esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Calibration and Characterization
### Calibration Procedure  
According to (12), the power measured by a radiometer is a linear function of the antenna temperature, and a number of a priori unknown parameters. The calibration process is required to obtain the values of these parameters, which vary with temperature and frequency band. As detailed in Section 2.3.2, thermal stabilization is of crucial importance in this case, in order to keep constant the values of the unknown parameters, and in particular, the gain of the amplifiers.  
Two different calibration methods are used depending on the frequency band. In the case of the three lower bands (L-, S-, and C-band), the simple hot-cold calibration ([1], pp. 402-404) is applied using the microwave absorber at a known temperature set in front of the antennas as hot load, and the radiometer pointing to the sky at zenith position as cold load. On the other hand, the upper bands are calibrated combining the tipping curves method [34; 35], which is used to retrieve several cold calibration points, with the hot-cold method. Calibration using tipping curves cannot be applied at the L-, S-, and C-band because of the wide antenna pattern and its non-negligible side lobes, which start collecting radiation from ground when the beam is not pointing to the zenith.
## 3 Results
### Electro-Optical Measurements of the Optical Links
#### 3.3.4 Data Transmission Parameters of the IR Link  
This section examines the data transmission parameters achieved in the IR link (uplink).  
Bit Rate of the IR Link  
In order to measure the transmission speed, the infrared link was configured to operate at its highest possible speed, and the time taken by the IR system to send 1 MB was recorded. Using OOK 8b10b modulation and coding, the system achieved a maximum rate of 1.91 MB/s, which is equivalent to 15.25 Mbps.  
Bit Error Rate (BER) of the IR Link  
Employing OOK modulation for the IR link resulted in no detectable errors within the packets. This is because the average power level was maintained constant, meaning if the power level of a symbol '1' decreased, the level of all subsequent symbols also decreased, resulting in packet errors. To mitigate this, the packet size was set to 1 byte for command transmission in the IR channel, reducing the overall probability of errors as long as the amplitude level of the symbols remained distinguishable.  
Optical Link Length of the IR Link  
The maximum distance for maintaining command reception was measured using an optical table and rails, allowing for the distance between the receiver and transmitter to be varied without altering other factors such as the direction of the IR light beam or the distance between the receiver and the lens, as shown in Figure 34.  
Commands were successfully received without errors at distances ranging from 60 cm to 110 cm. For distances from 72.5 cm up to 110 cm, the set-up depicted in Figure 27 was employed. Distances beyond 110 cm were not measured, as the video transmission link's limit is 110 cm, and both the visible and infrared links are part of an integrated system that must operate at the same distances.
###### Abstract  
Flexible pavements stand out as the most commonly used worldwide, compared to rigid and composite pavements, owing to their versatility and widespread application. The use of hot mix asphalt (HMA) in flexible pavements causes significant environmental concerns due to high CO2 emissions and energy consumption, whereas warm mix asphalt (WMA) technologies have gained popularity in recent decades, offering a more sustainable alternative by enabling asphalt production at lower temperatures. WMA technologies can be categorized into three main groups: foaming, organic additives, and chemical additives, with each offering distinct benefits for performance and environmental impact. One of the chemical additives used in WMA production is Cecabase RT BIO10. In this study, virgin bitumen with 50/70 penetration was modified by adding Cecabase RT BIO10 at four levels: 0%, 0.3%, 0.4%, and 0.5% by weight. The experimental design employed a Taguchi L16 orthogonal array to systematically evaluate the effects of various factors on modified bitumen performance. Binders were prepared at four temperatures (110 \({}^{\circ}\)C, 120 \({}^{\circ}\)C, 130 \({}^{\circ}\)C, and 140 \({}^{\circ}\)C), four mixing durations (15, 20, 25, and 30 min), and four mixing speeds (1000, 2000, 3000, and 4000 rpm), enabling an efficient analysis of each parameter's impact. The prepared binders were subjected to a series of tests, including penetration, softening point, flash point, rotational thin film oven test (RTFOT), elastic recovery, Marshall stability, ultrasonic pulse velocity (UPV), and FTIR analysis. These tests were conducted to investigate the effects of various parameters and levels on the binder properties. Additionally, stiffness and seismic modules were evaluated to provide a more comprehensive understanding of the binder's performance. The experiment results revealed that the penetration, elastic recovery percentage, and Marshall stability increased with increasing additive content while the softening point and RTFOT mass loss decreased. At a high service temperature of 40 \({}^{\circ}\)C, the stiffness modulus of the modified bitumen decreased slightly. At a low service temperature of \(-\)10 \({}^{\circ}\)C, it decreased further. Additionally, the incorporation of Cecabase RT BIO10 led to an increase in the seismic modulus. Through optimization using the Taguchi method, the optimal levels were determined to be a 0.4% Cecabase RT BIO10 ratio, 140 \({}^{\circ}\)C mixing temperature, 30 min mixing time, and 1000 RPM mixing speed. The optimal responses for each test were identified and integrated into a unified optimal response, resulting in a comprehensive design guide with 95% confidence level estimates for all possible level combinations.  
