esa-sceva/satcom-chunk-collection huggingface dataset:
# FedSeq: Personalized Federated Learning via Sequential Layer Expansion in Representation Learning
## 3 Proposed Algorithm
### Method 2: Anti Scheduling
```
1:Total global rounds \(T\), Total clients \(N\), join ratio \(r\), Learning rate \(\eta\), Total base layers \(K\), Fine-tuning rounds \(F\), Scheduling mode \(Mode\), Layer unfreeze rounds \(t_{1},t_{2},\ldots,t_{K}\)Initialize: Global base parameters \(\theta^{(0)}_{\text{G},b_{1}},\theta^{(0)}_{\text{G},b_{2}},\ldots,\theta^{(0)} _{\text{G},b_{K}}\) and Global Head parameter \(\theta^{(0)}_{\text{G},h}\)
2:for\(t=1\)to\(T\)do
3: Randomly select \(M=\lfloor r\times N\rfloor\) clients
4:for Each selected client \(i=1\)to\(M\)do
5:if\(Mode=\)'Vanilla' then
6:if\(t\geq t_{k}\)then
7: Unfreeze \(\theta_{i,b_{k}}\)
8:endif
9:\(\theta^{(t+1)}_{i}=\theta^{(t)}_{i}-\eta\
abla_{\theta_{i}}\mathcal{L}(D_{i}, \theta^{(t)}_{i,b_{1}},\ldots,\theta^{(t)}_{i,b_{K}},\theta^{(0)}_{i,h})\)
10:elseif\(Mode=\)'Anti' then
11:if\(t\geq t_{k}\)then
12: Unfreeze \(\theta_{i,b_{K-k+1}}\)
13:endif
14:\(\theta^{(t+1)}_{i}=\theta^{(t)}_{i}-\eta\
abla_{\theta_{i}}\mathcal{L}(D_{i}, \theta^{(t)}_{i,b_{1}},\ldots,\theta^{(t)}_{i,b_{K}},\theta^{(0)}_{i,h})\)
15:endif
16: Keep \(\theta_{i,h}\) frozen
17: Send updated base parameters \(\theta^{(t+1)}_{i,b_{1}},\ldots,\theta^{(t+1)}_{i,b_{K}}\)
18:endfor
19: Aggregate global model \(\theta^{(t+1)}_{G}=\sum_{i=1}^{N}\frac{|D_{i}|}{|D|}\theta^{(t+1)}_{i,b}\)
20:endfor
21:for\(f=1\)to\(F\)do
22: Unfreeze all layers for each client
23:for each client \(i=1\)to\(N\)do
24: Update all parameters \(\theta_{i}\) for fine tuning
25:\(\theta^{(T+f)}_{i}=\theta^{(T+f-1)}_{i}-\eta\
abla_{\theta_{i}}\mathcal{L}(D _{i},\theta^{(T+f-1)}_{i,b_{1}},\ldots,\theta^{(T+f-1)}_{i,b_{K}},\theta^{(T+ f-1)}_{i,h})\)
26:endfor
27:endfor
```  
**Algorithm 1** FedSeq: Layer Decoupling Algorithm with Vanilla and Anti Scheduling
## 1 Introduction
Since certain physical restrictions and distinctive features of underwater environments can be considered during the development of MAC, UWSNs has been a valuable research direction in approaching the impact of underwater acoustic characteristics [33]. [PERSON] et al. (2014) analyzed the impacts of node deployment strategies on localization performances in a 3D environment. To achieve this goal, three different deployment schemes in 3D UASNs were presented and compared [34]. As for the routing design of UASNs in uncertain ocean environments, [PERSON] et al. (2021) presented the AAD-FPVR routing algorithm. The proposed algorithm considers node drifting information, uncertain ocean ambient noise [35]. Underwater acoustic radiation has always been our research focus. The purpose of the work is to determine the optimal scheme for applying damping. To achieve this goal, coupling between the ring-stiffened conical shell and fluid medium is simulated by using the FEM, and the underwater acoustic radiation field is predicted by using the BEM. By analyzing and quantifying the damping parameters in different conditions, the effect of viscoelastic damping on the underwater acoustic radiation is obtained, which provides references for the acoustic stealth performance of submarine.
