esa-sceva/satcom-chunk-collection huggingface dataset:
# Many-Objective Optimization for Anomaly Detection on Multi-Layer Complex Interaction Networks
## 4 Results
### Case Study IV: Real-World Multi-Layer Network of World Trade Network  
In our fourth experiment, we consider a multi-layer network modeling world trade relationships. These data originate from import-export interactions on commodities between some countries of the world in 2011, c.f., [8; 29; 68], disaggregated for different traded commodities. This network can be defined as a multiplex network composed of many layers, where each layer is given by a different commodity. Figure 8 shows the first layer of the network. The nodes are given by the 207 countries contained. A link between two countries in a layer is given if there is trade between them in the respective commodity. The data are presented in matrix form: rows and columns represent countries, and the entries of the matrices are the volumes of trade. It is therefore a weighted multiplex network. The general classification is based on 96 different commodities. The classification was performed by grouping together similar commodities; this procedure leads to 11 aggregated'super-commodities'.  
In our experiment, we applied anomaly detection using the method explained above. Our results indicate a special outcome compared to other dataset--we can observe a specific special case here. Having obtained the Pareto Front and after having computed the respective ACE scores, we observe that, as a result, only one single node in the Pareto Front is provided, c.f., Table 11. Therefore, assessing a set of nodes using the ACE score cannot be performed-since we only have one candidate, which is already our final result. To this end, we conclude that, for our specific case, if we found a single node in the Pareto Front, then this node has quite different characteristics compared to all other nodes. For further (but weaker) candidates, we could resort to the nodes at the second rank of the dominated solution from the Pareto Front, using the ACE score for assessment as well.  
Figure 8: Visualization of world trading network on layer 1. Here, can be seen how the nodes (representing the countries) connect to the others, From the picture we can see that Ant (Antilles) node has no single connection in this layer.
# A Co-Design-Based Reliable Low-Latency and Energy-Efficient Transmission Protocol for UWSNs
## 3 Design Challenges
### Energy Constraints  
The energy consumption of sensor nodes in underwater environments is a non-negligible problem. Further, the energy-consumption problem in UWSNs is different from that in TWSNs. Due to the special features of the underwater environment, the sensors in UWSNs are generally equipped with battery power and their relative inaccessibility renders it difficult to replace this limited power supply when exhausted. Energy issues directly affect the lifetime of the entire sensor network. Under the influence of some underwater routing protocols, redundant copies of data packets are inevitably generated for making the transmission more reliable. To prolong the lifetime of UWSNs, redundant copies of data packets among sensor nodes should be reduced without reducing transmission reliability. Thus, the number of redundant copies of packets must be kept low to conserve energy. In addition, the retransmission mechanism and large numbers of control packets previously mentioned will further increase the energy consumption.
## 2 Related Work  
Current SLAM systems primarily focus on improving the accuracy of reconstruction using visual sensors. Methods based on monocular, stereo, and RGB-D cameras have achieved significant progress in creating dense and detailed environmental maps. However, visual sensors often perform poorly in environments with low lighting, textureless surfaces, or dynamic objects, limiting their applicability in real-world scenarios. In contrast, LiDAR (Light Detection and Ranging), with its millimeter-level positioning accuracy and adaptability to diverse environments, plays a crucial role in SLAM systems. LiDAR excels in capturing precise depth information, particularly in large-scale and unbounded environments, making it an indispensable component in fields such as autonomous driving and robotics.  
To overcome this limitation, recent research has attempted to leveraging the complementary strengths of both modalities by combining visual data with LiDAR data. Among these efforts, 3 DGS-based LiDAR-Visual fusion and NeRF-based LiDAR-Visual fusion have emerged as two critical research directions. The following sections discuss the relevant work in these two areas in detail.
## 3 Material and Methods
### Analysis of Volatile Compounds  
Higher alcohols were quantified by the direct injection of wine in split mode (25:1), using an Agilent 7890A gas chromatograph (Santa Clara, CA, USA) with a flame ionization detector and the chromatographic conditions previously described [56]. The identification was carried out using spectra obtained with commercial standard compounds and from the NIST library. Quantification was carried out following the internal standard quantification method, using the selected quantification ions and chosen IS for each compound as well as the calibration curves described in [56].  
