esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Algorithm
### Mutual Coupling Elimination
Thus, for \(J\) snapshots, the data matrix \(\mathbf{X}\) in Equation (7) transfers into:  
\[\bar{\mathbf{X}}=\bar{\mathbf{A}}\mathbf{D}\mathbf{\Lambda}_{\rho}\mathbf{S}_{ r}+\bar{\mathbf{N}} \tag{12}\]  
where \(\bar{\mathbf{A}}=[\bar{\mathbf{a}}(\theta_{1}),\bar{\mathbf{a}}(\theta_{2}), \ldots,\bar{\mathbf{a}}(\theta_{P})]\in\mathbb{C}^{\bar{M}\bar{N}\times P}\) and \(\bar{\mathbf{N}}=\mathbf{\Gamma}\mathbf{N}\in\mathbb{C}^{\bar{M}\bar{N} \times J}\) are the new transmit-receive steering matrix and noise matrix, respectively; in addition, \(\mathbf{D}=\text{diag}[\omega_{t1}\omega_{r1},\omega_{t2}\omega_{r2},\ldots, \omega_{tP}\omega_{rP}]\in\mathbb{C}^{P\times P}\). It can be observed in Equation (12) that non-zero mutual coupling coefficients are extracted into a diagonal matrix \(\mathbf{D}\). Hence, for the new steering matrix \(\bar{\mathbf{A}}\), the errors of mutual coupling are eliminated.
# An Automatic ECG Signal Quality Assessment Method Based on Resnet and Self-Attention
## 4 Results
### Datasets  
This study used the PhysioNet/Computing in Cardiology Challenge 2011 dataset from the Research Resource for Complex Physiologic Signals (PhysioNet) platform. Each record is a standard 10s ECG segment with a sampling frequency of 500 Hz and a resolution of 16 bit. Since the original dataset is a standard 12-lead ECG signal, and this experiment investigates the quality assessment of single-lead ECG signals, only single-lead (V6) ECGs were extracted from the original dataset to form the dataset for this experiment. Each record was given a label that read \"accepted\" or \"unaccepted.\" The total amount of valid sample data following data screening was 863, which was utilized for the final model training and performance assessment. This is because some of the initial labels were unsatisfactory for reasons unrelated to V6. Thus, 706 of them were given the \"accepted\" label, while 157 were given the \"unaccepted\" label. Table 3 shows the composition of the dataset used in this experiment. The dataset was divided in the ratio of 8:2, and 80% were randomly selected as the training dataset and the remaining 20% as the test dataset.
# Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches
## 6 Conclusions  
This paper presented an SLR of physical layer authentication used to secure wireless communication and networks. We selected several well-known databases as reliable electronic sources, such as IEEE Xplore, ACM Digital Library, Science Direct, Springer, and Hindawi. First, 248 papers published between 2015 and 2022 were selected. Then, according to the inclusion/exclusion criteria, 31 of the 248 papers were selected to analyse and exploit the appropriate data. We arranged this paper based on three research questions: RQ1, RQ2, and RQ3 were answered by analysing the selected papers.  
Regarding RQ1, the selected studies determined the algorithm types, evaluation types, features, and tools used. In the overview of papers, it was observed that 45% of selected papers were machine learning-based, and 55% were deep learning-based. In addition, a comparison of evaluation types showed that 69% applied a simulation environment to appraise the PLA in different wireless network environments. Based on RQ2, the PLA techniques in the studied papers were discussed; the comparison of the PLA techniques category indicated that 52% of the studied papers employed RF characteristics, and 48% used channel characteristics to implement PLA systems. Finally, according to RQ3, to develop more efficient PLA approaches in the future, we have described the open challenges, issues, and future trends of PLA in wireless networks and communication.  
As a result of the rapid development of wireless communications and increasing security threats, the physical layer security of wireless networks is becoming increasingly important. We expect these findings to aid other researchers in developing PLA systems more effectively. Although earlier research has achieved promising results, some significant issues should be investigated further in future studies. Furthermore, research into ML and DL approaches to physical layer security of wireless networks is still in its early stages and deserves further investigation.  
Writing--original draft, L.A.; review and editing, D.A., R.A. and O.B.J.R. All authors have read and agreed to the published version of the manuscript.  
