esa-sceva/satcom-chunk-collection huggingface dataset:
# CarrDet: Attention-Symmetry-Based Microscopic Carrier Detection for Wastewater Treatment
## 5 Discussion
### Ablation Study
#### 5.1.2 Impact of SDCD  
In a manner analogous to the experiments conducted to assess the impact of CFBA, we conduct experiments specifically to evaluate the individual impact of SDCD on carrier detection. For this purpose, we design the following experiments for the SDCD module: (c) utilizing the SDCD module alone without CFBA, and (d) incorporating both CFBA and SDCD simultaneously. The results presented in Table 2 indicate that using the SDCD module alone improves mAP by 4.32% compared to the baseline. Moreover, employing both CFBA and SDCD outperforms the experiment (c) with an improvement of 1.39%.  
The poor performance of general object detection methods can be partly attributed to the presence of defective carriers, which act as background noise. However, CarrDet uses the cosine similarity of the symmetric positions of the candidate boxes to perform secondary verification on the results of the detection head. Through this process, defective carriers are accurately filtered out, which leads to a substantial reduction in false detection results.
## Materials and Methods  
### General Framework  
The Sac-BiLSTM algorithm proposed in this paper for text sentiment analysis is illustrated in Figure 1. The algorithm consists of three main parts.  
Firstly, the tokenizer utilizes Word2 Vec for preprocessing the dataset. The hierarchical classifier feature of Word2 Vec is employed to separately train word embeddings and character embeddings.  
Secondly, considering the characteristics of Chinese text, the algorithm treats word embeddings and character embeddings as two distinct channels. This allows for the extraction of data features generated in different contexts. As a result, word embeddings capture semantic nuances more precisely, while character embeddings reflect the fundamental characteristics of the text.  
Thirdly, the algorithm combines BiLSTM with CNNs of various kernel sizes and DilatedConvolution. This encourages the character-level network to learn deep semantic information and local relationships within sentences, while the word embedding matrix network captures global contextual features and information. Subsequently, the Self-Attention mechanism is integrated into BiLSTM as a mapper for contextual semantic structure, while CNN serves as a feature extractor for semantic information. Self-Attention is used as a weight allocator to discover deep semantic information within sentences.  
The sequential order of the BiLSTM and CNN modules in the model is a crucial consideration. Placing the BiLSTM module before the CNN module allows for the exploitation of the BiLSTM's ability to capture global context information. By leveraging the bidirectional nature of the BiLSTM, it can effectively learn long-term dependencies and extract contextual information within the text sequence. This is particularly advantageous for handling lengthy texts that contain rich contextual information and semantic associations. In contrast, the CNN module is primarily designed to extract local features and patterns. Due to the limited receptive field of convolutional kernels, CNN may struggle to capture the entire global context of lengthy texts. It may not fully consider the global semantic information that is crucial for understanding the overall structure and meaning of the text. Considering the specific characteristics of the dataset used in this study, which consists of lengthy texts exceeding 1900 characters, the decision to place the BiLSTM module before the CNN module is justified. This configuration ensures that the global context information  
Figure 1: Sac-BiLSTM architecture.  
is adequately captured by the BiLSTM, while the CNN module can focus on extracting local features and patterns within the context provided by the BiLSTM.
# Future Industrial Networks in Process Automation: Goals, Challenges, and Future Directions
## 4 Evolution Challenges
### Reliability Challenges  
Providing a reliable system is essential for any type of network, and TSN is not an exception. Implementing fault-tolerance solutions is one of the major steps towards system reliability, which is mainly addressed through redundancy mechanisms. In TSN, fault tolerance can be achieved through two main substandards: (i) path control and reservation (IEEE 802.1 Qca) that enable the creation of multiple paths in the network; (ii) frame replication and elimination (IEEE 802.1 CB) that allow replication of streams and deploying them through the paths created by the Qca substandard--see Table 1. However, the way that those aforementioned mechanisms are set is very dependent on the application requirements [16]. One setting, called the decoupled approach, allows for arbitrary redundancy protocols to be utilized by decoupling the stream reservation from the redundancy mechanism. This setting is more appropriate for applications that have less stringent reliability requirements. Another setting, called harmonized, integrates the establishment of the reservation and the redundancy requirements at the cost of higher protocol overhead and bandwidth demands.  
