esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results
### Analysis of Path Smoothing  
The influence of the smoothing parameters on the slice coverage is analyzed in this section. The indices introduced in Section 5.2 are used. As Figure 22 shows, \(k_{tot}\) rises asymptotically with rising \(\Phi\) and falls with rising \(\Psi\). \(k_{Vol}\) rises approximately linearly with rising \(\Phi\) and falls with rising \(\Psi\), especially for \(\Psi\in[0.6,1]\). In addition, the coverage influence of \(\Phi\) and \(\Psi\) is greater with rising path width. Subsequently, the influence of \(\Phi\) and \(\Psi\) on the joint angular velocity is analyzed. Table 3 shows the \(k_{\omega,j}\) values for the limit cases (\(k_{tot}\approx 1\)) in comparison with the quasi unsmoothed path (QU). It is evident that smoothing offers a promising option to reduce the maximum angular joint velocities of the robot.  
However, the table also shows that only the case (\(\Phi=0\), \(\Psi=1\)) is manipulable (all \(k_{\omega,i}<1\)). Thus, it can be deduced that the manipulability of the path depends on \(\Phi\) and \(\Psi\). To further analyze the influence of \(\Phi\) and \(\Psi\) on the manipulability, \(k_{\omega,i}\) is plotted for all joints of a Kuka KR 6 as a function of \(\Phi\) and \(\Psi\) (cf. Figure 23).  
Figure 21: Executable path planned by the decomposition path planner and the smoothing algorithm.  
Figure 23 shows that \(k_{\omega,j}\) rises asymptotically with rising \(\Phi\) and falls asymptotically with rising \(\Psi\), for all joints. Furthermore, it is evident that the relationship between \(\Phi\) and \(\Psi\) decides if the path is manipulable. When viewing the graphs in Figure 23, the manipulable combinations can be separated from the non-manipulable ones by approximately linear inequalities between \(\Phi\) and \(\Psi\) (cf. Figure 24a for \(k_{\omega,1}\)). These inequalities define the solution set of smoothing parameters which allow a manipulable path. They can be shown as planes in the \(k_{tot}\) 3D plot (cf. Figure 24b), defining what combinations are manipulable and allowing for the choice of the combination which generates the greatest slice coverage.  
Based on this analysis, the optimal smoothing combination for the Kuka KR 6 and the welding path velocity of \(14\,\mathrm{mm}\,\mathrm{s}^{-1}\) is selected as \((\Phi,\Psi)^{T}\approx(0.6,1)^{T}\). Figure 21 shows the example slice after planning including a path smoothing with \(\Phi=0.6\) and \(\Psi=1\). It can further be seen that the path is divided into two sub-paths. While the red path only covers one of the four convex partial polygons, the blue one allows for the continuous covering of the remaining three polygons.  
Figure 24: (a) \(k_{\omega,1}\) graph shown from above with a green separation line between manipulable (blue points) and non-manipulable (red crosses) combinations of \(\Phi\) and \(\Psi\). (b) Separation lines for each joint as a plane for determining the manipulable combinations of \(\Phi\) and \(\Psi\).  
Figure 23: Influence of \(\Phi\) and \(\Psi\) on \(k_{\omega,i}\) for all robot joints. The light blue plane shows \(k_{\omega,i}=k_{\omega,i,crit}=1\) under which the path is manipulable.
