esa-sceva/satcom-chunk-collection huggingface dataset:
# A Testbed for GNSS-Based Positioning and Navigation Technologies in Smart Cities:
## 4 Hansel Services
### Front-End/Back-End Service (FBS)  
The Front-end and Back-end (i.e., FBS service) is hosted in the central server of the _testbed_. It is connected with every positioning service of the _testbed_ and communicates with them in order to provide the necessary input data (input parameters, setup, etc.), as shown in the scheme in Figure 6. It also receives the output data of each service and processes it accordingly to the different application requirements (displaying data, configuration, and geographical information of services).  
The FBS is a graphical user interface to provide an easy, intuitive, and clean way of maintaining and configuring the services and visualising the output of each of them. Figure 7 shows a screenshot of the _testbed_ dashboard summarizing on a map all the users of each service. FBS is fed from the data mainly from the API of each service depending on the particular characteristics of each of them. The API is responsible for the communication between the server and all services. It is bidirectional, being able to both, post and get data from and to the services.The front-end visualisation is customised to adapt to each particular service requirements.  
Figure 6: Schema of the front-end role into the Hansel _testbed_.  
Figure 7: Dashboard of the front-end showing the _testbed_ map, satellite constellation, and shortcuts to all services.  
FBS also provides a database service with all the information related with operators (i.e., authentication, permissions) and locations (geographical info, configuration). This allows the _testbed_ operators to manage the _testbed_ users and its usage permissions.
## Appendix A
#### a.1.3. Olb  
Given the equivalence between the number of tasks and the number of packets, the energy consumption results for tasks in OLB [15] naturally apply to their corresponding number of packets. Thus, for 500,000 packets, the energy consumption is reflective of 8 KWH. For example, when considering a mere 20 packets, this energy consumption translates to (8 \(\times\) 20)/500,000 KWH, which further converts to (8 \(\times\) 3600 \(\times\) 20)/500,000 KJ, resulting in 1.15 KJ.  
Similarly, for 1,000,000 packets, the energy consumption aligns with 18 KWH. Thus, for 40 packets, the energy consumption calculation stands at (18 \(\times\) 40)/1,000,000 KWH or (18 \(\times\) 40 \(\times\) 3600)/1,000,000 KJ, yielding 2.6 KJ.  
In a similar vein, when dealing with 1,500,000 packets, the energy consumption approximates 24 KWH. Consequently, for 60 packets, the energy consumption computes as (24 \(\times\) 60)/1,500,000 KWH, which equates to (24 \(\times\) 60 \(\times\) 3600)/1,500,000 KJ, culminating in 3.4 KJ.  
Similarly, for 2,000,000 packets, the energy consumption aligns with 29 KWH. Correspondingly, for 80 packets, the energy consumption computation stands at (29 \(\times\) 80)/2,000,000 KWH or (29 \(\times\) 80 \(\times\) 3600)/2,000,000 KJ, yielding 4.1 KJ.  
Finally, for 2,500,000 packets, the energy consumption is in the vicinity of 35 KWH. Correspondingly, for 100 packets, the energy consumption translates to (35 \(\times\) 100)/2,500,000 KWH, resulting in (35 \(\times\) 100 \(\times\) 3600)/2,500,000 KJ, amounting to 5.04 KJ.  
This coherent pattern persists, demonstrating a consistent relationship between energy consumption and the number of tasks (or packets), highlighting the scalability and predictability of the presented results.
# DC Converter with Wide Soft Switching Operation, Wide Input Voltage and Low Current Ripple
## 6 Conclusions  
A new wide voltage operation \(LLC\) converter with current-fed input is presented and experimented to realize soft switching operation and input current ripple reduction. A hybrid \(LLC\) resonant converter with a half-bridge-type or full-bridge-type structure is employed to realize wide input voltage operation. Owing to the fundamental leg voltage of the full-bridge-type resonant circuit being double the leg voltage of the half-bridge-type resonant circuit, a 4:1 input (\(V_{in,\ max}=4V_{in,\ min}\)) \(LLC\) converter is achieved in the presented converter. Two interleaved boost circuits are used at the input side to reduce input current ripple. Owing to the circuit characteristics of the \(LLC\) converter, all switches can turn on at zero-voltage switching. The proposed single-stage current-fed hybrid \(LLC\) converter has less switch components. To verify the effectiveness of the presented circuit, a design procedure of the prototype circuit is presented first to obtain the circuit components. Finally, the experimental verifications are provided to show the circuit performance. Due to the wide input voltage operation, the converter at the low input voltage condition has the serious power losses compared to the high input voltage case. Therefore, the selection of power devices and the design of the magnetic components are very important to achieve a high efficiency converter. These issues will be analyzed and investigated in the future work for the studied converter.  
