esa-sceva/satcom-chunk-collection huggingface dataset:
# Marine Internet for Internetworking in Oceans: A Tutorial
## 5 Internetworking Strategies of Marine Internet
### Internetworking Structure  
Figure 6 depicts an internetworking architecture for marine Internet. Here, the ICN plus NANETs acts as a backbone network, which connects marine Internet users directly to the terrestrial Internet. This backbone network can further connect HAPs or ANAETs if any to intensify its service density and expand its coverage, while all these networks can connect satellites to provide reliable services. The backbone network can also interconnect various types of underwater networks to realize underwater internetworking, and connect them to other networks on/over the water surface such as the terrestrial Internet. Actually, if we treat each above network as a special networking node, the marine Internet is just a super-scale WANET.  
Marine Internet tries to provide cost-effective network access according to application requirements by exploiting any networking opportunities as much as possible with the following preference: ICNs are always selected first if any, then WANETs and/or HAPs are exploited with satellites as the backup, as described below.  
1. When a node is covered by an ICN, it is better to use this ICN for network access since it is cost-effective.
2. When a node is out of the coverage of an ICN, while there is a NANET around, it tries to use this NAENT for network access.  
Figure 6: Internetworking architecture of marine Internet.  
In other cases, if there is a HAP that can also move around quickly on demand to provide network access, the node tries to link this HAP.
4. If all the above attempts fail, while the data to be transmitted can tolerate delay and loss, the node can try opportunistic networking by looking for AANETs.
5. If the data are critical or the node is in an emergency situation, the node tries to connect to a satellite.
# Research on Dynamic Modeling and Vibration Characterization of Integrated Bearings
## 2 Integrated Bearing Dynamic Performance Analysis
### Impact of Operating Conditions on Vibration of Integrated Bearings
#### 2.3.3 Impact of Bearing Speed on the Vibration of Integrated Bearings  
Under the operating conditions of a radial load of 200 N in the Y-direction, a radial load of 0 N in the Z-direction, and an axial load of 0 N, the relationship between the speed of the integrated bearing and its vibration is illustrated in Figure 15. As shown, with the gradual increase in the operating speed of the integrated bearing, both the vibration velocity and vibration acceleration levels tend to increase progressively. Therefore, selecting an appropriate operating speed can effectively reduce bearing vibration.
## 5 Point Cloud Uncertainty Evaluation: Gaussian Process Error Models
### Model Involving 18 Kinematic Errors  
The standard kinematic error model for a CMM [21; 33] involves first the specification of 6 error functions associated with the 6 degrees of freedom motion of a rigid body along an axis, e.g., \((e_{xx}(x),e_{xy},e_{xz}(x),r_{xx}(x),r_{xy}(x),r_{xz}(x))^{\top}\), where \(e_{xx}\) models the scale error along the axis; \(e_{xy}\) the straightness error in the \(xy\)-plane; and \(r_{xx}\) is the rotation about the \(x\)-axis, in this case, the roll; \(r_{xy}(x)\) is rotation about the \(y\)-axis (pitch); and \(r_{xz}(x)\) is rotation about the \(z\)-axis (yaw). Thus there is a scale error function, two straightness functions and three rotation functions corresponding to roll, pitch and yaw. There are 6 such functions associated with each axis, 18 in all, sometimes augmented by 3 scalar squareness parameters, depending on the convention for specifying the straightness error functions. These error functions are usually modelled in terms of empirical functions such as polynomials or splines, the coefficients of which are collectively represented by parameter vector \(\mathbf{b}\). The combined contribution of these \(18+3\) errors to the CMM measurement can be written as  
\[\mathbf{x}_{i}=\mathbf{x}_{i}^{*}+\mathbf{e}(\mathbf{x}_{i}^{*},\mathbf{b})+R(\mathbf{x}_{i}^{*},\mathbf{b}) \mathbf{p}, \tag{18}\]  
involving a translation component \(\mathbf{e}(\mathbf{x}_{i}^{*},\mathbf{b})\) and a component \(R(\mathbf{x}_{i}^{*},\mathbf{b})\) modelling angular errors where \(R\) is a rotation matrix depending on \(\mathbf{b}\) and location \(\mathbf{x}^{*}\). The rotational component also involves the probe offset \(\mathbf{p}\), the vector pointing from the centre of rotation of the probe housing to the probe tip centre. The explicit dependence on the probe offset allows different probe configurations to be modelled using the same kinematic error functions. Estimates of the function coefficients \(\mathbf{b}\) along with their associated variance matrix can be determined from repeated measurements of calibrated artefacts such as ball or hole plates [20; 34; 35].  
