esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Measuring Principle
#### 2.2.3 Data Analysis Methods  
The multi-variable nonlinear regression method was used to fit the reflection coefficient spectra obtained from the experiment according to Equation (11), thereby inversely obtaining the density and sound speed values of the fluororubber specimens [26]. To avoid the multi-parameter fitting algorithm from falling into the local optima, the value range of the core intermediate parameters was restricted during the iterative computation process. Specifically, the density of fluororubber was limited to the range of 1200-1600 kg/m\({}^{3}\), the longitudinal wave speed was 650-1300 m/s, and the shear wave speed was 150-700 m/s. The selection of initial values was carried out by screening based on the reflection coefficient curve \(H_{0}^{r}(0,\omega)\) for normal incidence.  
Among the three indicators \(K_{s}\), \(H\), and \(\beta\), \(K_{s}\) and \(H\) can be obtained from the reflection coefficient spectrum fitting via MATLAB's inline and nlinfit functions. The attenuation factor \(\beta\), caused by scattering effects, is coupled with the acoustic attenuation due to the viscosity coefficient, which requires an estimation. The estimation method is as follows: the power spectrum of the data between the IF0 interface echo and the IF1 interface echo is calculated and then correlated with the power spectrum of the IF1 interface echo data.
###### Abstract  
The purpose of this study is to evaluate and compare how well different routing protocols perform in terms of load sharing, link failover, and overall network performance. Wireshark was used for packet-level analysis, VMWare was used for virtualization, GNS3 was used for network simulation, and Iperf3 was used to measure network performance parameters. Convergence time, packet losses, network jitter, and network delay are the parameters that were selected for assessment. To examine the behaviors of Open Shortest Path First (OSPF) and Enhanced Interior Gateway Routing Protocol (EIGRP) routing protocols in a variety of network settings, a simulated network environment incorporating both protocols along with Border Gateway Protocol (BGP) is created for the research. The setup for the experiment entails simulating different network conditions, such as fluctuating traffic loads and connection failures, to track how the protocols function in dynamic situations. The efficiency metrics for OSPF and EIGRP with BGP are measured and evaluated using the data generated by Wireshark and Iperf3. The results of this research show that EIGRP has a better failover convergence time and packet loss percentage as compared to the OSPF.  
Keywords:comparative study; routing protocols; IPv6 transition; OSPF; EIGRP; BGP; load sharing; link failover; routing protocols evaluation; network delay; network jitter; packet loss; convergence time; GNS3; VMware; Wireshark; Iperf3 +
Footnote †: journal:
## 2 Mathematical Formulation  
This section formulates the problem regarding the optimal location and size of D-STATCOMs in electrical distribution networks as a general mixed-integer nonlinear programming (MINLP) model. The continuous variables of the model denote the voltages, angles, and active and reactive power variables, while the discrete variables correspond to the possibility of integrating D-STATCOMs in all the nodes of the distribution network. In addition, these equations take a nonlinear form due to the products between them and the trigonometric functions in the power balance equations. The complete MINLP model of the studied problem is presented below.
## 6 Results and Discussions
### Comparative Studies of the Proposed Methodologies  
The confidence interval of the SVM power curve, being smaller for the critical wind speed range (between cut-in and cut-out), may have better ability to reject the anomalous or faulty data, and thus be helpful in optimising WTs, and have more remarkable ability to detect anomalies in the early stages; this section addresses this as follows.  
The developed SVM power curve, together with estimated pointwise and simultaneous Cis' results are, along with the measured power curve, shown in Figure 11. They suggest that pointwise CIs are relatively smaller across the entire range of wind speed and therefore have a superior capability to reject invalid or faulty data as compared to simultaneous CIs.  
Figure 10: Analysis of simultaneous CIs for estimated power values in time-series.  
Figure 9: Simultaneous CIs for SVM-based power curve.  
This difference can be further seen in the limited time-series plot (Figure 12) where dashed circles highlight significant smaller pointwise CIs and thus have reduced uncertainty for the SVM power curve, as compared to simultaneous CIs. It should be noted that the SVM model's accuracy weakens in dealing with extensive datasets due to cubic inversion issues and, therefore, dealing with a large data size can be challenging and consequently affects the proposed uncertainty model's accuracy. Therefore, finding appropriate data management is vital for the effective use of SVM models.
