esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Evaluation of the Heavy Metals Contamination Level
#### 3.4.2 Igeo  
The Igeo values must be interpreted as follows [46]:  
Igeo \(\leq\) 0 denotes no contamination;  
For 0 < Igeo \(\leq\) 1, no/a medium degree of contamination;  
For 1 < Igeo \(\leq\) 2, a medium degree of contamination;  
For 2 < Igeo \(\leq\) 3, a medium/high degree of contamination;  
For 3 < Igeo \(\leq\) 4, a high degree of contamination;  
For 4 < Igeo \(\leq\) 5, a high/very high degree of contamination;  
Igeo > 5, a very high degree of contamination.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Metal**} & \multicolumn{4}{c}{**Index of Contamination**} \\ \cline{2-5}  & **EF** & **Igeo** & **CF** & **PLI** \\ \hline As & 0.07 & \(-\)4.49 & 0.07 & \\ Cd & 0.10 & \(-\)3.91 & 0.10 & \\ Cu & 1.58 & 0.07 & 1.57 & \\ Hg & 0.10 & \(-\)3.91 & 0.10 & \\ Ni & 0.13 & \(-\)3.49 & 0.13 & \\ Pb & 0.45 & \(-\)1.75 & 0.45 & \\ Sb & 0.04 & \(-\)5.23 & 0.04 & \\ Tl & 0.01 & \(-\)6.71 & 0.01 & \\ Zn & 0.53 & \(-\)1.51 & 0.53 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Calculated values of EF, Igeo, CF and PLI for the investigated sample.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**ICP-MS Analysis**} \\ \hline  & & _Threshold limit_ \\ \hline C\({}_{\rm As}\) & 0.87 & 20 \\ \hline C\({}_{\rm Cd}\) & 0.03 & 2 \\ \hline C\({}_{\rm Cu}\) & 70.8 & 120 \\ \hline C\({}_{\rm Hg}\) & 0.04 & 1 \\ \hline C\({}_{\rm Ni}\) & 9.09 & 120 \\ \hline C\({}_{\rm Pb}\) & 8.90 & 100 \\ \hline C\({}_{\rm Sb}\) & 0.06 & 10 \\ \hline C\({}_{\rm Tl}\) & 0.02 & 1 \\ \hline C\({}_{\rm Zn}\) & 50.1 & 150 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Heavy metals content (mg kg\({}^{-1}\) d.w.) for the analyzed basalt aggregate for concrete.  
The obtained I\({}_{\text{geo}}\) values, reported in Table 4, were found to be <0 with the only exception being copper, probably because it is often used as a soil defense product, as well as the soil texture and its high pH [46; 47].
## 3 Wavefront Sensorless AO-RL Environment
### Episodic Environment  
In real-world scenarios, this RL problem can be characterized as either a finite or infinite horizon problem. In the finite horizon case, each episode lasts for the duration of the satellite's communication with the receiver. Here, we focus on the episodic form of the problem. We assess the effectiveness of an RL policy that can map the deformable mirror from its neutral position (flat mirror) to a shape that focuses the beam on the single-mode fiber. We conducted a parametric study with respect to the episode's length, ranging from 10 to 100 time steps. Our findings indicate that the adoption of an episode length of 20-30 time steps is sufficient to achieve optimal action.  
In the generated AO-RL environment, the option of selecting quasi-static, semi-dynamic, or dynamic environments is made available. As previously mentioned in Section 2.2, in a quasi-static environment, it is assumed that the atmosphere can be considered to be in a quasi-static state and a static turbulence profile for training purposes is considered valid. In a semi-dynamic environment, the configuration of the quasi-static atmosphere changes in each episode. The utilization of a semi-dynamic environment can be found to be helpful in the generation of the initial configuration of neural network weights before transitioning to a dynamic environment. In a dynamic environment, the movement of the atmosphere is influenced by a velocity vector where the final timestep of episode \(i\) is the same as the initial timestep of episode \(i+1\).
