esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid RF/VLC Network Architecture for the Internet of Things
## 2 Related Work
Nevertheless, most works are not designed under the premises of low power, a low rate, and a long range, as the architecture proposed in this work. Authors in [24] proposed an optimization problem that minimized the total energy consumed by LED lamps when used for data transmission. However, the system did not provide long-range communications such as that in the present work, where LoRa is used as the backbone. Finally, [PERSON] et al. proposed in [25] an architecture very similar to the one presented in this work. LiFi was proposed as an enabling technology for IoT in indoor environments. Nonetheless, the system does not allow for integration with other networks, and in order to provide a solution, the authors simply stated that the integration can be performed at the IP layer by defining an adaptation layer between IPv6 and the LiFi link layer. However, this should comprise header compression, optimized neighbor discovery, and fragmentation, and the authors do not provide any solution in this regard. The work presented in this paper proposes not only a theoretical layered architecture as [PERSON]'s, but also an actual implementation (including the cited fragmentation mechanism), demonstrating the feasibility of this heterogeneous network approach for IoT.
## 3 Control Units of the Hybrid MG
### Control Units of PEMFC System  
Several factors, including temperature, pressure, and the partial pressures of hydrogen and oxygen, influence the MPP of PEM fuel cells. These factors collectively determine the optimal operating point of the fuel cell, where energy conversion is most efficient. Given PEM fuel cells' inherently nonlinear voltage-current characteristics, the integration of MPPT within PEMFC-based MGs is crucial. This integration is essential to adapt to the varying conditions of operation, maximize energy extraction, and enhance the overall efficiency and reliability of the system. To achieve this, PEM fuel cells are typically connected to DC-DC converters, which are key in adjusting the input resistance to match the maximum power point. In this study, a boost converter is interfaced with the PEMFC, as depicted in Figure 5, to perform this adjustment. The boost converter increases the output voltage to a level suitable for subsequent power conversion stages and ensures that the system operates at its maximum power point.  
To optimize the performance of the boost converter, the P&O algorithm is employed, as illustrated in Figure 4. This algorithm is a widely adopted MPPT technique that continuously adjusts the operating point of the PEMFC to maximize power output under varying environmental and load conditions. The P&O algorithm works by perturbing the system's operating point and observing the resulting changes in power output. If the power increases, the perturbation is continued in the same direction; otherwise, it is reversed. A PI controller regulates the reference voltage signal calculated by the P&O algorithm to further refine the system's response. The PI controller ensures that the system maintains the desired operating conditions by minimizing the error between the actual and reference signals, thereby enhancing the overall stability and efficiency of the PEMFC-based microgrid.
## 3 Context-Aware Positioning Model
### Context Sensing
#### 3.1.1 Characterizing the Physical Environment
We decided to use an XML representation not only because it is a standard format, but also because it is flexible and allows for information integration using well-known mechanisms. Thus, several feature vectors can be integrated into a large one, involving low computing effort. Given the diversity of organizations participating in disaster relief efforts, counting on a common representation of the shared information is mandatory to ensure data interoperability. In this sense, the use of XML to represent the shared information seems to be the best option. Note that for purposes of the model, access points are treated as stationary devices, and therefore each has its own feature vector. Once this task is completed, the XML file is sent to the next stage, _i.e._, the management of contextual information.
# Exploring the Effectiveness of the Phase Features on Double Compressed AMR Speech Detection
## 6 Experimental Setup
### Performance Criteria  
Given that DC AMR speech signal detection involves a binary classification task aimed at distinguishing between DC signals and those of SC, the evaluation of system performance revolves around true positive and true negative rates (TPR and TNR, respectively). In this context, the DC speech class is designated as the positive class, while the SC speech class is regarded as the negative class. TPR, also referred to as sensitivity, represents the probability of a DC signal being correctly classified as such by the system. It is computed by  
\[\text{TPR}[\%]=\frac{\text{TP}}{\text{TP}+\text{FN}}\times 100. \tag{6}\]  
where TP and FN are true positive and false negative values, respectively. TNR, also known as specificity, in turn denotes the probability of classifying a signal as belonging to the SC class given that the signal actually is SC. It is computed by  
\[\text{TNR}[\%]=\frac{\text{TN}}{\text{TN}+\text{FP}}\times 100 \tag{7}\]  
where TN and FP correspond to true negative and false positive values, respectively.  
