esa-sceva/satcom-chunk-collection huggingface dataset:
# Mobility Management Scheme with Mobility Prediction in Wireless Communication Networks
## 6 Conclusions  
In this study, we presented a registration scheme considering mobility prediction and corresponding flexible tracking area list (TAL) forming. In this scheme, based on prediction of moving direction and corresponding TAL forms, a new TAL was constructed such that the registration cost can be minimized.  
This study also presented a semi-Markov process model for the registration scheme with flexible TAL forming assuming two different environments: urban and rural. In this scheme with flexible TAL forming, the moving direction of a user's equipment (UE) could be predicted (with the probability \(q\)) using UE's moving information in the geographical area.  
To validate the semi-Markov process model, computer simulations were performed using flowchart-based RAPTOR. Through simulation studies, it was observed that analytical and simulation results were very close with a relative error of 1.4%. Numerical results for various system environments using the semi-Markov process model showed that the registration cost decreased as the moving probability to the predicted direction, \(q\), increased. Results also showed that when call-to-mobility ratio (CMR) was less than or equal to 1 that corresponds to current small cell configurations, the registration scheme with flexible TAL forming outperformed the distance-based registration scheme when \(q\) was high (\(q\geq 0.75\) in an urban case with a small sector, \(q\geq 0.85\) in a rural case with a big sector). In addition, results showed that the total cost would be decreased on average 2.2% by adopting the implicit registration.  
This study presented analytical results only for a small number of TAs (7 and 10). Further studies on the registration scheme with flexible TAL need to be performed by taking into account a larger number of TAs. In that case, the registration scheme with flexible TAL forming under the selective paging method can also be studied to reduce the paging cost.  
Modeling and data analysis, H.-S.J. and J.-H.B.; writing--original draft, J.-H.B.; writing--review and editing, H.-S.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No.2021R1F1A1049933) and by the Ministry of Education, Republic of Korea (2016R1D1A1B01014615).  
Authors would like to thank the anonymous reviewers for their careful reading of our manuscript and their many insightful comments and suggestions.  
The authors declare no conflict of interest.
# Fault-Free Protection Level Equation for CLAS PPP-RTK and Experimental Evaluations
## 4 Evaluation of the Proposed Protection Levels for PPP-RTK  
This section discusses the GNSS observation data and parsing process of the CLAS L6 broadcast messages used in this study. Subsequently, the resultant positioning error of the VRS-RTK positioning and computed protection levels are presented.
## 2 Results and Discussion  
### Layer Optimization  
The relationship of MIP composition in relation to the sensor signals obtained is revealed in Table 1. A normalized response of the MIP-layers to limonene and eucalypt respectively is given. Optimized signals were obtained with a 1/1.5 styrene/DVB copolymer composition, using 10 \(\mu\)L of templates, whereas other layers and template concentrations show lower responses. It is obvious that an increase in crosslinker will lead to a more rigid polymer capable of functionalizing the material in respect to cavities. Furthermore, the amount of template is very decisive. Only a few number of  
Figure 1: Structures of terpenes used for molecular imprinting and chemical sensing.  
template molecules will generate less interaction centers in the coating, thus a diminished sensor effect will result. A high amount of imprinted molecules causes a dilution of the monomers and an effective polymerization is hindered. The sensor properties of the coatings are tailored according to these ideas [16]. We used different layer heights of polymers in the range of 1-6 kHz, where 1 kHz corresponds to a thickness of 40 nm. This variation of sensor signal is demonstrated in Figure 2 and an optimum layer height is observed. Obviously this finding can be attributed to a bulk behavior since the response increases in parallel to layer height. At a layer height of approximately 4-5 kHz a plateau formation is followed by some hint for a slight decrease in frequency response. This behavior is easily understood by analyte diffusion in the interior of the layer. Thin layers incorporate analytes easily due to the pores which are generated additionally by the templates. An increasing layer height will statistically block the excess to the diffusion channels and thus the behavior in Figure 2 is explained.  
