esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Data Description
### Description of Real Data Collection
Dataset KTE1 is used to test the performance of the trained multipath detector in a changing multipath environment (i.e., more multipath signals when the rover antenna moves close to a wall when comparing with rover away from the wall). The first 20 min of KTE1 is used in Test 5 to demonstrate the detection results in position domain because there are always more than five available satellites during that period, so that the effect of satellite geometry on positioning quality can be reduced when down-weighting the detected multipath measurements. The reference station of Dataset KTR1 is set on the centre of the rotation arm rig. The reference station of Datasets KTR2, KTU, and KTE1 is set on the North Pillar at SEB. 'True' double-differenced carrier phase residuals can be computed from the fixed solution after ambiguity resolution using the least-squares ambiguity decorrelation adjustment (LAMBDA) method [35]. Calculating the true trajectories of rover movement is compulsory for the determination of 'true' double-differenced carrier phase residuals. Total stations were used to measure the length of rotation radius and the inclination of the rotation plane. The phase centre variation of antenna is much smaller than phase multipath errors and it is neglected in kinematic applications. The reference station on the North Pillar is in a low multipath environment that is shown in a 24-h test presented in [36] with true carrier phase residuals. The position of the centre of the rotation arm rig is determined by static GPS surveying with similar satellite geometry when collecting Datasets KTR2, KTU, and KTE1. Dataset KTE2 further validates the detector (trained with Datasets KTR1 and KTR2) in real scenarios. Dataset KTE2 was collected on the UNNC campus near a building (shown in Figure 6e) with a moving antenna passing through five pegs with the known position determined by total stations and static GPS surveying. When antenna moves to a peg, only the first epoch was used for validation. There are 24 such epochs available in Dataset KTE2. Figure 7: The rotating arm rig for testing of the proposed method with real multipath data.
## 3 Procedures for Assessing the Characteristics of the E/A
### The Calibration of DA Part of Measurement Channel  
The calibration of the DA part is easy to perform. It relys on determining the relationship between the output signal and the input signal (under stabilized conditions) over the range of the input signal. The calibration of the input analog circuits, starting from the voltage dividers (for three voltage channels) and the Rogowski coils (for six current channels) to the ADC's (Figure 2), was performed using the pattern sources of the alternating voltage and current. In Figure 8, the exemplary characteristics obtained as a result of the calibration of one of the current probes used, together with a designation of its trend line, are shown. Figure 9 contains the deviation of the trend line from the probe characteristics. The absolute value of this deviation (\(\Delta_{\mathrm{approx}}\)) slightly exceeds the value of 0.8 A. Taking into account the range of the current probe LFR 1/15 (peak current to 3000 A), it is an acceptable value in the application under consideration.  
Figure 6: The diagram of operations performed in the measurement algorithms [30].  
Figure 7: The simplified block diagram of the digital measurement track.  
The equations of the trend lines, obtained in the calibration procedures of the DA part of the measurement track (Figure 8), were used in the DDP part for the scaling of digital data in the corresponding channels.  
Current research on the configuration of the E/A is carried out in a way which leads to the provision of its input circuits to the system for the self-calibration of the DA part of the measurement track. The DDP processing of the DA data, obtained for the standard test signal \(S_{t}\), allows one to designate the actual DA characteristics and correct the DA data obtained for the measurement signal \(S_{m}\), as well as to determine the corrected result, along with the estimation of its uncertainty. The detailed description of the procedures and the calibration circuit is shown, e.g., in [38]. It is assumed that the additional operations performed in the DDP (shown in Figure 10) enable the determination of the uncertainty of the results.  
Figure 8: The exemplary calibration report of current probe No. 1.  
Figure 10: The idea of the implementation of the auto-calibration procedures in the E/A operations.  
Figure 9: The deviation between the trend line and the current probe characteristics No. 1.