The Deanship of Scientific Research (DSR) at King Abdulaziz University (KAU), Jeddah, Saudi Arabia has funded this project, under grant no. (RG-91-611-42).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} PLS & Physical layer security \\ PLA & Physical layer authentication \\ RFF & Radiofrequency Fingerprint \\ CSI & Channel State Information \\ RSS & Receive Signal Strength \\ CIR & Channel Impulse Response \\ \end{tabular}
## 3 Proposed Scheme
### Mathematical Modeling  
In the semi-static scheme, it is inevitable to use the same frequency sections in neighboring zones. Thus, it requires an inter-zone interference manage scheme. In the proposed FFR-3 scheme, interference is mitigated without throughput loss by appropriately combining orthogonal resource allocation and resource reuse scheme for each zone. FFR-3 obtains more FRF gain compared to the typical semi-static scheme. As described in the previous section, in FFR-3, FRF of cellular link and D2D link are one and two, respectively. It means that the proposed FFR-3 is superior to the conventional scheme in terms of interference mitigation but also throughput gain. In FFR-3, orthogonal frequencies are allocated to each zone in the same cell. However, there may be inter-cell interference from other cellular links in the adjacent cells, but the number of interferers is reduced and the distance from the interferer is increased. Therefore, we define SINR of FFR-3 as \(SINR_{ffr,CE}^{sensor_{1}(\mathcal{Z}_{An})-eNB_{c}}\) where sensor \(k\) is located in zone \(Z_{An}\), and it is connected with \(eNB_{c}\) using cellular link. It can be derived with Table 1 as  
\[SINR_{ffr,CE}^{sensor_{1}(\mathcal{Z}_{An})-eNB_{c}} =\frac{P_{t,b}G_{kc}}{IF_{An}+(IF_{Bu}+IF_{Ct})+N_{0}}\] \[=\frac{P_{t,b}G_{kc}}{\sum_{i\in(\mathcal{Z}_{An}\cap\mathcal{K} _{CE}\cap\mathcal{K}_{CE})}P_{t,b}G_{ic}+\sum_{i\in((\mathcal{Z}_{Bu}\cup \mathcal{Z}_{Cn})\cap\mathcal{K}_{D2D})}P_{t,s}G_{ic}+N_{0}}, \tag{1}\]  
where the interferers are classified into three groups, \(IF_{An}\), \(IF_{Bn}\), and \(IF_{Cn}\). Each group means that interference from each zone. \(IF_{An}\) is interference from the devices in all of zone \(An\), the same zone with the transferring device. \(IF_{An}\) can be described as \((\mathcal{Z}_{An}\cap\mathcal{K}_{CE}\cap\mathcal{C}_{c}^{c})\). It means that the interferers are in every zone \(\mathcal{Z}_{An}\), where \(n\in\{1,2\}\). In addition, the interferers use the cellular link so they are elements of the set \(\mathcal{K}_{CE}\) and they are not included in the cell of \(eNB_{c}\), which is \(\mathcal{C}_{c}\). \((IF_{Bn}+IF_{Cn})\) describes interference from the the other zones, which is \(((\mathcal{Z}_{Bu}\cup\mathcal{Z}_{Cn})\cap\mathcal{K}_{D2D})\). It is a set of interferers using D2D links, \(\mathcal{K}_{D2D}\) and is a union set of \(\mathcal{Z}_{Bn}\) and \(\mathcal{Z}_{Cn}\), where \(n\in\{1,2\}\). For a D2D link, two frequency sections are used. For example, the frequency sections, \(bn\) and \(cn\) are used as D2D links in the zone \(An\). With the \(bn\) case, it is also used as a cellular link in \(Bn\) and used
# QiOi: Performance Isolation for Hyperledger Fabric
## 3 Motivation
### Performance Interference Analysis  
Next, we explore the cause of performance degradation in Figure 3 at the component-level and show that the components have different impacts on performance degradation. We measure the system-level metrics in Table 2 for each component and compare the results when Fabric components run without the sysbench containers (i.e., _Alone_) and with eight sysbench containers (i.e., _Together_).  
First, Figure 3(a) depicts that the average time slice of peers, orderers, and chaincodes in _Together_ increases by 22%, 35%, and 3%, respectively, compared to those of _Alone_. This indicates that the Fabric components in _Together_ run on CPUs longer than in _Alone_. In addition, Figure 3(b) shows that the average scheduling delay for each component increases by 3.2 times, 4.4 times, and 1.9 times, respectively. This is because the Fabric components need to wait until the sysbench containers consume their time slice, which is quite a long time, i.e., 58 ms as illustrated in Table 3. As a result, the Fabric components experience severe scheduling delay, which leads to performance degradation of Fabric. The number of context switches in _Together_ in Figure 3(c) decreases by 22%, 39%, and 48%, respectively, compared to that of _Alone_. This means that the total runtime of the Fabric components decreases, which results in the decrease in Fabric CPU utilization.  
