esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
### Extraction of GGPP and LC-MS Analysis  
For isolation of GGPP, 10 mL of culture were harvested at 4000 rpm and 15 min. The supernatant was removed and the cells stored till further use (\(-\)80 \({}^{\circ}\)C). The cell pellet was defrosted on ice and resuspended in 600 \(\mu\)L acidified methanol (pH 5). Pyrorophsates were extracted by 3 \(\times\) 30 s shaking in silamat (Ivoclar Vivadent AG, Schaan, Liechtenstein) in the presence of 300 \(\mu\)L silica beads. The clear supernatant was used for LC-MS analysis after subsequent centrifugation for 10 min at 13,000\(\times\)_g_. LC-MS measurement was performed on a LaChrom ULTRA system (San Jose, CA, USA) using a SeQuant Zic-pHILJC column (5 \(\mu\)m 150 \(\times\) 2.1 mm) (Merck Millipore, Darmstadt, Germany). As a buffer system, 10 mM ammonium bicarbonate pH 9.3 (A) and acetonitrile (B) was used with a flow rate of 0.2 mL/min; 0-5 min 5% A (const.), 5-20 min 35% A (gradient), 20-25 min 5% A (gradient), 25-35 min 5% A (const.); pre-run 15 min, with 2 \(\mu\)L. Isoprenoid pyrophosphates were identified usinga micrOTOFQ (Bruker Daltonics, Billerica, MA, USA) according to their masses (GPP 313.0601; FPP 381.1227; GGPP 449.1853 [M-H]\") and elution time in accordance to a standard ([PERSON], Merck, Darmstadt, Germany).  
**Supplementary Materials:** The following figures are available online at [[http://www.mdpi.com/1422-0067/21/15/5482/s1](http://www.mdpi.com/1422-0067/21/15/5482/s1)]([http://www.mdpi.com/1422-0067/21/15/5482/s1](http://www.mdpi.com/1422-0067/21/15/5482/s1)). Figure S1: Biosensor plasmids, Figure S2: Validation of the pTEST vector system for promoter activity assay and expression of crtR orthologs.  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] planned and designed the experiments. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] performed the experiments. [PERSON], [PERSON] and [PERSON] analyzed the data. [PERSON] and [PERSON] drafted the manuscript. [PERSON] coordinated the study and finalized the manuscript. All authors read and approved the final manuscript.  
**Funding:** This research was funded by the European Regional Development Fund (ERDF) and the Ministry of Economic Affairs, Innovation, Digitalization and Energy of the State of North Rhine-Westphalia, grant number EFRE-0400184 (Biomer). Support for the Article Processing Charge by the Deutsche Forschungsgemeinschaft and the Open Access Publication Fund of Bielefeld University is acknowledged.  
**Acknowledgments:** We thank [PERSON] for optimization of the GGPP extraction protocol.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Real-Time and Robust Hydraulic System Fault Detection via Edge Computing
## 8 Performance Evaluation
### Experiment on Feature Selection
#### 8.2.2 Performance of Server Types  
We evaluated the usage of the CRFS in different architectures, with results listed in Table 6 and presented in Figure 7. The detection time by the edge server is below 30 ms, even when dealing with all features, while the detection time by the cloud server exceeds 200 ms. The packet size is calculated from the total of the selected features packet size. The Cooler component maintains the smallest packet size because its selected features are those with a small sampling rate. The difference between the detection times by the cloud is large (i.e., unstable) and of at least 10 ms, while in the edge it is at least 1-2 ms. The cloud has a high detection time because of the long-distance connection with a significant amount of data being sent. These results suggest that edge computing is a suitable architecture to apply to fault detection systems by providing computing resources near the data sources while maintaining low latency. Hence, an edge server is incorporated in the next experiment.