# SC-FDE Layer for Sensor Networks in Remote Areas Using NVIS Communications
## 1 Introduction
The main drawback is that the subdivision of the bandwidth into subcarriers presents peaks in the envelope that cause a reduction in amplification capability. The SC-FDE modulation has attracted significant interest in the field of research. In [21], a study of of SC-FDE synchronization technology in HF wireless communication using Minimum Mean Square Equalizer (MMSE) was proposed. In [22], an online Long Short-Term Memory (LSTM) estimator using HF Multiple-Input Multiple-Output (MIMO) SC-FDE systems was discussed. Furthermore, [23] proposed an efficient-channel parameters-estimation design for SC-FDE in HF wireless communications. This research endorsed the idea that SC-FDE can (1) be used for scenarios with challenging and noisy channels and (2) outperform OFDM in terms of power consumption by solving high Peak-to-average Power Ratio (PAPR) issues [24]. This paper defined a protocol for low-power NVIS communications that applies SC-FDE modulation to link nodes from wireless sensor networks deployed in remote areas. Compared with OFDM, SC-FDE improves the PAPR and reduces the complexity of transmitting nodes, as it is less sensitive to Inter-Block Interference (IBI) and Inter-Carrier Interference (ICI) caused by the multipath effect [25; 26; 27]. The outline of the paper is as follows. First, a background study and a summary of the theoretical concepts used in the protocol definition is provided in Section 2. Secondly, the use case is explained in Section 3 to contextualize and analyze the design of the system explained in Section 4 and the framework design is detailed and schematized in Section 4. Section 5 exhibits the tests results and, finally, Section 6 presents the conclusions.
## 7 Computational Worst-Case Complexity  
The aforementioned computation times indicate that the procedure is feasible in practice, notwithstanding the fact that the monitoring problem under metric uncertainty actually is NP-complete:  
Lemma 3: _The model-free exact monitoring problem under imperfect information (given as interval-bounded additive absolute measurement error) is NP-complete._  
Proof: The linear reduction of the model-free monitoring problem to SMT-LA exposed in Section 5 shows that the monitoring problem is in NP, as SMT-LA is NP-complete.  
NP-hardness follows from a straightforward reduction of the NP-complete problem of propositional satisfiability solving (SAT) [30] to model-free monitoring: Consider a propositional SAT formula \(\phi\). From \(\phi\) derive an STL monitoring condition \(\phi^{\prime}\) by replacing each positive literal \(x\) from \(\phi\) by \(x>0\) and each negative literal \(\
eg x\) by \(x<0\). Then the SAT formula \(\phi\) is satisfiable if the monitoring verdict for the STL formula \(\phi^{\prime}\) is different from \(\bot\) when applied to a measurement where all observed variables \(x\) obtain a measurement \(m_{x}=0\) under a non-zero random measurement error \(\delta_{v}>0\) for all \(v\in Var\).  
As the above reduction of SAT only requires a positive noise margin \(\delta_{v}\) w.r.t. random measurement error and is independent from any assumption concerning the offset \(\epsilon_{v}\), it applies to [PERSON] et al.'s noise model [12] as well. Exact monitoring for the error model from [12] consequently also is NP-complete.  
NP-completeness thus seems to be the inherent price to pay for uncertain information: bounded STL monitoring under certain observation, in contrast, is polynomial in the discrete-time case, as it only has to check an existing valuation given by the measurements for satisfaction of the (bounded) monitoring condition.  
The NP-completeness result for the model-free case expressed in Lemma 3 transfers to the case of model-based monitoring problem under metric imperfect information. NP-hardness can be shown by considering a discrete-time dynamical system with constant state 0 throughout within the very same SAT reduction as in the proof of Lemma 3. The reduction of the monitoring problem to SMT-LA from Section 6 again proves the linear model-based monitoring problem to be in NP.  
For linear uncertain discrete-time models, model-based monitoring under uncertainty consequently is NP-complete as well.  
Note that the above NP-hardness results only apply to situations where measurements remain completely uninformative due to the measurement error, whereas more informed cases converge, depending on their level of informedness, towards checking assignments rather than finding satisfying assignments. The hardness results consequently are of limited relevance to actual applications, as these are extremely unlikely to feature an investment into completely uninformative sensor equipment.