## 4 Experimental Setup and Results
### Description of the Legacy GPON Testbed  
The commercial GPON (Figure 4) used to demonstrate the viability of our approach consists of an OLT (SmartOLT 350 of Telnet-RI vendor [30]), that supports 2.5 Gbps at the downstream channel and 1.25 Gbps at the upstream channel. The GPON testbed shows a tree topology configuration with a single splitting level. In this way, the OLT is connected to the optical splitter by means of three spools of Standard Single Mode Fiber (SMF) of different lengths (total length of 20 km). Then, the splitter (1:8) is connected to every ONT using SMF distribution fibers and the distance to each ONT is configurable, from 100 m up to 5 km, using a connection panel, as it can be noticed in Figure 5. This configurable network scenario allows setting up realistic situations where ONTs are located at different distances from the Central Office (OLTs). Finally, the GPON is equipped with L3 (with router functionalities) and L2 (without router functionalities) ONTs that comply with the ITU-T G.984.x/G.988 recommendations. The ONT models are the Wave Access 3021 (L3) and the Wave Access 512 (L2) [30]. Finally, in our particular approach, we attach computers (mini computers) to build routers with flexible capabilities by means of OVSs. For our experimental study, we connect five ONTs, two L2 ONTs, and three L3 ONTs.  
Figure 5: Real structure of the GPON testbed and its devices.
# Phase Centre Corrections of GNSS Antennas and Their Consistency with ATX Catalogues
## 3 KRAW and KRA1 Stations  
The pair of antennas KRAW and KRA1 are located on a five-storey university building about 3.5 m away from each other. This is an unusual circumstance, which is beneficial for GNSS networks [25], but very few permanent reference stations are located in this way. The oldest station KRAW has continuously collected data since the end of 2002, while the younger KRA1 has been collecting data since 2010 [26]. In 2005, the characteristics of the gravity field (acceleration and gradient) were measured for the KRAW station, and a station normal height was determined [27]. Up to 2018 to 2019, the station's equipment did not change (brand and model). The KRAW used an Ashtech UZ-12 receiver with an ASH70195C_M SNOW antenna. In 2019, a new receiver was introduced, a LEICA RX1200 GGPRO (without changing the antenna). The KRA1 uses a Trimble NET8 receiver with a TRM57971.00 NONE antenna. In 2018, the KRA1 antenna was replaced by another TRM57971.00 NONE model with individual PCC. For the KRAW station, the ASH70195C_M SNOW antenna was not calibrated individually.  
During the experiments on quasi-geoid modelling, it was found that the antenna's calibrations might be inaccurate [28]. It was suspected that the ellipsoidal height difference between the stations is of the opposite sign to their coordinates in the EPN network [29]. To verify this situation, a test survey was organised. It was based on a comparison of the height differences between the stations (KRAW and KRA1) and temporary stations KR01 and KR02 (with TRM57971.00 NONE antennas), as well as an analysis of the archival data. The Trimble antennas at KR01 and KR02 are commonly used in GNSS permanent networks. Exactly the same type was tested at Wuhan University by the absolute method (the field robot); the test showed the high repeatability of the obtained calibrations: \(\sigma_{\text{N}}\) = 0.17 mm, \(\sigma_{\text{E}}\) = 0.12 mm, and \(\sigma_{\text{U}}\) = 0.30 mm [12]. The same model of antennas on KRA1, KR01, and KR02 guaranteed that the impact of the differences between the antennas would not be significant. The only difference was the power supply system (different model). The antennas were located at similar height levels, with a distance of about 3.5 m from each other (Figure 5).  
Figure 5: Location of the KRAW and KRA1 permanent stations on the roof of the AGH-UST buildings with the temporary KR01 and KR02 stations.  
The observation sessions took place over 12 days in December 2016. The precise differences in height were defined by GPS post-processing. As the height difference reference value, the results of geometric levelling (Zeiss KoNi 007) were used. The levelling was conducted both at the beginning and the end of the GPS session.
