esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Simulation Results on Flow Characteristics
### Flow Distribution of the Improved Model  
In response to the phenomenon of excessive flow in the first- and second-row T/R components, the pipes of the first- and second-row liquid supply branch pipes were gradually reduced in size, keeping the thickness of the cross-section of the branch pipe unchanged. The width was half the original branch pipe, making the cross-sectional dimensions 0.01 m \(\times\) 0.02 m. In contrast, the cross-sectional dimensions of the other branch pipe flow channels are 0.02 m \(\times\) 0.02 m square, to reduce the flow of the fluid and achieve a uniform flow distribution effect. The details of the model's gradual reduction are shown in Figure 10.  
Figure 10: Detail of model tapering.  
Figure 9: Group 1 component traffic distribution.  
The modified model is numerically simulated again, and the actual flow distribution of the first row and the second row is analyzed. The actual flow is shown in Figure 10. According to the distribution of the actual flow of each TR component in the first and second rows shown in Figure 11, it can be seen that the error between the flow and the rated flow in these two rows is reduced from 20% to less than 15%. It can be concluded that the diameter of the branch pipe is an important factor affecting the flow distribution.  
The middle section of the model consists of rows three to ten, with each row having ten TR components. An analysis and processing of the coolant flow through each component is conducted. In Figure 11(a), it can be seen that the component flow from the third to the sixth row is essentially within \(\pm\)15% of the set flow. In Figure 11(b), the actual flow of components a, b, and c in each row is less than the designed flow by \(-\)15%.  
This indicates that in the distribution of flow in each branch pipe, the actual flow of each TR component from the third to the tenth row has an error within 15% of the rated flow. The flow variation trend of the TR components in each row is consistent with the first and second rows, with the flow increasing sequentially from the liquid supply main pipe  
Figure 11: Flow distribution of T/R components in the first and second rows after branch-thinning process.  
Figure 12: Group 2 component traffic distribution.  
towards the liquid return main pipe. Analyzing the overall flow distribution from the third to the tenth row, it can be seen that the flow generally decreases from the third row to the tenth row, meaning that the flow of the TR components in the third row is greater than that in the tenth row.
## 3 Results and Discussion
### Evaluation of Cytotoxicityoxicity
#### 3.2.5 A. agallocha and A. malacensis  
Extract Inhibited the Increased Levels of Inflammation-Related Cytokines  
An important role for proinflammatory mediators in the development of inflammatory diseases [81]. Asthma, arthritis, and cancer are examples of chronic inflammatory illnesses where these factors, such as _iNOS_, _TNF-\(\alpha\)_, _Cox-2_, _IL-18_, and _IL-6_, are overexpressed by macrophages [82]. Important inflammatory response signaling pathways including _NF-\(\kappa\)B_ and _MAPKs_ regulate a range of innate and adaptive immune responses. Both NF-\(\kappa\)B and _MAPKs_ signaling serve as key regulators of the production of pro-inflammatory mediators during the inflammatory response. Nuclear transcription factors including _MAPKs_ and _NF-\(\kappa\)B_ can be activated by LPS, stimulating signaling pathways in macrophage cells. Inducible _iNOS_, _TNF-\(\alpha\)_, _IL-6_, _COX-2_, and other proinflammatory mediators can all be initiated by macrophage cells when LPS is expressed [83]. Therefore, this study focused on exploring the _A. agallocha_ and _A. malacensis_ affected gene expression on those factors by RT-PCR and qRT-PCR. In the LPS-treated group, there was a substantial increase in the mRNA expression of _COX-2_, _TNF-\(\alpha\)_, _iNOS_, _IL-6_, and _IL-8_. However, the _A. agallocha_ and _A. malacensis_ extracts reduced the gene expression in a dose-dependent way. The _A. agallocha_-treated cell significantly suppressed the gene expression of COX-2, _TNF-\(\alpha\)_, _iNOS_, _IL-6_, and  
Figure 9: Effects of A.A and A.M on the apoptosis-related genes’ levels of mRNA expression in A549 cells. A.A and A.M were applied to A549 cells at a dose of 1000 \(\upmu\)g/mL for 24 h. Following the extraction of total RNA, qPCR was used to analyze the transcript expression levels using primers that targeted (**A**) _p53_, (**B**) _Bax_, (**C**) _BCL-2_, (**D**) _Caspase 9_, and (**E**) _Caspase 3_. Every bar displays the mean and standard deviation of both samples from all three separate trials (****\(p<0.01\) utilizing Student’s \(t\)-test contrasted against the control).  
