esa-sceva/satcom-chunk-collection huggingface dataset:
# Magnetic Resonance Roadmap in Detecting and Staging Endometiosis: Usual and Unusual Localizations
## 3 Localization
### Deep Infiltrating Endometrosis (DIE)
#### 3.2.3 Torus Uterinus and Sacrouterine Ligaments  
The uterosacral ligaments, also known as rectouterine ligaments, extend from the uterus to the sacrum; specifically, they are attached to the cervix creating the torus uterinus and posteriorly to the presacral tissues [43]. The ligaments consist of smooth muscle, connective and adipose tissue, and they are a common site of endometriosis associated with dyspareunia.  
In the literature, there is still a high variability regarding the diagnostic presentation of disease in the uterosacral ligaments, which is likely to lead to an over-diagnosis at that level.  
[PERSON] et al. and the European Society of Urogenenital Radiology (ESUR) consider the irregular margins, thickening and asymmetry as parameters of diagnosis, while [PERSON] et al. referred to a specific thickness (\(>\)9 mm) as a criterion for endometriic involvement [11].  
[PERSON] et al. and the ENDOVALIRM group, in order to avoid false positive findings, prefer to use the following morphologic abnormalities in at least two planes as criteria of disease: asymmetry between each other, irregular or nodular margin, a spiculated or retracted aspect, and/or thickness of more than 5 mm. In the case of hemorrhagic spots, a diagnosis is made without any other alteration needed [39].  
On T2 WI, a hypo/isointense signal compared to myometrium may indicate the presence of endometriosis. Hemorrhagic foci corresponding to a high signal on fat-suppressed T1 WI and/or T1 WI and cystic alterations may also be seen as hyperintense cavities on T2 WI [14].  
Involvement of the proximal portion of ligaments (distance less than 2 cm from the cervix) may, in some cases, be associated with thickening at the level of the torus uterinus; in such cases, it is essential to report any mass or thickening in the upper portion of the posterior cervix, as this could cause the surgeon to proceed with a laparoscopic transvaginal approach [7] (Figures 2-4).  
In patients with retroflexion of the uterus, evaluation of the cervical uterosacral ligament insertion may be particularly difficult at MRI.  
Figure 2: Deep infiltrating endometriosis of the anterior and posterior compartments in 33-year-old female with dysmenorrhea, chronic pelvic pain, dyspneauria and dyschezia. (**A**) Sagittal T2 WI. Evidence of external anterior adenomas and plaque of the vesico-uterine pouch (white arrow); (**B**) Sagittal T2 WI. Retrocervical localization of DIE (white arrow); (**C**) Axial T2 WI. Left uterosacral ligament (USL) localization (white arrowhead), plaque of vesico-uterine pouch (white arrow) with involvement of the round ligaments, particularly on the left (white dotted arrows), and adhesions with the ovaries (retraction of the broad ligaments).
## 5 Experiments and Results
### Performance of the Proposed Anomaly Detection Method
This superior performance can be ascribed to our method's integration of the autoencoder and  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**The Proposed Scheme**} & \multicolumn{4}{c}{**Autoencoder-Only**} \\ \cline{2-9}
**Cell Index** & **P** & **R** & **F1** & \ \begin{tabular}{c} **Average** \\ **F1** \\ \end{tabular}  & **P** & **R** & **F1** & \begin{tabular}{c} **R** \\ \end{tabular}  & **F1** \\ \hline \hline \multicolumn{9}{c}{Internet of Vehicles} \\ \hline
0 & 0.933 & 0.921 & 0.927 & & 0.836 & 0.821 & 0.828 & \\
1 & 0.938 & 0.913 & 0.925 & & 0.827 & 0.831 & 0.829 & \\
2 & 0.917 & 0.941 & 0.929 & 0.851 & 0.817 & 0.834 & \\
3 & 0.926 & 0.926 & 0.926 & & 0.826 & 0.832 & 0.829 & \\
4 & 0.942 & 0.908 & 0.925 & & 0.831 & 0.826 & 0.828 & \\ \hline \multicolumn{9}{c}{Intelligent manufacturing} \\ \hline
0 & 0.864 & 0.919 & 0.891 & & 0.811 & 0.876 & 0.842 & \\
1 & 0.863 & 0.923 & 0.892 & & 0.817 & 0.874 & 0.845 & \\
2 & 0.860 & 0.937 & 0.897 & 0.894 & 0.815 & 0.871 & 0.842 & \\
3 & 0.856 & 0.939 & 0.896 & & 0.809 the GAN. By leveraging the strengths of both networks, our proposed method enhances detection precision, surmounting limitations such as GAN non-convergence. These results provide strong support for the effectiveness of our proposed anomaly detection method. By bridging the capabilities of the autoencoder and GAN, the proposed method not only improves the detection accuracy across various 5G vertical applications, but also mitigates some of the key challenges commonly faced in network anomaly detection. However, as with all models, the performance of the proposed method could be influenced by various factors, and thus further investigations are warranted to fully understand its potential and limitations in different contexts.
