esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Results
One resulting concentration curve was prepared based on the averaged values from three samples made of the same concrete (Figure 2). The mean chloride ion concentration in each section was obtained after rinsing chloride ions from the powdered concrete with distilled water. One sample of powdered concrete was washed two times (each time with water in amount which was in mass equal to mass of powdered concrete sample). The powdered concrete material was obtained by using a drilling rig with a diamond drill \"Profile Grinding Kit\" produced by German Instruments AS. The concentrations of chloride ions obtained from the chemical analysis of the tested solutions were then converted into the mean volumetric concentrations in the concrete 2 mm sampling sections of total chlorides ions (\(\overline{\text{C}}_{\text{t exp}}\) \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Concrete** & \(f_{\text{cm}}\) & \(\rho\) & \(\varepsilon\) \\  & **(MPa)** & **(kg/m\({}^{3}\))** & **(m\({}^{3}\)/m\({}^{3}\))** \\ \hline C1 & 54.2 & 2271 & 0.12 \\ C2 & 45.8 & 2241 & 0.10 \\ C3 & 49.5 & 2269 & 0.07 \\ C4 & 58.4 & 2258 & 0.09 \\ C5 & 46.5 & 2280 & 0.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mean compressive strength (\(f_{\text{cm}}\)), bulk density (\(\rho_{\text{b}}\)), and open porosity (\(\varepsilon\)) of tested concretes (data also partially published in [21]). (mol/m\({}^{3}\))) and their concentrations (\(p\) (%)) expressed for reference as a percentage of the cement mass per a concrete unit volume according to the following formulas:  
\[\overline{C}_{\text{t exp}}=\frac{m_{\text{t exp}}}{M_{\text{CI}}V_{\text{c}}}= \frac{2\,c_{\text{sol}}\cdot\rho_{\text{cb}}}{M_{\text{CI}}\rho_{\text{w}}},\ p=\frac{ \overline{C}_{\text{t exp}}\cdot M_{\text{CI}}}{\rho_{\text{cem}}}\cdot 100\% \tag{2}\]  
where: \(c_{\text{sol}}\) chloride ion concentration (kg/m\({}^{3}\)) determined in the washed solution, \(m_{\text{t exp}}\)--mass of total chlorides (kg) in the powdered concrete drilled from a sampling section, \(V_{\text{c}}\)--volume of the drilled concrete (m\({}^{3}\)) from sampling section in an intact state, \(\rho_{\text{cb}}\)--bulk density (kg/m\({}^{3}\)) of concrete, \(M_{\text{CI}}=0.035453\) kg/mol--molar mass of chloride, \(\rho_{\text{w}}=1000\) kg/m\({}^{3}\)--density of water, \(\rho_{\text{cem}}\)--density of cement (kg/m\({}^{3}\)) per a concrete unit volume. The precise details of the used experimental and calculation procedure for the determination of \(\overline{C}_{\text{t exp}}\) can be found in article [27]. Figure 1: View of the diffusion test stand (**a**) and its diagram (**b**): 1—concrete sample, 2—container for 3% NaCl solution, 3—conventional plugs in order to eliminate evaporation of water from the container of the solution, 4—3% NaCl solution, 5—distilled water. Dimensions in mm.
