esa-sceva/satcom-chunk-collection huggingface dataset:
# Monolitic Hybrid Transmitter-Receiver Lens for Rotary On-Axis Communications
###### Abstract  
High-speed rotary communication links exhibit high complexity and require challenging assembly tolerances. This article investigates the use of optical wireless communications (OWC) for on-axis rotary communication scenarios. First, OWC is compared with other state-of-the-art technologies. Different realization approaches for bidirectional, full-duplex links are discussed. For the most promising approach, a monolithic hybrid transmitter-receiver lens is designed by ray mapping methodology. Ray tracing simulations are used to study the alignment-depended receiver power level and to determine the effect of optical crosstalk. Over a distance of 12.5 mm, the lens achieves an optical power level at the receiver of \(-16.2\) dBm to \(-8.7\) dBm even for misalignments up to \(3\) mm.  
hybrid lens; optical wireless communications; Li-Fi; freeform lens; optic design; rotary interfaces; rotary joint; wireless rotary electrical interface; rotating electrical connectors; full-duplex data transfer; Gigabit-Ethernet; industrial communications; real-time +
Footnote †: journal: Applied Sciences
# Towards Collaborative and Dynamic Spectrum Sharing via Interpretation of Spectrum Access Policies
## 4 Ontology
### A DSA Ontology
Any of the quantities can be considered as a dimension of such a space. Each hyperspace defines its frequency range, time interval and the geospatial region, to which the associated rule is applied and more. While the first two properties are defined using the SpectralSPARQL concepts, the latter is defined using GeoSPARQL. An example of a policy is shown in Figure 7 later in the paper, after we discuss policies.
# Simulation and Evaluation of the Radiation Dose Deposited in Human Tissues by Atmospheric Neutrons
## 3 Results and Discussion
In polar zones (\(\sim\)80\({}^{\circ}\)), the flux of cosmic rays is greater due to the low-rigidity cutoff which implies a greater production of atmospheric neutrons and consequently a higher RD deposited in the tissue samples. The RD in polar zones is approximately 4.3 times higher than the RD in equatorial zones for an altitude of 10 km. The statistical errors of the data presented in Figures 4 and 5 are dominated by the propagation of the statistical error that is generated when calculating the neutron flux from the energy spectra, therefore, they present variations with the altitude and the latitude. We also found that statistical errors calculated as a percentage are the same for all tissue samples used. The statistical errors of the RD deposited in each tissue sample of Figure 4 are 5.0%, 4.9%, 4.8%, 4.9%, 5.1%, 5.4% and 5.7% for altitudes of 0, 1, 2, 3, 4, 6, 8 and 10 km above  
Figure 4: Radiation dose deposited in blood, adipose, brain and bone tissue samples by atmospheric neutrons with energies between 1 and 1000 MeV, as a function of the altitude for a latitude of 0\({}^{\circ}\) and a longitude of 0\({}^{\circ}\). This result was obtained by a numerical simulation, the RD was calculated for a period of one hour in spherical samples with a radius of 5 cm. See text for more detail. Figure 5: Radiation dose deposited in blood, adipose, brain and bone tissue samples by atmospheric neutrons with energies between 1 and 1000 MeV, as a function of the latitude for a altitude of 10 km and a longitude of 0\({}^{\circ}\). This result was obtained by a numerical simulation, the RD was calculated for a period of one hour in spherical samples with a radius of 5 cm. See text for more detail. sea level, respectively, while for the data in Figure 5 are 5.7%, 5.7%, 5.7%, 5.6%, 5.4%, 5.2%, 5.0%, 5.0% and 5.0% for latitudes of 0\({}^{\circ}\), 10\({}^{\circ}\), 20\({}^{\circ}\), 30\({}^{\circ}\), 40\({}^{\circ}\), 50\({}^{\circ}\), 60\({}^{\circ}\), 70\({}^{\circ}\) and 80\({}^{\circ}\), respectively.
## 4 Enhancements to ShinyAnonymizer Hashing Techniques
### Implementation of Advanced Techniques such as Hashing
#### 4.1.1 MD5  
* Description: MD5 is a common cryptographic hash algorithm that creates a 128-bit hash value, which is often expressed as a 32-character hexadecimal integer. It was intended to be a function with one direction, so it is computationally difficult to go through the steps and recover the initial data from the hash value.
* Application: MD5 is frequently utilized as a checksum for confirming data integrity. But because of weaknesses allowing for hash collisions (in which two distinct inputs exchange the same hash), MD5 is rarely suggested for secure applications demanding cryptographic power [19].