## 4 Data Integration
For instance, in the context of the CAPICE project, the important variables are those concerning demographics (i.e., sex, family structure, parental educational attainment, parental employment, socio-economic status (SES), individual's school achievements, mental health measures (both for psychopathology as well as for wellbeing and quality of life) by various raters (mother, father, self-report, teacher)), pregnancy/the perinatal period (i.e., alcohol and substance use during pregnancy, birth weight, parental mental health, breast feeding), general health (i.e., height, weight, physical conditions, medication, life events), family (i.e., divorce, family climate, parenting, parental mental health), and biomarkers (genomics, epigenetics, metabolomics, microbiome data, etc.). All of these pieces of data gathered in children and parents are harmonized while using various procedures. Variable manipulation is not essential if the query asked/responded and the answer noted in both datasets is the same [29]. If the response verified is not the same, the response is re-categorized/re-organized to improve the comparability of records from both of the datasets. Missing values are generated for each subsequent unmatched variable and are switched by multiple imputations if the same pattern is calculated in both datasets, even if using different methods/scales. A scale that is applied in both datasets is recognized as a reference standard [29]. If the variables are calculated several times and/or in distinct periods, these are harmonized by gestation trimesters data. Lastly, the harmonized datasets are assembled into a single dataset.
## 3 Case Study Results
### Metropolitan Lima Stations
However, the \({}^{\mathbf{c}}\)**STLD\({}_{\mathbf{c}}^{\mathbf{c}}\)** (2.141, 19.925, 1.605, 88.958, and 0.988), \({}^{\mathbf{c}}\)**STLD\({}_{\mathbf{c}}^{\mathbf{b}}\)** (2.143, 19.669, 1.603, 89.367, and 0.988), and \({}^{\mathbf{c}}\)**STLD\({}_{\mathbf{b}}^{\mathbf{b}}\)** (3.190, 21.490, 2.298, 95.063, and 0.972) models produced the second, third, and fourth best results. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline
**Measure** & **Min** & **Q1** & **Median** & **Mean** & **Mode** & **Var** & **S.D** & **Skewness** & **Kurtosis** & **Q3** & **Max** & **ADF (Statistic)** \\ \hline ATE & 0.80 & 5.50 & 8.50 & 28.36 & 5.20 & 1606.08 & 40.08 & 1.89 & 2.30 & 29.30 & 165.80 & \(-\)8.61 \\ log(TAE) & \(-0.22\) & 1.70 & 2.14 & 2.55 & 1.65 & 1.50 & 1.23 & 0.49 & \(-\)0.54 & 3.38 & 5.11 & \(-\)8.70 \\ DCM & \(-0.80\) & 8.98 & 24.50 & 28.13 & 1.00 & 454.02 & 2.131 & 0.53 & \(-\)0.67 & 44.03 & 117.10 & \(-\)6.03 \\ log(CMD) & \(-0.22\) & 2.19 & 3.20 & 2.86 & 0.00 & 1.37 & 1.17 & \(-\)0.89 & \(-\)0.19 & 3.78 & 4.76 & \(-\)6.53 \\ SB & \(-0.20\) & 8.30 & 15.10 & 17.09 & 6.50 & 12.02 & 11.05 & 0.83 & 0.51 & 24.00 & 8.39 & \(-\)13.02 \\ log(SDM) & \(-1.61\) & 2.12 & 2.71 & 2.56 & 1.87 & 0.78 & 0.88 & \(-\)1.46 & 3.38 & 3.18 & 4.43 & \(-\)10.35 \\ STA & \(0.10\) & 1.80 & 6.20 & 10.56 & 0.40 & 140.99 & 12.21 & 1.94 & 5.54 & 14.80 & 152.60 & \(-\)16.17 \\ log(STA) & \(-2.30\) & 0.59 & 1.82 & 1.59 & \(-\)0.92 & 2.02 & 1.42 & \(-\)0.44 & \(-\)0.61 & 2.69 & 5.03 & \(-\)14.38 \\ \hline \end{tabular}
\end{table} 
Table 2: This table contains descriptive statistics for the time series of ozone concentration and the logarithmic time series of the ozone concentration for all considered monitoring stations.