# Steel Surface Defect Detection Algorithm Based on Improved YOLOv8 Modeling
## 3 Construction of EB-YOLOv8  
### Model Architecture  
To address issues such as poor feature extraction capability, low detection accuracy, and model instability, the multi-scale attention mechanism (EMA) and the weighted bidirectional feature pyramid network (BiFPN) were integrated into the YOLOv8 network to create the EB-YOLOv8 model (Figure 2). Here, \"E\" refers to the EMA attention mechanism and \"B\" refers to the BiFPN pyramid structure. The EMA is added before the three C2f modules in the neck section and the SPPF in the backbone section to perform global encoding and calibrate channel weights. After each model parameter update, smoothing is applied to accelerate convergence and enhance stability. A new Concat_BiFPN structure is constructed in the neck section. Following the Upsample module, which ensures that feature maps of different scales are size-aligned, the Concat_BiFPN structure replaces the Concat structure in the YOLOv8 structure. This achieves the bidirectional fusion of feature map information, significantly enhancing the expressive capability of feature maps. In the cross-layer feature fusion process, learnable weight parameters are introduced, and weighted fusion is performed to automatically adjust recognition priorities of different scales in real time. This enables a precise identification of targets of varying sizes.  
Figure 1: YOLOv8 structure.  
Figure 3 shows the workflow of this model. First, the input image (640 \(\times\) 640) is passed through the backbone for five rounds of downsampling to generate multi-scale feature maps. Next, the feature pyramid network performs bidirectional feature fusion. First, deep semantic features are fused from top to bottom. Then, shallow detail features are fused from bottom to top. The EMA attention mechanism is embedded during this process to enhance key features. Finally, three feature maps of different resolutions (80 \(\times\) 80, 40 \(\times\) 40, and 20 \(\times\) 20) are used to detect small, medium, and large objects simultaneously, achieving efficient, multi-scale object recognition.  
Figure 3: EB-YOLOv8 flowchart.  
Figure 2: Structure of EB-YOLOv8.
# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 6 Taxonomy
### Graph Attention and Structural Reasoning
#### 6.10.3 Multidimensional Class Evaluation  
According to Table 14, this class comprises four papers with an equal distribution between general concept drift and data drift, underscoring graph-attention methods' balanced applicability to changes in model behavior and underlying data distributions. The identified application contexts are varied, including IoT, video surveillance, general analytical contexts, and healthcare. These areas benefit from structural reasoning due to their naturally interconnected or spatially structured data, making visual and structural explanations particularly intuitive and effective. Notably, this class equally addresses both local and global interpretability scopes, reflecting the dual capability of graph-based methods in providing comprehensive system-level insights and pinpointing specific nodes or edges responsible for drift. The audience encompasses both data scientists and end-users, illustrating the natural interpretability afforded by graph visualizations, which can directly communicate shifts to both technical experts and operational stakeholders. Explicit metrics such as fidelity-sparsity and robust validity are present, indicating a conscious effort within the group to quantitatively validate interpretability and robustness, enhancing credibility in operational decision-making contexts. The learning mode demonstrates substantial variability (hybrid, offline, and online), suggesting graph-based methods flexibly handle real-time drift detection and retrospective structural analysis, aligning well with the complexity and dynamics inherent in graph-structured data environments.
# Towards Efficiently Provisioning 5G Core Network Slice Based on Resource and Topology Attributes
## 4 Heuristic 5G Core Network Slice Provisioning Algorithm Design
### Heuristic Slice Provisioning
#### 4.2.4 Time Complexity of RT-CSP+ Algorithm  
In this section, we analyze the time complexity of the RT-CSP algorithm. Its time complexity is the sum of time complexities of slice node provisioning algorithm (i.e., Algorithm 1) and the slice link provisioning algorithm (i.e., Algorithm 2). The complexity of Algorithm 1 is dominated by the calculation of closeness centrality, whose complexity is \(O(|V^{l}||E^{l}|+|V^{l}|^{2})\). Then, the \(k\)-shortest path algorithm [30] is implemented in the slice link provisioning stage. Its complexity is \(O(k|V^{l}|(|E^{l}|+|V^{l}|log|V^{l}|))\). Therefore, the time complexity of RT-CSP algorithm is \(O(|V^{l}||E^{l}|+|V^{l}|^{2})+O(k|V^{l}|(|E^{l}|+|V^{l}|log|V^{l}|))\). It can run in polynomial time.  
```
0: Infrastructure network \(G^{I}\) and the \(i\)th slice request \(SR_{i}=(G^{S}_{i},t^{a}_{i},t^{l}_{i})\)
0: Slice provisioning result
1: Check slice requests whose lifetime ends at \(t^{a}_{i}\), release physical resources they occupied and update physical resources.
