esa-sceva/satcom-chunk-collection huggingface dataset:
# A Joint Dual-Frequency GNSS/SINS Deep-Coupled Navigation System for Polar Navigation
## 2 Availability Analysis of GNSS in Polar Areas and Grid SINS Mechanization  
### Coverage and Visibility of the GNSS Constellation in Polar Areas  
In this subsection, the coverage and visibility of the GPS constellation in polar areas are briefly reviewed; this subject was discussed in [20]. The elevation angle, visibility, and dilution of precision (DOP) values for GPS satellites at the position of \((90^{\circ}\ \mathrm{N},\ 0^{\circ}\ \mathrm{E},\ 0\ \mathrm{m})\) during one day are discussed as follows.  
Table 1 shows the visibility of all GPS satellites in polar areas during one day with mask angle change from \(10^{\circ}\), to \(20^{\circ}\), to \(30^{\circ}\), to \(40^{\circ}\). The results indicate that in polar areas, the visibility of GPS satellites is acceptable (11 satellites on average with mask angle \(10^{\circ}\)) but most of the satellites have lower elevation angles (only 3 satellites more than \(40^{\circ}\) on average).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mask Angles** & **Minimum** & **Maximum** & **Mean** \\ \hline \(10^{\circ}\) & 7 & 14 & 11 \\ \(20^{\circ}\) & 6 & 11 & 8 \\ \(30^{\circ}\) & 4 & 9 & 6 \\ \(40^{\circ}\) & 2 & 6 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: GPS satellite visibility for different mask angles.  
Table 2 show the DOP values of different schemes in polar areas with a mask angle of 10\({}^{\circ}\), where GDOP is the geometric dilution of precision, PDOP is the position dilution of precision, HDOP is the horizontal dilution of precision, VDOP is the vertical dilution of precision, and TDOP is the time dilution of precision. The results indicate that in polar areas, the GPS can provide a receivable coverage and geometric distribution.
## 2 Supervisory and Control System Based on Real-Time Power Management
### Energy Cost Optimization
#### 2.3.3 Grid Connection  
The smart grid transmits messages to IIREVs to respect the maximum grid supply \(P_{G\_S\_max}\) and injection \(P_{G\_I\_max}\) limits set by the grid [45], as in (11), where \(p_{G}(t_{i})=p_{G\_I}(t_{i})-p_{G\_S}(t_{i})\):  
\[-P_{G\_S\_max}\leq p_{G}(t_{i})\leq P_{G\_I\_max}. \tag{11}\]
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 5 BackCom: Wireless Communications
### BackCom Modulation Techniques
#### 5.2.1 Amplitude Modulation in BackCom  
The simple type of AM that is based on amplitude shift keying (ASK) is OOK. It shows binary data in the absence or presence of carrier signals. The waveform of the OOK backscattered signals is delineated in Figure 18, showing the fluctuation in magnitude, corresponding to 0 and 1. Similarly, OOK in AM is used with BiS to increase the range in BackCom [50]. [PERSON] et al. showed that the phase could be modulated in addition to AM [98].  
Figure 19 depicts a block diagram of an ASK and phase shift keying (PSK) modulator, consisting of two blocks. Load impedance \(Z_{1}\), in the first block, causes the reflection of signals after the modulation of data with the help of an \(S_{1}\) switch. A power rectifier in the second block supply constant power to the tag. Similarly, forward link demodulation in the second block detects incoming signals to extract the information with the help of logic and memory of the tag. Furthermore, a simple linear model for simulation of the complex reflection coefficient for RFID tags is considered based on an equivalent circuit diagram [110]. This model can further be utilized in derivations, to elaborate modulation by tags. [PERSON] et al. utilized M-PSK modulation with Amb-BackCom and derived equations for an optimal multi-level energy detector [111]. Likewise, a 4-PAM modulation scheme for Amb-BackCom was suggested for ultra-low power devices [112]. The quantization index modulation technique was presented for Amb-BackCom [113]. This technique embeds messages into wireless signals.
## 4 Proposed Framework
### Ontology Design and Deployment
#### 4.2.4. Local Ontology Alignment  
At each edge server, IoT node-collected information is merged with ontology through a systematic process as follows:  
* **Schema mapping:** Synchronizes local data items with ontology-created attributes, thus providing semantic homogeneity.
