esa-sceva/satcom-chunk-collection huggingface dataset:
# Interference Alignment in Multi-Hop Cognitive Radio Networks under Interference Leakage
## 3 Outage Probability Analysis
### Outage Probability Performance of the Perfect IA Scheme  
As described in the previous section, when the PN-SN interference is aligned, the system works in the SISO fashion if one data stream is sent at each transmitter [15]. With the aid of (7), \(P_{\text{out},P}^{(i)}\) can be expressed as:  
\[P_{\text{out},P}^{(i)}=\Pr[\Gamma_{R_{i}\to R_{i+1}}^{(i)}<\gamma_{th}], \tag{10}\]where \(\gamma_{th}\) is the threshold value for acceptable communication quality. As we assume that all paths are modeled with independent and identically-distributed Rayleigh fading, the cumulative distribution function (cdf) of \(\Gamma_{R_{i}\to R_{i+1}}^{(i)}\) can be expressed as:  
\[P_{\text{out},P}^{(i)}=1-\exp\bigg{(}-\frac{\gamma_{th}}{\tilde{\gamma}_{R_{i} \to R_{i+1}}}\bigg{)}, \tag{11}\]  
where \(\tilde{\gamma}_{R_{i}\to R_{i+1}}\) is the average SNR of the \(R_{i}\to R_{i+1}\) hop describing the outage probability of the first \(K-2\) hops. For the last two hops, the outage probability can be expressed as [35]:  
\[\begin{split} P_{\text{out},P}^{(K-1)}&=\Pr[\min( \Gamma_{R_{K-2}\to R_{K-1}}^{(i)},\Gamma_{R_{K-1}\to D}^{(i)})<\gamma_{th}]\\ &=1-\Pr[\Gamma_{R_{K-2}\to R_{K-1}}<\gamma_{th}]\Pr[\Gamma_{R_{K- 1}\to D}<\gamma_{th}].\end{split} \tag{12}\]  
Similar to (11), \(P_{\text{out},P}^{(K-1)}\) can be expressed as:  
\[P_{\text{out},P}^{(K-1)}=1-\exp\bigg{(}\frac{-\gamma_{th}}{\tilde{\gamma}_{R_{ K-2}\to R_{K-1}}}\bigg{)}\exp\bigg{(}\frac{-\gamma_{th}}{\tilde{\gamma}_{R_{ K-1}\to D}}\bigg{)}. \tag{13}\]  
By substituting (13) and (11) into (9), outage probability can be obtained.
## 4 Simulation Results
### Computational Complexity
The nonlinear parameters \(\tilde{\alpha}_{2}\) and \(\tilde{\alpha}_{3}\) are set as independent random variables with a Gaussian distribution. The results are obtained by performing 1000 Monte Carlo simulations. The input signal is the same as in Example 1. The SNDR decreases by about 20 dB per decade of the nonlinear strength for the uncalibrated signal. After the \(i\) stage calibration, the dropping rate increases to about \(20\times i\) per decade of the nonlinear strength, which corresponds with the analysis in Section 3.3. Figure 16: The mean square deviation (MSD) of one-stage calibration and two-stage calibration. Figure 15: The spectra of 4-TIADC output. (**a**) Without calibration; (**b**) one-stage calibration; (**c**) two-stage calibration.
# A Precise Modeling Method for PMSM Servo System of Spacecraft
## 4 Experimental Testing and Validation
### Servo System Verification  
In order to verify the theoretical analysis mentioned above, experimental verification was conducted in a certain spacecraft servo system. The system parameters used in the experimental testing were identical  
Given the position instructions of the triangle wave and sinusoidal wave, the position feedback can be well-tracked and consistent with the experimental results, which verifies the correctness of the modeling. And the simulation model shows the impact of the two-mass elastic system with backlash and the friction model on the actual results.
