration of SED model. Finally, for artificial sounds with continuous behavior, such as car sounds, a short filter mask is best; however, for non-continuous sounds, such as speaking, it is preferable to utilize a longer length of filter mask in the post-processing stage. The rest of the article is organized as follows. Section 2 presents existing works related to the SED model. A brief overview of the DCASE dataset is presented in Section 3. The proposed methodology is described in Section 4. Our results are discussed in Section 5. Finally, Section 6 presents concluding remarks and future research directions.
## 4 Proposed System Evaluation
### Outage Probability Calculation  
The outage probability, given by Equation (13), represents the probability that the BS fails to serve the user or fails to fulfil the QoS requirements of the user. This is given by:  
\[P_{outage}=P(BER\leq BER_{TH}), \tag{13}\]  
where \(P(.)\) is the probability that the BER of the user falls below a certain threshold, \(BER_{TH}\).
# Research and Implementation of Axial 3D Printing Method for PLA Pipes
## 3 Path Planning Method for Axial Printing
### Bottom Curve Extraction and Parameter Calculation  
#### 3.3.1 Bottom Curve Extraction  
Figure 8 shows the extraction process of the bottom curve of the pipe part model.  
Import the STL file into the Repetier-Host software (Hot-World GmbH & Co. KG, Willich, Germany), make the end face of the model coincide with the bottom of the receiving platform by adjusting the position of the model, and obtain the coordinate point set of the contour outside the first layer of the model through the traditional slicing method. By fitting these point sets with MATLAB (The MathWorks, Inc., Natick, MA, USA), the equation of the outer contour of the first layer is obtained, which is called the bottom curve equation. The equation of the bottom curve generated by fitting is as follows.  
\[L(t)=\left\{\begin{array}{ll}x_{c}(t)\\ y_{c}(t)&0\leq t<2\pi\\ z_{c}(t)&\\ \end{array}\right. \tag{4}\]  
The bottom curve equation is adjusted, so that the center point of the image displayed by the equation is at the origin of coordinates.  
Where \(z_{c}(t)=0\), and the point \(Q(0)=(x_{c}(0),y_{c}(0),0)\) is the starting point coordinate of the equation.  
Figure 8: Bottom curve extraction process: (**a**) Designed model, (**b**) STL import, (**c**) Position adjustment, (**d**) Model slice, (**e**) Bottom points, (**f**) Bottom curve
## 4 Current Issues and Challenges of Realising the Tactile Internet
### Challenges Related to Haptic Devices  
There are also challenges related to the sensors and actuators at operator and teleoperator ends. Related to haptic devices, haptic sensors and actuators are used for communication. These sensors are usually mounted at the teleoperator end, which is used to sense the tactile data. It also senses the activity going on at the teleoperator end and is relayed back to the operator/user end as feedback. The haptic actuators handle the feedback process, often known as haptic feedback devices.
## 2 Materials and Methods
### Displacement Back-Analysis Model Based on LSTM
Let there be \(i\) groups of training samples and let each group of training samples consist of \(m\) displacements \((y_{i1}\ y_{i2}\ \dots\ y_{im})\) and \(n\) target parameters \((x_{i1}\ x_{i2}\ \dots\ x_{in})\); then, the initial information matrix of the target parameters is  
\[X^{\prime}=\big{(}x^{\prime}_{in}\big{)}_{m\times n} \tag{4}\]  
where \(x^{\prime}_{in}\) is the value of the ith target parameter of the nth training sample. Figure 5: (a) LSTM network model structure; (b) LTSM cell structure. Step 2: Eliminate the different magnitudes between the different parameters, and the initial information matrix \(X^{\prime}\) can be normalized by min-max normalization or zero-mean normalization to obtain the dimensionless matrix \(X\). Step 3: Determine the parameters of the LSTM network model, where the input feature dimension \(L\), the number of network layers K, and the number of neurons S, are considered to be the key parameters. In the DBA-LSTM model, the dimension of the input features is the number of displacement data in a set of training samples, i.e., \(L=m\); the number of neurons is generally taken as the Nth power of 2; the more layers K in the LSTM network, the stronger the nonlinear fitting ability of the model and the better the learning effect; thus, the scheme with a better effect and fewer layers is generally chosen to ensure the computational efficiency. Step 4: Network training: The training algorithm of the LSTM network is the BP algorithm, which can be regarded as the training process that alternates between forward propagation and backward propagation, and mainly uses the error principle of backward propagation to feedback the errors generated during the network training process to the LSTM network by continuously adjusting the connection weight of each neuron in the network until the error is reduced to the specified range, at which time the system stops learning. Step 5: Network testing. After establishing the DBA-LSTM inversion model, the performance of the model is generally evaluated by calculating the goodness of fit \(R^{2}\), which is calculated as follows:  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}{{{({{\widetilde{x}}_{n}}-{x_{n}})}^{2}}}}{ \sum\limits_{i=1}^{n}{{{({{\widetilde{x}}_{n}}-{\overline{x}})}^{2}}}} \tag{5}\]  
where \({{{\widetilde{x}}_{n}}}\) is the true value, \({x_{n}}\) is the inversion value, and \({\overline{x}}\) is the average of the true value. Besides calculating \(R^{2}\), the simulated calculation results of each measurement point can be calculated using the inversion results for positive analysis and comparing with them the actual measurement results.
