esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Handover Management in Mobile HetNets: Current Challenges and Future Directions
## 3 Background
### Handover Decision Approaches in HetNets  
In HetNets, the HO processes are getting more complex, mainly when it is only based on traditional techniques like RSRP along with some HOM value. Macro cells transmit signals at high power and in the HetNet scenario, UEs receive these strong signals at every point in the cell compared to small or micro cells with low transmission power and coverage area. Resultantly, most of the HOs will be performed with macro cell, which will create several issues, including HOs and load balancing. Therefore, in HetNet scenarios, the HO decision must require coordination or updates from various collaborating parameters that can be used along with RSRP to enhance this decision-making process. These parameters and updates can be in the form of some threshold value, SINR, bandwidth, cell load, cost function, weight function, the velocity of the UE, or some optimized values. And these HO decision approaches perform evaluation on the basis of number of HOs, PLR, HOL, throughput, HOPP, HOF and CDR etc. Details about these HO decision categories will be discussed in the coming section below. Tabular categorizations are shown in Table 4, where we collected the data according to the information collection and decision-making point of view.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**HO Information Collection**} & \multicolumn{2}{c}{**HO Decision Making**} \\ \hline
**Network Related** & **UE Related** & **Strategy Related** & **Criteria Based** \\ \hline Coverage area & Velocity & ML/AI & Velocity based \\ \hline Link quality & Location & Weight function & RSRP based \\ \hline Cell load & User preference & Cost function & RSRQ based \\ \hline Cost & RSRP & Fuzzy logic & SINR based \\ \hline \hline \end{tabular}
\end{table}
Table 4: HO Information collection and Decision making.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Serial. No.** & **Performance Parameter** & **Research Articles** \\ \hline
1 & Handover Ping Pong (HOPP) & [37,38,44,77,79,83–100] \\ \hline
2 & Radio Link Failure (RLF) & [37,77,83–86,91–93,95,96,98,101] \\ \hline
3 & Handover Latency (HOL) & [37,44,84,102,103] \\ \hline
4 & Handover Failure (HOF) & [37,38,44,77,79,86,87,89,90,93,97,103,104] \\ \hline
5 & Handover Probability (HOP) & [37,38,44,76,85,87–92,94,98,101,105–107] \\ \hline
6 & Handover Interuppttion Time (HIT) & [37,38,44] \\ \hline
7 & Throughput & [68,102–105,108–117] \\ \hline
8 & No. of Handovers & [100,104,108,109,112,115–119] \\ \hline
9 & Signal to Interference and Noise Ratio (SINR) & [83,106,120] \\ \hline
10 & Packet Loss Ratio (PLR) & [103,109,116,117,121] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Research Articles based on Performance Parameters.
# Generation and Distribution of Quantum Oblivious Keys for Secure Multiparty Computation
## 3 Results and Discussion
### Efficiency
Thus, for the cases in which both parties being at the same location is not an inconvenience, the oblivious key rates can be further raised, as the effects of channel noise are minimized. Direct comparisons of OT generation speed between asymmetric cryptography techniques and quantum techniques are difficult because the algorithms run on different hardware. Nevertheless, as quantum technologies keep improving, the size and cost of devices capable of implementing quantum protocols will decrease and their use can result in significant improvements of OT efficiency, in the short-to-medium term future.
# Antimicrobial Peptides--Mechanisms of Action, Antimicrobial Effects and Clinical Applications
## 4 Clinical Application of AMPs
### Challenges Linked with Clinical Applications--From Screening to Delivery  
When designing or striving to optimize AMPs for therapeutic applications against an array of infectious agents, the initial step is to appropriately screen recognized clusters or anticipated peptide sequences for their expected antimicrobial properties. After in silico process (often with the use of cost-effective machine learning) [153], screening is most commonly pursued with the use of standardized and validated assays that aim to measure minimal inhibitory or minimal microbicidal concentration [36]. Nonetheless, differences in environmental conditions represent an important obstacle for corroborating such in vitro findings with in vivo results [22]. Consequently, this can be identified as a crucial hurdle in the more pervasive usage of AMPs for treatment purposes.  
As low oral bioavailability and low metabolic stability are inherent traits of oral AMP formulations, this route of administration is not favored, which leaves topical formulations as a preferred option [154]. Likewise, the use of intravenous formulation is hampered by the proteolytic cleavage that takes place in the blood and liver, resulting in a short half-life of such compounds [22]. This means enhancing the stability of AMPs for their broad usage is currently one of the priorities in the pharmaceutical industry, which is already pursued with the use of nanoparticles [155].  
