esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussion
### CLP Optimization Results
This novel approach can help companies identify factors that need the most changes for achieving their targeted CLP and, consequently, to prioritize the management practices that focus on factors with the greatest deviation from average value in the HFS-RF-PSO model. The proposed HFS-RF-PSO model presented in this paper has a few limitations that need to be addressed in future research. First, the hybrid model was developed, using field data collected for concrete placing activities. In order to develop a generic model of CLP for different types of labor-dependent activities, new field data need to be collected. Second, although the PSO algorithm is computationally efficient compared to other optimization techniques and is robust with respect to control parameters, it can fall into a local optimum \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Selected Factor and CLP**} & **Optimum** & **Deviation** \\  & **Value (\(F_{\textit{Prodi}}\))** & (\(F_{\textit{Prodi}}\)–\(F_{\textit{Avgi}}\)) \\ \hline \multirow{2}{*}{(1) Crew size} & 0.326 & 0.024 \\  & (2) Crew composition & 0.364 & 0.075 \\ \multirow{2}{*}{(2) Treatment of craftsperson by foreman} & 0.587 & 0.018 \\  & (4) Craftsperson trust in foreman & 0.535 & 0.017 \\ \multirow{2}{*}{(5) Level of interruption and disruption} & 0.043 & \(-\)0.119 \\  & (6) Complexity of task & 0.549 & 0.0490 \\ \multirow{2}{*}{(7) Working condition (dust and fumes)} & 0.108 & \(-\)0.110 \\  & (8) Location of work scope (elevation) & 0.176 & 0.044 \\ \multirow{2}{*}{(9) Congestion of work area} & 0.452 & 0.014 \\  & (10) Fairness in performance review of crew by foreman & 0.808 & 0.114 \\ \multirow{2}{*}{(11) Ground conditions} & 0.372 & 0.004 \\  & (12) Quality audits & 0.733 & \(-\)0.099 \\ \multirow{2}{*}{(13) Risk monitoring and control} & 0.271 & 0.007 \\  & (14) Crisis management & 0.629 & \(-\)0.005 \\ \hline \multicolumn{2}{c}{Construction labor productivity} & 0.522 & 0.263 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Result of the RF-PSO algorithm for selected factors and CLP. in high-dimensional space. In future research, an adaptive PSO algorithm can be developed and added to the hybrid model to improve diversity of the algorithm and avoid falling into local optimum.
# Neighborhood Aggregation Collaborative Filtering Based on Knowledge Graph
## 2 Related Work
### Graph Convolutional Network  
The graph convolutional network (GCN) is a deep learning method used to extract spatial features of topological graphs [25], which includes the spectral method [26] and non-spectral method [27]. The spectroscopy method defines the convolution calculation in the Fourier domain by calculating the eigen decomposition of the graph Laplacian. But the learning of the convolution kernel depends on the eigen decomposition of graph laplace matrix, which has certain requirements for the structure of graph and cannot be migrated to the model with other structure. The non-spectral method directly defines the convolutional calculation on the graph and takes into account neighbor nodes of different sizes while maintaining local convolution invariance.  
Many of the existing recommendation methods import graph convolution network. PinSage [16] is a recommendation framework for advertising and shopping recommendations in social network. This algorithm combines random walks and graph convolution to generate node embedding that encompasses graph structure and node features. NGCF [17] uses the users' recorded interactions to generate graph and introduces the GCN to extract the potential features of user. KGCN [18] applies the GCN to the knowledge graph to generate the embedded features of the recommended entity.  
In our work, we combine the user-item interactions with the corresponding KG, and introduce the idea of non-spectral GCN to dynamically extract the potential interests of the user.