_IL-8_ (1.56, 0.98, 1.91, 1.93, 1.52) fold, respectively. In contrast, _A. malaccessis_ treated cells that significantly suppressed the gene expression of COX-2 (1.44 fold), _TNF-a_ (0.81 fold), _iNOS_ (1.79 fold), _IL-6_(1.53 fold), and _IL-8_ (1.39 fold), respectively (Figure 10). The quantities analysis showed that _A. malaccessis_ has more anti-inflammatory activity than _A. agallocha_.
# Error Investigation on Wi-Fi RTT in Commercial Consumer Devices
## 3 Wi-Fi RTT Distance Error Measurements
### Experimental Sites
#### 3.2.2 Range-Fixed and Position-Varied Environment  
A volunteer was employed to hold the smartphone and collect the samples following a specific route in this environment. Similarly, a LOS condition was first set in this environment. As shown in Figure 5a, a circular route was stipulated surrounding the AP at the radius of \(d\) m so that the range between the smartphone and the AP was fixed (\(d=1,3,5,7,9\)) while the smartphone's position changed. At each distance, samples were collected at the sampling period of 200 ms for at least 250 s. This data collection provided us with more than 1000 samples in each case and ensured that the entire circular route could be finished at least once.  
Then, an NLOS condition was constructed by setting the same blockers used in RFPF. As shown in Figure 3b, different from the NLOS settings in RFPF, we added three more blockers to entirely block the signal transmission to all directions. Identical to the LOS condition, we collected samples at the sampling period of 200 ms for at least 250 s for each circular route. Once all routes had been finished, the material of the blocker was changed and the same sample collection procedure was repeated.  
Similarly, we adopted the same sample collection procedures for all three different smartphones in both LOS and NLOS conditions.  
Figure 4: Schematic of the RFPF environment: (**a**) LOS condition; (**b**) NLOS condition.  
Figure 5: Schematic of the RFPV environment: (**a**) LOS condition; (**b**) NLOS condition.
## 4 Simulation and Analysis of Magnetic Field Distribution
### Magnetic Field Generated by Sideband Current and Harmonic Current
#### 4.3.3 EMI Noise Current  
When switching devices such as mosfet and diode are switched on and off, they will produce large voltage change rate \(\mathrm{d}u/\mathrm{d}t\) and current change rate \(\mathrm{d}i/\mathrm{d}t\), which will generate abundant high frequency signals, as shown in Figure 20, thus forming conduction and radiation electromagnetic emission. Magnetic field distribution of a vehicle with WCS is shown in Figure 21.  
Figure 19: Comparison of different magnetic field: (**a**) alignment; (**b**) 30% offset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Offset**} & **Primary 3 rd** & **Secondary 3 rd** & **Primary 5 th** & **Secondary 5 th** \\  & **Harmonic** & **Harmonic** & **Harmonic** & **Harmonic** \\ \hline
0 & 0.5399 & 0.3724 & 0.1034 & 0.1034 \\
10\% & 0.5291 & 0.3661 & 0.1020 & 0.1060 \\
20\% & 0.5140 & 0.3611 & 0.1103 & 0.1056 \\
30\% & 0.5776 & 0.3681 & 0.1286 & 0.1191 \\
40\% & 0.7801 & 0.3211 & 0.954 & 0.1152 \\
50\% & 0.9573 & 0.1659 & 0.736 & 0.0625 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Harmonic currents with the offset (A/m).  
Considering the magnetic field, radiation emission will also occur in a certain frequency range from 9 kHz to 108 MHz. The test points are located around the vehicle and three meters away from the body according to the requirement of SAE J2954, and the magnetic field distribution is shown in Figure 21.  
