esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion and Conclusions
### Limitations and Future Work  
The FCC authorization is limited to U.S.-manufactured and U.S.-launched satellites. Therefore, the method presented in this paper is currently applicable only to satellites launched from the United States. However, as discussed in Section 2.2.2, if the ITU were to adopt the requirement for submitting a NASA Debris Assessment Software (DAS) report, it would be possible to extend this method to satellites launched globally. The method outlined in this paper is for the C + D phase represented in Figure 2, and it is limited to the system boundary discussed in the LCA and system boundary section. It does not account for any other sections other than platform, payload and propellant, as represented in Figure 2. Future work could involve developing a framework for quantifying the environmental and economic impact by incorporating the circular approach for low Earth orbit satellites. For instance, in this paper, we discussed the environmental impacts on Earth due to the production of satellites. This is just one aspect that could be considered while designing a space mission. The other aspect is re-entry pollution caused by discarded satellites and the economic aspect. Hence, a framework that can give insights into economic and environmental aspects (production as well as re-entry pollution caused by discarded satellites in the atmosphere) on Earth could be developed. The outcomes could serve as a basis for subsequent investigations aimed at formulating a sustainability framework conducive to the integration of more circular practices for the space industry on Earth and in space.  
Conceptualization, S.T.K. and C.T.; methodology, S.T.K.; software, S.T.K.; validation, S.T.K. and C.T.; formal analysis, S.T.K.; investigation, S.T.K.; resources, S.T.K. and C.T.; data curation, S.T.K.; writing--original draft preparation, S.T.K.; writing--review and editing S.T.K., C.T. and M.E.; visualization, S.T.K.; supervision, C.T. and M.E.; project administration, S.T.K.; funding acquisition, C.T. and M.E. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Science and Technology Facilities Council [grant number ST/X508858/1].  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
We would like to thank the STFC for funding this research and the European Space Agency for providing access to the LCA guidelines handbook and LCI database.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Thinger.io: An Open Source Platform for Deploying Data Fusion Applications in IoT Environments
## 2 Challenges for the Deployment of Modern Data Fusion Applications
* **Autonomy:** Autonomy is an important challenge since many data fusion applications require the deployment of distributed sensors and actuators in a geographic area, which are powered by batteries. Therefore, it is necessary to carry out optimizations at different levels of the IoT architecture to extend the lifetime of an implementation [22]. It is possible to optimize in different areas such as protocols to save bandwidth, detection periods, energy saving mode of devices and sensors, etc. [23]. As we will see later, Thinger.io has been designed for the use of a highly optimized protocol that reduces the delay when sending information to the cloud, saves bandwidth, reduces memory space and saves the device's battery. * **Security:** Devices deployed in our environment can monitor personal information, or even interact with our resources and nearby elements, like those present in homes or the industry [24]. As such, the security in the IoT ecosystem is a real concern, to avoid leaking sensitive data, or granting access to non-authorized actors to actuate over our environment, like our home, or car. Thus, it is required to have secure clouds, secure connections, anonymity in the information stored in the cloud, etc. [25]. Thinger.io provides a layer of security to avoid granting access to unauthorized actors. There are several platforms trying to satisfy the increasing demand of connected devices, as well as trying to solve some of the aforementioned challenges. There are many global manufacturers that are developing their own platforms for building IoT ecosystems--from Facebook with its Parse platform, Samsung with the ARTIK technology, ARM with ARM mbed, to other emerging platforms such as Golgi, Temboo, Xively, etc. All of these platforms are proprietary and closed, meaning that they are confined to a cloud deployment managed by a third party. They