esa-sceva/satcom-chunk-collection huggingface dataset:
# Calculation of Main Circuit Steady-State Parameters for Capacitor Commutated Converter System
## 3 Calculation Model and Method of the Main Circuit Parameters
### Determination of the Capacitance Value
#### 3.1.3 Selection Principles of the Commutation Capacitor  
From the above analysis, it is important to determine the value of the two state variables before determining the commutation capacitor. The selection principles are as follows:  
* Principle 1: The valve stress of a CCC is limited typically 10% higher than that of a conventional LCC [25].
* Principle 2: The total reactive power consumption is defined as 15% of the rated DC power [26].  
For Principle 1, the insertion of the commutation capacitor will add the additional voltage stress on valve thyristors. When selecting the capacitance value, the peak voltage across the valves of the CCC is 1.1 times that of the LCC under the same conditions, which can meet the design requirements. Therefore, the state variable \(u_{\text{VTMaxCCC}}\) is determined.  
For Principle 2, once \(Q_{\text{d}}\) is determined as 15% of the rated DC power, the consumption of the total reactive power is diminished as compared to about 50% in the case of the conventional LCC. Thus, the insertion of the commutation capacitor can reduce the amount of devices for the reactive power compensation and improve the power factor at the AC side.  
When system parameters of the CCC are determined, \(u_{\text{VTMaxCCC}}\) and \(Q_{\text{d}}\) can be calculated. The equations in Section 3.1.2 can be solved by MATLAB to obtain the unique solution and the capacitance value can also be determined.
# An Improved Cuckoo Search Algorithm and Its Application in Robot Path Planning
## 3 The Proposed Bascs
### Strategies
#### 3.1.3 Parameter Dynamic Adjustment Strategy  
To tackle the limitations of fixed step size and discovery probability in the CS algorithm, which fail to adapt to the varying demands of different search stages, BASCS incorporates adaptive adjustments for both parameters.  
The elimination probability \(Pa\) and step-size factor \(\partial\) are pivotal parameters in CS. Initially, larger values of \(Pa\) and \(\partial\) are preferred to expand the search area and bolster global optimization performance. Conversely, in later iterations, a gradual reduction in \(\partial\) and \(Pa\) allows for fine-tuning near the optimum, yielding high-precision solutions and acceleratingconvergence. Hence, a Levy flight step size that adapts to iterations and a dynamically adjusted discovery probability are essential.  
The adjustment equation for the Levy flight step-size factor \(\partial\), which changes with CS iterations, is presented as Equation (17):  
\[\partial=1-log_{T_{max}}t, \tag{17}\]  
The elimination probability \(Pa\), which varies with iterations, is calculated as Equation (18):  
\[Pa=Pa_{max}-(Pa_{max}-Pa_{min})\times\frac{t}{T}, \tag{18}\]  
where \(Pa_{max}\) and \(Pa_{min}\) represent the upper and lower bounds of the dynamic adjustment range, respectively. According to [36], extensive simulation experiments indicate that the optimal range for the discovery probability \(Pa\) is within [0.1, 0.75].
## 4 Discussion
### Data Privacy Attacks and Protections
#### 4.4.1 Data Privacy Attacks on Federated Learning
Secondly, the attacker create a shadow model using the shadow dataset \(\mathcal{D}^{\prime}\) which mimics the target model behavior. In this step, the attacker observed the shadow model behavior in response to instances known to have been provided during training against those that were not. This behavior is utilized to create an attack dataset that captures the different instances in the training data and data that have not been seen previously. Finally, this attack dataset is used to construct a binary classifier that predicts whether an instance was previously used in the target model output.
# Spatiotemporal Feature Enhancement for Lip-Reading: A Survey
## 2 Classification of Lip-Reading Methods  
Lip-reading methods are typically classified into two categories based on how features are processed: machine learning-based methods and deep learning-based methods, as illustrated in Figure 3.  
