esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Electrical Conductivity  
Electrical conductivity is a function of food components and a complex function of temperature and other physical properties; thus the components of salt, acids and moisture are very effective in increasing the conductivity of electricity, while total solids content, sugar, fats, lipids and alcohols decrease it [62; 63]. It was demonstrated that with the increase of temperature, the electrical conductivity increases due to an increase in mobility of the tissue constituents as a result of higher temperature [63]. In addition, the nature of the product itself influences the conductivity of the food materials. According to [63], the interest in the _EC_ of food is increasing greatly as advances in food processing technology, food quality assessment and food preservation increase. The mean values obtained for the electrical conductivity of apple samples (fresh and dried) are presented in Figure 5. Results show that fresh apple samples have _EC_ values between 626 ('Golden Delicious' cultivar) and 727 ('Florina' cultivar) \(\upmu\)S/cm, while apple samples dried in the oven have _EC_ values ranging from 548 ('Florina' cultivar) to 1854 ('Starkrimson' cultivar) \(\upmu\)S/cm. From Figure 5, it is possible to observe the increase of the _EC_ values for all the samples of apple chips dried in the dehydrator; thus values between 1880 ('Florina' cultivar) and 2020 ('Golden Delicious' cultivar) \(\upmu\)S/cm were obtained. Over time, a decrease in almost all the values recorded for the electrical conductivity of the dried apple samples can be observed (Figure 5). The _EC_ values of dried 'Starkrimson' cultivar apple samples decrease with time with 42% in the case of samples dried in the oven and 45% for the samples dried in the dehydrator. In the case of the other apple cultivars, it was observed that the _EC_ values increased with time for apple chip samples dried in the oven and decreased for the samples obtained by drying in the dehydrator.
## 3 Results and Discussion
### Linear Resistance and Specific Resistance of Xylem and Phloem  
The conductivity of the upper integumentary tissues of a pear tree was evaluated by the dependence of the impedance on the frequency of the signals supplied, compensating for the capacitance of the contact pads of the electrodes and the insulating layer under the bark of the plant. Figure 6 shows that starting from the frequency of 2 kHz, the impedance modulus decreases with increasing frequency. At low frequencies, the total impedance is equal to the phloem impedance, and when the frequency increases, the impedance decreases due to an increase in current flow through the capacitive layer between phloem and xylem [17]. With an increase in frequency, the real part of the xylem impedance (the active resistivity) is more pronounced.  
At a frequency of 3 kHz, the linear resistance corresponds to 258 ohms/m, and at a frequency of 100 kHz, about 76 ohms/mm. Accordingly, the linear resistance of the phloem can be assumed to be 252 ohms/mm, and the linear resistance of the xylem is 98 ohms/mm.  
Figure 5: Dependence of the impedance modulus on the distance between the electrodes at different frequencies after compensation of the parasitic capacitance (for a pear tree 13 mm in diameter).
# Authenticity at Risk: Key Factors in the Generation and Detection of Audio Deepfakes +
## 5 Experimental Settings
### Data Preprocessing  
The audio notes from _WELIVE_ were used as the _source audios_ for deepfake generation, i.e., those for which the linguistic content would be preserved.  
As detailed in Section 3, participants could record audio notes associated with emotion and location tags using the smartphone application. The audio notes were captured by the pendant, encoded using the OPUS codec [6], and transmitted to the mobile phone via Bluetooth(r). Then, the compressed data were sent to the server.  
To conduct this research, we decoded the audio notes, which resulted in 252 audio files from 13 different users, all of whom were women. All audios were recorded using a sampling rate of 16 kHz. These audio files were organized according to the user and the day of recording, ranging in length from 2.5 s to 45 s approximately. In each recording, the user described the situation that led her to select a specific emotion in the application. Of the 252 audio notes available in the dataset, only 176 were used because the rest contained no speech data (only background noise), or could not be decoded at all, since their duration was less than 1 s. The latter is probably due to the fact that the user pressed the record button accidentally, without the intention of making a recording. The remaining audios were used to preserve the linguistic content. Table 1 shows the information about the duration of the dataset and the number of samples per user.  
As for the reference audios, i.e., the voices we want to convert to, audios from different datasets were used for different tests. Initially, recordings of audio notes from a user other than the source voice were used in the _WELIVE_ dataset. Subsequently, tests were performed using the _CSS10 Spanish: Single Speaker Speech Dataset_[50], sampled at 22 kHz. Finally, eight of the audios from the _VCTK_ corpus [38] were used, which were randomly chosen and sampled at 96 kHz, and downsampled at 48 kHz. This dataset was the one we finally used to generate the audio, as it was the one with which we obtained the best results. To perform the conversion, each of the selected voice notes was converted using eight different audios: four with male voices and four with female voices. This approach made it possible to evaluate the model's ability to preserve linguistic content while adapting to different vocal characteristics.  
