esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Leading Process
#### 3.1.2 Effect of MnO\({}_{2}\)Dosage  
Based on the above discussion, the oxidation of low-valence vanadium is of great importance to further improve its leaching efficiency. It is known that E\({}^{0}\)MnO2/Mn2+ = 1.21 V and E\({}^{0}\)(VO\({}_{2}\)+/VO\({}^{2+}\)) = 1.00 V, and therefore MnO\({}_{2}\) could be used for the oxidation of V(IV) during the leaching process [26,27]. With a summarization of the potential-pH diagram of vanadium and manganese shown in Figure 4, it was clear that the position of MnO\({}_{2}\)was always higher than that of V(IV) and V(III), indicating that the electrode potential of MnO\({}_{2}\)was higher than that of V(IV) and V(III). Therefore, MnO\({}_{2}\) could oxidize V(IV) and V(III) into V(V). The effect of MnO\({}_{2}\) dosage on the leaching efficiency of chromium and vanadium was investigated under the following reaction conditions: reaction time of 180 min, reaction temperature of 90 \({}^{\circ}\)C, liquid-to-solid ratio of 5:1 mL/g, and concentration of H\({}_{2}\)SO\({}_{4}\) at 30 wt.%. The results are detailed in Figure 5.  
Figure 3: Effect of concentration of H\({}_{2}\)SO\({}_{4}\) on the leaching efficiency of vanadium and chromium.  
Chromium leached out much easier in acidic medium, with the leaching efficiency increasing to 99.80% at the selected reaction conditions. The leaching efficiency of vanadium was increased linearly with the increase of dosage of MnO\({}_{2}\). The vanadium in low valences, like V(IV) and V(III), was oxidized to V(V) by MnO\({}_{2}\), and then dissolved in acidic medium and achieved high leaching efficiency. The results confirmed that MnO\({}_{2}\) could oxidize low-valence vanadium ion of V(IV) and V(III) into V(V).
# Enhancing Data Security Through VLSM Subnetting and TCP/IP Model in an ENT
## 2 Literature Review
### Routing Protocols
#### 2.3.2 Enhanced Interior Gateway Routing Protocol (EIGRP)  
Diffusing Update Algorithm (DUAL) enhances network scalability and convergence of EIGRP by computing and creating tables to assess the route [13; 24]. The algorithm locates an alternative trajectory in the router. The message format of EIGRP, including bits sizes, covers version, opcode, checksum, flags, sequence, ack, autonomous system number, and TLVs. EIGRP uses both distance vector and link state algorithms to determine the best path, and selects the fastest path based on bandwidth, delay, reliability, and load even though it defaults to using only bandwidth and delay. The DUAL considers various parameters to determine the path to deliver the packets quicker to its destination as an autonomous system [12]. When configured in its traditional way, EIGRP is inefficient and introduces the need for network automation using scripting and software such as Ansible [25]. The EIGRP metric has the capacity to support lots of hops. Enabling EIGRP can increase the network width to 224 hops. Transport Control Field (TCF) as a limitation to scaling the network can be mitigated by increasing the TCF when IPv4/6 packets have travelled to about 15 hosts, the EIGRP would identify the next hop to the destination.
###### Abstract  
Conventional methods that have been developed to immobilize the mouth and tongue for radiotherapy (RT) in head and neck cancer (HNC) treatment have been unsatisfactory. We, therefore, developed three-dimensional (3D), customizable, silicone bite blocks and examined their clinical feasibility. For HNC patients, before RT, the 3D printed bite blocks were fabricated based on primary computed tomography (CT) simulation images. The placement of the 3D bite blocks was followed by a secondary CT simulation before RT planning was finalized. Dosimetric parameters and positioning verification achieved with the propose bite blocks were compared with conventional universal oral corks. The 3D printed bite blocks were conformal to the occlusal surface, ensuring immobilization of the tongue without eliciting a gag reflex, and an elastic and firm texture that supports opening of the mouth, with a smooth surface with tolerable intraoral tactility. The dosimetry of patients using the proposed bite blocks showed better coverage of the planning target volume and surface of a tumour bed along with reduction in normal tissue doses. Good concordance of positioning by 3D printed bite blocks during the RT course was verified. The 3D printed bite blocks with silicone might be a customizable, safe, and practical advanced technology in RT for HNC.  