cannot be deployed inside a private business, industry, or being used without approved devices and sensors for the platform. So, the alternative is to rely on open source initiatives such as Thinger.io, Kaa, ThingSpeak, Particle, or OpenRemote. These platforms can be easily used and adapted for academic, industry, and scientific research. In the following section, after a description of the Thinger.io platform, the research efforts that it incorporates will be discussed. These research efforts focus on the transmission efficiency, real-time bidirectional communication, interoperability and the ease of modeling data fusion applications in ecosystems where the integration of IoT, Cloud and Big Data technologies is necessary. Thinger.io has its own modeling language that facilitates the modeling of these services in a transparent way to the designer, being supported by the use of efficient communication protocols and a highly scalable architecture.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{3}{c}{**Affected**} & \multicolumn{3}{c}{**Intact**} & \multirow{2}{*}{**Control**} \\ \cline{2-2} \cline{5-8}  & **Before** & **3 months** & **1 year** & **Before** & **3 months** & **1 year** & \\ \hline X1 & \(2.9\pm 1.8\) & \(4.0\pm 2.5^{\star}\) & \(4.5\pm 2.2\) & \(4.2\pm 2.0\) & \(4.8\pm 2.9\) & \(5.9\pm 3.0^{\#}\) & \(3.8\pm 2.1\) \\ \hline A1 & \(21.1\pm 7.3^{\#}\) & \(19.4\pm 4.4^{\#}\) & \(20.7\pm 5.8^{\#}\) & \(22.4\pm 5.6^{\#}\) & \(20.7\pm 5.1^{\#}\) & \(21.1\pm 5.1^{\#}\) & \(26.3\pm 4.1\) \\ \hline X2 & \(58.2\pm 3.1^{\#}\) & \(58.1\pm 2.7^{\#}\) & \(58.0\pm 2.2^{\#}\) & \(58.6\pm 4.6^{\#}\) & \(57.4\pm 3.1\) & \(57.9\pm 1.3^{\#}\) & \(56.0\pm 2.1\) \\ \hline A2 & \(-7.6\pm 8.6\) & \(-1.3\pm 5.3^{\star}\) & \(-13.3\pm 5.2\) & \(-7.9\pm 7.2\) & \(-10.3\pm 5.0^{\star}\) & \(-10.8\pm 6.2\) & \(-10.6\pm 4.2\) \\ \hline Adduction & \(11.4\pm 4.6\) & \(12.6\pm 5.6\) & \(15.0\pm 4.2\) & \(13.0\pm 4.0\) & \(13.1\pm 4.3\) & \(18.7\pm 5.9\) & \(13.5\pm 3.8\) \\ \hline Rotation & \(10.7\pm 3.6\) & \(10.9\pm 3.2\) & \(10.9\pm 1.3\) & \(12.0\pm 4.7\) & \(11.4\pm 3.6\) & \(11.0\pm 2.5\) & \(13.1\pm 5.6\) \\ \hline \hline \end{tabular}  
* significant difference from before-surgery value. # significant difference from the control group.  
\end{table} 
Table 2: Hip movement amplitudes. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{3}{c}{**Affected**} & \multicolumn{3}{c}{**Intact**} & \multirow{2}{*}{**Control**} \\ \cline{2-2} \cline{5-8}  & **Before** & **3 months** & **1 year** & **Before** & **3 months** & **1 year** & \\ \hline X1 & \(17.9\pm 3.4^{\#}\) & \(17.8\pm 2.9\) & \(17.3\pm 3.2\) & \(15.4\pm 2.7^{\#}\) & \(15.9\pm 3.0\) & \(16  
In patients before surgery, both on the affected and unaffected sides, the first knee flexion phase (X1) ended slightly, though significantly, earlier than in the control group (p < 0.05). Before and three months after surgery, the extension phase (X2) on both sides (affected and unaffected) ended considerably earlier than in the control group. In the post-operative period, a significant delay of maximum knee flexion in the swing phase (X3) persisted for a year or more on the affected side and up to three months on the intact side. Abduction-adduction and rotation movements were slightly decreased on both sides and showed no distinct trends.
# Sensor-Level Mosaic of Multistrip KOMPSAT-3 Level 1R Products
## 2 Methods
### Piecewise Discrepancy Compensation  
High-resolution satellite image strips are acquired using a push-broom sensor that uses a line of detectors arranged perpendicular to the flight direction of the spacecraft. As the spacecraft flies forward, the image is collected one line at a time, with all of the pixels in a line being measured simultaneously.  
This mechanism should produce an irregular geometric discrepancy between the adjacent strips along the image line. We applied a piecewise discrepancy compensation that models the local difference for some image lines, as depicted in Figure 4. However, it is a possibility of discontinuity between adjacent image pieces. Therefore, we model each local discrepancy with a spline curve.  
The sensor model for the mosaic image strip should be generated for photogrammetric processes. Since the mosaic image consists of several image strips of different sensor  
Figure 4: Piecewise discrepancy compensation.  
modeling information, the RPCs for the mosaic can be generated by bias-compensating the RPCs of the reference considering the estimated compensations to the adjacent images [14].