Cecabase RT BIO10; Marshall stability; modified bitumen; warm mix asphalt (WMA); FTIR
# Lightweight and Security-Enhanced Key Agreement Protocol Using PUF for IoD Environments
## 6 Proposed Protocol
### Informal Analysis
#### 7.2.1 User Impersonation Attacks  
This work assumes that an attacker (\(A\)) eavesdrops on a public-channel message to impersonate a legitimate user and obtains the values of \(\{PID_{D},K_{M}^{*},G_{M},r_{M}\}\) stored on the mobile device via a power analysis attack. In this case, \(A\) must compute the authentication request request message \(\{HID_{M},V_{M},R_{1},T_{1}\}\). However, to compute the authentication request message, \(A\) must calculate \(K_{M}=K_{M}^{*}\oplus F_{M}\), which is impossible because \(F_{M}\) is masked by the user's identity, password, and a random number that \(A\) does not know. Therefore, the proposed protocol is resilient to user impersonation attacks.
# Assessment of Energy Use and Photovoltaic Energy Potential in Saudi Arabian Governmental Schools
## 2 Literature Review
Compounds 1, 2, and 3 were shown to have low LCOEs of $0.183, 0.224, and 0.244/kWh, respectively, for their hybrid energy systems. Because of the present pricing and the system used in Saudi Arabia, the net present value and IRR for PV systems are inappropriate, and the LCOE of PV systems is $0.024/kWh. The SPBP is 5.14 years if the electricity generated by PV systems is fed into the grid at a rate of $0.084/kWh, equivalent to the grid tariff for the government or mosque, as reported by [PERSON] and [PERSON] [36].  
Table 1 shows application data regarding location, type, capacity, SPBP, and LCOE for mostly grid-connected PV projects. The focus was mainly on projects installed in the Gulf region. The results show that the capacity of the systems ranged between 3.08 and 5300 kW, the SPBP between 3.7 and 18.6 years, and the LCOE between 0.022 and $0.244/kWh. This indicator variation can be attributed to the study's date, the system's type and capacity, the price and type of conventional energy adopted, and the kind of support and facilities granted. As a result of the discrepancy in indicators found in the reference study in the Gulf region, it became clear that this topic needs a new study that considers the current conditions. This study evaluates the energy consumption in existing government school buildings and, technically, economically, and environmentally, assesses the possibility of integrating PV systems in these buildings and their associated facilities.
# Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours Ahead
## 7 Applications and Contributions
### Smart Wind Energy Deep Decarbonization (SWEDD) Framework
Real-time data (RTD) and real-time computing (RTC) capabilities are employed to ensure that the energy supply is optimized in response to varying wind speeds and power needs. SWEDD framework is tightly integrated with sensing and data collection systems, which can utilize the Internet of Things (IoTs) to gather and communicate essential greenhouse gas (GHG) data, thereby enhancing the accuracy of carbon footprint assessments and enabling smarter energy supply management. Figure 23: Smart wind energy deep decarbonization (SWEDD) framework. In addition, the SWEDD framework incorporates a strategic pathway for energy supply, feeding into smart and zero-carbon city initiatives. The Deming cycle of \"Plan, Do, Check, Act\" is fundamental within the framework, allowing for continuous improvement in strategies aimed at deep decarbonization [84; 85; 86]. \"Plan\" is formulating decarbonization strategies, \"Do\" is developing intelligent and automated decision systems for green energy management, and \"Check\" is connected to Carbon Metering and Evaluation (CCI and CCTC), which tracks and verifies the impact of implemented strategies on reducing GHG emissions. \"Act\" is to provide feedback to policymakers and key stakeholders to take corrective and preventive actions [87; 88; 89]. SWEDD facilitates precise monitoring and management of energy generation and consumption and ensures that cities can progressively transition towards lower carbon footprints, in alignment with global sustainability goals.