# Anomaly Detection in Network Traffic via Cross-Domain Federated Graph Representation Learning
## 5 Experiments and Results
### Results
#### 5.2.2 Convergence Analysis  
Figure 4 compares the trends in the accuracy and F1 score of AD-FG with other baselines during multiple communication rounds. When processing distributed network traffic data, AD-FG showed better convergence for both the accuracy and F1 score as the number of communication rounds increased and could achieve high performance metrics within a relatively small number of communication rounds and eventually stabilize at a high level, which is advantageous compared with other methods. This suggests that, unlike the feature-based encoders in other methods, AD-FG can better capture and utilize the structural information in graph data by explicitly encoding the graph structure. Other methods mainly focus on the feature vectors of the nodes, encode structural information implicitly with features, and ignore the topological relationships and structural patterns among the nodes in the graph. This approach often fails to fully exploit the complex dependencies of graph data and tends to lose sensitivity to structural information. In  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Methods** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline Local & 85.21 & 83.68 & 61.26 & 70.71 \\ FedAvg & 85.02 & 82.25 & 61.94 & 70.66 \\ FedProx & 85.14 & 85.38 & 59.10 & 69.85 \\ FedSage & 85.37 & 86.82 & 58.70 & 70.04 \\ GCFL & 84.43 & 79.18 & 63.15 & 70.27 \\ MemAE-EIF & 83.50 & 74.31 & 69.72 & 71.46 \\ CTGCN & — & 87.80 & 50.23 & 63.90 \\ DyGCN & — & 77.77 & 66.48 & 71.68 \\ AD-FG (Ours) & **90.20** & **89.63** & **70.94** & **79.19** \\ \hline \end{tabular}
\end{table}
Table 3: Results of different methods.  
contrast, AD-FG captured the topological information of the graph more accurately by encoding the graph structure using degree and random walk methods, enabling it to explicitly understand the relationships between nodes and effectively learn potential patterns and anomalies. This encoding of structural information is more flexible and powerful than encoding that relies solely on node features, as it is able to deal with connectivity, adjacency, and global topological patterns in the graph, thus enhancing the model's performance when dealing with complex graph data.
# NextDet: Efficient Sparse-to-Dense Object Detection with Attentive Feature Aggregation
## 4 Experimental Analysis
### Model Evaluation Metrics  
An object detector is assumed to have detected target objects successfully upon fulfilling the following two constraints. Using a grid search approach, IoU threshold of 0.2 is selected and utilized throughout all models and datasets outlined in this paper. A two-step evaluation procedure is performed to evaluate the proposed object detector, NextDet as follows:  
1. Calculating the predicted bounding box and the ground-truth bounding box ratio: IoU defined in (7) is utilized to calculate this ratio of overlap. If the calculated IoU value is greater than a pre-determined threshold, it is determined that the object detector has detected the target object within an image successfully.
2. Matching the ground-truth and predicted bounding box class labels: After it is determined that an object has been successfully detected in step 1, class label of the predicted bounding box is matched to the ground-truth bounding box accordingly.  
Different metrics are used to evaluate an object detector [40]. The Mean Average Precision (mAP) is a popular evaluation and benchmarking metric defined as the average of the Average Precision (AP) calculated for all classes. AP corresponds the area under the Precision-Recall (PR) curve where Precision (P) measures the ability for a model to identify  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Number of Classes**} & \multicolumn{3}{c}{**Number of Images**} & \multirow{2}{*}{**Resolution**} & \multirow{2}{*}{**Class Overlap**} \\ \cline{3-3} \cline{5-6}  & & & & **Train** & & **Test** \\ \hline Argoverse-HD & Road & 8 & 39,384 & 15,062 & 1200 \(\times\) 1920 & True \\ \hline COCO & Common & 80 1 & 118,287 & 5000 & Multi-scale & True \\ \hline \hline \end{tabular}  
* COCO dataset features 80 object classes and an additional 11 _stuff_ categories with missing object categories/labels.  
\end{table}
Table 1: Statistics of the two datasets utilized in training and testing the proposed object detector.  
target objects i.e., the percentage of predictions that are correct, and Recall (R) measures the ability a model to find all positives among all relevant ground truths. These metrics can be defined mathematically as follows:  
\[P=\frac{TP}{TP+FP}=\frac{TP}{all\ detections} \tag{9}\]  
\[R=\frac{TP}{TP+FN}=\frac{TP}{all\ detections} \tag{10}\]  
\[AP=\frac{\Sigma P}{number\ of\ objects} \tag{11}\]  
\[mAP=\frac{\Sigma AP}{number\ of\ classes} \tag{12}\]  
Here, True Positives (TP) denotes a correct detection when IoU value is greater than or equal to the threshold usually set between 50% to 95%, False Positives (FP) denotes a wrong detection when IoU value is smaller than the threshold, and False Negative (FN) indicates ground truth not being detected.