## 4 Discussion
### Surface Properties (Roughness, Topography and Wettability)  
Regarding surface roughness, it was found that this parameter depends on the concentration of the compound incorporated into the resin. Surface roughness is a parameter that influences both the esthetics and the mechanical properties of the restoration [14]. It has been described that rough materials are more susceptible to the adhesion of microorganisms, which can lead to diseases in the oral cavity such as stomatitis [13; 30]. It is influenced by various factors, e.g., the manufacturing method, the type of resin used, and the monomer elution [13]. However, if the surface roughness of the investigated resins is below the recommended limit for dental materials (Sa \(\leq\) 0.2 um), this may not have a major impact on debris accumulation or biofilm adhesion [14]. The compounds incorporated into the resins that showed values below the limit were GNPs [14] and Ag-MSN [15], while in the case of ZrO\({}_{2}\) NPs [13; 30] and nanodiamonds [36], the surface roughness values were above the established limit. Although the roughness values are above the limit, the article states that the incorporation of zirconia nanoparticles did not have a major impact on microbial adhesion [13]. The article with the nanodiamonds described that roughness values above 2 um would increase the possibility of bacterial colonization of the resin surface, which was not observed in the groups studied [36]. It can be concluded that even if the roughness of the tooth material is above the recommended limit, this does not mean that bacterial adhesion will occur. It is necessary to analyze the properties of the individual materials.
# Remote Sensing of Environmental Changes in Cold Regions: Methods, Achievements and Challenges
## 3 Changes and Trends  
Climate warming in cold regions has been altering the phenology of snow, lake, and river ice, and vegetation, causing accelerated ice melting in polar regions, and leading to complex wetting and drying trends, and greening and browning patterns in northern high latitudes and TP. Multi-decade remote sensing observations are essential in documenting the environmental changes and revealing the long-term trends
# Human Activity Recognition Using CSI Information with Nexmon
## 8 HAR Classification and Performance Analysis
### Multiclass Classification Using SVM for Experiment 1.1  
The SVM is suitable for classification when the data has precisely two classes and is linearly separable. Yet, it becomes unsuitable for the multiclass when the data is not linearly separable because the hyperplane of SVM can divide only for binary classes. Nevertheless, the same principle of dividing the class with hyperplane can be used by clustering the data in multiple clouds and using soft-margin SVM to find the hyperplane of maximal margin within an acceptable tolerance of nonlinear data points on the decision boundaries.  
Two different approaches can be used to achieve multiclass SVM classification: One-to-One and One-to-Rest. In the One-to-One approach, every two classes are divided with the hyperplane and by ignoring the rest of the classes. In the One-to-Rest approach, choosing the hyperplane separates one class from all other classes, as shown in Figure 8.
# On the Fidelity of NS-3 Simulations of Wireless Multipath TCP Connections
## 5 Simulation Model
### Simulation Setups  
This research uses the ns-3 version 3.25 with DCE 1.8. The DCE tool allows utilizing the Linux networking stack within the ns-3 simulator. Generally, the network protocols in an early development phase are first implemented in the Linux kernel. Testing such new protocols using their Linux kernel implementation is more realistic than implementing them using a simulator.  
As mentioned in Section 2, some researchers have proposed different implementations of MPTCP in ns-3. However, none of them are integrated with the ns-3 release, as they are not fully compliantwith the MPTCP specifications. Alternately, the DCE framework provides the flexibility of using the Linux kernel implementation of MPTCP within ns-3. Therefore, in this research, we use the Linux kernel MPTCP implementation provided by the MPTCP protocol designers through the DCE framework. We use in the simulator the same version of MPTCP (0.89) employed in the hardware testbed. The MPTCP parameters such as path manager and packet scheduler are configured with the same values as the testbed. Likewise, ns-3 DCE also gives the flexibility of selecting the congestion control algorithm (Cubic/LIA/OLIA/wVegas) to be used. The ns-3 DCE also provides the benefit of utilizing POSIX-socket based applications. This allows using the traffic generation tool _iperf_ the same as in the testbed. Thus, the DCE feature offers an emulation mode that allows the simulated experiments to run in real time, similar to the hardware testbed.  
