esa-sceva/satcom-chunk-collection huggingface dataset:
# Light-YOLO: A Study of a Lightweight YOLOv8n-Based Method for Underwater Fishing Net Detection
## 3 Model Improvement
### Integration of the SEAM Module  
In studying the detection of underwater fishing net vulnerabilities that the recognition system may miss due to water currents superimposing the vulnerability with the intact net behind it, we developed advanced techniques to enhance detection accuracy. We incorporate the SEAM module [23] into the YOLO detection framework to enhance the spatial transformation invariance of the model. SEAM effectively bridges the gap between fully supervised and weakly supervised semantic segmentation by constructing a twin-network structure with shared weights to achieve covariant regularization. In this structure, one branch directly processes the original input image. In contrast, the other branch first applies affine transformations (e.g., scaling, rotating, or flipping) to the input image before forward propagation. This design ensures that the generated class activation maps (CAMs) remain consistent even when the input image is transformed, mimicking the nature of pixel-level labels as the image transforms under fully supervised conditions, where SEAM introduces covariant regularization to ensure that the predicted CAMs from various transformed images [24] provide self-supervision for network learning. F(.) denotes the network, A(.) denotes the affine transformation, and I is the input image.  
\[\text{R}_{\text{ER}}=||\text{F}(\text{A}(\text{I}))-\text{A}(\text{F}(\text{I} ))||_{1} \tag{7}\]  
Further, SEAM introduces a pixel correlation module (PCM) at the end of the network to further refine the CAM by capturing the contextual information of each pixel through a self-attentive mechanism. The PCM measures the feature similarity between pixels using the cosine distance and calculates the affinity by normalizing the inner product in the feature space to optimize the CAM to fit the object boundary more accurately. This approach not only ensures the consistency of the CAM under different transformations but also effectively improves the detection performance by integrating low-level features and adjusting the inter-pixel similarity.The block diagram of the PCM module is shown in Figure 4.  
In integrating SEAM and PCM into the YOLO framework, PCM has the following advantages. By removing unnecessary jump connections, reducing parameters, and using the ReLU activation function instead of the sigmoid, the model structure is streamlined, overfitting is avoided, and the sensitivity of the model to contextual information is improved. These improvements enable the model to identify and localize vulnerabilities more efficiently when detecting underwater fishing net vulnerabilities, even if they are accurately detected under different spatial transformations, enhancing the accuracy of underwater fishing net detection.
## 3 Experimental Results
### Surface Comparison
The color images were obtained from a deconvolution of the Raman signal; the yellow map represents the distribution of the LDPE phase (based on the band at 1300 cm\({}^{-1}\)) and the red map, that of silicon nitride (based on the band at 200 cm\({}^{-1}\)). The Raman spectra confirm the presence of silicon nitride particles at the time when no peaks were observed in the 520 cm\({}^{-1}\) region. This was related to the Si-Si bonds and confirmed that the composition of the silicon nitride particles was virtually stoichiometric. Figure 4 shows the Raman imaging results acquired on the Ti6 Al4V substrate. In this case, the silicon nitride powders underwent transformation (degradation) during the laser cladding process. The blue and green image shows the distribution of the amorphous silicon phase (shoulder at about 495 cm\({}^{-1}\)) and the crystalline silicon phase (band at about 520 cm\({}^{-1}\)). The red image shows the regions +in which the silicon nitride phase is still present, as estimated by the presence of triplet peaks at about 200 cm\({}^{-1}\). The red areas correspond to optical image regions in which small silicon nitride powder crystals are still clearly visible. Figure 5 shows the Raman imaging results obtained on the two ceramic substrates. For both materials, the blue/green compositional maps show a strong prevalence of both amorphous and crystalline silicon signals, covering the whole surface. In the case of ZTA, the two phases are equally dispersed, with a slight (about 60%) amorphous signal prevalence, while for Y-TZP the crystalline phase is mainly localized in a few spots, with a strong (80%) amorphous phase prevalence. Both materials showed stoichiometrically-similar residual Si\({}_{3}\)N\({}_{4}\) particle dispersion. Figure 6 shows a qualitative Si-Si bond dependency (as measured by XPS in previous literature reports [37]), with respect to the applied voltage for each material tested. If the applied voltage is under a certain threshold, the coating does not adhere to the substrate and the powder is scattered by the laser beam irradiation. The threshold seems dependent on the physical properties of the materials, such as melting point and absorbance coefficient. Polymer cladding can be achieved at relatively low potentials, due to the melting of the matrix. This results in a virtually stoichiometric coating. In the case of ceramics, the coating requires a higher applied potential to be deposited, but the stoichiometry of the coating changes into a silicon-rich phase. Although metals require high voltage to attach the ceramic coating to the surface, this high power enables the formation of a high content silicon-rich ceramic coating. Figure 6: Qualitative representation of the applied voltage/Si-Si bond relationship as measured by X-ray Photoelectron Spectroscopy, as an indicator of substrate chemical composition.
