esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Spatial Patterns in \(\Delta\Gamma\) Values  
Median \(\Delta\Gamma\) values (\(\Gamma_{\rm F}-\Gamma_{\rm C}\)) range from 0.19 to 2.46 dB (5 th to 95 th percentile) with a median of 1.21 dB (Figure 6a). We calculated the median magnitude of \(\Delta\Gamma\) for each SMAP observation in 2020. Then, we calculated the median \(\Delta\Gamma\) value for each 9 km grid cell.  
The highest median \(\Delta\Gamma\) values are found in north-central Mexico, southeast Asia, Japan, and western Australia. \(\Delta\Gamma\) values are zero and near-zero in regions with high topographic roughness and in densely forested areas, due to low signal-to-noise ratios in the CYGNSS DDMs. The high median \(\Delta\Gamma\) values seen over the Amazon and Central African rainforests are water signals, sometimes from beneath the canopy, and essentially map the regional tributaries [28]. Looking closely at these densely forested regions, \(\Delta\Gamma\) values are zero or near-zero everywhere besides the regional tributaries.  
We expect \(\Delta\Gamma\) values to be consistently low in dry and barren areas, due to limited changes in soil moisture and vegetation. However, median \(\Delta\Gamma\) values fluctuate over the Sahara Desert. This might be due to sand dunes, rock outcrops, or other topographic and surface roughness variations throughout the desert.  
The global spatial patterns in median \(\Delta\Gamma\) (Figure 6a) are not very similar to the spatial patterns in the median RMSD between 9 km SMAP TBs and 3 km SMAP/CYGNSS TBs (Figure 6c). The correlation between median \(\Delta\Gamma\) and median RMSD TB is 0.45. This suggests \(\Delta\Gamma\) is not the primary source of SMAP/CYGNSS TB spatial heterogeneity, and instead \(\beta\) is the dominant factor.  
Figure 6: (**a**) Map and histograms of median \(\Delta\Gamma\) values (described in Section 3.2), calculated using data from 2020. (**b**) Map and histograms of SMAP/CYGNSS \(\beta\) values (described in Section 2.2.2). (**c**) Map and histograms of the median RMSD between 9 km SMAP TBs and 3 km SMAP/CYGNSS TBs (described in Section 3.1), calculated using data from 2020. Gray histograms include all data. Red histograms only include data for 9 km grid cells with \(\beta\) values where R \(<-0.4\) (Section 4.3).
# Sensor Technologies for Transmission and Distribution Systems: A Review of the Latest Developments
###### Abstract  
The transmission and distribution systems are essential in facilitating power flow from the source multiple loads over large distances with high magnitudes of voltages and currents. Hence, the monitoring and control of various components of these structures are crucial. Traditionally, this was implemented by sensing only the grid current and grid voltage parameters through coils, clamps, or instrument transformers. However, these have bulky structures that restrict them to the substation and have installation and maintenance issues due to their direct contact with high voltage conductors. Currently, the power grid is undergoing various developments e.g., penetration of renewable energy sources, remote control, and automation, bidirectional power flow, etc. These developments call for compact and energy-efficient sensors to sense multiple grid parameters such as the magnetic field data, temperature, humidity, acoustics, etc., to enable real time, wide area monitoring and the predictive maintenance of the power grid. The goal of this paper is to summarize the advancements in sensing technologies on transmission and distribution systems over a decade and to explain their role in the forthcoming expansion of the power grids. This paper aims to outline the current state-of-affairs of sensor technology as well as to fill research gaps by exploring their limitations.  
big data; condition-based grid maintenance; pervasive sensing; predictive grid maintenance; smart sensors; wide area monitoring system; wireless sensing network (WSN) 2022157339. [[https://doi.org/10.3390/en15197339](https://doi.org/10.3390/en15197339)]([https://doi.org/10.3390/en15197339](https://doi.org/10.3390/en15197339))
# Rogue Node Detection Based on a Fog Network Utilizing Parked Vehicles
###### Abstract  
Rogue nodes in the Internet of vehicles (IoV) bring traffic congestion, vehicle collision accidents and other problems, which will cause great social losses. Therefore, rogue node discovery plays an important role in building secure IoV environments. Existing machine learning-based rogue node detection methods rely too much on historical data, and these methods may lead to long network delay and slow detection speed. Moreover, methods based on Roadside Units (RSUs) have poor performance if the number of RSUs is insufficient. Based on the widespread presence of ground vehicles, we propose a rogue node detection scheme based on the fog network formed by roadside parked vehicles. To achieve efficient rogue node discovery, a fog network composed of stable roadside parked vehicles is dynamically established, in which each fog node firstly collects the information of moving vehicles on the road in its coverage range, and then fog nodes use the U-test method to determine the rogue nodes in parallel, so as to find the bad nodes efficiently. Simulation results show that the proposed algorithm has higher detection accuracy and stability than the other rogue node detection schemes.  