# Laser Irradiation on Limestone and Cracking: An Experimental Approach
## 5 Conclusions  
In this study, the effects of immersion conditions on rock-breaking by laser irradiation on the temperature, hole size, rock-breaking efficiency, macro-fracture after laser irradiation, and fracture length were investigated. In addition, the changes in rock weight loss with temperature were determined using a thermogravimetric analyzer. The main conclusions can be drawn:  
1. The temperature area over 100 \({}^{\circ}\)C increases with the increase of irradiation time and the one between 2.27 cm\({}^{2}\) and 13.20 cm\({}^{2}\) varies with the change of laser power between 90 W and 135 W. The specimen has a larger _MSE_ and a smaller _ROP_ at a power of 90 W compared with the 135 W, indicating that increasing the laser power improves the efficiency of rock-breaking. With the increase of the irradiation time, the _MSE_ gradually increases, while the _ROP_ gradually decreases;
2. The cracks on the surface of the rock samples developed considerably after being placed for one month. This is because the composition of limestone is dense, hard, and brittle. After the sample cools and shrinks, it is difficult for the cracks to be filled with cementitious substances, resulting in the continuous increase of the spacing and the gradual extension of the rock under the influence of its gravity;
3. The fracture length of the dried sample increased from 0.61 to 5.09 mm, and that of the soaked sample increased from 2.24 to 8.7 mm under a laser power of 90 W. The fracture length of the dried sample increased from 6.30 to 9.85 mm, and that of the soaked sample increased from 9.04 to 11.38 mm under a laser power of 135 W;
4. The soaked sample began to show differences when heated at 100 \({}^{\circ}\)C, which was caused by the evaporation of some free water molecules in the rock. The main weight-loss temperatures of the samples occur in the range of 640 \({}^{\circ}\)C to 900 \({}^{\circ}\)C. This is because dolomite and calcite are thermally decomposed during the high temperature area. Inflection-point temperatures for the weight-loss ratio of soaked and dried samples were 842 \({}^{\circ}\)C and 859 \({}^{\circ}\)C, respectively.  
Figure 14: Plots of: (**a**) thermogravimetric curve; and (**b**) weight-loss rate with temperature.  
**Author Contributions:** Methodology, B.R.; Validation, W.L. and Y.Z.; Data curation, Y.X.; Writing--original draft, H.P.; Writing--review & editing, J.L. and Y.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by the National Natural Science Foundation of China (No. 52174004), Natural Science Foundation of Hubei Province, China (2022 CFB265), and the Fundamental Re-search Funds for the Central Universities (2042022 kf1025).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Activation of Persulfate for Groundwater Remediation:
## 2 Activation Mechanism of Persulfate for ISCO Application  
Persulfate anion (S2 Os2-) can be converted into reactive radicals, including sulfate radical (SO4-), peroxymonosulfate radical (SO5-) and sulfate radical (SO3-), depending on the activation methods and conditions. The above mechanism can be described as follows [46]:  
\[\text{S}_{2}\text{O}_{8}\text{{}^{2-}}+\text{Activator}\to 2\text{SO}_{4} \text{{}^{\bullet-}} \tag{1}\]  
\[\text{SO}_{4}\text{{}^{\bullet-}}+\text{H}_{2}\text{O}\rightarrow\text{SO}_{4 }\text{{}^{\bullet-}}+\text{OH}\text{{}^{\bullet}}+\text{H}\text{{}^{+}} \tag{2}\]  
\[\text{SO}_{4}\text{{}^{\bullet-}}+\text{OH}\text{{}^{\bullet}}\rightarrow\text{ HSO}_{5}\text{{}^{\bullet-}}\rightarrow\text{SO}_{5}\text{{}^{\bullet-}}+\text{H}\text{{}^{+}} \tag{3}\]  
Common activation methods include alkali treatment [28], metal catalyzed processes (chelated and unchelated, homogeneous and heterogeneous) [47; 48; 49], heat [36; 50], radiation (UV, visible light) [51], H\({}_{2}\)O\({}_{2}\)[52] and electrical activation [53]. While some of the activation methods, e.g., UV and visible light, have proven to be successful to some degree at the laboratory scale, challenges in field applications remain due to the lack of perfect supporting equipment and feasible pathways [29; 52]. Heating and electrokinetic activation are widely used activation technologies in field practices for the temperature of soil and groundwater to be controlled and monitored [54; 55; 56]. Alkaline- and metal-based materials were also widely used to activate persulfate in field applications because they can directly be injected into the polluted designation together with persulfate [57; 58; 59].