Furthermore, as a minimum inhibitory concentration of AMPs (which is arguably the best proxy to evaluate its effectiveness) is habitually lower when compared with conventional antibiotics [156; 157], some authors suggest that the optimal AMP molecule should have effects that surpass just antimicrobial activity [158]. One of the solutions would be a multifunctional compound that combines direct activity against pathogenic agents with certain indirect effects, such as immunomodulation. That way, the antimicrobial effect can be enhanced via effective suppression of pro-inflammatory cytokines and vigorous activation of neutrophils/macrophages [159; 160; 161].  
The high cost of production represents another major impediment in scaling up the production of AMPs and subsequently putting them into the market. There is also a lack of toxicology studies that influence the development of AMPs, as well as a sparsity of clinical data that often translates to myriad regulatory issues [154]. Therefore, we recognize an evident need for a robust body of evidence before we can truly reorient clinical practice toward AMPs and adequately inform their development and production.
# Modeling and Analysis of Wearable Antennas
## 1 Introduction
Specific attention has been devoted to antenna layouts that avoid weak soldering connections [17] and also preferring antenna configurations exhibiting a lower sensitivity to the variations of both the non-conductive layer thickness and the textile material permittivity and conductivity. However, the above literature lacks consideration of many important factors such as lack of one- and two-dimensional analysis, power and information transmission and flexibility. As an approach to sheet waveguide, we applied functions such as power supply, location search, time synchronization, direction search, communication, etc. to the surface using an array of microcoils and electromagnets for application to sensor networks, and an intelligent surface which utilized the MAGIC-Surface [18; 19]. There was a problem of cost because magnetic devices were used. Also, a two-dimensional communication sheet which is easy to manufacture has been studied using a structure in which an insulating material such as a foamed urethane is sandwiched between a ground plate and a conductive mesh layer [20; 21; 22]. A sheet-like waveguide (free access mat) that is easy to manufacture and can excite the waveguide with an arbitrary antenna has been proposed [23]. In this paper, we describe the electromagnetically coupled ribbon wire interconnect used to cross the line which becomes the basis of this waveguide [24]. We describe the characteristics of one - dimensional and two - dimensional line using this structure. We also provide an example in which a two-dimensional waveguide using a conductive cloth is applied to a wearable transmission line will also be described. This also clarifies that electric power, as well as information, can be supplied.
## 2 Materials and Methods
### Data  
#### 2.2.1 Remote Sensing Datasets  
We selected GOME-2 SIF [21; 31; 32] as the primary satellite-retrieved SIF used in this study due to its spatial continuity, relatively high spatiotemporal resolution (0.5\({}^{\circ}\), daily), and longer time series (2007-2018). This decision was made after evaluating various SIF datasets retrieved by satellites (Table A1) [33]. Additionally, we utilized GOSIF (0.05\({}^{\circ}\), 8 days), a modeled SIF dataset spanning 2000-2020, which was generated using a data-driven approach based on discrete OCO-2 SIF soundings, MODIS EVI, and meteorological information [23]. For land surface phenology metrics based on the vegetation index, we  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Site-ID** & **Site-Name** & **Years** & **Latitude** & **Longitude** & **IGBP** \\ \hline CA-Gro & Ontario—Groundhog River, Boreal Mixedwood Forest & 2003–2014 & 48.22 & \(-\)82.16 & MF \\ CA-Man & Manitoba—Northern Old Black Spruce (former & 1994–2008 & 55.88 & \(-\)98.48 & ENF \\  & BOREAS Northern Study Area) & 1996–2010 & 53.63 & \(-\)106.20 & DBF \\ CA-Oos & Saskatchewan—Western Boreal, Mature Black Spruce & 1997–2010 & 53.99 & \(-\)105.12 & ENF \\ CA-Qfo & Quebec—Eastern Boreal, Mature Black Spruce & 2003–2010 & 49.69 & \(-\)74.34 & ENF \\ FI-Hyy & Hyytiala & 1996–2014 & 61.85 & 24.29 & ENF \\ FI-Let & Lettosuo & 2009–2012 & 60.64 & 23.96 & ENF \\ FI-Lom & Lompolojankka & 2007–2009 & 68.00 & 24.21 & WET \\ FI-Sod & Sodankyla & 2001–2014 & 67.36 & 26.64 & ENF \\ RU-SkP & Yakutsk Spasskaya Pad larch & 2012–2014 & 62.26 & 129.17 & DNF \\ \hline \hline \end{tabular}  
* IGBP vegetation type at site—DNF: deciduous needleleaf forests; DBF: deciduous broadleaf forests; ENF: ever-green needleleaf forests; MF: mixed forests; WET: wetlands.  