## 2 Materials and Methods
### Statistical Analysis  
The normality of the data distribution was assessed using the Shapiro-Wilk test and by visually inspecting Q-Q and box plot graphs. Differences between the two groups regarding anthropometric measurements were assessed using independent samples \(t\)-tests. Differences in spatiotemporal parameters and gait phase durations between groups (hyperpronated vs. neutral foot type, between-subject factor) and walking conditions (slope/speed condition, within-subject factor), as well as the group-by-walking condition interaction, were assessed using a mixed-design two-way ANOVA. A mixed-design ANOVA also was used to assess differences in vGRFs between groups and plantar foot regions (LRF vs. MRF vs. MID vs. LFF vs. LFF vs. MFF vs. TOES), as well as the group-by-plantar foot region interaction. The sphericity of the data was determined based on Mauchly's test, where a significant Greenhouse-Geisser correction was used. Pairwise comparisons were performed using the Bonferroni adjustment. The statistical analysis of the data was performed with SPSS 29.0 (IBM Corp, Armonk, NY, USA), while the significance level was set at the level of \(p\leq 0.05\).
# Multi-Objective Defocus Robust Source and Mask Optimization Using Sensitive Penalty
## 4 Conclusions  
In conclusion, we proposed the DRSMO to compensate for uncertain defocus and OPE in real lithography processes. The inverse optimization framework was based on a new cost function that constrained the uniformity of an aerial image in different defocus disturbances, and a more robust lithographic source and mask with lower defocus sensitivities were designed. Using this method, the robustness against focus shifting was dramatically improved and the DOF and PW were extremely enlarged as well. It created a larger exposure tolerance in the actual lithography process and it was especially applied to high fidelity exposures in cutting-edge technical nodes.  
Conceptualization, P.W. and Y.L.; methodology, P.W. and T.L.; software, P.W. and T.L.; validation, P.W. and T.L.; formal analysis, N.S. and Y.L.; investigation, P.W. and T.L.; resources, T.L.; data curation, N.S. and Y.S.; writing--original draft preparation, P.W. and Y.L.; writing--review and editing, P.W. and Y.L.; visualization, E.L. and Y.S.; supervision, Y.L.; project administration, Y.L.; funding acquisition, Y.L. This research was funded by the General Program of National Natural Science Foundation of China (Grant No.61675026) and the National Science and Technology Major Project (Grant No. 2017 ZX02101006-001).  
We gratefully acknowledge KLA-Tencor Corporation for providing academic use of PROLITH. We thank Mentor Graphics Corporation for providing academic use of Calibre.  
**Conflicts of Interest:** The authors declare no conflict interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Algorithm** & \(S_{\beta}\) & **DOF (EL = 5\%)** & **DOF (EL = 8\%)** & **Run Time** \\ \hline \multirow{2}{*}{**DRSMO (\(\omega\) = 0.1)**} & MBGD & 25.5 & 122 & 107 & 22,072.7 \\ \cline{2-6}  & SGD & 34.3 & 111 & 92 & 14,846.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The values of \(S_{\beta}\), DOF (nm) corresponding to ELs equal to 5% and 8% and run time (seconds) for DRSMO proceeded by the MBGD and SGD, respectively.  
Figure 9: PWs of target 1 for the DRSMO with the same weight factor \(\omega\) = 0.1 that were proceeded by the SGD algorithm (jasper curve) and MBGD algorithms (red curve), respectively.
## 4 Experiments and Analysis of Results
### Analysis of Results
#### 4.4.1 Performance Comparison of Different Models
Experiments demonstrated that the MD-SERel model proposed in this study can effectively extract deeper semantic and syntactic features, improving representational capabilities. It has shown excellent performance in the pesticide domain, where text information is dense and logical structures are complex.
