esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
In this paper, a static bi-objective resource optimization model is established for MCF-EONs with limited resources. To effectively solve the model, a joint routing and core coding scheme with core switching is designed for the first time, and a new hybrid algorithm called MOEA/D-AMSF is proposed. The algorithm integrates multiple strategies, such as the IPHG, single-point crossover and bi-layer single and multiple mutations with repair operators into the MOEA/D framework. An adaptive mechanism is distinctively designedto adjust both crossover and mutation probabilities to accelerate algorithm convergence and enhance solution diversity. Finally, the algorithm performance is evaluated in NSFNET topology. The results show that, compared with the baseline algorithm, the proposed algorithm can obtain more dominated and diverse solutions in most cases. It can also search for solutions that the baseline algorithm cannot find in such an objective space with a smaller blocking ratio. However, it evolves slowly due to the exponential growth of the search space as the number of requests and cores increases. The research has also shown that core switching has a significant effect on reducing the network RBR and crosstalk ratio under lighter traffic loads. In the case of a heavier traffic load, spectrum fragments become the main factor affecting the network service performance, and the performance with core switching deteriorates a little since the number of solutions dominated by the baseline algorithm increases.  
There are still many scenarios to be studied further: (a) only static connection requests are studied in this paper, and dynamic connection requests that arrive randomly to the network can be studied in the next work, for example, the RSCA problem modeling and algorithm design considering spectrum fragments; (b) considering the cost of the network node with core switching, an optimization model that minimizes cost and blocking probability simultaneously can be established to make research more realistic; and (c) facing more complex network scenarios or optimization models, the introduction of computer parallelism, clustering algorithms, prediction algorithms, and machine learning into MOEA/D-AMSF could further accelerate algorithm convergence.  
Conceptualization, Z.X. and Q.X.; methodology, Z.X.; software, Q.X.; validation, Q.X. and T.M.; formal analysis, Q.X.; investigation, Q.X.; resources, T.C.; data curation, J.L.; writing--original draft preparation, Q.X.; writing--review and editing, Z.X.; visualization, Q.X. and T.C.; supervision, Z.X.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Key Research and Development Program of China (2019 YFB1803605, 2020 YFB1805601) and the National Science Foundation of China (No. 61572391).  
Not applicable.  
We thank the National Key Research and Development Program of China (2019 YFB1803605, 2020 YFB1805601) and the National Science Foundation of China (No. 61572391).  
The authors declare no conflict of interest.
## 2 System Model
### Areas of Interest  
Two different urban areas are considered in the simulation. The first area (Area-A) is the city center of Hong Kong, and the second area (Area-B) is Manhattan, New York. In addition to being realistic examples of urban areas, both areas are characterized by a large number of buildings with various towering heights, which makes them appropriate choices for developing multipath channel models. Maps are obtained from the OpenStreetMap (OSM) project [41], which includes areas' features, such as street and building heights. MATLAB SiteViewer 2023 is used to import and visualize 3D building data.
## WT1 Immunohistochemical Expression in Human Embryonal/Fetal and Neoplastic Tissues
### WT1 Expression in Breast Carcinoma  
Several studies have demonstrated WT1 expression in breast carcinomas, with low expression in adjacent normal breast tissues. [PERSON] et al. [101], confirmed WT1 protein expression by using Western blotting. These results suggested that WT1 (17 AA+) might be a crucial isoform in cancer progression and development, and might work together with WT1 (17 AA-) as a protein partner. In the same study, the authors showed that WT1 plays an oncogenic role in ER\(\alpha\) and HER2 protein regulation.  
In addition, they observed that ER-\(\alpha\) and HER2 proteins were highly expressed in breast cancer, but expressed at low levels in adjacent normal breast tissues, while WT1 (17 AA+) was strongly expressed in breast cancer and slightly in adjacent normal breast tissues. The result of WT1 (17 AA+) mRNA expression was confirmed at the protein level.  