Another important aspect of process automation systems is that a single point of error must be avoided in many scenarios. In order to meet that requirement, the end-nodes themselves need to be redundant. From a network perspective, this means that there are two independent network ports on the devices, which are required to be connected to two different switches to avoid a single point of error. In this perspective, the IEEE 802.1 CB and IEEE 802.1 Qca standards are not sufficient as they only provide network redundancy and not end-to-end redundancy. Moreover, an IEEE 802.1 Qca update is needed to associate dual network ports of the redundant end-points.
## 2 Models and Principles
### LAC Block
#### 2.2.4 The Structural Principle of the LAC Block  
Many scholars have applied and improved the AC block since its discovery. High applicability is one of the highlights of the AC block, and it can be used to replace almost any convolution in a CNN. It makes the replaced CNN reach a new height in feature extraction capability.  
However, the three convolutional branches in the AC block are all ordinary convolutions, so depthwise separable convolution can be used to construct an LAC Block with lightweight improvement. Figure 5 depicts the architecture of the AC Block and LAC Block. As the network's depth expands, we switch from the original three branches' conventional convolution to a group convolution, where the number of groups g and g takes 16, 128, and 256, respectively. This corresponds to the depthwise convolution in the depthwise separable convolution. Then, we add a 1 \(\times\) 1 convolution before each group convolution, which corresponds to the pointwise convolution in the depthwise separable convolution.  
It is mentioned in the literature [33] that different before-and-after order of depthwise convolution and pointwise convolution has an equivalent influence on the effect, so our design is reasonable. After the depthwise separable improvement of the three convolutional branches, we designed a channel shuffle for each convolutional branch. Then, each of them performs group convolution again. The channel shuffle reorders the current feature information so that the feature information of each group in the next group convolution comes from the different groups in the last group convolution. In this way, the information from different information groups can be blended and enrich the feature information within each group. Additionally, we included the GELU [36] activation function in place of the original ReLU activation function. The very effective activation function known as GELU has been applied in the Transformer model, which has lately gained popularity. It uses the idea of random regular, which is intuitively more in line with the understanding of nature. Additionally, it can assist in improving the model's degree of nonlinearity.  
Figure 4: Schematic diagram of the channel shuffle process.  
Figure 5: Structure diagram of AC Block and LAC Block. (a) AC Block. (b) LAC Block.
## 5 Results
### Power Balance and Autonomy Evaluation
#### 5.1.2 Autonomy Evaluation  
To assess the platform's autonomy, the authors carried out tests to determine hydrogen consumption and battery subsystem SoC. Figure 17 shows the hydrogen flow consumed by the PEM fuel cell under Profile 3. It remains stable at around 3.95 \(\mathrm{dm^{3}/min}\) (constant electrical power delivery, with stable hydrogen consumption), with the recorded values ranging from 3.78 \(\mathrm{dm^{3}/min}\) to 4.36 \(\mathrm{dm^{3}/min}\) (measured by FT-01). Additionally, periodic peaks were recorded, which were associated with the hydrogen purge needed to guarantee the best PEM fuel cell performance. The purge happens every 10 s, and the valve is left open for 0.07 s. Based on the data reported in the test, the average hydrogen required by the Hygrobot platform is \(\dot{V}_{H2_{consignign}}~{}=~{}4.05~{}\mathrm{dm^{3}/min}\).  
Figure 16: Hygrobot power balance detail for Profile 3.  
Taking into account the storage capacity of the hydrogen tank, shown in Table 4, the experimental continuous operating time range of the PEM fuel cell for the demand profile, as shown in Figure 16, is calculated in Equation (13):  
\[T_{FC_{exper}}\ =\ \frac{V_{H2}}{\mathrm{V}_{H2_{\mathrm{consumption}}}} \tag{13}\]  
where:  
\(T_{FC_{exper}}\) is the experimental continuous operating time of the PEM fuel cell (min).  