Volatile compounds found in lower concentrations in the wine were quantified by headspace solid-phase micro-extraction (autosampler PAL RSI 120) and gas chromatography with mass spectrometer (Agilent 78902B CG coupled to a 5977B MSD). In total, 10 mL of wine was diluted (1:3 with an hydroalcoholic solution and the addition of four internal standards (IS): methyl 2-methylbutyrate, methyl octanoate, heptanoic acid, and 3,4-dimethylphenol) and placed into a 20 mL glass vial with 3.5 g L\({}^{-1}\) of sodium chloride.  
The samples were incubated 5 min at 40 \({}^{\circ}\)C, and after that the volatiles in the headspace of the vial were extracted with a 1 cm 50/30-\(\upmu\)m DVB/Carboxen/PDMS SPME fiber (Supelco) at the same temperature and with an agitation speed of 500 rpm during 60 min. After extraction, the fiber was desorbed for 3 min in the injector at 250 \({}^{\circ}\)C using the splitless mode. Chromatographic analyses were carried out with a DB-WAX Ultra Inert capillary column (60 m length, 0.25 mm i.d., and 0.50 mm film thickness, Agilent). The volatile composition of the wines was analyzed in triplicate after one month of bottling (T1) and after twelve months of bottle aging (T12). The odor activity value (OAV) was used to evaluate the potential contribution of a chemical compound to wine aroma. This parameter provides a rough pattern of the sensory importance of odorants by converting quantitative data into sensory information [57]. We have considered that odorants with higher OAVs (>0.2) contribute more strongly to overall aroma.
# Prediction of Ocean Weather Based on Denoising AutoEncoder and Convolutional LSTM
## 3 Deep Learning Model for the Prediction of Ocean Weather
### Correlation Analysis of Ocean Weather  
Eight parameters of ocean weather used in this study (SST, MWH, MWD, MWP, WU, WV, CU, and CV) not only have their own characteristics but also have correlated characteristics. For this reason, it is challenging to analyze and predict them. For example, in the case of SST, seasonal and trend components are very distinctive. On the other hand, MWD does not have a particular periodicity, and its prediction is difficult because the surrounding ocean weather profoundly influences it. As described in Section 2.3 and Section 3.1, we have to consider the spatial and correlation of each parameter of ocean weather. When considering the correlation of all eight parameters of data among each other, the input data may be composed of eight channels. However, training between data that lacks correlation decreases training efficiency. Therefore, in this study, we attempted to develop a learning model considering the correlation between each parameter of data.  
[PERSON]'s correlation coefficient (PCC) [20], which is traditionally used in statistics, was used to analyze the correlations among the eight parameters of data. PCC is a numerical value quantifying the correlation between data X and Y. It has a value between \(+1\) and \(-1\), with a positive correlation for a positive value, a negative correlation for a negative value, and no correlation for a zero value. Generally, it is judged that there is a meaningful linear correlation with values of \(+0.5\) or more or \(-0.5\) or less.  
Table 2 shows the absolute values of PCC for the eight parameters of data. For the calculation of PCC, one year of weather data in 2017 was used. The PCC results indicate that the wind speed (WU) and wave height (MWH) have a relatively high correlation compared to others, and the wave height and wave period (MWP) have a relatively high correlation. However, meaningful correlations, among other data, are difficult to find. The PCC can only analyze linear correlations. Therefore, considering that there will be a nonlinear correlation, we should review models that consider six ocean weather events at once. Among the input data obtained from ECMWF, SST, which does not correlate with other data, is employed as a single learning model, and the remaining five parameters of data are utilized as a single learning model. Finally, the two current-related data obtained from HYCOM are also used as a single learning model.