# Data-Driven Social Security Event Prediction: Principles, Methods, and Trends
## 4 Trends in Data-Driven Social Security Event Prediction
### Model Interpretability  
Existing research tends to focus solely on the temporal accuracy of event predictions, with experiments evaluated mainly based on metrics like accuracy and recall. Model interpretability is particularly crucial in social security contexts, where decisions based on predictions can have significant real-world consequences. Without understanding the reasoning behind predictions, the effectiveness of interventions may be compromised. Prediction results often directly provide a probability value for the occurrence of an event or probability values along with information on the time and location of the event. However, they fail to explain the basis on which the predictive models make their judgments, significantly impacting the decision support provided by the prediction results. Some studies that consider spatiotemporal precursors often make simplifying assumptions that do not align with the high noise, multiple reports, and redundancy in the input data, thus necessitating improvements in the practicality of their methods. There is an urgent need to conduct interpretability analyses of the internal mechanisms of predictive models based on the features of the input data. Describing the differential contributions of different input features to the effectiveness of predictive models and generating precursor descriptions that lead to event occurrence in the spatiotemporal dimension are key directions for future breakthroughs in this field.
# Curiosity-Driven Camouflaged Object Segmentation
## 4 Experiments
### Extension Applications  
In the medical field, automatic polyp segmentation is an essential step in modern polyp screening systems, which can help clinicians accurately locate polyp regions for further diagnosis or treatments. Similar to camouflaged object segmentation, polyp segmentation also faces several challenges, including (1) variations in the shape and size of polyps and (2) non-sharp boundary between a polyp and its surrounding mucosa [2; 3; 5]. In the industry field, industrial defects usually originate from the undesirable production process, e.g., mechanical impact, workpiece friction, chemical corrosion, and other unavoidable physical, whose external visual form is usually with unexpected patterns or outliers, e.g., surface scratches, spots, holes on industrial devices; color difference, indentation on fabric surface; impurities, breakage, stains on the material surface, etc [1; 6]. Previous techniques work on the assumption that defects are easily detected, but they ignore those challenging defects that are \"seamlessly\" embedded in their materials' surroundings. Therefore, to further validate the robustness of our CDNet, we extend it to the polyp-segmentation task and industrial defects segmentation.  
From the quantitative comparison results in Table 3, it can see that our method is superior to other competitors on the datasets in the medical and industrial fields. Figure 7 presents a visualization of the results obtained by our model on downstream tasks. The 1 st and 2 nd rows showcase the outcomes of polyp segmentation, while the 3 rd and 4 th rows depict the results of industrial defect segmentation. Overall, quantitative and qualitative results provide compelling evidence of the success of our CDNet in addressing both  
Figure 6: Visualization of intermediate results in our CDNet in several challenging scenarios, including large objects, small objects, multiple objects, occluded objects, and objects with background matching.  
the polyp segmentation and industrial defect-segmentation tasks, thus reinforcing the robustness and effectiveness of our method.
## 3 Dynamics Model of the APK Joint with VSA
### Dynamic Model of the APK Joint  
The hip joint and residual thigh are controlled by human in Figure 2, and the knee joint dynamic model can be extracted from Equation (1):  
\[M_{kk}\ddot{\theta}_{k}+C_{kk}\dot{\theta}_{k}+G_{k}+M_{kh}\ddot{\theta}_{h}+C _{kh}\dot{\theta}_{h}+\omega_{ek}=\tau_{k}+\omega_{Gk} \tag{3}\]  
As can be seen from the above equation, the knee joint dynamic model includes the independent term of the knee joint and the coupled term between the knee joint and the hip joint. Furthermore, the knee joint is driven by VSA, and the dynamic model of the APK joint is also related to the VSA driver. The schematic diagram of the VSA knee joint model is shown in Figure 3.  