## 3 Results and Discussion
### Effect of Antibiotics on the Decontamination Performance of BRC-MFC
#### 3.1.1 Nutrient Removal Performance
A similar study pointed out that TC has a significant promotion effect on NH4\({}^{*}\)-N removal, probably because the process of forming complexes between TC and catalytic iron in the BRC-MFC submerged layer consumes some Fe2\({}^{+}\), resulting in a weakening of the reduction of nitrate-nitrogen to NH4\({}^{*}\)-N by catalytic iron [48], leading to a lower effluent concentration of NH4\({}^{*}\)-N. In addition, MFC connects the surface layer with the deep layer of the BRC system through a wire, forming a closed electronic supply loop, which strengthens the electronic migration within the system and provides additional electrons for the cathode on the surface layer. The nitrification reaction mainly takes place in the surface layer and is strengthened by the influence of additional electrons. The more additional electrons there are, the higher the remove rate of ammonia nitrogen, so NH4\({}^{*}\)-N was gradually consumed and removed. Figure 2: _Cont._  
The comparison of stage I and stage II data showed that the four antibiotic-stressed reactors did not differ significantly in COD removal, and antibiotics did not have a significant effect on COD removal capacity (\(p\) > 0.05), probably due to the presence of antibiotic resistance  
Figure 2: **(a)** Removal performance of chemical oxygen demand (COD). **(b)** Removal performance of total nitrogen (TN). **(c)** Removal performance of ammonia nitrogen (NH\({}_{4}\)*-N). genes within the BRC-MFC system so that COD removal did not receive much influence from antibiotic stress. In addition, dehydrogenase was thought to play an important role in organic matter degradation. In a study of antibiotic-stressed microorganisms, it was found that single tetracycline antibiotic stress or a combination with other types of antibiotics will have a significant stimulating effect on dehydrogenase activity to enhance the removal of organic matter [49]. The greater decrease in TN removal in stage II compared to stage I throughout the reaction may be due to the fact that the addition of TC inhibited the expression of microbial denitrification functional genes and the inhibition depended on the concentration of TC [50]. However, as shown in Figure 2b, the average TN effluent concentration of each reactor in both stage I and stage II was less than 20 \(\mathrm{\SIUnitSymbolMicro g\cdot mL^{-1}}\), which met the primary B standard for urban wastewater treatment plant discharge. The removal rate of NH\({}_{4}^{+}\)-N was lower in stage II than in stage I. The reason may also be that the absolute abundance of denitrification function genes decreases due to the combined stress of two antibiotics, which weakens the nitrification reaction in the BRC-MFC system. However, the NH\({}_{4}^{+}\)-N removal rate is always higher than the TN removal rate, which proves that nitrification is always stronger than denitrification in a BRC-MFC system.
# Galileo: The Added Value for Integrity in
## 3 Integrity Algorithm
### Preliminary Check  
Classical RAIM algorithms are applied without a check on the satellite geometry: in signal-degraded scenarios, this can cause a reduction of the performance of the integrity algorithm. For example, in the case of large dilution of precision (DOP), the navigation accuracy degrades; moreover, unfavorable geometry conditions may degrade the performance of the RAIM algorithms. In order to verify the geometric condition, a check has to be performed before the quality monitoring algorithm. This test has to screen out bad geometries, which could imply erroneous detections. With this in mind, a preliminary check has been introduced before the application of the FB technique; the test that screens out bad geometries [10; 19] is based on the weighted approximate radial-error protected (WARP) defined as:  
\[\text{WARP}=\text{WSlope}_{max}\cdot T_{G} \tag{15}\]where \(T_{G}\) is a decision threshold, which will be better defined in the following, and \(WSlope_{max}\) is the maximum among the computed \(WSlope\)[10]. \(WSlope\) is defined as [19; 20]:  
\[WSlope_{i}=\frac{\sqrt{A_{1,i}^{2}+A_{2,i}^{2}}}{S_{i,i}} \tag{16}\]  
where:  
\[A=\left(H^{T}WH\right)^{-1}H^{T}W \tag{17}\]  
and:  
\[S=I-H\left(H^{T}WH\right)^{-1}H^{T}W \tag{18}\]  
The indices in Equation (16) are used to identify a specific element of \(A\).
## 5 Connected Vehicles
### Prospects
#### 5.4.2 Data Analytics  
Nowadays, vehicle manufacturers can easily collect a large amount of data through V2X and store it on cloud storage. However, Big Data analytics are essential for extracting information regularities and generating business value from it. A reasonable analytic model automatically filters out irrelevant or even distracting factors while retaining the strongly relevant ones. Big Data analytics are accurately measured using the four Vs: Volume, Variety, Velocity, and Veracity [119]. All aspects associated with Telematics, such as position information, electronic trading, human intercourse, business substance, sensors feedback, and e-devices, are internal or external sources of Big Data [120]. Through the rational extraction of valid information, vehicle manufacturers can not only adjust their products accordingly to satisfy user needs better and optimize the operation of services, but also discover new business opportunities to expand service models. The main benefit channels are as follows:  
* Promoting the development of intelligent transportation: vehicle information and customer feedback will enable product managers and R&D teams to formulate a reasonable product upgrade route;
* Improving customer stickiness: analyzing specific user preferences based on stream data and customer information, and customizing information and service delivery using predictive models;
* Optimizing comprehensive vehicle health management: based on vehicle data streams, conducting model analysis to provide more accurate and timely warnings, thus giving valuable suggestions for predictive maintenance;
* Upgrading after-sales services and local inventory: using real-time vehicle location information to match the appropriate after-sales sites to provide customized repair services and avoid blind inventory accumulation.