\begin{table}
\begin{tabular}{c|c|c|c|c|c} \hline \multirow{2}{*}{**Layers**} & \multicolumn{3}{c|}{**Composition (\(\mu\)L)**} & \multicolumn{2}{c}{**Normalized Response (Hz)**} \\ \cline{2-6}  & \multirow{2}{*}{**Styrene**} & \multirow{2}{*}{**DVB**} & \multirow{2}{*}{**Template**} & **25 ppm** & **50 ppm** \\  & & & & **Limonene** & **Eucalypt** \\ \hline
1 & 1 & 1.5 & 0 & 0.98 & 1.23 \\
2 & 1 & 1.5 & 5 & 3.81 & 4.84 \\
3 & 2 & 1.5 & 5 & 3.13 & 4.17 \\
4 & 1 & 1.5 & 10 & 6.92 & 6.39 \\
5 & 2 & 1.5 & 10 & 4.81 & 3.45 \\
6 & 1 & 1.5 & 15 & 5.41 & 6.01 \\
7 & 2 & 1.5 & 15 & 4.97 & 5.77 \\ \hline \end{tabular}
\end{table}
Table 1: Sensor responses to limonene and eucalypt as function of MIP composition.  
Figure 2: Variation of sensor response with changing layer heights of terpinene MIP at 50 ppm.
## 3 Method
### Part 3: Processing of Instantaneous Albedo to Daily Mean RSF (Level-3)  
The third part of the TOA flux retrieval consisted of temporally integrating all available instantaneous observations for a given day, which makes it a multi-satellite product. Figure 2a shows the different orbits as a function of local solar time: typically there is at least one (mid-)morning and one afternoon observation, and depending on latitude this may be complemented by an early morning and/or late afternoon observation. This section describes how all these instantaneous albedo observations are fused together to construct a daily mean value.  
For each nested 0.25\({}^{\circ}\) grid box, the UTC day (24 h) was subdivided in 288 temporal bins of 5 min each (referred to as _5 min bins_). For each of these temporal 5 min bins, the solar zenith angle was calculated based on the grid box's location and time in the 5 min bin center. The day was subdivided into three types of 5 min bins (Figure 7a): (1) daylight bins, for which \(\theta_{0}<84^{\circ}\), cf. Section 3.3.1, (2) twilight bins, for which 84\({}^{\circ}\)\(\leq\theta_{0}<100^{\circ}\), cf. Section 3.3.2, and (3) nighttime bins, for which \(\theta_{0}\geq 100^{\circ}\), cf. Section 3.3.3.  
Figure 6: Nested regular CM SAF grid 0.25\({}^{\circ}\) (unprojected, a.k.a. plate carrée).
# Prediction of Cutting Parameters of HVOF-Sprayed Stellite 6
## 3 Experimental Work
### Experimental Procedure  
In the experiments, a total of 28 turning cuts on the Stellite 6 coating layer were realized. The base materials of the Stellite 6 layers were steel S235 JRG1 and carbide WC-Co. This work aimed to find out and optimize the influence of different combinations of cutting parameters. The combinations used for this analysis are as follows: speed \(v_{c}\), feed \(f\), depth of cut \(a_{p}\), and tool back angle \(\lambda\). The parameters were selected in the following ranges: \(v_{c}\) = 30-80 [m-min\({}^{-1}\)], feed \(f\) = 0.2-0.3-0.5-0.7-0.8 [mm], depth of cut \(a_{p}\) = 0.15-0.23-0.31 [mm] and back angle of the tool \(\lambda\) = 48-60-72 [\({}^{\circ}\)].The cutting parameter composition has been selected sufficiently varied to provide sufficient discrete results in the structure and texture values of the finished surfaces by subsequent analysis. In this work, a series of measurements and analytical work described below were performed on machined surfaces.