# GraphDBSCAN: Optimized DBSCAN for Noise-Resistant Community Detection in Graph Clustering
## 4 Evaluation
### Comparative Evaluation of Community Detection Methods
The results confirm that GraphDBSCAN outperforms all baseline methods, demonstrating its effectiveness in improving modularity, reducing noise sensitivity, and refining community granularity. Its ability to adapt across both real-world and synthetic datasets makes it a scalable, noise-resilient, and parameter-adaptive solution for large-scale networks. By integrating optimized DBSCAN clustering, proximity-aware feature extraction, and an improved edge removal strategy, GraphDBSCAN effectively balances modularity, structural integrity, and noise reduction, making it robust and scalable.
## 4 Modeling and Inversion of GNSS-R Reflectivity
### Modeling of GNSS-R Reflectivity  
The tau-omega model is used to model the vegetated soil reflectivity as [35; 36]:  
\[\Gamma_{p}^{soil\ + vegetation}(\theta)=\Gamma_{p}^{soil}(\theta).e^{-2\tau_{p}^{ canopy}/\sin\ \theta} \Big{(}1-\omega_{p}^{ canopy}\Big{)}^{2} \tag{5}\]  
where \(\Gamma_{p}\) is the reflection coefficient for circularly polarized (\(p\)) radiation, \(2\tau_{p}^{ canopy}\) is the two-way vegetation opacity, \(\Big{(}1-\omega_{p}^{ canopy}\Big{)}^{2}\) accounts for the two-way path of the GNSS signal, \(\theta\) is the elevation angle and \(\omega_{p}^{ canopy}\) is the single scattering albedo.  
In the case of low-lying vegetation, different studies have proposed linear relationships between the optical thickness and various vegetation parameters (_LAI_, vegetation water content) [26; 27]. In the present study, we use the _LAI_ parameter as a linear proxy for the optical thickness:  
\[\tau_{p}=a\cdot LAI. \tag{6}\]  
where \(a\) is a constant applied to all vegetation cover.  
At the L-band, since the single-scattering albedo can be neglected for low vegetation cover development [35], Equation (5) can be simplified to:  
\[\Gamma_{p}(\theta)=\Gamma_{p}^{soil}(\theta)\cdot e^{-2\alpha LAI/\sin\ \theta} \tag{7}\]  
When a logarithmic scale is applied, several studies have revealed a linear relationship between \(\Gamma_{p}^{soil}\) and the soil moisture (_Mv_). As this behavior is also observed in the present study, we consider the approximation:  
\[\Gamma_{RL}^{soil}(\theta)_{dB}=\alpha Mv+\delta \tag{8}\]  
Therefore, by applying a logarithmic function to Equation (7), we can write:  
\[\Gamma_{p}(\theta)_{dB}=\alpha Mv+\frac{\beta LAI}{sin(\theta)}+\delta \tag{9}\]  
where \(\alpha\) is the sensitivity of the reflectivity to soil moisture, \(\beta\) is the sensitivity of the reflectivity to the vegetation _LAI_, and \(\delta\) is a constant related to the roughness effect. \(\alpha\), \(\beta\) and \(\delta\) are empirical parameters retrieved from the experimental data. The parameters \(\alpha\), \(\beta\) and \(\delta\) are estimated by minimizing the sum of the squared differences between the simulated and measured \(\Gamma_{RL}\) data.
# Overexpression of _PtDXS_ Enhances Stress Resistance in Poplars
## 2 Results  
### Molecular Cloning and Sequence Analysis of PtDXS  
A 2900 bp full-length cDNA, named _PtDXS_ (GenBank accession no. XM_024607716.1) was isolated from leaves of _P. trichocarpa_. It contained a 2166 bp ORF encoding a peptide of 721 amino acids, a 351 bp 5\({}^{\prime}\)-untranslated region, and a 383 bp 3\({}^{\prime}\)-untranslated region (Figure S1). The PtDXS protein had a predicted molecular mass of 77.72 kDa and an isoelectric point of 6.62 ([[http://web.expasy.org/cgi-bin/protparam/protparam](http://web.expasy.org/cgi-bin/protparam/protparam)]([http://web.expasy.org/cgi-bin/protparam/protparam](http://web.expasy.org/cgi-bin/protparam/protparam))). The deduced amino acid sequence of _PtDXS_ was similar to those of _DXS_s from other species; e.g., _Alpinia officinalisum_ (AEK69518.1, 80.72% identity), _Hevera brasiliensis_ (ABD92702.1 86.78% identity), _Ricinus communis_ (XP_015573388.1, 86.17% identity), and _Thebroma cacao_ (XP_017975597.1, 86.56% identity) (Figure S2).  