Second, Table 3 shows the system metrics of the eight sysbench containers when the containers run without the Fabric components (i.e., _Alone_) and with the Fabric components (i.e., _Together_). Although the time slice of sysbench containers in _Together_ decreases by 5.7 times, the number of context switches increases by 5.65 times. Therefore, the average CPU utilization of sysbench containers slightly decreases from 34% to 33%, and throughput of sysbench containers does not decrease.  
From these results, we identify that the scheduling delay is a key bottleneck in performance interference. Therefore, a design goal of QiOi is to control the scheduling delay via CPU allocation of Fabric components. We focus on the orderers and the peers because their impacts have been known on determining Fabric performance [16; 36; 37; 38].
# Deep-Reinforcement-Learning-Based Intelligent Routing Strategy for FANETs
## 4 Proposed Intelligent Routing Method
### Reward  
The proposed method aimed to realize the reliability, stability, and integrity of data transmission through node selection. Under the guidance of this goal, if the next hop is the destination node, the agent should obtain the maximum reward, and then we have  
\[F_{j}=\left\{\begin{array}{cc}1,&\text{Node $j$ is the destination node}\\ 0,&\text{Node $j$ is not the destination node}\end{array}\right. \tag{20}\]  
Meanwhile, we note that the selection of ordinary relay nodes should also be rewarded in order to guide the convergence of the routing process. Therefore, the above four factors were taken into account in the setting of rewards. Specifically, denote \(C_{j}\) and \(T_{j}\), respectively, as the channel capacity and the link lifetime between the current node and the selected next node \(j\), and \(D_{j}\) and \(L_{j}\) as the distance to the destination and the MAC queue length of \(j\). In the proposed method, the reward was set as  
\[r=-\left(\mu_{1}e^{-\frac{C_{j}}{\text{max}}}+\mu_{2}\frac{D_{j}}{D_{\text{max }}}+\mu_{3}\frac{L_{j}}{L_{\text{max}}}+\mu_{4}e^{-\frac{T_{j}}{\text{max}}} \right)F_{j}, \tag{21}\]  
where \(C_{max}\), \(T_{max}\), \(D_{max}\), and \(L_{max}\), respectively, represent the maximum channel capacity, link lifetime, distance to the destination, and MAC queue length among the neighboring one-hop nodes, while \(\mu_{*}\) is the weight factor, and we have \(\mu_{1}+\mu_{2}+\mu_{3}+\mu_{4}=1\).
# Topic-Oriented Text Features Can Match Visual Deep Models of Video Memorability
## 5 Predictive Models of Video Memorability
### Textual Branch: SBERT-Based Models  
Although UMAP provides us with features that apparently capture the semantics of our captions, this method applies non-linear transformations to the input data, potentially causing undesirable side-effects. In fact, after reducing the dimensionality of the original SBERT embeddings under a _Principal Components Analysis_ (PCA), the relationship between said vector representation and their memorability labels seems to emerge in a direct way (Figure 7). This approach applies only linear operations over the input space, thus keeping a representation space closer to the original one. It can be seen that the least memorable samples tend to cluster together in all the cases, whereas highly memorable ones display a more diverse spatial distribution. Because even in a very low dimensionality space, samples display such alignment with the task of predicting video memorability, we use a PCA-projected version of the SBERT embeddings as input features to our models.  
The lower branch of the diagram in Figure 8 shows that linear regression models are used when modeling memorability from text encodings. The input to these models is an embedding from a pretrained Sentence-BERT, one vector at a time. We also explore the effect of reducing the size of the original embeddings (768-dimensional) via a PCA decomposition. We reckon there is some degree of redundancy in the original captions describing the visual semantics, partially manageable by extracting the most relevant features within data. We do not carry out any data augmentation procedure or text processing over the original sentences or their representation.  
Figure 7: 2-dimensional PCA projection of SBERT embeddings by their memorability score. (**a**) VideoMem (short-term). (**b**) VideoMem (long-term). (**c**) Memento10K (early AVG). (**d**) Memento10K (late AVG).
