esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This paper presents and implements an innovative electronic driver designed to power a UV-C LED disinfection and sterilization lamp. The lamp comprises ten UV-C LED modules connected in parallel, powered by a unique single-stage, single-switch power conversion circuit that combines a modified buck converter with a flyback converter. Additionally, this driver recycles energy stored in the transformer's leakage inductance, enhancing circuit efficiency. A prototype driver circuit was developed and tested, achieving a DC-input voltage of 18 V, rated at 3.3 W of power, a 10 V output, and a 330 mA current. The experimental results show efficiency above 91%, confirming the driver's effectiveness for UV-C LED disinfection and sterilization applications.  
[PERSON] developed the circuit topology of the lamp driver and designed the methodology; [PERSON], [PERSON] and [PERSON] arranged the software resources, set up the simulation, and performed the circuit simulations for the proposed lamp driver; [PERSON] and [PERSON] implemented the prototype circuit of the proposed lamp driver, and carried out the measurements of the lamp driver circuit as well as analyzing the experimental results with guidance from [PERSON]; [PERSON] prepared the first draft of the article and revised the manuscript for submission. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science and Technology Council (NSTC) of Taiwan for its grant numbers NSTC 112-2813-C-214-039-E, NSTC 112-2221-E-214-005, and NSTC 113-2221-E-214-020.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available in the article.  
**Acknowledgments:** The authors would like to convey their sincerest gratitude to National Science and Technology Council of Taiwan for supporting this study (grant numbers NSTC 112-2813-C-214-039-E, NSTC 112-2221-E-214-005, and NSTC 113-2221-E-214-020).  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Existing DC-DC LED Driver in Ref. [27]** & **Proposed DC-DC LED Driver** \\ \hline Circuit Structure & Modified buck converter with a coupled inductors & Modified buck–flyback converter \\ DC-Input Voltage & 12 V & 18 V \\ Output Power (Output Voltage/Current) & 8 W (8 V/1 A) & 3.3 W (10 V/0.33 A) \\ Number of Power Switches Needed & 1 & 1 \\ Number of Capacitors Needed & 2 & 2 \\ Number of Magnetic Elements Needed & 1 & 1 \\ Number of Diodes Needed & 3 & 2 \\ Measured Circuit Efficiency & 91\% & 91.22\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: A comparison between the existing DC-DC LED driver in [27] and the proposed one.
# Analysis of Multi-Antenna GNSS Receiver Performance under Jamming Attacks
###### Abstract  
Although antenna array-based Global Navigation Satellite System (GNSS) receivers can be used to mitigate both narrowband and wideband electronic interference sources, measurement distortions induced by array processing methods are not suitable for high precision applications. The measurement distortions have an adverse effect on the carrier phase ambiguity resolution, affecting the navigation solution. Depending on the array attitude information availability and calibration parameters, different spatial processing methods can be implemented although they distort carrier phase measurements in some cases. This paper provides a detailed investigation of the effect of different array processing techniques on array-based GNSS receiver measurements and navigation performance. The main novelty of the paper is to provide a thorough analysis of array-based GNSS receivers employing different beamforming techniques from tracking to navigation solution. Two beamforming techniques, namely Power Minimization (PM) and Minimum Power Distortionless Response (MPDR), are being investigated. In the tracking domain, the carrier Doppler, Phase Lock Indicator (PLI), and Carrier-to-Noise Ratio (C/N\({}_{0}\)) are analyzed. Pseudorange and carrier phase measurement distortions and carrier phase position performance are also evaluated. Performance analyses results from simulated GNSS signals and field tests are provided.  
GNSS; array processing; measurement distortions; carrier phase positioning Article
# A Decentralized Receiver in Gaussian Interference
## Appendix A Optimization Method  
Evaluation of the Gaussian distortion and distributed compression bounds in each environment requires optimization of a quasi-convex objective (Remark 4). In the case of distributed compression, the space of acceptable parameters is not convex: consider compressing the encodings for two highly-correlated sources. By the Slepian-Wolf theorem, if the encodings are high-bitrate, then perturbing the encodings' bitrate does not greatly affect the region of feasible compression rates, since the encodings have high redundancy. On the other hand, perturbing the bitrate of low-bitrate encodings greatly affects the region of feasible compression rates, since uncorrelated distortion from encoding dominates source redundancies. Then, a compression to an average between minimum feasible compression rates for low-bitrate encodings and ones for high-bitrate encodings is infeasible, and the distance from the feasible set becomes large as the difference between high and low bitrates is made large. This is the mechanism that governs \(\mathcal{R}_{\overline{DC}}(\mathbf{r})\subseteq\mathbb{R}_{+}^{K}\), hence the domain's non-convexity.  