If \(V_{B}\) is the variance matrix associated with \(\mathbf{b}\) (derived from a ball plate exercise or otherwise) and \(G_{X|B}\) is the sensitivity matrix of \(\mathbf{x}_{1:m}\) with respect to \(\mathbf{b}\), then the variance matrix associated with \(\mathbf{x}_{1:m}\) is given by  
\[V_{X|B}=G_{X|B}V_{B}G_{X|B}^{\top}.\]  
The full kinematic error model and its use in generating variance matrices is very much a specialist undertaking. Typically, each error function is defined in terms of 5 or so parameters, e.g., polynomial coefficients, so that the complete error model involves the order of 100 parameters \(\mathbf{b}\). Consequently, assigning the associated variance matrix \(V_{B}\) involves estimating the order of \(10^{4}\) elements. Below, we consider kinematic error models developed in terms of Gaussian process models that can be specified by a small number of statistical parameters.
For example, in the initiation stage, they act as antioxidants by reversing oxidative damage to cells and antimutagenic by repairing damage caused to DNA; in the promotion stage, by exerting an anti-inflammatory effect, delay of cell cycle progression as well as induction of apoptosis [32]; and, finally, in the progression stage, by acting as inhibitors of angiogenesis [33; 34]. As mentioned above, apoptosis is one of the key molecular mechanisms currently studied for the development of new drugs for the treatment of cancer; due to the cell suffering apoptosis, it is rapidly recognized and phagocytosed, thereby reducing inflammation and tissue damage [35]. This process can occur from two central pathways, the extrinsic death receptor pathway and the intrinsic pathway, which are initiated by extracellular death receptors and by intracellular stimuli such as hypoxia, irreparable genetic damage, and severe oxidative stress [36]. The intrinsic pathway, also called mitochondrial, involves alteration of the mitochondrial membrane potential and promotes the release of cytochrome c, which activates caspase-9 and cleaves caspase-3, primarily responsible for cell apoptosis [27; 37]. On the other hand, the extrinsic pathway is mediated by external surface receptors such as Fas and TNFR1, which will subsequently activate the effectors caspase-3 and caspase-7 [38]. Extracts from natural sources, such as plants, have been widely used in preclinical breast cancer studies due to their rich compound profile, low toxicity, and good efficacy [39; 40], as these can target apoptosis-related signaling pathways to induce death in breast cancer cells; making them a valuable resource in the search for drug candidates for breast cancer treatment [41; 42; 43]. For this reason, it is important to conduct more research on the antiproliferative mechanisms of action of these extracts and know the cytotoxicity that they can present when exposed to normal human cells. Therefore, in this study, we determined the apoptotic potential of the polyphenolic extract of _L. gravelens_ in the breast cancer cell line MDA-MB-231 from a non-cytotoxic concentration in human CCD-18 Co cells.
# A Dynamic Measurement System Based on Adaptive Clustering and Multi-Classifier
## 3 Materials and Methods
### Robust Detection of Dynamic Targets
#### 3.2.2 Head Network Optimization  
The head network primarily consists of two components: the loss function and non-maximum suppression. This article introduces a decision function to the existing framework. For each frame's detection process, an online decision function is formulated based on the depth value and the size of the pre-selected box. The optimal classifier, or a combination of classifiers, is determined according to the decision function's evaluation. The decision function is defined in terms of Equation (3):  
\[\{x,c\}=\left\{\begin{array}{ll}\{0,1\}&if\ c_{a}<\theta_{1}\ \&\ c_{b}<\theta_{2}\\ \{x_{a},c_{a}\}&if\ c_{a}\geq\theta_{1}\ \&\ c_{b}\geq\theta_{2}\ \&\ c_{a}\geq c_{b}\\ \{x_{b},c_{b}\}&if\ c_{a}\geq\theta_{1}\ \&\ c_{b}\geq\theta_{2}\ \&\ c_{a}<c_{b}\end{array}\right. \tag{3}\]  
The target size state can be represented as \(\{x,c\}\), where \(x\) denotes the target position, and \(c\) is the outcome of the decision function at that position. The first classifier is termed \(a\), and the second classifier is termed \(b\).  
The target detection box is obtained through the synergistic effects of the aforementioned decision functions and a loss function, followed by a non-maximum suppression post-processing technique. This process filters numerous target boxes produced by each classifier. Typically, the classifier designated for large-scale targets employs a higher screening threshold, while a lower threshold is used for smaller scales.