# Practical I-Voting on Stellar Blockchain
###### Abstract  
In this paper, we propose a privacy-preserving i-voting system based on the public Stellar Blockchain network. We argue that the proposed system satisfies all requirements stated for a robust i-voting system including transparency, verifiability, and voter anonymity. The practical architecture of the system abstracts a voter from blockchain technology used underneath. To keep user privacy, we propose a privacy-first protocol that protects voter anonymity. Additionally, high throughput and low transaction fees allow handling large scale voting at low costs. As a result we built an open-source, cheap, and secure system for i-voting that uses public blockchain, where everyone can participate and verify the election process without the need to trust a central authority. The main contribution to the field is a method based on a blind signature used to construct reliable voting protocol. The proposed method fulfills all requirements defined for i-voting systems, which is challenging to achieve altogether.  
blockchain; i-voting; stellar; blind signature Article
## 3 Models and Methods
### Spatial Convergence Model
\(C_{k}\) is the parameter of the condition variable that needs to be estimated. \(\alpha\) is a constant term and \(\beta\) is used to measure the convergence of regional transport efficiency. When \(\beta\) is less than 0, it indicates that that particular province contains a convergence with regard to integrated transport efficiency. In the opposite case, this convergence does not exist. Formulas (6) and (8) represent the Spatial Lag Model (SLM) and Spatial Error Model (SEM) of the absolute \(\beta\)-convergence, respectively. Formulas (7) and (9) represent the SLM and SEM of the conditional \(\beta\)-convergence, respectively. By setting the dependent variable in the regression model of spatial autocorrelation, the SLM model was used to study that a province is affected by spillover effect of its adjacent province, i.e., the degree of influence of the impact of the variable error of each province's neighbors. SEM highlighted that the interactions between the provinces were comprised of the different differences in their relative position, and measured the degree of influence of the impact of the variable error of each province's neighbors. If the study object was found to have convergent characteristics, we were further able to calculate the corresponding convergence speed \(\theta\), and the half life cycle \(\tau\), i.e., the time taken by provinces with a low level of integrated transport efficiency to catch up with provinces that had high integrated transport efficiency. The specific calculation formula was as follows:  
\[\theta=-\frac{\ln(1+\overline{\beta})}{T} \tag{10}\]  
\[\tau=\frac{\ln 2}{\theta} \tag{11}\]  
where \(T\) represents the time length of the study and \(\overline{\beta}\) is the estimated value of the convergence coefficient.
# Development of Phenomenological Model for RC Circular Columns
## 3 The ModIMK Model and Parameters' Calibration  
### The ModIMK Hysteretic Model  
The ModIMK model consists of two parts, the backbone curve and the hysteresis rule, and the backbone curve of the model is shown in Figure 1. The ModIMK hysteretic model has a critical difference compared with other models; its backbone curve is not acquired by linking the peak moment point of every hysteresis loop directly, but it is obtained based on the monotonic loading curve (see Figure 1). The backbone curve connecting the peak moment points varies greatly depending on the loading history, whereas the monotonic loading curve is stable.  
Because most test groups did not have monotonically loaded components, the backbone curve is generally approximated by calibrating the hysteresis curve.  