Machine learning-based lip-reading methods employ traditional feature extraction algorithms to capture visual features such as spatial, textural, color, and other visual features from the Region of Interest (ROI) of the lips [5,6]. In contrast, deep learning-based lip-reading methods build feature extraction networks to extract features encompassing visual, temporal, and semantic attributes.  
Based on the type of features, deep learning-based lip-reading methods are further subdivided into those based on visual features, spatiotemporal features, and pulse features, as shown in Figure 2. The key distinction between visual features and spatiotemporal features lies their temporal and spatial information. In particular, visual features predominantly emphasize the extraction of spatial elements, i.e., visual and semantic features of the lips [4,7], which are primarily spatial features [9,10] with a small amount of temporal information, whereas spatiotemporal features concentrate on deeper and comprehensive features, intricately weaving temporal, spatial, and semantic features of the lips [11,12], which are characterized by a rich integration of spatial and temporal attributes to establish a richer, more holistic representation of the features. For example, based-pulse feature methods, as one of the based-spatial feature methods, leverage spiking neural networks to extract pulse features [13,14], which contain significant rich temporal and spatial information.
## 4 A Mathematical Model
### Model Parameters  
The following parameters are used in the mathematical model:  
\(D_{t}\) denotes the duration of the \(t\)-th period of unchanged conditions, during which, according to the plan, it is necessary to perform the agro-technical work in question (in working days);  
\(T_{max}\) denotes the maximum duration of the working shift, i.e., the longest possible time that can be used for performing agricultural operations during the day by one tractor or combine (in hours);  
\(V_{i}\) denotes the total volume of mechanized work of type \(i\) (in the relevant units of measurement: tonnes, hectares, etc.);  
\(l_{j}^{+}\) denotes the number of available tractors (or combines) of grade \(j\);  
\(r_{k}^{+}\) denotes the number of available agricultural machines (implements) of grade \(k\);  
\(a_{j}\) denotes the average annual costs for the purchase of a tractor (or combine) of grade \(j\);  
\(b_{k}\) denotes the average annual costs for the purchase of agricultural machinery (tools) of grade \(k\);  
\(a_{j}^{0}\) denotes the leasing payments for a tractor (or combine) of grade \(j\) (for one year);  
\(b_{k}^{0}\) denotes the leasing payments on agricultural machinery (gun) of \(k\) grade (for one year);  
\(f_{j}\) is the annual normative fund of a tractor (or combine) of grade \(j\) (in hours);\(P=\left[p_{ijk}\right]\) denotes the performance matrix of the MT unit as a part of a \(j\)-grade tractor (or combine) and an agricultural machine (implement) of grade \(k\) when performing mechanized work \(i\); \(U=\left[u_{ijk}\right]\) denotes a price matrix of one hour of MT unit operation consisting of a tractor (or combine) of grade \(j\) and an agricultural machine (implement) of grade \(k\) when performing mechanized work \(i\); \(C=\left[c_{ijk}\right]\) denotes a matrix of the cost of work \(i\) by the equipment of grade \(j\) with an agricultural machine of grade \(k\), taking depreciation into account.  
The objective Function (1) with Equality (2) determine the total average annual cost of performing the entire complex of mechanized works calculated in monetary units:  
\[F\big{(}X,Y,L,T,L^{0},R^{0}\big{)}= \tag{1}\] \[=\sum\limits_{i=1}^{I}\sum\limits_{j=1}^{I}\sum\limits_{k=1}^{ K}\sum\limits_{t=1}^{T}x_{ijkt}\,y_{ijkt}\,c_{ijk}(X,Y)\cdot D_{t}+\sum\limits_{j=1}^{I}j _{i}\,a_{j}+\sum\limits_{k=1}^{K}r_{k}\,b_{k}+\sum\limits_{j=1}^{J}l_{j}^{0}\, a_{j}^{0}+\sum\limits_{k=1}^{K}r_{k}^{0}\,b_{k}^{0}\to min\]  
where \(c_{ijk}(X,Y)=u_{ijk}+a_{j}\cdot g_{j}(X,Y)\) and the following equality hold  
\[g_{j}(X,Y)=\left\{\begin{array}{ll}1,&\mbox{if }\sum\limits_{i=1}^{I}\sum \limits_{k=1}^{K}\sum\limits_{t=1}^{T}y_{ijkt}\cdot x_{ijkt}\cdot D_{t}\leq f _{j}\cdot l_{j},\\ \tau,&\mbox{if }\sum\limits_{i=1}^{I}\sum\  
The number of tractors (combines), agricultural machines (implements) and lorries should be expressed in an integer non-negative number (zero is also allowed). The number of hours of operation during the shift of each tractor (combine) shall be expressed as a real non-negative number.