Figure 3 shows the replication of testbed Setup 1 in the ns-3 simulator. The simulated nodes are installed with MPTCP Linux stack instead of the default ns-3 stack. Like the testbed, the sending nodes are equipped with LTE and Wi-Fi network interfaces (\"network devices\" in ns-3 terms). The LTE network device is connected to the LTE network by attaching to an eNodeB, whereas the Wi-Fi network device is connected to the Wi-Fi network via Access Point-1. One interface of the receiving nodes is also connected to a Wi-Fi network via Access Point-2. A wired CSMA link is created between the PGW of the EPC and the receiving nodes to replicate the Ethernet switch as in Setup 1 of the testbed.  
Setup 2 of the testbed, where a bottleneck link exists, is recreated in the simulator, as shown in Figure 4. The configuration of the sending network and the connectivity of the network nodes are the same as in Setup 1. The bottleneck link leading to the receiving network is created using a bridge (similar to the Ethernet switch in the testbed) that connects the receiving nodes to the LTE network via the PGW and the Wi-Fi network via AP-1. As revealed by Figures 3 and 4, the LTE network includes two eNodeBs. Since the S1-based handover is still not implemented in ns-3, we only simulate the X2-based handover between the eNodeBs.  
Figure 3: Simulation Setup 1.
## 2 Numerical Methods
### Inclined Leading Edge in the Diffuser Vane  
Generally in centrifugal compressor with vaned diffuser, the leading edges of the vanes are perpendicularly extended in the spanwise direction with respect to the direction of the upstream incoming flow. Therefore, a high incidence flow angle is generated near the leading edge of the diffuser vane, which results in the formation of separated flows within the diffuser channels. These separation regions lead to the significant loss in aerodynamic  
Figure 5: Comparison of the numerical results and experimental data [29] at full compressor map; top: total pressure ratio, bottom: total-to-total efficiency.  
Figure 6: Comparison of the time-averaged numerical data and measurements [37]; normalized velocity profiles in the spanwise direction at 1.05 \(R_{2}\) in the vaneless gap.  
performance of the compressor. In this research, diffuser vanes with the inclination angles in the spanwise direction with respect to the height of the vane are proposed.  
Two types of inclined leading edge of diffuser vanes with different angles from hub-to-shroud (\(+\alpha\)) and shroud-to-hub (\(-\alpha\)) were presented, and numerically analyzed. Figure 7 shows the parameter \(\alpha\) and configuration of various types of inclined leading edges applied in the diffuser vanes compared to the original leading edge. As shown in Figure 7, the diffuser vane profiles for various inclination angles are exactly the same, and the only difference is observed at the vane leading edges. First, the performance map was calculated using steady simulations for two cases of \(\alpha=(30\) and \(-30)^{\circ}\), to evaluate the stall margin improvement. Then, transient simulations for different inclination angles of \(\pm\alpha\) = (30 and 75)\({}^{\circ}\) were numerically analyzed, and were compared to the original diffuser. It should be noted that all the unsteady simulations were conducted at the design condition (21,789 rpm and 4.536 kg/s).
# Synthesis and Biological Activity of 1,3,4-Oxadiazoles Used in Medicine and Agriculture
## 3 General Methods for the Synthesis of 1,3,4-Oxadiazololes  
The preparation of unsubstituted 1,3,4-oxadiazolole was first described by [PERSON] in 1965. The synthesis was carried out by applying thermolysis at atmospheric pressure to formylhydrazone ethylformate (Scheme 1) [50; 51].  