## 5 The Proposed AFB-GPSR Strategy  
In ad hoc networks, it is preferable to generate beacons at a high rate to increase the freshness of the exchanged information (i.e., increase neighborhood awareness), but this may increase network congestion and resource consumption. On the contrary, transmitting fewer beacons saves bandwidth and reduces congestion. This may result in outdated information. Therefore, it is essential to employ adaptive schemes that adjust the beacon rate based on various criteria such as network topology, mobility, and link quality.  
A fuzzy logic system (FLS) is described in general as a nonlinear mapping of a data input vector to a scalar output. FLS is made up of four modules [30];1. Fuzzification module--The system's crisp values are converted into fuzzy sets utilizing fuzzy linguistic variables and terms, as well as membership functions.
2. Knowledge base--contains expert-provided IF-THEN rules.
3. Inference engine--performs fuzzy inference on inputs using IF-THEN rules from the knowledge base.
4. Defuzzification module--converts the inference engine's fuzzy set into a crisp value.  
In particular, in geographical routing, each node makes forwarding decisions based on the position information contained in the neighbor database. Therefore, to accommodate the rapid topology changes in MANET, the AFB-GPSR strategy is proposed as an optimization of the original GPSR routing algorithm. The improvements are made primarily for the GPSR beaconing strategy in both GF and perimeter modes to ensure the precision and truthfulness of routing table information and ensure optimal routing achievement. Therefore, a fuzzy logic controller (FLC) is modeled to evaluate the optimal beaconing interval time (OBIT) based on the variations of three key network parameters that have a large effect on the network performance:  
1. The node speed (NS) of the current sender, regarded as the main cause of link failure;
2. The one-hop neighbors' density (OHND), which affects the availability of next-hop resources; and
3. The link reliability (LR) between the sender and its neighbors.
# Investigation into the Fire Hazards of Lithium-Ion Batteries under Overcharging
## 3 Results and Discussion
### Burning Process  
Figure 5 shows several typical moments during the burning process of LIB under the effect of a 2 kW power electric heater. The two types of LIBs exhibited similar burning phenomena during tests; these can be divided into five stages: (a) heating stage; (b) rupture and ignition; (c) violent ejection; (d) stable combustion; and (e) flame abatement. During the heating stage, the battery remained stable and only part of the packing melted. With the continuous rise of temperature, the safety vent cracked, accompanied by a clear sound. Hereafter, t smoke was released and then the smoke ignited. The flame lasted until a large quantity of smoke was liberated, resulting in the violent ejection, and then stable combustion could be observed. With the depletion of the combustibles, the fire began to abate and be extinguished. These phenomena were consistent with those observed in previous works [16; 17; 18].  
Comparing the fire behaviors of 4.2 V LIB and 5.0 V LIB, it can be seen that the overcharged LIB exhibits earlier ignition, ejection, and stable combustion than the normal LIB. Moreover, its ejection and combustion are much more violent. This reveals that the overcharged LIB possesses a more serious combustion process and a lower stability than the normal LIB. It is the result of unstable electroactive materials, where highly delithiated electroactive materials become more reactive in the overcharged LIB [6].  
Some photographs of batteries before and after tests are presented in Figure 6. It is obvious that LIBs were severely damaged after burning. The surface packing was entirely burned away after tests. The iron can of battery deformed under the expansion of generated gases and there were some electrolyte residuals on the surface of the battery. Also, it is clear that the positive pole of battery was carbonized totally.  
Figure 5: \(Cont.\)
# Improving Spaceborne GNSS-R Algal Bloom Meteorological Data
## 3 Detection Method  
### Employed CYGNSS Observations  
GNSS-R is sensitive to the roughness change over lake surfaces, and this change can be seen clearly in the DDMs of CYGNSS (Figure 2). The open water surface tends to be rough when wind blows [41], but when a large-scale floating object exists, it will turn smoother, making the GNSS-R reflected signals coherent [42]. The coherent reflections are easily distinguished through observables extracted from DDMs, which are also the basis of GNSS-R sea ice and oil slick detection.Once the plankton in water reproduce quickly, a kind of green paint-like substance will appear over the water surface, leading to an increase in water surface tension and reduction in the formation of waves, making the GNSS reflected signals coherent. Therefore, monitoring cyanobacteria bloom with the employment of GNSS-R is theoretically feasible. The relationship between the forward scattered GNSS signals and delay-Doppler is represented by a two-dimension function. The maximum power point in the DDM is usually associated with the SP. When the area of SP is covered by cyanobacteria bloom, the reflected GNSS signals are typically coherent, and the power is concentrated near the maximum power point in DDM, but the power will spread into a 'horseshoe' shape when the signals come from a rough water surface. In this way, the cyanobacteria bloom can be detected by the observables extracted from DDM (Figure 2).  