## 4 Experiment and Result
### Evaluating Learning Performance: A Comparison with Alternative Reinforcement Learning Methods
As shown in Table 3, the performance of EMAC was evaluated based on the average reward and the standard error calculated over 100 iterations after training. The results show that EMAC outperforms A2C, DDPG, and PPO in terms of both higher rewards and more stable learning overall. Similarly, when comparing EMAC with SAC and TD3, it was observed that EMAC achieved higher average values and lower standard errors in most environments, with a few exceptions.
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 2 Highly Adaptive Reconfigurable Receiver Front-End (HARRF) for 5G and Satellite Applications
### Conceptual Design Objectives  
The design of the switchable receiver front-end operating at X-band (8-12 GHz) and K/Ka-band (18-40 GHz) frequencies must consider key objectives such as:  
* High Linearity: Ensuring minimal distortion under high-power signal conditions.
* Low Noise Figure (NF): Optimising the sensitivity of the receiver.
* Adequate Gain: Maintaining sufficient signal amplification while minimising losses.
* Efficient Switching Mechanism: Seamless transition between X-band and K/Ka-band with minimal insertion loss.
* Wideband Operation: Supporting mmWave 5G FR2 applications with bandwidth flexibility.
# A Review on Decentralized Finance Ecosystems
## 4 The Taxonomy
### Product/Services
#### 4.2.3 Asset Management  
DeFi protocols for asset management use smart contracts to pool investors' tokens, often for use on other decentralized applications [19]. Smart contracts provide security and enable investors to allocate their tokens to other DApps. Blockchain technology facilitates decentralized transactions and registries of assets, promoting transparency and facilitating rapid settlement without the need for validation from central authorities. It encompasses both fungible and nonfungible assets [19; 42; 92]. Fungible assets are those interchangeable into individual units [92]. Similar to conventional investment funds, on-chain funds are primarily utilized for diversifying portfolios. They provide an avenue for investing in a collection of crypto assets and implementing various strategies without the need for individual token management. In contrast to conventional funds, on-chain funds do not necessitate a custodian. Instead, the crypto assets are secured in a smart contract [42]. Users can transparently and automatically manage their assets with third parties at a relatively low cost. Smart contracts ensure the property rights of users.  
Asset management within the DeFi space features prominent examples such as the _DeFi Pulse Index_ (DPI) and _Balanced Crypto Pie_ (BCP). _DPI_ serves as a blockchain financial product designed to lower entry barriers for newcomers, providing exposure to DeFi for those lacking specialized expertise while offering experienced users a single-asset exposure to the DeFi sector. _Conversely_, _BCP_ offers a convenient ERC20 token that grants equivalent exposure to Bitcoin, Ethereum, and DeFi assets. _BCP_ underlying assets continuously generate profits and enhance holdings through automated rebalancing to maintain the set allocation. For this category to mature fully, it necessitates sufficient data, aggregators, companies, and analytics providers to assist users in making informed asset decisions. That is why products like _DPI_ play a pivotal role by providing extensive industry information and aiding investors in decision-making.
## 3 Experiment Results and Discussion  
### Dataset  
The KITTI dataset [31; 32] provides sensor data of diverse weather conditions obtained on clear, rainy, or cloudy days and of various driving environments including downtown areas, outskirts, and highways using a camera, LiDAR, and GPS, along with annotation data for segmentation and detection. In this paper, we used the object detection database in the KITTI dataset. The KITTI object database consist of 7481 frames of labeled data and 7518 frames of unlabeled data, and the labels include object locations and pose information in each frame for object detection. To develop SSADNet, we used both label data and their LiDAR data.
## 5 Conclusions and Future Works  
### Particularities of This Research  
This study focused on the evolution of online assessment practices through Moodle, exploring Moodle's question types and import formats. In addition, the impact of FTP on educators' assessment methods during and after the COVID-19 pandemic was studied. It has been concluded that the pandemic has marked a shift in assessment methods. Beforethe pandemic, 47% of educators used Moodle quizzes frequently, while 31% responded that they had never used it. After the pandemic, 86% of the instructors started using Moodle question types for their teaching.  