## 5 Empirical Test and Result Analysis
### Influencing Mechanism Analysis
#### 5.4.2 Enhancing Innovation Capacity
Columns (3) and (4) reported the regression results of the mediating effect of adding control variables. The regression coefficients of _Ludigital_ and _Lntd_ to ESG were both significant at the 1% level, and the mediating effect was established in this case. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **(1)** & **(2)** & **(3)** & **(4)** \\ \hline  & _Lntd_ & _ESG_ & _Lntd_ & _ESG_ \\ \hline _Lndigital_ & 0.0761 *** & 0.0043 & 0.0875 *** & 0.0077 *** \\ \hline  & (0.0155) & (0.0031) & (0.0121) & (0.0029) \\ \hline _Lntd_ & & 0.0571 *** & & 0.0159 *** \\ \hline  & & (0.0023) & & (0.0027) \\ \hline lnsize & & & 0.776 *** & 0.0637 *** \\ \hline  & & & (0.0164) & (0.0041) \\ \hline lev & & & \(-\)0.294 *** & \(-\)0.0464 *** \\ \hline  & & & (0.0970) & (0.0221) \\ \hline growth & & 0.0287 & \(-\)0.0055 \\ \hline  & & (0.0188) & (0.0037) \\ \hline roa & & 1.477 *** & \(-\)0.0676 \\ \hline  & & (0.288) & (0.0587) \\ \hline cashflow & & 1.464 *** & 0.0942 * \\ \hline  & & (0.257) & (0.0552) \\ \hline mb & & \(-\)0.179 *** & 0.0079 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The mediating role of innovation ability (* \(p<0.1\), ** \(p<0.05\), *** \(p<0.01\)).
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 3 Techniques for Dense Video Captioning
### Transformer-Based Approaches
#### 3.3.3 Hierarchical Transformer Models  
Hierarchical transformer models are designed to capture the intricate hierarchical structure of videos, which encompasses scenes, actions, and objects at multiple levels of granularity. [PERSON] et al. [36] proposed a Hierarchical Modular Network (HMN) specifically for video captioning. This network consists of three key modules: the entity module, which identifies principal objects within the video; the predicate (or action) module, which learns action representations; and the sentence module, which generates the final captions. As illustrated in Figure 9, the HMN adheres to the conventional encoder-decoder paradigm, with the HMN itself serving as the encoder.  
Similarly, [PERSON] et al. [37] introduced COOT, another hierarchical model for video captioning. COOT includes components for capturing relationships between frames and words, producing clip and sentence features, and generating final video and paragraph embeddings. Both HMN and COOT decompose the video captioning task into sub-tasks, such as scene recognition, action recognition, and caption generation, and then combine the results of these sub-tasks to produce the final caption. By leveraging the hierarchical  
Figure 8: The SBAT architecture (redrawn by the authors, adapted from [34]) integrates a sparse boundary-aware strategy (Sp) into both encoder and decoder multihead attention blocks.  
Figure 9: The proposed Hierarchical Modular Network (redrawn by the authors, adapted from [36]) functions as a robust video encoder, linking video representations to linguistic semantics through three levels using entity, predicate, and sentence modules. Each sentence module has its own input, which is extracted from captions, and linguistic guidance.  
structure of videos, these models are able to generate captions that are more semantically meaningful and grammatically correct. However, the effective design of these modular architectures requires careful consideration to ensure seamless information flow across the different levels of hierarchy.
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & \multicolumn{3}{c}{**Characteristics**} \\ \hline Scientific objectives & \multicolumn{3}{c}{O\({}_{3}\), SO\({}_{2}\), NO\({}_{2}\), BrO, O\({}_{3}\) profile, Aerosol, Cloud, etc.} \\ \hline Channel & UV1 & UV2 & UVIS \\ \hline Spectral range/nm & 250–300 nm & 300–320 nm & 310–495 nm \\ \hline Spectral resolution/nm & \textasci{}1 & 0.5 & \textasci{}0.6 \\ \hline Field of view & \multicolumn{3}{c}{\(112^{\circ}\)} \\ \hline Spatial resolution & 28 km \(\times\) 21 km (at nadir) & 7 km \(\times\) 7 km (at nadir) & 7 km \(\times\) 7 km (at nadir) \\ \hline SNR & \textasci{}50 & \textasci{}200 & \textasci{}200@ UV \\  & & & \textasci{}1000@ VIS \\ \hline Wavelength calibration accuracy & 0.05 nm & 0.05 nm & 0.05 nm \\ \hline BRDF calibration accuracy & \multicolumn{3}{c}{3\%} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Specifications of UVS. Figure 1: Functional schematic of the UVS. To ensure stable operation over its 8-year lifespan, on-orbit calibration plays a pivotal role. To monitor variations in radiation response during orbit, an on-orbit solar calibration mode for UVS was designed. This mode employs the instrument's in-flight diffusers to introduce solar radiation for detection. UVS incorporates two diffusers: a primary diffuse reflector for monthly solar and white light calibration, and a backup diffuser for monitoring changes in the primary diffuse reflector every six months while in orbit. Monitoring the degradation of UVS necessitates calibration of the angular characteristics and irradiance response of UVS\({}^{2}\). Additionally, wavelength shift monitoring is performed using solar Fraunhofer lines during solar calibration. Passive radiator plates achieve cooling for the optical bench and detector module. The optical bench operates at a temperature of 293.15 \(\pm\) 2 K, while the detector operates at an even lower temperature of \(-\)243 K.