[PERSON] proposed and designed this project and wrote this paper. [PERSON] measures the circuit waveforms in the experiment. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Acknowledgments: This research is financial supported by the Ministry of Science and Technology (MOST), Taiwan, under grant number MOST 108-2221-E-224-022-MY2.**
## Numerical Implementation
### Grid and Time-Step Refinement Studies  
In this section, three sets of meshes are performed to do a grid convergence study as shown in Table 2. All the present numerical simulations are conducted for a minimum duration of 140 s (35 wave periods), which ensures that the stable results can be obtained.  
\begin{table}
\begin{tabular}{c c} \hline
**Case** & **Transverse Inclined Angle \(\theta\) (\({}^{\circ}\))** \\ \hline A & 0 \\ B & 15 \\ C & 30 \\ D & 45 \\ E & 60 \\ F & 75 \\ \hline \end{tabular}
\end{table}
Table 1: Overview of six cases with different transverse inclined angles (\(H\) = 1.3 m, \(T\) = 4.0 s, \(d\) = 3.8 m).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Mesh** & \begin{tabular}{c} **Near the Cylinder (m)** \\ **Max(Ax, \(\Delta y\), \(\Delta z\))** \\ \end{tabular} & \
\begin{tabular}{c} **At Wave Generator (m)** \\ **Max(Ax, \(\Delta y\), \(\Delta z\))** \\ \end{tabular} &
\begin{tabular}{c} **Number of Total Grids** \\ \end{tabular} \\ \hline Coarse mesh & (0.12, 0.1, 0.08) & (0.15, 0.10, 0.08) & 5,791,152 \\ Medium mesh & (0.10, 0.06, 0.05) & (0.12, 0.08, 0.05) & 7,787,600 \\ Fine mesh & (0.08, 0.06, 0.045) & (0.10, 0.08, 0.045) & 8,564,864 \\ \hline \end{tabular}
\end{table}
Table 2: The resolutions of three different meshes.  
Figure 2 shows the total horizontal breaking wave forces \(F_{T}\) on the vertical cylinder (\(\theta=0^{\circ}\)) with three different meshes over one wave period. The relative difference in peak value of the \(F_{T}\) between the fine mesh and the medium mesh is 2.2%, which is considered sufficiently accurate for the present simulations. Moreover, there finement study on time-step is also carried out by changing the maximum Courant number. It can be seen from Figure 3 that there is no significant improvement for \(F_{T}\) when the maximum Courant number decreases from 0.5 to 0.3. Therefore, the medium mesh with the maximum Courant number 0.5 is considered to give sufficient accuracy to capture the horizontal breaking wave forces and will be used for all other cases. The different views of the medium mesh near the vertical cylinder are shown in Figure 4.  
Figure 3: Time-step refinement study for the breaking wave forces on the vertical cylinder.  
Figure 2: Grid refinement study for the breaking wave forces on the vertical cylinder.
# Self-Organizing and Scalable Routing Protocol (SOSRP) for Underwater Acoustic Sensor Networks
###### Abstract  
Underwater Acoustic Sensor Networks (UASN) have two important limitations: a very aggressive (marine) environment, and the use of acoustic signals. This means that the techniques for terrestrial wireless sensor networks (WSN) are not applicable. This paper proposes a routing protocol called \"Self-Organizing and Scalable Routing Protocol\" (SOSRP) which is decentralized and based on tables residing in each node. A combination of the hop value to the collector node and the distance is used as a criterion to create routes leading to the sink node. The expected functions of the protocol include self-organization of the routes, tolerance to failures and detection of isolated nodes. Through the implementation of SOSRP in Matlab and a model of propagation and energy being appropriate for marine environment, performance results are obtained in different scenarios (varying both nodes and transmission range) that include parameters such as end-to-end packet delay, consumption of energy or length of the created routes (with and without failure). The results obtained show a stable, reliable and suitable operation for the deployment and operation of nodes in UASN networks.  