3D printing; 3D bite blocks; silicone; head and neck cancer; radiotherapy; oral fixation +
Footnote †: journal: Applied Sciences
# A Novel Anti-Jamming Technique for INS/GNSS Integration Based on Black Box Variational Inference
## 2 Problem Description
### INS/GNSS Integration System Model
\begin{array}{cc}V_{E}&V_{N}\end{array} \right]\) are the horizontal velocities, \(\left[ \begin{array}{cc}\varphi&\lambda\end{array} \right]\) are the horizontal positions, \(\omega_{ie}\) is the angular rate of the earth's rotation, \(R_{M}\) and \(R_{N}\) are the earth radius, \(\left[ \begin{array}{cc}f_{E}&f_{N}&fu\end{array} \right]\) are the specific force. In order to clearly describe the effect of time-varying noise on INS/GNSS integration, we conducted a simulation experiment. INS/GNSS integration operates without interference for 10 min. Then, the standard deviation of random white noise at the GNSS east and north position was increased from 5 m to 50 m. The standard deviation of random white noise at the east and north velocity was increased from 0.2 m/s to 2 m/s. The interference lasted for 5 min and then returned to the non-interference state. The total time of the simulation was 30 min. The influence of the time-varying noise on the velocity and position errors are shown in Figures 1 and 2. As demonstrated in Figures 1 and 2, the velocity and position have large unsteady errors when time-varying noise appears in the measurement information. Therefore, it is necessary to estimate the accurate value of the MNCM to suppress the interference caused by time-varying noise to the navigation system.
###### Abstract  
The partial nitritation (PN)-anammox (PN/A) process offers a sustainable alternative to nitrogen management in wastewater treatment, addressing the high costs and increasing the low eco-friendliness associated with traditional nitrification/denifrification processes. Stable partial nitritation (PN) is critical for effective PN/A operation, and this study specifically focused on the need to suppress nitrite-oxidizing bacteria (NOB) to facilitate the enrichment of ammonia-oxidizing bacteria (AOB). Utilizing two sequencing batch reactors (SBRs), PN1 and PN2 with different free ammonia (FA) concentrations, this study aimed to evaluate the NOB suppression strategy while enriching AOB. The PN2 reactor, which operated with a higher initial FA concentration (50 mg/L), successfully maintained high nitritation activity, with 96.1% ammonium removal efficiency (ARE) and 95.1% nitrite accumulation efficiency (NAE) at reduced influent NH\({}_{4}\)\({}^{-}\)N concentrations (50 mg NH\({}_{4}\)\({}^{+}\)-N/L, FA 10 mg/L). In contrast, PN1 showed inadequate NOB suppression due to lower FA concentrations (10 mg/L). These results suggest that initiating the nitritation process with higher FA concentrations can effectively suppress NOB, enhancing the stability and efficiency of PN/A processes in mainstream applications.  
free ammonia; nitritation; suppressing nitrite-oxidizing bacteria; nitrite accumulation; nitrifying bacteria +
Footnote †: journal: Applied Sciences
## 5 Gaussian Mixture Modeling
### Bayesian Information Criterion  
The Bayesian Information Criterion (BIC) is a useful metric for choosing between two or more alternative models [31]. Since we are modeling the data using a set of Gaussian models, BIC is used to determine the optimal number of components to characterize the set of time series. Given a dataset and a candidate model, BIC works by computing the logarithm of the likelihood of this model considering the set of likelihood-maximizing parameters \(\hat{\theta}\) (for GMM, a set with \(k\) pairs \((\mu;\sigma)\)); it also penalizes the model proportionally to the number of parameters (for GMM, the number of components), so to reduce the chances of overfitting--refer to Equation (3).  
\[BIC=-2*log(L(\hat{\theta}))+k*log(n) \tag{3}\]where \(n\) is the number of samples; \(k\) is the number of components to test (we ranged from 1 through 15); \(L(\hat{\theta})\) is the likelihood of the model, with \(\hat{\theta}\) as the parameter values that maximize the likelihood function.  