In addition to the TPR and TNR evaluation metrics, the accuracy criterion is employed to demonstrate the overall performance of the system. It is computed by  
\[\text{Acc}[\%]=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}}\times 100 \tag{8}\]
# On Resolvability- and Domination-Related Parameters of Complete Multipartite Graphs
## 5 An Interaction between Resolvability and Domination
### Metric-Locating-Dominating Number  
In this section, we compute the metric-locating-dominating number of complete multipartite graphs.  
First, we show the following lemma.  
**Lemma 1**.: _Let \(\Gamma\) be a connected graph such that either \(\beta(\Gamma)=r=\gamma(\Gamma)\) or \(\beta(\Gamma)=r=\gamma_{l-d}(\Gamma)\). Then, \(\gamma_{mld}(\Gamma)=r\)._  
Proof.: We divide our discussion into the following two cases:  
**Case 1:** Assume \(\beta(\Gamma)=r=\gamma(\Gamma)\).  
This implies that there exists a metric-locating-dominating set on \(r\) number of vertices, since both the metric dimension and domination number equal to \(r\). Moreover, such a metric-locating-dominating number is minimum because there exists a minimum resolving set and a minimum dominating set, both having cardinality \(r\). This implies that \(\gamma_{mld}(\Gamma)=r\).  
**Case 2:** Assume \(\beta(\Gamma)=r=\gamma_{l-d}(\Gamma)\).  
There exists a dominating set of cardinality \(r\), since \(\gamma_{l-d}(\Gamma)=r\). Moreover, \(\Gamma\) has a minimum resolving set of cardinality \(r\), since \(\beta(\Gamma)=r\). Thus, there exists a metric-locating-dominating set of cardinality \(r\), say \(M\). Note that \(M\) is minimum as the resolving set of cardinality \(r\) is minimum in \(\Gamma\). This shows that \(\gamma_{mld}(\Gamma)=r\), which completes the proof.  
By Lemma 1 and Theorems 4.1 and 4.1, we have the following corollary.  
**Theorem 5**.: _Let \(K_{t_{1},t_{2},\ldots,t_{r}}\) be the complete \(r\)-partite graph with \(1\leq t_{1}\leq t_{2}\leq\ldots\leq t_{r}\) and \(\sum\limits_{i=1}^{r}t_{i}=n\). Then:_  
\[\gamma_{mld}(K_{t_{1},t_{2},\ldots,t_{r}})=\sum\limits_{i=1}^{r}t_{i}-r.\]  
The following example illustrates an application of Theorem 5.  
**Example 7**.: _Let \(K_{2,3,5}\) be the complete multipartite graph as depicted in Figure 3. Then, its seven black vertices form a minimum metric locating-dominating set. By Theorem 9, we obtain that \(\gamma_{mid}(K_{2,3,5})=10-3=7\)._  
Theorem 9 computes the metric-locating-dominating number of complete multipartite graphs, which is the first result of its kind in the theory of metric-locating-dominating number of graphs.
###### Abstract  
As spectral resolution increases, the dimension of the Doppler Asymmetric Spatial Heterodyne (DASH) interferometer increases. The existing approach for stably mounting the interferometer is limited to mounting a normal-sized DASH interferometer. In this study, a novel and stable structure is proposed, with its effectilveness exemplified for a near-infrared (NIR) DASH interferometer. The mathematical model of a flexible structure was established. The parameters of the support structure were optimized by requiring the mechanical stress of the flexible structure and shear stress at the bonding surface to be less than the strength value. The spring constants were optimally designed to adjust natural frequency and minimize stress. The finite element analysis (FEA) results show that the maximum mechanical stress was 65.56 MPa. The maximum shear stress was 3.4 MPa. All stress values had a high safety margin. The mechanical material and adhesive area were optimally designed. Therefore, the thermal resistance of the structure was improved by 7.5 times. The test results indicate that the proposed flexible support structure could satisfy the requirements of the launch environment. The results from FEA and vibration tests were consistent with the model calculation results. Compared to existing structures, the mechanical performance and thermal resistance were improved.  