To overcome this, an iterative interior point method was used to find the maximum: each constraint \(f(x)<0\) is replaced with a stricter constraint, \(f(x)+\beta<0\) for some \(\beta>0\) and a minimization of the objective is performed under the new constraint. The optimal point is passed as the initial guess for the next iteration, where the objective function is minimized with updated constraints \(f(x)+\beta^{\prime}<0\) with \(0<\beta^{\prime}<\beta\). The method is iterated until the constraint is practically equivalent to \(f(x)<0\) or the optimal point converged. Each individual minimization was performed using sequential least-squares programming (SLSQP) through SciPy [26]. The code for this is included in the Supplementary Materials.
## 3 Adaptive TrimTree Mechanism  
This section presents the design of Adaptive TrimTree mechanism. Some of the key design features of Adaptive TrimTree are as follows:  
* A simple traffic-and-topology-based heuristic for resource consolidation: Resource consolidation saves energy by switching off the redundant network resources. We opt for a simple heuristic based on traffic analyzer output and DCN topology.
* Selective connectedness for traffic anomalies thus avoiding performance loss due to sudden traffic demands: Resource consolidation can oversimplify the need for redundant resources therefore identifying additional required resources and keeping those in sleep mode to be invoked as required provides better service guarantee.
* Link rate adaption of active network subset based on buffer occupancy and link utilization to scale the data rates of switches thus making the network subset energy proportional.  
The system design of our proposed mechanism is shown in Figure 1. It comprises of four modules; TrimTree, Route Update, Power Management and Rate Adaption. The first module \"TrimTree\" identifies two network subsets called \"active network subset\" and \"passive network subset\". The active network subset is a minimum segment of network to meet the current traffic demand based on the network topology and traffic. The passive network subset is a set of switches and links which can be required to meet future traffic demands and anomalies. This information is shared with the \"Route Update\" and \"Power Management\" modules. The \"Route Update\" module updates routing tables for the new network subset (active + passive). The \"Power Management\" module physically implements the active and passive networks by switching the active components to ON state, passive components to sleep state and shutting OFF the remaining network components. The \"Rate Adaption\" module adapts the link data rates of \"on\" components in accordance with the load thus maximizing energy savings.
## 5 Results
### Peak Throughput
#### 5.2.1 Laboratory  
For this environment, which presents higher spectrum saturation in the 2.4 GHz and 5 GHz bands, all technologies detailed in Table 3 have been evaluated. The results are presented separately in Figure 8 to account for the distinct performance characteristics of each frequency band. Specifically, on the left side of Figure 8, the results obtained for the 2.4 GHz band are shown, while on the right side, results for the 5 GHz and 6 GHz bands are shown. In the 2.4 GHz band, it is observed that the results for WiFi\({}^{7}\)_2.420 technology are slightly lower than those for WiFi\({}^{6}\)_2.420 technology. This result demonstrates that, even under optimal conditions, the achieved bitrates are at least 30% lower than the theoretical maximum bitrate. In the 5 GHz band, better results are obtained with WiFi\({}^{7}\)_580 technology than with WiFi\({}^{6}\)_5160. On the other hand, it is worth noting high peak throughput results obtained in both 5 GHz and 6 GHz bands due to low or no spectrum saturation in this environment, reaching up to 940 Mbps with WiFi\({}^{6E}\)_6160 technology.
# A Fine Fast Acquisition Scheme for a Communication and Navigation Fusion System
## 5 Simulation Results and Analysis
### Performance Analysis
#### 5.2.1 Detection Performance
Analysis above shows that the DD-MAC/TC-CACFAR method can realize a high probability of detection and stable low probability of false alarm (probability of error), which can effectively reduce MAT.