# Dual-Dense Convolution Network for Change Detection of High-Resolution Panchromatic Imagery
## 3 Change Detection with the Proposed Dual-Dense Convolutional Neural Network
### Training of the Proposed Dual-DCN for Change Detection  
Given a training set consisting of image pairs, the proposed network can be end-to-end trained by the backpropagation algorithm. For each image pair, let \(Y\) be a binary label of the ground truth in which \(Y=0\) if both inputs are similar, and \(Y=1\) if both inputs are dissimilar. The proposed dual-DCN is trained based on dissimilarity by computing the contrastive loss \(L(D,Y)\) as an objective function [30]. This loss function employs a partial loss function for similar and dissimilar of a pair image. It produces a low value of \(D\) for unchanged pixels pair and high value for a pair of change pixels.  
This proposed network is optimized using the stochastic gradient descent (SGD) optimizer. Each mini-batch arises from a single image pair that contains many changes and many absences of changes. The proposed algorithm randomly initializes all new layers by drawing weights from Glorot uniform [31]. The learning rate, decay rate, and momentum are set to 0.01, \(1\times 10^{-6}\) and 0.9, respectively. The epoch number is set to 30.
# A Novel RBFNN-CNN Model for Speaker Identification in Stressful Talking Environments
## 3 Speech Databases and the Extraction of Features
### Extraction of Features  
Our speech identification models selected the finest speech signal representation by feature extraction, such as Mel-frequency cepstral coefficients (MFCCs), after having been processed. The most extensively used feature of the speech was the Mel frequency scale, which involved easy calculation, suitable potential for the distinction, anti-noise, as well as other benefits [34; 35]. Sound processing also made use of MFCC as a feature extraction technique. It has been found that MFCCs function better than other coefficients in the two areas and award a high-level approximation of auditory perception of individuals [36; 37].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Prior Work** & **Classifier** & **Accuracy** \\ \hline
[31; 32; 33] & CSPHMM2s & 85.8\% \\ \hline
[20] & Speaker and Stress & \\  & Information based on & 56.74\% \\  & Compensation (SSIC) & \\ \hline
[24] & Gaussian Mixture Model & 97.62\% \\  & (GMM) & \\ \hline
[26] & Deep Time-delay Markov & 8.55 PER * \\  & Network (DTMN) & \\ \hline \hline \end{tabular}  
* PER refers to prediction error rate.  
\end{table}
Table 1: Comparison with previous studies.  
We used the Python library (python_speech_features), and both (mfcc) and (logfbank) were imported. The used parameters were: n_mfcc = 96, n_fft = 1024, win_length = int(0.025 \(\ast\) rate), hop_length = int(0.01 \(\ast\) rate).  
The used filter bank was the Mel filter bank that was introduced by the library.
## 7 Conclusions  
A novel multiport converter topology for integrating a photovoltaic power source with battery-supercapacitor hybrid energy storage for PV-powered UAV applications was proposed in this paper. The proposed converter provides two ports with voltage step-down/step-up capability for interfacing the battery and supercapacitor unit and a voltage step-up port for interfacing with the PV source. The buck-boost capability of the SC port enabled a wider operating voltage range for the SC, which increases the utilization of supercapacitor energy capacity that enabled a 20% reduction in the size of the supercapacitor unit compared to the conventional approaches that utilize (voltage step-up only) topologies to interface supercapacitors. A phase-shifted carrier modulation technique was proposed, which allowed further reduction in the sizes of the inductors for the battery and supercapacitor stages. The performance of the converter was experimentally evaluated on an integrated power system prototype, which was subject to power pulses/step transients. The results showed an excellent transient response of the proposed converter and its control.  