The validation of the sample was carried out by checking the proportion of STEM and non-STEM educators in each of the surveys carried out, as well as the age range. Regarding field of knowledge, there is not a significant difference. The maximum difference (field of knowledge is 3.1% and age range is 2.5%) indicates no significant variation.  
FTP notably impacted the question types used in Moodle. Before the training was received, the GIFT format was predominantly used for question imports, which had a limited range of question types. After the training on FTP usage, there was a significant change as educators started exploring a more comprehensive range of question formats. In this case, 15% of educators who initially did not use Moodle quizzes started using them after becoming familiar with FTP. The introduction of FTP has also facilitated the integration of images and videos into Moodle quizzes. Prior to the training, 50% of educators had used images in their quizzes, with STEM ones using them more frequently at 57%. After FTP training, 63% started inserting multimedia elements, with 68% of STEM educators reporting being very satisfied. Educators have also been observed to use a broader range of question types after the training, including Cloze questions, which increased in usage. In this case, only 11% of educators used Cloze questions before the training. This percentage increased to 22% among STEM educators after the training, pointing out that FTP had a positive impact on expanding the use of question format options.
# Edible Packaging: A Technological Update for the Sustainable Future of the Food Industry
## 3 Results
### History of Edible Packaging  
Edible packaging may seem like a new innovation, but it is a very ancient technology that has been used for centuries for food preservation. It started in the 12 th century in China for the preservation of oranges and lemons by coating them with wax to prevent water loss during transportation and storage [6]. The practice of preservation was called \"larding\" [6]. Boiling soy milk proteins in pans and further air-drying created the first edible films, known as \"Yuba\" films, in Japan at the beginning of the 15 th century [9]. During the 16 th century, the landing of fruits, vegetables, meats, and fish was common in England to prevent moisture loss, similar to waxing. The first gelatin film patent was granted in the United States in the 19 th century to safeguard several meat products. To limit gas transport through edible coatings, sucrose and sugar derivatives were used as protective coatings on nuts to prevent oxidative rancidity [10; 11]. In the 1930s, commercial waxing and lipid coatings were applied to vegetables and fruits, allowing natural respiration and preventing dehydration during transport. This business has grown a lot in the last century. Its primary use was to keep water from leaking out of fruits and vegetables and to give them a shine. In the first two decades of the 21 st century, the development of edible packaging remained stagnant due to the widespread use of plastic as a cost-effective packaging material [8]. Today's rising customer standards for quality, freshness, and sustainable packaging choices are facilitating a rise in commercial edible packaging materials. Moreover, increasing awareness of the necessity of environmental protection requires the widespread introduction of non-polluting food packaging as edible packaging.
# Grid Nodes Selection Strategies for Power Quality Monitoring
## 4 Problems and Future Prospects
### PQ--Part of Smart Grid and Its Communication Network
Measurements were non-remote, consequently it was impossible to immediately detect the data loss. In the future, data loss can be crucial for legal disputes about responsibility and penalties (between grid operators, regulator, consumers, and other parties), correct grid logic and operation (situation assessment, predictive maintenance, smooth and correct interoperability with other systems--\"self-healing\", relay protection, outage management, etc. ), reporting for statistical or political purposes. In our case, the corrupted weak array is the loss of legal value, since according to EN 50160, weak duration data are required for compliance verification of supply voltage RMS, negative sequence unbalance, and other events. During the project, many problems have arisen with only a few monitors; thus, a huge technological leap forward must be made in anticipation of massive installation. \begin{table}