\end{table}
Table 1: Flux tower sites used in the analysis.  
Figure 1: Distribution of vegetation types across boreal forests, along with the locations of the flux tower sites and PEP725 phenological observation sites used in this study. DBF: deciduous Broadleaf Forests; MF: Mixed Forests; ENF: Evergreen Needleleaf Forests; DNF: deciduous Needleleaf Forests; WS: Woody Savannas; S: Savannas; CS: Closed Shrublands; OS: Open Shrublands; G: Grasslands; PW: Permanent Wetlands.  
employed the MODIS Land Cover Dynamics (MCD12Q2) Version 6 data, derived from EVI2, which provides annual metrics from 2001 to 2019.
## 3 Simulation Experiments
### Comparative Analysis of Models
To calculate confidence intervals for RMSE, MAE, and MRE for each model, we performed multiple (10) independent training and testing sessions. For each assessment metric, we calculated its mean and standard error, and further calculated 95% confidence intervals. The specific formulas were calculated as follows:  
\[\text{Confidence Interval}=M\text{ean}\pm 1.96\times\frac{S\tan dardError}{ \sqrt{n}} \tag{25}\]  
where n is the number of repetitions of the experiment. The standard error is calculated as follows:  
\[S\tan dardError=\frac{S\tan dardDeviation}{\sqrt{n}} \tag{26}\]  
In Figure 15, we will add confidence intervals for the RMSE, MAE, and MRE for each model. This can be accomplished by adding error bars to the bars, which will show the range of confidence intervals for each metric. The steps are as follows:  
Figure 14: Comparison of model results. As can be seen in Figure 15, the confidence intervals for the RMSE, MAE, and MRE of the SVR model are relatively narrow, which indicates that the model performs more consistently over multiple experiments. In contrast, the confidence intervals of other models, such as LSTM and Random Forest, are wider, indicating that there may be greater variability in the performance of these models. This result further validates the superiority and stability of the SVR model in short-term traffic flow prediction.
## 5 Conclusions  
In this paper, we present a novel approach for integrating deep learning and deterministic inversion for electrical resistivity surveys. The proposed workflow involves generating a target-oriented training dataset based on field borehole data and training the deep-learning model in three stages: base model training, borehole mixer training, and fine-tuning. The base model performs pure resistivity inversion without borehole information, while the mixer network incorporates borehole data into the inversion result. In the fine-tuning stage, we slightly adjust the model parameters for better performance. In addition, to overcome the limitation of deep-learning models when synthetic training data differs from target field data, we use the recovered model from DL-ERT inversion as an initial model for the deterministic inversion. We tested our approach using both synthetic test data and field data from a railway tunnel construction site in South Korea, and our proposed method, which combines deterministic inversion with a DL-ERT initial model, was designed to outperform DL-ERT or deterministic inversion alone in terms of accuracy and data fitting. Our findings suggest that the combination of deep learning and deterministic inversion methods holds great promise for solving inversion problems. The proposed approach can potentially be extended to other geophysical methods and geological scenarios to improve the accuracy and efficiency of inversion solutions.  
Conceptualization, D.Y.; Methodology, S.K. and D.Y.; Software, S.K., J.O. and D.Y.; Validation, S.K.; Formal Analysis, H.-S.K.; Data Curation, D.-W.R. and H.-S.K.; Writing--Original Draft Preparation, S.K.; Writing--Review and Editing, S.K., D.Y. and H.-S.K.; Visualization, S.K.; Supervision, D.Y.; Project Administration, D.Y.; Funding Acquisition, D.Y. All authors have read and agreed to the published version of the manuscript.  
Figure 18: Results of the proposed method applied to different electrode configurations: (**a**) ground truth, (**b**) apparent resistivities, (**c**) DL-ERT inversion results.  
**Funding:** This work was supported in part by the Basic Research and Development Project of Korea Institute of Geoscience and Mineral Resources (KIGAM) under Grant 23-3117 and in part by the Korea CCUS Association(K-CCUS) grant funded by the Korea Government (MOE, MOTIE) (KCCUS20220001, Human Resources Program for Reduction of greenhouse gases).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data are not publicly available owing to confidentiality.  