DASH interferometer; spectral resolution; optimal structure; spring constant; mechanical stress; shear stress
# Non-Mypopic Beam Scheduling for Multiple Smart-Target Tracking in Phased Array Radar Networks
## 4 Simulation and Analysis
### Performance Results with Multi-Dimensional States
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Target Type**} & \multirow{2}{*}{**Policies**} & \multicolumn{2}{c}{\(K=1\)} & \multicolumn{2}{c}{\(K=2\)} & \multicolumn{2}{c}{\(K=3\)} \\ \cline{3-8}  & & **Cost** & **AWRMSE (m)** & **Cost** & **AWRMSE (m)** & **Cost** & **AWRMSE (m)** \\ \hline \multirow{4}{*}{Reckless} & US-MP index & **269,150** & **110.09** & **219,078** & **74.89** & **195,336** & **61.78** \\  & US-MP index & **(16.84\%)** & **(32.09\%)** & **(12.48\%)** & **(18.24\%)** & **(9.32\%)** & **(10.97\%)** \\  & myopic & 323,655 & 162,12 & 250,311 & 91.61 & 215,410 & 69.40 \\  & / & / & / & / & / & / \\  & TEC & 280,613 & 119.43 & 227,659 & 78.69 & 200,572 & 62.93 \\  & (13.29\%) & (26.33\%) & (9.05\%) & (14.10\%) & (6.88\%) & (9.32\%) \\ \hline \multirow{4}{*}{Cautious} & US-MP index & **274,512** & **92.07** & **217,766** & **60.05** & **193,309** & **48.61** \\  & & **(13.32\%)** & **(30.32\%)** & **(13.08\%)** & **(28.15\%)** & **(7.47\%)** & **(13.29\%)** \\  & myopic & 316,710 & 132.13 & 250,550 & 83.59 & 208,912 & 56.07 \\  & / & / & / & / & / & / \\  & TEC & 280,745 & 98.84 & 225,040 & 65.42 & 196,884 & 50.53 \\  & (11.36\%) & (25.19\%) & (10.18\%) & (21.73\%) & (5.76\%) & (9.88\%) \\ \hline \multirow{4}{*}{Reckless and \multirow{4}{*}{Cautious}} & US-MP index & **268,109** & **97.75** & **212,290** & **64.84** & **186857** & **52.69** \\  & **(14.89\%)** & **(25.15\%)** & **(12.60\%)** & **(21.51\%)** & **(8.76\%)** & **(12.13\%)** \\ \cline{1-1}  & \multirow{2}{*}{myopic} & 315,013 & 130.61 & 242,897 & 82.61 & 204,795 & 59.96 \\ \cline{1-1}  & & / & / & / & / & / & / \\ \cline{1-1}  & TEC & 277,192 & 103.29 & 220,694 & 68.41 & 190,387 & 54.08 \\ \cline{1-1}  & & (12.01\%) & (20.91\%) & (9.14\%) & (17.19\%) & (7.03\%) & (9.81\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performances with different numbers of radars for \(N=8\) targets with multi-dimensional states. Figure 8: WRMSE of policies for reckless and cautious targets. (**a**) \(K=1\) (**b**) \(K=2\) (**c**) \(K=3\). Figure 7: WRMSE of policies for cautious targets. (**a**) \(K=1\) (**b**) \(K=2\) (**c**) \(K=3\). simulations are shown in Figures 9 and 10. As the number of radars and targets increases, the US-MP index policy obtains better tracking performance in all of the cases. Above all, the adapted US-MP index policy improves the non-myopic beam scheduling performance and outperforms other greedy policies. Numerical simulation results with different parameters of the problem model indicate the superiority of this policy. However, the indexability validation and the lower bound need further calculations and verifications. Additionally, for other optimization metrics, such as Bayesian CRLB [4], PCRLB [12], the associated state transition process can still be modeled using the US method. Therefore, the US-MP index policy can be extended to accommodate the new optimization objective functions.