# QuietPlace: An Ultrasound-Based Proof of Location Protocol with Strong Identities
## 6 QuietPlace Communication Specification
### QuietPlace Characteristics  
Following the analysis in Section 2, we present the characteristics of QuietPlace in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Characteristic** & **Solution** & **Description** \\ \hline Proof creation architecture & Hybrid & Witnesses can be other users’ devices or nodes trusted to the CA or/and the verifier. \\ \hline Proof verification architecture & Indirect & The CA is involved in the verification process in order to verify the identities and signatures of the participants. \\ \hline Use of a trusted third party/certificate authority & Yes & We use a certificate authority, which is responsible for keeping the public keys of the users, verifying their identities and signatures and detecting malicious nodes. It can optionally run a trust scheme to assess the users according to their behavior. \\ \hline Physical layer & Ultrasound & Prover and witnesses communicate via ultrasound during the proof creation phase. The physical layer used in the verification phase is indifferent. \\ \hline Geocoding & Plus codes & We represent location using plus codes of different size, according to the desired accuracy. \\ \hline Geolocation & No & The protocol does not calculate the users’ location but verifies it. The location is obtained by the device OS. \\ \hline Proximity test & Yes, timeout-based & The round-trip time between witness’s challenge and prover’s response is measured. Exceeding a specified threshold will cause the prover’s claim to be rejected. \\ \hline Incentive to participate & Yes & The CAs can provide their services to verifiers with a cost. This motivates them to execute identity-checking procedures and expand their networks of trusted witnesses. \\ \hline Location granularity levels & Yes & Using hash chains, the prover can reveal its location to the verifier with the accuracy of its choice. \\ \hline Verifier-agnostic proofs & Yes & After CA performs its checks, the prover possesses verifier-independent information. The proof becomes verifier-specific with the creation of the final LP message. \\ \hline Proof of location chains & No & The proposed protocol does not support proof of location chains. Every proof produced can be used by itself and is unrelated to others. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of QuietPlace.
## 5 Discussion  
From the above analyses of the effectiveness and efficiency of RMO_SODSDA, we found the following:  
(1) In the Americas-large, Apj, and Emea datasets, percentage of RSSOD constraints that can be successfully enforced exceeded 70%. However, when the length of RSSOD constraints increased to 3-5 RSSOD and 5-10 RSSOD, the percentage in the Americas-small and Customer datasets was below 50% and 17%, respectively. Thus, the Americas-large, Apj, and Emea datasets were more appropriate for the effectiveness evaluations of RMO_SODSDA.  
(2) We compared the execution time of RMO_SODSDA from Tables 11-14 with that of the unconstrained role mining from Table 6 in Table 15. Obviously, the time of our method was much higher than that of any unconstrained role mining method. We constructed lots of different variants of SMER constraints to correctly implement the given RSSOD constraints, although it was time-consuming.  
Figure 4: Query comparison when \(match=min\).  
Figure 3: Query comparison when \(match=max\).  
Figure 2: Query comparison when \(match=exact\). MAX-SAT, maximal satisfiability.  
(3) For the given cases of permissions request in this paper, the query time of RMO_SODSDA was below one second in any case. However, the method did not outperform the MAX-SAT solver in the \(min\) case.  
(4) We considered a similar set of experiments as the ones from the MAX-SAT solver to evaluate the efficiency of RMO_SODSDA, and we found that the query time does not obviously vary in the \(exact\) and \(max\) cases when the number of roles exceeds 300. Thus, we did not consider and analyze further the similar results in these cases.  
(5) Similar to the number of roles chosen from the MAX-SAT solver, we chose the similar set of constraints as another parameter for our method, and only the RSSOD constraint was taken into consideration. Particularly, it was observed from Table 6 that the number of mining roles from the Americas-small and Customer datasets was close to 230 and 270, respectively. However, according to the first discussion in this section, the percentages of RSSOD constraints that can be successfully enforced were very low. As the results in Figure 4, the query time irregularly floated up and down once the two datasets were chosen for the performance evaluations.