## 6 Conclusions  
The vibration magnetic interference that is caused by the mechanical motion of the probe has a significant influence on the detection performance of low-frequency magnetic field of a helicopter aeromagnetic system. The traditional aeromagnetic compensation (T-L) model considers only the interference magnetic fields that are related to the attitude of the aircraft and is generally suitable for compensation with a frequency below 2 Hz. To eliminate these interference magnetic fields, a method based on the improved coherent noise suppression method (ICNS) with a pair of magnetometers is proposed in this paper. Since the distance between the two magnetometers and the helicopter is far less than the distance between the helicopter and the target, the ICNS method can adaptively eliminate the vibration interference fields and has little effect on the field to be measured. The results of the calibration and detection with a flight test show that the proposed method can effectively eliminate the interference field that is caused by the vibration in accordance with the approaching target. The proposed algorithm shows its effectiveness in detecting ocean targets that generate low-frequency magnetic field signals or anomalies.  
Conceptualization, Y.F.; methodology, Y.F. and L.C.; software, L.C. and Y.Z.; validation, Y.Z. and L.C.; formal analysis, X.Q.; investigation, Y.F. and G.F.; resources, G.F.; data curation, Y.F. and L.C.; writing--original draft preparation, Y.F.; writing--review and editing, Y.Z.; visualization, L.C.; supervision, G.F.; project administration, X.Q.; funding acquisition, G.F. All authors have read and agreed to the published version of the manuscript.  
Figure 15: The time–frequency diagrams of the survey line with the target, the pre-processing data (**a**), post-processing data with AIC method (**b**) and post-processing data with ICNS method (**c**).  
Funding: This research work was supported by the National Key R&D Program of China under grant 2019 YFC1408102, and the National Natural Science Foundation of China under grant 61901441. The authors wish to express their gratitude to the editor and the anonymous reviewers. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.
# New Approach of Blind Adaptive Equalizer Based on Genetic Algorithms
## 3 Methods
### Adaptive Blind Equalization
#### 3.3.3 Adaptive Blind Equalization Using Linear Programming
The functions \(\rho_{i}^{l}w_{l}^{l}(k)\), \(\rho_{i}^{l}w_{l}^{Q}(k)\), \(\rho_{i}^{Q}w_{l}^{l}(k)\), and \(\rho_{i}^{Q}w_{l}^{Q}(k)\) are linear in \(\mathbf{w}^{l}\) and \(\mathbf{w}^{Q}\); their moduli \(|\rho_{i}^{l}w_{l}^{l}(k)|\), \(|\rho_{i}^{l}w_{l}^{Q}(k)|\), \(|\rho_{i}^{Q}w_{l}^{l}(k)|\), \(|\rho_{i}^{Q}w_{l}^{l}(k)|\), and \(|\rho_{i}^{Q}w_{i}^{Q}(k)|\) are convex functions in \(\mathbf{w}^{l}\) and \(\mathbf{w}^{Q}\). Comparing Equation (18) with those presented in [8; 9; 10], it can be stated that the cost function remains convex in nature, with a different global minimum.
## 3 Results
### Unimodal Data Approaches  
As is discernible from Figure 4, studies centered on the analysis of unimodal data were further categorized into those employing signals or physical data and those employing signals or physiological data. Amongst studies utilizing physical signals, it is evident that, over the period covered by this study, emotion analysis using facial analysis, speech analysis, and textual analysis has been dominant. The latter, in particular, experienced an increase of 200% in the year 2023 compared to the previous year. This remarkable uptick can be attributed to the abundance of emotional textual data available on various platforms such as social networks, product reviews, and blog comments.  
From the articles analyzed in this systematic review, 155 examined text-based information. Among these, 59 indicated that they had extracted data from social networks, blog comments, or review-type comments. Notably, 41% of these articles specifically sourced data from Twitter.  