Table 9. indicates that, at different frequencies, radiation emission values at four directions exceed the emission limit due to the large corresponding current. Radiation emission values under coil alignment and offset are consistent. Taking the operating characteristics of WCS into account, it is necessary to set protection to the wireless charging system to reduce the leakage magnetic field that might induce the radiation emission problems.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Frequency** & **Front** & **Rear** & **Left** & **Right** & **Limit** \\ \hline
85 kHz & 88.79 & 87.45 & 97.63 & 98.29 & 28.5 \\
255 kHz & 76.23 & 60.37 & 69.89 & 70.24 & 18.95 \\
85 kHz (30\% offset) & 86.12 & 84.24 & 91.23 & 92.11 & 28.5 \\
255 kHz (30\% offset) & 76.07 & 60.19 & 69.47 & 69.67 & 18.95 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Magnetic field strength at three meters around the vehicle.  
Figure 20: PWM signal of mosfet.  
Figure 21: Far-field magnetic field distribution on fundamental current and third-order harmonic current: (**a**) alignment; (**b**) 30% offset.
## 5 Discussion
### Policy and Market Barriers to Digestate Utilization
#### 5.5.2 Legal and Commercial Feasibility of Digestate as Fertilizer  
The regulation of digestate as a fertilizing product is governed both at the European Union level and by individual Member States. Digestate can be marketed under EU harmonized rules or according to national legislation, each with distinct requirements and implications.  
Regulation (EU) 2019/1009 [124] governs the placing of fertilizing products on the EU internal market and establishes a harmonized approval and labeling system. Within this regulation, digestate is addressed under specific Component Material Categories (CMCs): CMC4: Digestate derived from fresh crop materials (excluding food and feed waste); and CMC5: Digestate derived from non-food/non-feed biodegradable waste.  
To qualify under these categories, digestate must meet stringent input material restrictions, processing conditions (e.g., sanitization), and contaminant limits. The final product must correspond to a Product Function Category (PFC), such as fertilizer, soil improve, or growing medium, and comply with associated quality and safety criteria.  
Upon meeting these requirements, digestate products obtain CE marking through conformity assessment, enabling free marketing across the EU without additional national authorizations. This facilitates cross-border trade and regulatory harmonization. Nonetheless, manufacturers may elect to operate under national regulations if EU criteria are unmet or inapplicable.  
In Spain, the commercialization and use of digestate as a fertilizer are primarily governed by Royal Decree 506/2013 [126] on fertilizing products and Law 7/2022 [125] on waste and contaminated soils, which regulate conditions for end-of-waste status. Digestate can be marketed in Spain either as a finished fertilizer, complying with specificationsin Royal Decree 506/2013 [126] annexes, or as a raw material for compound fertilizer formulation, subject to prior evaluation and approval.  
If derived from waste, digestate must have undergone an end-of-waste assessment ensuring quality, environmental safety, and traceability. Royal Decree 1051/2022 [123] aligns with Regulation (EU) 2019/1009 [124] in recognizing digestate conforming to CMC4 or CMC5 as a recovered material suitable for fertilizer use. Additionally, operators marketing digestate-based fertilizing products must register with the National Register of Fertilizer Manufacturers and Importers and comply with relevant reporting and traceability obligations.  
It is important to note that obtaining fertilizer status--either at an EU or national level--does not automatically authorize land application. Use in agriculture must comply with national and regional environmental regulations, including restrictions related to nitrate vulnerable zones, application methods, and nutrient limits.
# Joint Location-Dependent Pricing and Request Mapping in ICN-Based Telco CDNs For 5G
## 7 Conclusions  
In this paper, we first propose a ICN-based telco CDN framework that integrates the benefits of both ICN (i.e., information-centric forwarding) and telco CDNs (i.e., powerful distributed edge caching) to provide high-quality content delivery service in the 5G era. Then, we design a location-dependent pricing (LDP) strategy for ICN-based telco CDNs by taking into account the spatial heterogeneity of the traffic usage of geo-distributed CDN sites. Thereafter, on the basis of the LDP strategy, we formulate a price-aware request mapping optimization which minimizes both user perceived latency and application providers' bandwidth payment. Simulations under four representative traffic patterns show that our design helps ICN-based telco CDNs efficiently manage the sites' congestion and thus better match their revenue to cost. In addition, the impacts of key parameters in LDP and PARM on the performance metrics are analyzed and setting suggestions are provided for adopters.  