# Fuzzy Risk Evaluation and Collision Avoidance Control of Unmanned Surface Vessels
## 3 Design of Smart Collision Avoidance Control System for USVs
### Decision-Making Procedure
#### 3.2.2 Fuzzy Collision Avoidance Acting Timing Indicator  
The fuzzy collision avoidance act timing indicator is designed to estimate the level of residual time, \(\mu_{CA}\), to allow the controlled vessel to take an evasion action. This indicator has three input parameters: 1. the velocity of the controlled USV, \(V_{O}\); 2. The heading angle of the target ship, \(C_{O}\); and 3. the modified relative distance, \(D_{L}\), which is a function of the total length, \(L_{S}\).  
\(D_{L}\) can be expressed as:  
\[D_{L}=\frac{D}{L_{S}} \tag{10}\]  
This proposed fuzzy collision avoidance act timing indicator with two inputs, \(D_{L}\) and true bearing (\(\phi\)) are shown in Figure 7:  
According to COLREGs, alteration of sailing course alone is the first action to take. Vessels are allowed to decrease their speed when alteration to the sailing course will not be effective in avoiding collision. Based on the expert experiences of captains, altering the sailing course is the most preferable method when the detected indices are \(\mu_{CRI}\geq 0.3\) and \(\mu_{CA}\geq 0.7\). Decreasing the speed is the only avoidance method when the detected indices are \(\mu_{CRI}\geq 0.8\) and \(\mu_{CA}\geq 0.7\).  
Figure 6: The relationship between output \(\mu_{CRI}\) and inputs \(\text{DCPA}_{L}\) and TCPA for a 1.72-m USV.  
Figure 7: The relationship between output, \(\mu_{CA}\), and inputs, \(D_{L}\), and true bearing, \(\phi\), at a velocity of 10 knots.
## 2 Materials and Methods
### Flux Tower GPP Data  
The monthly gap-filled flux estimated gross primary production (GPP) was selected as the drought impact data to evaluate the VIUPD-derived VCI in this study. The _in situ_ data on GPP were measured by an eddy covariance instrument set on the flux tower. The GPP data can be downloaded from the AmeriFlux website [30]. The months from April to October are chosen. As the VIUPD is a vegetation index and VIUPD-derived VCI is mainly suitable for monitoring agricultural drought, we chose the grass and crop vegetation types covering comparatively longer period sites in this study. Table 1 shows the details of the chosen flux sites and Figure 1 shows their geographic positions.
## 2 Materials and Methods
### Effect of Losartan on Human NP Cells  
Passage 3 NP cells were seeded in 12-well plates at a density of 20,000 cells/well. Overall, four donors with three replicates per donor were assessed in this experiment. Additionally, three wells per group and donor were assessed for DNA content. Cells were seeded in 2 mL DMEM with 10% FCS to allow cell attachment. On the day of seeding the 12-well plates (Day 0, baseline), samples were taken for normalization. After 24 h, the medium was changed to the experiment medium with six groups. The control group was cultured with 2 mL DMEM, 1% non-essential amino acids, 50 \(\upmu\)g/mL L-ascorbic acid 2-phosphate, and 1% ITS+. We prepared high-dose (1000 \(\upmu\)M) and low-dose (100 \(\upmu\)M) stock solutions of losartan by dissolving 47.9 mg losartan potassium and 4.79 mg, respectively, in 1 mL PBS. After sterile filtering with a 0.2 \(\upmu\)m filter, we prepared 250 \(\upmu\)L aliquots and stored them at \(-\)20 \({}^{\circ}\)C. Losartan at concentrations of 100-1000 \(\upmu\)M in PBS was then added to the medium for the experiments. Human recombinant TNF-\(\upalpha\) (10 ng/mL) was added simultaneously to induce a pro-inflammatory stimulus in human NP cells.  
Experimental groups:  
- Control group  
- Losartan 100 \(\upmu\)M  
- Losartan 1000 \(\upmu\)M  
- TNF-\(\upalpha\) 10 ng/mL + Losartan 100 \(\upmu\)M  
- TNF-\(\upalpha\) 10 ng/mL + Losartan 1000 \(\upmu\)M  
After treatment for 72 h, the medium was collected and analyzed for nitric oxide (NO) content. Three wells/group were digested with 0.5 mg/mL proteinase K (Sigma-Aldrich, St. Louis, MO, USA) to measure DNA content in the monolayer. Three wells/group were lysed in TRI reagent (Molecular Research Center, Cincinnati, OH, USA) and PolyAcryl-Carrier (Molecular Research Center, Cincinnati, OH, USA), and stored at \(-\)80 \({}^{\circ}\)C for gene expression analysis.  