Figure 6 depicts the value of metric BIC considering 1 through 15 Gaussian components. We verified that using eight components, the fitting of the model, discounted the overfitting penalization, reaches an optimal minimum value.
## 2 Materials and Methods
### Assessment of Olive Trees' Health State  
We evaluated the health state of the centennial olive trees by capturing infrared images and extracting temperature information from their trunks. For each tree, we captured two separate infrared images: one from the side of the trunk with the majority of cavities (referred to as \"Side A\") and the other from the opposite side (referred to as \"Side B\") (Figure 3). To enhance measurement accuracy, we maintained a closer distance of 3.0 m during image acquisition, ensuring comprehensive coverage of the entire tree trunk while minimizing potential interference from overlying leaves. During camera calibration, we further improved precision by setting the emissivity value to e = 0.95, which is ideal for tree trunks [115].  
Figure 3: Sample infrared images from different sides of the trunk and the methodological procedure for extracting temperature and humidity values from the tree trunks using the ArcGIS Analysis toolbox: (**a,b**) correspond to the calibrated images imported into the TESTO IRSoft® (v. 4.3) software, utilizing the temperature palette; (**g,h**) represent the calibrated images imported into the TESTO IRSoft® (v. 4.3) software, employing the humidity palette; (**c,d,i,j**) depict the tree trunks following polygon creation using a shapefile format; (**e,f,k,l**) illustrate the temperature and humidity histograms of the olive trees’ trunks.  
In the subsequent analysis of these trunk infrared images, we followed a procedure similar to the one used for canopy temperature analysis. However, in this case, we incorporated not only the temperature image palette but also integrated a humidity image palette (Figure 3a,b,g,h). The humidity palette was computed for each pixel, considering the relative surface moisture of the tree trunk. We then meticulously extracted temperature and humidity values from the defined tree trunk areas (Figure 3c,d,i,j).  
To characterize the thermal and humidity profile of the tree trunks and establish relevant indices, we extracted temperature and humidity data from their respective histograms. Ensuring the assessment's robustness required addressing data outliers and skewed distributions, we determined the interquartile ranges for trunk temperature and humidity on both Side A (T\({}_{\text{IQR}}\) Side A, H\({}_{\text{IQR}}\) Side A) and Side B (T\({}_{\text{IQR}}\) Side B, H\({}_{\text{IQR}}\) Side B) of each tree. For a comprehensive overview, we employed inter-percentile ranges from each trunk histogram, collectively forming the outer percentile ranges for both trunk sides (T\({}_{\text{OPR}}\) Side A, H\({}_{\text{OPR}}\) Side A, T\({}_{\text{OPR}}\) Side B, H\({}_{\text{OPR}}\) Side B). These indices encompassed temperature values associated with any external or internal irregularities present on the trunk. Furthermore, we calculated the differences between the two trunk sides (T\({}_{\text{IQR}}\) difference, H\({}_{\text{IQR}}\) difference, T\({}_{\text{OPR}}\) difference, H\({}_{\text{OPR}}\) difference).
# Node Screening Method Based on Federated Learning with IoT in Opportunistic Social Networks
## 3 Methods
### Community Model Design  
In opportunistic social networks, communication between nodes is generally carried out through the store-carry-forward mode, so it also has a certain degree of mobility and randomness. When analyzing the problem of data transmission in the opportunistic social network, we must first have a certain understanding of the structure of the opportunistic social network. In the traditional opportunity network, each node seems to be independent of each other; however, due to the existence of various social relationships, nodes may aggregate into a community, and this will be explored through a few inferences.  
According to the definition of the weighted network above, the current degree of community structure is defined as:  
\[\Psi(t)=\frac{\kappa_{a}}{K}-\frac{\varphi_{s}}{\left(2K\right)^{2}} \tag{1}\]  
where \(\Psi\) represents the degree of modularity of the community, \(K\) represents the total weight, \(\kappa_{a}\) represents the total weight of all edges in the community \(a\), and \(\varphi_{s}\) represents the sum of the degrees adjacent to the node \(s\) in the community.  