multi-hop routing; transmission range; distance; self-organization; energy consumption; end-to-end delay; fault tolerance; isolation recognition +
Footnote †: journal: Sensors
## 3 Uncertainties, Limitations, and Sources of Error in SEM Measurements
### Constraints in Phase Identification by EDS Spectra  
It is a common claim in SEM-based automated mineralogy studies that minerals can be detected, identified, and quantified by their characteristic EDS spectrum (an example is shown in Figure 9, indicating feldspar mineral albite [84]). However, this claim cannot be fully correct, as minerals are characterized by their lattice structure indicated by XRD first, and then comes the use of elemental composition information provided by EDS spectrum quantification. Therefore, mineral identification remains incomplete with use of the EDS spectrum only, based on its foundations on elemental composition. Identifying a mineral by chemical composition alone can be misdirecting, as there are examples of minerals with similar chemical compositions but different crystal structures based on the crystallization conditions of minerals. For instance, pseudorutile and ilmenite are both titanium-iron oxide minerals, but they exhibit different crystal structures [84].  
Another challenge in mineral detection, identification, and distinction using EDS spectra is that some minerals have very similar elemental compositions, such as hematite (Fe\({}_{2}\)O\({}_{3}\)) and magnetite (Fe\({}_{3}\)O\({}_{4}\)). Hematite is composed of 70% by weight Fe and 30% by weight O, while magnetite is made up of 72% by weight Fe and 28% by weight O. The EDS  
Figure 9: Classification modes of EDS spectra: (**a**) FEI-QEMSCAN, and (**b**) FEI-MLA for feldspar mineral albite [84]. CC-BY.  
spectra for both minerals appear to be very similar, and the very trivial differences in Fe and O peaks cannot be resolved by appearance. In such scenarios, it is a good idea to use the BSE image gray level as an additional distinguishing standard. It must be noted that for such a measurement, a specific BSE brightness and contrast calibration is needed. Another challenge is the detection range of EDS spectra, as it does not cover the whole elemental periodic system. For example, the first light elements cannot be detected by EDS, such as H, He, Li, and Be. It is, therefore, recommended to complement EDS spectra with XRD and XRF methodologies for mineral identification and quantification [84]. Other limitations of EDS spectra include longer mapping causing damage to the samples, low sensitivity of light elements, low quantitative accuracy, information about the chemical composition only (not about functional groups or chemical bonds), and overlapping peaks, making it difficult to distinguish among elements present in the sample.
## 4 Methods
### Node Feature Learning
#### 4.2.2 User Embedding Learning  
After learning the embeddings of all POIs, the user sub-trajectory \(T_{k}^{u_{i}}\in T_{u_{i}}\) is fed into the Lite-GRU-based model, where the sub-trajectory \(T_{k}^{u_{i}}\) contains \(T_{k}\) time steps, and \(x_{k,t}\) represents the feature input at time step \(t\) for sub-trajectory \(T_{k}^{u_{i}}\), i.e., the embedding of POI \(v\), \(\Psi_{v}^{final}\in\mathbb{R}^{d}\), where \(\mathbb{R}\) represents the set of real numbers.  
First, the hidden state \(h_{k,0}\in\mathbb{R}^{d}\) is initialized, and, then, at each time step \(t\), the reset gate \(r_{k,t}\) is computed as shown in Equation (2), the update gate \(z_{k,t}\) is calculated as shown in Equation (3), the candidate hidden state \(\overset{\sim}{h_{k,t}}\) is calculated as shown in Equation (4), and the hidden state \(h_{k,t}\) is updated as shown in Equation (5).  
To reduce computational redundancy and decrease the model storage requirements, we merge the reset gate and update gate into a single common gate by sharing the weight matrices. The computation method is shown in Equation (2):  
\[z_{k,t}=\sigma(W\cdot[h_{k,t-1},x_{k,t}]+b) \tag{2}\]  
To avoid the gradient vanishing problem of tanh, we use the ReLU activation function to compute the hidden state, which effectively improves the training performance of deep networks. The final hidden state \(\text{h}_{k,t}\) is calculated as shown in Equation (3):  
\[\text{h}_{k,t}=(1-\text{z}_{k,t})\cdot\text{h}_{k,t-1}+\text{z}_{k,t}\cdot \text{ReLU}(W_{h}\cdot[z_{k,t}\cdot h_{k,t-1},x_{k,t}]+b_{h}) \tag{3}\]  
where \(W\in\mathbb{R}^{d\times d}\), \(W_{h}\in\mathbb{R}^{d\times d}\) represent the shared weight matrix and the candidate hidden state weight matrix, respectively, and \(b\in\mathbb{R}^{d}\), \(b_{h}\in\mathbb{R}^{d}\) represent the biases for the merged  
Figure 2: Schematic of user trajectories. Different colors represent different users, while solid and dashed lines represent different sub-trajectories of the same user. On one day, a went to school, b went to the shop and bar, and c went to school. On another day, a went to the gym, b went to the restaurant and gym, and c went to the hospital and company, t hasm’t gone anywhere in the past two days.  
