esa-sceva/satcom-chunk-collection huggingface dataset:
# Practical Submodule Capacitor Sizing for Modular Multilevel Converter Considering Grid Faults
## 3 Transient SM Capacitor Voltage during Grid Faults  
### System Dynamics and Control Schemes  
Based on Figure 2, the dynamics of an MMC can be described as  
\[L_{a}\frac{\dot{d}c_{j}}{dt}=\frac{V_{dc}}{2}-v_{cj} \tag{9}\]  
\[\frac{L_{a}}{2}\frac{\dot{d}j}{dt}=v_{sj}-v_{j} \tag{10}\]  
\[C_{\omega}\frac{dv_{uj}}{dt}=n_{uj}i_{uj} \tag{11}\]  
\[i_{uj}=i_{cj}+\frac{1}{2}i_{j} \tag{12}\]  
where \(v_{cj}\) is the voltage driving the circulating current through each phase-leg, and \(v_{sj}\) is the differential voltage controlling the current injected into the grid. Accordingly, \(v_{cj}\) and \(v_{sj}\) are given as  
\[v_{cj}=\frac{n_{uj}v_{uj}+n_{lj}v_{lj}}{2} \tag{13}\]  
\[v_{sj}=\frac{\text{-}\ n_{uj}v_{uj}+n_{lj}v_{lj}}{2} \tag{14}\]  
By applying the Park transformation on Formula (10), the output current dynamic can be described in the \(dq\)-frame as  
\[\frac{L_{a}}{2}\frac{d}{dt}\left[\begin{array}{c}i_{d+}\\ i_{q+}\end{array}\right]=\left[\begin{array}{c}v_{sd+}\\ v_{sq+}\end{array}\right]-\left[\begin{array}{c}v_{d+}\\ v_{q+}\end{array}\right]+\frac{L_{a}}{2}\left[\begin{array}{cc}0&\omega_{1} \\ -\omega_{1}&0\end{array}\right]\left[\begin{array}{c}i_{d+}\\ i_{q+}\end{array}\right] \tag{15}\]\[\frac{L_{a}}{2}\frac{d}{dt}\left[\begin{array}{c}i_{d-}\\ i_{q-}\end{array}\right]=\left[\begin{array}{c}v_{sd-}\\ v_{sq-}\end{array}\right]-\left[\begin{array}{c}v_{d-}\\ v_{q-}\end{array}\right]+\frac{L_{a}}{2}\left[\begin{array}{cc}0&-\omega_{ 1}\\ \omega_{1}&0\end{array}\right]\left[\begin{array}{c}i_{d-}\\ i_{q-}\end{array}\right] \tag{16}\]  
where \(v_{sd+}\), \(v_{sq+}\), \(v_{sd-}\), and \(v_{sq-}\) are the positive- and negative-sequence components of \(v_{sj}\) in the \(dq\)-frame.  
According to Equations (15) and (16), a typical output control loop can be constructed for the MMC as shown in Figure 4. A positive-sequence control and a negative-sequence control using PI regulators are adopted, where \(K_{op}\) and \(K_{i}\) are the gains. The positive- and negative-sequence components of the grid voltage can be extracted with methods in [31] and used as the input of a grid current reference generator, which is applied to provide the active and reactive current references (\(i^{*}_{d+}\), \(i^{*}_{q+}\), \(i^{*}_{d-}\), \(i^{*}_{q-}\)) for the controller.  
According to Equation (14), a circulating current suppression control (CCSC) of the MMC can be designed as shown in Figure 5[14]. A PR controller is applied to eliminate the dominant double-line frequency ripple in the circulating current, where \(K_{cp}\) and \(K_{r}\) represent its gains. The DC reference of the circulating current control will be given by a circulating current reference generator.
###### Abstract  
**Background:** Multiple studies have examined the potential of growth factors (GF) to enable mesenchymal stromal cells (MSC) to nucleus pulposus (NP) cell-like cell differentiation. Here we screened a wide range of GF and GF combinations for supporting NP cell-like cell differentiation.  
**Methods:** Human MSC were stimulated using 86 different GF combinations of TGF-\(\beta\)1, -2, -3, GDF5, -6, Wnt3a, -5a, -11, and Shh. Differentiation potency was assessed by alcian blue assay and NP cell marker expression (e.g., COL2A1, CD24, etc.). The top four combinations and GDF5/TGF-\(\beta\)1 were further analyzed in 3D pellet cultures, on their ability to similarly induce NP cell differentiation.  