Internet of vehicles; rogue nodes; parked vehicles; fog networks Article
## 2 Related Work
### Volume Estimation
#### 2.3.1 Depth Camera  
In volume estimation research, the depth camera method [8; 9] is one of the most commonly used approaches. This method uses depth sensors to capture depth images of food and calculates the volume by measuring the distance of each pixel in the depth map from the reference plane. For example, the Depth Calorie Cam method [27] estimates the volume of food placed on a reference plane, such as a plate or table, by calculating the depth of each pixel relative to the reference plane. However, the accuracy of this method highly depends on the initial setup, such as whether the camera is parallel to the reference plane. The prediction results may be affected if the camera angle is not perfectly perpendicular to the reference plane.
## 4 Results and Discussion
### Comparison of SC Calculated Using Different Methods with Experimental Results  
Figure 10 presents a comparative analysis between the SC generated by laser pulses at 3.8 \(\upmu\)m with a peak power of 500 kW and a duration of 150 fs and the corresponding experimental results obtained under identical conditions. In Figure 10a, the Raman response  
Figure 8: Variation of the dispersion profile with wavelengths for Ge–Sb–Se SIF.  
Figure 9: Variation in the effective mode area with wavelengths for Ge–Sb–Se SIF.  
function utilized is generated using the multiple vibrational mode model, whereas in Figure 10b, a simple damped harmonic oscillation based on the single Lorentzian model reported in Ref. [21] is employed. The observed disparity between the SC generated using the Raman response function is based on the simple damped harmonic oscillation and the experimental result and is particularly noted in the longer wavelength range. Conversely, the SC generated using the Raman response function derived from the multiple vibrational mode model exhibits a remarkable agreement with the experimental results across the entire range of wavelengths. Figure 10c,d shows the spectral evolution of the simulation in Figure 10a,b, respectively. This outcome highlights the significant impact of the Raman response function on the SC generation process and serves as compelling evidence that the Raman response function obtained through the multiple vibrational mode model offers superior accuracy when compared to the single Lorentzian model. Consequently, this model presents a more precise approach for studying and simulating materials characterized by a multi-peaked Raman gain spectrum, which holds significant potential for enhancing the performance and output characteristics of Raman lasers as well as for facilitating the design and evaluation of fiber amplifiers.
# C-V2X Centralized Resource Allocation with Spectrum Re-Partitioning in Highway Scenario
## 2 Assumptions and Problem Formulation
### C-V2X Assumptions
\(\gamma_{\min}\) can be obtained with the following calculations:  
* Coding Rate \(R_{rate}\) in C-V2X can be obtained as: \[R_{rate}=\frac{Bits_{tot}}{N_{RB}N_{eff}N_{SC}N_{pps}}\] (9) where \(Bits_{tot}\) is the number of bits that are carried by \(N_{RB}\) resource blocks, \(N_{eff}\) is the number of OFDM symbols carrying \(Bits_{tot}\) (equal to 9 [33]), \(N_{SC}\) is the number of sub-carriers in each resource block (equal to 12 [11]), \(N_{pps}\) is the number of bits in each data symbol. \(Bits_{tot}\) and \(N_{RB}\) are found from Table 7.1.7.2.1-1 in [11]. It is important to mention that the number of resource blocks \(N_{RB}\) in [11] is given for one time slot. * Spectrum efficiency \(\Theta\left[\mathrm{b/s/Hz}\right]\) is the second step in calculation, according to the following formula: \[RB_{free}=(RB_{T}-SCH_{sf}RB_{scl})N_{SF_{guide}} \tag{2}\]0 (10) where \(N_{TS}\) is total number of OFDM symbols in a sub-frame (equal to 14 [33]), the sub-frame duration is denoted by \(T_{sf}\) (1 ms), and \(RB_{BW}\) is the bandwidth of a resource block, namely 180 kHz. * The minimum signal-to-interference plus noise ratio \(\gamma_{\min}\) can be obtained by inverting the Shannon equation taking into account the implementation loss. Finally, \(\gamma_{\min}\) can be found by the following formula: \[RB_{free}=(RB_{T}-SCH_{sf}RB_{scl})N_{SF_{guide}} \tag{2}\]1 (11) where \(\eta\) is the loss in implementation and is assumed to be equal to 0.6 according to [34].