Figure 15: Experimental results during sudden absence/presence of PV power.  
**Author Contributions:** Conceptualization, A.M.F.; methodology, A.M.F. and C.K.; software, A.M.F.; validation, A.M.F.; formal analysis, A.M.F. and C.K.; investigation, A.M.F. and C.K.; data curation, A.M.F.; writing--original draft preparation, A.M.F.; writing--review and editing, A.M.F., C.K. and M.S.; visualization, A.M.F.; supervision, C.K. and M.S.; funding acquisition, A.M.F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Ministry of Higher Education, Egypt, through a Ph.D. scholarship funded by the Cultural Affairs and Missions Sector.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors also gratefully acknowledge support from the ESTEEM project, which received funding from the Clean Sky 2 Joint Undertaking under the European Union's Horizon 2020 research and innovation program under grant agreement No 755485.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Usable Encryption Solution for File-Based Geospatial Data within a Database File System
## 6 Discussion
### GIS Security Model and Security Analysis
#### 6.1.3 Integrity  
To verify the originality of the spatial files, users can generate their hash values just before they are encrypted when the user logs off and can compare this with the respective hashes when the files are decrypted the next time they log in.
# Signals Intelligence System with Software-Defined Radio
## 2 Materials and Methods
### Experimental Setup
The SDR framework used to develop the applications that send and receive data used for testing is GNU Radio. GNU Radio is a dedicated framework for SDR applications. It is used to create topologies consisting of the interconnection of several dedicated blocks for digital signal processing. These topologies can be created and tested both graphically and textually using the Python programming language. Once created, a topology can be run both locally and across the network. GNU Radio also offers the possibility of making dedicated blocks for signal processing. If the resources required by the processing blocks are high, they can be moved and integrated to run in Soc (FPGA) or DMA (direct memory access) devices. In addition to the ease with which the application can be used, there is also very good connectivity with the most used SDR hardware, such as USRP, HackRF, or BladeRF. For these and more, there are dedicated drivers that are compatible with GNU Radio and allow use with great ease. This framework can be installed on Windows, Ubuntu, Fedora, and macOS operating systems. The programming languages that can be used for development are \"C/C++\" and \"python\" [26].
# Using ChatGPT in Software Requirements Engineering: A Comprehensive Review
## 7 Future Research Directions  
Given the transformative potential and limitations identified in the use of ChatGPT in software requirements engineering, we propose future research focused on the following critical areas:  
* Prompt generation: New prompt construction techniques suitable for each stage of the software development lifecycle need to be explored. This will result in clearer and more comprehensive requirements. Experimentation with different prompt construction strategies can improve the quality of the generated requirements.
* Mitigate bias and increase equity: Investigate methods for identifying, measuring, and mitigating bias in ChatGPT-generated software requirements engineering. This includes developing techniques for training on more balanced datasets and exploring the implementation of fairness-aware algorithms for software requirements engineering.
* Suggest ways of clarifying the decision-making processes to improve clarity and transparency: Possible ways include more interpretable models or the development of tools to provide insight into model reasoning.
* Reliable data handling and processing protocols that address data privacy and security concerns: Research could explore encryption methods, differential privacy techniques, and secure mechanisms to ensure the secure storage of sensitive information.
* Improve ChatGPT's error detection and correction capabilities, focusing more on logical and factual inaccuracies: This could involve using external knowledge bases, improved model training techniques, or a human-in-the-loop verification system.
* Strategies to minimize overreliance and maintain a well-orchestrated interaction between GenAI and human intellect in problem-solving: We suggest defining and setting standards for the use of ChatGPT in software requirements engineering, how GenAI can assist in performing specific tasks, and how human oversight can be emphasized.
* Resistance to adversarial attacks: It is necessary to study ways to improve ChatGPT's ability to resist counterattacks and malicious manipulation in order to promote reliability and consistency in terms of system requirements.