\(V_{H2}\) is the hydrogen bottle storage capacity (2000 \(\mathrm{d}\mathrm{m}^{3}\)).  
\(\mathrm{V}_{H2_{\mathrm{consumption}}}\) is the average hydrogen consumption flow (4.05 \(\mathrm{d}\mathrm{m}^{3}\)/min).  
This results in \(T_{FC_{exper}}\) = 8.23 h. Therefore, the autonomy obtained from experimental tests with a variant demand profile is close to the theoretical autonomy calculated in Equation (3). The difference is due to the impact of the purge and variations in efficiency with respect to the maximum power operating point.  
Finally, to evaluate the battery subsystem SoC, a long-duration test was performed with Profile 3 and Figure 15c, a duration of 3600 s (1 h) at 20 \({}^{\circ}\)C, and 60% RH. Figure 18 shows the evolution of the hydrogen tank and the battery subsystem SoC, as well as the DC bus voltage.  
Figure 17: Hydrogen consumption by the PEM fuel cell during the autonomy test.  
Figure 18: _Cont._  
From Figure 18, it can be concluded that the hydrogen tank reduced by 15%, while the battery subsystem SoC increased by up to 80%. In addition, the DC bus voltage increased from 35.5 V to 37 V.
## 3 Research Framework of Internet of Emergency Services
### Real-Time Data Collection and Analysis  
Real-time data are of utmost importance in emergency management and response. They provide a timely and accurate understanding of the situation, enabling emergency responders to make informed decisions and take appropriate actions [4; 5; 6]. In the event of a disaster or emergency, time is of the essence. Real-time data can help emergency responders quickly identify the location, scale, and nature of the emergency, as well as track the progress of response efforts. This information can be used to allocate resources effectively and efficiently, prioritize response efforts, and coordinate communication and collaboration among response teams. Real-time data can be obtained from a variety of sources, including sensors, social media platforms, and mobile applications. These sources can provide information on a range of factors, such as weather conditions, traffic patterns, and the location of individuals in need of assistance. By analyzing this data in real-time, emergency responders can gain valuable insights into the situation on the ground, enabling them to respond effectively to the emergency. Furthermore, real-time data can also help emergency managers and responders to assess the impact of their actions and adjust their response strategies as needed. By monitoring the progress of response efforts in real-time,  
Figure 4: Example of a robot: Clearpath Husky robot with CSIRO’s Navigation Pack and a RadEye G-10 dosimeter for data collection in a possible nuclear disaster environment [93].  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Robot** & **Context of Use** & **Description of Use** \\ \hline [PERSON] et al. (2021) [94] & IOT-based universal gas detector and health monitoring device & Workplace & Detects toxic gases, alerts workers, and sends emergency messages to a supportive person and nearby ambulance and hospital \\ \hline [PERSON] et al., (2021) [72] & Inherent of robotic things (IoRT) & Search and rescue & IoRT connected to a feedback information system (FIS) distributed among multi-access edge computing (MEC) centers to rescue victims \\ \hline [PERSON] et al. (2021) [95] & Autonomous and IoT controlled assistance robot (AMED) & Hospitals and quarantine zones & Limits person-to-person contact, delivers medicines, food, and collects waste, while monitoring the patient’s condition \\ \hline [PERSON] et al. (2021) [96] & Mobile robots & Dissaster recovery & Uses optimal localizable k-coverage (OLKC) strategies to preserve connectivity and robustness in IoT networks \\ \hline \hline \end{tabular}
\end{table}
Table 5: The use of robots for emergency monitoring and response.  
emergency responders can evaluate the effectiveness of their actions and make adjustments to improve their response efforts.