## 5 Conclusions  
The ASPSHS under the zero-state response control strategy can effectively improve the indoor thermal environment of farmhouses, reduce operating energy consumption and carbon emissions. The main research conclusions of this paper are as follows:  
1. The temperature difference of an attached sunspace with a room provides power for passive heating. The zero-state response control strategy of the interior active window should be related to the temperature difference. The temperature difference is about 10 \({}^{\circ}\)C, which can provide better heating effect.
2. During the day, an attached sunspace's passive heating can offer a more suitable thermal environment for the farmhouse. When solar radiation is intensive, opening the interior active window can raise the internal temperature to an average of 25.45 \({}^{\circ}\)C higher than the outside, with a 23% discernible indoor average temperature differential indoor.
3. Using an ASPSHS with a zero-state response control strategy, farmhouses in cold places can maintain an internal temperature of 14 \({}^{\circ}\)C for more than 37.99% of the time in winter and meet 73.33% of the daytime heating time.
4. The farmhouse with attached sunspace under zero-state response control strategy can greatly reduce operating energy consumption and carbon emissions. Compared with a farmhouse without an attached sunspace, the energy for heating can be saved by 39.86%; compared to a farmhouse with an attached sunspace but not under the zero-state response control strategy, the energy for heating can be saved by 24.06%, and carbon emission reduction can be increased by 51.73%.  
Conceptualization, J.Z., S.L. and D.L.; methodology, J.Z. and D.L.; software, D.L.; validation, formal analysis, D.L.; investigation, D.L.; experiments, D.L.; data curation, D.L.; writing--original draft preparation, D.L.; writing--review and editing, S.L.; visualization, D.L.; supervision, S.L.; project administration, J.Z.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by \"National Key R&D Program of China\" (Grant No.2018 YFD1100700).  
Not applicable.  
The authors declare no conflict of interest.
## 5 Results and Discussion
### RQ1: What State-of-the-Art Methods Exist for Optimizing LoRaWAN Energy Efficiency?  
To answer RQ1, we have reviewed various studies that have presented state-of-the-art methods and techniques for LoRaWAN optimization. These included various DL, ML, and RL techniques and algorithms. Several datasets and simulators have been used by various authors in their research studies. DL and NN techniques have shown significant improvement in optimizing various aspects of LoRaWAN networks. For instance, author [32] has applied ANNs to optimize transmit power, and in [33], author utilized a GRU for dynamic resource allocation in LoRaWAN. The author [34] proposed the AI-ERA approach using DNNs to enhance resource assignment in IoT applications. A study [35] used ML with NNs to improve indoor localization accuracy within LoRaWAN networks. In terms of RL techniques, authors [36] used DRL to optimize network resources and [37] developed an adaptable RL-based strategy to optimize LoRaWAN's ADR function. Authors [25; 31] utilized supervised learning to predict optimal network parameters, and [46] introduced a Dynamic Transmission Priority Scheduling Technique based on an unsupervised learning clustering algorithm.  
Table 3 provides a summary of the DL and NN techniques adopted by the authors.
## 2 Materials and Methods
Elemental composition and mapping were measured by an EDS analyzer (X-MaxN) with a 20 mm2 SDD detector (Oxford instruments) and AZtecEnergy software. For the purposes of SEM and EDS measurements, the ceria layers on both the silicon glass and the alumina ceramics were coated by 5 nm of Au. Experiments assessing the catalytic activity of the nanoparticles were performed on a 2,4,6-trichlorophenol aqueous solution, which was measured with an Avantes AvaSpec UV-VIS spectroscope. For the experiment, 60 mg of sample TT-25, TT-600 and TT-1100 were put into three vials, and 10 mL of \(9.5\times 10^{-4}\) mol/L concentrated 2,4,6-trichlorophenol (TCP) aqueous solution was then added to each. The light source provided a combination of both deuterium and halogen discharge tubes, which covered wavelengths from 180 nm to 880 nm. All measurements were carried out in a 0.5 cm infrasil cuvette, and for the evaluation of data, Avasoft 8.2 software was used. The thermal behavior was analyzed by simultaneous thermal analysis (STA). The DTA and TG curves were recorded simultaneously on a Linseis STA PT1600 apparatus at a heating rate of 10 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\) in a dynamic air atmosphere (50 mL\(\cdot\)min\({}^{-1}\)).