* **Unit standardization:** Converts raw measurement values to uniformly normalized units. For example, \[T^{\prime}=\frac{5}{9}(T-32),\quad\text{for temperatures initially in Fahrenheit.}\] (14)
* **Data enrichment:** Entails adding metadata, and in doing so, enriches semantic data representation and enables uniformity in all edge servers.
# Data Compression Based on Stacked RBM-AE Model for Wireless Sensor Networks
## 3 Results and Discussion
### Transfer Learning
For nodes located near node 7, such as node 6, node 8 and node 9, the reconstruction error varies little and the performance of the model does not  
Figure 11: The reconstruction error of each node on testing with different model parameters. Figure 9: The \(PRD\) value of each node on testing with different model parameters. decrease significantly by using model parameters that are not corresponding to the nodes themselves. The digital difference of reconstruction error of node 6, node 8 and node 9 is 0.0875 \({}^{\circ}\)C, 0.1127 \({}^{\circ}\)C and 0.0720 \({}^{\circ}\)C, respectively. This means that for some areas of the WSNs with strong spatial correlation, when a node in the area is trained, all nodes in the area can share the trained model parameters. These results prove that our algorithm has a good transfer learning ability. The error reduction speed during the model training helps judge whether the training is completed or not. When the training error value drops slowly and approaches a stable value, it means that the training is complete. In our experiments, when the training of the model was completed, the number of training iterations was mostly less than 200. This can further reduce the storage and communication energy consumption of node model parameters. The transfer learning ability can be applied to the design of clustering routing protocol in WSNs.
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Sea Ice (SI)
#### 2.2.3 Microwave Radiometer  
Due to the higher microwave radiation emitted by SI compared to clouds, microwave radiometers can gather data during day and night and regardless of the cloud condition. This feature makes microwave radiometers suitable for SI studies. The most important parameter determining the amount of microwave radiation emission from SI are its physicalproperties, such as atomic composition and crystalline structure [91]. Among different microwave radiometers, the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSMIS) are the most frequently used radiometers for SI mapping and monitoring [92]. Moreover, the Scanning Multichannel Microwave Radiometer (SMMR), Advanced Microwave Scanning Radiometer for EOS (AMSR-E), and Advanced Microwave Scanning Radiometer 2 (AMSR2) have provided valuable data for SI studies. For instance, Ref. [92] retrieved SI concentration from microwave radiometer data. The National Aeronautics and Space Administration (NASA) Team algorithm and the artist SI algorithm were utilized in this study to retrieve SI concentration. The developed algorithm was applied to the brightness temperatures measured by the SSM/I instrument in different channels. Furthermore, the Wide Swath Mode ASAR images with 150 m \(\times\) 150 m spatial resolution and MODIS band-1 images with 250 m \(\times\) 250 m spatial resolution were used for the evaluation.  
The main limitation of microwave radiometers is their relatively coarse spatial resolution due to the low emitted microwave radiation. The coarse spatial resolution restricts many SI applications, such as SI lead detection, and increases the mixed-pixels problem [92; 93].
## 5 Case Study
### Generic Architecture for CPS Framework  
To comply with the above requirements, the architecture that was chosen to support this application was based on that presented by the authors in [52], which is a generic architecture for a CPS Framework. Using this architecture, as presented in Figure 1, it is possible to perform the integration of any type of device and send its data in real time, as required. It also allows for the integration of data sinks such as data visualization interfaces, databases, business logic implementation applications, etc.  
This architecture comprises a central component (Mediator) that routes information between Devices (connected to Device Ecosystems) and Data Sinks. This component also routes commands between components that can actuate in devices (Device Actuation) and the CPS devices. For data exchange between the components of the architecture and the Mediator, a set of Interfaces was defined. Device Ecosystems use the Device Interface to send data to the mediator and to receive commands from it. For each data transmission protocol that needs to be supported, a Device Ecosystem must exist. Devices that use the same protocol can share the same Device Ecosystem (independently of their make or model), therefore making the integration components of the architecture reusable.  
Besides this integration at the protocol level, integration at the data compatibility level must also be ensured. To accomplish this, an interface to connect Data Format Translators (the Translation Interface) was defined. Data from devices are translated into a common format that can be then sent to the data sinks (using the Data Interface) or translated again into a particular format needed by the Data Sink.  
