esa-sceva/satcom-chunk-collection huggingface dataset:
# The Plasma Ozonizer System for Mangosteen Storage Container to Preserve the Quality of Mangosteen
## 3 Results  
### Characteristics of the Ozone Production System  
The ozone tube is shown in Figure 1. The ozone tube acts as a capacitor in the resonance circuit when connecting with the RF power supply. The high-voltage, high-frequency power supply produced a controllable frequency and sinusoidal alternating voltage output. The minimum breakdown voltage was about 6 kV from peak to peak. While applying with the load, the operating frequency is between 10 kHz to 1 MHz. The resonance frequency of the ozone tube in this work was 224 kHz. The external electrodes are stainless steel pipes. The inner electrode is a rod of stainless steel. There is borosilicate glass as a dielectric between the electrodes. The ozone tube will turn oxygen molecules into ozone gas, where the molecules of oxygen gas pass through the electrical field between the electrodes. Due to the collision of electrons in plasma powered by radio sources, oxygen atoms combine with oxygen gas molecules to form ozone gas.  
The relationship between ozone concentration and oxygen flow rates is shown in Figure 3a. At a minimum breakdown voltage of 6 kV\({}_{\text{P-P}}\) and resonance frequency of 224 kHz when the oxygen flow rate was 1, 2, 3, and 4 L/min, the ozone concentration was about 55, 80, 55, and 30 ppm, respectively.  
The dependence of ozone concentration on the discharge time, while the oxygen flow rate 2, 3, and 4 L/min, was shown in Figure 3b. At first, the ozone concentration will gradually increase. After that, the ozone concentration will become constant after about 15 min for all flow rates.
# A Robust and Effective Two-Factor Authentication (2 FA) Protocol Based on ECC for Mobile Computing
## 4 Security Analysis
### Heuristic Analysis
#### 4.2.1 Timely Password Typo Detection  
The proposed protocol decreases the computation and communication overhead in cases of input errors or illegal user-initiated attacks. More precisely, in the login phase, the smart card \(SC_{i}\) verifies the password's validity by checking whether \(A_{i}^{\prime}\)? = \(A_{i}\) after the user inputs \(\{ID_{i}^{\prime},PW_{i}^{\prime}\}\). If \(A_{i}^{\prime}\) = \(A_{i}\), then the request message is transmitted to the service provider by \(SC_{i}\). Otherwise, this session is terminated. Therefore, the 2 FA provides timely password typo detection.
## 2 Airborne Instruments and Interface with the ATR-42
### Antenna and Cable Losses
#### 2.1.2.2 Linearity  
The linearity of the radiometer receiver, over a range of temperature covering natural ocean and land emissions, is crucial to obtain consistent data. A proven technique for linearity validation involves the use of a cryogenic load providing a very cold brightness temperature at the input, a variable  
Figure 2: The CAROLS radiometer inside the ATR42 research aircraft, **(a)** illustration of nadir antenna inside the aircraft, **(b)** illustration of CAROLS system (receiver and antennas) inside the aircraft.  
attenuator, and a data collection system at the system output. For each attenuation value, the receiver output is determined with the noise diode being switched on and off. This experiment is run when the system is correctly regulated, and it is assumed that the noise diode adds a constant noise, at the receiver input. The linearity measurements indicate worst-case scale variations of the order of 0.4% (Figure 4).