## 5 Enhancements to ShinyAnonymizer Data Encryption Techniques
### Complete Data Security: Combining Hashing, Encryption, and Anonymization  
In the digital age, data create new ideas and knowledge, so providing complete data protection is important. Adjusting rules, issues with privacy, and incidents of data loss need a holistic strategy. This talk analyzes the utilization of data anonymization, hashing, and encryption to avoid exposing important data, ensure data integrity, and covers the ethical and legal standards of a privacy-conscious society in general.  
Data anonymization is the very first weapon in response, with an emphasis on maintaining separate identities inside databases. Taking away or changing private data (PII) requires using techniques such as perturbation, suppression, and generalization. A complicated technique must be used for employing anonymization together with hashing. Hashed identification codes with sensitive features are utilized for generating pseudonyms. Without revealing the primary names of the individuals in question, this relationship enables the connecting of significant data between numerous datasets while preserving individual privacy protection. By protecting essential features before the data get transmitted, merging anonymization with encryption minimizes the risk of re-identification across the anonymization procedure.  
Hashing is an essential process for ensuring the accuracy of data, because it converts data into hashes, which are unrecoverable types. Outside its usual role, hashing plays an important part in combining multiple methods. Hashed values, which are used as passwords or credentials for encryption methods, make the encryption process tougher [26]. Furthermore, when utilized in tandem with data anonymization, encrypted IDs enable safe data linkage without revealing real names. Integrating hashing, encryption, and data anonymization leads to an extensive approach that covers the entire data lifetime. This integrated strategy could guarantee data security from the time of collecting until all stages of processing, collaborating, and storage. It minimizes the risk of re-identification and unauthorized access to data. Also, the analytical significance of the data is retained by this method. Appropriate studies and evaluations continue to benefit from associated and anonymized datasets as soon as they obtain an appropriate balance between privacy and utility.  
In short, hashing, encryption, and data anonymization collaborate to give a flexible and effective platform for general data security. This collaborative method maintains sensitive data, while resolving the challenges imposed by a constantly evolving data environment. As technology advances and worries about privacy expand, both of these strategies are going to stay at the top of data security, promoting trust and suitable handling techniques in a rapidly expanding digital world.
## 3 Experimental Results and Analysis
### Evaluation Metrics and Experimental Settings  
In this paper, the average accuracy rate is selected as the evaluation index, and its formula is shown in Formula (8).  
\[Accuracy=\frac{1}{I_{c}}{\sum_{j=1}^{I_{c}}{\frac{I_{jj}}{I_{j}}}} \tag{8}\]  
In the formula, \(I_{c}\) is the total number of sample classes, which is six in this paper; \(j\) is the class label, which is 1-6 in this paper; \(I_{j}\) is the total number of samples with the class \(j\); and \(I_{\bar{j}}\) is the total number of samples with category correctly predicted as a class \(j\).  
The whole experiment was conducted on PyCharm ([[https://www.jetbrains.com/pycharm/](https://www.jetbrains.com/pycharm/)]([https://www.jetbrains.com/pycharm/](https://www.jetbrains.com/pycharm/)), accessed on 7 March 2022) with the number of iterative rounds set to 100, the learning rate initialized to 0.1, the hyperparameter in the cross-entropy loss set to 1, and the hyperparameter in the distillation loss set to 0.1. Stochastic Gradient Descent (SGD) was used as the optimization method, and the batch size was 16.
# Composition and Basic Physical Properties of the Phobos Surface: A Comprehensive Review
###### Abstract  
The surface of Phobos is an intriguing subject of research for many scientists. This is associated, among other things, with the fact that it is perceived as a potential launch site for future human Mars exploration. Additionally, measurements conducted on its surface would not only deepen our knowledge about Phobos but also provide insights into geochemical processes occurring on similar small bodies in the Solar System. Therefore, understanding the physical-mechanical properties of regolith is a crucial aspect of planetary exploration. These properties are key factors needed for both planning safe landings and establishing future bases on celestial bodies. In this paper, information is compiled regarding hypotheses about its origin, the probable composition of Phobos' surface (spectral properties and HiRISE data), as well as its morphology. The article also presents the process of regolith formation covering Phobos' surface and its presumed physical properties. It has been established that the estimated bulk density of Phobos, compared to the densities of other asteroids and meteorites, is most similar to C-type asteroids. It was also found that C-type asteroids, in terms of total porosity, best reflect Phobos. However, determining the surface composition of Phobos and its detailed physical properties requires additional information, which could be obtained through in situ studies or sample return missions.  