**Results:** Almost all 86 GF combinations showed their ability to enhance proteoglycan production in alcian blue assay. Subsequent qPCR analysis revealed TGF-\(\beta\)2/Wnt3a, TGF-\(\beta\)1/Wnt3a, TGF-\(\beta\)1/Wnt3a/GDF6, and Wnt3a/GDF6 as the most potent combinations. Although in pellet cultures, these combinations supported NP marker expression, none showed the ability to significantly induce chondrogenic NP matrix production. Only GDF5/TGF-\(\beta\)1 resulted in chondrogenic pellets with significantly enhanced glycosaminoglycan content. **Conclusion:** GDF5/TGF-\(\beta\)1 was suggested as an optimal GF combination for MSC to NP cell induction, although further assessment using a 3D and in vivo environment is required. Wnt3a proved promising for monolayer-based NP cell differentiation, although further validation is required.  
nucleus pulposus; growth factors; differentiation; mesenchymal stromal cell; growth factor; Wnt; differentiation factor; chondrogenesis; cell therapy
# Research on Network Attack Traffic Detection HybridAlgorithm Based on UMAP-RF
## 4 Experiment and Analysis
### Comparison of the Running Time of UMAP-RF Hybrid Algorithm and Other Algorithms  
Time is a critical factor in preventing and reducing the effects of the attack. The network security connection data after dimensionality reduction by the UMAP algorithm highlights key feature information and effectively clusters data with different features, which significantly reduces the time required for subsequent detection of abnormal data and directly improves the efficiency of network traffic attack detection. In the binary classification experimental section, the running time of the hybrid algorithm UMAP-RF (including the running time of UMAP dimensionality reduction) is compared with other machine-learning algorithms, as shown in Table 5.  
As shown in Table 5, the UMAP algorithm reduces the dimensionality and then classifies by the random forest algorithm compared to the direct random forest algorithm in terms of running time by one-third, proving that the UMAP dimensionality reduction algorithm can significantly reduce the subsequent time for detecting abnormal data. The hybrid algorithm UMAP-RF is not only more accurate than the other two algorithms in detecting abnormal data but also requires the lowest running time. By comparing the running time and accuracy of different algorithms in dichotomous experiments, it is demonstrated that the hybrid algorithm UMAP-RF can effectively detect abnormal data in a shorter time and outperform the other algorithms in terms of comprehensive performance.
## 2 Materials and Methods
### Euler Deconvolution  
When used on magnetic data, the Euler deconvolution method permits the establishment of magnetic sources. A quick and accurate assessment of the position, depth, direction, and dip of magnetic contacts is produced by the procedure [4]. The structural index of zero, which has been used to solve the Euler deconvolution problem, refers to contacts or lineaments [77; 78]. It is necessary for the window size to be sufficiently large to account for major field and gradient variations while being consistently small enough to exclude the effects of many sources [79; 80; 81].
The top-down approach is intended to change the bodily and emotional responses (the lower part of the brain) through reconstructing cognition (the upper part of the brain). In other words, the top-down approach is applied to the cerebral neocortex. It addresses cognition by seeking meaning and understanding to facilitate control of the affective disorder and bodily sensory experiences and to increase linguistic self-esteem [13]. The proposed intervention program consists of both the bottom-up approach and the top-down approach to treat CPTSD symptoms, which helps to stabilize the arousal state of the autonomic nervous system (ANS) and to restructure negative cognition and thinking. In psychology, the effectiveness of an intervention program is typically evaluated based on the subject's subjective response to scaled questionnaires. Although clinical scales have been widely utilized for their validity and reliability, there are some limitations in consistency with this approach because it is a subjective self-report method. In biomedical engineering and digital healthcare fields, heart rate variability (HRV), which is a quantitative measure of the variation in intervals between heartbeats, has been widely utilized to quantify the state of the subject [14; 15]. In psychology, it has been increasingly employed to assess the effects of psychotherapy [16; 17]. However, HRV has rarely been utilized for CPTSD. HRV serves as a sensitive indicator of the functionality of the autonomic nervous system (ANS) [18; 19; 20]. This system's parasympathetic and sympathetic branches modulate the heart rate via influences on the sinoatrial node pacemaker [21]. The high-frequency (HF) peak of HRV, derived from spectral analysis of inter-beat intervals, is believed to represent the parasympathetic or vagal tone, though there is some debate regarding the sensitivity and specificity of commonly utilized HRV measures [22; 23]. Reduced HRV, which is often observed in post-traumatic stress disorder (PTSD), implies autonomic inflexibility, potentially resulting from sympathetic overactivity, parasympathetic deficiency [20; 21; 24; 25], or the exacerbation of common cardiovascular issues associated with PTSD [26]. Lower HRV values have also been noted in various psychiatric disorders, including schizophrenia, depression, bipolar disorder, and panic disorder [27; 28; 29; 30], linking decreased HRV to pathophysiology, psychopathology, and increased mortality [21; 31]. Therefore, this study investigated the relationship between HRV and the psychological status of one subject who suffers from CPTSD. The principal hypothesis was that checking the subject's HRV in real time could help a psychotherapist to determine the subject's psychological status.