It is important to note that StarGAN is a pre-trained network using the VCTK Corpus, a dataset in English. In addition, this dataset was used as the reference audio in the conversion, while our source audios, coming from the WELIVE dataset, are in Spanish. For the detection process, all the analysis has been carried out with Spanish audio, initially using the HABLA dataset and subsequently, the WELIVE dataset, which includes both real and synthetic audio.
# A Novel Wideband Splitter for a Four-Element Antenna Array
## 3 Proposed Antenna Array Splitter
### Prototype of the Proposed Antenna Array Splitter  
Based on the above analysis, the prototype of the proposed antenna array splitter was designed and manufactured. The material used for the realisation of the prototype was double-sided FR4 with a substrate height of 1.48 mm, a copper thickness of 50 \(\upmu\)m, and \(\epsilon_{r}\approx 4.3\). The dielectric loss tangent of the material has a value of 0.021 and is assumed to be constant. The physical dimensions of the realized design are shown in Figure 2, where the black shape represents the copper structure of the proposed splitter. According to calculations performed using the online solver [24], for the given parameters of the structure, the electrical length is 91.02\({}^{\circ}\) at the center frequency of 650 MHz.  
Figure 2: Schematic of the proposed antenna array splitter design with dimensions.  
The characteristic impedance of the used RF system is 75 \(\Omega\) and thus \(Z_{0n}=75\)\(\Omega\). To achieve the required bandwidth, the characteristic impedance of the symmetrical transmission line \(\xi\times Z_{0s}\) should be equal to 185 \(\Omega\). To minimize the dimensions of the splitter, the distance between the shielding cover and the structure of the splitter was set to 10 mm.  
This value of the correction factor \(\xi\) was estimated empirically based on the performed experiments. Since the distance of the shielding cover from the structure of the splitter is relatively low, the correction factor \(\xi\) reaches the value of 0.65. Therefore, it is necessary to design the characteristic impedance of the symmetrical transmission line to a value of 285 \(\Omega\). The manufactured prototype of the proposed splitter is shown in Figure 3a).  
The proposed splitter structure was installed into the shielding cover, as can be seen from Figure 3b). The shielding cover is an important part of the proposed splitter and is uniquely designed and constructed according to the physical dimensions of the splitter structure. The fundamental part of the shielding cover is the carrying plate. On the carrying plate, there is a front plate, a rear plate, and right and left plates. Four input connectors are located in the front plate; one output connector is in the rear plate.  
The cover also includes one support bar and one shorting bar, which are located inside the shielding cover. The splitter structure is covered by an appropriate lid. All parts of the shielding cover except for the support bar have a high specific conductivity. Connections of all parts of the shielding cover and connections of input and output connectors are precise without any electrical imperfections.
## 3 Results
### Application of Abiotic Factors for Control of Rhizoctonia solani AG 2-2 IIIB  
#### 3.2.1 Effect of pH on Rhizoctonia solani AG 2-2 IIIB Growth  
The response of _R. solani_ AG 2-2 IIIB depending on the pH of the culture medium was investigated. Peptone broth with yeast extract was used as the cultivation medium. The pH values (5.5; 7.0; 8.5) were maintained via the buffer solutions. The experiment resulted in the conclusion that alkaline conditions strongly suppressed the fungus growth (Figure 2). A visually detectable mycelium was observed only on the fourth day of cultivation. No changes were seen after 7 days of study. However, the alkaline conditions did not cause the death of the fungus. The recovery of mycelium growth was observed after the transfer of the agar block with mycelium onto nutrient agar on Petri plates. It evidenced that slightly alkaline conditions did not cause the devastation of _R. solani_ AG 2-2 IIIB.  
Neutral and acidic conditions were suitable for the growth. Although the growth dynamics were similar, the increase in mycelium biomass was more intense at pH = 5.5. The lower growth efficiency compared to the control without pH regulation may have been caused by the higher ionic power of the solution with buffers. Thus, the thermodynamic calculations of the effect of pH on control of _R. solani_ AG 2-2 IIIB were confirmed. The conclusion that unfavorable pH conditions cause only growth inhibition without the death of mycelium was the key point of the study.
## 3 Theoretical Foundation
### Relevant Theories Supporting the Theoretical Foundation
#### 3.6.5 The Communication Theory (TCT)  
TCT studies the process of information, interdisciplinary disciplines of interpersonal communications, psychological paradigm, and philosophical and social dimensions [36].