# Recent Progress on Irradiation-Induced Defect Engineering of Two-Dimensional 2H-MoS2 Few Layers
## 6 Irradiation-Induced Properties
### Catalytic Properties  
Molybdenum is relatively abundant and cheaper than noble metals. Thus, molybdenum compounds have been used as electrocatalysts and photoelectrocatalysts to supersede noble metal catalysts. It was found that MoS\({}_{2}\) edges were catalytically active for hydrogen evolution reaction (HER) while the basal surfaces were catalytically inert. So MoS\({}_{2}\) nanomaterials have been employed as catalysts to generate hydrogen [69; 176; 177; 178; 179] because of more catalytic edges. MoS\({}_{2}\) nanoparticles were first used as photocatalysts [180] and electrocatalysts [181] to generate hydrogen in the 2000s. The catalytic properties were controlled by edge sites of the MoS\({}_{2}\) materials. MoS\({}_{2}\) 2D films were more active and employed as photocatalysts [182] late in 2016 for water purification,  
Irradiation can introduce defects in MoS\({}_{2}\) layers, which create more HER active sites. Sulfur vacancies can activate and optimize hydrogen evolution [20]. Additionally, electrical conductivity can be tuned by the introduced defects. Therefore, catalytic activities of MoS\({}_{2}\) HER can be significantly enhanced by irradiation defect engineering. [PERSON] et al. [183] treated the CVD-grown MoS\({}_{2}\) thin films by Ar plasma. The active site density of MoS\({}_{2}\) thin films increased five times to \(7.74\times 10^{16}\) cm\({}^{2}\) after the Ar plasma treatments. HER performance was enhanced significantly too. However, long-time Ar treatments etched MoS\({}_{2}\), not benefited the HER behaviors. The MoS\({}_{2}\) sheets were also treated by O\({}_{2}\) plasma [183]. The O\({}_{2}\) plasma treatments led to the formation of both Mo-O and S-O bonds, generating more active sites with the increasing plasma time, enhancing the HER activity significantly.
## 3 Control Strategy
### Coupling Error Control Model
Similar to the studies of [34; 35; 36], taking the wheel 1 of the FC as an example, a Lyapunov function is constructed to judge the system stability, as shown by Equation (21):  
\[V_{1}=\frac{E_{1}^{2}}{2}>0. \tag{21}\]  
If Equation (22) exists and Equation (23  
+++ ==WARNING: Truncated because of repetitions==
) is true, the system is stable when \(E_{1}\) approaches zero:  
\[\dot{E}_{1}=-c_{1}E_{1}(c_{1}>0),\] (22)  
\[\dot{V}_{1}=E\dot{E}_{1}=-c_{1}E_{1}^{2}<0.\] (23)  
Combining Equations (17), (19), and (20) allows the following equation to be derived:  
\[\eta_{p}^{*}=\frac{\eta_{p}}{\eta_{k}}=\frac{\omega_{p}}{\omega_{k}}. \tag{15}\]0 (24)  
\[\eta_{p}^{*}=\frac{\eta_{p}}{\eta_{k}}=\frac{\omega_{p}}{\omega_{k}}. \tag{15}\]1 (25)  
\[\eta_{p}^{*}=\frac{\eta_{p}}{\eta_{k}}=\frac{\omega_{p}}{\omega_{k}}. \tag{15}\]2 (26)  
where  
\[\eta_{p}^{*}=\frac{\eta_{p}}{\eta_{k}}=\frac{\omega_{p}}{\omega_{k}}. \tag{15}\]3  
For any positive real number \(c_{1}\), \(\mu_{1}\) can be constructed such that \(E_{1}\) approaches zero. Therefore, we can construct Equation (28) to make \(E_{1}\)\(\rightarrow\)0. Similarly, \(\mu_{i}\) can be constructed to make \(E_{i}\)\(\rightarrow\)0. Based on the converse derivation of Equation (28), it also can be found that, if Equation (28) is established, then Equation (22) can be guaranteed. As one of the control parameters, \(c_{1}\) will affect the regulation time and overshoot of the control system. Through pre-research simulation, it was found that, if \(c_{1}\) is too small, the adjustment time of the system will increase, and, if \(c_{1}\) is too large, the overshoot will increase.