gate and candidate hidden state, respectively. \(\sigma(\cdot)\) is the nonlinear activation function of the neuron, the sigmoid function, whose calculation method is shown in Equation (4):  
\[f(x)=\frac{1}{1+e^{-x}} \tag{4}\]  
Finally, the embedding of sub-trajectory \(T_{k}^{u_{i}}\) is represented by the hidden state at the last time step \(h_{k,TK}\), and the embedding of user \(u_{i}\), \(E_{u_{i}}\), is obtained by averaging the embeddings of all sub-trajectories of user \(u_{i}\), as shown in Equation (5):  
\[\mathrm{E}_{u_{i}}=\frac{1}{N}\sum_{N}^{k=1}\mathrm{h}_{k} \tag{5}\]  
where \(E_{u_{i}}\) is the embedding of user \(u_{i}\), \(N\) is the total number of sub-trajectories for user \(u_{i}\), and \(h_{k}\) is the final hidden state of the \(k\)-th sub-trajectory of user \(u_{i}\).
## 5 Potential Benefits  
Potential benefits of the proposed prototype are derived from the analysis and exploitation of different data analysis techniques, as proposed in [28]. For example, by leveraging the capabilities for data analysis offered by the logical layer (ThingSpeak), it is possible to react to changes and make decisions that result in improved community services and more efficient management of existing resources. The resources analyzed were as follows:  
1. Optimization for dumpsters: the data provided by the sensor, placed in dumpsters, records the times when residents throw out the trash and determines the time of collection. The aim is to establish a schedule for the deposit of waste so that the containers are full for the shortest possible time, especially in summer.
2. Rationalization of water consumption for watering gardens: the analysis of soil moisture at different times of the year can rationalize the consumption of water by adjusting watering schedules according to the time of the year. In addition, possible future work could include installing sensors in order to activate sprinklers, based on humidity measured in real time.
3. Measurement of noise levels: within the established norms for hours in which noise levels should be considerably lower, or respecting the schedules for using the pool in summer. Sound sensors allow continuous logging of noise levels in those areas allowing measurement of the times when noise levels exceed the maximum allowed.
4. Optimizing power consumption: sensors enable electricity consumption to be read in real time for different devices. This allows users to adapt their consumption at times when rates are lower. In addition, the light sensors allow public lighting levels to be set depending on the light intensity at a given moment, allowing for a more efficient consumption without being subject to strict schedules.  
Figure 10: JSON data to graph Figure 9.  
5. Pollution levels: one of the current concerns of city dwellers is environmental pollution. SmartResidents can continuously record pollution levels based on different parameters that provide insightful statistics on times, days, or time intervals of contamination.
6. Swimming pool remote measurement: measuring oxidation-reduction potential (ORP), pH, and calorie levels of water can determine if the water quality in the swimming pool is sufficient for recreational purposes. Otherwise, the pool maintenance person will be alerted and will undertake the necessary actions to restore water sanitation.  
Use of certain statistical resources: managing a residential community means balancing neighborhood expenses as effectively as possible. With the SmartResidents platform, statistics on the use of certain common resources can be calculated, for example, the maintenance required by the paddle court, according to usage by residents. The contrast between the potential benefit and the prototype's low cost justify the investment. The cost of the hardware components needed for the prototype deployment came to $1400 (each sensor node costs $87 and each control unit costs $117).