Formal analysis, M.J.; Funding acquisition, S.G.; Methodology, M.J. and B.F.; Project administration, S.G.; Supervision, H.L.; Validation, M.J.; Writing--original draft, M.J., H.L. and B.F.  
This research is funded by the project of Beijing New-Star Plan of Science and Technology under Grant No. Z151100000315052.  
The authors declare no conflict of interest.
# Local Batch Normalization-Aided CNN Model for RSSI-Based Fingerprint Indoor Positioning
## 5 Conclusions  
This study presented a CNN model enhanced with LBN to improve the performance of RSSI-based indoor fingerprint positioning systems. Through extensive simulations and real-time experiments, the effectiveness of BN, LN, and LBN in enhancing model accuracy was validated. The proposed CNN + LBN model consistently outperformed the baseline CNN, CNN + BN, and CNN + LN models in real-time positioning tasks, achieving a real-time accuracy of 92.97%. This marked a significant improvement over the traditional CNN approach. While real-time accuracies for all models were slightly lower than their simulation results, the CNN + LBN model demonstrated the most robust performance across both scenarios. These findings underscore the potential of LBN in enhancing the generalization and precision of CNN-based indoor positioning systems, particularly in real-time applications. Future research should focus on further improving the model by utilizing larger and more diverse datasets and exploring additional normalization techniques. Such advancements could help address the residual performance gap between simulation and real-time results, further strengthening the model's reliability and scalability for practical deployment.  
[PERSON] and [PERSON] conceptualized the core idea of this study. [PERSON] handled data collection, developed the computational code, and conducted simulations and experiments. Both [PERSON] and [PERSON] verified the numerical findings. The research was designed and supervised by [PERSON]. Additionally, [PERSON], [PERSON] and [PERSON] contributed to the preparation of the manuscript. [PERSON] and [PERSON] reviewed, revised, and finalized the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research did not receive any external funding.  
All data supporting the findings of this study are included within the article.  
The authors declare no conflicts of interest.  
Figure 9: Cumulative distribution function curves comparing positioning accuracy among CNN baseline, CNN + BN, CNN + LN, and CNN + LBN.
# Drone SAR Imaging for Monitoring an Active Landslide Adjacent to the M25 at Flint Hall Farm
## 3 Materials and Methods
### Hardware Development  
The drone radar hardware is a combination of commercially available and custom-made components. The payload components are considered in relation to the SWaP-C design criteria for drone deployment, of size, weight, power and cooling. The payload and its components have a small form factor and are lightweight for easier mounting to the drone and to maximise flight durations, respectively; the power consumption of electrical components such as the SDR and Single Board Computer (SBC) is considered, with internal rather than external power sources, and appropriate cooling mechanisms to dissipate any thermal energy that is produced.
## 4 Implementations with DSD Reaction Networks
### The Enzymatic Protein Hydrolysis Model  
Taking the equation \(X_{P}+X_{E}\overset{k_{1}}{\longrightarrow}X_{P,E}\) in Equation (1) as an example, its DSD reactions are in Equation (20).  
\[\left.\begin{array}{l}X_{P}+L_{1}\overset{q_{1}}{\underset{q_{\max}}{ \rightleftarrows}}H_{1}+Sp1\\ X_{E}+H_{1}\overset{q_{\max}}{\longrightarrow}O_{1}+Sp2\\ O_{1}+T_{1}\overset{q_{\max}}{\longrightarrow}X_{P,E}+Sp3\end{array}\right\},q_{1}=k_{1} \tag{20}\]  
Using DNA strand displacement mechanism, the reaction \(X_{P,E}\overset{k_{2}}{\longrightarrow}X_{A}+X_{E}\) can be translated into  
\[\left.\begin{array}{l}X_{P,E}+G_{1}\overset{q_{2}}{\longrightarrow}Sp4+O_{2 }\\ O_{2}+T_{2}\overset{q_{\max}}{\longrightarrow}X_{A}+X_{E}\end{array}\right\},\quad q_{2}=\frac{k_{2}}{C_{\max}} \tag{21}\]  
The degradation reaction \(X_{A}\overset{k_{3}}{\longrightarrow}\varnothing\) is converted into  
\[X_{A}+G_{2}\overset{q_{3}}{\longrightarrow}\varnothing,\quad q_{3}=\frac{k_{ 3}}{C_{\max}} \tag{22}\]  
Figure 10: Schematic diagram of BC-IPAR scheme for a process.  