* Ethical and social issues: The use of ChatGPT may raise ethical issues regarding privacy breaches and redundancy. We recommend in-depth research into the two main issues causing unemployment and increasing economic inequality, and promoting equal access to GenAI tools.
For example, [PERSON] et al. [28] and [PERSON] et al. [29] stated that an improvement in crude oil soil achieved with a different proportion of Portland cement increased its strength and reduced its permeability and plasticity, making the soil more suitable after the cement treatment. Similarly, [PERSON] et al. [3] reported better results of soil geotechnical properties with the utilization of various additives, such as cement, lime, and fly ash to stabilize contaminated soils with crude oil. In further experimental work, [PERSON] et al. [30] indicated that the results of the geotechnical properties presented a notable increase in undrained shear strength, solid content (water content), and Arterberg limit values of the soil, achieved through using different curing times and various doses of cement after stabilizing the mature fine tailings. Additional research carried out by [PERSON] [31] examined the sand's strength behavior when contaminated with oil by utilizing the cement kiln dust (CKD) to determine the stabilized soil's engineering properties for use in rural road construction. Results showed that with the addition of CKD, the unconfined compressive strength and California bearing ratio (CBR) values of the oil-contaminated sand were increased. The stability of polluted sand decreases with the increase in oil percentage. Consequently, [PERSON] et al. [32] concluded that oil-contaminated land could be reused when stabilized with cement or cement bypass dust, due to the enhanced geotechnical properties of construction and engineering applications, offering practical, safe, and cost-effective solutions. This study investigated the effectiveness of using two different types of cements to stabilize oil-contaminated soil. Additionally, the physical, mechanical, and chemical behavior of both polluted crude-oil soil and stabilized soil has been studied in order to enable their reuse as earth construction materials. To date, this kind of research has not been carried out in the oil fields in Kurdistan Region, Iraq.
# Photovoltaic Prediction Software: Evaluation with Real Data from Northern Spain
This work provided disaggregated errors for the different PV installations, so it was not possible to establish which tool performed better. Recently, [PERSON] et al. [8] conducted a comparative study of different PV power simulation software tools (PVsyst, PV*SOL, and HOMER) using the production data measured at the 336.96 kWp PV plant of the University of Campinas (Brazil). The results revealed that PV*SOL presented more conservative results and HOMER was the most optimistic software. According to the annual errors obtained by comparing the output of the software tools and the actual values of the PV plant, PVsyst showed the best performance, with an error of 1.02%, followed by HOMER, with an error of 2.04%. PV*SOL showed a higher, although a more conservative, error (\(-10.38\%\)). With this background in mind, five of the most commonly used system sizing tools were evaluated in this study, i.e., RETScreen, Solar Advisor Model (SAM), PVGIS, PVsyst, and PV*SOL. In the first place, the characteristics of each of the programs under analysis in the study were described: geographical data needed, way to compute solar irradiation, the PV system simulation capacity, etc. The results of the simulation with computer programs are compared with real production data over one complete year of operation of three facilities located in Spain, each one with a different tracking system: fixed installation, installation with one axis sun-tracker and two-axis tracker PV system. The main novelty of the study is that data used to verify simulation came from grid tied facilities in operation: the data correspond with the actual system turnover values and was provided directly by the company that owned the facility. The lengthy data-collection period, from 2008 to 2020, allows us to discount the influence of either warm or cold years. In addition, annual comparisons have been complemented with a monthly analysis where the deviation error throughout the year is discussed.
# Low-Power Wake-Up Receivers for Resilient Cellular Internet of Things
## 6 State-of-the-Art NB-IoT WUR  
In this section, we present a state-of-the-art standard-compatible NB-IoT WUR with a receiver front end and an integrated digital baseband [60]. The chip was fabricated in 28 nm CMOS, successfully demonstrated on-chip Narrowband Wake-Up Signal (NWUS) demodulation and wake-up detection, and achieved a \(-\)107 dBm sensitivity with 2.4 mW power consumption. Overall, the presented chip achieves the highest degree of integration for an NB-IoT WUR and is the first fully standalone solution with on-chip digital processing.