Basic unsubstituted 1,3,4-oxadiazol can be obtained using the simplest \(N\),\(N^{\prime}\)-diformylhydrazine in a reaction with phosphorus pentoxide in the presence of polyphosphoric acid. The synthesis technique proposed by [PERSON] et al. is based on preheating the polyphosphoric acid to a temperature of about 100 \({}^{\circ}\)C in the first step and then adding P\({}_{2}\)O\({}_{5}\). Hydrazine can then be added to the mixture. The reaction is carried out at elevated temperature for several hours. Unsubstituted 1,3,4-oxadiazol thus obtained can then be neutralized with sodium bicarbonate [52]. The most popular methods of obtaining 1,3,4-oxadiazol derivatives are based on the use of \(N\),\(N^{\prime}\)-diacylhydrazines or \(N\)-acylhydrazines (Scheme 2). The most frequently used cyclodehydrating agents for \(N\),\(N^{\prime}\)-diacylhydrazines are polyphosphoric acid (PPA) [53], H\({}_{2}\)SO\({}_{4}\)[54], POC\({}_{3}\)[55], SOCI\({}_{2}\)[56], (CF\({}_{3}\)SO\({}_{2}\))\({}_{2}\)O [57], P\({}_{2}\)O\({}_{5}\)[58], BF\({}_{3}\)-OE\({}_{2}\)[59] or Burgess reagent [60]. In addition, it is possible to obtain 1,3,4-oxadiazol derivatives by oxidative cyclization of \(N\)-acylhydrazines with oxidizing substances such as ceric ammonium nitrate (CAN) [36], Br\({}_{2}\)[61], KMnO\({}_{4}\)[62], PbO\({}_{2}\)[63],chloramine T [64], DDQ [65], or hypervalent iodine reagents [66; 67]. One-pot syntheses of oxadiazole groups from acid hydrazides with carboxylic acids and ortho-esters in the presence of an acid catalyst are also described in the literature [68; 69; 70; 71]. Other possibilities of synthesis include acylation, subsequent opening and closure of the tetrazole ring [72], conversion of 1,2,4-oxadiazol derivatives under the influence of UV ra-diation [73], and heterocyclic of semicarbazides, thiosemicarbazides or selenosemi-carbazides [74; 75; 76].
# A Passively Q-Switched Holmium-Doped Fiber Laser with Graphene Oxide at 2058 nm
###### Abstract
demonstrated a Q-switched Ho-doped fiber laser incorporating a CNT-based SA [72], [PERSON] et al. reported a Q-switched Ho-doped fiber laser with a heavily Ho-doped fiber SA [74], and [PERSON] et al. demonstrated the use of a heavily Ho-doped fiber SA for the realization of high-power Q-switched pulses from a Ho-doped fiber laser [75]. In this work, a Q-switched Ho-doped fiber laser using a low-cost SA based on GO is experimentally demonstrated. The SA, which has an all-fiber structure, was implemented through deposition of GO particles onto the flat polished surface of a side-polished fiber. In order to pump a Ho-doped fiber gain medium in the laser ring resonator, our built continuous-wave (CW) Tm-doped fiber laser at 1928 nm, which was followed by a power amplifier operating was used. By inserting the GO-based SA in the prepared Ho-doped fiber-based ring resonator, the pulsed output was found to be readily obtainable at a wavelength of 2058 nm due to passive Q-switching. The measured temporal width of the output pulses was 1.56 \(\upmu\)s at a 56.12 kHz repetition rate. Both output pulse width and repetition were tunable by changing the pump power. It should be noticed that this study's constructed laser has an all-fiber ring configuration unlike the Q-switching-based Ho-doped fiber laser incorporating a SA based on CNT, demonstrated by [PERSON] et al. in which a Fabry-Perot cavity was employed with a free-space coupled mirror-type SA [72]. The main goal of this work is to demonstrate the ultimate potential of graphene oxide (GO) as a base material for the implementation of our SA since it is a low-cost alternative to expensive high-purity graphene in terms of saturable absorption. Note that GO-based SAs have not been used for passive Q-switching of a fiber laser operating at the wavelength band beyond 2 \(\upmu\)m, even if they have been well-investigated at wavelengths of 1, 1.5, and 1.9 \(\upmu\)m.
# Error Sources of Interferometric Synthetic Aperture Radar Satellites
## 4 Random Errors
### Temporal Decorrelation (\(\delta_{temp}\))
The largest coherence decrease occurred in non-forested vegetation areas (i.e., 30.6%), then in agricultural regions and water bodies (i.e., 20%), while the coherence in urban regions remained above 0.5 [57]. Given the same surface change over a period of time, temporal decorrelation occurs with varying severity for each SAR sensor. A longer wavelength platform is less sensitive to small changes of scattering properties, so L-band and S-band sensors are less susceptible to temporal decorrelation, while C-band and X-band sensors are easily contaminated by phase noises caused by temporal decorrelation [28; 34; 39; 145; 146; 147]. [PERSON] and [PERSON] [32] showed that to achieve complete decorrelation, it requires 10 cm RMS motion of scatterers for L-band sensors, while it only needs 2-3 cm for C-band sensors. That said, it is about four times easier for C-band sensors to completely lose coherence than L-band sensors.