Because CPS devices need to receive commands (e.g., an actuation command or a configuration-related request), a Command interface was designed.  
With this architecture, if we have N device types and M data sink types, instead of having N \(\times\) M integration solutions, which would result in an ad-hoc integration between device and data sink, we will have N + M integration solutions.  
A reference implementation of this Framework was developed, where the Mediator component was implemented using Java and packed in a Docker container, making it ready  
Figure 1: Generic architecture for CPS Framework.  
to use and integrated in projects that require this component. To make the integration easier, two integration libraries were developed, one using Java and another in Python. These two libraries implement the communication interfaces, using gRPC, and allow system integrators not to need to care about the details of the communications between the architecture components.
# Lateral Stability of a Mobile Robot Utilizing an Active Adjustable Suspension
###### Abstract  
Mobile robots are expected to traverse on unstructured terrain, especially uneven terrain, or to climb obstacles or slopes. This paper analyzes one such passively-actively transformable mobile robot that is principally aimed at the above issue. A passive locomotion traverses on a rough and flat terrain; an active reconfiguration with an active suspension. This paper investigates the lateral stability of this mobile robot when it reconfigures itself to adjust its roll angle with the active suspension. The principles and configurations of the robot and its active suspension are presented. To analyze the effects of the suspensions' inputs on robot stability, a mathematic model of the robot on side slopes is presented. Based on the evaluation method of the stability pyramid theory, an analytical expression representing the relationship between the input of the active suspension (linear actuator length) and stability evaluation index on transverse slopes is obtained. The results show that there is an increase in both the lateral stability and minimum lateral tip-over angle under different ground clearances when adjusting the active inputs. Furthermore, the models presented here provide theoretical references and optimization directions for the design and control of mobile robots with adjustable suspensions.  
stability; active suspension; mobile robot; reconfigurable robot; kinematics Article
## 4 Results and Discussions
### Mechanical Response Considering Softening Effect
#### 4.2.3 Failure Characteristics  
The heterogeneity of minerals and meso-structures within rocks has a significant impact on the initiation and propagation of cracks, as well as the macroscopic failure patterns. Since the failure modes of the numerical models under different immersion times exhibit a high degree of similarity, the heterogeneous model in its natural state is taken as an example to investigate the influence of heterogeneity on the failure characteristics of soft rock. The crack types and failure modes under uniaxial compression and Brazilian splitting conditions are shown in Figure 14.  
Figure 14: Diagram of crack types in heterogeneous model: (**a**) uniaxial compression; (**b**) Brazilian splitting. Note: Q—quartz; F—feldspar; C—cement.  
Figure 13: The relationship between damage parameters and immersion time in heterogeneous models: (**a**) crack threshold and its damage factor; and (**b**) tensile and shear crack ratio and damage degree.  
Generally, the failure of the numerical model is the result of the combined action of tensile cracks (T) and shear cracks (S), both of which can be comprehensively determined by the direction of displacement between block units and the magnitude of displacement on either side of the crack. According to Figure 14a, the failure mode can be observed as \"X-type conjugate shear failure\" and the tensile cracks can be observed locally within the model. According to Figure 14b, it can be seen that the model is primarily tensile fracture. It is worth mentioning that under uniaxial compression, tensile and shear cracks can occur at six types of contact: quartz-quartz, quartz-feldspar, quartz-cement, feldspar-feldspar, feldspar-cement, and cement-cement, among which the three types related to the cement are the main locations where cracks are generated. In the Brazilian splitting numerical simulation, cracks mainly occur at three types of contacts: quartz-cement, feldspar-cement, and cement-cement. The fundamental reason for the above phenomena is that the mechanical properties of cement and the strength of such contact type are significantly lower than those of quartz and feldspar. Therefore, the direction of crack propagation is closely related to the heterogeneity of material composition and contact types.