**Theorem 1**.: _In an opportunistic social network, increasing the weight can increase the degree of association with the community._  
**Theorem 2**.: _If the connection weights of two sub-communities have the following relationship._  
\[\frac{\varphi_{i}\varphi_{j}}{2K}<\kappa_{ij}<\Delta\kappa+\frac{\varphi_{i} \varphi_{j}+\varphi_{s}\Delta\kappa+\Delta\kappa^{2}}{2(K+\Delta\kappa)} \tag{2}\]  
_then these two communities are separate._  
**Theorem 3**.: _When the weight of an edge decreases, two nodes are connected by this edge, and this edge is the only edge of one of the nodes. If the weight of this side changes, then the community is not divided._  
The proof of the above theorem is attached in the Appendix A. From the above several theorems and their proofs, we can obtain some characteristics of the opportunistic social network, and the most important one is its \"community\" nature. There are many relationships between nodes in the opportunistic network, such as social relationships, because these nodes will be divided into communities. There will be more connections between nodes in the same community, and the data transmission between them is more changeable. Therefore, below we will focus on the data transmission scheme between nodes in the same community.
# Review of Renewable Energy-Based Charging Infrastructure for Electric Vehicles
## 8 Pricing Programs  
To date, there is a small number of utility programs that provide EV charging based on renewable sources. Even though a limited number of programs have offered EVs' renewable charging, the existing programs in Austin (USA) [115] and Saint Paul (France) [116] indicate that the third EV drivers choose renewable energy. An annual billing of renewable energy can be used for EV electricity demand. However, programs that use the charging times can request suitable and renewable energy supplies, thus providing significant advantages to customers and utilities. Customers can charge at a beneficial rate, and utilities can manage system peaks [117].  
Giant utilities can provide network solutions for charging electric vehicles with renewable sources for retail, employee, and commercial customers. The network solutions are outcomes of promising programs that make EV drivers depend more on renewable sources [115]. Another program works on encouraging EV drivers to charge during preferred times of the day by offering financial incentives, for instance, linking the charging times with renewables' availability [118, 119]. Renewables' pricing and time of charge are  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Configuration** & **Aim** & **Method** & **Remarks** \\ \hline \multirow{4}{*}{[108]} & \multirow{4}{*}{PV-Grid} & Improving & Simulation-based on & \multirow{4}{*}{Proves the proposed} \\  & & self-consumption of PV & real-time data acquisition & & strategy’s efficiency that \\  & & energy and lower its & of the demand and & & can be used in embedded \\  & & impacts on the grid & generation & & systems for real-time \\  & & & & without forecasting & & allocation of the EV \\  & & & & & charging rate \\  & & & An hourly simulation was & & Confirms feasibility \\  & & Comparing an optimal & & used by considering & of PV-based infrastructure, \\  & & charge-scheduling strategy & & statistical data for driving & benefits to EVs’ drivers and \\  & & with an uncontrolled & & distances, different types of & the garage owner and the \\  & & charging case & vehicles, parking time, & need for an optimal \\  & & & installation cost, tax rebates & & charging controller \\  & & & Determining optimal & & Demonstrates the \\  & & schedules of EV according & & effectiveness of the & \\  & & to the predicted PV power & & proposed smart EV \\  & & and demand & & charging method \\  & & & & Confirms applicability of & \\  & & & & the strategy to DC &  
factors influencing participant interest. In other programs, the green pricing has been paired with time of use (TOU) rates to attract small commercial and residential customers to charge EVs with renewables; this leads to cost savings of charging at periods of excess renewable generation or during off-peak periods [117]. A comparison of the existing charging programs is depicted in Table 4.