The dynamic model of the APK with VSA, which is shown in Figure 3, can be described as follows:  
\[I_{m}\ddot{\theta}_{m}+D_{m}\dot{\theta}_{m}=\omega_{u}-\omega_{k} \tag{4}\]  
\[\omega_{k}=K_{s}(\theta_{k}-\theta_{m})+D_{s}(\dot{\theta}_{k}-\dot{\theta}_{ m}) \tag{5}\]  
where \(\theta_{m}\) denotes position of the main motor (hereinafter, called'motor'), and \(\theta_{k}\) denotes position of the prosthetic knee. \(I_{m}\) represents the motor inertia, \(D_{m}\) denotes the motor damping, \(\omega_{u}\) denotes the input torque to the motor, and \(\omega_{k}\) denotes joint compliance torque defined in Equation (5). \(K_{s}\) is the joint stiffness and \(D_{s}\) is the joint damping.  
From Equations (3)-(5), the dynamic model of VSA-driven APK can be summarized as:  
\[\frac{M_{kk}\ddot{\theta}_{k}+C_{kk}\dot{\theta}_{k}+G_{k}}{from knee joint dynamics}+\frac{I_{m}\ddot{\theta}_{m}+D_{m}\dot{\theta}_{m}}{from motor dynamatism}+\underbrace{M_{kk}\ddot{\theta}_{h}+C_{kh}\dot{\theta}_{h}}_{ hip joint coupled dynamics}+\omega_{ek}=\omega_{u}+\omega_{Gk} \tag{6}\]
###### Abstract  
Nowadays, the demand for connection between the remote offices of a company, or between research locations, and constantly increasing work mobility (partly due to the current pandemic emergency) have grown hand in hand with the quality and speed of broadband connections. The logical consequence of this scenario is the increasingly widespread use of Virtual Private Network (VPN) connections. They allow one to securely connect the two ends of a connection via a dedicated network, typically using the Internet and reducing the costs of Content Delivery Network (CDN) lines (dedicated connections). At the same time, Virtual Local Area Networks (VLANs) are able to decrease the impact of some scalability issues of large networks. Given the background above, this paper is focused on overviewing and surveying the main progresses related to VPNs and VLANs in wireless networks, by collecting the most important contributions in this area and describing how they can be implemented. We state that security issues in VLANs can be effectively mitigated through the combination of good network-management practices, effective network design and the application of advanced security products. However, obviously, the implementation of VPNs and VLANs poses specific issues regarding information and network security; thus some good solutions are also surveyed.  
QoS; VPN; mobile VPN; security; MANETs 2021  
1
## 3 Multidisciplinary Data and Methods
### Atmospheric Data Analysis  
As regards the atmospheric analysis, the data used come from climatological re-analysis datasets, in particular, European Center for Medium-range Weather Forecasts (ECMWF) ERA5 reanalysis dataset ([[https://www.ecmwf.int/](https://www.ecmwf.int/)]([https://www.ecmwf.int/](https://www.ecmwf.int/)), accessed on 2 September 2023). This is a comprehensive reanalysis, from 1940 to five days behind present time, which assimilates as many observations as possible in the upper air and near surface. In our case, however, the dataset of the parameters that we use dates back to 1980 [58], because we are confident that the shorter dataset is more homogeneous.  
We focused our attention on some of the parameters which usually are reported as influenced by the preparation phase of the impending earthquake, such as skin temperature (SKT) and outgoing longwave radiation (OLR): in fact, supported by many pieces of evidence in literature (e.g., [17; 59]), LAIC models consider them as directly affected by the \"thermodynamic\" channel.  
The ECMWF time series of each atmospheric quantity has been collected in order to apply CAPRI algorithm [17]: for each day, in a temporal window of some months precedingthe seismic event, the average and standard deviation of the atmospheric parameter of the forty year (1980-2022) historical time series is computed, after the removal of the linear trend (to take into account the effect of global warming); then, the comparison of each day's present value with the corresponding historical daily average (and standard deviation) is made for around forty years (1980-2022). If the observable of interest exceeds, with certain persistence, the mean of the time series by twice the standard deviation, an anomaly is identified. In this work, we considered an interval of four months before the EQ. The area sensitive to the EQ preparation phase energy was chosen and it corresponds to 2\({}^{\circ}\)\(\times\) 2\({}^{\circ}\) in latitude and longitude, respectively.  