## 4 Numerical Results and Discussions
### Comparisons
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\(n\)} & \multirow{2}{*}{\(N_{\gamma\text{e}}\)} & \multirow{2}{*}{\(N_{\gamma\text{pe}}\)} & \multirow{2}{*}{\(N_{\gamma\text{e}}\)} & \multirow{2}{*}{\(p_{\text{ce}}/\text{kPa}\)} & \multirow{2}{*}{**Relative Error/\%**} \\ \hline
2 & 37.85 & 28.65 & 47.89 & 1106.02 & - \\
3 & 28.03 & 21.36 & 35.26 & 818.42 & 26.37 \\
4 & 26.17 & 20.07 & 33.02 & 766.44 & 6.62 \\
5 & 25.50 & 19.61 & 32.24 & 748.05 & 2.56 \\
6 & 25.19 & 19.40 & 31.88 & 739.49 & 1.24 \\
7 & 25.01 & 19.29 & 31.68 & 734.81 & 0.69 \\
8 & 24.91 & 19.22 & 31.56 & 731.99 & 0.42 \\
9 & 24.84 & 19.18 & 31.48 & 730.15 & 0.28 \\
10 & 24.79 & 19.15 & 31.43 & 728.89 & 0.19 \\
11 & 24.76 & 19.12 & 31.39 & 727.99 & 0.14 \\
12 & 24.73 & 19.11 & 31.36 & 727.32 & 0.10 \\
13 & 24.72 & 19.10 & 31.34 & 726.81 & 0.08 \\
14 & 24.70 & 19.09 & 31.33 & 726.41 & 0.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Values of bearing capacity factors versus the number of blocks.
## 3 Results and Discussion
### ATR-FTIR Analysis of the Adhesive Mixtures  
Figure 1a shows the ATR-FTIR spectrum collected from the neat and uncured pMDI together with the spectra of cured pMDI adhesive mixtures with soy, and with soy and wheat. Neat and uncured pMDI presents all the characteristic bands as supported in numerous references [38; 39; 40]. The difficulty to observe FTIR bands in the 2300-2200 cm\({}^{-1}\) region is noted, due to the diamond ATR accessory that was employed for the collection of the spectra. For this reason, the isocyanate band at 2278 cm\({}^{-1}\)--according to the bibliography--is biased and located at 2242 cm\({}^{-1}\).  
Figure 1: (**a**) ATR-FTIR spectra of neat and uncured pMDI, and its mixtures with soy, and soy/wheat; (**b**) ATR-FTIR spectra of all the cured adhesive mixtures.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Solids, \%** & **pH at 21 \({}^{\circ}\)C, [ ]** & **Buffer Capacity, mL** \\ \hline EOP-non-extracted (NE) & 92.14 & 5.95 & 63 \\ \hline EOP-extracted (E) & 91.76 & 7.10 & 18 \\ \hline ROS-non-extracted (NE) & 92.28 & 5.50 & 44 \\ \hline ROS-extracted (E) & 90.95 & 7.01 & 12 \\ \hline OS-non-extracted (NE) & 88.9 & 7.27 & 12 \\ \hline OS-extracted (E) & 89.87 & 7.60 & 11 \\ \hline OL-non-extracted (NE) & 94.58 & 5.59 & 46 \\ \hline OL-extracted (E) & 89.61 & 5.95 & 18 \\ \hline HS-extracted (E) & 96.00 & 5.85 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Properties of biomasses olive and hemp biomasses.  
Both the cured adhesive mixtures--with plain soy flour and soy/wheat flours--present the same spectral characteristics (Figure 1a). The isocyanate band decreases and shifts to 2270 cm\({}^{-1}\), but is not fully consumed; this is indicative that the pMDI polymerization with the additives is not full but isocyanates remain to react with the wood of the veneers. A new band of C=O at 1663 cm\({}^{-1}\) is observed, indicative of NH-CO-NH linkages [38]. The bands at 1610 and 1577 cm\({}^{-1}\) of neat and uncured pMDI disappear and a new band at 1595 cm\({}^{-1}\) rises, indicating the formation of secondary urea linkages. Finally, the shoulder at 1530 cm\({}^{-1}\) is attributed to CHN groups of secondary urethane formation.  
Figure 1b shows the ATR-FTIR spectra of the cured adhesive mixtures, as they are described in Table 2. In Figure 1a, the glue mixtures with soy flour and with both soy and wheat flours are again presented for comparative reasons. In every case, all the spectra of the cured glue mixtures present similar characteristics in comparison with the cured mixtures of soy flour, and of soy and wheat flour, as already discussed; the band of the isocyanate group at 2270 cm\({}^{-1}\) is decreased in comparison with the neat and uncured pMDI, but the group is not fully consumed, as isocyanates remain to react with the wood of the veneers.