## 3 Fault Diagnosis of the Protection System Based on Gradient Boosting Decision Tree (GBDT)  
The gradient boosting decision tree intelligent algorithm belongs to the ensemble algorithm, which has a good processing ability for discrete data and is very prominent in dealing with small sample data. Gradient boosting is the core idea and step of this intelligent algorithm for the classification task. When it carries out'multi-classification' work, it is based on 'two classifications' and adopts the idea of 'one positive class, multiple negative classes'. The training process of the gradient boosting decision tree intelligent algorithm is the main work of this section, which mainly includes selecting the optimal value of the learning rate and the number of iterations, and finally gives the fault diagnosis process of the protection system based on the gradient boosting decision tree.
## 4 Performance Analysis
### Energy Consumption  
To evaluate the effect of utilizing blockchain technology on the sources of the education sector, we calculated the average utilized energy per hour for the servers. Figure 6 displays a comparison between the average utilized energy by such servers with blockchain. It indicates that the proposed blockchain consumes less power than the conventional methods, and hence it proved to be effective in terms of energy utilization.
# YOLOv8-WD: Deep Learning-Based Detection of Defects in Automotive Brake Joint Laser Welds
## 4 Experimental Results and Analysis
### Ablation Experiment in the Study  
To assess the performance of the proposed enhanced algorithm, we evaluated and compared the training outcomes of the standard YOLOv8 model against those of models integrated with different enhancements. Specifically, the comparison focuses on the average precision (mAP@50) and F1 score. In the results table, a cross (\(\times\)) represents the absence of an improvement, while a check mark (\(\surd\)) denotes its inclusion. The ablation study results are summarized in Table 1.  
In the experiment, we tested eight different model configurations by sequentially adding or removing three major improvements: EfficientViT, CAFM, and DynamicConv. The comparison between Model 1 (no improvements) and Model 8 (all improvements) reveals the contribution of each modification to the model's performance.  
Model 8 demonstrated the best overall performance, achieving a mAP@50 of 90.5% and an F1 score of 86.1%. This indicates that the combined application of EfficientViT, CAFM, and DynamicConv significantly enhances the model's detection capabilities, thereby confirming the effectiveness of the integrated improvement strategy.  
When introduced individually, EfficientViT (Model 2), CAFM (Model 3), and DynamicConv (Model 4) did not show substantial performance gains. The mAP@50 values were 88.9%, 87.1%, and 87.6%, respectively, and the F1 scores also declined. These results suggest the limitations of each improvement when applied in isolation, highlighting that although these modifications have potential, their impact is limited without the support of complementary improvements.  
Notably, Model 5 exhibited improved mAP@50 and F1 scores compared to Models 2 and 3, suggesting some degree of complementarity between the improvements. However, Model 6 showed a 0.9% decrease in F1 score compared to Model 4, and Model 7 experienced a 0.2% drop in mAP@50 compared to Model 2, indicating that in certain cases, the combination of improvements may result in performance degradation.  
In summary, this ablation study demonstrates the effectiveness of the proposed improvements by comparing different model configurations. Furthermore, the results indicate that an appropriate combination of enhancements can significantly improve the performance of the YOLOv8 model, while inappropriate combinations may lead to performance deterioration.
# Bistatic Forward ISAR with DVB-T Transmitter of Opportunity
## 3 DVB-T Waveform and BFISAR Signal Synthesis  
### DVB-T Waveform Synthesis  
DVB-T is a terrestrial-based digital video broadcasting system that transmits compressed digital audio, digital video, and other data in a Moving Picture Experts Group (MPEG) transport stream, using coded orthogonal frequency-division multiplexing (OFDM) modulation. The DVB-T television station emits waveforms, whose analytical description is based on the multiplication of the following complex exponential functions [42]:  
\[s(t)=c_{m,J,k}.\psi_{m,J,k}(t).\exp(j2\pi f_{0}t), \tag{6}\]  
where \(f_{0}=c/\lambda_{0}\) is the central carrier frequency, \(\lambda_{0}\) is the central wavelength, and \(c\) is the speed of light:  
\(m=\overline{0,\,\infty}\) stands for the DVB-T frame index;  
Figure 1: BFISAR geometry.  