## 4 Analysis of Experimental Results
### Comparative Analysis of Coarse Alignment Algorithms  
On the basis of 1847 and 4496 ISS feature points extracted from the source and target point clouds, the 4 PCS coarse alignment method is used with PCA, SAC, K-4 PCS, and the original 4 PCS algorithms for coarse alignment of model 60 rails, and the resulting alignment results are shown in Figure 9.  
From Figure 9b,c, it can be seen that the source and target point clouds of the PCA algorithm and SAC algorithm have obvious deviations, especially the PCA algorithm, which has the largest deviation. From Figure 9d,f, it can be seen that the 4 PCS algorithm, K-4 PCS algorithm, and the 4 PCS algorithm based on the ISS feature points of this paper reach a basic overlap of the two point cloud models after the coarse alignment, which can provide a good initial position for the fine alignment. Table 3 shows the average values of the results obtained by the algorithm running 10 times, and the reliability of the algorithm is verified by comparing the two evaluation criteria of the algorithm's running time and RMSE. From the data in the table, when the number of the extracted source point clouds is 1847 and the number of the target point clouds is 4496, this paper's 4 PCS algorithm based on the ISS feature points takes 1.48 s less time than the optimal K-4 PCS algorithm, 1.48 s less time than the 4 PCS algorithm, and 3.64 s less than the 4 PCS algorithm; the alignment accuracy is improved by about 0.27 mm or so compared with these two algorithms.  
Figure 8: The effect of coarse alignment with different number of feature points. (**a**) Total of 996 points; (**b**) total of 1502 points; (**c**) total of 2000 points; (**d**) total of 2500 points; (**e**) total of 3003 points; (**f**) total of 3497 points; (**g**) total of 3998 points; (**h**) total of 4496 points; (**i**) total of 4998 points; (**j**) comparison chart of the number of different points.
## 3 Results
### Reliability  
The examination of the ICC revealed an excellent level of agreement between instruments in the change of direction times for the right leg (ICC = 0.92; 95% CI = 0.88 to 0.94) and the left leg (ICC = 0.91; 95% CI = 0.87 to 0.93). Figure 5 shows the coefficient of variation (CV) values of the different measurements performed with each instrument and leg. Similar levels of reproducibility and reliability were also determined in both instruments (Photocell right leg: CV = 2.60 \(\pm\) 2.96%, ICC = 0.57, 95% CI = 0.38 to 0.71; Photocell left leg: CV = 2.21 \(\pm\) 1.93%, ICC = 0.76, 95% CI = 0.64 to 0.85; Fitnessmeter right leg: CV = 2.45 \(\pm\) 3.07%, ICC = 0.66, 95% CI = 0.49 to 0.78; Fitnessmeter left leg: CV = 2.94 \(\pm\) 2.76%, ICC = 0.72, 95% CI = 0.58 to 0.82).
# WLQRP: A Weighting Link-Quality-Based Routing Protocol for Underwater Sensor Networks
## 6 Conclusions  
Taking into account the distinctive features of communication underwater using acoustic signals, the design of routing protocols must address factors such as the properties of signal propagation in water, the limited energy capacity of sensor nodes, and the ever-changing network topology. In order to improve the dependability of sensor networks and decrease energy usage, this research paper suggests a routing protocol called WLQRP that is both reliable and energy-efficient. WLQRP not only effectively bypasses node void areas by considering the link quality to the next hop and the next-to-next hop nodes, thus improving communication reliability, but also reduces energy consumption by pre-determining the optimal relay node through handshake messages, reducing redundant transmissions. The experimental simulation findings indicate that WLQRP improves network performance by increasing the data transmission rate, reducing energy consumption, and decreasing end-to-end latency. This implies that WLQRP is effective in minimizing network energy usage and enhancing network reliability. Unlike other protocols that sacrifice latency for other performance metrics, WLQRP maintains lower latency while achieving superior  
Figure 12: Deviation factor.  
performance in these metrics compared to the VBF protocol. For future work, we will focus on researching how to adaptively adjust the weight coefficients for link quality based on the network's channel conditions and topology, thus enhancing the performance of the routing algorithm.  
Conceptualization, [PERSON]; methodology, [PERSON] and H.Y.; software, [PERSON]; validation, [PERSON]; formal analysis, [PERSON]; investigation, [PERSON]; resources, [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON], Y.G. and Z.W.; writing--review and editing, [PERSON], J.Y., Z.P. and H.Y.; visualization, [PERSON]; supervision, J.Y. and H.Y.; project administration, J.Y. and H.Y.; funding acquisition, J.Y. and H.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number: U22A2012); the National Natural Science Foundation of China (grant number: 72064007); and the Research Initiation Fund of Guilin University of Technology (grant number: GUTQDJJ2017).  