# Multi-Vehicle Tracking Based on Monocular Camera in Driver View
## 3 Proposed Algorithm
### Loss Function  
There are three subtasks of each prediction head in our multi-vehicle tracker, so the total loss function \(\mathcal{L}\) consists of classification loss, regression loss, and embedding loss of all prediction heads,  
\[\mathcal{L}=\sum_{i=1}^{N}\left(w_{cls}^{i}\mathcal{L}_{cls}^{i}+w_{reg}^{i} \mathcal{L}_{reg}^{i}+w_{emb}^{i}\mathcal{L}_{emb}^{i}\right) \tag{1}\]  
where \(N=3\) is the number of prediction heads, \(w_{cls}^{i}\) and \(\mathcal{L}_{cls}^{i}\) are the weight and loss function for the foreground/background classification task, \(w_{reg}^{i}\) and \(\mathcal{L}_{reg}^{i}\) are the weight and loss function for the bounding box regression task, \(w_{emb}^{i}\) and \(\mathcal{L}_{emb}^{i}\) are the weight and loss function for the appearance embedding task, and \(i=1,2,3\). All \(w_{*}^{i}\) are carefully tuned for optimal performance.  
In addition, \(\mathcal{L}_{cls}\) is formulated as a cross-entropy loss as below:  
\[\mathcal{L}_{cls}=-(y\log(p)+(1-y)log(1-p)) \tag{2}\]  
where \(y\) is a binary indicator (0 or 1) for foreground and background classification, and \(p\) is a probability which predicted as positive class.  
When it comes to regression loss, we use smooth \(L1\) loss:  
\[\mathcal{L}_{reg}=\begin{cases}0.5x^{2},&|x|<1\\ |x|-0.5,&\text{otherwise.}\end{cases} \tag{3}\]  
Moreover, \(\mathcal{L}_{emb}\) is formulated as the same in [7] as below:  
\[\mathcal{L}_{emb}=-\log\frac{\exp(f^{T}g^{+})}{\exp(f^{T}g^{+})+\sum_{i}(\exp (f^{T}g^{-}_{i}))} \tag{4}\]  
where \(f^{T}\) is a selected anchor instance in a mini-batch, \(g^{+}\) is the weight of the positive sample with respect to \(f^{T}\), and \(g^{-}_{i}\) is the weight of the negative sample.  
Let \(m\) and \(v\) denote the 1 st moment vector and 2 nd moment vector, respectively. \(\alpha\) is the learning rate of the model. \(\beta_{1},\beta_{2}\) and \(\varepsilon\) are hyper-parameters. The parameters \(m\), \(v\), and \(\theta\) are updated during training as follows:  
\[\left\{\begin{aligned} & m_{t}\leftarrow\beta_{1}m_{t-1}+(1-\beta_{1}) \frac{\partial Loss}{\partial\theta_{t-1}},\\ & v_{t}\leftarrow\beta_{2}v_{t-1}+(1-\beta_{2})\frac{\partial Loss }{\partial\theta_{t-1}}^{2},\\ &\hat{m}_{t}\leftarrow\frac{m_{t}}{1-\beta_{1}^{t}},\\ &\hat{v}_{t}\leftarrow\frac{v_{t}}{1-\beta_{2}^{t}},\\ &\theta_{t}\leftarrow\theta_{t-1}-\alpha\frac{\hat{m}_{t}}{ \sqrt{\hat{v}_{t}}+\varepsilon}.\end{aligned}\right. \tag{5}\]
## 6 Challenges and Future Trends in LEV Charging
### Fast Charging and High-Current Converter Topologies
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Reference** & & **Salient Features** \\ \hline \multirow{4}{*}{1.} & \multirow{4}{*}{[4]} & \(\bullet\) & Receiver coil is embedded into the e-bike’s kicksstand. \\  & & \(\bullet\) & Use of capacitors in the circuit helps in enhancing power transfer capability. \\  & & \(\bullet\) & Misalignment issue is addressed through the use of auto-resonant frequency control, but this may also increase control complexity. \\  & & \(\bullet\) & Additional DC-DC stage may affect efficiency. \\  & & \(\bullet\) & Two/three coil hybrid wireless charging topology is presented. \\  & & \(\bullet\) & Scheme is used to charge multiple bikes at once. \\
2 & & \(\bullet\) & For compensation of reactive power, an inductor array is attached on the primary side. \\  & & \(\bullet\) & Issue of misalignment is not addressed in this work \\  & & \(\bullet\) & Charging efficiency of approximately 91\% is achieved. \\  & & \(\bullet\) & Three-coil structure-based wireless charger for e-bike is presented. \\  & & \(\bullet\) & A primary-side load identification technique is employed to control the transition \\
3 & [84] & & between constant current and constant voltage mode, thus eliminating sensing \\  & & \(\bullet\) & Charging efficiency of approximately 91\% is achieved. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of some recent wireless LEV charger implementations. battery. The concept of battery swapping has gained traction for conventional EVs in recent years, in particular because of their potential to facilitate V2G capability [98; 99; 100]. Battery swapping stations also allow for easier integrating with renewable energy sources [101]. However, there are safety risks in battery swapping for conventional EVs [102]: sparks during battery changing and degradation of contacts. Safety risks are relatively low for the case of LEVs, mainly due to their lower operating voltage. However, there are still disadvantages such as larger infrastructure requirements.