# Optimal Power Dispatch of an Offshore Wind Farm under Generator Fault
## 2 Wind Farm Model
### Wind Turbine Model  
The WT model used here is a static model, which is based on the modified NREL 5-MW turbine model in SimWindFarm Simulink toolbox [18], as shown in Figure 2. The input of the model is power reference, \(P_{\text{r}}\), and wind speed, \(v\). The WTC adopted the region control [19] in the non-down-regulation condition and the maximum \(\omega\) strategy [20] in the down-regulation condition. According to the WTC, when the wind speed and power reference are determined, the references of pitch angle, \(\beta\), and TSR, \(\lambda\), can be obtained. Then, \(\beta\) and \(\lambda\) will determine the specific power coefficient, \(C_{\text{p}}\), and thrust coefficient, \(C_{\text{t}}\). Both \(C_{\text{p}}\) and \(C_{\text{t}}\) are functions of \(\beta\) and \(\lambda\). They are derived from the \(C_{\text{p}}\) and \(C_{\text{t}}\) surfaces. The output of the WT model is the power output and \(C_{\text{t}}\). The power output can be expressed as:  
\[P=\frac{1}{2}\rho\pi R^{2}v^{3}C_{\text{p}}\eta \tag{1}\]  
where \(\rho\) is the air density, \(R\) is the radius of the turbine rotor, and \(\eta\) is the conversion efficiency. Figure 3 shows the power output of the WT in the non-down-regulation condition.  
Because power dispatch and power optimization are involved in this paper, the down-regulation strategy of WTs has to be mentioned. The down-regulation strategy directly affects the operation state of the WT and further affects the aerodynamic interaction among the WTs. This is due to the fact that the \(C_{\text{t}}\) values are different when the \(C_{\text{p}}\) values are the same under different down-regulation strategies. The maximum \(\omega\) strategy was adopted in the WT model. In this strategy, the rotor speed is set as the maximum value when the WT is down-regulated. One of the advantages of this strategy is that  
Figure 3: Power curve of the wind turbine in the non-down-regulation condition.  
Figure 2: Wind turbine static model.  
some of the energy is converted to the kinetic energy of the rotor. When the power reference is set to a higher value, the power recovery is fast. The specific operation state of the WT in this strategy is shown in Figure 4.  
The Figure 4a-d are the pitch angle, rotor speed, and \(C_{\text{p}}\) and \(C_{\text{t}}\) surfaces, which respectively show the operation points of these four important operating parameters of the WT in the non-down-regulation and down-regulation conditions. The x axis is the wind speed, the y axis the power reference, and the z axis the respective surface for each graph. It can be seen from Figure 4b that when the power reference is less than the available power, the rotor speed is adjusted to the maximum value.
# Towards Mitigating Jellyfish Attacks Based on Honesty Metrics in V2X Autonomous Networks
## 5 Result Analysis and Performance Evaluation  
In this section, we present the tools, parameters, and metrics used to create the network scenario and validate our proposed SecE-V2X protocol for defending against Jellyfish attacks. The simulator for urban mobility (SUMO) [52] was employed to generate nodes' mobility traffic urban scenarios, as shown in Figure 8. INET [53] and vehicles in network simulation (Veins) [54] frameworks were developed using the objective modular network testbed in C++ (OMNET++) network simulator [55], and are used for communication between nodes.  
The simulation scenario used is an urban environment of 2500 m \(\times\) 2500 m in Erlangen. The speeds of the nodes vary from 0 to 14 m/s. We perform each simulation both under and without attacks for 500 s with a node density of 100 nodes. The main parameters of our simulation are summarized in Table 4.  
The proposed protocol was tested against all three Jellyfish attack types for various numbers of malicious nodes (0, 3, 6, and 9). The malicious nodes are placed in the middle of node pools without any collusion. Figure 9 shows a real-time simulation scenario with a simulation time of 200 s, where malicious nodes are identified in red. We compare the performance of our proposed protocol with two secured routing approaches from [37], 2017, and [20], 2018, while considering the effects of malicious nodes on the network. We re-simulated the algorithms in the mentioned approaches in order to ensure a correct and fair comparison in terms of tools, network density, mobility models, node speed, etc. As Jellyfish attacks degrade network performance and reduce all flows' throughputs by dropping and delaying packets, through this experimental study we intend to evaluate the efficiency of SecE-V2X in terms of optimizing nodes' packet delivery ratios as well as data throughputs, in addition to reducing the transmission delay.