The data presented in this paper are available after contacting the corresponding author.  
The authors declare no conflict of interest.
## 1 Introduction  
Computerization of modern science and technology has raised many concerns about the methods traditionally used to ensure the integrity and utility of data ([1], and references therein). It is particularly important that sensor deployments and their associated information systems follow scientific fundamentals to ensure the integrity and validity of the data collected and presented. These principles were recently formalized by the U.S. National Academy of Sciences Committee on Ensuring the Utility and Integrity of Research Data in the Digital Age in their report [2].  
Addressing the report recommendations and the basic principles of scientific research, this paper develops a methodology and implementation procedures for automatically assessing the quality of research data gathered from sensors. In particular, it concentrates on calculating and presenting data quality metrics along with the research data themselves, crucial as modern measurement theory and practice assumes getting a measurement result along with some characteristics of its uncertainty [3]. Since the early 1990s and in particular since the publication of the ISO Guide [4], there has been an increasing recognition that the uncertainty of a measurement is no less critical than the value of the measurement result itself. Uncertainty may have a number of different components. Some of these may be evaluated by statistical methods but others may require expert estimates, reasoning and judgment ([4], and references therein). This paper combines both approaches. It derives the total uncertainty characteristics from statistical processing of the available data and includes a contribution from domain expert judgment.  
The paper's goal is twofold:  
* to describe, for the first time, an automated method that uses a prototype fuzzy rules expert system to generate error bars. This will provide a more meaningful measure of uncertainty than a hard classification using data flags;
* to demonstrate this approach's success through its application to marine water monitoring.
## 2 Main 3-HP Biosynthesis Pathways for the Construction of Microbial Cell Factories
### Other Pathways  
The lactate pathway and the oxaloacetate pathway have also been successfully constructed to biosynthesis 3-HP. It was found that D-lactate and 3-HP are hydroxyl position isomers by a chemical structural formula analysis, and the synthesis of 3-HP from D-lactate can be achieved by a hydroxyl shift reaction. However, the lactate pathway is thermodynamically infeasible because the overall standard Gibbs free energy change is positive [12]. [PERSON] et al. [25] introduced the lactate pathway into a lactate-producing _E. coli_ chassis, but less than 5% of lactate could be converted to 3-HP, and the titer of the engineered _E. coli_ was about 2.0 g/L in fed-batch fermentation. In addition, the similar chemical structure of lactate and 3-HP contributed to the difficulty of downstream isolation. Therefore, the lactate pathway is an unwelcome option in 3-HP biosynthesis.  
Due to the long \(\beta\)-alanine pathway, the oxaloacetate pathway was recently constructed as a simpler alternative. In the oxaloacetate pathway, benzoylormate decarboxylase (MdLC) catalyzes the direct conversion of oxaloacetate to MSA, avoiding the three-step reaction found in the original \(\beta\)-alanine pathway [26]. Notably, the oxaloacetate pathway (\(\Delta\)G\({}^{\circ}\) = \(-\)34.7 kJ/mol) demonstrated to be thermodynamically more favorable and feasible than the malonyl-CoA pathway (\(\Delta\)G\({}^{\circ}\) = \(-\)14.5 kJ/mol) [26]. Therefore, this simplified pathway provides an attractive alternative to the existing 3-HP synthesis routes. At present, the oxaloacetate pathway has only been constructed in _Saccharomyces cerevisiae_, and the genetically engineered _S. cerevisiae_ showed good potential for 3-HP production, reaching a titer of 18.1 g/L. Further, it is still necessary to conduct additional studies on the systematic comparison of key enzymes from different sources and the construction of the oxaloacetate pathway in other chassis.
# Adaptive Impedance-Based Fault Location Algorithm for Active Distribution Networks
## 3 Adaptive Fault Location Algorithm for Active Distribution Systems
### Step 2: Processing of DER Information  
In this step the DER available information is processed, in order to consider its effect on the fault location algorithm. For the adaptive feature, the proposed method has two options: using DER electrical models or using the information provided by IEDs installed at the DER location. The algorithm was developed considering one DER. Nevertheless, multiple distributed generators can be considered by using the superposition and [PERSON]'s theorem.