# MuIm: Analyzing Music-Image Correlations from an
## 3 Multi-Layered Music-Image Correlation
### Analysis of Multilayered Information  
As illustrated in Figure 1, music and image data exhibit complex, multilayered interactions where similar semantic content (e.g., depicting a landscape) can evoke different emotions due to factors such as aesthetics, individual subjectivity, and context. Conversely, audio-visual content with divergent semantics (e.g., fast-paced music paired with calm imagery) may still produce similar emotional responses, highlighting the necessity for a multi-layered analytical approach for cross-modal AI systems to better interpret music-image correlations.  
To represent these correlations mathematically, let \(M\) denote the set of music data and \(I\) represent the set of image data. We define a correlation function \(f(M,I)\) that captures both semantic (\(S\)) and emotional (\(E\)) layers:  
\[f(M,I)=\alpha d_{s}(S_{m}(M),S_{i}(I))+\beta d_{c}(E_{m}(M),E_{i}(I)) \tag{1}\]  
where \(\alpha\) and \(\beta\) are weighting coefficients, \(d_{s}\) denotes a similarity distance function (e.g., cosine similarity) for semantic alignment between semantic vectors \(S_{m}(M)\) and \(S_{i}(I)\), and \(d_{c}\) is a correlation distance function (e.g., Mahalanobis distance) for measuring the emotional congruence or divergence between emotional state vectors \(E_{m}(M)\) and \(E_{i}(I)\).
## 4 Scheduling  
Radio communication dominates the power consumption in wireless sensor networks [12]. Sensor nodes waste a lot of energy for receiving signals. The energy consumption for receiving is approximately equal to that for data transmission. In addition, a node in idle mode also consumes energy. Therefore, idle listening, the time spent for waiting packets, consumes a significant amount of energy. [PERSON] _et al._[13] observed that idle listening dominated the energy consumption of network interfaces in hand-held devices. It becomes clear that using scheduling to reduce listening and turn off the radio during idle time is an effective way to prolong network lifetime.
## 2 Instrument Design
### Radiometer Assembly
#### 2.1.1 Antenna Set
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Frequency Band**} & \multicolumn{1}{c}{**Beamwidth**} & \multicolumn{1}{c}{\(\boldsymbol{\eta_{\text{mbe}}}\)} & \multicolumn{1}{c}{**CPI**} & \multicolumn{1}{c}{\(\boldsymbol{\eta_{A}}\)} \\ \hline L & 1.400–1.427 GHz & \(\sim\)25\({}^{\circ}\) & 95\% & 35 dB & \(\sim\)95\% \\ S & 2.690–2.700 GHz & \(\sim\)25\({}^{\circ}\) & 95\% & 35 dB & \(\sim\)95\% \\ C & 7.140–7.230 GHz & \(\sim\)25\({}^{\circ}\) & 95\% & 35 dB & \(\sim\)95\% \\ X & 10.60–10.70 GHz & \(6^{\circ}\) & 98\% & 40 dB & \(\sim\)99\% \\ K & 18.60–18.80 GHz & \(5^{\circ}\) & 98\% & 40 dB & \(\sim\)99\% \\ K & 23.60–24.00 GHz & \(4^{\circ}\) & 98\% & 40 dB & \(\sim\)99\% \\ Ka & 36.00–37.00 GHz & \(4^{\circ}\) & 98\% & 40 dB & \(\sim\)99\% \\ W & 86.00–92.00 GHz & \(3.2^{\circ}\) & 98\% & 37 dB & \(\sim\)99\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Main parameters of the eight dual-polarization microwave antennas of MERITXELL. They have been measured at UPC anechoic chamber as mounted in the whole system. Figure 3: Corrugated horn with its Fresnel lens used in the Ka-band with the corresponding output waveguides for V and H polarizations. Figure 2: Front view of the antenna set and additional sensors of MERITXELL.