Based on the reasons above, the pixel number (PN) observable of each DDM, GNSS-R reflectivity (SR, \(\Gamma\)) and signal-to-noise ratio (SNR) of every CYGNSS data point are selected to detect the suspicious coherent signals from the lake surface. The PN observable, called the DDM spreadness, that can effectively determine the surface roughness surrounding SP sensitively [42], is employed here and defined as the number of pixels with its value > 0.1 in the normalized DDM.  
Assuming that the reflected signals are coherent, the surface reflectivity \(\Gamma\) is also employed and calculated by [22]  
\[\Gamma=\frac{\sigma(R_{t}+R_{r})^{2}}{4\pi(R_{t}R_{r})^{2}}, \tag{2}\]  
where \(\sigma\) is the bistatic radar cross section, \(R_{t}\) and \(R_{r}\) are the distance from the SP to GNSS satellite and from the SP to CYGNSS satellite, respectively.  
The last observable employed is SNR, which can be read directly from the CYGNSS dataset. An approximate correlation between the cyanobacteria bloom condition and the three observables mentioned above can be seen in Figure 1.
## 2 Materials and Methods
### Hydrodynamic Forces  
Once the pressure field is obtained, the oil film resultant forces can be evaluated as shown in Equation (8). This approach calculates nonlinear forces' components in radial and tangential directions, as can also be seen in Figure 1.  
\[\left\{\begin{array}{c}F_{r}\\ F_{t}\end{array}\right\}=\int_{0}^{2\pi}\int\limits_{\frac{-I}{2}}^{\frac{I}{2}}p \left\{\begin{array}{c}\cos\theta\\ \sin\theta\end{array}\right\}dzRd\theta \tag{8}\]  
Through the attitude angle, it is possible to bring the forces from the reference frame to the inertial one by decomposition, as follows in Equation (9).  
\[\left\{\begin{array}{c}F_{x}\\ F_{y}\end{array}\right\}=\left\{\begin{array}{c}-F_{y}\text{sen}\phi+F_{t} \cos\phi\\ F_{r}\cos\phi+F_{t}\text{sen}\phi\end{array}\right\} \tag{9}\]  
These forces are directly inserted into the system's equations of motion, when considering the nonlinear model.  
For characterizing the oil film behavior, the linear approach considers the lubricant inside the journal bearing as a set of springs and dampers [5; 6]. Using a first-order Taylor series expansion, one can write the linearized force as presented in Equation (10), in which the bearing's dynamic coefficients of stiffness \(\left(K_{xx},K_{yy},K_{xy}\right)\) and damping \(\left(C_{xx},C_{yy},C_{xy}\right)\) are the partial derivatives of the forces calculated around the equilibrium position (Equation (11)).  
\[\begin{array}{c}F_{x}=F_{x0}+K_{xx}\Delta x+K_{xy}\Delta y+C_{xx}\Delta\dot{ x}++C_{xy}\Delta\dot{y}\\ F_{y}=F_{y0}+K_{yx}\Delta x+K_{yy}\Delta y+C_{yx}\Delta\dot{x}++C_{yy}\Delta \dot{y}\\ K_{xy}=\left(\frac{\partial F_{x}}{\partial y}\right)_{0};\;\;C_{xy}= \left(\frac{\partial F_{x}}{\partial\dot{y}}\right)_{0}\end{array} \tag{10}\]  
\(F_{x0}\) and \(F_{y0}\) are the hydrodynamic forces at the journal equilibrium position and \(\left(\Delta x,\Delta y\right)\) and \(\left(\Delta\dot{x},\Delta\dot{y}\right)\) disturbances around it.
# Multi-Band Unmanned Aerial Vehicle Antenna for Integrated 5G and GNSS Connectivity
## 2 Dual-Port Inverted T-Shaped MIMO Antenna and Analysis
### Envelope Correlation Coefficient  
As a diversity parameter, the envelope correlation coefficient (ECC) can be derived from radiation patterns or S-parameters, but ECC values obtained from far-field radiation patterns are generally preferred. This preference stems from the fact that the ECC calculated from radiation patterns effectively illustrates the independence of radiation patterns among multiple radiating elements in MIMO systems. Additionally, it is essential to consider that many planar antennas experience loss, underscoring the need for caution when relying on  
Figure 11: Axial ratio of the single-patch, MIMO, and MIMO + FSS antennas.  