\(l=\overline{0}\), \(67\) stands for the Orthogonal Frequency Division Multiplexing (OFDM) symbol index;  
\(k=\overline{-(K/2)+1,(K/2)}\) stands for the index of the carrier frequency, \(K=6817\) denotes the total number of DVB-T carrier frequencies, and \(c_{m,l,k}\) denotes a Quadrature Phase Shift Keying (QPSK) or Quadrature Amplitude Modulation (QAM) amplitude defined for the \(k\)-th carrier, \(l\)-th data symbol, and \(m\)-th frame.  
The values of the exponential function \(\psi_{m,l,k}(t)\) depend on the time intervals in which it is defined. If the current time \(t\) accepts values in the interval \(t=\overline{(l+68.m).T_{S}}\), \((l+68.m+1).T_{S}\), the exponential function \(\psi_{m,l,k}(t)\) can be written as:  
\[\psi_{m,l,k}(t)=\exp\biggl{\{}j2\pi\frac{k}{T_{U}}.[t-\Delta-(l+68.m)T_{S}] \biggr{\}} \tag{7}\]  
where \(T_{S}=\Delta+T_{U}\) is the general symbol duration, \(T_{U}=0.896\) ms is the symbol's part duration, \(\Delta=0.224\) ms is the guard interval in the general symbol, and \(T_{S}=1.12\) ms.  
Otherwise, the exponential function \(\psi_{m,l,k}(t)\) is equal to zero, i.e., \(\psi_{m,l,k}(t)=0\).  
A DVB-T waveform-synthesizing algorithm includes the following steps.  
1. For \(m=0\), \(l=0\), \(k=\overline{-(K/2)+1,(K/2)}\), compute \(\psi_{m,l,k}(t)\). If \(k=K/2\), then \(l=1\).
2. For \(m=0\), \(l=1\), \(k=\overline{-(K/2)+1,(K/2)}\), compute \(\psi_{m,l,k}(t)\), then \(l=l+1\).
3. When \(l=67\), then \(m=m+1\), go to step 2.  
Denote \(\widetilde{t}=\Delta+(l+68.m)T_{S}\) as a slow time parameter measured on the azimuth direction. The complex DVB-T waveform can be written as  
\[s(t)=\exp\biggl{\{}j.2\pi.\biggl{[}\frac{k}{T_{U}}.(t-\widetilde{t})+f_{0}.t \biggr{]}\biggr{\}} \tag{8}\]  
which can be rewritten as:  
\[s(t)=\exp\bigl{\{}j.2\pi.\bigl{[}(f_{0}+k.\Delta f)t-k.\Delta f.\widetilde{t }\bigr{]}\bigr{\}} \tag{9}\]  
where \(\Delta f=(T_{U})^{-1}\) is the carriers' spacing.
## 5 Results
### Example 2
The estimation results and deviations of the pitch angle velocity are visualized in Figures 8 and 9. Figure 5: The estimation error of heavy velocity of the FSHC. Figure 6: The estimation of pitch angle of the FSHC. Figure 7: The estimation error of pitch angle of the FSHC. Considering the evidence from the experiments presented in Figures 2-9, it can be deduced that in scenarios where heavy-tailed non-Gaussian measurement disturbances and system uncertainties are present concurrently, the MKMC-CSVSF algorithm outperforms the CSVSF and CKF methods in longitudinal motion estimation. The estimation errors associated with the MKMC-CSVSF are notably reduced in comparison to those of the CSVSF and CKF methods. The RMSE and ARMSE of the MKMC-CSVSF demonstrate a higher level of effectiveness relative to the other methodologies, as illustrated in Figures 10-13 and Table 3. Furthermore, the fuzzy rule-based selection of multi-kernel bandwidths proves to be appropriate for handling multiple kernel bandwidths, while the maximum correntropy criterion effectively addresses non-Gaussian noise scenarios. Consequently, the proposed MKMC-CSVSF performs better than the CSVSF and CKF method in the presence of a higher level of effectiveness relative to the other methodologies. Figure 12: The RMSE of pitch angle of the FSHC. \[\text{If}\;\Big{\|}z_{k}-\hat{z}_{k|k-1}\Big{\|}<0.1\text{, then decrease the }\rho,\;\rho=400 \tag{71}\]0 The RMSE of pitch angle velocity of the FSHC. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Motion of FSHC** & **CKF** & **CSVSF** & **MKMC-CSVSF** \\ \hline Heave motion & 0.1094 & 0.1091 & 0.1088 \\ Heave velocity & 2.1785 & 2.1422 & 2.0869 \\ Pitch angle & 0.6985 & 0.6952 & 0.6945 \\ Pitch velocity & 1.0272 & 1.0263 & 1.0242 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The ARMSE of different methods. of multivariate non-Gaussian noises and system uncertainties, thereby enhancing filtering accuracy and robustness against system uncertainties.