# A Rectangular Toroidal Current-Based Approach for Lung Biopsy Needle Tracking
## 5 Conclusions  
This study introduces a novel electromagnetic localization system for lung biopsy needle tracking, aimed at improving real-time procedural guidance and localization accuracy. The system utilizes a rectangular toroidal current distribution, with a printed circuit board (PCB) serving as the magnetic field emitter. An alternating electromagnetic field at approximately 70 kHz was modeled using the Biot-Savart law. Induced voltages from multiple transmitting coils were separated through Fourier transform algorithms, enabling independent signal extraction. A five-degree-of-freedom positioning model was solved using the least squares method, ensuring both computational efficiency and spatial precision. Experimental validation demonstrated that the proposed system achieved an average localization error of less than 1.76 mm, effectively addressing core challenges in biopsy needle tracking. The results highlight the system's potential to enhance accuracy while reducing dependency on imaging techniques such as CT, thereby contributing to safer and more efficient clinical procedures. Future work will focus on enhancing calibration strategies, minimizing susceptibility to environmental interference, and evaluating the system's performance in biological tissues to support its translation into clinical settings.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app15094613/s1](https://www.mdpi.com/article/10.3390/app15094613/s1)]([https://www.mdpi.com/article/10.3390/app15094613/s1](https://www.mdpi.com/article/10.3390/app15094613/s1)) and Zenodo ([[https://zenodo.org/records/146361](https://zenodo.org/records/146361)]([https://zenodo.org/records/146361](https://zenodo.org/records/146361)) 30 (accessed on 13 January 2025)); (1) Video: Accurate Biopsy Needle Localization Using a Rectangular Toroidal Magnetic Field: A Computational Model and Experimental Validation. (2) 2000-times position errors.  
Conceptualization, H.P.; methodology, H.P.; software, Y.D.; validation, Y.D.; formal analysis, Y.D.; investigation, Y.D.; resources, Q.F.; data curation, Q.F.; writing--original draft preparation, Y.D.; writing--review and editing, Y.D.; visualization, Y.D.; supervision, Y.D.; project administration, L.X.; funding acquisition, L.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Sichuan Provincial Department of Science and Technology under grant No.2022 YFG0235 and Sichuan University Huaxi Hospital under grant No. H2021201005.  
Not applicable.  
All our data are available, and we have uploaded relevant experimental videos to Zenodo ([[https://zenodo.org/records/14636130](https://zenodo.org/records/14636130)]([https://zenodo.org/records/14636130](https://zenodo.org/records/14636130)) (accessed on 13 January 2025)).  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### Satellite Data  
A Landsat-8 Satellite Scene (Path 93/Row 85) covered the study area. The image of this scene was taken from Landsat-8 Collection-2 Tier-1, which was calibrated as top-of-atmosphere (TOA) reflectance as follows [35]:  
\[\rho\lambda=\frac{\pi\cdot L_{\lambda}\cdot d^{2}}{\text{ESUN}_{\lambda}\cdot \cos\theta_{\text{s}}} \tag{1}\]  
where  
\(\rho\lambda=\) TOA reflectance [-];  
\(\pi=\) Constant equal to \(\sim\)3.14159 [-];  
\(L_{\lambda}=\) Spectral radiance [W/(m\({}^{2}\) sr \(\mu\)m)];  
\(d=\) Earth-Sun distance [Astronomical units];  
\(\text{ESUN}_{\lambda}\)= Mean solar irradiance [W/(m\({}^{2}\)\(\mu\)m)];  
\(\theta_{\text{s}}=\) Solar zenith angle [Degrees].  
Pixels with clouds, cloud shadows and cirrus clouds were masked out in the selected image.
# Data Imputation Based on Retrieval-Augmented Generation
###### Abstract  
Modern organizations collect increasing volumes of data to drive decision-making, often stored in centralized repositories such as data lakes, which consist of diverse structured and unstructured datasets. However, these repositories often suffer from issues such as incomplete, inconsistent, and low-quality data, which hinder data-driven insights. Existing methods for data imputation, including statistical techniques and machine learning approaches, often rely heavily on large amounts of labeled data and domain-specific knowledge, making them labor-intensive and limited in handling semantic heterogeneity across data formats. To address these challenges, this study proposes a novel retrieval-augmented generation (RAG) framework for data imputation that effectively combines the strengths of retrieval mechanisms and large language models (LLMs). This approach constructs semantic-based indexes for heterogeneous data, employs a recall and re-ranking strategy to enhance retrieval relevance, and proposes a method to reduce inference cost while ensuring imputation quality. Extensive experiments on real-world datasets demonstrate that the proposed framework significantly outperforms machine learning and deep learning approaches.  
retrieval-augmented generation; data imputation; data mining Article