esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methodology
### Network Architecture of CCS-YOLOv8
#### 2.2.2 CBAM Attention Mechanism  
By focusing on key characteristics of the detected object, the attention process may be viewed as an allocation mechanism that improves target localization and classification accuracy. The central concept is to give the raw data varying weights to identify underlying relationships and highlight important information [34]. Since the majority of the livestock targets to be recognized are small- and medium-sized, and the background makes up a sizable component of the information that is used; it is especially advantageous to incorporate an attention mechanism. To improve the model's representation of livestock features, we integrated CBAM [35] into the C2f module of YOLOv8n. CBAM is a lightweight and end-to-end attention mechanism. For each feature map, CBAM sequentially applies attention to the channel and spatial dimensions, achieving comprehensive attention in both aspects.  
For the input feature map F1, CBAM initially refines it using the channel attention module to derive the intermediate feature map F2. Subsequently, F2 undergoes further refinement through the spatial attention module to produce the final feature map F3, as Figure 3 illustrates.  
In the channel attention module, important feature information is extracted from the input feature map through GAP and GMP. The channel attention weights are then determined using an MLP, and normalization is accomplished using the sigmoid function. Ultimately, channel-wise multiplication is used to apply the acquired weights to the original input feature maps. This process may be stated as follows:  
\[\begin{split}\mathcal{M}_{c}(\mathcal{F})&=\phi(MLP_{1}( AvgPool(\mathcal{F}))+MLP_{2}(MaxPool(\mathcal{F})))\\ &=\phi(\mathcal{W}_{3}(\mathcal{W}_{2}(\mathcal{F}_{avg}^{c}))+ \mathcal{W}_{3}(\mathcal{W}_{2}(\mathcal{F}_{max}^{c}))),\end{split} \tag{1}\]  
The spatial attention module mainly performs GAP and GMP in the spatial dimension. This mechanism captures the correlation between spatial features through convolution while maintaining the input and output dimensions, facilitating the extraction of salient livestock features. The specific calculations are as follows:  
\[\begin{split}\mathcal{S}_{\text{s}}(\mathcal{F})&= \psi(g^{3\times 3}([AvgPool(\mathcal{F});MaxPool(\mathcal{F})]))\\ &=\psi(g^{3\times 3}([\mathcal{F}_{avg}^{s};\mathcal{F}_{max}^{s}])),\end{split} \tag{2}\]  
The newly constructed C2 fCBAM module replaces all of the Bottleneck modules with the BottleneckCBAM module, as shown in Figure 4. To increase the diversity of learned features at various network levels, this structure combines a cross-stage feature fusion technique with a truncated gradient flow approach. Consequently, it lessens the effect of redundant gradient information and enhances the network's capacity for learning. The C2 fCBAM module is included in the model to improve output feature representation, which raises the model's detection accuracy and boosts algorithmic performance as a whole.
# Research on Output Voltage Stability of Non-Contact Excitation Motor
## 5 Simulated Analysis  
To test the applicability of the controller, the simulation software MATLAB is used to simulate and compare the traditional sliding mode control and power fast discrete terminal sliding mode control methods. Compare how fast the output voltage stabilizes. The effects of improved sliding mode control and traditional sliding mode control in stabilizing the output voltage of the non-contact excitation motor are discussed in the following three working conditions. The system parameters are shown in the Table 1 below.
# Multi-Player Tracking for Multi-View Sports Videos with Improved K-Shortest Path Algorithm
## 3 Methodology
### Graph Construction  
Based on the above theoretical analysis, we can convert the multi-player tracking problem into a graph model to solve; the graph is a directed acyclic one (a path can visit every node once at most); and the paths are node-disjoint (different paths cannot share the same node), as shown in Figure 2. For a graph, nodes and edges are two key elements. Thus, constructing a graph means defining these two elements and assigning the linking weights of the edges.