Figure 3: Quantity of articles from each category published from 2018 to 2023.  
Concerning studies employing unimodal physiological signals, EEG signals are a prominent choice, showcasing a remarkable capacity for emotion generalization, with a substantial acceleration in the quantity of studies in the years 2022 and 2023. Additionally, various other physiological signals are starting to be explored for their potential in emotion generalization, including ECG, EDA, HR, and PPG signals, although they are still very limited in number.
## 4 Experimental Results
### Outer Race  
In this section, the bearing outer race fault signal measured from the test rig is analyzed and the corresponding results are shown in Figure 5.  
Figure 4: Test rig for bearing signals.  
Figure 3: Histogram of related signals.  
The time domain waveforms of the noisy outer race fault signal measured are shown in Figure 5a. The length of this signal was 76,800. The envelope spectrum analysis of the noisy bearing fault signal is shown in Figure 5b. Also, the fault feature frequency cannot be extracted directly. The results of the estimated transient components in the time domain is shown in Figure 5c. The envelope spectrum analysis of the estimated transient components is shown in Figure 5d. In Figure 5d, it can be seen that the fault feature frequency of the outer race was \(f_{1}=142.8\) Hz, which is equal to the theoretical value (142.8 Hz).
## 2 Basic Path Diagram (BPD)  
Due to the complexity of the mechanism, developing the structure of the system with a simplified topological diagram is easier and more efficient than using a complete mechanical schematic. The graphical method, BPD, in this paper applies blocks and lines to represent the connection among the units of the system. The procedure for developing the BPD will be described in this chapter.
## 1 Introduction
### Challenges in Message Routing for EnFVs  
The routing of messages in EnFVs presents numerous challenges that need to be addressed to ensure efficient and reliable communication [10; 11]. One of the primary challenges is the dynamic nature of these transportation systems. EnFVs operate in dynamic environments characterized by constantly changing traffic conditions, charging station availability, flight restrictions, and environmental factors. Message routing algorithms must be able to adapt in real-time to these dynamic conditions to provide efficient and uninterrupted communication. Another key challenge is the multi-objective nature of message routing in EnFVs. Efficient routing must consider multiple objectives simultaneously, such as minimizing travel time, reducing energy consumption, maximizing reliability, and incorporating environmental sustainability. However, optimizing these objectives simultaneously is a complex task, as they often conflict with each other [12]. Traditional single-objective optimization approaches are inadequate to capture the trade-offs and achieve optimal solutions in these multi-objective scenarios. Furthermore, the integration of genetic algorithms (GAs) offers a promising solution for addressing the multi-objective message routing problem in EnFVs [4]. GAs are evolutionary optimization techniques that mimic the process of natural selection to develop a population of potential solutions. However, the design and parameterization of GAs for message routing optimization requires a careful consideration to ensure effective convergence towards optimal or near-optimal solutions. Additionally, the computational complexities associated with genetic algorithms can pose challenges in terms of scalability and efficiency.
# An Intrusion Detection System for RPL-Based IoT Networks
## 3 Related Work
### IDS Improvements in the Literature  
As part of the objective of securing IoT environments, there are some authors that aimed to improve the way an IDS works through the use of new techniques. A relevant work whose objective was to enhance IDSes focused on IoT environments is the one presented by [PERSON] et al. [50], where the authors introduced a lightweight technique to secure systems with the use of an innovative malicious pattern-matching engine. The authors presented the topic by explaining how a specific type of IDS works. In fact, they focused on IDSes using a pattern-matching algorithm to verify predefined sets of signatures in order to detect malicious intentions. The motivation of this work came from the idea that a conventional engine for pattern-matching is not suitable for IoT devices due to their resource-constrained characteristics. Hence, [PERSON] et al. proposed a novel algorithm that would ignore unnecessary matching processes, with the objective of reducing the resources used by the matching operation. First of all, the authors introduced a new method called auxiliary shift value, which would skip to a higher number of characters when matching a text as compared with the fast traditional method called the Wu-Manber algorithm [51]. Later on, they applied a technique to sort prefixes in order to reduce matching operations as well and to allow the final algorithm to be more efficient when making comparisons.  