Several studies focused on immunohistochemical nuclear expression of WT1 in breast cancer, predominantly in the mucinous histotype [43; 102; 103; 104], while it is occasionally co-expressed in non-mucinous carcinoma components of mixed mucinous carcinoma [102]. In addition, WT1 expression was observed, not only in mucinous carcinoma, but also in associated solid papillary carcinoma. A good correlation of WT1 immunohistochemical expression between solid papillary carcinoma and associated mucinous carcinomas does exist, indicating that the former could be the precursor of the latter [105]. With the advent of immunotherapy in malignancies, expression of WT1 in mucinous breast carcinomas provides a molecular target in these relatively indolent breast tumors [102]. Conflicting results are reported regarding WT1 expression and the prognosis of breast carcinoma. A study demonstrated that WT1 expression in breast cancer is correlated to poor prognosis, due to cancer-related epithelial-to-mesenchymal transition (EMT) and poor chemotherapy response [106]. Conversely, another study displayed that the immunoreactivity for WT1, together with RSPO1 and P16, was significantly associated with a more favorable disease-free survival [107]. Furthermore, high levels of Wilms' Tumor 1 (WT1) protein and mRNA had been associated with aggressive phenotypes of breast tumors, because HER2/neuoncogene increases WT1 expression. Increased levels of WT1 are due to the engagement of Akt, resulting in HER2/neu overexpression [108].
# Internet of Things: A Scientometric Review
## 3 Country and Author Research Analysis
### Country Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**N.** & **Country** & **Total** & **Average Growth** & **h-Ind.** \\ \hline
1 & China & 4822 & 16\% & 47 \\
2 & United States & 1561 & 116\% & 42 \\
3 & India & 1089 & 169\% & 15 \\
4 & South Korea & 894 & 206\% & 16 \\
5 & Italy & 874 & 61\% & 32 \\
6 & Germany & 811 & 64\% & 24 \\
7 & United King. & 711 & 71\% & 25 \\
8 & France & 543 & 126\% & 21 \\
9 & Spain & 463 & 42\% & 23 \\
10 & Japan & 449 & 166\% & 11 \\
11 & Taiwan & 438 & 68\% & 16 \\
12 & Brazil & 272 & 90\% & 9 \\
13 & Finland & 266 & 50\% & 20 \\
14 & Canada & 259 & 104\% & 15 \\
15 & Australia & 249 & 59\% & 22 \\
16 & Sweden & 216 & 68\% & 17 \\
17 & Switzerland & 193 & 31\% & 19 \\
18 & Portugal & 191 & 45\% & 13 \\
19 & Greece & 180 & 46\% & 14 \\
20 & Romania & 169 & 72\% & 9 \\
21 & Belgium & 164 & 87\% & 11 \\
22 & Austria & 146 & 113\% & 12 \\
23 & Malaysia & 137 & 71\% & 9 \\
24 & Russian Fed. & 134 & 271\% & 8 \\
25 & Ireland & 126 & 116\% & 9 \\
26 & Netherlands & 109 & 122\% & 12 \\
27 & Singapore & 109 & 112\% & 8 \\
28 & Poland & 104 & 77\% & 6 \\
29 & Czech Rep. & 101 & 153\% & 5 \\
30 & Turkey & 92 & 319\% & 5 \\
31 & Pakistan & 82 & 210\% & 7 \\
32 & Saudi Arabia & 80 & 122\% & 7 \\
33 & Norway & 72 & 119\% & 11 \\
34 & UAE & 71 & 180\% & 6 \\
35 & South Africa & 60 & 162\% & 9 \\
36 & Denmark & 59 & 39\% & 11 \\
37 & Tunisia & 55 & 163\% & 6 \\
38 & Serbia & 53 & \(-\)1\% & 6 \\
39 & Croatia & 51 & 159\% & 6 \\
40 & Hungary & 51 & 24\% & 6 \\
41 & Indonesia & 51 & 410\% & 3 \\
42 & Egypt & 49 & 159\% & 4 \\
43 & Morocco & 47 & 163\% & 4 \\
44 & Iran & 42 & 94\% & 5 \\
45 & Colombia & 39 & 146\% & 3 \\
46 & Algeria & 38 & 113\% & 5 \\
47 & Jordan & 38 & 108\% & 5 \\
48 & New Zealand & 38 & 86\% & 6 \\
49 & Mexico & 36 & 172\% & 5 \\
50 & Thailand & 32 & 107\% & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Internet of Things top 50 countries of first author’s corresponding address. Country number position (N.), total number of publications (Total), average percentage growth from the last 3 years (2014 to 2016), and h-index (h-ind.) from 2006 to 2016.  