\begin{tabular}{c c c c} \hline
**Group** & **Technology** & **Bandwidth** & **Coverage** \\ \hline \multirow{4}{*}{Wireless LPWAN} & LoRa & 0.3–50 kbps at 868 MHz (Europe) & 10–15 km (3–5 km in urban area) \\  & NB-IoT & 200 kbps downlink, 20 kbps uplink & 10 km (1 km in urban area) \\  & Sigfox & 100 bps (140 uplink messages per day limit, 4—downlink) & 40 km (10 km in urban area) \\ \hline \multirow{4}{*}{Wireless WAN} & WiMAX & 72 Mbps in 48 km radius, 100 Mbps in 10 km radius, max 1 Gbps (802.16m) at 2–66 GHz 2G and 2.5G: up to 250 kbps (2G GSM—14.4 kbps); 3G HSPA+: 22 Mbps uplink, 168 Mbps downlink; 4G LTE: 86 Mbps uplink and 326 Mbps downlink (for 20 MHz \\  & Cellular & bandwidth with 4x4 MIMO); 4G LTE-Advanced: 500 Mbps uplink, 1 Gbps downlink (for 70 MHz bandwidth with 4x4 MIMO), 1 Gbps uplink, 3.3 Gbps downlink in 10 km radius; 5G: larger than 1 Gbps expected \\ \hline \multirow{4}{*}{Wired} & PLC & BB-PLC: 200 Mbps at 1.8–250 MHz; Does not propagate through \\  & NB-PLC: 800 kbps at 3–500 kHz & transformer \\  & DSL & VDSL (ITU-T G.993.2): 10 Mbps for 500–750 m; \\  & & G.fast (ITU-T G.9700 and G.9701): 1 Gbps for 500 m \\  & & 100 Base-Tx (802.3u): 100 Mbps; P802.3 bs: 200 Gbps and 400 Gbps (1 Tbps is expected in \\  & Ethernet cables & the near future); Cat 8: up to 40 Gbps for 30 m for 2 GHz bandwidth; Cat 7: 10 Gbps for 100 m for 600 MHz bandwidth (Cat 7A—1 GHz) \\  & Optical & Up to 100 Gbps & AON: 80–100 km (typical); \\  & & (1–40 Gbps typical) & PON: 10–20 km (typical) \\ \hline \end{tabular}
\end{table} 
Table 8: _Cont._
# Transcriptome Analysis of Egg Yolk Sialoglycoprotein on Osteogenic Activity in MC3T3-E1 Cells
###### Abstract
The Wnt/\(\beta\)-catenin signaling pathway increases bone formation by stimulating osteoblast proliferation, and inhibiting GSK3 activity disrupts the protein complex, resulting in no longer phosphorylated \(\beta\)-catenin [23], thereby activating transcription factor activity for regulation [24]. Besides, GSK-3\(\beta\)/\(\beta\)-catenin signaling pathway was found to be one of the key downstream pathways of the PI3K/AKT pathway, which regulates bone formation [23]. It has been found that PI3K/AKT increased phosphorylation of GSK-3\(\beta\) in murine osteoblastic MC3T3-E1 cells, leading to \(\beta\)-catenin stabilization and \(\beta\)-catenin-mediated transcription, which indicated that the PI3K/AKT/\(\beta\)-catenin axis is functional in regulating osteoblasts [25]. Eggs from hens are considered to be a traditional food rich in a variety of bioactive ingredients, including polyunsaturated fatty acids, immunoglobulin, lecithin, and others [2; 26]. Recently, some studies have indicated that the sialoglycoprotein of crucial carp egg can show good anti-osteoporosis ability in vivo and in vitro [27; 28]. A lot of research has been done on the extraction of sialoglycoprotein from egg yolk [29; 30]. There is a study finding that the bone marker serum bone alkaline phosphatase (BALP) was significantly increased after feeding the egg hydrolysate in ornicectomized dogs [31]. Egg yolk protein from hen eggs could significantly improve bone growth rate and expression of BMP-2 in adolescent male rats [32]. Besides, egg yolk soluble protein from hen eggs promoted the proliferation and differentiation of osteoblastic MC3T3-E1 cells [33]. It has been found that the yolk-derived functional factor from hen eggs has a protective effect on bone metabolism and has a potential effect on the prevention of osteoporosis both in MC3T3-E1 cells and ovariectomized rats [26]. However, there is still a lack of research on the effective factors and mechanisms of osteogenic activity in egg yolk extract. On that basis, we explored the effects of egg yolk sialoglycoprotein (EYG) on proliferation, differentiation and mineralization in MC3T3-E1 cells, and investigated the osteogenic mechanism by transcriptome analysis. The study provided several enriched DEGs (differentially expressed genes) and pathways for future research into the molecular mechanisms of bone formation.