Plant DXS proteins contain a chloroplast transit peptide, which is consistent with the subcellular localization of the MEP pathway [23]. A predicted chloroplast transit peptide with a conserved VXA cleavage site [24] was present at the N-terminus of PtDXS and was rich in hydroxylated serine and threonine residues and lacked acidic amino acids such as aspartic acid and glutamic acid (Figure S2). Thiamin pyrophosphate (TPP) is a cofactor of DXS and is indispensable for its activity [25]. PtDXS also contains an N-terminal TPP-binding domain, which begins and concludes with the highly conserved sequences GDG and LNDN, respectively (Figure S2). A glutamic acid residue, which is thought to be related to transketolase activity, was found in the middle of PtDXS. Moreover, DRAG and PSD domains, which are considered to be related to pyridine binding, were present at the C-terminus, similar to the DXSs of other plant species (Figure S2).
## 5 Use Case: Dynamic Radio/MEC Resource Allocation and Slicing Management in High Mobility Scenarios
### System Evaluation
#### 5.2.1 FL Evaluation  
To evaluate the FL framework in this use case, the environments for the Model 1 instances were configured on each deployed node to align with the parameters defined by their respective traffic profiles. The aggregation server was then set up to ensure all  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Traffic Profile** &
\begin{tabular}{c} **Arrival Rate** \\ **(Requests/Second)** \\ \end{tabular} & **Life Cycle Rate (Minutes)** \\ \hline Dense urban mobility & 4–6 & 2–5 \\ Moderate suburban & 2–3 & 6–10 \\ mobility on highways & 1–2 & 10–15 \\ Event-based mobility & 8–12 & 1–4 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Selected values for request generation.  
nodes were available after each round of local training, facilitating the construction of the global model.  
In this stage, five rounds of training were conducted, with each round consisting of 100,000 timesteps. The results of the FL process are presented in Figure 13 for the nodes corresponding to the different traffic profiles: dense urban mobility (Figure 13a), moderate suburban mobility (Figure 13b), highway mobility (Figure 13c), and event-based mobility (Figure 13d).  
The results indicate that, after two rounds of training, the clients were unable to achieve the maximum number of rewards. However, by the third round, the global model created by the server through the aggregation of parameters from all clients demonstrated the ability to process all types of slice requests. This is confirmed by observing that the same global model achieved the maximum number of rewards across all defined traffic environments.  
Figure 13: FL evaluation results in the use case for Client 1 (**a**), Client 2 (**b**), Client 3 (**c**), and Client 4 (**d**).  
This federated approach used approximately one-third of the training time required to achieve convergence in the centralized approach, thus justifying the advantages it brings to the proposed solution.
## 3 Thermographic Techniques for Skin Cancer Diagnosis
### Dynamic Infrared Thermography
#### 3.2.2 Dynamic IR Thermography Using a Cold Provocation
Thermal images were recorded using an FLIR Thermovision Camera E60 (sensitivity of 50 mK), placed 0.5 m away from the skin lesion, before and immediately after illumination, as well as after 15, 30, and 45 min. Tests were performed in a special room whose temperature was maintained at 23 \(\pm\) 1 \({}^{\circ}\)C. According to the authors, knowledge of the observable changes in the temperature gradient of the lesion due to the photodynamic treatment can provide useful information about chemical and physiological processes occurring during therapy.
# Performance Evaluation of TCP BBRv3 in Networks with Multiple Round Trip Times
## 5 Performance Evaluation  
The primary objective of the simulations was to evaluate the performance of BBRv3 in terms of fair bandwidth sharing, bandwidth utilization, drop ratio, and buffer filling within multi-RTT environments. This section presents the outcomes derived from the simulations conducted across diverse network configurations.