Phobos; composition of the surface; morphology; regolith; physical properties +
Footnote †: journal: Physics Letters
## 3 Methodology
### Feature Extraction Methodology
#### 3.1.1 Characteristics of Time Domain  
In this paper, three common statistical parameters, which are mean, variance, and range of the signal, are selected to reflect the time domain characteristics of the signal. These features can be used to analyze the time domain features of signals and as the input of machine learning classifiers containing time domain features. The calculation formulas are as follows:  
Mean of differential pressure signal:  
\[m=\frac{1}{N}{\sum\limits_{i=1}^{N}{s(i)}} \tag{1}\]  
where \(x(i)\) refers to each point of the signal, and \(N\) indicates the number of signal points.  
The standard deviation of differential pressure signal:  
\[S=\sqrt{\frac{1}{N}{\sum\limits_{i=1}^{N}{(s(i)-m)^{2}}}} \tag{2}\]  
where \(m\) refers to the mean of the differential pressure signal  
Range of differential pressure signal:  
\[R=s(i)_{\text{max}}-s(i)_{\text{min}} \tag{3}\]
# Survey on Quality of Experience Evaluation for Cloud-Based Interactive Applications
## 6 Stages of the Quantification of QoE in CIAs
### Processing Stage: Computing QoE
However, pixel-by-pixel comparison allows for the calculation of metrics such as which regions differ or the percentage of updated pixels. To calculate the image degradation experienced by users on the client side, traditional image-processing techniques offer comparative measurements such as the MSE, PSNR, or SSIM. In some cases, image processing is used to identify specific elements within them. To achieve this, strategies must employ computer vision to recognise objects or use character recognition such as OCR to identify text. Artificial intelligence-based processing techniques employed machine learning (ML) or deep learning (DL) techniques as the core of their input parameter processing. These algorithms differ from traditional combinational methods because they employ a much larger number of input parameters. These approaches attempted to find complex relationships among input parameters and automatically relate them to QoE. To do this, researchers generated large datasets that they used to train the models. Among the algorithms we analysed are support vector machines, random forests, naive Bayes, decision trees, k-nearest neighbours, extreme gradient descent, and neural networks such as long short-term memory, recursive, or gated recurrent units.
## 4 Elevator People-Counting System
### Crowd Detection Module Porting  
The embedded development board used in this research was the NVIDIA Jetson Nano Developer Kit, equipped with a 128-core NVIDIA Maxwell GPU and a quad-core ARM CortexA57 MPCore processor. It has 4 GB of 64-bit LPDDR4 memory. After migrating the image model and algorithm to the embedded development board, the environment creation process involves the following steps. Firstly, a microSD card with a capacity of greater than 16 GB is prepared, and the image file is burned using Etcher, a process that takes approximately 15 min. Then, the SD card is inserted into the development board slot, and power is supplied to initiate the system. Upon successful booting, the development board enters the interface and establishes a connection. The environment installation process begins by adjusting the power mode. The command \"$sudo jetson_clocks\" is used to lock the power to avoid overloading, and \"$sudo nvpmodel - m 0\" is executed to set the board to high-performance mode (10 W). In this mode, the power supply must reach DC 5V 4A; otherwise, sudden shutdowns may occur. Next, the following four commands are used to install the Python environment: \"$sudo apt-get install python3-pip python3-dev build-essential\", \"$sudo -H pip3 install -upgrade pip\", \"$sudo -H pip3 install jetson-stats\", \"$sudo systemd restart jetson_statservice\". Regarding the frame rate and confidence interval adjustments, hardware limitations prevent frame rates of higher than 10 frames from running accurately, resulting in counting errors. Thus, the frame rate is adjusted to between five and ten frames. As for the testing videos, the original resolution of 540 P is reduced to 480 P to ensure smoother processing. Due to the reduced resolution, the confidence threshold is adjusted to between 0.5 and 0.7. The recognition accuracy results are presented in Section 5.3, showing that even with the decreased image quality, the counting accuracy remains satisfactory.