# Assessment of CYGNSS Wind Speed Retrievals in Tropical Cyclones
## 2 Datasets and Methodology
### Data Colocation Methodology  
In order to accurately assess CYGNSS wind measurements, for each CYGNSS observation (from the L2 files), it is necessary to only select radiometer observations that are both retrieved within a short time window and lie within the same 0.25\({}^{\circ}\) grid box from the CYGNSS observations. As noted above, these radiometer observations are taken from the RSS daily L3 files, which include both ascending and descending satellite passes. The local ascending node times for the SMAP and WindSat sensors are both around 18:00, while the local ascending node time for the AMSR2 sensor is around 13:30. Sample maps of observation time (UT) for ascending and descending SMAP passes on a typical day are illustrated in Figure 1b,c, respectively. Notice that, despite the good global coverage for both CYGNSS and SMAP and the other polar orbiting radiometers, when focusing on a single storm scene over a small region (e.g., a 10\({}^{\circ}\)\(\times\) 10\({}^{\circ}\) box), it is challenging to find many observations for the two types of sensors that are colocated within a short period of 1-4 h, as will be evident from the analyses presented in the next sections. For analyses over the full tropical band and using one or more years of observations, there are enough colocations within a short time window of 1 h to build robust statistics. For assessment of each individual TC, we expanded the time colocation window to 4 h to obtain a better coverage of the storm. Restricting the colocation time window to 1 h would have resulted only in a couple of CYGNSS tracks colocated with each radiometer TC-wind dataset within a storm. Because of the relatively large colocation window in TCs, some mismatch between them is to be expected, as winds can vary significantly over 4 h in rapidly changing storms. The overall assessment of CYGNSS winds in TCs is determined from a collection of 86 storm scenes between 2017 and 2020, where we found enough colocations within 1 h to build summarizing statistics from the following intense TCs: Irma, Maria, Jose, Mangkhut, Florence, Jebi, Hagibis, Dorian, Maysak, Hayshen, Uesi, Harold, Paulette, Teddy, and Delta. More details about the storms included in the database are listed in the Supplementary Material Table S1.
## 3 Control Program Representation Accommodated for Direct Hardware Synthesis
### Optimization of Intermediate Representation  
The initially created EFG is a subject to optimization. The optimizations are applied iteratively, as long as their application is possible. They start from constant propagation for the arithmetic and logic nodes. Each node representing arithmetic operations can have no more than one constant argument, while nodes representing logic operations propagate constant values. Next, there are eliminated nodes, representing operations with a single argument, as a result of applying a general generation pattern or due to constant propagation. The arithmetic or logic operation nodes of the same type, linked by a single directed edge, are merged.  
Control programs, in large part, consist of logic operations [3,26]. It is important to investigate the ability to optimize logic expressions using EFG. Figure 9 depicts logic optimizations for exemplary LD program fragments. A limitation of logic optimization using data flow graphs can be observed in the proposed form. Case A depicts the situation where [PERSON] law is applied to merge AND and OR operation nodes, connected with an inverting edge (nodes surrounded by a dashed line rectangle). Analyzing the set of the node's arguments evaluates to a constant 1 (true). The constant is propagated, resulting in optimized expression of \(y=d\). Case B depicts an almost similar situation, where surrounded nodes also evaluate to constant 1. In this case, the optimization procedures that analyze node arguments are not able to introduce any optimization. To correctly minimize the logic function, [PERSON]'s exact method should be used or a heuristic derivative, called Espresso [27]. In both cases, the multiple layer logic function, that is a subject to minimization, is translated to the sum of products form or the product of sums form.  
When implementing the logic optimization method, the final target of the implementation should be taken into consideration, which is an FPGA device. In such a case, all logic functions are implemented using look-up tables (LUTs) [28]. The essential problem of logic functions mapping to LUTs is the limited number of inputs. It is required to decompose a function to fit into available blocks. The sum of the product's representation is not the best choice for decomposition algorithms. Looking for satisfactory partitioning is computation-intensive and is limited to several variables (<20) [27]. To overcome this limitation, a binary decision diagram (BDD) has been used [27,29]. It offers the efficient handling of logic functions end enables their mapping.  
Figure 8: Actions mapping scheme for: default action (**A**), conditional execution and variable assignment (**B**), and general mapping scheme for actions with the N, P, S, and R attributes (**C**).
## 4 Computational Simulations and Experimental Results
### System Simulation Design
#### 4.2.3 Variations in BER with SNR  
Based on the aforementioned simulation results, a suitable selection of \(\lambda_{s}\) and \(\lambda_{b}\) was made for BER simulations. Across different optical noise environments, the response of BER to variations in \(\lambda_{s}\) was observed through simulations. Additionally, with a constant optical noise power density represented by \(\lambda_{b}\), simulations were conducted to explore the effects of \(\lambda_{s}\) on BER for different symbol rates denoted by \(R\).  