# A Comprehensive Evaluation Model for Optimizing the Sensor Array of Electronic Nose
## 4 Results
### Weight Values of Evaluation Indicators  
The above results analyzed each single evaluation indicator independently, so the optimization schemes obtained according to these indicators were different. Given that different evaluation indicators may have different impacts on the performance of the E-nose, it is not comprehensive to evaluate the sensor array only by a single evaluation indicator. Table 4 lists the weight value of each of the evaluation indicators calculated by EWM (Section 2.2). The weight value of the selectivity equaled 0.452, which was obviously higher than that of the other three indicators. This result indicates that selectivity was the most important among the four indicators. On the contrary, the correlation had the lowest weight value of 0.015. Therefore, it can be concluded that, compared with the other three indicators, the correlation had the least influence on the performance of the E-nose.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Evaluation Indicator** & **Weight Values** \\ \hline Sensitivity & 0.357 \\ Selectivity & 0.452 \\ Correlation & 0.015 \\ Repeatability & 0.176 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The weight values of the evaluation indicators calculated based on EWM.  
Figure 8: Repeatability results of all sensors in the initial sensor array.
## 2 Materials and Methods
### Proposed CO\({}_{2}\) Sensor Node
#### 2.3.2 Hardware Design  
The sensor- node hardware was designed based on the block diagram in Figure 2. A single board unit composed of sensor-node component circuitry builds a compact device. This development involved the main board and software designs, as well as the components' descriptions as follows.  
Figure 2: IAQ sensor node architecture.  
Concerning a compact design, all sensor-node components were applied on a double-layer printed circuit board (PCB) with a size of 25 mm \(\times\) 50 mm. A compact size was appropriately considered for the indoor application requirement, which is easy to install without occupying a large space. Moreover, the developed sensor node will be coupled with an energy harvester module in further development. Therefore, a compact-size sensor node is suitable for this purpose. The sensor node will be installed close to the indoor ambient energy source (artificial light, fan, air conditioning, etc.) to harvest ambient micro energy to prolong the sensor node's main power. This scenario can extend the sensor-node life for a long operation period (autonomous sensor-node system). The PCB layout was designed using Altium Designer software, as shown in Figure 3.  
The main sensor node components were integrated and soldered on the PCB's top layer, including the microcontroller, Wi-Fi module, and power circuit components. An on-off switch was placed on the PCB's bottom layer. All electronic components integrated to build the sensor node are surface mount devices (SMDs) available in the market, small in size, and consume low power. A CO\({}_{2}\) sensor board was integrated on the top layer side of the sensor node's PCB using a bus connector.
## 4 Building the Reputation System
### Action Phase
For protocols in which the false-positive ratio of 0% is not reached, this metric will be higher the earlier the action scheme is applied. For the detection ratio for good nodes, it is also expected that the performance will decrease once the action takes place, as in any of the action schemes selected, there is a disconnect from the nodes that are considered malicious. Thus, no exchange of messages with malicious nodes will happen. The messages from nodes considered malicious no longer circulate on the network, which prevents some nodes from being identified as malicious, also preventing some nodes from proving themselves as good. The results for these metrics were simulated using the defined settings in Table 2. The 95% confidence intervals are very small, showing great trust in the achieved results presented in Figure 8.  
The results concur with those expected. The false-positive ratio for good nodes reaches zero before 4 h of the simulation, so it is not affected by the actions taken. However, when messages from black-hole nodes start being dropped, fewer messages circulate in the network, reducing the opportunities for good nodes to prove themselves as good, so the detection ratio for good nodes grows slower when traffic in the network reduces as a result of the action schemes. Furthermore, it does not appear to be a difference between the action schemes in which the action scheme starts at 8H. This can be because creating messages only for nodes that are considered good does not guarantee that those messages will be  
Figure 8: Comparison of detection ratio for good nodes and false-positive ratio for good nodes, between best performance action schemes, for Prophet, with 20% malicious nodes. Figure 7: Comparing best performance action schemes’ delivery ratio for good nodes for Prophet with 20% malicious nodes. exchanged. Furthermore, it limits the messages created to only some good nodes, ending up excluding all nodes that are good but have not yet been identified as such. After analyzing the scenarios, the best combination of an action scheme is 8H-Disconnect + Delete. It is the second action scheme with the best delivery ratio for good nodes, not being outperformed by the 4H-Disconnect + Delete action scheme by a large value, but outperforming the 4H-Disconnect + Delete action scheme by a lot in terms of detection and false-positive ratios. Due to how the nodes are classified, in a binary way, always being good or bad and not having any \"status\" in-between, the reputation scheme was named Binary Reputation System, or for short, BiRep. Naturally, nodes that are not yet in the good node list or black-hole node list are not yet classified, assumed as being good by default.