# High-Efficiency Quantum Dot Lasers as Comb Sources for DWDM Applications
## 1 Introduction  
Cloud-based computing continues to fuel the demand for more internet protocol traffic, thrusting hyperscale data centers into the \"Zeta-byte era\" [1]. To reduce power spent on input-output (IO) and to eliminate the IO bottleneck at the front panel of server rack units [2], data center links will see photonics move into electronics packages in the near future [3, 4]. This seriously constrains the power consumption, footprint, and spectral efficiency of next-generation links. Significant breakthroughs in silicon photonics have led to compact, power-efficient transceivers, which benefit from the high-volume production capability of silicon [5].  
One technology in particular, micro-ring modulators (MRM), has allowed silicon photonic transceivers to produce optical links with extremely high IO bandwidth per mm\({}^{2}\)[6]. MRMs benefit from a compact footprint and are resonant structures, allowing them to modulate a single wavelength in a dense wavelength division multiplexing (DWDM) signal, thus eliminating optical multiplexers and demultiplexers (MUX/DEMUX) [7]. This capability has dramatically increased the need for a monolithic light source that can deliver all DWDM channels. The three approaches typically considered to meet this need are distributed feedback laser (DFB) arrays, nonlinear optical frequency combs (NOFC), and semiconductor mode-locked lasers(MLLs) [8].
## 2 Materials and Methods
### Optimal Feature Extraction  
For feature extraction, the TSADL-LULCCD technique uses dense EfficientNet model. A novel dense CNN approach was utilized to mix the existing EfficientNetB0 with dense layers. An EfficientB0 has 230 layers and 7 MB Conv blocks [19]. It features a thick block infrastructure containing 4 tightly connected layers with rate of development is 4. All the layers under this infrastructure utilize the resultant mapping features of the previous  
Figure 1: Workflow of TSADL-LULCCD approach.  
levels as input mapping features. The dense block attains benefits of earlier convolutional layer resultant mapping features for generating further mapping features with some convolutional kernels. This CNN method recovered LANDSAT 7 image database. The dense EfficientNet is an alternative drop-out and dense layer. The dense layer is fundamental layer that provides every result in the preceding layer to every neuron, all the neurons offering one outcome to next layer. It starts by adding a pooling layer, then 4 dense layers and 3 drop-out layers to ensure the method runs smoothly. At the final stage, a dense layer collected of 4 FC neurons in conjunction with Softmax outcome layer for computing and classifying the probability score to each class.  
In order to change the hyper parameters related to the dense efficient approach, the Adam optimizer is used [20]. The Adam algorithm performs dynamic adjustment of different parameters by evaluating \(m_{t}\) and \(v_{t}\) first-order and second-order moment estimate, as given in Equations (1)-(3), where \(\beta_{1}\) and \(\beta_{2}\) correspondingly describe the first-order and the second-order exponential damping decrement. Parameter \(g_{t}\) is a gradient at \(t\) timestep in \(J_{sparse}(W,\ b)\) loss function.  
\[m_{t}=\beta_{1}m_{t-1}+(1-\beta_{1})\cdot g_{t} \tag{1}\]  
\[v_{t}=\beta_{2}v_{t-1}+(1-\beta_{2})\cdot g_{t}^{2} \tag{2}\]  
\[g_{t}\leftarrow\
abla_{\theta}J_{t}(\theta_{t-1}) \tag{3}\]  
Computer bias-corrected for \(m_{t}\) and \(v_{t}\):  
\[\hat{m_{t}}=\frac{m_{t}}{1-\beta_{1}^{t}} \tag{4}\]  
\[\hat{v_{t}}=\frac{v_{t}}{1-\beta_{2}^{t}} \tag{5}\]  
Update parameter:  
\[\theta_{t+1}=\theta_{t}-\frac{\gamma}{\sqrt{\hat{v_{t}}+\hat{\xi}}}\cdot\hat{m_ {t}} \tag{6}\]  
\(\gamma\) represents the update stepsize, \(\xi\) takes a smaller constant to prevent the denominators to be 0
###### Abstract  
The modified Cam-clay model (MCC model) is capable of representing the consolidation process of the soil under a complex stress path and thus is extensively adopted in the numerical analysis of excavation engineering. For reliability problems of ground surface settlement of excavation, minimal attention has been paid to investigating the effect of spatial variable MCC model parameters on the settlement during the staged construction of the excavation. Based on laboratory tests and sensitivity analysis, this study launched the probabilistic transient hydro-mechanical coupling analysis of excavation settlement considering spatial variable deformation parameters (\(\lambda\) and \(\kappa\)) using an MCS-FORM hybrid approach. The results show that: (1) Both the deformation parameters have a positive relationship with the settlement, and \(\kappa\) has a more significant effect than \(\lambda\). (2) The observed maximum settlement and the location with maximum settlement follow a log-normal distribution. (3) An increasing COV of parameters leads to an enhanced surface settlement, expansion of the significant influence region, and decreased reliability. (4) The reliability index is greatly enhanced with the delimited controlled standard value of the surface settlement, \(H_{\text{conv}}\), and it decreases significantly at the preliminary stage and then decreases progressively until stable during excavating. Overall, adopting the deterministic analysis without considering the spatial variability of MCC parameters leads to underestimating the risk due to the settlement and the significant influence region.  