## 3 Hybrid Network Implementation Using LoRaWAN and LoRaMESH
### Implementing Hybridization through DAP Device  
Based on the understanding of how the main communication metrics behave in the infrastructure using LoRaWAN and LoRaMESH topologies, this work has as main motivation to hybridize these two topologies in order to propose a scalable, long range and low cost communication infrastructure, making it feasible to be implemented in a real scenario. With this in mind, the implementation of Data Aggregator Point (DAP, from the  
Figure 21: Result of LoRaMESH scenario (a) RSSI and (b) SNR for packages sent by SMs or EPC.  
Figure 20: LoRaWAN behavior **(a)** RSSI, **(b)** SNR, **(c)** ToA and **(d)** Delay of packets sent by EECPortuguese Data Aggregator Point) emerged, which is a hybrid module that contains two channels, being one LoRaWAN and the other LoRaMESH. This module will be able to hybridize the network, so that the two can interact with each other. This device is being shown in the Figure 22.  
In this case, DAP will be a coordinator for the LoRaMESH network and an end-node for the LoRaWAN network, making the communication between the two technologies and hybridizing it, thus having a LoRaMESH topology on the left and a LoRaWAN topology on the right. In the Figure 23, it is possible to visualize how the architecture of this network will be.  
The end-node present in the SM will send the package via MESH until it reaches the coordinator present in the DAP, the DAP will make a queue of packets to be sent to the LoRaWAN end-node also present in the DAP. As soon as the packets are sent to the LoRaWAN end-node via serial communication, they will be forwarded to the LoRaWAN gateway, which will send them to the EPC TTN network server, which will then view the data in the Power BI application.
# Simulink Model of a Thermoelectric Generator for Vehicle Waste Heat Recovery
## 3 TEM Properties Identification Model
### Model Validation  
The model has been validated for two commercially available thermoelectric modules which are presented in Table 1. This data has been used in the Simulink model presented in Figure 3 to determine the effective parameters.  
Figure 3: Simulink model for determining effective properties (values for TGM-127-1.4-2.5).  
The effective parameters obtained with the model presented in Figure 3 were used to calculate the voltage, current, and power at matched load resistance conditions, as well as the power, current, voltage, and efficiency at the maximum temperature gradient for various values of the load resistance. The results show that there is a relatively good agreement be between the measured and calculated values (Figures 4 and 5). The major differences appearing here can be attributed to the temperature dependency of the electrical resistivity, Seebeck coefficient, and thermal conductivity, which is not captured in the proposed model. Despite these differences, the predicted power output of the module is in relatively good agreement with the measured values. A possible reason for this result is that the maximum power was chosen as an input parameter in deference to the maximum voltage.  
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|} \hline
**TEM** & **nTC [-]** & **PeLmax [W]** & **Imax [A]** & **nmax [\%]** & **Ap/n [mm2]** & **Lp/n [mm]** & **Th [’C]** & **Tc [’C]** \\ \hline TGM-199-1.4-0.8 [22] & 199 & 11.40 & 5.10 & 4.3 & 1.96 & 0.8 & 200 & 30 \\ TGM-127-1.4-2.5 [23] & 127 & 4.46 & 2.37 & 5.5 & 1.96 & 2.5 & 200 & 30 \\ \hline \end{tabular}
\end{table}
Table 1: Thermoelectric parameters of commercially available thermoelectric modules.  
Figure 4: Manufacturer data compared to calculated data at matched load resistance conditions for TGM-199-1.4-0.8: (a) Module power; (b) module current; (c) module voltage; (d) module power and voltage with respect to current; and (e) module efficiency.
## 3 Security Certification of Services
### Security Assurance Certificates  
When test-based certification is used, test cases and related evidence can be attached to Web service interfaces in order to evaluate the assurance level. According to [PERSON] _et al._, three different certificate types can be identified, each one characterized by specific metadata and a certification process [14].  
**Self-certificate (SC):**: SCs are characterized by a four-way interaction between service provider (\(SP\)) and consumer (\(C\)). First of all, \(C\) sends a request to \(SP\) specifying the list of security properties to be certified on the service. \(SP\) already mapped to each service consistent test suites, including functional and QoS-based test cases; those tests are then used to build the reply, sending to \(C\) the test cases related to the specified set of properties. If the reply is satisfactory, \(C\) can directly execute the test cases on the service and analyze the results. It is important to note that this mechanism does not require a trust relationship between \(C\) and \(SP\); however, it reduces the number of actors involved in the certification process and, therefore, the certification time.