## 4 Conclusions  
It can be concluded from the results, that all five varieties of _Brassica_ genus studied (cauliflower, red cabbage, Chinese cabbage, white cabbage and broccoli) are rich sources of polyphenols. Among all _B. oleraca_ vegetables, red cabbage (_Brassica oleraca_ var. _Capitata F. rubra_) proved to be a good source of potential dietary antioxidants and thus showed high antioxidant activity. HPLC analysis showed the variation in the phenolic acid and flavonoid contents in all the _Brassica_ species analyzed. The Brassicaceae species are also proved to have moderate anti-proliferative and antibacterial potential. _Brassica_ vegetables are excellent source of polyphenols and may be used for pharmaceutical applications and it may be used as the food supplements.  
Conceptualization, I.Z. and A.I.H.; methodology, I.Z.; software, I.Z.; validation, I.Z. and A.A.; formal analysis, I.Z.; investigation, I.Z.; resources, I.Z. and A.I.H.; data curation, I.Z.; writing--original draft preparation, I.Z. and T.F.; writing--review and editing, A.I.H., A.A. and S.M.A.A.; visualization, I.Z. and T.F.; supervision, A.I.H. and A.A.; project administration, A.I.H. and A.A.; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c} \hline
**Extracts** & **\% Inhibition (40 mg/mL)** \\ \hline Cauliflower & \(54.12\pm 3.3\,\mathrm{b}^{,c}\) \\ Broccoli & \(53.16\pm 2.3\,\mathrm{b}^{,c}\) \\ Red Cabbage & \(58.19\pm 1.6\,\mathrm{c}^{\,c}\) \\ White Cabbage & \(51.93\pm 2.9\,\mathrm{b}^{\,\mathrm{b}}\) \\ Chinese Cabbage & \(30.3\pm 1.6\,\mathrm{a}^{\,\mathrm{a}}\) \\ \hline \end{tabular} The values are mean \(\pm\) standard deviation of three independent observations. Different letters in superscript show significant differences among selected _Brassica_ species.  
\end{table}
Table 5: Anti-proliferative activity of ethanol extract of selected _Brassica_ species against human A549 lung cancer cell line.