# A Geostationary Instrument Simulator for Aerosol Observing System Simulation Experiments
## 2 Methodology and Data
### Surface Reflectance Models
For the 354 and 388 nm channels, which fall outside of the range of the MODIS observations, we used a global 0.25\({}^{\circ}\) monthly climatology of Lambertian surface reflectance that was based on TOMS observations ([PERSON], personal communication) interpolated to the satellite grids. Figure 8 shows the December Lambertian surface reflectance from the climatology dataset on the three instrument domains. \[\rho_{iso}=1 \tag{2}\]2 Simulated surface reflectance according to the MAIAC BRDF retrieval at 670 nm for the TEMPO sun-sensor geometry at 17–22 Z on 31 December 2005. Changes in the surface reflectance throughout the day are due to the bidirectional nature of the surface reflectivity.
# Auditing Accessibility of Pavements and Points of Interest in Urban Areas: The 'Seek & Go' Tool +
## 5 Technical Approach
### Handling Offline Content  
The 'Seek & Go' application is designed and implemented to facilitate concurrent usage by multiple users who have installed it on their tablet or smartphone with the appropriate screen size. Notably, the implementation of the 'Seek & Go' application takes into account the possibility of unstable internet connectivity, such as when users enter underground parking lots, buildings, or elevators. Moreover, such a design feature ensures access to users who may encounter internet connection issues due to economic or other social complications, thus enabling them to engage in the process and upload to the system when they obtain access to free or unlimited Wi-Fi.  
In this light, the 'Seek & Go' application has been engineered to function even without internet access, utilizing a progressive web app approach, which employs smart caching and the browser's local storage to maintain collected data. Additionally, a mechanism for continuously monitoring content updates was introduced to ensure that local data remain in synchronization with the central database.  
In the event of connectivity issues, users may experience a lack of synchronization of their collected data. However, through the use of the 'Seek & Go' application, users can store and take photos offline, with the ability to upload them to the server either as a batch selection or per survey on demand. It is important to note that special management is required for the handling of photos locally and offline. To comply with local storage limitations, photos are encoded to a Base64 sequence on the fly. Upon upload to the server, the photos are decoded to their original binary form and stored as JPG image files. This transformation process also helps to reduce the actual size of the photos in terms of bytes on the server. Additionally, public links to the uploaded images are available in various resolutions, including thumbnail, medium, and original.  
One limitation of the current version of the application implementation approach is the challenge of supporting high-resolution images generated by advanced mobile cameras, which is predominantly determined by device specifications. To mitigate the issue of handling extremely large photos without encountering memory errors, particularly on low-end devices, users may opt to choose a lower resolution in their camera settings or frequently synchronize and then clear the local storage of their devices after each synchronization. Experienced users can only delete local storage through the front-end UI as an additional security layer against unintended data loss.
## 6 Conclusions  
To solve the drawbacks of synchronous FL and asynchronous FL, we propose a semi-synchronous FL, i.e., DecantFed, which performs the following: (1) Jointly clusters clients into different tiers and allocates bandwidth to different tiers so that the clients in different tiers would have different deadlines/frequencies to upload their local models; (2) Dynamically allocates training workload in terms of training data samples to different clients to enable high computational capacity clients to derive better local models, while keeping the clients in their original tiers. Simulation results demonstrate that the model accuracy incurred by DecantFed and FedAvg is similar but is much higher than FedProx. The convergence rate over time incurred by DecantFed is much faster than FedAvg. In addition, the deadline is an important parameter that significantly influences the performance of DecantFed, and we will investigate how to dynamically adjust the deadline to maximize the performance of DecantFed in the future. Finally, our results demonstrate that dynamic workload optimization for clients is vital to improving FL performance. In future work, we aim to conduct an ablation study to assess the contribution of individual design components, including client clustering and bandwidth allocation, dynamic workload optimization, and dynamic learning rate, to the overall performance of DecantFed. This analysis will provide further insights into optimizing federated learning models under semi-synchronous and asynchronous settings. Such studies are expected to guide more efficient strategies tailored to heterogeneous client capabilities.  
Figure 5: Comparison of DecantFed (with dynamic workload optimization) and UniformDecant (without dynamic workload optimization) for \(\beta=0.1\) and \(\beta=1\).  