Pathway Study with the Perxisome Proliferator-Activated Receptor Gamma (PPAR\(\gamma\)) Antagonist T0070907 under Inflammatory Conditions  
To determine a potential pathway for the anti-inflammatory effect of losartan, we conducted a pathway study with the PPARy antagonist T0070907 (Tocris Bioscience, Bristol, UK). Human recombinant TNF-\(\alpha\) (10 ng/mL) was used as an inducer of inflammation. T0070907 was added to the medium to investigate whether losartan's anti-inflammatory effect would be inhibited by blocking PPARy. DMSO was used to solubilize T0070907 and the same amount (1 \(\mu\)L/mL medium) was also added to the other groups to provide equivalent culture conditions. As before, the cells were seeded on day 0 and exposed to the experimental group medium after 24 h. Gene expression analysis was performed after 24 and 48 h of exposure. The experimental groups were:  
- Control  
- TNF-\(\alpha\) 10 ng/mL + T0070907 1 \(\mu\)M  
- TNF-\(\alpha\) 10 ng/mL + Losartan 1 mM + T0070907 1 \(\mu\)M
## 2 Problem Description  
First, the data transmission problem in satellite communication can be considered a combinatorial optimization challenge. Second, the process includes assigning transmission resources from the ground station when each satellite is in view. This problem can be broken down into three key decision stages:  
1. Ensuring that the allocation order meets all constraints;
2. Determining the order of time window allocation for each satellite;
3. Minimizing the satellite transmission time and maximizing task gain.
## 3 System Modeling  
In this paper, we assumed that all sensor nodes are being deployed in the NAN randomly and independently and the mobile sink is considered as the NAN gateway for collection of monitoring and control data from the sensor nodes. Also, we assumed that all sensor nodes follow asynchronous working-sleeping cycle strategy with their working time as \(W_{V}\) and their sleeping time as \(S_{V}\). The residual energy of the sensor nodes is based on the simplified energy consumption model [18]. Figure 2 depicts the asynchronous working-sleeping cycle strategy of nodes along with their residual energy slope. Both of these features are used to visualize the possible link connectivity between adjacent sensor nodes, \(V_{i}\), \(V_{j}\), and \(V_{k}\), which are within their radio range r. With reference to [18], the energy consumed during transmission and reception for adjacent nodes \(V_{i}\) and \(V_{j}\) is given by:  
\[E_{T}(V_{i},V_{j})=(E+\varepsilon_{amp}d_{(V_{i},V_{j})}^{2})B \tag{1}\]  
\[E_{R}(V_{j})=EB \tag{2}\]  
where \(E\) is the basic energy consumed during transmission and reception per bit, \(B\) is the number of bits transmitted or received, \(\varepsilon_{amp}\) is the energy consumed by transmission amplifier, \(d_{(V_{i},V_{j})}\) is the distance between node \(V_{i}\) and \(V_{j}\), \(E_{T}\) and \(E_{R}\) are the energy consumed by nodes for transmission and reception, respectively. Figure 2 shows that the link connectivity between adjacent nodes depends on asynchronous working-sleeping cycle as well as residual energy of nodes. When the node is in working mode, its residual energy starts depleting and follows the residual energy slope. The residual energy at the start of the working mode is high and is low at the end of the working mode. So, the opportunistic link connection if established at the start of the working mode will lead to higher link connectivity value as compared to the connection established at the center or end of the working mode.  
Figure 2: Illustration of link connectivity based on asynchronous working–sleeping cycle strategy and residual energy of nodes deployed in NAN. SGs—smart grids.