**Acknowledgments:** The authors would like to thank HSGEO INC. for providing the field data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Link Quality Estimation from Burstiness Distribution Metric in Industrial Wireless Sensor Networks
## 2 System Model  
### System and Network Model  
In this study, we focused on convergecast multi-hop networks that consist of one coordinator (sink) node and some sensor nodes in a tree topology. All nodes are deployed randomly during network formation and they have identification numbers. The communication between two nodes is called a tree-link if they have a parent-child relationship and exchange data through this link. The data of the sensor are generated by sensor nodes and transmitted periodically through the tree-link to the sink.
## 3 Results and Discussion
### Ni Renoval Mechanism  
#### 3.6.1 Sem-Eds  
SEM-EDS is an effective method to observe the surface morphology of a material while quantitatively analyzing its elemental composition. The SEM-EDS images of nZVI@ATP after adsorption are shown in Figure 10. Compared with the surface before the reaction (Figure 1), the surface of the adsorbed material was rough and had irregular flocs after the reaction (Figure 10a), probably due to Fe\({}^{0}\) oxidation on the material surface and the generated iron oxides during the reaction. In addition, the adsorbent might have some insoluble complexes generated via the coordination reaction with Ni\({}^{2+}\) in water [29]. As can be seen in Figure 1, no Ni was observed on the material surface before the reaction, while the Ni\({}^{2+}\) content in the EDS pattern after the reaction (Figure 10b) was 11.82%, indicating the successful adsorption of Ni\({}^{2+}\) by nZVI@ATP on the material surface.  
Figure 9: Material recycling diagram (initial concentration of 200 mg/L; initial pH of 5; temperature of 298 K; dosing amount of 1 g/L; and stirring time of 12 h).
# Origin and Composition of Exosomes as Crucial Factors in Designing Drug Delivery Systems
## 7 Targeted Delivery
### Cancer Cells  
Targeted delivery of cancer exosomes is mediated by expressed surface proteins, e.g., tetraspanins, interacting with different cell types in a different manner [108]. Tetraspanins also seem responsible for further malignancy spread; thus, therapeutic usage of this type of exosomes is not recommended [109]. It is worth mentioning that cancerous exosomes were used for stimulating the immune response to prevent the remaining cancer cells to grow after resection. The immune response was mediated by tumor-specific antigens presented at the surface of exosomes administered to patients after surgery [110; 111]. Additionally, exosomes released from a dorsally implanted cell-graft composed of HT29 and HCT116 cell lines preferably locate at the stomach and intestine. This may be due to the origin of the cells composing the implant, which were derived from colorectal and colon tumors [112]. This effect is also supported by higher autologous exosomes uptake by the colorectal cell line than allogenic cell-derived exosomes both in vitro and in vivo. Latter studies also revealed an increased accumulation of autologous exosomes in the tumor site [113]. This effect can be overcome by expressing on the vesicles surface cancer-targeting particles such as integrins. Designed in this manner, exosomes will specifically target cells other than autologous cells, also increasing their uptake ratio [114].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Moiety** & **Targeted Cells/Tissue** & **Source** \\ \hline Rabies viral glycoprotein & Brain & [94] \\ \hline Mannose- and sialic acid- enriched glycoproteins & Cancer cells & [95] \\ \hline Integrins \(\alpha_{6}\beta_{4}\) and \(\alpha_{6}\beta_{1}\) & Lung metastasis & [96] \\ \hline Integrins \(\alpha_{\kappa}\beta_{5}\) & Liver metastasis & [97] \\ \hline Cd63 & Neuronal dendrites & [98] \\ \hline Phosphatidylethanolamine & Glioblastoma cells & [99] \\ \hline Sphingovelin & Tumor microenvironment & [100] \\ \hline Metalloproteinase 15 & Breast cancer cells (integrin \(\alpha_{\kappa}\beta_{3}\)) & [8] \\ \hline Folate & Breast cancer cells (FA receptors) & [101] \\ \hline CpG-STAT3 ASO & Glioma microenvironment & [102] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Examples of moieties for exosome targeting.
# A BDGIM-Based Phase-Smoothed Pseudorange Algorithm for BDS-3 High-Precision Time Transfer
## 4 Results and Discussion  
For BDS single-frequency time transfer, the ionospheric delay is an error that cannot be ignored, and it directly determines the performance of time transfer. One of the main methods used to remove the ionospheric delay is the broadcast ionospheric model. Furthermore, the release of the BDS-3 BDGIM means that it will supersede the BeiDou Klobuchar, so we first evaluate the BDGIM before conducting time transfer experiments. The performance of the BDGIM is evaluated with reference to GIM products. By making a comparison with the Klobuchar model, the ionospheric delays over different sites are analyzed. Then, the performance of phase-smoothed pseudorange SF SPP time transfer based on the BDGIM is evaluated.