CAPRI algorithm has been applied also to some gas time series. In particular, carbon dioxide (CO\({}_{2}\)), carbon monoxide (CO), and sulphur dioxide (SO\({}_{2}\)) have been retrieved from climatological physical chemical model Modern-Era Retrospective Analysis for Research and Applications, version 2, [60] (MERRA-2) provided by NOAA in the sub-dataset M2T1 NXAER version 5.12.4. The data have a spatial resolution of 0.625\({}^{\circ}\) longitude and 0.5\({}^{\circ}\) latitude and a temporal resolution of 1 h.
# Induction Heating-Assisted Compaction in Porous Asphalt Pavements: A Computational Study
## 1 Introduction
### Induction Heating-Assisted Asphalt Compaction  
Asphalt compaction enhances interlocking of the aggregate-sized particles that increase the internal friction of the mix and this, in turn, provides a material with adequate stiffness and strength. However, inadequate compaction results in low mix density, high air void content, and a reduction of mix fatigue life [9]. Moreover, temperature is an important factor in mix compaction. As temperature drops, the viscosity of asphalt mix is increased and hence coated aggregate mobilization reduction will result to diminished air-void content of mix and the time required to obtain the same degree of compaction increases [9; 12].  
Within this framework, in order to control the temperature profile of laid asphalt mix during compaction and consequently improve the mix compactability, the implementation of induction heating (IH) technology is proposed. IH is a technique recently introduced in pavement engineering [13; 14; 15; 16]. When an alternating electrical current is applied across an induction coil, an alternating magnetic field is developed. If the coil is placed in the vicinity of a material with inductive particles (e.g., asphalt including inductive fibers or filler-sized particles), then eddy currents are induced in the particles and heat is generated through [PERSON]'s law. The heat generated by electromagnetic induction increases the temperature of the mix and enables viscosity reduction through local melting of the binder. In this paper, a continuous IH compaction process, which consists of a continuously moving induction coil system and a roller compactor (Figure 1), was applied in LTPA-type mixes.
## 3 The Proposed Protocol
### Registration Phase
#### 3.2.1 Registration Phase of BASN MS  
The registration of \(MS_{j}\) includes R11-R13:  
R11: \(MS_{j}\Longrightarrow GWN:\ MIS_{j}\); the \(MS_{j}\) transmits identity \(MIS_{j}\) to gateway node \(GWN\) via a secure channel. Then, gateway \(GWN\) collects \(MIS_{j}\) and stores it in identity set \(S_{M}=\left\{MIS_{j}\right\}\).  
R12: \(GWN\Longrightarrow MS_{j}:\left\{x_{j}\right\}\); GWN calculates secret value \(x_{j}=h\left(MIS_{j}\parallel x\right)\) for \(MS_{j}\) and also returns secret value \(x_{j}\) to \(MS_{j}\) via a secure channel.  