In Figure 8a, the relationship between \(\lambda_{s}\) and BER is illustrated for a symbol rate of 2.5 ksps under different environmental noise levels. It is observed that as \(\lambda_{s}\) increases, the BER decreases. When \(\lambda_{s}\) exceeds 2.1 \(\times\) 10\({}^{-3}\), the BER can drop below 10\({}^{-5}\). This phenomenon occurs due to the system receiving a greater number of effective photons, thereby accentuating different frequency characteristics. Consequently, the system can more readily discern the four carrier frequencies, resulting in a lower bit error rate. Higher noise levels correspond to higher BERs at the same \(\lambda_{s}\). This is attributed to an increased misinterpretation of ambient light noise as signal light, thereby compromising the system's ability to distinguish the four carrier frequencies. To alleviate this issue, employing carrier frequencies that are more distinct can enhance the system's resilience to ambient light interference.  
Figure 7: Decoding Stability Index from simulations: (**a**) different scenarios for \(\lambda_{b}\); (**b**) different scenarios for \(R\).  
Figure 8b depicts the relationship between \(\lambda_{s}\) and BER for different symbol rates \(R\), with a noise level of 0.00015. It is evident that the symbol rate has a significant impact on the BER. Particularly, at a symbol rate of 2.5 ksps, the BER can decrease below \(10^{-6}\) when \(\lambda_{\text{s}}\) exceeds \(2.4\times 10^{-3}\). Reducing the rate by half means doubling the time for transmitting carrier frequencies. In terms of signal processing, this implies a doubling in the number of available signal photons. Consequently, within a single symbol time, twice as many signal photons enter the detector. This significantly reduces the system's bit error rate, primarily due to the relationship between the effective photon intensities within a single symbol width. To address this situation, the following mitigation measures can be taken: in hardware design, increase the optical transmission power; for longer communication distances or higher path losses, opt for a lower rate communication.  
Based on the aforementioned simulation results, the proposed signal processing algorithm under weak UV light signals using 4 FSK modulation has been validated to effectively handle weak signals and recover data. Additionally, it offers guidance for hardware design parameters.
## 2 Related Works
In this study, the authors proposed a non-orthogonal multiple access multicellular communication system to simultaneously share subchannels, which results in improved spectral efficiency. The disadvantage of their study is that the optimal interference mitigation scenario could not be achieved due to intercell interference.
## 3 Attack Surface Description
### Attack Surface Taxonomy and Definition  
In the context of WLANs, an attack surface can be defined as the aggregate of all possible points of unauthorized interaction through which an attacker can attempt to extract data, gain access, or disrupt the operation of a network. Formally, let:  
\[\mathcal{A}=\bigcup_{i=1}^{n}\mathcal{V}_{i} \tag{1}\]  
where the following are true:  
* \(\mathcal{A}\) denotes the total attack surface;
* \(\mathcal{V}_{i}\) represents individual vulnerability vectors (e.g., radio interface, insider device, ISP gateway);
* \(n\) is the number of distinct attack vectors.  
Each vector \(\mathcal{V}_{i}\) can be evaluated based on three critical dimensions: exposure risk, ease of exploitation, and potential impact.  
We categorize WLAN attack surfaces into three fundamental domains:  
1. Radio Access Surface (\(\mathcal{V}_{1}\))--vulnerabilities in wireless protocols and authentication schemes.
2. Compromised Insider Nodes (\(\mathcal{V}_{2}\))--internal network devices acting as intrusion points.
3. ISP Gateway Surface (\(\mathcal{V}_{3}\))--ISP-level routing, DNS control, and firmware-based access mechanisms.  
To address the research question, the attack surface is classified into three distinct areas susceptible to attacks within WLANs. The initial surface pertains to Radio Access Control, focusing on the authentication procedures for WLAN network access. This paper specifically focuses on Wi-Fi-based WLANs. The second attack surface involves malicious insiders, examining the potential compromise of already-connected internal devices to the WLAN through malware or other means. The third attack surface, the back-end, investigates the extent of access the Internet Service Provider (ISP) has to the WLAN. These attack surfaces will be explored in detail in the respective subsections.
## 4 System Simulation and Performance Analysis
### Performance Degradation Analysis
#### 4.2.1 Effect of Reset Spring  
The actuator performance degrades due to wear and aging in the long running process. Especially, the stiffness of the reset spring in the actuator changes greatly due to fatigue caused by the repetitive duty cycle, which affects system regulation accuracy. Hence, the system dynamic characteristics and the variation law of the buffer tank pressure under different degradation degrees of the reset spring stiffness were studied.  