start-up, Xeelas (Arnhem, Netherlands), and Turkish group, Sade (Ankara, Turkey), partnered to build Turkey's largest LoRaWAN (LoRa, Long Range Wide-area network network) in Istanbul to enable business, local governments, and conservation groups to collect and analyze from connected devices [37]. In Russia, the Internet of Things market is expected to reach USD 74 Billion by 2023, where the Russian government's Internet start-up fund (FRII) has joined forces with tech giants GS Group (Saint-Petersburg, Russia) and mobile operators to launch a national Internet of Things (IoT) consortium [38]. In Pakistan, by January 2017, 17 Internet of Things start-ups were launched, on their own or incubated, at Plan9 (Lahore, Pakistan), NEST i/o (Karachi, Pakistan), and i2i (Islamabad, Pakistan) [39].
## 5 Data Analysis Method
### Spectrograms  
In past studies on the micro-Doppler effect, the most common way to analyze the data has been by creating spectrograms and power spectral densities (PSDs) [28; 29; 30; 31; 32]. Spectrograms are especially useful because they show how the frequency content of a signal changes over time. A short explanation of how a spectrogram is made helps explain the data processing methods used in this study.  
To create a spectrogram, the Short-Time Fourier Transform (STFT) is applied to complex radar in-phase and quadrature (I/Q) data. This process involves dividing the signal into short time segments using a chosen window, then converting each segment into the frequency domain. The result is a linear spectrum (LSP) that shows the frequency content at  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Accel Type Sensor SN** & **Locations** \\ \hline PCB 353B16-224635 & driver’s window \\  & windshield \\ PCB 353B16-224636 & driver’s door \\  & vehicle hood \\  & rear door window \\ PCB 353B16-224637 & license plate \\  & above rear logo (jeep only) \\  & rear door \\ PCB 353B16-59353 & bumper cover \\  & license plate (leep only) \\  & below license plate (GMC only) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Acoustic accelerometer placement on vehicle surfaces.  
that moment in time. These results are then visualized over time by mapping the amplitude of each frequency to a color scale.  
This method works well for showing motion, like the spinning of helicopter blades [33; 34], sounds from loudspeakers [35; 36], how people walk [37; 38], and the flight of drones or birds [39]. In our study, we used radar to detect vibrations from different parts of a vehicle's chassis.