The ModIMK hysteresis model has three types: bilinear, peak-orientated, and pinching; the circular columns collected in this work are more compatible with the peak-orientated type. The main model parameters are listed in Table 3, and other ones can be viewed on the official website of OpenSees.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameter** & **Description** & **Parameter** & **Description** \\ \hline \(\rho_{t}\) & volume stirrup ratio & \(d\) & diameter of circular column, mm \\ \(\lambda\) & ratio of stirrup spacing to longitudinal bar diameter & \(f_{ly}\) & yield strength of stirrup, MPa \\ \(c\) & thickness of concrete cover, mm & \(\rho_{l}\) & longitudinal reinforcement ratio \\ \(f_{ls}\) & ultimate strength of longitudinal reinforcement & \(f_{ly}\) & yield strength of longitudinal reinforcement, MPa \\ \(L\) & shear span, mm & \(V_{p}/V_{n}\) & shear capacity ratio, Calculated according to the U.S. \\ \(s_{n}\) & rebar buckling coefficient \(s_{n}=(s/d_{l})\big{(}f_{y}/100\big{)}^{0.5}\) & \(v\) & axial load ratio \\ \(s\) & stirrup spacing, mm & \(d_{s}\) & stirrup diameter, mm \\ \(\lambda_{sp}\) & shear span ratio & \(f_{c}\) & cylinder compressive strength of concrete, MPa \\ \(d_{l}\) & longitudinal reinforcement diameter, mm & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design parameters of circular components.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value Range** \\ \hline \(d\) (mm) & 152–609.6 \\ \(f_{c}\) (MPa) & 23.1–90 \\ \(\mathbf{v}\) & \(-0.01\)–\(0.419\) \\ \(\lambda_{sp}\) & 1.5–10 \\ \(f_{ly}\) (MPa) & 296–496.4 \\ \(f_{ly}\) (MPa) & 230.68–1000 \\ \(\rho_{l}\) & 0.0052–0.0563 \\ \(\rho_{t}\) & 0.0013–0.0304 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Value ranges of design parameters.
## 4 Maneuver Assessment and POD Results
### Maneuver Assessment by POD
reduced compared with those without maneuver estimation. Moreover, such values for the TH-2A with maneuver estimation are comparable with those for the TH-2B, whose thrusters did not fire during the test periods. Similar to the LC residuals, the ionosphere-free linear pseudorange combination (PC) residuals for the POD can also be reduced with maneuver handling. These results indicate that the impact of thrusts on the POD results of the LEOs can be eliminated by maneuver handling, and their orbit precision is on par with that of the maneuver-free LEOs. The maneuver accelerations derived from the GPS data have proven to be in good agreement with the onboard telemetry data [21]. Therefore, the GPS-derived results can be treated as references to evaluate the maneuver estimations by the BDS2-based POD. Figure 10 shows the relative errors of velocity changes obtained by the BDS2-based POD with respect to those obtained by the GPS-based POD, which are less than 7.0%, and the mean relative error is 1.2%. The result indicates that the maneuver estimations of the LEOs derived from the regional BDS2 data are on par with those derived from the global GPS data.
## 1 Introduction
Separate Path Routing (SPR) creates multiple paths from source to sink which means each packet of an event uses different route to destination. Network Location Anonymization works on hiding the identity of the source by using pseudonyms. Network Coding divides a packet into smaller pieces. These pieces will follow different routes to the sink. Dummy Data Sources creates fake sources, which create dummy traffic to hide and obfuscate the real traffic inside. This approach will be used in the proposed techniques, because it provides higher anonymity than other approaches. Three different techniques, Dummy Uniform Distribution (DUD), Dummy Adaptive Distribution (DAD) and Controlled Dummy Adaptive Distribution (CAD), were developed to provide a high level of anonymity with an acceptable delivery ratio, delay and overhead. DUD does not have a mechanism to reduce the delay, which can take a long time to deliver the real event. However, DAD performance in the case of delay is better than DUD, because it increases the transmission rate of nodes that detect a real event or forward packets of a real event. After nodes transmit all the real packets they have, the transmission rate is reduced for those nodes to make the average transmission rate equal to other nodes in the network. This makes the adversary unable to distinguish the difference between real and fake packets. CAD is an improvement of DAD in the case of the delay because it forces the real packet to be transmitted after n-intervals if the node fails to transmit the real packet within n-intervals. This guarantees the arrival of a real event within a specific time. The rest of this paper is organized as follows. Section 2 describes the related work. Section 3 presents the network, routing, adversary, and anonymity models used. Section 4 describes the proposed techniques. Section 5 discusses the metrics, which affect the introduced techniques. Section 6 demonstrates simulation and results. Section 7 discusses the anonymity analysis of the results. Section 8 presents the conclusion.