# Non-Maximum Suppression Performs Later in Multi-Object Tracking
## 3 Materials and Methods
### Performing NMS Later  
In most of the work now, NMS is used in the detection stage. After NMS, some low-scoring or redundant detection boxes can be filtered out, and some high-scoring detection results can be obtained. This paper believes that the application of NMS in the detection stage will make the results more biased towards the detection task, and the tracking in the multi-target tracking task is the main task of the research. Therefore, it is unreasonable to filter out low-scoring and redundant boxes in the detection stage, which would filter out candidate boxes that are useful for tracking. In this paper, we try to postpone NMS, that is, use NMS in the tracking stage instead of NMS in the detection stage, so that the results are more focused on the main tracking task.
## 5 Stability and Robustness
Figure 24: The robustness test for the PID\({}_{\text{b}}\) controller with \(b\) = 0.5. The responses are for a process steady-state gain and time-delay parameter perturbation of \(\pm\)10%. Note that, for clarity, the controller output response is zoomed in for the reference step change. Figure 25: The robustness test for the PID\({}_{\text{th}}\) controller. The responses are for a process steady-state gain and time-delay parameter perturbation of \(\pm\)10%. As can be seen from the process output responses, a change in the process gain or time-delay does not deteriorate the tracking or disturbance-rejection responses. Nevertheless, the PID\({}_{\text{fh}}\) controller seems to be slightly more sensitive to process perturbations (especially the increased process gain).
## 3 System Design Configuration
### Procedure to Determine the Time Delay  
To detect the time delay, the satellites in the ISL network require SS signals and 1 PPS time stamp references for both the outgoing (_Tx_) and incoming (_Rx_) signals. On the receiving side, data transmitted at both frequencies (\(f_{1}\) and \(f_{2}\)) is saved as text or recorded wave files. The files are analyzed onboard the satellite to detect the delay following the procedure indicated in Figure 5 below.
# Research on Low Energy Consumption Static Postures of Bionic Feet
## 5 Static Postures Analysis Considering Energy Consumption and Maximum Step Size
### Given the Yaw Angle \(\alpha\), the Foot Tip Falls in the Middle of the Maximum Step Size  
#### 5.3.1 Maximum Step Analysis of the Robot  
The maximum step \(R_{i}\) of a single leg is the maximum distance between a certain direction and the intersection with the movable area. It is assumed that in the joint coordinate system, the coordinate of \(K_{i1}\) is \((x_{i1},\ x_{i2},\ x_{i3})\), the coordinate of \(K_{i2}\) is \((y_{i1},\ y_{i2},\ y_{i3})\), and the maximum step size is obtained by Equation (9):  
Figure 8: Minimum \(M\) under different center of gravity heights.  
Figure 7: Joint angle of \(\theta_{2}\) and \(\theta_{3}\) when \(M\) is minimum under different center of gravity heights.  
\[R_{i}=|K_{i1}K_{i2}|=\sqrt{\left(x_{i1}-x_{i2}\right)^{2}+\left(y_{i1}-y_{i2} \right)^{2}+\left(z_{i1}-z_{i2}\right)^{2}}. \tag{9}\]  
At any joint angle, the single-leg support state of the robot is shown in Figure 9, where \(h\) is the thickness of the robot body.  
According to the geometric relationship, the following constraint equation can be established:  
\[H\;=\;-\;l_{2}s_{2}\;-\;l_{3}s_{23}. \tag{10}\]  
Equation (9) is the postures constraint equation of the robot used to define the effective range of the joints of the robot legs, and \(H\) is a function of \(\theta_{2},\;\theta_{3}\). According to the trunk structure and leg structure of the robot, the range of \(H\), that is, \(H\;\in\;\left[\frac{h}{2},l_{2}+l_{3}\right]\), can be determined.  