## 4 Results and Discussion
### Mechanisms of the Effect of Inclination Angle on Heat Pipe Performance  
The mechanisms in the literature to explain how the inclination angle affects the heat pipe performance are limited. It has been widely accepted that an insufficient or excessive inclination angle could harm the heat pipe performance [30]. An insufficient inclination angle may induce a poor return of backflow to the evaporator [52], while an excessive inclination angle will reduce the heat dissipation efficiency by decreasing the heat exchange time at the condenser [31; 52]. However, a quantitative prediction of the optimal inclination angle is still lacking. In this study, we are trying to obtain the optimal inclination angle from the perspective of the liquid film profile in the condensation section. It could provide a general criterion to explain various optimal inclination angles of the MF-AHPAs with different working fluids and heat loads.
## 2 Proposed Method
### Model Integration
After this initial HMM iteration, we make the dynamic dictionary voice type assignments using the following equation:  
\[\text{VoiceType}(V_{i})=\arg\max_{v}\sum_{p,t}P_{t}(p|v)P_{t}^{a}(p|V_{i}), \tag{19}\]  
such that each voice \(V_{i}\) from the HMM is assigned the voice type \(v\) from the dictionary that gives the greatest correlation between the (initial, non-uniform) PLCA voice probabilities \(P_{t}(p|v)\) and the HMM voice priors \(P_{t}^{a}(p|V_{i})\). This alignment procedure begins with the HMM's lowest voice and performs a greedy search, such that for each subsequent voice, the \(\arg\max\) only searches over those dictionary voice types not already assigned to a lower HMM voice. This dynamic dictionary voice type assignment allows the model to decide which voice types are present in a given song at runtime. For all subsequent iterations, this voice type assignment is saved and used during integration. Additionally, the HMM is now run with \(M=4\), and the voice type assignment is used to ensure that the PLCA output \(P_{t}(p|v)\) estimates correspond to the correct voice indices in the HMM. This dynamic dictionary type alignment is a novel feature of the proposed model compared to our previous work [38]. We also place certain constraints on the HMM during its first iteration. Specifically, where \(O_{t}\) is the set notes observed at frame \(t\): (1) if \(|O_{t}|\leq M\), each note in \(O_{t}\) must be assigned to a voice in \(S_{t}\); and (2) if \(|O_{t}|>M\), the voices in \(S_{t}\) must contain exactly the \(M\) most likely pitch activations from \(O_{t}\), according to the \(P(p,t)\) from the acoustic model, where ties are broken such that lower pitches are considered more likely (since overtones are the most likely false positives). The final output of the integrated system is a list of the detected pitches at each time frame that are assigned to a voice in the most probable final HMM state \(S_{t_{max}}\), along with the voice assignment for each after the full 30 EM iterations. Figure 6 shows an example output of the integrated system and is discussed more in depth in Section 3.4.
## 4 Performance Evaluation
### Simulation Parameters  
We adopt the first order model [12] for the simulations. Here, the energy consumed for sending a \(k\)-bit message for a distance \(d\) is calculated as:  
\[E_{Tx}(K,d)=K(E_{elec}+\varepsilon_{amp}\times d^{2}) \tag{2}\]  
where \(E_{amp}\) is the energy consumed by the amplifier circuit to send one bit for a distance of one meter. \(\varepsilon_{amp}\) can be expressed as \(\varepsilon_{fs}\) or \(\varepsilon_{mp}\) according to the distance between the source node and the destination node.  
For the simulations, the nodes are randomly distributed over the area of the WSN. The sensors are connected to their BS via a single level of CHs. Each node sends five packets per round, with each packet containing 500 bytes.  
Table 3 shows the common simulation parameters for the WSN through the simulation experiments [56].  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Data Packet Size & 500 bytes \\ Initial Energy & 2 J \\ \(E_{elec}\) & 50 nJ/bit \\ \(\varepsilon_{fs}\) & 10 nJ/bit/m\({}^{2}\) bytes \\ \(\tilde{E}_{fus}\) & 5 nJ/bit/signal \\ \(\varepsilon_{mp}\) & 0.0013 nJ/bit/m\({}^{4}\) \\ Threshold Distance \(d_{0}\) & 87 m \\ Deployment Method & Random \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.  