Figure 10: 3D gain of the single-patch, MIMO, and MIMO + FSS antennas.  
S-parameters for ECC determination. The computed ECC values are below 0.01, which is very good for the proposed application. Figure 13 shows the ECC values using an S-parameter, and Figure 14 shows the ECC values using a radiation pattern.
# Enhancing Data Security Through VLSM Subnetting and TCP/IP Model in an ENT
## 4 Results
### Tracer  
Demonstrates the route or path through which messages (echo packets) travel to its destination.  
In Figure 16, the first line shows that the first hop (router) in the path to 192.168.5.5 has an IP address of 192.168.5.193, and the response time is 0 milliseconds (ms) for all three attempts. A response time of 0 ms typically indicates that the hop is within the local network. The second line represents the second hop with the IP address 192.168.5.226. The response time is 1 ms for the first attempt and 0 ms for the other two attempts. Like the first hop, the low response time suggests that this hop is also within the local network. The third line shows the third hop with the @P address 192.168.5.230. The response time is 0 ms for the first attempt and 1 ms for the other two attempts. Again, the low response time implies that this hop is within the local network. The fourth line represents the final hop, reaching the destination IP address 192.168.5.5. The response times vary between 10 ms and 45 ms, suggesting that there might be some network congestion or latency between the current location and the destination.  
Figure 15: (**a**) PC2 in Location A pings another PC in Location C. (**b**) PC0 in Location B pings Laptop0 in Location C. (**c**) PC2 in Location B pings another PC in Location A.  
Overall, the output suggests that the destination 192.168.5.5 is reachable, and the path appears to be within the local network without any significant delays.
## 3 Results and Discussion
### Rainfall-Runoff Simulation and Water Balance  
Table 4 summarizes the results of runoff generation for different climate/land-use scenarios. For the four scenarios investigated, the amount of runoff gradually increased from 79 to 85 between the 2000s and 2020s. This substantial increase can be correlated with the rapid urbanization observed in Multan over the last few decades. The SCS curve number was found to have a direct relation with the rate of infiltration and runoff discharge. The correlation between runoff discharge and curve number is shown in Figure 11. It should, however, be noted that no discharge gauges are installed in the study area to calculate the observed runoff and confirm the HEC-HMS simulation performance. Nevertheless, the simulated results are in good agreement with previously published research, which considered the same LULC, meteorological and soil conditions [44]. Moreover, in the first five-year scenario, the outlet point achieved a maximum peak discharge of 133.3 (m\({}^{3}\)/s) on 25 July 2001. The second five-year scenario reached a maximum discharge of 310.8 m\({}^{3}\)/s on 08 August 2010. The third and fourth five-year scenarios yielded peak discharges of 162.6 m\({}^{3}\)/s and 959.6 m\({}^{3}\)/s on 09 September 2012 and 31 August 2017, respectively. Despite a slight difference in curve number values, similar results were obtained by [PERSON] et al. [44] for Hyderabad city, where 221.4 mm rainfall transformed into 590.4 m\({}^{3}\)/s. These results indicate that the increase in urbanization reduces the rate of water infiltration and increases runoff generation, which is one of the main causes of groundwater depletion in the study area.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{**Peak Runoff Date**} & \multirow{2}{*}{**Rainfall** **(mm)**} & \multicolumn{2}{c}{**Curve**} & \multirow{2}{*}{**Volume**} & \multirow{2}{*}{**Simulated**} \\  & & & **Number** & & **(INC)** & **Runoff (m\({}^{3}\)/s)** \\ \hline
2000–2005 & 25 July 2001 & 83.4 & 79 & 2.02 & 133.3 \\
2006–2010 & 8 August 2010 & 120 & 79.94 & 2.52 & 310.8 \\
2011–2015 & 9 September 2012 & 76.5 & 81.50 & 3.23 & 162.6 \\
2016–2020 & 31 August 2017 & 300 & 84.9 & 11.85 & 959.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Runoff generation for different climate/land-use scenarios.  
Figure 10: Land-use land cover changes from 2000 to 2020 of the study area.  
In summary, using the climatic and Land-use land cover data in HEC-HMS model, it is found that the soil infiltration rate influences overall water balance in the area. Due to the decrease in the rate of infiltration, the groundwater is depleting over time. Extensive urban infrastructure, extreme climate events, and groundwater exploitation also contribute altering the hydrologic cycle. Most of the rainwater is transformed into runoff, while the remaining part evaporates due to arid climate, without recharging the aquifers: causing the depth to water level to drop day by day.