# A Systematic Survey on 5G and 6G Security Considerations, Challenges, Trends, and Research Areas
## 3 5G Security Architecture: A Systematic Approach
### Core Network
#### 3.1.2 Key Exchange  
As discussed in the previous subsection on authentication, 5G key exchanges are performed via two primary protocols: 5G-AKA and EAP-AKA. During an initial connection to the network, the UE uses its secret key stored in the USIM and the same key stored in the CN, specifically in the UDM (Unified Data Management) function, as a shared secret. Unlike previous methods in 3G and 4G LTE, the UE will send its (SUPI) concealed and encrypted by way of the SUCI (Subscriber Concealed Identifier) to offer heightened security. Once the device is admitted to the network, the device is secured via derived keys, ultimately generating a master root session key KAUSF (Key [from] Authentication Server Function) [27].  
One concern with key generation is the duration and updating of keys in order to maintain confidentiality. 3 GPP TS 33.501 [2] defines the following events when keys are updated:  
1. The UE establishes a new PDU session or modifies an existing session.
2. The UE is handed over to a new base station or RAN cell.
3. The UE moves to a different serving network.
4. The UE performs a registration or de-registration from the network.
5. The UE requests a key change, or the network requests a key change, put in place by the operator's policy.  
If a UE is solely connected to one base station without disconnection or handover for a prolonged amount of time, TS 33.501 specifies that the network shall request a key change every 24 h. It is also stated that the amount of time can be reduced at the discretion of the network operator.
## 4 Conclusions  
1. The concentrations of H\({}_{2}\)S increased with the increase in temperature and time. The aquathermolysis reaction resulted in the presence of H\({}_{2}\)S, CO\({}_{2}\), and H\({}_{2}\) in the gas products. The effects of the oil-water ratio, rock minerals, and pressure on the concentrations of H\({}_{2}\)S need to be further investigated, and the oil products after the aquathermolysis need to be comprehensively analyzed in future research.
2. The established kinetic model can be used to predict H\({}_{2}\)S production during the CSS processes. The activation energy and frequency factor were estimated to be 76.12 KJ/mol and 404.03 1/day, respectively.
3. The H\({}_{2}\)S concentrations increased from 0.77 ppm to 1.94 ppm during the one-dimensional CSS experiment. As the CSS cycle increased, the oil production gradually dropped while the water cut gradually increased. The oil recovery at the end of the eighth cycle reached 40.10%.
4. The measured and simulated H\({}_{2}\)S production had a good consistency, indicating that the developed model has high precision in predicting H\({}_{2}\)S production during CSS processes.
5. The H\({}_{2}\)S production increased with the increase in soaking time, steam quality, and steam injection volume due to the strengthened aquathermolysis reactions. Based on the reservoir simulations, the H\({}_{2}\)S production was predicted in the range of 228 m\({}^{3}\) to 2895 m\({}^{3}\) within the parameters of this study.  
Conceptualization, T.W.; methodology, T.W. and R.Y.; formal analysis, L.Z. and W.Z.; investigation, Y.S. and Y.B.; data curation, Y.S.; writing--original draft preparation, T.W. and W.Z.; writing--review and editing, T.W. and L.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science and Technology Major Project of the Ministry of Science and Technology of China (2016 ZX05025-004) and the Major Science and Technology of China National Offshore Oil Corporation (YXXY-ZX 06 2021).  
Not applicable.  
Data Availability Statement: Data sharing is not applicable to this article.  
The authors declare no conflict of interest.  
Figure 12: Effects of steam volumes on H\({}_{2}\)S production.
## 5 Convergence and Complexity Analysis
### Complexity  
The complexity of the algorithm directly affects its performance. In the algorithms adopted, the complexity of the hierarchical clustering algorithm can be calculated according to the connection algorithm, similarity measurement criteria and hierarchical grouping process, and the algorithm complexity can be expressed as \(O\big{(}LK^{2}N\big{)}\). The complexity of the joint user scheduling and hybrid beamforming design algorithm is closely related to the number of multicast groups and scheduling users. In addition, the number of optimization variables and constraints in the CCCP algorithm and the penalty iteration algorithm can also affect the complexity [38]. In the problem \(Q_{10}\), the number of optimization variables is \(2\sum\limits_{l=1}^{L}|U_{l}|+2L\), the number of convex constraints is \(\sum\limits_{l=1}^{L}|U_{l}|\) and the number of linear constraints is \(\sum\limits_{l=1}^{L}|U_{l}|+3L\). Let the number of iterations in the penalty iteration algorithm and the CCCP algorithm be \(I_{p}\) and \(L_{c}\), respectively. In conclusion, the overall complexity of the proposed algorithm is \(O\bigg{(}LK^{2}N+I_{p}I_{c}\bigg{(}2\sum\limits_{l=1}^{L}|U_{l}|+2L\bigg{)} \bigg{(}2\sum\limits_{l=1}^{L}|U_{l}|+3L\bigg{)}\bigg{)}\).  