## 3 Results and Discussion
However, the residual dislocations in the matrix (as shown in Figure 11c) ensure the relatively higher strength of SCFs/2024 Al composites compared to that of 2024 Al. The addition of SCFs results in the deduction in fracture strain for SCFs/2024 Al composites. As shown in Figure 10a,b, the fracture surface of the SCFs/2024 Al composite at room temperature is mainly characterized by the shear failure of carbon fiber. This indicates the strong interfacial bonding between carbon fiber and aluminum matrix. The formation of Al\({}_{4}\)C\({}_{3}\) is observed at the interface between carbon fiber and aluminum matrix by TEM characterization (Figure 4a). Al\({}_{4}\)C\({}_{3}\) is reported to have negative and positive effects on the interfacial bonding between aluminum matrix and carbon materials. On one hand, the formation of nano-sized Al\({}_{4}\)C\({}_{3}\) could increase the wettability between aluminum and carbon materials and enhance the interfacial bonding [22; 33]. On the other hand, a larger size of Al\({}_{4}\)C\({}_{3}\) is adhesive to the interfacial bonding due to its intrinsic brittleness [31]. It is worth noting that due to the residual stress generated at the interface during the cooling process, carbon fiber is in compress. The interfacial residual stress in radial direction remarkably strengthens the interfacial bonding and increases the friction between carbon fiber and aluminum matrix, which makes the carbon fiber difficult to be pulled out. Therefore, the cracks propagate towards carbon fiber and cause stress concentration at the tip of cracks, and finally result in the shear failure of carbon fiber. In contrast, the SCFs/2024 Al composite after the tensile test at high temperature exhibits a different fracture morphology. The amount of debonding and pulled-out carbon fibers are obviously increasing, revealing the weakening in interfacial bonding between carbon fiber and aluminum matrix. The deterioration in interfacial bonding is mainly caused by the following three key factors: The decreasing residual stress at the interface due to the increasing temperature, the coarsening of Al4C3 (Figure 11a) and segregation (Figure 11d-f). It is generally known that the interfacial debonding is caused by crack propagation. When the interfacial bonding strength is lower than the fracture strength of carbon fiber and the strength at the tip of cracks, the cracks are more likely to deflect along the fibers at the interface. The fibers debonding and pulled-out occur due to the growth of cracks at the interface. Therefore, the fracture behavior for SCFs/2024 Al composites shows much difference at high temperature. At room temperature, carbon fibers are mainly fractured in shear failure because of the strong interfacial bonding induced by residual stress. At high temperature, carbon fibers are more likely to be debonded and pulled out from the matrix, owing to the weakening in interfacial bonding.
## 2 Materials and Methods
### Characterization Methods  
Scanning electron microscopy (SEM, S4800 and SU8000, Hitachi High-Tech Corporation, Tokyo, Japan) was used to observe the surface structure morphology before and after biochar modification. The content and distribution of elements such as C, O and Mn on the surface of biochar were analyzed with an energy dispersive X-ray spectrometer (EDS, HORIBA EX-350, Kyoto, Japan); X-ray powder diffraction (XRD, Bruker D8 advance, Karlsruhe, Germany) was used to characterize the structure of biochar crystals before and after modification.
# Bot Datasets on Twitter: Analysis and Challenges
## 3 Database Analysis
### [PERSON] et al. Database (2019)
#### 3.5.3 Concluding Remarks  
The introduction of Spanish-speaking politician accounts operated by humans presents a new opportunity to evaluate the problem of detection of social bots on Twitter from another perspective. While it is harder to separate between bots and humans as they are very similar, this third class has marked differences in both its behavior and characteristics. We have verified that just as bots can be of different types (content polluters, statistics enhancers, or political influencers) according to their programming, humans can also have varied behavior in accord to their agenda on the platform.  
The new feature model proposed by the authors of this database provides exciting tools to separate classes. We think the frequency of posting for accounts and the sentiment analysis for tweets are impactful enough to observe hidden patterns, especially in bot and politician accounts. Combining them with the original features such as the number of favorites given or the source of tweets can be helpful for correct classification.  
We consider this database the most complete regarding available features. It also incorporates Spanish-speaking accounts that enabled us to analyze them with their counterparts that speak in English. The main disadvantage we found is that it does not include follower/following relationships.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Feature** & **Key Findings** \\ \hline _lang_ & 92\% of bots and humans set their language to English and all Mexican politicians \\  & picked Spanish. \\ _listed\_count_ & All politicians appear in more than 950 public lists, and bots have the lowest appearance in \\  & public lists. \\ _location_ & Most of bots do not have a location (87\%), which is higher than humans (27\%) and \\  & politicians (0\%). \\ _source_ & Bot accounts prefers to use different applications (TweetAdder) than the other classes \\  & to tweet. \\ _retweet\_count_ & 97\% of the tweets made by bots do not get retweeted, but for politicians it is only 10\%. \\ _num\_mentions_ & Bots mention other users scarcely (11\%) while humans (59\%) and politicians (35\%) do \\  & it more. \\ _sunday\_twetal_ & Politics have a busier schedule, bots are half less active, and humans have the least. \\  & Majority of bot and human tweets do not express a sentiment, while politician tweets are \\  & most positive. \\ \hline \hline \end{tabular}
\end{table}
Table 4: A summary of our findings when analyzing our graph partitions of the chosen user (first three), tweet (middle three), and tweet sentiment analysis and usage frequency (last two) features from the human, the bot, and the politician classes of [PERSON] et al.’s [33] 2019 database.  