# RAC1B: A Rho GTPase with Versatile Functions in Malignant Transformation and Tumor Progression
## 4 Biochemical Properties, Generation and Degradation of RAC1B
### Binding Partners and Downstream Effectors  
As mentioned above, RAC1B is unable to interact with RHO-GDI [7; 8], resulting in most RAC1B protein being bound to the plasma membrane and not sequestered by RHO-GDI in the cytoplasm. Interestingly, RAC1B is able to bind the GTPase-binding domain of p21-activated kinase (PAK) but not full-length PAK [8] in a GTP-dependent manner. When compared with RAC1 and RAC1-GV12, RAC1B binds with much lower affinity or not at all to many common effectors of Rho GTPases, e.g., RHO-GDI, GIT-1, and IQGAP, but it does display enhanced binding to proteins involved in transcriptional regulation, cell-cell adhesion, and motility, such as SmgGDS, an atypical GEF with multiple armadillo repeats, RACK1 (receptor for activated C kinase 1), a scaffolding protein involved in key signaling pathways, and p120(ctn) [21]. The interaction between RAC1B and p120(ctn) is crucial for RAC1B-induced chemotaxis and is dependent upon protein regions predicted to be unstructured in the absence of molecular complex formation. This suggests that the interaction between RAC1B and p120(ctn) involves coupled folding and binding [21]. Finally, in hepatocellular carcinoma (HCC), the GAP Rho GTPase-activating protein 11A (ARHGAP11A) has been shown by coimmunoprecipitation assay to directly interact with RAC1B independent of Rho GTPase-activating activity and to promote the HCC malignant phenotype [22].
## 3 Experiments
Figure 3b showed the simulating results and the experimental results of the 250 km fiber link with both forward- and backward-pumped FRA. In Figure 3b, the simulating results of our model were obviously different from those of the simplified NLSE. The RMSE between the EVM of the simplified NLSE and the experimental results was 0.491%; the RMSE between the EVM of our model and the experimental results was 0.491%. Figure 3: Comparisons of the simulations and experiments: (**a**) 180 km fiber link with only backward-pumped FRA, (**b**) 250 km fiber link with both forward- and backward-pumped FRA, (**c**) 6-span 480 km fiber link with backward-pumped FRAs. NLSE and the experimental results was 0.889%; the RMSE between the EVM of our model and the experimental results was 0.492%. In particular, when the launch power was about 4 dBm, the EVM curve simulated through the simplified NLSE began to increase. However, in the simulating results of our model, the EVM curve increased when the launch power was higher than 5 dBm and increased more slowly than that of the simplified NLSE. Compared with the experimental results, our model could obtain the optimal launch power more adequately. The simulating results and the experimental results of the 6-span 480 km fiber link are shown in Figure 3c. The fiber link included six spans, each with an 80 km fiber, a 16 dB-gain back-pumped FRA, and a VOA. In the experiments, we adjusted the VOA to keep the launch power of each span the same. In the multi-span fiber link, the optimal launch power was much lower than that of the single-span system, because of the more apparent nonlinear effects. In Figure 3c, when the signal launch power was higher than \(-\)5 dBm, it was obvious that the simulation results based on our model fit better with the experiments than those of the simplified NLSE. When the signal launch power was lower than \(-\)5 dBm, the performances of our model and the simplified NLSE were not sufficiently different. Another remarkable difference between our results and those of the simplified NLSE was the different optimal launch power. In simulations based on our model, the optimal launch power was about \(-\)7 dBm, but the optical launch power obtained by the simplified NLSE was about \(-\)4.5 dBm. In the experiments, the optimal launch power was in the range of \(-\)5 dBm to \(-\)6 dBm. The simplified NLSE model had a higher accuracy on the simulation of the optimal launch power. The RMSE between the EVM of the simplified NLSE and the experimental results was 7.396%; the RMSE between the EVM of our model and the experimental results was 1.299%.
## 3 Experimental Measurements
### Experimental Results without CPM  
The values obtained from the experimental measurements are directly used for further processing. After making the initial preparations, we began the actual measurement following the established procedures described in the previous section. Prior to measuring the values, we accurately determined the distance between the antenna and the chest phantom.  
Once the measuring set was connected to the electrical network, we gradually obtained the power level values for each predetermined distance. We repeated this procedure ten times for each distance to ensure an adequate number of measurements. Upon completion of all measurements, we calculated statistical parameters such as the arithmetic mean, median, maximum value, and standard deviation for each set of values. These statistical data provide a more detailed understanding of the characteristics of the measurements and their variability at individual distances from the radiation source.  
The highest effective electric field intensity values of measurements 1 without CPM are placed in the Table 4.