[PERSON] et al. in [37] provided a protocol to be used in a 6 LoWPAN network using RPL called lightweight heartbeat protocol. The main idea of this protocol was to send heartbeat messages between nodes in order to know if other nodes in the network were available or not. This kind of technique can be included in an IDS environment in order to detect attacks that cannot be identified by using conventional detection techniques. The heartbeat protocol and other existing techniques to design an IDS are used in our work to mitigate several RPL attacks. In addition, two new techniques are also implementedand evaluated to demonstrate the way they work. The next section explains the proposed IDS design.
## 3 Energy-Saving Optimization Model  
### Automatic Recognition of Appliance Status  
Before optimization, it is necessary to achieve real-time recognition of the operating state of the electrical appliances. The recognition method consists of two parts: The offline dictionary formulation, and online recognition. The automatic recognition algorithm was revealed in our previous work [26], and it is not the contribution of this paper, so here we introduce it in a simple form. First, based on HEMS [8], which include smart plugs and gateways, various experiments can be used to collect and store different kinds of electric appliances' operation data. Then, we can use the K-Means algorithm to cluster the power data to several centers [27], in which each center corresponds to a typical operating state. It is worth noting that the number of centers depends on some previous knowledge, but is not like the method in [27]. For example, it is easy to know that a water dispenser has two typical operation states, including heating and insulation. After the offline experiments, a dictionary is obtained which is a set of typical operation states, and can be used for online recognition. In the real-time operation, the new data collected in real time is compared with the typical operation states in the dictionary. In this way, we can obtain real-time operation states of an appliance at a given time slot.  
An example is given as follows. We used the K-Means algorithm to cluster several days of real power data of an electric light and water dispenser (time-type appliances), and an air conditioning unit (temperature-type appliance). The test data comes from the filed data in a pilot project in Guangzhou Province, China. The respective clustering centers were obtained for each appliance, which corresponded to the typical operation status of the appliances. Table 1 shows our clustering results. For instance, it is not difficult to know that a water dispenser has two typical operating statuses: Insulation and heating. Accordingly, two cluster centers are obtained, including 0.469 W and 426.26 W. As a result, we can assert that if the collecting power value in real time is near to 0.469 W, the operating status should be insulation, while if the collecting power value in real time is near to 426.26 W, the operating status should be heating. We also undertook real-time recognition tests, as shown in Figure 1. In the test, the real-time monitored data from smart plugs were compared with the clustering results in Table 1 to obtain real-time operation status. Figure 1a shows the operation status recognition result of the air conditioning unit, and Figure 1b displays the operation status recognition result of the water dispenser. For instance, the blue dots in Figure 1b are thought to occur during heating status, because the values of these blue dots are near to the reference value of the heating status of a water dispenser in Table 1, of 426.26 W.
# Joint Optimization of Data Transmission and Energy Harvesting in Relay Satellite Networks
## 4 Solutions and Algorithms
### Decomposition Strategy
#### 4.2.2 Data Acquisition Control  
For the DACO problem, we need to minimize the second part in (17) under the constraint of (3), i.e.,  
\[\text{DACO}: \min_{\varphi_{i}(t)}\sum_{i=1}^{M}\tau\bigl{[}\mathcal{D}_{i}(t )-\frac{P_{r}}{\varphi_{\max}}(E_{i}(t)-\mathcal{B})\bigr{]}\varphi_{i}(t)-V \mathcal{H}(t)\] \[s.t. (\ref{eq:BMO_1}).\]  
Note that \(\mathcal{H}(t)\) is a concave function of \(\varphi_{i}(t)\), and the other part of DACO is linear. Therefore, the DACO problem is convex, which can be solved based on the convex optimization theory. Denote \(\varphi_{i}^{*}(t)\) as the optimal solution of DACO, we have  
\[\varphi_{i}^{*}(t)=\left\{\begin{array}{ll}\varphi_{\max},&\varphi_{\max} \leq F_{L},\\ F_{L},&0<F_{L}<\varphi_{\max},\\ 0,&\mathit{others},\end{array}\right. \tag{19}\]  
where \(F_{L}=(V\varphi_{\max})/(\tau D_{i}(t)\varphi_{\max}-\tau P_{r}(E_{i}(t)- \mathcal{B}))-1\).
## 2 Basic Theory of Signal Noise Reduction Method  
### Wavelet Noise Reduction Principle  
The wavelet threshold noise reduction method can be carried out according to the processing flow, as shown in Figure 2. The main processing steps include the wavelet basis function selection, multi-scale decomposition, wavelet threshold selection and signal reconstruction.