# Service-Aware Clustering: An Energy-Efficient Model for the Internet-of-Things
## 2 The Energy-Efficient Internet-of-Things Model
### The Service-Aware Clustering Model
#### 2.3.2 The Service-Aware Clustering Solution
Its main steps consist of building a routing tree for the signaling messages, as illustrated by Figures 5-7. The main steps of the signaling tree construction algorithm are described as follows:  
* Initialization: The routing process starts with an initial network depicted by the graph in Figure 5a where Node 1 is the sink. * Beaconing process: As shown in Figures 5b,c and 6a,b a beaconing process is initiated by a sending node, which announces to the receiving node the intention of the sender of becoming the parent of the receiver. In all figures, the beaconing process is identified by red arrows. This beacon message will reach only a set of neighbors \(J\in\mathbf{N}[s]\) of a sender (\(s\)) that meet the constraint \(\mathcal{D}_{is}(s,J)<\mathcal{C}_{ov}\) expressing that the distance between the sender \(s\) and any of its neighbors \(J\) should be inferior to the wireless coverage range \(\mathcal{C}_{ov}\). While Step 1 shows the initial connected graph of the network, Step 2 reveals the initial beaconing process. Step 3 reveals the initial acknowledgment and forwarding of the beaconing process. * Acknowledgment and relaying: In the steps in Figure 5c and Figure 6a,b,c, acknowledgments are illustrated by green arrows, which reflect the selection of a parent node chosen among the nodes that sent a beacon message. In contrast to the LIBP protocol where the acknowledgments are used to select a parent among many candidates, the acknowledgment in the SAC protocol is used to both select a parent and to carry a node's information to the sink/gateway. Note that while all neighbors of the sink have a unique parent selection choice (the sink), the other different nodes select their unique parent from different potential originators of the beacon message. Steps 3, 4 and 5 show how the acknowledgment (in green arrows) and beaconing (in red arrows) processes are recursively repeated, with the expectation of having the beacons reaching the rest of the network, as well as acknowledgments sent back to the root for building a routing topology for the clustered network configuration computation. * Weight: The least interference paradigm maps the acknowledgment process into a weight setting by: (1) having each node receiving only one acknowledgment from each of its potential children; and (2) the sum of received acknowledgment messages representing a weight on a node expressing the interference on the node of the paths carrying the sensor readings from sensing locations to the sink. * Termination criteria: The beaconing process is repeated recursively using a breadth-first approach, resulting in the formation of a spanning tree when the beacons reach the leaf nodes, which are marked with weight zero, as they do not receive any acknowledgment message from any node, as expressed by Figure 7a. In such a tree, each node will exhibit a weight corresponding to the number of children it has.
# A Hybrid STL-Deep Learning Framework for Behavioral-Based Intrusion Detection in IoT Environments
## 1 Introduction
To validate this approach, we pose the following research questions (RQs):  
* How effectively can STL pre-filtering separate normal, predictable IoT network traffic patterns from potentially anomalous traffic based on time series decomposition? * Does integrating STL pre-filtering with deep learning models (LSTM, RNN, and DNN) significantly reduce FPR and FNR compared to standalone deep learning models for IoT intrusion detection? * Which deep learning architecture (LSTM, RNN, or DNN) performs best within the proposed hybrid STL-DL framework in terms of overall accuracy, F1-score, FNR, and FPR on a realistic IoT dataset? * How does the performance (accuracy, F1-score, FNR, FPR, and execution time) of the proposed hybrid STL-DL framework compare to existing state-of-the-art IDS solutions evaluated on similar IoT datasets? The main contributions of this paper can be summarized as follows. (1) We propose a novel hybrid IDS framework that combines STL with deep learning techniques to enhance behavioral analysis in IoT networks. (2) The framework is systematically evaluated across three variants--STL + LSTM, STL + RNN, and STL + DNN--and compared against their respective standalone deep learning counterparts. (3) Our analysis demonstrates that STL pre-filtering significantly improves detection accuracy by reducing both FNR and FPR. (4) Extensive experimental validation is conducted using the BoT-IoT dataset, a large-scale and realistic benchmark, with detailed analysis of the effects of class imbalance and feature selection. (5) Finally, a comparative study with existing state-of-the-art IDS approaches shows the superiority of our proposed framework, particularly in achieving lower error rates and more effective intrusion detection in complex IoT environments. The remainder of this paper is organized as follows. Section 2 reviews related work in IoT intrusion detection. Section 3 details the proposed hybrid STL-Deep Learning framework. Section 5 describes the experimental setup, including the dataset, evaluation metrics, and implementation details. Section 6 presents and discusses the experimental results, addressing the research questions. Finally, Section 7 concludes the paper and outlines directions for future research.