R13: \(MS_{j}\) stores \(x_{j}\) secret  
\begin{table}
\begin{tabular}{c|l|c|l} \hline
**Notations** & **Descriptions** & **Notations** & **Descriptions** \\ \hline \(GWN\) & Gateway node & \(S_{M}\) & The set of BASN’s identity \\ \(\oplus\) & XOR operation & \(T_{ng}^{Li}\) & Registration timestamp of \(U_{i}\) \\ \(T_{c}\) & Current timestamp & \(PID_{i}\) & A pseudo-random identity of \(U_{i}\) \\ \(GID\) & \(GWN\)’s identity & \(f_{U_{i}}(t)\) & Authorization check polynomial \\ \(\parallel\) & Bit concatenation & \((ID_{i},PW_{i})\) & The identity and password of \(U_{i}\) \\ \(MIS_{j}\) & The identity of \(MS_{j}\) & \(FE_{.}REP(\cdot)\) & Fuzzy extraction and recovery function \\ \(h(\cdot)\) & Secure hash function & \(A\Longrightarrow B:M\) & The message M is sent from A to B through a secure channel \\ \(U_{i}\) & \(i\)th user (medical staff) & \(A\to B:M\) & The message M is sent from A to B through a public channel \\ \(x_{j}\) & The secret value of \(MS_{j}\) & \(PUI_{sum}(\cdot)\) & The physically unclonable function with embedded \(sum\) \\ \(x,y\) & \(GWN\)’s long-term key pair & \(\Delta T_{auth}^{Li}\) & Time threshold for \(U_{i}\) to be authorized in order to obtain \\ \(MS_{j}\) & \(j\)th body area sensor node & \(S_{M}^{Li}\) & The set of BASN’s identity for \(U_{i}\) to be authorized in order \\  & & & to obtain authentication \\ \hline \end{tabular}
\end{table}
Table 1: Notations with related descriptions in the proposed protocol.  
Note that the secure channel can be understood here as comprising the user and node devices that are within the same physical space (such as a computer room managed by a hospital administrator); then, the registration and sharing of secret values can be completed in a face-to-face manner.
# TinyGS vs. SatNOGS: A Comparative Analysis of Open-Source Satellite Ground Station Networks
###### Abstract  
In recent years, two of the largest open-source ground station (GS) networks capable of enabling Earth-satellite communication have emerged: TinyGS and SatNOGS. These open-source projects enable anyone to build their own GS inexpensively and easily, integrate into a GS network, and receive data from satellites listed in the database. Additionally, it enables satellite developers to add satellites to the databases of these projects and take advantage of this GS network to receive data from the satellites. This article introduces the TinyGS and SatNOGS projects and conducts a comparative analysis between them. Generally, the TinyGS project seems to have simpler implementation as well as lower associated costs. In a deeper analysis, it was observed that on the 29 July 2023, the TinyGS project had a higher number of online GSs and a more favorable geographic distribution. On the other hand, the SatNOGS project managed to communicate and decode a larger number of satellites up to 29 July 2023. Additionally, in both projects, it was noted that frequencies between 436 and 437 had the highest number of satellites with decoded data. Ultimately, the choice between these projects depends on critical parameters defined by the reader.  
SatNOGS; TinyGS; ground station network; LEO satellites 202452282-254[[https://doi.org/10.3390/](https://doi.org/10.3390/)]([https://doi.org/10.3390/](https://doi.org/10.3390/))
# The Less Known Cyclins--Uncovered
## 11 Cyclin O  
Cyclin O encoded by the CCNO gene is a protein whose molecular weight is 38 kDa. The gene encoding cyclin O is located on the long arm of chromosome 5 at position q11.2. Cyclin O mostly resembles the A- and B-type cyclins. It binds to Cdk1 and Cdk2, which leads to their activation. Cyclin O downregulation blocks the caspase activation [93].  
In normal cells, cyclin O is involved in the process of central cell amplification. [PERSON] et al., 2015 [94] researched the functions of cyclin O during deuterostome-mediated centriole amplification of multiciliated cells (MCCs). In the study, it was demonstrated that CCNO expression is limited to MCCs and its deletion in mouse reduces numbers of multiple motile cilia. This is also characteristic of MCCs dysfunctions, like severe hydrocephalus and mucociliary clearance deficits. CCNO\(-/-\) MCCs are unable to generate deuterostomes sufficiently so only a few fully functional centrioles that maturate to ciliary basal bodies are created [94].  
In normal cells, cyclin O plays a key role in the development of oocytes. [PERSON] et al., 2013 [95] studied mouse oocytes and showed that the highest expression of cyclin O occurs just in the case of these cells. The authors reported that the reduction of cyclin O expression also caused oocyte retention in the germinal vesicle (GV) stage, by CDC2 dephosphorylation. The authors suggest that cyclin O is necessary to resume oocyte meiosis [95].  