Assuming that the yaw angle \(\alpha\) and the center of gravity height H are known, the coordinates of the two points \(\mathrm{K_{i1}}\) and \(\mathrm{K_{i2}}\) are obtained by the following kinematic equation:  
\[\left\{\begin{array}{c}K_{i1}(0,\;0,\;-H)\\ K_{i2}(d,\;d*\cos\alpha,\;-H)\end{array}\right., \tag{11}\]  
where  
\[d\;=\left(l_{1}+\sqrt{\left(l_{2}+l_{3}\right)^{2}-H^{2}}\right).\]  
Bringing Equation (11) into Equation (9), we find the maximum step size \(R_{i}\) of each leg of the robot. The maximum step size \(R\) defining the overall robot is the minimum value of the maximum step size of each leg of the robot:  
\[R\;=\;\min\;\{R_{i}\}. \tag{12}\]  
Therefore, after obtaining the maximum step size, the landing point could be located at the midpoint of the maximum step size.
## 2 Methodology
### Process Modelling
#### 2.2.2.2. SMR H\({}_{2}\) Plant with MDEA Absorption CO\({}_{2}\) Capture  
For completeness of the benchmarking plants, a CO\({}_{2}\) capture SMR plant was developed using an absorption unit with MDEA prior to the PSA, in order to remove 95% of the CO\({}_{2}\) present in the shifted syngas stream. A process diagram is provided in Figure 7. The MDEA section was modeled with a dedicated thermodynamic model (DBR Amine Package: Kent-Eisenberg) facilitated by Honeywell to accurately represent the chemical absorption process. The use of a chemical solvent is suitable for the intermediate partial pressures of CO\({}_{2}\) at the inlet stream to the absorption unit. Furthermore, MDEA presents a low regeneration heat requirement amongst amine-based solvents: The Unisim model with partial flash solvent regeneration predicted a value of 1.07 MJ\({}_{th}\) per kg of CO\({}_{2}\), very closely matching the values reported in [44]. The integration of conventional CO\({}_{2}\) capture through amine absorption has several implications: the S/C ratio was increased to 4,as in [11; 43], to achieve maximum methane conversion in the tubular reformer (92.5%), while a second, low-temperature shift (LTS) unit is added to fully convert the CO present in the syngas produced. Furthermore, a portion of the purified H\({}_{2}\) stream after the MDEA unit must be employed in the burner to satisfy the energy balance without increasing CO\({}_{2}\) emissions. Lastly, since the steam demand is large, all the steam produced in the heat recovery systems is either employed to reach the required S/C ratio in the reformer or in the MDEA stripper column for solvent regeneration. A dedicated LP boiler after the LTS unit is used to fulfill the LP steam demand. The resulting decreased steam turbine output and the extra auxiliary consumption imposed by the 5-stage intercooled CO\({}_{2}\) compressor and downstream supercritical CO\({}_{2}\) pump results in negative net electricity production for this plant. Both H\({}_{2}\) and CO\({}_{2}\) products are delivered at a pressure of 150 bar. Given that the GSR and GSPOX H\({}_{2}\) plants are assumed to import a substantial amount of electricity, the SMR with MDEA CO\({}_{2}\) capture model was designed to rely as well on electricity imports to enable a fair comparison, and only sufficient steam is raised to satisfy the S/C require  
Figure 6: Process diagram of the SMR plant without CO\({}_{2}\) capture.  
ments. Therefore, the steam turbine (ST) output will decrease considerably, but less pure H\({}_{2}\) product will be combusted in the reformer, ultimately leading to higher H\({}_{2}\) efficiencies and lower penalties than in the case where the steam turbine is operated to satisfy the plant energy demand.
## 4 Global Path Planning Based on Network Analysis Method  
The global path is based on the ArcGIS Engine 10.2 [25]. We build a map layer to place the start point and the end point. Furthermore, we search a route through the road network by the Dijkstra algorithm, and build with the network analysis tool of the ArcGIS Engine-interface of INASolver.  
After analysis through the tool, we get some context regarding the result. Thus the result should be the optimal path. And this result is based on length or time, which includes all feature edges and junctions. After global path planning, the feature edges and junctions information are processed.  
Figure 6 depicts some insertion points in the path. These points are mapped in some special locations, such as the crossroad or the construction site. Except for some reminding information, such as the traffic signal light, all points have a unique ID number, and the unique ID number helps the global plan to seek the GPS path of this location in the special road database.  
Figure 4: A road (edge) feature only has one start point and one end point, but no break point.  
Figure 5: The special points (junction) are placed nearby in the crossroad.  
Figure 3: Every lane has its own GPS data line in the high accuracy Geographic Information System (GIS).  
Figure 6: Some insertion points are placed in the path.