# A Channel Allocation Mechanism for Cellular Networks
## 2 Related Work
### Cellular Network Architecture
#### 2.1.2 Universal Mobile Telecommunications System  
The UMTS is the third generation (3G) and is the standard of mobile communication system. UMTS has a feature which is compatible with existing GSM/General Packet Radio Service (GPRS), so it is considered the best cellular system in mobile communication systems. Figure 2 illustrates the architecture of the UMTS Circuit-Switched (CS) service domain [16; 17; 18]. The network system of UMTS includes User Equipment (UE, Figure 2a), and UMTS Terrestrial Radio Access Network (UTRAN, Figure 2b). Moreover, it has to build the standard communication interface between any two components in UMTS to transmit information and control command. Through the standard, all mobile users worldwide can use a common set of compatible services. URTAN consists of Node B (Figure 2d) and Radio Network Controllers (RNC, Figure 2e). UE is equal to user end equipment such as MS. Node B is responsible for capability of physical layer, including controlling information of spread spectrum and modulation. RNC is responsible for radio resource management and the capability to connect to MSC via the interface of IuCS (Figure 2a). In general, any coverage of Node B is called Cell. HLR is responsible for recoding the information of users subscribed to the system, including what service users subscribed to and the location of MS.
## 3 Results and Discussion
### Method Validation
#### 3.2.7 Quantification of Metformin Uptake by HEI-OC1
To a lesser extent, OCT1-mediated uptake of metformin was also reported in HEK-293 cell lines transfected with both OCT1 and OCT2 [62]. In a similar investigation on the Chinese hamster ovary cell lines (CHO-K1) transfected with rat OCT1, [PERSON] et al. reported a saturable and time-dependent uptake of metformin [63]. Their in vivo results also revealed that metformin uptake was 30 times higher in wild-type mice compared to the OCT1 knocked-down group [63]. OCT1 was also involved in the lactic  
Figure 5: Uptake of metformin by the auditory cell line (HEI-OC1). (**A**) represents metformin cellular uptake in \(\mathrm{\SIUnitSymbolMicro g}\)/mL, (**B**) represents metformin cellular uptake in %, (**C**) represents chromatograms of residual metformin detected outside the auditory cells and (**D**) represents images of untreated (HEI-OC1) and cells exposed to different concentrations of metformin (1 mM, 2.5 mM, 5 mM and 10 mM). (**) and (NS) in subfigures (**A,B**) represent the level of statistical significance of the results. (**) means highly significant when \(p\)-value < 0.001. (NS) means statistically non significant. (*) in subfigure (**C**) represent the peaks produced by metformin detected outside HEI-OC1 cells after exposure to different concentrations at the same retention time. acidosis associated with metforminin [64]. Altered metforminin pharmacokinetics were also reported by ablation of both OCT1 and OCT2 in another study [65]. Of significance, OCT2 was reported to be expressed in the cochlear organ of Corti [66; 67]. The ototoxicity and nephrotoxicity associated with cisplatin chemotherapy is reported to be mediated through OCT2 [66; 67]. [PERSON] et al. highlighted the significance of OCT2 as a therapeutic target for cisplatin toxicity through competitive cation trafficking [66]. Quantitative PCR results also revealed the expression of OCT1 in HEI-OC1 cells and localisation of OCT1 as well as OCT2 in mouse cochlea [68]. In their investigation of the metforminin optoprotective effect against cisplatin toxicity on HEI-OC1 cells, [PERSON] et al. suggested the possibility of competitive uptake between the hydrophilic cations (metformin and cisplatin) for mediating transporter(s) [48]. To date, the exact uptake mechanism of metformin by HEI-OC1 has not been reported, and the exact transporters/carriers involved in this uptake by HEI-OC1 cells are not fully understood. Our results showed a concentration-dependent and saturable pattern of metformin uptake by HEI-OC1 cells (Figure 5), and further investigations are needed to reveal whether metforminin uptake by HEI-OC1 cells is mediated by OCT1, OCT2 or both or different transporter(s), and what factors could affect this uptake in healthy and diseased conditions. The validated HPLC method outlined herein could be a useful tool in the quantification process of metforminin uptake in HEI-OC1 cells or other cell lines and can be trialled for the quantification of the uptake of glipizide and gliclazide in similar cell culture experiments.