**Lightweight Certificate (LC):**: This type of certificate introduces a new actor in the certification process, namely a Third-Party Certifier (\(3 PC\)). In this case, all tests are executed by the \(3 PC\). A consumer \(C\) can send a request to \(3 PC\) specifying the requested security properties, and a list of candidate providers. Then, \(3 PC\) can contact each specific provider, supply the test cases, and apply them to services. \(3 PC\) can also play the role of certificate repository, storing available certificates for future usage.
**Collaborative Certificate (CCert):**: Certificates are generated and stored independently from \(C\) requests. An extension of the UDDI protocol could support CCert, as well as the storing and managing of test suites. Test suites are signed by \(SP\) during the service registration phase; then \(3 PC\) can access them periodically to verify the test results, to generate missing certifications, and to invoke one or more tests as needed to reconfirm or strengthen service quality.  
In the remainder of this paper, we further develop the CCert scenario to enable classifying services based on their level of assurance. We adopt this well-established approach [14] to provide users with a subset of certified services. Then, we show how the CCert scenario can be implemented, using our approach so that real, practical implementations can properly meet user needs.
## 6 Measure Optimization Approach  
Drawing upon the findings of our analysis, the optimal choice of DMs is influenced by four key criteria: efficiency, practicality, proficiency, and relativity. It is worth noting that the assessments of efficiency and practicality can be skewed due to the dimensional attributes of the DM, thereby leading to inconsistencies in the evaluation outcomes for these criteria. This issue is mitigated through the use of \(\zeta\), a dimensionless parameter, in proficiency analysis. Additionally, relativity serves as a robust measure of the linear association between IMs and DMs, thereby acting as a compensatory factor for the aforementioned limitations. Consequently, the optimal selection of an optimal DM is determined by considering both proficiency and relativity, in addition to efficiency and practicality.  
When it comes to choosing an ideal IM, three primary requirements must be met: scaling robustness, hazard computability, and site-specific sufficiency. Specifically, scaling robustness ensures that the IM will not disproportionately influence the structural response during amplitude modulation. Meeting the hazard computability criterion allows for a more streamlined seismic analysis, facilitating the derivation of a precise seismic hazard curve with minimal computational effort. Finally, fulfilling the site-specific sufficiency criterion guarantees that variations in earthquake magnitude and fault distance will not compromise the analytical results.  
In the context of IM selection, the pillars of efficiency, practicality, and proficiency serve as central guiding principles. The chosen IMs can be further verified through their relativity and relative sufficiency compared to other IMs. According to the analysis results of this study, the IMs selected based on efficiency, practicality, and proficiency remain basically the same, and also present higher relativity and relative sufficiency.
## 2 Literature Review and Hypotheses
### Information Quality and Flow  
Information quality has been widely studied in information system [35; 36]. Previous studies have indicated information quality as the user's assessment of \"whether the information output from the information system is useful to the end user\". High quality information is well-formatted, up-to-date, and complete [37; 38]. Thus, we define information quality as the degree to which a system provides useful content to their users. The majority of customers in a brand community are information consumers [39]. Information is important for the customers to continue using a brand community.  
Figure 1: Research model.  
When customers perceive great intensity, frequency, breadth and quality of information exchanged, they will feel enjoyment, involvement and time distortion. Thus, high quality information will lead customers to experience flow in the brand community. Accordingly, we propose that:  
**H2.**_Information quality has a positive effect on flow._
# Energy-Efficient Clustering Scheme for Flying Ad-Hoc Networks Using an Optimized LEACH Protocol
## 3 Related Work
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Protocol** & **No of CH’s** & **CH Selection** & \begin{tabular}{c} **No of Nodes in** \\ **Cluster** \\ \end{tabular} & \begin{tabular}{c} **Cluster Method** \\ \end{tabular} & \begin{tabular}{c} **Mobility** \\ \end{tabular} & \begin{tabular}{c} **Scalability** \\ \end{tabular} & \begin{tabular}{c} **Energy** \\ **Efficiency** \\ \end{tabular} & \begin{tabular}{c} **Residuary** \\ **Energy** \\ \end{tabular} & \begin{tabular}{c} **Localization** \\ **Localization** \\ \end{tabular} \\ \hline LEACH & Indeterminate & Random & Indeterminate & Disseminated & Static & Fixed & High & No & No \\ \hline LEACH-B & Determinate & \begin{tabular}{c} Random, \\ Residual Energy \\ \end{tabular} & Indeterminate & Disseminated & Static & Effective & Very High & Yes & Yes \\ \hline LEACH-C & Determinate & \begin{tabular}{c} Residual Energy \\ Mobility \\ \end{tabular} & Indeterminate & Centralized & Static & Effective & Very High & Yes & Yes \\ \hline LEACH-I & Indeterminate & \begin{tabular}{c} Residual Energy, \\ Mobility \\ \end{tabular} & Indeterminate & Disseminated & Mobile & Very effective & Very High & Yes & Yes \\ \hline LEACH-I & Determinate & \begin{tabular}{c} Residual Energy, \\ Industrial Energy, \\ Industrial Energy, \\ Industrial Energy, \\ Industrial Energy, \\ \end{tabular} & Indeterminate & Disseminated & Static & Very effective & Very High & Yes & Yes \\ \hline LEACH-T & Determinate & \
\begin{tabular}{c} Residual Energy, \\ Random \\ \end{tabular} & Indeterminate & Disseminated & Static & Effective & High & Yes & No \\ \hline Improved- &
\begin{tabular}{c} Undeterminate \\ Residual Energy \\ \end{tabular} & Indeterminate & Disseminated & Static & Fixed & High & No & No \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison based on certain metrics.