## 5 Simulation Results
### Coordinated Preheating Results and Comparison
On the contrary, the one-by-one coordination method requires 20,000 rounds of adjustment in this example, whose calculation time is linearly related to the user size, so it is not scalable. Table 4 presents the performance comparison under different models, and two conclusions can be drawn: (1) A coordination mechanism is necessary. Preheating without coordination will lead to the failure of adjustment. Its net peak and generation cost are even higher than those without preheating; (2) A grouping mechanism is necessary, which  
Figure 9: Changes of Evaluation Indexes with the Adjustment Rounds. can achieve scale-independence, while coordination one by one is unacceptable in terms of computing time. In order to analyze the impact of each round of adjustment on the system, the important process curves are given in Figure 10, and their meanings have been marked in the figure. It can be seen from Figure 10a that both the morning and evening peaks of the total load decrease with the adjustment. The peak decreases more obviously in the first few rounds, and gradually approaches the balance in the later rounds. It is worth noting that compared with the morning peak, the evening peak has dropped more significantly. This is because the preheating period before the evening peak is long and there is no specific heat demand (the user has not yet arrived home), so the adjustable range is large. The preheating period before the morning peak has strict temperature restrictions (to ensure a proper sleep temperature), and the adjustment range is limited. In addition, since the earlier the preheating is, the greater the heat loss will be, the preheating period will not be too long. The gray part in Figure 10b represents the wind power curtailment area, and it can be seen that the curve gradually leaves the area with adjustment. Figure 10c shows the change process of the marginal cost curve, which becomes smoother with adjustment. \begin{table}
\begin{tabular}{c|c|c|c|c|c} \hline  & **Generation Cost** & **User Bill** & **Net Peak** & **Wind Power** & **Required** \\  & **(10,000 \%)** & **(10,000 \%)** & **(MW)** & **Curtailment (MW)** & **Rounds** \\ \hline No preheating & 15.90 & 21.92 & 73.33 & 88.34 & — \\ Preheating without & 22.42 & 33.16 & 92.89 & 50.32 & 1 \\ coordination & 10.04 & 11.33 & 38.40 & 1.03 & 20 \\ Coordinated preheating one & — & — & — & — & 20,000 \\ by one & — & — & — & (Unacceptable) \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of different preheating effects. Figure 10: Process display of each round.
# Petrophysics Parameter Inversion and Its Application Based on the Transient Electromagnetic Method
## 2 Theory
### Inversion Algorithm
The relationship between resistivity, porosity, and water saturation is  
\[S_{w}=\left(\frac{\rho_{w}}{\rho\phi^{-m}}\right)^{1/n},\phi=\left(\frac{\rho_ {w}}{\rho S_{w}^{-n}}\right)^{1/m} \tag{15}\]  
Inversion is performed using the nonlinear least squares method. To prevent over-fitting and enhance the uniqueness and stability of the solution, a regularization term is incorporated into the objective function. The objective function for inverting the water saturation and porosity is defined as follows:  
\[\Phi(\mathbf{m})=\frac{1}{2}\parallel\mathbf{W_{d}}(F(\mathbf{m})-\mathbf{d_{ obs}})\parallel_{2}^{2}+\frac{1}{2}\beta\Big{(}\lambda_{\phi}\parallel\phi-\phi_{ \text{ref}}\parallel_{2}^{2}+\lambda_{S_{w}}\parallel S_{w}-S_{w,\text{ref}} \parallel_{2}^{2}\Big{)} \tag{16}\]  
where \(\phi_{\text{ref}}\) and \(S_{w,\text{ref}}\) are the prior values of porosity and water saturation, respectively, and \(\lambda_{\phi}\) and \(\lambda_{Sw}\) are the corresponding regularization parameters. Since both porosity and water saturation range between 0 and 1, boundary constraints are imposed during the inversion process to ensure the physical plausibility and convergence of the results.
## 2 Methodology
### Three-Dimensional Numerical Simulation
#### 2.2.3 Selection of Checkpoints  
The turbine blades were subjected to complex loads, including centrifugal loads, thermal loads, and aerodynamic loads. This process involved the integration of fluid-thermal-structural finite element analysis, executed separately through the ANSYS fluid flow module, steady-state thermal analysis module, and static structural analysis module, as shown in Figure 5. Due to the limited degree of deformation and deflection in the turbine blades, the one-way fluid-structure interaction (FSI) was sufficient to ensure accuracy in this study. This approach effectively demonstrates the influence of temperature/pressure and centrifugal force distribution on structural deformation, eliminating the need for mesh revision in both the fluid and solid domains [28]. The boundary conditions were established with the actual operational data, and the mesh model was based on the reconstructed geometric model.  