# Feed Rate Variation Strategy for Semi-Conical Shell Workpiece in Ball Head End Milling Process
###### Abstract  
The semi-conical shell workpiece is a special kind of thin-wall part that is commonly used in aerospace and mold industries. Due to the special stiffness distribution and weak rigidity for the area with a large radius, the machining quality of the semi-conical shell is sensitive to both cutting force and vibration. Conventionally, constant conservative machining parameters are chosen to ensure the workpiece deformation and surface quality, which will reduce the machining efficiency. Based on the cutting force and vibration response simulation of the whole milling process. A feed rate variation strategy is proposed for the ball head end milling process of the semi-conical shell workpiece. The cutting force, dynamic performance and stability prediction are obtained considering the shape and boundary conditions of the workpiece and the contour tool path of the milling process. Variable feed rate is used in the milling simulation to find the harmony between machining quality and efficiency. User-defined vibration amplitude and another user-defined cutting force threshold are used to find the optimal feed rate for each simulation segment. Both continuous and discrete feed rate variation strategies are proposed, and the improved discrete feed rate variation is applied in the milling experiment. About 25% of the consumed time is saved with almost the same machining quality by the experimental results.  
semi-conical shell workpiece; feed rate variation strategy; milling cutting force; milling process simulation; modal analysis Article
## 4 Rock Damage Recognition
### Ib-Value  
[PERSON] (1966) defined the b-value with AE magnitude and frequency to interpret AE information. The formula was given as:  
\[\text{lg}N=a\ -bM, \tag{2}\]  
where \(M\) is the Richter magnitude for earthquakes, \(N\) is the incremental frequency whose magnitude is greater than \(M\), and \(a\) and \(b\) are correlation coefficients. [PERSON] (1966) developed b-values using the _lb_-value to clearly present rock damage:  
\[lb=\frac{\text{lg}N(w_{1})-\text{lg}N(w_{2})}{(\alpha_{1}+\alpha_{2})\sigma}, \tag{3}\]  
where \(\alpha_{1}\) and \(\alpha_{2}\) are user-defined constants between 0.5 and 5.0 (we set both constants to 1 in this study), \(w_{2}\) is the upper amplitude limit, and \(w_{1}\) is the lower amplitude limit. An increasing _lb_-value indicates that rock damage is dominated by micro-cracks, while a decreasing _lb_-value indicates that rock damage is dominated by macroscopic cracks. A stable trend in the _lb_-value indicates that the damage status of the rock has not changed.  
Figure 8: Relationship between axial stress-strain curve and ultrasonic velocity ratio at different deformation stages for sample SA-2 under uniaxial compression.  
In this study, we used the mean amplitude of AE hits and _lb_-values to analyze the damage process (Figure 9). Mean amplitude fluctuated within the first 200 s, as the sample was under the recoverable deformation state. It then entered a constant phase for a period of time before decreasing with increasing loading stress. _lb_-values had consistent features with mean amplitude. In the compression fissures and linear deformation stages, it did not obviously change, only fluctuated slightly, which indicated the rock damage was in a stable state. However, it declined after the stabilized-growth cracks stage, indicating that a large number of cracks had been produced and connected inside the sample. The _lb_-value fell to 0.43 at the peak loading stage.
# Enhancing Machine Learning Prediction in Cybersecurity Using Dynamic Feature Selector
## 3 Algorithms Used for Dimensionality Reduction
### Information Gain  
Information gain or mutual information refers to the probability theory of the mutual dependency between variables. In this scenario, it represents how much information can be obtained about the dependent feature from any of the independent features selected for evaluation. Information gain is a metric used in decision trees to evaluate how good a split has been made in a decision tree to classify the dataset. A higher information gain is derived when there is a pure split in a node [37]. A pure split denotes that the node is split in such a way that all the records belong to a single class. An impure split denotes a node that produces a split where the records are evenly distributed among all the classes. An impure split is not useful since it is not able to classify records with higher accuracy [38]. Although information gain is a useful metric for evaluation, it suffers from cardinality where it favors attributes with larger number of values. To compensate for this problem, information gain ratio is used to decide a successful split [39]. It is a ratio of the Information Gain to Split Entropy. Split entropy is also referred to as the Intrinsic Value. Equation (2) denotes the Information Gain from X on Y. Here H(\(Y\)) denotes the entropy of feature \(Y\) and H(\(Y|x\)) denotes the entropy of Y given the attributes of feature \(x\). Information gain ratio is obtained by dividing the information gain by intrinsic information.  