# A Method of Free-Space Point-of-Regard Estimation Based on 3D Eye Model and Stereo Vision
## 3 Methods
### 3D Point-of-Regard Estimation
#### 3.3.1. Eyeball Features and Personal Calibration
\right] \tag{10}\]  
Then, the distances between these points and the corresponding watched target points are added, obtaining two objective functions about \(\theta_{l}\) and \(\theta_{r}\) as follows:  
\[ \begin{array}{l}f(\theta_{l})=\sum\limits_{i=1}^{n}\left(\left(\frac{a_{1i }^{T}}{a_{3i}^{T}}(Z_{oi}-Z_{li})-(X_{oi}-X_{li})\right)^{2}+\left(\frac{a_{2 i}^{T}}{a_{3i}^{T}}(Z_{oi}-Z_{li})-(Y_{oi}-Y_{li})\right)^{2}\right)\\ f(\theta_{r})=\sum\limits_{i=1}^{n}\left(\left(\frac{b_{1i}^{T}}{b_{3i}^{T}}(Z _{oi}-Z_{ri})-(X_{oi}-X_{ri})\right)^{2}+\left(\frac{b_{2i}^{T}}{b_{3i}^{T}}(Z _{oi}-Z_{ri})-(Y_{oi}-Y_{ri})\right)^{2}\right)\end{array}  \tag{11}\]  
In this paper, Levenberg-Marquardt method is used to optimize \(\theta_{l}\) and \(\theta_{r}\). The initial values of \(\theta_{l}\) and \(\theta_{r}\) can be decided by Equations (5) and (6). Then we analyze the influence of the user's fixation times on the calibration results through experiment. In this experiment, we assume that the increase of the user's fixation times will reduce the calibration error. Therefore, we compare the calibration results in different fixation times with the real position of the corresponding calibration target to analyze how much fixation times can take stable personal calibration results. Detailed experimental results are described in Section 3.3.3.
## 2 Materials and Methods
### Odometry Errors: Systematic and Non-Systematic  
The main disadvantage of odometry is its unbounded accumulation of errors in the evaluation of the trajectory of a mobile robot (see Equation (4)). The error sources that cause inaccuracies in the determination of the odometry depend largely on the type of terrain practiced by the mobile robot [3], and are classified as systematic and non-systematic. In general, the concept of odometry calibration is focused on the minimization of systematic errors because they remain constant during the displacement. In general, the errors affecting the orientation of the mobile robot have the worst cumulative effects because once they are incurred they grow into lateral position errors [23; 24].
## 6 Results and Discussion  
### Experimental Results  
The results of the experiments with the CIC-IDS2017 and UNSW-NB15 datasets were primarily evaluated based on the metrics presented in Section 5. First, we focused on the stability of the model, as demonstrated by its convergence. This can be assessed through the training process depicted in Figures 9 and 10. With the selected parameters mentioned above, the proposed model nearly achieves convergence around the 800 th epoch and remains stable thereafter.  
Next, the accuracy achieved by the model during the testing phase demonstrates the effectiveness of this model in accurately detecting malicious flows within network systems. Based on effective detection, the proposed model becomes sufficiently robust for deployment in the network systems of organizations and enterprises. Twenty percent of each dataset was used for testing, corresponding to 84,831 flows from the CIC-IDS2017 dataset and 9997 flows from the UNSW-NB15 dataset. The model's prediction results were depicted in the confusion matrix in Figures 11 and 12. The prediction results indicated that all incorrect predictions (both false positives and false negatives) make up less than 1%. This means that the model achieves a high and consistent detection rate for both malicious and normal flow cases.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Hidden units & 500 & Loss function & Cross-entropy \\ \hline Learning rate & 0.001 & Optimizer & Adam \\ \hline Epoch number & 3000 & & \\ \hline \end{tabular}
\end{table}
Table 4: Hyperparameter values used in FN-GNN.  
The metrics used to evaluate the effectiveness of the model were calculated based on the formula presented in Table 1. The final evaluation results on two benchmark datasets are described in Table 5.  
Figure 11: Prediction results on the test set of CIC-IDS2017.  
Figure 10: The accuracy of the training history on the UNSW-NB15 dataset.  
Figure 9: The accuracy of the training history on the CIC-IDS2017 dataset.
## 4 Driving Simulation Experiment
### Driving Simulation Experiment Design  
#### 4.2.1 Experimental Protocol  
This experiment aimed to investigate the awakening effects of different voice interaction styles on driver alertness under varying levels of fatigue. The protocol consistedof two main parts: a fatigue induction phase and an alertness stimulation phase, with a total duration of approximately 50 min. Each participant was required to complete nine experimental sessions, representing all combinations of the three fatigue levels and three types of voice interactions (Table 4).  