2: Provision slice nodes of \(G^{S}_{i}\) using Algorithm 1.
3:if Slice nodes of \(G^{S}_{i}\) provisioning failed then
4:return\(slice\)ProvisioningFailed
5:else
6: Provision links of slice \(G^{S}_{i}\) using Algorithm 2.
7:if Links of slice \(G^{S}_{i}\) provisioning failed then
8:return\(slice\)ProvisioningFailed
9:else
10: Provision slice request \(G^{S}_{i}\), allocate physical resources and update physical resources.
11:return\(slice\)ProvisioningSucceeded
12:endif
13:endif
```  
**Algorithm 3** Slice provisioning algorithm RT-CSP.
## 6 Discussion  
In recent years, convex combinations algorithms to improve performance in ANC systems have been reported [12; 13; 14; 15]. The proposals generally improve the MSE by paying a significant penalty in terms of computational burden. However, this factor consequently limits its use for practical ANC systems. Specifically, single-channel ANC systems have been exhaustively studied [13; 14; 15]. The results have demonstrated that the performance of a single-channel ANC can be improved using two filters with complementary capabilities, which operate during the entire filtering process. However, these solutions at least double the computational cost when compared with the classic adaptive filters. For a multichannel ANC system, the computational cost is sufficiently high; thus, their implementation in practical applications is not infeasible. For example, [PERSON] et al. [12] proposed a costly solution that can improve the steady-state MSE of ANC multichannel systems using convex solutions (C-FXLMS). The convex approaches promise to be potential solutions in the development of effective and robust algorithms in terms of convergence speed and steady-state MSE performance; however, they incur a significant penalty in terms of computational cost. In this work, we propose an alternative solution that exhibits high computational cost at the beginning of the filtering processing. However, the computational cost is reduced significantly once the steady-state MSE is reached. At this time, the FXLMS is used during the remaining filtering time. The final computational cost is comparable to the proposals based on conventional FXLMS, thus having potential for use in practical ANC systems.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Multiplications** & **Additions** \\ \hline FXLMS & 2,232,000,000 & 2,212,000,000 \\ FXAPL-I & 16,888,000,000 & 17,912,000,000 \\ FXA-AP & 53,496,000,000 & 47,256,000,000 \\ Proposed algorithm with fixed scaling factor & 7,925,438,400 & 9,779,913,600 \\ Proposed algorithm with dynamic scaling factor & 10,558,618,612 & 12,000,526,248 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Total number of multiplications and additions of the FXAPL-I, FXLMS, FXA-AP and the proposed algorithms for a multi-channel ANC system using a Gaussian noise input.  
Figure 6: Learning curves of the FXAPL-I algorithm, the FXLMS algorithm, the FXA-AP algorithm and the proposed algorithm as measured at error sensor 1 for a Gaussian noise input.
###### Abstract  
In recent years, the THz frequency band (0.3 THz-10 THz) has attracted an increasing research interest for the realization of emerging high-speed wireless communication links. Nevertheless, the propagation of THz signals through the atmospheric channel is primarily subjected to signal attenuation due to free space path loss (FSPL), water vapor, adverse weather conditions along with atmospheric turbulence-induced and misalignment-induced scintillations. Therefore, in this work, a multi-hop line-of-sight THz system that utilizes serially connected decode-and-forward relays is proposed to extend the total THz coverage distance under the presence of fog, rain or clear weather conditions, as well as water vapor, atmospheric turbulence, non-zero boresight pointing errors and FSPL. Under these circumstances, an average bit error rate (ABER) analysis is performed. In this context, novel closed-form ABER expressions are derived. Their analytical results demonstrate the influence of each of the above limiting factors as well as their joint impact on the ABER performance. Finally, the feasibility of extending the total THz link distance through multi-hop relaying configurations is also evaluated.  
t  
e 2022 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# New Approach of Blind Adaptive Equalizer Based on Genetic Algorithms
## 3 Methods
### Genetic Algorithm Setting
#### 3.5.2 Encoding  
The values to be optimized are the auxiliary variables \(\tau_{1}\) and \(\tau_{2}\), and the phase and quadrature components of the equalizer weights. Thus, each chromosome is composed of \(n=2N+2\) variables (\(N\) is the equalizer length) and represented by a double-valued vector \(c(j)\), expressed by  
\[c(j)=[\tau_{1}\ \tau_{2}\ w_{0}^{I}(k)\ w_{0}^{Q}(k)\ \ldots\ w_{N-1}^{I}(k)\ w_{N-1}^{Q}(k)]. \tag{29}\]  
where \(1\leq j\leq S\), and \(S\) is the population size, which must be suitably chosen to ensure a variety of genes and to avoid affecting the performance of the GA. The problem addressed in this work is a restricted linear problem; therefore, all genes should fit in the hyperspace delimited by all restrictions (Equations (24)-(28)).