# Secure and Time-Aware Communication of Wireless Sensors Monitoring Overhead Transmission Lines
###### Abstract  
Existing transmission power grids suffer from high maintenance costs and scalability issues along with a lack of effective and secure system monitoring. To address these problems, we propose to use Wireless Sensor Networks (WSNs) as a technology to achieve energy efficient, reliable, and low-cost remote monitoring of transmission grids. With WSNs, smart grid enables both utilities and customers to monitor, predict and manage energy usage effectively and react to possible power grid disturbances in a timely manner. However, the increased application of WSNs also introduces new security challenges, especially related to privacy, connectivity, and security management, repeatedly causing unpredicted expenditures. Monitoring the status of the power system, a large amount of sensors generates massive amount of sensitive data. In order to build an effective Wireless Sensor Network (WSN) for a smart grid, we focus on designing a methodology of efficient and secure delivery of the data measured on transmission lines. We perform a set of simulations, in which we examine different routing algorithms, security mechanisms and WSN deployments in order to select the parameters that will not affect the delivery time but fulfill their role and ensure security at the same time. Furthermore, we analyze the optimal placement of direct wireless links, aiming at minimizing time delays, balancing network performance and decreasing deployment costs.  
wireless sensor networks; smart grid; overhead transmission lines; security; reliability Article
# Quantitative Scale Analysis of the Channel Bar in a Braided River and Its Internal Architecture
## 4 Conclusions  
1. This study quantitatively measures the shape of some typical braided rivers and the channel bar sand body, and conducts statistical analysis of the length and width of single braided river and channel bar sand body, and establishes functional models of the relationship between the width and length of single braided river.
2. Through the statistical analysis of sand body configuration parameters, it is found that there is a good positive correlation between the length and width of the core beach and the width and thickness. The calculation shows that the length of the channel bar in PI3 layer in the Nanwu area is 600-1000 m. The width of the channel bar is 400-500 m. The width of braided river is 90-140 m.
3. According to the logging data of the core of the cored well and the dense well location, a quantitative method based on the calculation of single accretion scale is established from three perspectives: the architecture interface identification of the accretion, the occurrence and the scale calculation of interlayer, and the scale calculation of the single accretion. The inclination angle of the accretion interface in the direction of the long axis is 0.78-1.32\({}^{\circ}\), and the inclination angle of the accretion interface in the direction of the short axis is 2.02-3.78\({}^{\circ}\). The density of a single well group is generally 2-3 per well. The length of the single accretion in the channel bar is 700-1500 m.
4. Based on the quantitative scale analysis of single accretion, the architecture model of the channel bar is established. There are four stages of accretions inside the channel bar in the target research area. A sedimentary interface exists between the accretions, where two sedimentary architecture units--a channel and silty layer--are developed.  
Conceptualization, H.L.; Methodology, Z.L.; Validation, Z.L.; Investigation, J.L.; Data curation, J.L.; Writing--original draft, H.L. All authors have read and agreed to the published version of the manuscript.  
The authors are grateful for science and technology innovation ability cultivation project of Hebei provincial planning (22E50590D), and priority research project of Langfang education sciences planning (JCJY202130).  
Figure 11: Spatial distribution characteristics of the architecture elements within channel bar (modified by Reference [40]).  
Data Availability Statement: The data used to support the findings of this study are available from the corresponding author upon request. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
# Integrated Energy System Optimization Based on Standardized Matrix Modeling Method
## 2 Integrated Energy System Standardized Matrix Modeling  
The integrated energy system is a system that integrates multiple energy inputs, multiple energy outputs, and multiple energy conversion devices, seen in Figure 1. Comprehensive and accurate system definition and scientific physical structure description are the bases for system modeling and optimization planning of the integrated energy system.  
Figure 1: Schematic diagram of the integrated energy system.  
Note: CHP is the abbreviation of the combined heat and power system; PV is the the abbreviation of the photovoltaic power.  
The integrated energy system is a regional energy internet, as shown in Figure 1. From the perspective of system engineering, the integrated energy system can be composed of the following four subsystems.  
1 External energy supply subsystem  
The external energy supply subsystem supports the operation of the entire integrated energy system, supplying primary nonrenewable energy (natural gas, fuel oil, and coal) and secondary energy (main grid power supply) for the entire system operation demand, and ensures the energy security for the entire system operation.  