Before the experiment began, the driver's fatigue level was preliminarily assessed using the Karolinska Sleepiness Scale (KSS) to ensure their state met the target fatigue level required for this study. A KSS score of \(\leq\)3 was defined as no fatigue, 4 to 6 as mild fatigue, and \(\geq\)7 as severe fatigue [31; 32]. Considering that fatigue naturally increases with the duration of simulated driving, the experiment was designed to progress sequentially from low to high fatigue levels: awakening trials were first conducted under no fatigue or mild-fatigue conditions, and after each trial, the KSS score was reassessed. Once the next fatigue level was reached, the corresponding awakening trial was carried out.  
To effectively induce the target fatigue state, the experiment included a long, monotonous straight-line driving task, requiring participants to drive continuously for 30 min in the simulator. Research shows that driving for 30 min in a monotonous environment can induce significant fatigue [33]. After each driving session, participants completed the KSS to evaluate their fatigue level; if the required fatigue level was reached, they proceeded to the next fatigue-awakening trial; if not, fatigue induction continued until the target state was achieved. To protect participants' rights, the experimental procedures were fully explained beforehand, informed consent was obtained, participants could withdraw at any time, and appropriate compensation was provided after the experiment.  
The experimental driving scenario consisted of a simulated two-way, two-lane road with a total length of 4000 m, including a 3000 m tunnel section. The starting point was located 600 m before the tunnel entrance, and the endpoint was 400 m beyond the tunnel exit. The designed driving speed for the entire segment was 60 km/h. To further evaluate drivers' vigilance and responsiveness under different voice interaction stimuli, a car-following task was introduced: the lead vehicle traveled at a constant speed of 60 km/h but suddenly decelerated to 30 km/h approximately 2000 m before the tunnel entrance, thereby prompting the participant's attentional and braking responses to a simulated sudden event.
## 3 Comparative Validation
### Numerical Simulation
#### 3.1.2 Simulation of Segment Joints  
The segment ring adopts the form of \"7 + 1\", including four standard blocks, two adjacent blocks and one key block. There are two bolt joints at the longitudinal joint of each segment. Three bolt joints are provided at the circumferential joints of the standard block and the adjacent block, and only one bolt joint is provided at the circumferential joints of the key block. All nodes between segments are connected with connector units.  
Since the opening of circumferential joints after thrust unloading is the focus of the study, the simulation of circumferential joints must be very accurate. For the connector unit at the non-bolted joint, the nonlinear compressive stiffness and linear shear stiffness are set to simulate the sealing gasket and rubber gasket in the circumferential joints, and the failure criterion is given to ensure that the connector unit at the non-bolted joint does not produce tensile stress. The connector unit at the bolted joint not only sets nonlinear compressive stiffness and linear shear stiffness, but also adds flexural stiffness and tensile stiffness to simulate the connection effect of bolts.  
Table 1 lists the parameters of each material.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & **Compression Modulus** & **Poisson’s Ratio** \\ \hline Segment (C50) & 34.5 Gpa & 0.2 \\ Bolt (Q235) & 210 Gpa & 0.3 \\ Sealing gasket (EPDM) & 7 Mpa & 0.3 \\ Rubber gasket (NBR) & 25 Mpa & 0.35 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material parameters in circumferential joints.  
Figure 7: Schematic diagram of connector unit.  
In this simulation, according to engineering practice, a 20 mm interval is set at the segment circumferential joint, which is the total width before thrust is applied. The thickness of the sealing gasket is 20 mm, and the thickness of the rubber gasket is 4 mm. The sealing gasket is compressed within the initial 16 mm range, and the rubber gasket begins to be compressed within the 16 mm-20 mm range. When the compression exceeds 20 mm, the segments contact each other and begin to be compressed. Since the segment will not be compressed in the actual project, the compression stiffness of the connector element \(\mathrm{k_{s}}\) in the circumferential joints is assumed to be the following piecewise function:  
\[\mathrm{k_{s}}=\left\{\begin{array}{ll}\mathrm{k_{s1}}&\qquad 20\leq \mathrm{t}\leq 4\\ \mathrm{k_{s2}}&\qquad 4\leq\mathrm{t}\leq 0\end{array}\right. \tag{22}\]  
Then, according to the following formula, the compressive modulus of the sealing gasket and the rubber gasket is converted into the compressive stiffness of the connector unit:  
\[\mathrm{k}=\frac{\mathrm{ES}}{1} \tag{23}\]  
where t is the opening of the circumferential joints, l is the length of the connector unit and S is the cross-sectional area of the material.