## 2 Pharmacology of Piperine: In Vitro and In Vivo Studies
### Anti-Cancer Effect of Piperine
#### 2.6.1 Breast Cancer  
The study on HER2 overexpressing breast cancer cells demonstrated inhibited proliferation and induced apoptosis by activating caspase-3 and cleavage of PARP [47]. Moreover, it was determined that piperine inhibits HER2 gene expression at the transcriptional level and enhances sensitization of HER2 overexpressing cells to paclitaxel killing. The same study found that it inhibits AP-1 and NF-kB activation, blocks extracellular signal-regulated kinase (ERK1/2), p38 mitogen-activated protein kinase (p38 MAPK), and Akt signaling pathways, and suppresses epidermal growthy factor (EGF)-induced MMP-9 expression [47]. Without affecting normal mammary epithelial cell growth, piperine inhibits the in vitro growth of triple-negative breast cancer cells (TNBC), and hormone-dependent breast cancer cells. Also, it increases the expression of p21(Wafl/Cip1) and inhibits survival-promoting Akt activation [48], inhibits mammosphere formation, breast stem cell marker aldehyde dehydrogenase (ALDH), and Wnt signaling pathway without causing toxicity to differentiated cells [49]. In another study, on TNBC cells, the efficacy of factor-related apoptosis-inducing ligand (TRAIL)-based therapy has been enhanced when piperine was added as an adjuvant [50]. In a model of 4T1 murine breast cancer cells, injection of piperine into tumors (35-280 umol/L) inhibited the growth of 4T1 cells in a time- and dose-dependent manner, and decreased the expression of MMP-9 and MMP-13 [51]. It has been reported that piperine analogs, formed by replacing the piperidine nucleus with different amino acids and substituted aniline, exhibit significantly enhanced activity against human breast cancer cells [52]. The best cytotoxic activity (IC\({}_{50}\)--0.74 umol) was obtained by a histidine analog of piperine containing imidazole ring structure.
## 3 Deficit Round Robin with Fragmentation (DRRF) (for Best Effort Traffic)
### Max-min Fairness  
In the previous section, to achieve throughput fair allocation, we assume that all MSs have infinite traffic to send, and can use the resources allocated to them. In other words, at the scheduling stage all MSs always have packets waiting in their queues. However, in practice, the available traffic is finite. Some MSs may not have enough traffic, may not be able to use a fair share, or may have too much traffic.  
Our goal is to make a fair allocation among MSs. In the case where some MSs cannot use a fair share, their left-over share should be fairly allocated to other MSs. This leads to a commonly used max-min criterion for fair allocation. An allocation is said to be max-min fair if it maximizes the allocation for the user who received the minimum. In IEEE 802.16e Mobile WiMAX Networks, we apply the max-min concept by _maximizing the minimum number of requested slots_.  
Figure 2 shows steps in computing the max-min fair allocation. The first step is to compute the number of requested slots from the number of bytes requested by an active mobile station and its MCS. Then, we sort the requested slots for all active MSs in ascending order (Step 2). In Step 3, for each active MS the number of fair share slots is derived. Next, the number of granted slots, the minimum of number of fair share, and the number of requested slots are updated. Then, the number of free slots is updated. This loop continues until there are no more free slots, or the number of requested slots for all active MSs is satisfied. The results are used to as the actual quantum for DRR and DRRF, and the actual fair share allocation for GPS. Note that the quantum size and fair share may change over a frame period according to the MCS level.
## 3 Case Study
### Results and Analysis  
#### 3.2.1 Risk Assessment Results  
Construction of Membership Function  
According to the data of each indicator in the set of evaluation factors, the parameters of the sigmoidal membership function are fit to obtain the sigmoidal membership function and images of the indicators. We selected three indicators, namely, wave height, visibility, and vessel age, and their images are shown in Figures 4-6.  
Figure 3: Dividing the Towing Manual planned route into TMrouteA and TMrouteB.  
Figure 4: Gaussian membership function image of wave height.  
Figure 5: Gaussian membership function image of visibility.  
Figure 6: Gaussian membership function image of vessel age.  
Risk Zoning Results  
Following our risk assessment model in Section 2.1, the results of the risk assessments of the two sections in this case are shown in Figures 7 and 8.  
From Figure 7, during the time of the first section of the ship's route, from 1 May to 12 May, the overall risk in the navigation area is low, and the level of safety of the sailing ship is mostly high.  
From Figure 8, during the time of the second section of the ship's route, from 16 June to 7 July, the risk of sailing in the navigation area gradually increased from 29 June, and the scope of the high-risk area gradually increased. According to an investigation report, the sea area entered a seasonal monsoon period at this time, which is consistent with the risk change in the results in Figure 8. We can see that the risk assessment based on the assessment model in this study is accurate.  
Figure 8: Risk regionalization result maps selected from 20 June 2022 to 5 July 2022.  
Figure 7: Risk regionalization result maps selected from 1 May 2022 to 12 May 2022.