According to the algorithm complexity, the proposed joint user scheduling and hybrid beamforming design algorithm has a strong timeliness in small dimensional communication systems. However, for large dimensional communication systems, such as the satellite communication system, the number of active users is usually large. According to the complexity analysis, with the increase in the total number of active users, the convergence speed of the algorithm would gradually slow down, and the complexity would gradually increase. Considering the characteristics of the LEO satellite communication system, the delay caused by the high complexity is unacceptable, which would affect the overall performance of the communication system. To handle this problem, considering the balance of the algorithm performance and complexity, before the joint user scheduling and hybrid beamforming design, we can appropriately reduce the system dimension by adding the user preselection step in the algorithm process. The user preselection step can reduce the total number of active users in each transmission, and then we carry out the joint user scheduling and hybrid beamforming design for preselected users.
## 3 Materials and Methods
### Optical Images (Sentinel-2/MSI and Sentinel-3/OLCI)  
Within this study 33 S3/OLCI and 27 S2/MSI images acquired from July and October 2018 have been analyzed. The 60 optical images are analyzed to map chlorophyll a (Chl-a) concentrations using two different band ratio algorithms applied to atmospherically-corrected data. Given the very low transparency typical of the waters of the Curonian Lagoon [32] and the tendency of algae dispersed in the water column to accumulate near the surface, satellite-based estimates of Chl-a concentrations are presumably relevant only for the upper layer of water and thus can be considered as a proxy of the scum presence. Indeed the shallow, weakly stratified lagoon remains very turbid due to solids (mainly sand and silt) being resuspended as a result of water mixing caused by local winds and intensive primary production [33; 34]. The Secchi disk depth varies from 0.3 to 2.2 m [29] and is always relatively low, so it is impossible to see the bottom at this shallow optical depth.  
The atmospheric correction is carried out using the Second Simulation of the Satellite Signal in the Solar Spectrum-Vector code (6 SV) [35] previously used in other satellite applications for the Curonian Lagoon [20]. The parametrization of 6 SV code in case of the Curonian Lagoon is performed with Aerosol Optical Thickness (AOT) values gathered from AERONET station Gustav Dalen Tower located in the Baltic Sea and with water vapour values retrieved from daily MODIS products, via NASA Giovanni interface [36]. Chl-a concentration is produced after the application of a semi-empirical band-ratio model that uses the reflectance in the red and near-infrared (NIR) spectral regions [21; 37]. The scum presence is calculated according to the approach by [PERSON] et al. [20], with the ratio between reflectances in the near infrared (NIR) range. As indicated in INFORM D5.15 [38] (p. 140), Equation (1) is used for MSI  
\[\text{Chl-a}\left[\frac{\text{mg}}{\text{m}^{3}}\right]=(76.36\pm 2.29)\frac{ \text{Ref}_{705}}{\text{Ref}_{665}}-(51.57\pm 0.26), \tag{1}\]  
and Equation (2) for OLCI  
\[\text{Chl-a}\left[\frac{\text{mg}}{\text{m}^{3}}\right]=(52.19\pm 1.81)\frac{ \text{Ref}_{708}}{\text{Ref}_{665}}-(32.07\pm 0.57), \tag{2}\]  
where \(Ref_{x}\) indicates the reflectance of the band with central wavelength \(x\).