# An Observer-Based Current Sensor-Less Control Scheme for Grid-Following Converters
## 3 Conventional Control of Grid-Following Inverters
### Current Control Loop  
As shown by (3), there is a tight coupling between the \(i_{d}\) and \(i_{q}\) dynamics, and the grid voltages also affect the system dynamics. Hence, the proper decoupling actions and the feed-forward-based disturbance rejection approaches are required. Equation (15) provides the necessary control effort to cover both demands.  
\[\begin{array}{c}v_{td}=L_{f}u_{d}+v_{gd}+R_{f}i_{d}+L_{f}wi_{q}\\ v_{tq}=L_{f}u_{q}+v_{gq}+R_{f}i_{q}-L_{f}wi_{d}\end{array} \tag{17}\]  
where, \(u_{d}\) and \(u_{q}\) are intermediate control variables, which are introduced later on in (19). Substituting (17) into (3) yields to the resulted current dynamics of (16):  
\[\begin{array}{c}\frac{di_{d}}{dt}=u_{d}\\ \frac{d}{dt}=u_{q}\end{array} \tag{18}\]By defining the intermediate control variables of (17), the current control loop transfer functions are as given by (18), in which \(k_{c}\) is the current control loop bandwidth (BW):  
\[\begin{cases}u_{d}=K_{c}\left(\dot{i}_{d}^{ref}-i_{d}\right)\\ u_{q}=K_{c}\left(\dot{i}_{q}^{ref}-i_{q}\right)\end{cases} \tag{19}\]  
\[\begin{cases}i_{d}=\frac{K_{c}}{s+K_{c}}\dot{i}_{d}^{ref}\\ i_{q}=\frac{K_{c}}{s+K_{c}}\dot{i}_{d}^{ref}\end{cases} \tag{20}\]  
\(K_{c}\) usually is chosen at least 10 times smaller than that of the switching frequency of VSC, to preserve the proper system stability in the presence of the time delay in the current control loop, imposed by computations and PWM switching in the control loop.  
The current references of \(i_{q}^{ref}\) and \(i_{d}^{ref}\) are obtained from the reactive power and DC-link control loops, respectively.
# Numerical Simulation of Co-Barrier Efficiency of Air Curtains in Mine Refuge Chambers
## 2 Model Development
### Evaluation Metrics  
The barrier efficiency formula is as follows [34]:  
\[\eta_{z}=\frac{Q_{o}-Q_{m}}{Q_{o}}\times 100\% \tag{9}\]  
where \(\eta_{z}\) represents the air curtain barrier efficiency (%), \(Q_{o}\) represents the airflow through the door frame without air curtain (m\({}^{3}\)/s), \(Q_{m}\) represents the airflow through the air curtain door frame (m\({}^{3}\)/s).  
The airflow finally enters a certain volume of space \(V\), so the formula is as follows:  
\[\eta_{z}=\frac{\left(Q_{o}-Q_{m}\right)/V}{Q_{o}/V}\times 100\% \tag{10}\]  
The formula is as follows when \(V\) is a constant:  
\[\eta_{z}=\frac{C_{o}-C_{m}}{C_{o}}\times 100\% \tag{11}\]  
where \(C_{o}\) represents the CO gases concentration in the transition room without an air curtain (ppm) and \(C_{m}\) represents the CO gases concentration in the transition room with air curtain (ppm).