# Assessment of Bioactive Surfactant Levels in Selected Cereal Products
###### Abstract  
Per- and polyfluoroalkyl substances (PFASs) are bioactive surfactants that are widespread in the environment and living organisms. This study presents measurements of PFAS in selected food of plant origin that are part of the healthy eating pyramid, including bread, rolls, flour, bran, buckwheat, millet, rice, and noodles. A simple and reliable analytical method was developed for the simultaneous determination of seven perfluorocarboxylic acids (PFACs) and three perfluoroalkane sulfonates (PFSAs) in cereal-based products. Out of the 10 PFASs, 5 PFASs were detected at levels above LOQ. The most frequently detected compound was perfluorooctanoic acid (OFOA), which was quantified in 65.2% of samples, while none of the others were present in more than 40.0% of tested products. Perfluorobutanoic acid (PFBA) was the predominant PFAS in cereal-based products, and its maximum measured concentration was 202.85 ng/g for wheat bran. The soil-root-shoot interactions in relation to PFAS transfer to the above-ground parts of plants and PFAS's ability to interfere with proteins are most likely the sources of these compounds in commonly consumed cereal products. As PFBA contributes greatly to total PFAS concentration, this food group should be included in future dietary exposure assessments.  
perfluoroalkyl substances (PFASs); micro-HPLC-MS/MS; QuEChERS; crops; cereal products; health risk +
Footnote †: journal: Applied Sciences
# Keyframe Insertion: Enabling Low-Latency Random Access and Packet Loss Repair
## 2 State-Of-The-Art
### Network-Based Methods  
Within the network, different types of processing can be performed to enable faster channel switching behavior. The type of processing can be separated into caching behavior and complex processing.  
In-network caching allows the end user device to quickly receive past frames up to the last keyframe at the network speed. Although such caching eliminates the delay dependency to the buffer duration, network throughput becomes the bottleneck instead [15]. In an alternative work, a similar caching behavior is proposed, but the cached portions of the multicasted stream are delivered using unicast to the end-user device [16]. Additionally, this strategy is not able to solve any packet loss related problems.  
When processing is allowed in the network, a solution can be found in a zapping accelerator server. Such a server generates several time-shifted replicas of the video stream from which one is sent until there is an I-frame in the regular channel [17]. Note that this paper considered generating these streams in the network rather than at the source, otherwise this work could be considered a content-based technique. In different works, transcoding at the cloud edge is proposed to transform the requested video to specific formats [18]. Transcoding videos at channel change events requires a lot of processing complexity and even with complexity reductions around 82% compared to reference encoders [19], such processing is excessively expensive. The only solution capable of limiting transcoding complexity with 99.2% uses coding information calculation (CIC) modules and residual encoder (RE) modules [20] such that only entropy decoding and encoding for each channel switching or packet loss event is required. In the proposed solution, we opt to keep the complexity in the network minimal.
# Video Super-Resolution with Regional Focus for Recurrent Network
## 3 Proposed Model
### Network Structure
The depth-space transformation can reduce the complexity of the model without losing local information, as shown in Figure 3.  
Equation (1) represents the process:  
\[[y_{LR}]^{H\times W\times 2s^{2}}\leftrightarrow[y_{HR}]^{sH\times W\times 2} \tag{1}\]  
where \(H\times W\) represent the size of the low-resolution frame, \(s\) represents the reconstruction factor, and \(y_{LR}\) and \(y_{HR}\) represent the low-resolution feature and high-resolution features, respectively. In the designed network structure of this paper, the current frame \(x_{t}\) to be reconstructed can be spliced with the hidden state of the positive feedback network, and the reconstruction result can be obtained from previous frame, and then input into the network. In addition, we also designed a skip connection structure for the current frame to be reconstructed, which adds the input network frame \(x_{t}\) to be reconstructed with the output reconstruction  
Figure 3: Depth–space transformation. Figure 2: The video super-resolution recurrent network structure proposed in this paper. results via bicubic interpolation up-sampling. The high-frequency information of the frame to be reconstructed is seriously lost, but it contains a lot of low-frequency information. The skip connection after interpolation and up-sampling can guide the network to pay maximum attention to the high-frequency components of the reconstructed frame, avoid the complex transformation of the network learning from the LR frame to the HR frame, and greatly simplify the training difficulty of the network.