# Blockchain-Based Decentralized Federated Learning Method in Edge Computing Environment
## 3 Methodology  
### System Architecture  
To implement blockchain-based decentralized federated learning, we first designed the system architecture. Figure 1 shows the architecture of BD-FL, which mainly includes the demand release module, aggregation module, training module, and verification module. Since the nodes participating in the consensus in BD-FL are only a limited number of edge servers, the alliance chain is selected as the implementation platform of the architecture.  
The demand release module is mainly composed of model demanders, whose main role is to release the demand task and pay the model training fee and the verification fee. After the model demander pays the fee and provides the initial model data, the system will send the relevant information of the initial model to each edge server for download by local devices. The aggregation module is mainly composed of edge servers that are close to local devices or data sources, such as base stations with certain computing and storage capabilities. It will save the gradient parameters of the local model, and other block data uploaded by local devices, and aggregate the global model on edge servers. It will also verify the accuracy of the uploaded gradient parameters and prevent dishonest local devices from maliciously providing wrong information. In the consensus process of blockchain, the local device will not participate, and the edge servers of the aggregation module will participate in the consensus to reduce the system communication delay. The training module is mainly composed of local devices, and its main role is to train local models using local data samples. In the model training, the system will bind local devices with edge servers according to our proposed matching algorithm. The local device will only upload the local model parameters to its bound edge server and only download the global model from its bound edge server. The verification module is also composed of some local devices. In each round of global model aggregation, the edge server sends the received local model gradient parameters uploaded by the local device to the verification device, which uses its own dataset to verify the quality of the local model, and returns the results to its bound edge server.
# Pan-Cancer Analyses Reveal Genomic Features of FOXM1 Overexpression in Cancer
###### Abstract  
FOXM1 is frequently overexpressed in cancer, but this has not been studied in a comprehensive manner. We utilized genotype-tissue expression (GTEx) normal and The Cancer Genome Atlas (TCGA) tumor data to define FOXM1 expression, including its isoforms, and to determine the genetic alterations that promote FOXM1 expression in cancer. Additionally, we used human fallopian tube epithelial (FTE) cells to dissect the role of Retinoblastoma (Rb)-E2F and Cyclin E1 in FOXM1 regulation, and a novel human embryonic kidney cell (HEK293T) CRISPR FOXM1 knockout model to define isoform-specific transcriptional programs. FOXM1 expression, at the mRNA and protein level, was significantly elevated in tumors with FOXM1 amplification, p53 inactivation, and Rb-E2F deregulation. _FOXM1_ expression was remarkably high in testicular germ cell tumors (TGCT), high-grade serous ovarian cancer (HGSC), and basal breast cancer (BBC). FOXM1 expression in cancer was associated with genomic instability, as measured using aneuploidy signatures. FTE models confirmed a role for Rb-E2F signaling in FOXM1 regulation and in particular identified Cyclin E1 as a novel inducer of FOXM1 expression. Among the three _FOXM1_ isoforms, _FOXM1c_ showed the highest expression in normal and tumor tissues and cancer cell lines. The CRISPR knockout model demonstrated that FOXM1b and FOXM1c are transcriptionally active, while FOXM1a is not. Finally, we were unable to confirm the existence of a FOXM1 auto-regulatory loop. This study provides significant and novel information regarding the frequency, causes, and consequences of elevated FOXM1 expression in human cancer.  
Keywords:FOXM1; pan-cancer; gene amplification; genomic instability; Retinoblastoma protein Cyclin E1; fallopian tube epithelial cells; testicular germ cell tumors; high-grade serous ovarian cancer; basal breast cancer +
Footnote †: journal: Cancer