## 4 Topic Models Based on Word Learning  
The document generating mechanisms of topic model and Event Model are completely different. In order to further compare the two mechanisms, we propose a topic model based on word learning.  
After obtaining word vectors and clustering them, words can be mapped to word class vector space. Word classes usually have specific concepts as shown in Table 1, so we can explore the relationship between word classes and continue to reduce dimensionality in linear or nonlinear ways.  
We use the following three ways to further reduce dimensionality: Event Model, WC-LDA, WC-RBM, as shown in Figure 6. WC-LDA and WC-RBM are topic models based on word learning. The dimensionality reduction process of Event Model is same as word-word class and we merge word classes with similar semantics into a bigger word class. For this process, we do not need to analyze the semantics of word classes and just let \(N\) be a value which is less than the number of word classes. Then generate the document according to the linear mode. The generative model of WC-LDA and WC-RBM is word class-topic-document, which replaces word in bag-of-words model with word class.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Word Class** & **Word** & **Word Class** & **Word** & **Word Class** & **Word** & **Word Class** & **Word** \\ \hline \multirow{4}{*}{Football} & [PERSON] & [PERSON] & Google & & C \\ \cline{2-7}  & [PERSON] & \multirow{2}{*}{Basketball} & [PERSON] & \multirow{2}{*}{Brand} & Facebook & \multirow{2}{*}{Language} & C\(++\) \\ \cline{2-7}  & [PERSON] & & [PERSON] & & Microsoft & & Java \\ \cline{2-7}  & [PERSON] & & [PERSON] & & Alibaba & Python \\ \hline \hline \end{tabular}
\end{table}
Table 1: Word classes and their corresponding words.  
Figure 6: **Left:** The Relationship of \(V\) and \(v\) is linear. \(V(1)=v(1)+v(2)\), _etc._**Right:** For WC-LDA, The Relationship of \(v\) and \(h\) is nonlinear, as shown in Equations 9–12.  
For WC-LDA, calculate the model parameters by  
\[P(wc|d)=\sum_{t}P(wc|t)\times P(t|d) \tag{9}\]  
where \(wc\) is word class, t is topic and \(d\) is document. For WC-RBM, firstly change the dimensions which are not equal to 0 in the vector to 1. The energy function of word classes (\(v\)) and latent topics (\(h\)) is defined as  
\[E(v,h)=-\sum_{t}a_{i}v_{i}-\sum_{j}b_{j}\,h_{j}-\sum_{i}\sum_{j}v_{i}w_{i,j}h_{j} \tag{10}\]  
where \(a_{i}\) are bias weights for \(v\) and \(b_{j}\) for \(h\). The joint probability distribution over \(v\) and \(h\) is  
\[p(v,h)=\frac{1}{Z}\,e^{-E(v,h)} \tag{11}\]  
The partition function is  
\[Z=\sum_{v,h}e^{-E(v,h)} \tag{12}\]
# Nonlinear Seismic Response of Multistory Steel Frames with Self-Centering Tension-Only Braces
## 3 Incremental Static (Pushover) Analysis
### Energy Dissipation  
For every single SC-TOB, the energy dissipation \(E=F\Delta\), where \(F\) is the friction force selected according to brace type from Table 1, and \(\Delta\) is the slip displacement, which equals to the corresponding elongation of MPE. As shown in Figure 10, the seismic energy dissipated in each story stably increases with increasing roof drift once the relevant SC-TOBs are activated with a \(\theta\) equal to or slightly greater than 0.4% as designed.  
Figure 10: \(Cont.\)  
Figure 9: Inter-story drift response under (**a**) LD-P and (**b**) LD-U.  
Figure 11 counts the cumulative energy dissipated in each story under both load distributions. The lower stories generally dissipate more seismic energy than the middle and higher stories do, with almost no energy dissipation observed in the top story under LD-P and top three stories under LD-U, suggesting that SC-TOBs on those stories could be replaced by conventional TOBs without influencing the self-centering capability. Although the energy dissipation distributions are quite different, being more uniform under LD-P, the cumulative dissipations are not significantly different: 664.15 kN\(\cdot\)m for LD-P is 88.47% of that for LD-U (750.71 kN\(\cdot\)m).