**Remark 3**.: _The reactions shown in Equations (20)-(22) are the results of the enzymatic protein hydrolysis model of Equation (2) being converted into DNA strand displacement reactions. To further demonstrate this process, the following schematic diagrams are constructed. The DNA strand displacement reactions of Equation (20) are shown in Figure 11. In addition, the corresponding DNA reactions as designed according to Equation (21), as shown in Figure 12. Then, a schematic diagram showing the idea of the DSD reaction of Equation (22) is illustrated in Figure 13._
# Software-Defined NB-IoT Uplink Framework--The Design, Implementation and Use Cases
## 5 Uplink Implementation
### NPUSCH Parameters  
At this stage of the uplink implementation description, it is important to introduce the basic transmission parameters that are predetermined by the higher stack layers before starting the NPUSCH communication. However, they should be already considered within channel coding calculations (e.g., estimation of the total number of the bits available for the transmission); from now, the context of the frame-based transmission is included in the performance of all modules. More explicitly, the channel coding section belonging to the uplink shared channel (UL-SCH) operates on the transport block concept, whereas the further NPUSCH procedures involve additional time-related factors with reference to the physical resources background.  
As mentioned in Section 1, there are two possible variants of the NB-IoT subcarrier spacing: 15 kHz and 3.75 kHz. The number of available subcarriers and the time dependencies regarding those two parameters are presented in Table 3.  
It is important to mention that \(T_{slot}\) represents the time duration of a single slot, containing seven symbols (#0-6). To properly understand the slot term in the context of the transmitted bits, or more precisely, symbols and subcarriers, the exemplary NB-IoT resource grid is presented in Figure 5.  
\begin{table}
\begin{tabular}{c c c} \hline
**NPUSCH Format 2 Bit** & **Output Codeword** \\ \hline
0 & \textless{}0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\textgreater{} \\
1 & \textless{}1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1\textgreater{} \\ \hline \end{tabular}
\end{table}
Table 2: Block encoder \(1/16\).  
Figure 5: NB-IoT resource grid [2].  
The 3 GPP technical specification [2] defines possible combinations of the number of active subcarriers and slots as well, which are summarized in Table 4.  
It should be noted that multitone mode is available only for the NPUSCH format 1 with 15 kHz subcarrier spacing, and \(N_{sc}^{RUI}\) indicates number of the active subcarriers for the resource uni (RU), i.e., all slots in the given transmission.
# Resonant Asymmetrical Half-Bridge Flyback Converter
## 2 Proposed Circuit Configuration
### State 8 (\(t_{7}-t_{0}-T_{s}\))  
During this interval, \(Q_{1}\) and \(Q_{3}\) remain on. \(Q_{2}\) is turned off. When \(i_{Lr}\) is demagnetized to zero, \(V_{in}\) magnetizes \(L_{r}\) to increase \(i_{Lr}\) positively. \(C_{r}\) is also charged to raise \(v_{Cr}\). Since \(i_{Lr}\) is the sum of \(i_{Lm}\) and \(i_{N1}\), both \(v_{N1}\) and \(v_{N2}\) are higher than zero when \(i_{Lr}\) is higher than \(i_{Lm}\). \(C_{0 ss3}\) is charged to increase \(v_{ds3}\) from zero. Once \(v_{ds3}\) is positive and detected by the SR control IC, which turns \(Q_{3}\) off immediately. Due to \(L_{m}>>L_{r}\) and \(C_{r}>>C_{0 ss3}\), \(L_{m}\) and \(C_{r}\) do not resonate with \(L_{m}\) and \(C_{r}\). Therefore, it only needs to consider the resonant operations of \(L_{r}\) and \(C_{0 ss3}\). \(L_{r}\) and \(C_{0 ss3}\) resonate for half a cycle to raise \(v_{ds3}\). When \(v_{ds3}\) is charged to (\(v_{N2}+V_{O}\)), it returns to state 1.  