Figure 3: Nearly half of the tweets from the human dataset do not express a sentiment in the tweet partition of the sentiment analysis feature _sa_score_tag_ from [PERSON] et al.’s [33] 2019 database. Each color depicts the category (score in sentiment analysis) and its percentage of nodes (tweets).
## 5 Discussion  
### Feature Visualization Analysis  
To more intuitively visualize the attention points of PSANet in the images, we used Grad-CAM to extract the weights from the model's final convolutional layer, generating attention heatmaps. Figure 12 shows the heatmap results for different scenes, from which it can be observed that in building area-dominated scenes, PSANet accurately focused on the building areas in the images while also recognizing the complete boundary contours and assigning lower heat values to the blurry pixels along the boundaries. Meanwhile, in scenes dominated by the background, PSANet was still able to accurately focus on the building areas in the images. This illustrates that PSANet successfully learned the spatial and contextual information of the building areas, thereby enabling accurate recognition of the building areas in the images. In addition, when there were objects in the image with similar echo intensity to the building areas, PSANet was able to correctly classify them as background while preserving the integrity of the building area pixels. This illustrates that PSANet has strong robustness to effectively cope with complex backgrounds and noise interference, ensuring the accurate identification of building areas.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Parameters** & \multirow{2}{*}{**Flops (G)**} & **Inference Time** & **Training Time on** & **Training Time on** & **Memory** \\  & **(M)** & & **(s)** & **Anhui Images** & **Jiangsu Images** & **(MB)** \\ \hline SegNet & 29.44 & 40.17 & 0.12 & 1 h 32 min 8 s & 2 h 35 min 17 s & 112 \\ DeepLabV3-Plus & 58.75 & 62.85 & 0.18 & 2 h 9 min 58 s & 3 h 40 min 54 s & 224 \\ MAFF-HRNet & 97.68 & 62.67 & 0.25 & 3 h 1 min 39 s & 5 h 30 min 49 s & 374 \\ UNet++ & 36.63 & 138.66 & 0.14 & 4 h 6 min 9 s & 7 h 32 min 17 s & 139 \\ SegFormer & 84.59 & 24.94 & 0.23 & 3 h 15 min 4 s & 5 h 44 min 36 s & 323 \\ PSPNet & 47.71 & 14.84 & 0.15 & 1 h 5 min 7 s & 1 h 41 min 59 s & 178 \\ PSANet (Ours) & 46.51 & 41.43 & 0.17 & 1 h 49 min 42 s & 3 h 7 min 11 s & 177 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The computational consumption of different networks.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Method** & **IoU** & **Recall** & **Precision** & **OA** & **F1-Score** \\ \hline \multirow{3}{*}{Jiangsu Province} & ASPP-Small & 71.19 & 79.39 & 87.33 & 94.33 & 83.17 \\  & ASPP-Large & 70.76 & 84.81 & 81.03 & 93.81 & 82.87 \\  & Ours & 71.38 & 82.23 & 84.39 & 94.18 & 83.31 \\ \hline \multirow{3}{*}{Anhui Province} & ASPP-Small & 73.77 & 80.64 & 89.64 & 96.69 & 84.89 \\  & ASPP-Large & 73.35 & 84.97 & 84.29 & 96.44 & 84.63 \\ \cline{1-1}  & Ours & 74.31 & 84.52 & 86.01 & 96.63 & 85.26 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Evaluation metrics obtained using different dilation rates.