The reset spring acts as the reverse force in the ejection process of the actuator and as the driving force in the reset process of the actuator. Combined with the oil supply pressure, the limit of the withdrawal speed of the actuator, and the inlet pressure, the optimal design stiffness of the low-pressure stage reset spring can be calculated, and its value is 38 kN/m. Figure 9 indicates that the change of spring stiffness mainly affected the reset process of the valve plate but had no effect on the opening process of the valve plate. The greater the spring stiffness was, the shorter the reset time. The response times of the valve plate were 12\({}^{\circ}\) (6.6 ms), 18\({}^{\circ}\) (10 ms), and 24\({}^{\circ}\) (13 ms) when the reset spring stiffness was 38 kN/m, 30 kN/m, and 15 kN/m, respectively.  
Figure 10 displays the pressure change rules of both the first-stage exhaust buffer tank and the second-stage exhaust buffer tank, respectively, when the spring stiffness of the high-pressure stage actuator is normal, and the spring of the low-pressure stage actuator has different degradations (slight degradation: 38 kN/m-30 kN/m; moderate degradation: 38 kN/m-25 kN/m; severe degradation: 38 kN/m-15 kN/m). The first-stage load and second-stage load were set as 85% and 80%, respectively. Under normal regulation conditions, the pressure of the first-stage exhaust buffer tank was 264 kPa, and  
Figure 8: Dynamic process under different control signals.  
the pressure of the second-stage exhaust buffer tank was 580 kPa. When the spring of the low-pressure stage actuator degenerated, both the pressure of the first-stage exhaust buffer tank and the pressure of the second-stage exhaust buffer tank decreased significantly. The more the spring degraded, the more the pressure dropped. The exhaust buffer tank pressure in stable state under different stiffness values is summarized in Table 3. As can be seen from the table, the spring stiffness changed from the design value of 38 kN/m to 15 kN/m, and the pressure of the first- and the second-stage exhaust buffer tank decreased by 34 kPa and 72 kPa, respectively. It means that the actual control load decreased with the degeneration of the spring. Therefore, it is conceivable that capacity regulation will fail if the reset spring stiffness degrades severely.
## 4 Performance Tests
### INSERT Operation  
For each database structure, the insert operations are presented in Table 1.  
\begin{table}
\begin{tabular}{l} \hline \hline \multicolumn{1}{c}{**RELATIONAL MYSQL: Insert operation**} \\ \hline Optional\textless{}Continent\textgreater{} continent = continentRepository.findByld(2L); \\ if (continent.isPresent()) { } \\ Country country = new Country(10, ”Italy”, ”EUR”, \"Roma\", 123455, 12345, continent.get()); \\ countryRepository.save(country); \\ \hline \multicolumn{1}{c}{**DOCMENT-BASED MYSQL: Insert operation**} \\ \hline Country country = new Country(”Italy\", ”EUR\", \"Roma\", 123455, 12345); \\ Continent continent = new Continent(10, new Date(), new Date(), \\ “Europe”, 123456789, 1234567, country); \\ collection.add (new ObjectMapper(). writeValueAsString(continent)).execute(); \\ \hline \multicolumn{1}{c}{**COUCHDD FIRST STRUCTURE: Insert operation**} \\ \hline Map continent = db.find(Map.class, “2”); \\ if (continent!= null) \\ Map\textless{}String, Object\textgreater{} map = new HashMap\textless{}String, Object\textgreater{}(); \\ map.put(“\(\_\)id”, 10); map.put(\"name\", ”Italy\"); map.put(\"population\", 12345); \\ map.put(“surface\", 123455); map.put(\"created\(\_\)at\", date); map.put(“updated\(\_\)at”, date); \\ map.put(“currency\", “EUR\"); map.put(\"capital\", \"Roma\"); \\ map.put(\"continent\(\_\)id\", continent.get(”\_\)id\"); \\ db.create(map); \hline \hline \end{tabular}
\end{table}
Table 1: Insert operations.  
In the case of data insertion in the CouchDB database, we have to create a key-value map with the fields used, and then insert through a predefined command. If we consider the scenario of using document-based MySQL, we created the object to be inserted, and by the predefined command add we add it after we mapped it as a string. The difference between the first structure in CouchDB and relational MySQL and between the second structure in CouchDB and document-based MySQL is the fact that, for the first two, we have to verify that there is the continent for which we want to add the country, and for the other two this is not necessary. This automatically takes longer. Figure 4 presents the average execution time of the INSERT operation. It shows that the NoSQL approach has the best performance for this operation when the number of elements increases to over 1,000,000; however, when the number of elements is decreased, the times were relatively close to the ones obtained for a relational approach.
# Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
## 5 Clustering Process Optimization
### Cluster Head Selection Phase
#### 5.1.2 Non-Probability-Based Clustering Optimization  
Non-probability-based clustering schemes consider more specific criteria for CH selection. These criteria depend on factors that can improve the performance of the CH selection schemes, such as density dispersion, energy expenditure, regional selectivity, and sensing coverage [49,50]. These protocols generally require more data exchanges and might sometimes lead to increased time consumption compared to random or probabilistic-based schemes. The energy consumption of the nodes selected as CHs is usually higher than that of the other nodes in the network; hence, the failure of a CH can cause data loss within the cluster member nodes [51]. The density dispersion-based clustering method selects the node with the highest residual energy as the CH [52]. Sensor nodes are deployed according  
Figure 6: Cluster head selection process.  
to sensing coverage schemes to sense a given parameter while avoiding coverage gaps in the network [53]. Cluster head selection is not the first step in regional selectivity-based techniques. First, the sensors have to find their neighboring nodes in a specified radius, or perform an initial regional cluster formation considering the location of the nodes in the network, before selecting the most qualified node from each region as the CH using a distributed algorithm [54].
# Design and Development of an SVM-Powered Underwater Acoustic Modem
## 4 Discussion
Regarding the key performance metrics previously mentioned in Section 3, they can be summarized as follows:  
* Data transmission rate: The system achieved a remarkable data transmission rate of 10,000 bands, which is capable of processing 1 bit every 100 microseconds. This metric is especially significant given the high-noise environments typical of underwater communication. * Noise resilience: At a low SNR of just 5 dB, the system demonstrated exceptional resilience, maintaining an accuracy of 98.28%. This resilience is indicative of the robustness of the approach, which effectively handled the substantial noise levels that often compromise other communication systems. * Future enhancements: There is considerable potential for incorporating Reed-Solomon error correction to further enhance the reliability. This addition would capitalize on the system's ability to handle errors and improve the data integrity without the complexity of additional hardware. By establishing these benchmarks, this study not only demonstrated the high performance of the current system but also set the stage for future innovations in underwater acoustic communication. The metrics provided served not just as evidence of the system effectiveness but as a foundation for ongoing improvements and adaptations.
# Resource Allocation of Multiple Base Stations for Throughput Enhancement in UAV Relay Networks
## 2 System Model
### Throughput  
The data rate in the bps/Hz of \(U_{m}\), \(R_{U_{m}}\), and that of \(U_{o}\), \(R_{U_{o}}\), can be calculated by the Shannon formula [33], as  
\[R_{U_{m}} = \frac{(1+\lambda)}{2}R_{mBS,U_{m}}^{1 st}+\frac{(1-\lambda)}{2}R_ {mBS,U_{m}}^{2 nd}, \tag{6}\] \[R_{U_{o}} = \min\biggl{(}\frac{(1+\lambda)}{2}R_{xBS,UAV}^{1 st},\frac{(1- \lambda)}{2}R_{UAV,U_{o}}^{2 nd}\biggr{)}, \tag{7}\]  
where \(R_{mBS,U_{m}}^{1 st}=\log_{2}(1+SINR_{mBS,U_{m}}^{1 st})\), \(R_{mBS,U_{m}}^{2 nd}=\log_{2}(1+SINR_{mBS,U_{m}}^{2 nd})\) and \(R_{UAV,U_{o}}^{2 nd}=\log_{2}(1+SINR_{UAV,U_{o}}^{2 nd})\). \(R_{xBS,UAV}^{1 st}=\log_{2}(1+SINR_{mBS,UAV}^{1 st})\) if data are transmitted from mBS to UAV, otherwise, \(R_{xBS,UAV}^{1 st}=\log_{2}(1+SINR_{xBS,UAV}^{1 st})\). The maximal ratio combining (MRC) [33] is applied to the calculation of \(R_{xBS,UAV}^{1 st}\). Moreover, (7) adheres to the fact that the data rate of the access link cannot exceed that of the wireless backhaul link. \(\lambda\)\((-1\leq\lambda\leq 1)\) is the adaptive time allocation parameter, which determines the length of each time slot, as depicted in Figure 2.