\[(L_{m}+L_{r})\cdot\frac{di_{Lr}}{dt}=V_{in}-v_{Cr}, \tag{57}\]  
\[C_{r}\cdot\frac{dv_{Cr}}{dt}=i_{Lr}=i_{Lm}, \tag{58}\]  
\[L_{r}\cdot\frac{di_{Lr}}{dt}=V_{in}-v_{Cr}-(-n\cdot V_{O}), \tag{59}\]  
\[i_{Lm}(t)=i_{Lm}(t_{7})-\frac{n\cdot V_{O}}{L_{m}}(t-t_{7}), \tag{60}\]  
\[i_{Lr}(t)=\frac{V_{in}-v_{Cr}(t_{7})+n\cdot V_{O}}{Z_{4}}\cdot\sin(\omega_{r 4}(t-t_{7}))+i_{Lr}(t_{7})\cdot\cos(\omega_{r4}(t-t_{7})), \tag{61}\]  
\[v_{ds3}(t)=i_{N2}(t)\cdot Z_{4}=n\cdot(i_{Lr}(t)-i_{Lm}(t_{7}))\cdot Z_{4}, \tag{62}\]  
\[v_{Cr}(t)=(V_{in}-v_{Cr}(t_{7})+n\cdot V_{O})[1-\cos(\omega_{r4}(t-t_{7}))]+[ i_{Lr}(t_{7})\cdot Z_{4}\cdot\cos(\omega_{r4}(t-t_{7}))], \tag{63}\]  
The formulas for \(Q_{1}\) and \(Q_{2}\) to achieve ZVS turn-on are shown as (64):  
\[\frac{1}{2}L_{r}\cdot i_{Lr}(t_{5})^{2}\geq\frac{1}{2}(C_{0 ss1}+C_{0 ss2})V_{ in}^{2}=\frac{1}{2}(2C_{0 ss2})V_{in}^{2}=2C_{0 ss2}\cdot V_{in}^{2}, \tag{64}\]  
The calculation of output current can be expressed as (68).  
\[i_{N2}=n\cdot i_{N1}, \tag{65}\]  
\[i_{N1}=i_{Lr}-i_{Lm}, \tag{66}\]  
\[i_{N2(Toff)}=n\cdot\int_{t_{6}}^{t_{1}}(i_{Lr}-i_{Lm})\cdot dt, \tag{67}\]  
\[I_{O}=i_{N2(avg)}=\frac{n}{T_{Sw}}\cdot\int_{t_{6}}^{t_{1}}(i_{Lr}-i_{Lm}) \cdot dt, \tag{68}\]
# Retrospective Study on Seismic Ionospheric Anomalies Based on Five-Year Observations from CSES
## 1 Introduction
At the same time, the existence of uncertainty factors, such as the high dynamics of the ionosphere and the complexity of the EQ preparation process, leads to complex and variable outcomes of seismic ionospheric anomalies [24]. One of the scientific objectives of the CSES mission is to explore the ionosphere's response to the strong EQ activities in the Lithosphere, as well as the monitoring of potential seismic risk [21]. It is important and meaningful to conduct more research on EQ precursor monitoring based on the observations from the CSES mission. This study primarily conducts a retrospective analysis of global strong shallow EQ cases with Ms \(\geq\) 7 and depth \(\leq\) 100 km based on CSES-01 observation data collected over the five years since its launch. After verifying the abnormal phenomena of each EQ case, a summary and statistical results of seismic ionospheric anomaly characteristics are presented. Section 2 introduces the CSES data and the processing methods. The typical EQ case analysis and retrospective studies are presented in Section 3. The conclusions and discussions are reported in Section 4.