## 4 Technological Advancements
### Automated and Robotized Systems  
Due to the congested gaps between IMUs, fully finished 3D IMU-fitted PFISBs have difficulties installing MEP facilities. A single 3D file combining architectural, structural, and MEP data allowed for the effective implementation of the BIM to visualize and identify hundreds of conflicts between MEP and structural systems [100]. Additionally, to lessen the IMUs and component damage during transit, both structural and nonstructural. The conveyance of IMUs is planned, optimized, and visualized using the integrated BIM platform, Geographic Information System (GIS), and Vehicle Routing Problem (VRP) [101]. The system was discovered to track the environment, the network of roads, and the best route for the safe transfer of IMUs. The traditional manual techniques for configuring, choosing, and positioning the cranes remained dangerous; as a result, the Hevilift automated crane planning and optimization approach to effectively deploy thousands of cranes simultaneously has recently been implemented [102]. The robotized crane assembly with automation allowed the IMUs to be hoisted and installed with outstanding autopilot precision and safety, greatly enhancing the safety and sustainability of PFISBs [103].
## 3 Results
### Diversity and Composition of Cultured Cheese Board Bacterial Communities  
To evaluate and compare the bacterial community from each board, alpha (within sample; richness and evenness) and beta (between sample) diversity were first compared among boards. For alpha diversity, no differences were found between board communities for either [PERSON]'s richness or [PERSON]'s diversity metrics (\(p>0.05\), Figure 3, Table A1). Beta diversity differed across boards for both the Bray-Curtis dissimilarity and the Jaccard index (\(p<0.05\), Figure 4, Table A1). Pairwise comparisons found cultured communities from boards A and B, A and D, A and E, B and C, B and D, B and E, C and E and D and E to be distinct for both metrics (\(p<0.05\)) (Table A2), indicating differing community compositions. These results mirror findings from our previous study on the uncultured bacterial communities from the same wooden cheese boards [11].  
Next, cultured board bacterial community compositions were compared at the genus level. Relative abundances of the 10 most abundant genera were found to be similar for  
Figure 4: NMDS (nonmetric multidimensional scaling) plots depicting the \(\beta\) diversity of cheese-ripening boards. Beta diversity of each board’s microbiota either with (experimental) or without (control) \(L\). _monocytogenes_, assessed using (**A**) Bray–Curtis dissimilarity or (**B**) Jaccard similarity analysis. Ellipses represent standard error of all samples collected for each respective cheese-ripening board.  
Figure 3: Box plots illustrating alpha diversity measures (Chao richness and Shannon diversity) of broth cultures of the bacterial communities from wooden cheese boards. Median values are indicated in the plots. Using Wilcoxon Rank Sum Tests (with FDR correction), it was determined that surface community alpha diversity did not significantly differ among boards (Group A–E) (\(p>0.05\)), nor did they differ with exposure to \(L\). _monocytogenes_ (Category, \(p>0.05\)), for either Chao’s richness (Chao1) or Shannon’s diversity (Shannon).  
boards A, B and C, which were distinct from boards D and E, which exhibited similar microbiota to each other (Figure 5). The microbiota of boards D and E were dominated by _Leuconostoc_ (>85-95% relative sequence abundance in each) with lesser contributions from _Carnobacterium_, _Staphylococcus_ and _Enterococcus_. The microbiota of boards A, B and C were dominated by _Carnobacterium_ (>87-99% relative sequence abundance) with lesser contributions from _Brevibacterium_, _Enterococcus_, _Nocardiopsis_, _Psychrobacter_, _Prerotella_ and _Staphylococcus_.
# Remote Water Quality Monitoring System for Use in Fairway Applications
## 2 Materials and Methods
### Main Server and Peripherals  
The ATmega32u4 microcontroller, which is the heart of the central unit, has a built-in multi-channel 10-bit analog-to-digital converter. This means that measurement data obtained from analog inputs, based on standardized 0-5 V signals, are mapped in digital form using an integer value from 0 to 1023. Such values, from 4 measurement channels, which correspond to appropriate chemical or physical quantities, are sent to the main server. Figure 5 shows a matrix of sample data as stored on the main server.  