Firstly, the temperature and pressure distributions of the blades were obtained by taking the actual operating data as the boundary conditions. Secondly, stress-strain calculations were carried out on the basis of pressure and temperature distribution. Finally, the checkpoints for lifetime estimation were selected according to the principles in Table 1. Except for the locations suffering maximum stress, strain, and temperature, the locations with special geometric features, such as cooling holes and blade edges, also needed to check out.
# Design and Application of Daylight-Based Lighting Controller on LED Luminaire
## 4 Results
The characteristics of an LED luminaire in terms of both illuminance and power consumption in the experiment setup with a daylight control circuit over a period of one day is illustrated in Figure 10a,b, respectively. It can be seen that the daylight control circuit can operate at the full capacity from 9.00 A.M. to 1.00 P.M., while at the period 1.00 P.M. to 4.00 P.M. the daylight control circuit cannot operate. The reason is in the afternoon, sun moves toward the western direction and the experimental setup does not consist of louver in that direction, leading to low daylight through louver, hence LED luminaire operates at maximum power in order to achieve setting illuminance around 300 lux. The proposed daylight control circuit can reduce power consumption in an experimental setup to 8W with both LED luminaires in some periods of time and is able to regulate illuminance within CIE standard value. Figure 10: _Cont._  
Figure 9: Effect of Louver angle on electrical and illuminance data obtained from experimental setup at 01.00 P.M.: (**a**) Voltage; (**b**) Current; (**c**) Power; (**d**) illuminance on working plane.
# Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information
## 4 Sybil Attack Detection System  
Before the system was proposed, we found that the AoA of each client can be accurately detected when it is stationary in the experiment. However, AoA cannot be effectively measured because of multipath interference when attackers or others move in the indoor WLAN environment. Therefore, we also need to design a motion detector to resolve client motion. The amplitude information in the CSI stream is selected from the detector because it is sensitive to the motion in the environment, whereas the original CSI amplitude data are affected by noise, so it should be de-noised. As a result, we design de-noising and motion detection modules for the system.
# Evaluating Satellite Sounding Temperature Observations for Cold Air Aloft Detection
## 3 Results
### Evaluating NUCAPS Skill in Detecting CAA
“Detected” indicates that a temperature of 208 K was detected within the profile for the listed product. COSMIC is the observational “truth” in this study.
## 4 Glyphosate and AMPA in Foods
### Analytical Method Validation
Moreover, references to regulatory standards such as SANTE/12682/2019 (European Commission) [104], AOAC [140], ICH Q2(R1) [141], and ISO 17025 [142] were often absent. These omissions pose challenges for regulatory acceptance and data reproducibility. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Analytical Method** & **Matrix Effect** & **Uncertainty** & **Guideline** & **Validation Summary** & **Ref.** \\ \hline LC-MS/MS & Controlled with standards & Estimated measurement uncertainty provided & SANTE/11312/2021 & Fully validated for GLYP and AMPA in water, high accuracy and reproducibility. & [133] \\ \hline LC-MS/MS & Matrix effects assessed; internal standards used & Not specified & Not specified & Fast and reliable for GLYP and AMPA; suitable for biomonitoring. & [134] \\ \hline LC-MS/MS & Matrix effects evaluated; internal standards used & Not specified & Not specified & Acceptable accuracy and precision good for GLYP/AMPA. & [135] \\ \hline LC-MS/MS & Matrix effects evaluated; internal standards used & Not specified & Not specified & Validated for GLYP/AMPA in soil; applicable to field samples. & [136] \\ \hline LC-MS/MS & Matrix effects evaluated; internal standards used & Not specified & Not specified & Robust method for large-scale GLYP/AMPA. & [137] \\ \hline LC-MS/MS & Matrix effects evaluated; internal standards used & Not specified & Not specified & Suitable for GLYP/AMPA & [138] \\ \hline LC-HRMS & Controlled with standards & Not elaborated & Not specified & Sensitive for GLYP in water; limited validation data. & [139] \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Summary validation practices in glyphosate and AMPA detection studies. To advance the field, future validation efforts should explicitly reference recognized guidelines, incorporate a comprehensive evaluation of matrix effects, and quantify uncertainty. This would enhance analytical comparability and increase confidence in reported results for regulatory and public health purposes. In this context, the ideal method for GLYP and AMPA quantification needs to be suitable for a wide range of matrices and easy to carry out for most laboratories while being robust, selective, accurate, and precise. However, most developed methods for GLYP and AMPA detection are generally suitable for only one matrix or a specific type of matrix in which they were validated. It is a challenge to expand the scope of food samples and complex matrices, and low concentration levels pose a barrier [68].