## 5 Experimental Results
### The Compared Experimental Results
#### 5.4.3. The Comparison Results of Wilcoxon Signed-Rank Test  
To further statistically compare PTBO with the other eight algorithms, a Wilcoxon signed-rank test [45] has been carried out to show the differences between PTBO and the other algorithms. The \(p\)-values on every function by a two-tailed test with a significance level of 0.05 between PTBO and other algorithms are given in Table 12.  
Figure 9: Convergence performance of the compared algorithms on parts of functions.  
From the results of the signed-rank test in Table 12, we can observe that our PTBO algorithm has a significant advantage over seven other algorithms in p-value, which are PSO, BA, CS, BSO, WWO, WCA, and SMS. Although PTBO has no significant advantage over DE, PTBO significantly outperforms DE in R\({}^{+}\) value. R\({}^{+}\) is the sum of ranks for the functions on which the first algorithm outperformed the second [46], and the differences are ranked according to their absolute values. So, according to the statistical results, it can be concluded that PTBO generally offered better performance than other algorithms for all 28 functions.  
The above comparisons between PTBO and other nature-based meta-heuristic algorithms may offer a possible explanation for why PTBO could obtain better results on some optimization problems and that it is possible for PTBO to deal with more complex problems better.
# First Retrieval of Sea Surface Currents Using L-Band SAR in Satellite Formation
## 4 Data Processing
### Cross-Track Baseline Component Phase Removal
Finally, by subtracting the fitted phase from the actual residual phase, the phase with the cross-track baseline component removed is obtained, as illustrated in Figure 9c. At this stage of processing, we obtained a fairly good-looking interferometric phase induced by the Doppler velocity. Figure 8: Acquisition geometry of hybrid interferometric SAR. Figure 9: **(a)** The residual fringe after the orbit-based method. **(b)** The fitted flat Earth phase. **(c)** The re-flattened interferograms. The color map represents the interferometric phase.
# Power System Reliability Evaluation Based on Chronological Booth-Balieriaux Method
## 4 Case Study
### Effect of Chronological Characteristic on the Power System Reliability
#### 4.2.2 Preventive Maintenance Schedule  
Preventive maintenance changes the availability of a generator for a temporal period. Because generators under maintenance cannot contribute to the system, the generalized generator model is expressed as follows:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & **LOLH** & **EUE** & **Time** \\  & **(Hour/Year)** & **(MWh/Year)** & **(s)** \\ \hline COPT method & 0.001898 & 0.233808 & 0.60 \\ Proposed method & 0.001898 & 0.233808 & 9.81 \\ Traditional method & 0.001898 & 0.233808 & 47.23 \\ \hline \hline \end{tabular}
\end{table}
Table 2: IEEE RTS 2020 reliability indices results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Indices**} & \multicolumn{5}{c}{**Uncertainty (\%)**} \\ \cline{2-5}  & **0** & **2** & **5** & **10** & **15** \\ \hline LOLH (hour/year) & 0.001898 & 0.003347 & 0.032086 & 1.189605 & 8.549189 \\ \hline EUE (MWh) & 0.234 & 0.438 & 5.123 & 319.870 & 3615.958 \\ \hline \hline \end{tabular}
\end{table}
Table 3: IEEE RTS 2020 reliability indices results considering the load uncertainty.  
\[G_{k,t}(x)=\sum_{i=1}^{S_{k}}P_{k,j,t}(1-u_{k,t})\delta(x+X_{k,j,t}), \tag{20}\]  
where \(u_{k,t}\) is a binary value indicating whether the \(k\)th generator is under maintenance at time \(t\). An optimization problem was solved to establish the maintenance schedule, which minimizes the weekly supply ratio variance. Table 4 lists the established schedule. Figure 1 shows the optimized availability capacity and netload profile. Table 5 lists the reliability indices of the base case, which neglects the influence of maintenance, the optimized case, which applies the optimized maintenance schedule, and the derated case, which uses the derated capacity listed in Table 4[18].  
The reliability indices were similar in the optimized case and the base case. This result was analyzed because the maintenance period of gas turbines (U55, U355), corresponding to 62% of the total capacity of the system, was low, and the optimization was effective. On the other hand, reliability indices changed significantly in the derated case. This was because the derated capacity affects availability even during high-demand periods when maintenance is not usually performed. Therefore, considering the influence of maintenance indirectly without dealing with the maintenance schedule may not adequately evaluate reliability.