modified cam-clay model; reliability; ground surface settlement; excavation
# Leveraging Advanced NLP Techniques and Data Augmentation to Enhance Online Misogyny Detection
## 5 Discussion
### Performance of Transformer Models  
Transformer models, already strong contenders in misogyny detection, showed considerable performance gains with dataset enrichment. Models like ALBERT and DISTILBERT, which initially had solid f1-scores of 0.671 and 0.610, now achieved near-perfect F1-scores of 0.929 and 0.975, respectively. These models excel at detecting the subtle and complex nature of misogynistic language, a challenge with which simpler ML and DL models often struggle. The enriched dataset not only improves Recall but also enhances the Transformers' ability to discern nuanced context, which is critical for ethical and fair detection. As shown in Figure 12, Transformer models, while performing better than ML models, still fall slightly short of the performance achieved by DL models. DistilBERT, for instance, stands out with balanced performance across all evaluation metrics, achieving high Precision, Recall, F1-Score, and ROC AUC. ELECTRA, BERT, and RoBERTa also maintain strong performance, with near-perfect scores across key metrics. While XLNet performs near-optimally, ALBERT shows relatively lower Recall, impacting its F1-Score. Nevertheless, these models highlight the advantages of Transformer architectures in handling complex, context-rich classification tasks.  
Generally, the performance of models in misogyny detection is heavily influenced by the Precision-Recall trade-off, with some models, like SVM, NB, and LR, prioritizing Precision at the cost of Recall. While this reduces false positives, it may result in missed instances of subtle misogynistic language. A more balanced approach between Precision and Recall ensures better overall detection. Data augmentation significantly improved the performance of all models, with machine-learning models, particularly Random Forest, achieving perfect classification results. Deep-learning models like CNN, LSTM, GRU, and BiLSTM also reached optimal performance, while Transformer models, such as BERT, DistilBERT, RoBERTa, and ELECTRA, outperformed traditional machine-learning models. These improvements highlight the importance of enriched training data for enhancing model robustness and generalization across nuanced datasets.
# Design of Two Compact Wideband Monopoles Through Loading with Linear Approximated Lumped Components
## 4 Performance Comparison  
To further illustrate the advantages of the presented colinear loaded monopoles, the comparisons with other loaded monopoles are listed in Table 3. Refs. [33; 35; 38] also design monopoles using the load technique but employ a constant loaded component in their simulation which might require more effort in tuning the prototype. And these works have a similar gain and VSWR in only one frequency band. Ref. [39] proposed a design with a dual wide band but their minimum gain is lower compared with other works. Ref. [40] achieved a better gain and VSWR with a dual band but its BWR is lower. Our proposed antenna has two frequency bands with a compact size, and both monopoles have a wider band with an acceptable gain and VSWR.  
Figure 14: (**a**) Radiation efficiency of lower monopole. (**b**) Radiation efficiency of upper monopole.  
Figure 13: Electrical property of upper monopole. (**a**) Measured and simulated VSWRs. (**b**) Measured and simulated gains.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Ref.** & **Operating Band** & **Gain (dB)** & **VSWR** \\ \hline
[33] & 30–450 MHz & \(>\)–2 & \(<\)3.5 \\ \hline
[35] & 2–30 MHz & \(>\)–4.5 & \(<\)3.5 \\ \hline
[39] & 30–600 MHz & \(>\)–21 & \(<\)3 \\  & 820–1200 MHz & \(>\)–5 & \(<\)2 \\ \hline
[38] & 120–520 MHz & \(>\)–4 & \(<\)2 \\ \hline
[40] & 3–5 GHz & \(>\)3 & \(<\)2 \\  & 6–14 GHz & \(>\)3 & \(<\)2 \\ \hline This work & 30–750 MHz & \(>\)–2.5 & \(<\)3.5 \\  & 800–3000 MHz & \(>\)0 & \(<\)2.5 \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of presented works with other reported wide-band monopoles.