**Author Contributions:** Conceptualization, L.Y. and X.S.; Methodology, L.Y. and X.S.; Software, L.Y.; Validation, L.Y.; Formal analysis, L.Y.; Investigation, L.Y.; Resources, L.Y.; Data curation, L.Y.; Writing--original draft, L.Y.; Writing--review & editing, L.Y., X.S., R.A., C.P. and S.S.; Visualization, L.Y.; Supervision, X.S.; Project administration, X.S.; Funding acquisition, X.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Science Foundation under Award under grant no. CNS-2323050, OIA-2417062, and CNS-2148178, where CNS-2148178 is supported in part by funds from federal agencies and industry partners as specified in the Resilient & Intelligent NextG Systems (RINGS) program.  
**Data Availability Statement:** The data presented in this study are openly available in [[https://arxiv.org/abs/2403.06900](https://arxiv.org/abs/2403.06900)]([https://arxiv.org/abs/2403.06900](https://arxiv.org/abs/2403.06900)) (accessed on 20 November 2024).  
**Conflicts of Interest:** 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.
###### Abstract  
Directional modulation (DM) technique has the ability to enhance the physical layer security (PLS) of wireless communications. Conventional DM schemes are usually based on a single antenna array with the basic assumption that eavesdroppers (Eves) and legitimate users (LUs) are in different directions. However, it is possible that Eves are in the same direction as LUs in practical applications. As a result, signals received by Eves will be approximately the same or even in better quality than those received by LUs. To address the neighbor security issue, we introduce a multiple antenna arrays model at the transmitter side with the help of the artificial noise (AN)-aided DM technique to achieve secure and precise DM transmission in this paper. Meanwhile, to recover the mixed useful signals, two novel DM schemes based on single- and multi-carrier multiple antenna arrays model are proposed, respectively. In addition, the symbol error rate (SER), secrecy rate, and robustness performance of the proposed DM schemes were analyzed and simulated. Simulations validate the effectiveness of the proposed DM schemes and demonstrate that multiple antenna arrays model based DM methods outperform single antenna array model aided DM methods in security.  
directed modulation; physical layer security; multiple antenna arrays; artificial noise; robustness +
Footnote †: journal: Sensors
## 3 Results and Discussion
### Optical Characterization of the Discharge  
It was visible with the naked eye that the color of the discharge was dependent on the treated liquid. The typical bluish/pinkish color of a non-thermal plasma operated in air changed to greenish when saline solution was treated instead of distilled water or tap water. The assumption that this was attributed to the emission of sodium was verified by optical emission spectroscopy (Figure 5). Significant emission of the Na-D-line at 589 nm (for the performed investigation it was not necessary to resolve the sodium doublet) was detected in the case of saline solution as the liquid. Although the nitrogen (N\({}_{2}\)) emissions dominated the spectra, for tap water and saline solution, small emissions of nitrogen monoxide (NO) were also detected. Additionally, it was clearly shown that in the case of distilled water the general emission intensity was the lowest in good agreement with the lowest discharge current.
## 3 Results and Discussion
### Optimization of the Electrochemical Aptasensor
#### 3.2.2 Optimization of Blocking and Reaction Time  
Normally, ethanolamine or 5% BSA is used to block the electrode. The use of BSA might interfere with our result. Therefore, ethanolamine was used as a blocking reagent in  
Figure 4: Optimization of (**A**) aptamer concentration, (**B**) immobilization time for aptamer, and (**C**) concentration of blocking solution, and (**D**) blocking time with 0.1 M ethanolamine.  
this study. Different concentrations of ethanolamine (0.5, 0.1, and 0.01 M) were screened for optimal concentrations. According to SWV results, using 0.1 M ethanolamine for blocking produced the largest current changes and most reproducible results (Figure 4C). Time required for blocking was also optimized. Blocking with 0.1 M ethanolamine for 15 min was enough to cover unbound aptamer on the sensor surface (Figure 4D). The incubation time for the target protein solution was optimized (data not shown). Incubation for less than 30 min was insufficient for aptamer and target binding. However, binding was achieved at 40 min with appropriate EIS and SWV signals.