## 3 Results and Discussion
### Semi Pilot Plant Operation: Linear Velocity Optimization  
In order to study the design parameters of the semi pilot reactor (Figures S1 and S2), which were completely coupled with UV lamps, the linear velocity was first optimized. For this purpose, the reactor was connected with a BTEX cylinder (50 \(\mu\)mol/mol) with a pressure of 10 atm. The mixture of VOCs passed through the reactor as a result of the intrinsic pressure of the storage cylinder. Three sampling ports were used to withdraw the sample at different times. The linear velocity was measured at the outlet of the reactor, with the help of a portable flow meter probe. For each port, an air suction pump was used to gather the representative sample. The BTEX concentrations were measured at linear velocities of 1, 3.5, and 5 cm/s for optimum flow and for maximum adsorption. Figure 5 shows the proportion removed (%) for all four components using different experimental linear velocities at three points (inlet, center, and outlet) of the reactor.  
Figure 4: Benzene, toluene, ethylbenzene, and xylene (BTEX) concentrations with respect to time for 10 cm/3 L of 50 ppm BTEX, namely: (**a**) bare tube, (**b**) just epoxy coated tube, (**c**) epoxy + Fe-ZSM-5 tube, and (**d**) epoxy + Fe-ZSM-5 + UV (30 min).  
Figure 5: Linear velocity optimization for the semi pilot reactor with an inlet concentration of 50 ppm and a running time of 3 min for each velocity.  
Although the removal efficiency is different with different components at the center and different outlets for all three of the velocities, 3.5 cm/s was found to be the optimal highest velocity needed in order to guarantee a high removal rate. We compared the removal of VOCs at 1 and 3.5 cm/s. We found that a linear velocity of 5 cm/s reduced the removal by almost 20% compared with the velocity of 3.5 cm/s. This can be explained by the ability to have a low interaction time (retention time) for the gases to react with an active surface area at a high linear velocity. Therefore, based on the findings of this experiment (Figure 5), we will use a linear velocity of 3.5 cm/s for the VOCs passing through the reactor.
## 4 Modified Ring Routing for the Energy Efficient Mobile Sensor Network
### Ring Construction  
The closed, one-node wide strip of nodes, known as the ring node, is used to construct the ring. As long as a ring provides a closed loop, its shape is imperfect. It is chosen simply because it is one-hop to two-hops away from the sink node and can preserve the sink position information. A centralized decision entity is not necessary for the simple and energy-efficient initial ring creation method. A beacon message sent by the sink node to  
Figure 2: Flowchart for the inter cluster communication in the WSN.  
its one-hop neighbors, is further forwarded to its next hop. The one-hop neighbors are logically connected in a ring fashion. The frame format for the beacon message or the SPI (sink position information) packet in the ring node retains the sink advertisement, as given in Figure 4.  
The pseudo code of the procedure to process the SPI packets is provided in Figure 5, as follows:  
1. **procedure**PROCESSSPIPacket(SPI packet)
2. **if**_role=sink node_ **then**
3. generate SPI packet with parameters
4. compute 1 hop neighbors list and 2-hop neighbors
5. 1 _hop neighbors_\(\xrightarrow{}\)_ring node_
6. send SPIpacket to ring node
7. **else if**_role=remote node_ **then**
8. **if**_ring node_\(\epsilon\)_1-hop neighbors of_ _Sink Node_ **then**
9. record Sink Node position and MAC address information
10. _sink Position_\(\leftarrow\)_SPIpacket.sinkPosition_
11. Send _ack_ to Sink Node
12. **endif**
13. **endif**
14. **end procedure**  
Since the ring nodes' communication ranges are not exactly impressive, the localization errors are anticipated to be below 15 m in areas where construction is guaranteed to occur. Ring routing, however, is tolerant to localization errors up to a 30 m mean error profile.
# LAAVOS: A DeAOT-Based Approach for Medaka Larval Ventricular Video Segmentation
## 2 The LAAVOS Model  
We name the proposed semi-automatic video segmentation model LAAVOS, which comprises three major components: an LA-ResNet encoder, a feature propagation module incorporating Transformer [9], and an APFViS decoder.  
As illustrated in Figure 1, specifically, the current frame (frame t), the preceding frame (frame t - 1), and a reference frame (frame 1) are sequentially fed into the LA-ResNet encoder to extract multi-scale visual features enhanced by local attention mechanisms.  
These attention-refined features are then passed into the intermediate feature propagation module, which consists of two branches: a visual branch and an ID branch. The visual branch is responsible for computing cross-frame attention maps and temporally propagating visual features across frames. In contrast, the ID branch reuses the attention maps generated by the visual branch to propagate object identity embeddings, enabling consistent identity tracking throughout the sequence.  
Figure 1: The LAAVOS structure.  
Finally, the enhanced features from both branches are fused and fed into the APFViS decoder to produce fine-grained segmentation masks for each frame.  
This architectural design effectively enhances segmentation robustness and enables accurate extraction of ventricular regions in medaka larval videos, maintaining stable performance even under adverse conditions such as motion blur, occlusion, and color distortion.