## 2 Materials and Methods
### Mathematical Description of UNets  
The U-Net model and other variants of it employ an encoder-decoder architecture at their core. This means they are mathematically divided into three types of 2D convolution functions: (1) downsampling/encoder functions \(e\), whose domain is four times the size of their range, effectively halving a 2D image; (2) upsampling/decoder functions \(d\), whose range is four times their domain, doubling the size of the image; and (3) transformation functions \(t\), whose domain and range have the same dimension so they effectively only apply some transformation to the input image without changing its size.  
All of the above functions are composed with a nonlinear function such as ReLU(x) = max(0, x) to allow stacking of complexity, since otherwise the stacked convolutions would be mathematically equivalent to a single convolution due to convolution being an associative operation.  
A Standard U-Net consists of four encoder functions, four decoder functions, and 2k transformation functions, where k is the sum of kernel sizes for each depth, used to add more complexity between downsampling and upsampling. The differentiating idea is to connect the transformed output of the encoder and decoder functions that have the same range, effectively transferring spatial information (that is lost during subsequent downsampling) to decoder functions. These are called \"skip connections\".  
For Attention U-Net, the core idea is to add an \"attention gating\" mechanism that highlights certain parts of an image via depthwise multiplication with a weight matrix. This weight matrix is computed in the attention gate as follows:  
\[AttentionGate(d_{i},e_{i-1})=\sigma\big{(}t\big{(}ReLU(t(d_{i})+t\big{(}t_{e_{i -1}}(e_{i-1})\big{)}\big{)}\big{)}\big{)}\]  
Here, \(d_{i}\) and \(e_{i}\) are the output of the ith decoder function and encoder functions, respectively. \(d_{e_{i}}\) is a function that is the composition of transformations applied after encoder \(e_{i}\). \(\sigma\) is the sigmoid function, which is applied elementwise. The output of the previously defined attention gate is a 2D matrix, which is depthwise multiplied with \(t_{e_{i-1}}(e_{i-1})\), which is equivalent to the skip connection at depth \(i-1\).  
For Nested U-Net (UNet++), the core idea is to extend the architecture of the U-Net by adding more transformation functions between skip connections and adding more skip connections in general. Since we have already laid the mathematical foundation of the constituents, their specific arrangement can be seen in Figure 2b.
# Seismic Resilience in Critical Infrastructures: A Power Station Preparedness Case Study
## 4 Case Study
### Power Station Structure and Vulnerability
#### 4.3.1 Water Treatment Facility  
The water arriving at the facility is subjected to distillation and mineral removal processes. This treated water is then utilized to cool the turbines and compressors. The water treatment facility is constructed of a steel truss with a concrete slab foundation, with dimensions of 10 m in height, 60 m in length, and 23 m in width. Damage to the water treatment facility can result from a loss of electricity supply, damage to processing equipment (such as pumps, pipes, and membranes), and harm to chemical storage tanks. The primary subcomponents of the water treatment facility, along with their associated fragility curves, are detailed in Table 3 and depicted in Figure 3. The expected probability of damage for these subcomponents is calculated using the fragility curves for a PGA value of 0.05 g. The estimated probabilities are presented in the table.  
Figure 3: Fragility curves for main subcomponents of the water treatment facility.  
A Fault Tree Analysis (FTA) was conducted, utilizing the specified damage probabilities for each subcomponent. Figure 4 depicts the FTA, outlining potential failure mechanisms within the water treatment facility through \"AND\" and \"OR\" logic gates, reflecting the interdependencies among the subcomponents. The overall probability of damage to the facility was determined to be 0.231.
# The Challenges of Advanced CMOS Process from 2D to 3D
## 4 Dopant Implantation in CMOS
### Challenge of Ion Implantation in 3D Structure Devices
#### 4.1.1 Conformal Doping  
For 3D structure devices like FinFETs, the requirement for S/D extension is not limited for highly dopant activation or less dopant diffusion and profile abruptness, but the conformal distribution within the fins [112; 113; 114]. The non-conformal doping profile results in degradation of the drive current of the transistor. Although large tilt beamline implantation could improve the conformality but there is still a serious limitation on the tilt angle due to shadowing by the neighboring fins.  
An alternative approach to improve conformality is using plasma doping [115; 116; 117]. The fins were immersed into plasma where the top surface and sidewall of the fins could be conformally doped by appropriate atoms. However, in order to prevent the dopants out-diffusion from the surface and make a better doping uniformity, a capping layer is always deposited before activation annealing.