# Stents in Congenital Heart Disease: State of the Art and Future Scenarios
## 3 Congenital Heart Disease
### Characteristic Features of Fetal Circulation
This fetal shunt, a vessel approximately 1.5 cm long with a diameter of 0.5-0.6 cm (which in utero is kept open by the high level of prostaglandandins coming from the placenta) ends in the descending aorta after the aortic arch and allows the right ventricle to feed the lower systemic circulation during gestation. In fact, the poorly oxygenated blood is sent to the lower part of the body requiring less oxygen supply, and to the placenta for oxygenation. According to the described vascular organization, it is evident that the resultant circulation is highly optimized to guarantee proper oxygenation of the fetal vital organs, i.e., myocardium and the brain. Due to the presence of intracardiac (foramen ovalae) and extracardiac shunts (ductus arteriosus and ductus venosus), the fetal circulation can be schematized as an intricate parallel circuit (Figure 2b) in which systemic circulation is fed both from the right and the left ventricle. Thus, the cardiac output is defined as a combined ventricular output [35] given by the sum of the blood flows from the two ventricles. At birth, a series of cardiopulmonary adaptions take place to accommodate extra-uterine life. The umbilical cord is clamped, arresting the delivery of oxygen, nutrients, and prostaglandandins through the placental circulation and increasing systemic vascular resistance. The blood flow in the ductus venosus drastically reduces until a complete passive closure of the vessel 3-10 days after birth [35]. At the first breath, the lungs expand, causing a strong decrease in pulmonary vascular resistance and an increase in pulmonary blood flow. These events determine a pressure shift, with the pressure in the left ventricle exceeding that in the right ventricle, causing the foramen ovalae to close immediately. Complete anatomical closure of the shunt occurs later, within 6-12 months from birth. Additionally, the increase in blood oxygen content, which causes constriction of the smooth muscle in the ductus arteriosus, together with the reduction in prostaglandandins level, determines the functional closure of the ductus arteriosus within 24 h from birth. Anatomical closure occurs later via diffuse fibrous tissue proliferation within 2-3 weeks [36]. Once a clear picture of how the physiological fetal cardiovascular system is obtained, it is easy to understand how blood circulation is strongly shunt-dependent during gestation. The three described shunts assure a flexible and adaptive circulation, which makes it possible for the fetus to survive gestation even in the presence of severe congenital malformations typical of CHD (e.g., the absence of underdevelopment of one of the two ventricles), thanks to the establishment of parallel pathways.
## 2 Materials and Methods
### Evaluation of Apnea Detection Results  
#### 2.4.1 Classification Performance over Detection Windows  
In this paper, five statistical metrics of accuracy (\(ACC\)), true positive rate (\(TPR\)), true negative rate (\(TNR\)), positive predictive value (\(PPV\)), and \(F_{1}\) score are applied to assess the performance of the proposed modeling framework over all detection windows (\(W_{m}\)):  
\[ACC = \frac{\sum TP+\sum TN}{\sum TP+\sum FP+\sum FN+\sum TN}\times 100\% \tag{16}\] \[TPR = \frac{\sum TP}{\sum TP+\sum FN}\times 100\%\] (17) \[TNR = \frac{\sum TN}{\sum FP+\sum TN}\times 100\%\] (18) \[PPV = \frac{\sum TP}{\sum TP+\sum FP}\times 100\%\] (19) \[F_{1} = 2\frac{TPR.PPV}{TPR+PPV}\times 100\% \tag{20}\]  
where \(TP\) (true positive) is the number of apneic windows that were correctly classified as such, \(TN\) (true negative) is the number of normal windows that were correctly classified as such, \(FP\) (false positive) is the number of normal windows that were falsely classified as apneic, \(FN\) (false negative) is the number of apneic windows that were missed by the classifier. \(ACC\) is a classical measure for binary classification but is not enough in this problem due to class imbalance between the apnea and normal classes [52; 53; 54]. Thus, \(TPR\), \(TNR\), and \(PPV\) are used to report a more detailed performance in detecting apneic and normal windows. The \(F_{1}\) score considers \(TP\) and \(FP\) detections simultaneously and thus accounts to the \(TPR/PPV\) tradeoff reporting a more comprehensive idea on the overall performance of the proposed model.