# EMI Threat Assessment of UAV Data Link Based on Multi-Task CNN
## 2 EMS Measurement and Prediction
### Original Data Acquisition  
The state parameters and their corresponding I/Q data of the UAV's data link interfered with by the above three types of interference are collected. As shown in Figure 1, the state parameters, including \(p_{\text{AGC}}\), \(p_{\text{SNR}}\), \(p_{\text{BER}}\) indicate the automatic gain control (AGC) voltage, signal-to-noise ratio (SNR) and bit error rate (BER), respectively, which are collected from the monitoring software of the data link. Meanwhile, the I/Q data of the received signal of the data link are collected by an electromagnetic spectrum monitoring receiver connected to the receiving antenna.  
The electromagnetic spectrum monitoring receiver is a zero intermediate frequency receiver used to demodulate the received electromagnetic signal to obtain the I/Q data. After receiving the electromagnetic signal \(r(t)\), which contains operating signal \(s(t)\) and jamming signal \(j(t)\), the band selection of band-pass filter (BPF) and the amplification of low noise amplifier (LNA) are applied. The RF signal is down-converted to the baseband signal through the demodulator by mixing with the local oscillator (LO). Then the baseband signal is filtered by the low-pass filter (LPF) and amplified by the amplifier (AMP).  
In order to get the I/Q signal \(r_{I}(n)\) and \(r_{Q}(n)\), the analog to digital converter (ADC) converts continuous analog signal into discrete digital signal. The received signal \(r(n)\) can be expressed by  
Figure 1: Schematic of original data collection.  
\[r(n)=a(n)\cdot e^{j\phi(n)}=r_{I}(n)+j\cdot r_{Q}(n) \tag{1}\]  
where \(r_{I}(n)\) and \(r_{Q}(n)\) represent the in-phase and quadrature components of \(r(n)\), \(n\) is the length of the signal. \(a(n)\) and \(\phi(n)\) stand for the amplitude and phase of \(r(n)\), respectively.
# Interference Mitigation for GNSS Receivers Using FFT Excision Filtering Implemented on an FPGA
## 3 Proposed Methodology
### Degradation of the GNSS Receiver Performance  
There are various ways to monitor the degradation of GNSS receiver performances. The first way is at the signal level (i.e., bit error rate, carrier-to-noise ratio, monitoring phase/frequency lock loop, saturation phenomenon of AGC). The second way is at the observation level (i.e., noise on pseudoranges and carrier-phase observations) or the navigation level (i.e., position accuracy).  
The first step for the simulations and experiments was the collection of the raw data. The raw data was logged for a typical terrestrial scenario, with all GNSS satellites set to a power level of \(-130\) dBmW. The reason behind this power level is that the minimum received power level of GPS L1 C/A code (signed used) is \(-128.5\) dBmW (from IS-GPS-200-M), assuming a 3 dBi receiver antenna. Therefore, \(-130\) dBmW is slightly conservative but reasonable for a low-cost antenna. The power spectral density and bit distribution of the sampled signal is shown in Figure 6. It illustrates the raw data before the addition of the interference in three different representations, which are the frequency domain, the time domain, and the histogram representations. The sampling frequency, IF, and bandwidth used were determined by the utilized GNSS front end, which is defined by the GNSS datalogger (NSL Stereo). The interference frequencies were chosen to be in line with [13, 20] to provide a comparison against the notch filter approach, which is widely used in mass market receivers. The front end was configured to down-convert the GPS L1 C/A signal to an intermediate frequency (_IF_) of 6.5 MHz, with a 3 dB bandwidth of around 2.75 MHz and frequency sampling at 26 MHz with a 2-bits resolution. It is noticeable that there is a self-generated CW in-band at 6.235 MHz, 10 to 12 dB above the noise, but it is not strong enough to interfere with the GNSS tracking after the matched filter of the spreading code demodulation was applied. The RF data was quantized to 2 bits during logging. As shown in Figure 6, the time domain's amplitude fluctuated between negative and positive values. The signal was heavily quantised down to 2 bits, taking on values of (\(\pm 1\), \(\pm 3\)). This is common in GNSS receivers, where the long averaging times result in only a 0.5 dB impact on SNR for 2-bit quantisation. However, in the simulation, we allowed 8 bits of the ADC range to handle the interference. To simulate the automatic gain control of the RF front-end, the standard deviation of the signal was always adjusted to around one-third of the signed 8-bit range (\(128/3\cong 42\)). The 8-bit signed has 7 bits of data and one bit of the sign (\(2^{7}=128\)).  
Figure 5: Example of a frequency ramp for the type of sweeping frequency jammer.