Figure 6: A snapshot of clustering output in _FL-EEC/D_.
## 2 Materials and Methods
### Preparation of FA-PAA  
FA-PAA was prepared via three steps, i.e., FA-EDA-Boc \(\rightarrow\) FA-EDA-TFA \(\rightarrow\) FA-PAA (Figure 1). To prepare FA-EDA-Boc (the first step in Figure 1), 0.9 mmol of FA was dissolved in 15 mL of DMSO at 60 \({}^{\circ}\)C under an N\({}_{2}\) flow and magnetic stirring in a 100-mL three-neck round-bottom flask. After the solution temperature decreased to room temperature, 0.1 mmol of DMAP and 1.0 mmol of DCC were added to the solution using a syringe, followed by magnetic stirring for another 1 h. Next, 1.8 mmol of EDA-Boc was added to the solution with a syringe and the reaction proceeded for 12 h. The obtained product solution was added slowly to cold ethyl acetate. The obtained yellow precipitate, i.e., FA-EDA-Boc, was washed with ethyl acetate several times. To prepare FA-PAA-TFA (the second step in Figure 1), 2 mL of TFA was added to the yellow precipitate obtained above in a 100-mL three-neck round-bottom flask, and the solution was magnetically stirred for 3 h at room temperature until it dissolved in TFA. Next, chloroform was slowly added to the solution until a yellow precipitate was formed. The upper clear solution was removed, and the remaining precipitate was washed thrice with ethyl acetate. The obtained product, i.e., FA-EDA-TFA, was dried to powder form using a rotary evaporator. To prepare FA-PAA (the third step in Figure 1), the above FA-EDA-TFA was added to 5 mL of DMSO in a 100-mL three-neck round-bottom flask, followed by the addition of 40 uL of TEA using a syringe. The mixture was magnetically stirred until a clear solution was obtained. Separately, 1.5 mmol of PAA was added to 20 mL of DMSO under an N\({}_{2}\) flow at 60 \({}^{\circ}\)C in a 100-mL three-neck round-bottom flask, and the mixture was magnetically stirred until a clear solution was obtained. After the solution temperature decreased to room temperature, 1.5 mmol of DCC and 0.15 mmol of DMAP were added using a syringe, and the reaction continued for 1 h. Thereafter, the above-prepared FA-EDA-TFA solution was slowly added to the PAA solution using a syringe. The reaction continued for 12 h with magnetic stirring. The obtained solution was dialyzed (MWCO = 1000 amu) against triple-distilled water for 24 h. After dialysis, the remaining solution inside the bag was filtered using a Whatman filter paper (Sigma-Aldrich, USA) and subsequently evaporated using a rotary evaporator to obtain a dark yellow solid product, i.e., FA-PAA.
# GraphDBSCAN: Optimized DBSCAN for Noise-Resistant Community Detection in Graph Clustering
## 1 Introduction
The primary contributions of this research include the development of GraphDBSCAN, an adaptive graph clustering algorithm that integrates DBSCAN with improved Newman-Girvan clustering, as well as the introduction of a KNN-driven DBSCAN parameter optimization mechanism, which enables automated selection of Eps and minPts, improving noise reduction and clustering stability. Additionally, the study incorporates autoencoder-based proximity feature extraction, capturing latent structural information to refine community assignments. Finally, extensive empirical validation is conducted on real-world datasets (DBLP, Amazon, Cora) and synthetic networks, demonstrating significant improvements in modularity (+30%) and community coherence compared to existing methods such as Louvain, MDBNG, and Label Propagation. The remainder of this paper is structured as follows. Section 2 presents an overview of related community detection and graph clustering methods. Section 3 details the proposedGraphDBSCAN framework, including adaptive DBSCAN parameter tuning, proximity feature extraction, and the improved Newman-Girvan clustering approach. Section 4 describes the experimental setup, datasets, evaluation metrics, and comparative results. Section 5 discusses key findings and limitations, and concludes with future research directions.