\[IG(Y,x)=\text{H}(Y)-\text{H}(Y|x). \tag{2}\]
## 1 Introduction
In [14], a three-dimensional hybrid code is called the perfect difference/multi-diagonal (3D-PD/MD) based on two sequences and one sequence of 2D-PD and 1D-MD code, respectively. Accordingly, this study proposes a new spectral/time/spatial code called the variable weight zero cross correlation for non-coherent OCDMA (3D-VWZCC-OCDMA) system, which is characterized by the property of zero cross correlation, simple construction, simple detection, supports large number of users, eliminates the effects of PIIN noise, and has low power consumption. As a result, the suggested code gives better performance in terms of bit error rate and signal-to-noise ratio at a higher data rate. In this context, the rest of this paper is composed as follows: Section 2 presents a literature review of some previous codes. Section 3 describes the method of code construction. The presentation and description for a non-coherent system based on the new code is in Section 4. Then, Section 5 introduces the mathematical formulas of the three-dimension code (3D-VWZCC). The numerical results and the simulation of the system using MATLAB and Optisystem, respectively, are presented in Sections 6 and 7, respectively. Finally, we end with a conclusion about the code and its performance in Section 8.
## 3 Results
### Water Yield (WYLD) Response to Changing Climate under Future Scenarios  
We also considered water yield, an important parameter estimated by the SWAT model for efficient water management and planning in watersheds. The contribution of each sub-basin in the watershed to the total water yield was analyzed for the observed data. The average annual basin values for the different water balance components during the calibration and validation periods simulated by the SWAT model are presented in Table 8. Evapotranspiration and groundwater contributed larger amounts to the water balance components. About 23% of the precipitation that occurs in the basin is returned to the atmosphere, when using evapotranspiration as a relative percentage of the average rainfall.  
Total water yield (WYLD) is the amount of streamflow leaving the watershed outlet during the time step. The total annual water yield for 1991-2001 was predicted to be 1310.86 mm, comprising 58.55 mm of surface runoff, 1211.45 mm of baseflow (groundwater flow + tile flow), and 13.50 mm of lateral flow; the rest are transmission losses. Water yield for each year from 1991-2001 was quantified, as reported in Figure 8. Observed medium floods in 1994 and 1998 in Hongshui river, a tributary to the Xijiang River Basin, generated a large water yield, as also concluded by [61; 62]. The lowest water yield was 82 mm in 1991, while a high value of water yield (1662 mm) was generated in 1998.  
The results of the simulated mean annual water budget components for the Upper Xijiang River Basin under mean historical and future scenarios of five global climate models (GCMs) are presented  
Figure 8: Annual water yield for the entire watershed.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Water Balance Components** & **Depth (mm)** \\ \hline Precipitation & 1865.32 \\ Surface Runoff & 58.55 \\ Lateral Flow & 13.50 \\ Baseflow & 1211.45 \\ Evapotranspiration & 365.21 \\ Total Water Yield & 1310.86 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Mean annual (1991–2001) water balance components for the entire watershed.  
in Table 9. The results also present the impacts of likely future climate change on the hydrologic conditions. The results are significant, especially in terms of evapotranspiration and water yield (WYLD). Evapotranspiration is increased by less than 5% under all future scenarios, compared to the baseline period.  
Water yield is the most significant component of the water budget for sustainable water resource management and decision-making processes. Water yield for the future period (2020-2050) is summarized in Figure 9, while Figure 10 shows the percent change in water yield under baseline and future scenarios. There will be a likely decrease of 30% in water yield under the average of the future scenarios. A decrease of 40%