## 3 Design of Approximate QAM Demodulation Circuits
### Piecewise LLR Architecture  
The piecewise LLR (PLLR) algorithm approximates Equation (1) by using a linear function [28]. This method considers \(k=1,2,\ldots,N\), as well as \(d_{x,k}\) and \(d_{y,k}\), which denote the half distance between the partitions boundaries of \(b_{x,k}\) and \(b_{y,k}\), respectively. Given that \(r\) is the received symbol with coordinates \((r_{x},r_{y})\), the LLR is calculated as  
\[LLR(b_{x/y,k})=|G_{ch}|^{2}\cdot D_{x/y,k},\quad\text{where}\quad D_{x/y,k}= \left\{\begin{array}{ll}r_{x/y}&k=1\\ -|D_{x/y,k-1}|+d_{x/y,k}&k>1\end{array}\right. \tag{3}\]  
Our PLLR architecture is presented in Figure 3, where \(N\) linear functions are calculated in parallel. The computation of \(D_{x/y,k}\) involves only additions, followed by a multiplication with \(|G_{ch}|^{2}\), and thus, the circuit complexity is significantly decreased compared to the previous demodulation methods. Similarly to ALLR, our PLLR design is fully pipelined, and performs the computations of the linear functions in streaming mode. The latency is 2 CCs and 3 CCs for the 64-QAM and 256-QAM, respectively (256-QAM requires extra additions).
# Experimental Study on Shear Strengthening of RC Beams with an FRP Grid-PCM Reinforcement Layer
## 3 Experimental Study on Reinforcement Amount
### Results and Discussion
#### 3.2.2 Load-deflection (\(P\)-\(\delta\)) Responses  
The load-deflection (\(P\)-\(\delta\)) values for the experimental specimens are plotted in Figure 10. Overall, the trend of the load-deflection responses contains one clearly visible inflection point changing the curve stiffness at load values of about 200 kN, corresponding to the first flexural cracking loads. A less clear inflection, but analyzed in data, occurs at about 500 kN, corresponding to the critical shear cracking. In addition, the initial fine flexural and shear cracks were observed during the test and then developed gradually on the surface of specimens as the loading value increased.  
When compared to the control beam (i.e., N0\({}^{\prime}\)), the ultimate load capacity and the curve stiffness are almost same to the specimen only reinforced by PCM (i.e., RHWP), indicating that little shear resistance contribution for load-carrying capacity is provided by PCM. The load capacity of specimens reinforced by the CFRP grid-PCM (i.e., RHWC4, RHWC6, RHWC8, RHWC4\({}^{\prime}\), RHWC64) layer are improved in a range from 29.3 to 42.9% in comparison with the control beam (i.e., N0\({}^{\prime}\)), thus it can be noted that the strengthening effect of the CFRP grid is adequate. Furthermore, compared to specimen RHWC4, the ultimate load capacity of specimen RHWC8 and RHWC4\({}^{\prime}\) increase by approximately 7% and 8%, respectively, demonstrating that the ultimate load capacity increases with the cross-sectional area's increase and the spacing decrease of the vertical grids. However, once debonding failure occurs at the interface between the CFRP grid and concrete substrate during the whole loading process, as observed for specimen RHWC64, there is a decline in the ultimate load.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Types** & \(P_{\text{fc}}\)**, **k**N & \(P_{\text{sc}}\)**, **k**N & \(P_{\text{u}}\)**, **k**N & \(\eta_{\text{u}}\)**, \%** & **Failure Mode** \\ \hline N0\({}^{\prime}\) & 240 & 332 & 665 & - & SC \\ RHWP & 242 & 392 & 677 & 1.8 & SC \\ RHWC4 & 242 & 421 & 881 & 32.5 & SC \\ RHWC6 & 240 & 450 & 949 & 42.7 & SC \\ RHWC8 & 239 & 481 & 943 & 41.8 & SC + PD \\ RHWC4\({}^{\prime}\) & 242 & 462 & 950 & 42.9 & SC \\ RHWC64 & 241 & 433 & 860 & 29.3 & SC + PD \\ \hline \hline \end{tabular} Note: \(P_{\text{fc}}\)—the first flexural cracking load, \(P_{\text{sc}}\)—the critical shear cracking load, \(P_{\text{u}}\)—the ultimate load, \(\eta_{\text{u}}\)—the increase ratio of ultimate load, SC—shear-compression failure, PD—partial debonding of CFRP grid.  
\end{table}
Table 8: Summary of experimental results.