# Radio Frequency Fingerprinting for Frequency Hopping Emitter Identification
## 3 Proposed RF Fingerprinting-Based Emitter Identification Method
### Signal Fingerprint Extraction  
The SF can be defined as any subtle differences in the demodulation and decoding of the FH signal, which can uniquely identify the emitter ID. However, in this study, our objective was to identify the emitter ID before passing through the MAC layer. Thus, we targeted the analog SF that could pass the physical layer in the form of RT, SS, and FT signals. We represent them by  
\[\mathbf{s}_{\text{SF}}=\mathbf{g}_{\text{SF}}(\mathbf{s}) \tag{9}\]  
where \(\mathbf{g}_{\text{SF}}\) is the extraction function of the SF, and \(\mathbf{s}_{\text{SF}}\in\mathbb{C}^{\text{N}_{\text{SF}}\times 1}\) is the SF selected from a set of possible lists, that is, \(\text{SF}\in\{\text{RT},\text{SS},\text{FT}\}\). Here, \(N_{\text{SF}}\) is the length of the SFs.  
Based on the definition of the SF signal in [6], the RT signal is defined as an increasing RF signal that increases from the noise level to the designed level. The SS signal is defined as a region of the RF signal that contains a modulated signal with a designed energy level, and the FT signal is defined as an inverse case of the RT signal, decreasing the RF signal from the designed energy level to the noise level.  
For accurate extraction, the extraction procedure is structured based on the energy variation of the SFs. For the windowed vector \(\mathbf{s}_{n}=s[i+(n-1)/2\times W_{E}:i+(n+1)/2\times W_{E}]\) with the extraction window size \(W_{E}\) and its \(L_{2}\) norm energy \(E_{n}\), the detection rule for the transient signals can be expressed as follows  
\[\begin{cases}E_{n}\geq(1+\delta)\times E_{n-1};&T^{\text{RT}}\leftarrow\left[ \begin{array}{cc}T^{\text{RT}}&i\\ T^{\text{FT}}&i\end{array}\right]\\ E_{n}\leq(1-\delta)\times E_{n-1};&T^{\text{FT}}\leftarrow\left[\begin{array}[ ]{cc}T^{\text{RT}}&i\end{array}\right]\end{cases} \tag{10}\]  
where \(\delta\) is the threshold value for detecting the energy variance and \(T^{\text{RT}}\) and \(T^{\text{FT}}\) are the detected time indices for the RT and FT signals, respectively.  
A sliding window method is applied to monitor the energy variation of the incoming signal, which is then used to detect the RT and FT signals. The RT signal is detected as a signal in which the \(L_{2}\)-norm energy of the window is increased by 10% or more. The FT signal is defined as a decreasing case. After detecting the RT and FT signals, the SS signal can be defined as the signal between the RT and FT signals using the following definitions:  
\[\begin{split}\mathbf{s}_{\text{RT}}&=s\big{[}T^{\text{RT}} [1]:T^{\text{RT}}[-1]\big{]}\\ \mathbf{s}_{\text{FT}}&=s\big{[}T^{\text{FT}}[1]:T^{\text{FT}}[-1] \big{]}\\ \mathbf{s}_{\text{SS}}&=s\big{[}T^{\text{RT}}[-1]:T^{\text{FT}}[1] \big{]}\end{split} \tag{11}\]  
The extraction results for the SFs are presented in Figure 4.