## 1 Introduction
Finally, an inner field-plate (IFP) structure was adopted to compensate the degradation of the RF characteristics caused by the FP structure. The optimum gate end-point of the FP structure was chosen and fixed to be taken as the starting point for the IFP structure. Then, the IFP structure was extended from that starting point toward the gate direction to analyze the variations of the cut-off frequency (f\({}_{\text{T}}\)) and maximum frequency (f\({}_{\text{max}}\)) with minimum loss in breakdown voltage. Consequently, we propose an optimum IFP construction for AlGaN/GaN HEMTs with enhanced breakdown voltage and RF characteristics.
# A Polarity Capturing Sphere for Word to Vector Representation
###### Abstract  
Embedding words from a dictionary as vectors in a space has become an active research field, due to its many uses in several natural language processing applications. Distances between the vectors should reflect the relatedness between the corresponding words. The problem with existing word embedding methods is that they often fail to distinguish between synonymous, antonymous, and unrelated word pairs. Meanwhile, polarity detection is crucial for applications such as sentiment analysis. In this work we propose an embedding approach that is designed to capture the polarity issue. The approach is based on embedding the word vectors into a sphere, whereby the dot product between any vectors represents the similarity. Vectors corresponding to synonymous words would be close to each other on the sphere, while a word and its antonym would lie at opposite poles of the sphere. The approach used to design the vectors is a simple relaxation algorithm. The proposed word embedding is successful in distinguishing between synonyms, antonyms, and unrelated word pairs. It achieves results that are better than those of some of the state-of-the-art techniques and competes well with the others.  
word to vector; word embeddings; antonymy detection; polarity +
Footnote †: journal: Applied Sciences
# Gaining Insights into Conceptual Models: A Graph-Theoretic Querying Approach
## 6 OPM Query Implementation
### Path Finding Queries Implementation
#### 6.1.1 Path Finding UI Implementation  
Once the user enters the \"Path Finding Queries\" section in the \"OPM Insights\" feature of OPCloud, a dialog pops up, requesting to select a specific path query. Each selection presents a list of properties or parameters to be defined for the query execution. Figure 16 shows an example for the shortest path query, where the display, source, and target nodes need to be specified.  
Clicking the \"Run Query\" button executes a method in the GDB Angular service, which calls additional methods based on the properties specified for that query in the dialog window. A main part of the flattening preprocessing is de-stating. When selecting an OPM state as the source and/or the destination of a query (such as the shortest path), we need to update the GDB Angular service to refer to the corresponding stateless object that represents that state instead of the original state. The query execution progress is displayed dynamically, and when the execution is complete, the dialog window closes automatically. The dialog window includes a checkbox to indicate whether or not to display the flat model in the background. In case of an error, a message in bold red font is displayed.  
Figure 17a is an example of executing a \"Longest Path\" query on an OPM model of the processes (legs) for a flight from Tel Aviv to San Francisco, which can be either direct or with a connection in New York. If the selected flat model visibility option is \"Show\", the entire OPM model in the flattened OPD appears in grey, as shown in Figure 17b with the OPM things and links resulting from the query execution highlighted and positioned at the top of the model, while the rest of the elements are positioned below them. The selected source and target things are highlighted by distinct yellow and green colors, respectively. If the selected option of \"Flat Model Visibility\" is \"Hide\", the result of the query, depicted in Figure 17c, will display an OPD with only the relevant OPM things and links, ordered according to the resulting path, positioned in a snake form. In both cases, the Object-Process Language (OPL) paragraph includes only OPL sentences that are part of the query result, and these OPL sentences are ordered according to the resulting route. The query source and target OPM things are highlighted in the OPL by larger font size, different color, and bold style. This enables reading the \"story\" of traversing the path.  
Figure 16: Shortest path query dialog window.