2 Energy conversion subsystem  
The first category of the energy conversion subsystem is a small-scale renewable energy power generation system including photovoltaic power generation, small wind power and small hydropower generation. The second category of the energy conversion subsystem is a cogeneration or a combined cooling, heat, and electric production system with an internal combustion engine, gas turbine, microgas turbine, fuel cell, and Stirling engine as prime movers, respectively. The third category of the energy conversion subsystem is the auxiliary energy conversion system, such as gas/oil boilers and energy storage equipment. The primary and secondary energy sources are efficiently converted into multiple forms of energy required by end users in a variety of ways by the energy conversion subsystem.  
3 Energy transmission network  
The energy transmission network (including the power grid, the heating network, and the cooling network) efficiently and reasonably transmits the energy generated by each energy conversion subsystem to each end user who needs different forms of energy.  
4 User terminal Subsystem  
The user terminal subsystem is a system that ultimately consumes the energy produced by the energy conversion subsystem. On the demand side, multiple types of users (such as resident users, factories, shopping malls, office buildings, hospitals, etc.) can be integrated into the same energy supply system to achieve load averaging through complementarity and interaction among users.
# Online Learning-Based Adaptive Device-Free Localization in Time-Varying Indoor Environment
## 4 The Proposed Method
### The Overview of DFL Based on ML-OSELM  
In the time-varying environment, the propagation pattern of WiFi signals will change. In order to adapt to such changes and ensure the accuracy of DFL in the time-varying environments, we propose a DFL method based on ML-OSELM, which is shown in Figure 6.  
Figure 5: Signal changes caused by environmental changes: (**a**) Signal changes when the transceiver device is restarted; (**b**) Signal changes when the transceiver is moved.  
Figure 6: The overview of ML-OSELM-based DFL.  
The proposed method consists of two phases, which are the offline phase and the online phase. In the offline phase, WiFi CSI signals are first collected, and the collected data are then transformed into weight-based fingerprints by ML-OSELM. We replace the original CSI with the hidden layer parameters of ML-OSELM as fingerprints. The CSI of each reference point is trained using ML-OSELM separately to obtain the corresponding weight-based fingerprints, which are constructed as a weight-based fingerprint database. Finally, the new fingerprints are used as input and trained using OS-ELM to obtain the adaptive DFL model.  
The online phase is divided into two cases: online localization phase and online learning phase. In the online localization phase, when real-time data are collected, it is first transformed into weight-based fingerprints using ML-OSELM and then fed into the model trained in the offline phase to obtain the location of the target. When the environment changes, we collect data from the new environment and use it to update the model to adapt to the environment, and we call this phase the online learning phase. During the online learning phase, the weights and biases of the model are fine-tuned to adapt to the changed environment.
# Dual Cancelled Channel STAP for Target Detection and DOA Estimation in Passive Radar
## 6 Conclusions  
In this paper, we proposed a dual cancelled channel STAP scheme for clutter rejection and slowly moving target detection and localisation in multichannel mobile passive radar. The proposed scheme aimed at reducing the computational complexity, as well as the number of required training data, compared to a conventional full-array solution.  
Specifically, the AB-STAP technique was considered, combined with an adjacent-bin post-Doppler strategy, and proved to be a suitable solution for multichannel mobile passive radar. The reduction of computational complexity was obtained both by reducing the number of adaptive DOF in the space-time processing steps and by providing a simple closed-form expression for the target DOA estimation.  
Despite its lower computational load, the proposed scheme was shown to yield comparable target detection and DOA estimation performance with respect to the equivalent full array solution. Moreover, it proved to be more robust against adaptivity losses, operating effectively even in the presence of a limited sample support. This plays a fundamental role in a practical passive radar scenario, where the potential non-homogeneity of the bistatic clutter may easily compromise the STAP performance by limiting the number of training data usefully exploitable.  
The effectiveness of the proposed scheme was demonstrated against simulated and experimental data from a DVB-T-based multichannel mobile passive radar. The benefits provided were shown to positively contribute to a practical implementation of the system.  
Conceptualisation and methodology, G.P.B., F.C. and P.L.; data curation, P.W. and D.C.; software, G.P.B.; validation, G.P.B.; formal analysis, G.P.B. and F.C.; investigation, G.P.B., F.C. and P.L.; writing--original draft, G.P.B.; writing--review and editing, F.C., P.L., P.W. and D.C.; funding acquisition, P.L.; project administration, P.L. and D.C. All authors have read and agreed to the published version of the manuscript.  
Figure 12: DOA estimation results for the aerial target Delphin over consecutive CPIs: (**a**) full array STAP; (**b**) AB-STAP.  
**Funding:** This research was funded by the Italian Ministry of University and Research Project SCN_00393 Safety and Security Systems for Sea Environment ($4E).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data sharing is not applicable to this article.  
**Conflicts of Interest:** The authors declare no conflict 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.