# Water IoT Monitoring System for Aquaponics Health and Fishery Applications
## 3 Materials and Methods
### Analysis of Requirements for Aquaponics System
The metrics indicated with * are metrics which should be measured using a cheap colorimetric test in providing \(NH_{4}\), \(NO_{2}\), and \(NO_{3}\) measures during the establishment phase of the aquaponics system. After that establishment, they will need to be measured manually, only if the \(EC\) and \(ORP\) values are not in a normal range. In terms of communication, the floor plan is extended within an area greater than 200 m\({}^{2}\), but the system should be designed to cover up to 1 km\({}^{2}\), as the farmers control room, i.e., where the central system is located and where the sensors will be located, cannot be assumed; thus, an average coverage of around 1 km\({}^{2}\) should be supported by the system. The technical specifications for the sensors included in the node are shown in Table 4. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & **Meas. Range** & **Resolution** & **Accuracy** & **Response Time** & **Data Protoc.** & **Function.Temp** \\ \hline DO (AtlasSci.) & 0.01–100 mg/L & & \(\pm\)0.05 mg/L & 1 read per 420 ms & UART/12C & \(-\)40 \(\lx@math@degree\)C to 88 \(\lx@math@degree\)C (Typ. 25 \(\lx@math@degree\)C) \\ \cline{2-6} pH (AtlasSci.) & 0.001–14.000 & 0.001 & \(\pm\)0.002 & 1 read per 420 ms & UART/12C & \(-\)40 \(\lx@math@degree\)C to 8\(\lx@math@degree\)C (Typ. 25 \(\lx@math@degree\)C) \\ \cline{2-6} EC (AtlasSci.) & 0.07–500,000 uS/cm & 0.07 uS/cm & \(\pm\)2\% & 1 read per 420 ms & UART/12C & \(-\)40 \(\lx@math@degree\)C to 85 \(\lx@math@degree\)C (Typ. 25 \(\lx@math@degree\)C) \\ \cline{2-6} ORP (AtlasSci.) & \(-\)1019.9–1019.9 mV & 0.9 mV & \(\pm\)1 mV & 1 read per 420 ms & UART/12C & \(-\)40 \(\lx@math@degree\)C to 8\(\lx@math@degree\)C (Typ. 25 \(\lx@math@degree\)C) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Technical specifications for Sensors. \begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **Type A** & **Type B** & **Type A + B** & **Minimal A + B** \\ \hline \(pH\) & X & X & X & X \\ \(EC\) & X & X & X & \\ \(DO\) & X & & X & X \\ \(TDS\) & & & X & X \\ \(Temp\) & X & & X & X \\ \(NH_{4}^{+}\) & X & & X & * \\ \(NH_{3}\) & & X & X & X \\ \(ORP\) & X & & X & * \\ \(NO_{2}\) & & X & X & * \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sensor Types and Monitoring Parameters.
# Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey
## 4 Resource Allocation
### Allocation Based on RL
The approach operated with a binary space of states where one indicated interference in the resource block assigned to a small cell base station of a particular tenant and zero the contrary. The action space was defined as the power level to allocate. Ref. [56] presented a Q-learning-based, dynamic, and autonomous computing resource allocation scheme intended to optimize the latency, energy consumption, and cost in 5G Fog-RAN. The scheme modeled the space of states as a vector comprising the allocated compute resource, average CPU utilization, and CPU reservation. The space of actions was defined regarding the resources to allocate at the Fog-RAN node level. Ref. [52] presented a dynamic resource allocation framework based on PPO (proximal policy optimization) and intended to maximize resource efficiency while meeting QoS in end-to-end NSL in multilayer mobile edge computing environments. The framework modeled the space of states considering service type, the utilization of resources allocated in the edge nodes, and the ratio of offloaded workload in the edge-cloud continuum. The actions corresponded to tuning resources size (increased or decreased CPU and bandwidth) in a chosen node. Ref. [57] introduced a resource allocation framework based on a two-stage Q-learning algorithm for increasing operators' revenue in a multitenant 5G network slicing environment. The first stage was devised to perform VNF mapping using a space of states based on the number of server nodes and computing resources available. The space of actions represented the association between a VNF and a physical server node. The second stage was conceived to carry out user association and power allocation, including a space of states associated with available radio resources and actions modeled as a set of vectors composed by the users and their corresponding power.