## 4 Conclusions  
A hyperspectral imaging system in the Vis/NIR region of 400-1000 nm was adopted to explore the potential of combination of spectral and textural data for classifying samples with different WHC. PLS-DA classification models were built to relate spectra, textures, or the fusion of the two to the corresponding drip loss values of the samples, respectively. The correct classification rate (CCR) of the prediction set for the model based on the spectra of seven informative wavelengths (441, 469, 506, 566, 596, 617, and 939 nm) was 73%, slightly lower than that of the model based on spectra of the full-band with a value of 78%. However, selecting optimal wavelengths reduced model complexity to a large extent and may also serve as practical industrial applications. The CCR for the prediction set of the model developed using textural variables was less optimistic with only a low value of 59%. Moreover, a new PLS-DA model was built using the combined data of spectra of the selected wavelengths and 16 textural variables. Compared to models developed from spectral or textural data individually, a model based on the fusion of this data showed the highest classification accuracy with a value of 86% for the prediction set. These results indicated that integrating spectral and textural data together could be an effective way to divide chicken breast fillets into high or low WHC groups. However, further studies should be carried out to explore more data to improve classification accuracy.  
This research was supported by the National Natural Science Foundation of China (No. 31772062). We really appreciate the three anonymous reviewers for their so many efforts and time to get this manuscript done. We will also pay tribute to them for the dedicated and responsible attitude, as well as the professional academic level.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON], and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# An Approach to Dynamic Sensing Data Fusion
## 5 Conclusions  
Sensor data dynamic adaptive acquisition is a meaningful research topic. In this paper, an adaptive data acquisition method based on time interval dynamic adjustment is proposed. Sensor networks are located in a variety of complex natural environments, which makes it necessary for sensor data acquisition to adapt to them. The existing data processing methods based on sensor networks adopt fixed acquisition time interval, which not only wastes bandwidth but also reduces the accuracy of sensor data acquisition. By detecting and judging the degree of data change, the proposed method can dynamically adjust the time interval of sensor acquisition according to the data change, thus ensuring the efficiency and accuracy of acquisition. In this study, a wireless communication system with sensor devices was constructed to verify the effectiveness and efficiency of the proposed method.  
Conceptualization, Y.Y.; methodology, Y.Y.; software, C.Z. and L.G.; validation, L.G. and C.Z.; investigation, L.G. and C.Z.; data curation, L.G. and C.Z.  
The research was supported by Basic Research on the Frontier and Application of Chongqing City under grant cstc2015 jcyjA40006; the National Natural Science Foundation of China (No. 61702059); Postgraduate Educational Grant of Chongqing City (Vig20163062); Educational Grant of Chongqing University (2016Y25); Research Fund of Guangxi Key Lab of Multisource Information Mining & Security (MIMS18-03); and Project No. 2018 CDGFCH0020 supported by the Fundamental Research Funds for the Central Universities.  
The authors declare no conflict of interest.