## 4 Experiments
### Method Analysis Results and Discussion
#### 4.3.6 Visualization of the Two Submodules in PCC  
Figure 7 visualizes the weights and behavior of the Multi-variants Correlation Constraint (MCC) and State Correction (SC) submodules using heatmaps. We used the DLinear model as the backbone, with the same look-back and prediction length of 96.  
To visualize the submodules' operational behavior while mitigating the negative impact of extreme or zero values on the visualization, we calculated the mean squared roots of the ratios between the submodules' absolute output and input values instead of using the ratios directly.  
The visualizations of the weights and ratio metrics demonstrate that the two submodules have different preferences for the time steps and variables. The MCC module is more active in the early prediction stages, which are closer to the reference point, as well as for those variables with strong correlation patterns, such as humidity, wind velocity, and precipitation. The SC module places more emphasis on the later stages, following the regular pattern that the more distant points are more likely to be unstable during model prediction. In addition, it focuses more on variables which are more likely to be unstable or have more complex and significant variance patterns, such as temperature, water vapor pressure, wind velocity and direction, and photosynthetically active radiation. After the initial period, the activity of both modules becomes stable and time-insensitive.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Noisy Input**} & \multicolumn{2}{c}{**0.1**} & \multicolumn{2}{c}{**0.3**} \\ \cline{2-5}  & **MSE** & **MAE** & **MSE** & **MAE** \\ \hline SegRNN & 0.167 & 0.211 & 0.176 & 0.228 \\ \hline SegRNN + PCC & **0.149** & **0.198** & **0.159** & **0.210** \\ \hline \multirow{2}{*}{**Noisy Sequence**} & \multicolumn{2}{c}{**0.1**} & \multicolumn{2}{c}{**0.3**} \\ \cline{2-5}  & **MSE** & **MAE** & **MSE** & **MAE** \\ \hline SegRNN & 0.178 & 0.240 & 0.267 & 0.357 \\ \hline SegRNN + PCC & **0.161** & **0.228** & **0.250** & **0.346** \\ \hline \end{tabular} Bold values (except the column headers \({}^{\text{\textregistered}}0.1^{\text{\textregistered}}\) and \({}^{\text{\textregistered}}0.3^{\text{\textregistered}}\)) indicate the better results.  
\end{table}
Table 8: Results of the PCC module under different noise conditions. The backbone is SegRNN with a look-back length of 96 and prediction horizons of 96 and 720.
## 2 Research Methodology
### 1-D Cnn  
Fourier transform has been the most popular feature extraction method used in analyzing signals. The one-dimensional convolution function in 1-D CNN can be similarly treated as the wavelet transform; thus, the convolutional neural network model achieves efficient performance in extracting the raw signal waveforms [39]. The greatest advantage of this method is that it does not require any feature extractors of transformation. This method can directly process the raw data. To extract the features automatically from the raw vibration signals, this study utilizes the 1-D CNN structure to extract the features. Figure 3 illustrates the 1-D CNN model.  
1-D CNN is composed of the following: input layer, convolutional layer, pooling layer, FCN layer, and output layer. The convolutional layer is the first layer that is used to extract features from the raw data, which could be reduced to sparse feature maps via convolutional kernels. The processing of the vibration signals is a sequential data analysis problem, for which one-dimensional kernels are adapted in this research. This model performs the one-dimensional filter operation by sliding over the  
Figure 2: Long short term memory (LSTM) framework.  
sequence data to obtain the corresponding feature maps. Next, the max pooling operation is used to determine the maximum value of the feature maps. The output of the convolutional layer and the max pooling operation can be expressed as follows:  
\[Y_{m,k}=f\left(\sum_{i=1}^{n}x_{i}*w_{m}+b\right) \tag{7}\]  
\[Z_{m,L}=\max(Y_{m,k}) \tag{8}\]  
where, \(Y_{m,k}\) is the output of the convolutional layer, \(X_{i}\) is the sample number, \(W_{m}\) is the convolutional kernels size, \(b\) is the bias, and \(f\) is the activation function. This experiment uses the max pooling operation as the pooling layer; thus, \(Z_{m,L}\) is the output of the pooling layer.