## 4 Experimental Results and Analysis  
### Performance on Training Datasets through Cross-Validation  
The CNN in the proposed CnnRBP model contains several hyperparameters. To tune these hyperparameters to achieve the best prediction performance, we carried out 10-fold cross-validation with different hyperparameters on the training datasets for three species separately. The details are described as follows:  
Firstly, the training dataset is divided into 10 independent subsets, in which the proportion of the positive to the negative is the same as that in the whole dataset. Then, one subset is taken as the test set, and the remaining nine subsets are taken together as the training set. This procedure is carried out 10 times to make each subset the test set once. Finally, for each validation, we use the SMOTE algorithm to balance the training set to train a CnnRBP model and then perform predictions on the test set with the trained model. The averaged prediction results for all 10-time validations are reported.  
By means of 10-fold cross-validation, we empirically explore several hyperparameters including the number of filters in the convolution layer (\(filters\) = 32, 64, 128), kernel size (\(k\) = 1, 3, 5), optimizer and learning rate (\(lr\) = 0.001, 0.002, 0.003, 0.004). As a result, the number of filters is set to 128. The kernel sizes of the five convolution layers are set to 5, 5, 3, 3 and 1, respectively. The learning rate is set to 0.001, and the Adam optimizer is selected as the optimizer. The prediction results with the above settings are shown in Table 2 through 10-fold cross-validation of the training datasets.  
As shown in Table 2, our models achieve AUC values over 96% on the three species datasets. More specifically, our models achieve ACC, MCC and AUC values on the human dataset of 99.91%, 99.81% and 99.98%, respectively. On the _E. coli_ dataset, the values of the ACC, MCC and AUC are 99.08%, 97.95% and 99.69%, respectively; On the Salmonella dataset, the values of the ACC, MCC and AUC are 95.34%, 89.79% and 96.72%, respectively. In addition, our models achieve BACC values of 99.88%, 98.72% and 94.60% on the three species datasets, respectively. As a comparison, TriPepSVM reports BACC values of 75.7%  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & **ACC (\%)** & **PRE (\%)** & **SEN (\%)** & **SPE (\%)** & **F1 (\%)** & **BACC (\%)** & **MCC (\%)** & **AUC (\%)** \\ \hline Human & 99.91 & 99.96 & 99.78 & 99.98 & 99.87 & 99.88 & 99.81 & 99.98 \\ _E. coli_ & 99.08 & 99.59 & 97.65 & 99.79 & 98.57 & 98.72 & 97.95 & 99.69 \\ Salmonella & 95.34 & 94.09 & 92.34 & 96.86 & 92.85 & 94.60 & 89.79 & 96.72 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance on training datasets through 10-fold cross-validation.  
on the human dataset, 84.1% on the _E. coli_ dataset and 82.7% on the Salmonella dataset through 10-fold cross-validation in their supplementary materials. This shows that the proposed method can achieve a great overall performance on unbalanced datasets.
## 4 Application in the City of Dortmund
### System Dynamics Simulation Model
#### 4.1.3 Model Formulation  
From a quantitative perspective, we present the evaluation of the APL components. Based on the CLD, we built the SFD, as shown in Figure 6. First, the variables of market size, potential e-customers, purchases per month, and APL users are defined as stocks (squared). Then, population growth rate, e-shoppers growth rate, online purchase rate, and APL market growth rate are defined as flows. Finally, population, accessibility, service level, average purchase per month, APL market share, and number of deliveries are auxiliary variables. The main output in this model is the number of deliveries, which are used as input values in the FLP model.  
Figure 5: The automated parcel locker (APL) system causal-loop diagram.
## 2 Background
### CTI Standard  
To share the CTI records among different organizations and individuals, there are several CTI standards populated by security organizations--e.g., OpenIoC [15], CybOX [16], and STIX [17]. After several years' development, the structured threat information expression (STIX) 2.0 standard [18] is published as the next version of OpenIoC and STIX.  
To share detailed information about cybersecurity threats, STIX 2.0 defines 12 STIX domain objects (SDOs)--including 'indicator','malware','report', 'vulnerability', and so on--which categorize each piece of information with specific attributes to be populated. Meanwhile, the STIX 2.0 defines two STIX relationship objects (SROs), which can chain multiple SDOs together through relationships and allow for easy or complex representations of CTI. An indicator SDO is shown in Figure 1. To describe the SDOs in detail, there are several properties defined. Some properties are common properties that can be used in all STIX SDOs and SROs, like type, id, created_by_ref. The other properties are specific properties that represent specific properties of the SDOs. As is shown in Figure 1, the pattern property is a specific property of Indicator SDO.