## 4 Results and Discussion
These results demonstrate the model's high efficiency in identifying security threats and emphasize the importance of using such algorithms in cybersecurity systems. The results in Tables 7 and 8 show that, for both 6-class and 15-class classifications, none of the normal samples were misclassified as attacks, nor were any attack samples classified as normal traffic. This indicates the model's high accuracy, precision, and recall (100%) in identifying normal traffic. In the 6-class classification results (Table 7), the model achieved strong weighted values, with a precision of 99.57%, recall of 99.48%, and an F1 score of 0.994. However, for specific attack types, some misclassifications were observed. For instance, the model correctly identified 57,045 DDoS-based attack samples, yet misclassified 577 as injection-based attacks, resulting in a high F1 score of 0.995, despite some errors. For injection attacks, 10,165 samples were accurately classified, but 1565 samples were incorrectly categorized as this attack type, yielding an F1 score of 0.929. Similarly, MITM-based attacks achieved an F1 score of 0.971, whereas malware-based attacks had the lowest classification performance, with an F1 score of 0.649. Overall, the errors mainly arose from distinguishing between different attack types rather than misclassifying benign traffic as attacks or vice versa. Misclassifications within attack categories are less critical compared to false positives (FP) or false negatives (FN) in practical intrusion detection applications. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Class/Prediction** & **Normal** & **Attack** \\ \hline
**Normal** & 272,800 & 0 \\ \hline
**Attack** & 0 & 109,134 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification results for 2-class. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class/Prediction** & **Normal** & **DDoS** & **Injection** & **Scanning** & **Malware** & **MITM** \\ \hline
**Normal** & 272,800 & 0 & 0 & 0 & 0 \\ \hline
**DDoS** & 0 & 57,045 & 577 & 0 & 0 & 0 \\ \hline
**Injection** & 0 & 0 & 10,165 & 0 & 0 & 0 \\ \hline
**Scanning** & 0 & 0 & 0 & 3995 & 0 & 0 \\ \hline
**Malware** & 0 & 20 & 988 & 0 & 930 & 0 \\ \hline
**MITM** & 0 & 4 & 0 & 0 & 0 & 68 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Classification results for 6-class.
## 3 Results and Discussion
### Empirical Correlations for the Rock Mechanical Properties  
The bulk density and P-wave velocity are parameters that can be obtained from geophysical well logging. In previous studies [17; 18; 19; 20; 21; 22; 23; 24; 29; 30; 31; 32; 33; 34; 35], there was evidence of strong relationships between both parameters and rock mechanical properties. Referring to previous research ideas, the relationships between single parameters (_V\({}_{\rm P}\)_ or _DEN_) or a combination of parameters considering the two parameters of _V\({}_{\rm P}\)_ and _DEN_ and rock mechanical properties were investigated. Then, the appropriate relationship can be selected based on the relative error. Finally, local empirical equations for evaluating the rock mechanical properties of the Permian Lucaogou Formation shale oil reservoirs in the Junggar Basin can be established, resulting in better prediction accuracy.
# On the Performance of LDPC-Coded Massive MIMO Schemes with Power-Ordered NOMA Techniques
## 4 Simulation Results and Analysis
On the other hand, even with the noise enhancement typical of the ZF receiver [13], it performs better than that of the MRC. Nevertheless, observing the results with cooperative NOMA, the MRC performs approximately the same as the ZF with and without LDPC. Figure 6 shows the performance in a scenario like that of Figure 5 but with four NOMA users with powers [1, 0.5, 2, 4] sharing the spectrum instead of three (baselines 7, 8). A fourth NOMA user with power 4 was added, i.e., with 6 dB more power than that of the reference user. The analysis performed for Figure 5 is also valid for Figure 6. As before, results with LDPC codes are always better than those without, and results with cooperative NOMA are always better than those with conventional NOMA. Figure 7 shows the performance results for three and four NOMA users sharing the spectrum with 8 \(\times\) 64 MIMO and with LDPC codes (baselines 3-8). In the case of three NOMA users, their relative powers are [1, 0.5, 2], while the relative powers of four NOMA users are [1, 0.5, 2, 4], i.e., a fourth user with power 4 was added (6 dB more power than that of the reference user). From Figure 7, we observe that the degradation of performance that results from the addition of the fourth user is residual with either the ZF or the MRC receiver and with conventional NOMA or with cooperative NOMA. Figure 5: Results for 3 NOMA users with powers [1, 0.5, 2], with 8 \(\times\) 64 MIMO (with and without LDPC codes). Figure 6: Results for 4 NOMA users with powers [1, 0.5, 2, 4], with 8 \(\times\) 64 MIMO (with and without LDPC codes). Figure 7: Results for 3 and 4 NOMA users, with 8 \(\times\) 64 MIMO, with LDPC codes. Figure 8 shows the performance results with three NOMA users with powers [1, 0.5, 2], comparing 8 \(\times\) 64 versus 8 \(\times\) 256 MIMO, with LDPC codes for both conventional NOMA and cooperative NOMA (baselines 5, 6, 9, 10). Since spatial multiplexing MIMO was adopted (also known as multi-layer transmission), the number of transmitting antennas corresponds to the number of parallel flows of data, while the diversity is provided by the number of receiving antennas. Therefore, 8 \(\times\) 256 has a diversity four times higher than 8 \(\times\) 64. This translates into an improvement of performance for both conventional NOMA and cooperative NOMA as well as for both receiver types. In the case of conventional NOMA and for 8 \(\times\) 64 MIMO, we observe that the ZF performs better than the MRC. Nevertheless, with 8 \(\times\) 256 MIMO and conventional NOMA, both receivers perform almost the same while the MRC presents a much lower level of complexity. This occurs because the level of residual interference mitigated by the iterative receiver of the MRC is more precisely estimated and cancelled due to the higher level of diversity provided by the 8 \(\times\) 256 MIMO. Moreover, as before, the performance obtained with cooperative NOMA is much better than that of conventional NOMA.
# Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges
## 4 Energy Market
### Grid Services and Ancillary Markets
#### 4.5.1 Market for Ancillary Services like Frequency Regulation and Voltage Control  
The shift towards decentralized power systems, fueled by DRESs, is transforming ancillary services in distribution networks. This necessitates novel market tools for energy storage integration and SG advancements [73] to tackle renewables' intermittency through state-of-the-art energy storage and grid management solutions. The associated challenges include regulatory barriers and the need for substantial investment in smarter grids capable of handling a larger share of renewable energy.  
The emergence of DG introduces competitive dynamics in ancillary service markets, with studies [129; 206; 207] highlighting the need for flexible energy resources and future market designs. These include developing pricing mechanisms for ancillary and reactive power services and optimizing such services at the distribution level.  
Research has identified barriers to integrating novel ancillary services at the distribution grid level, such as inertial response and voltage regulation [73]. The transition poses stability and security challenges and requires effective market tools for service procurement and addressing technical, regulatory, and financial challenges.  
The future directions will require dynamic and flexible market designs that support the quick dynamics of renewable resources and should aim to enhance system reliability and efficiency through innovative regulations and market strategies.
# CFD and Energy Loss Model Analysis of High-Speed Centrifugal Pump with Low Specific Speed
## 2 CFD Simulations and Energy Loss Model
### Empirical Correlations for Ordinary Pumps  
The external characteristic prediction of the ordinary pump usually adopts empirical correlations and the efficiency of the pump is calculated by Equation (8). The disc friction loss is determined by Equations (9) and (10) [23]. Since the specific speed of the centrifugal pump is lower than 65 under the design condition, the theoretical head is calculated by the Wiesner correlation (Equation (11)).  
\[\eta=\frac{1}{\frac{1}{\eta_{o}\eta_{h}}+\frac{P_{d}}{P_{u}}+0.03} \tag{8}\]  
\[P_{m}=0.0035k\rho\omega^{3}\bigg{(}\frac{D_{2}}{2}\bigg{)}^{5} \tag{9}\]  
\[k=0.027+4.75\Big{(}\frac{n_{s}}{100}\Big{)}-8.56\Big{(}\frac{n_{s}}{100}\Big{)} ^{2}+6.23\Big{(}\frac{n_{s}}{100}\Big{)}^{3}-1.61\Big{(}\frac{n_{s}}{100}\Big{)} ^{4} \tag{10}\]  
\[H_{t}=\frac{\pi D_{2}n}{60g}\bigg{(}\sigma\frac{\pi D_{2}n}{60}-\frac{Q_{t}}{3 600\pi D_{2}b_{2}\psi_{2}tan\beta_{2}}\bigg{)} \tag{11}\]  
where \(\eta_{o}\) is volumetric efficiency, \(\eta_{h}\) is hydraulic efficiency, \(P_{m}\) is disc friction loss, \(\omega\) is angular velocity, \(\sigma\) is slip coefficient, and \(\psi_{2}\) is blade outlet extrusion coefficient.  
Figure 3: Mesh independence study.  
Figure 2: The mesh of low specific speed high-speed centrifugal pump.