Data from the central unit, thanks to IoT modules, are sent to the main server at specific intervals, thanks to the use of GSM communication networks. Information is stored on the SQL server (Structured Query Language). Further data extraction from the main server can be achieved with a personal computer or mobile device (e.g., smartphone, tablet, etc.) with an Internet connection.  
An application has been developed in which data received from the main server, still in values ranging from 0 to 1023 as sent by the central unit, are converted into specific chemical or physical quantities that can be subjected to further analysis. The application is used to analyze and visualize measurement data from four different sensors simultaneously. Its main purpose is to enable complex monitoring of measurement data from the buoy at regular intervals. Data recording takes place every 30 min and includes values of four measured parameters, the date of reading, and the buoy identifier. The application can be installed and operated on a personal computer, as well as on a portable device equipped with the Android system. The application's capabilities are as follows:  
* **Automatic data collection:** enables recording of data from various sensors at regular intervals;
* **Data analysis:** possibility to analyze data according to patterns of individual parameters and sets of parameters;
* **Trend visualization:** display of trends, and minimum and maximum parameter values with the option to customize time ranges;
* **Flexible reporting:** color-coding of parameters, ability to zoom in, zoom out, and select different time ranges;
* **Editing conversion factors:** possibility to adjust conversion factors to calculate actual measurement values;
* **Charting:** generate and save your own charts directly on your device;
* **Automatic notifications:** the system sends a notification when the minimum and maximum values set for individual sensors are exceeded;
* **Server Switching:** the ability to switch between different servers to access different sets of measurements, allowing for the flexibility to manage and analyze data from different sources if needed.  
Figure 6 shows an example visualization of data received from the central unit.  
Figure 5: Examples of data on the main server.
## 7 Results and Discussion
### Explainability of Results  
To analyze the influence of specific features on the model's predictive performance, we employed _GradCam++_[47], an interpretive technique tailored for deep learning frameworks, particularly convolutional neural networks. _GradCam++_ builds upon _Grad-CAM_[48] by leveraging gradients from a designated target layer, typically the final convolutional layer, concerning the class of interest. This approach provides detailed insights into the contributions of individual neurons to the decision-making process. This analysis was confined to CNN's GRAM transformation segment in our architecture. As shown in Figure 8, our analysis identified nine distinct patterns (clusters) used by the con  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Sensitivity** & **Specificity** & **F1-Score** \\ \hline LLE + LDA & 0.555 (\(\pm 0.425\)) & 0.453 (\(\pm 0.321\)) & 0.440 (\(\pm 0.298\)) \\ CNN + Gram [10] & 0.871 (\(\pm 0.134\)) & 0.838 (\(\pm 0.102\)) & 0.859 (\(\pm 0.113\)) \\ Proposed model & 0.927 (\(\pm 0.016\)) & 0.997 (\(\pm 0.001\)) & 0.945 (\(\pm 0.008\)) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average and standard deviation over \(S=50\) test sets.  
volutional network to detect overloads. These clusters highlight how feature relationships and the network's focus on specific periods evolve to predict overloads. For instance, the first cluster emphasizes throughput, speed, and time since last maintenance in the final five minutes, whereas the fifth cluster focuses on coarse, intermediate, power, and pressure metrics in the first five minutes of the 15-min window. This adaptability demonstrates the network's ability to respond to temporal dynamics in operational data, independent of the physical rules layer. Figure 8 illustrates how the physical loss function influences the network's decisions. By integrating physical rules, the network shifts its focus, making throughput, velocity, pressure, power, and maintenance time more relevant across different moments, unlike previously observed isolated emphasis. An intriguing question arises: Why do physical rules emphasizing speed, power, and pressure lead the network to focus on granulometry? That could reflect the network's resilience in uncovering additional relationships beyond the physical rules. To deepen the analysis, we examined the first cluster (Figure 9). Initially, the model focuses on granulometry during the last five minutes to predict overloads. However, after incorporating physical rules, the focus broadens across all intervals, primarily focusing on throughput, mill speed, and elapsed time since the last maintenance. This shift underscores the enhanced interpretability and adaptability introduced by integrating physical rules into the model.  
Figure 8: Visualization of feature importance using GradCam++ in an specific cluster.  
Figure 9: Visualization of feature importance using GradCam++ in PINN-CNN model (\(k=9\) clusters).