In normal cells, cyclin O also participates in the normal development of the body. [PERSON] et al., 2017 [96] studied two types of mice: CCNO+\(/-\) and CCNO\(-/-\). Studies on mice with cyclin O knockout showed many developmental defects, i.e., hydrocephalus and growth retardation, and decreased ciliary motility in the fallopian tube, which resulted in infertility. Besides, they showed impairment of the central nervous system (CNS), which was manifested in irregularities in the development of the hippocampus and thinning of the cerebral cortex [96].  
Cyclin O in cancer cells is overexpressed in gastric cancer (GC). [PERSON] et al., 2018 [97] studied 80 patients with stomach cancer. In their studies, they showed that cyclin O is overexpressed in this type of cancer, and also localizes mainly in the cell nucleus. The authors also showed that the reduction of cycO expression in GC cells induces the process of apoptosis, which is associated with inhibition of cell proliferation [97].
# Optical Studies in Red/NIR Persistent Luminescent Cr-Doped Zinc Gallogermanate (ZGGO:Cr)
## 3 Results
### Temperature-Dependent PL
The same trend is observed for the effective Debye temperature, which can be estimated by \(T_{D}=\hbar\overline{\omega}/k_{B}\)[34], resulting in 319 K and 449 K from the R1- and R2-lines behaviour, respectively. It is worth noting the increase of the fitted parameters when the sample is excited with 442 nm, resulting in much higher effective Debye temperatures, as indicated in Table 4. According to the measured Raman spectra (Figure 2b, Table 1), the effective phonon energies obtained from the R-lines shift when the sample is excited with 325 nm are closer to the energy modes, mainly due to the Zn ions vibrations (e.g., ZnO\({}_{4}\) tetrahedrons) and to the one corresponding to a local lattice disorder (e.g., inverse spinel). On the other hand, when the sample is excited with 442 nm, the effective phonon energies \begin{table}
\begin{tabular}{c c c c c c} \hline \(\mathbf{\lambda_{exc}}\) (nm) & **Lines** & \(\hbar\overline{\omega}\) **(meV)** & \(\beta\) **(meV)** & \(\textbf{E_{R}}\)**(0) (eV)** & \(\textbf{T_{D}}\) **(K)** \\ \hline
325 & R1 & 27.5 \(\pm\) 5.1 & 4.9 \(\pm\) 1.5 & 1.804 \(\pm\) 6.91 \(\times\) 10\({}^{-5}\) & 319.1 \\
325 & R2 & 38.7 \(\pm\) 4.9 & 9.4 \(\pm\) 2.3 & 1.809 \(\pm\) 5.26 \(\times\) 10\({}^{-5}\) & 449.1 \\
442 & R1 & 49.5 \(\pm\) 5.3 & 17.6 \(\pm\) 4.4 & 1.804 \(\pm\) 5.16 \(\times\) 10\({}^{-5}\) & 574.4 \\
442 & R2 & 52.8 \(\pm\) 4.0 & 21.1 \(\pm\) 3.9 & 1.809 \(\pm\) 3.78 \(\times\) 10\({}^{-5}\) & 612.7 \\ \hline \end{tabular}
\end{table} 
Table 4: Fit parameters obtained for the R1- and R2-lines’ peak position shift using Equation (3). Figure 11: Temperature dependence of the R-lines’ peak position obtained with 325 nm and 442 nm photon excitation, and correspondent fittings according to Equation (3). well match the ones that are likely to contain the Ga\({}^{3+}\) cation vibrations, suggesting that the observed behaviour is induced by the presence of the Ge ions in the alloy. Additionally, it should be emphasised that the studied ZGGO:Cr polycrystalline sample exhibits a wide range of crystallite sizes as indicated in Figure 3, explaining, therefore, the observation of distinct Debye temperatures.