## 2 Materials and Methods
### Photocatalytic Activity  
The photocatalytic activity of the TiO\({}_{2}\)/zeolite composite was evaluated by decolorizing aqueous solutions containing methyl orange (MO) at concentrations of 10 and 20 ppm. In each test, 0.6 g of the photocatalyst was dispersed in 200 mL of the corresponding MO solution. Subsequently, the suspension was illuminated with a 300 W OSRAM Ultravitalux lamp, where 70 Wm\({}^{-2}\) was measured in the UV-A intensity range. The experiments were realized at room temperature. During irradiation, 10 mL of the treated solution was collected every 10 min and centrifuged at 6000 rpm for 4 min. Then, the supernatant was removed, and another centrifugation was performed at 6000 rpm for 2 min, resulting in a clear solution. The remaining concentration of methyl orange was analyzed using a Lambda 365 UV-vis spectrophotometer (PerkinElmer, Waltham, MA, USA) at 507 nm. A comparison of the photocatalytic efficiency of the TiO\({}_{2}\)/zeolite composite and pure TiO\({}_{2}\) was carried out. The methyl orange removal efficiency in percentage degradation (%\(Eff_{deg}\)) was calculated with Equation (3) [27]:  
\[\%Eff_{deg}=\left[\frac{C_{0}-C_{t}}{C_{0}}\right]\times 100\% \tag{3}\]  
where \(C_{0}\) and \(C_{t}\) represent the initial concentration of the MO solution and the concentration of MO analyzed after light irradiation during a specific time interval, respectively.
## 2 Synchronization Design and Dynamic Chaos-Based Random Key Generators
### Dynamic Chaos-Based Random Key Generator  
From the above discussion, it was possible to suppress the butterfly effect between master-slave systems through the effect of the controller, and the states of the controlled slave system could be synchronized with those random states of the master system. Next, we designed a dynamic random key generator. There are three types of AES advanced encryption technologies, which use cryptographic keys of 128, 192, and 256 bits [24], respectively. Obviously, from the above chaotic systems, it was possible to generate three random states; however, if these random states were used as the AES encryption key, the lengths would be somewhat insufficient. Therefore, to solve this problem, dynamic chaotic signals were integrated with the hash function to establish the dynamic key generator. Because of the avalanche effect of Secure Hash Algorithm 256 (SHA256) [25] and the butterfly effect of the chaotic systems, each dynamic key was never repeated, which made frequency analysis impossible and thus was difficult to crack. The dynamic random key generator with SHA256 is shown in Figure 5.
## 3 Determine the Weights of PQ Indices
### Determination of Objective Weight Based on the EW Method  
[PERSON], the founder of information theory, introduced entropy theory into information theory as a measure of information disorder [30, 31]. In this paper, the EW method is used to determine the objective weight of each index of PQ. As an objective method of weighting, the entropy method can obtain objective weights independently of subjective factors. The smaller the entropy value, the lower the information disorder, and the higher the available value, so the corresponding index weight should be larger. On the contrary, the larger the entropy value, the higher the information disorder, and the lower the available value, so the corresponding index weight should be smaller. The detailed relationship is shown in Table 2.  
Let the evaluation matrix be \(A=(a_{ij})_{m^{*}n}\), and the objective weights calculation procedure of the indices is as follows [32]:  
Standardization processing of evaluation data using standard 0-1 transformation:  
When the index is a benefit attribute index, that is, the larger the index value, the better. The standardization equation is as follows:  
\[a_{ij}^{*}=\frac{a_{ij}-\underset{j}{min}a_{ij}}{\underset{j}{max}a_{ij}- \underset{j}{min}a_{ij}},\ i=1,2,\ldots,m;\ j=1,2,\ldots,n. \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Disorder of Information** & **Value of Information** & **Weight to Be Given** \\ \hline Large entropy value & High & Low & Small \\ Small entropy value & Low & High & Large \\ \hline \hline \end{tabular}
\end{table}
Table 2: Relationship between entropy weight and entropy value.  
When the index is a cost-based attribute index, that is, the smaller the index value, the better. The standardization equation is as follows:  
\[a^{*}_{ij}=\frac{\underset{j}{max}a_{ij}-a_{ij}}{\underset{j}{max}a_{ij}- \underset{j}{min}a_{ij}},\ i=1,2,\ldots,m;\ j=1,2,\ldots,n, \tag{4}\]  
where \(b_{ij}\) is the standardized value of the \(j\)th index of the \(i\)th evaluation sample.  
1 According to the standardized decision matrix, the characteristic proportion of the \(j\)th index of the \(i\)th evaluation sample:  
\[p_{ij}=a^{*}_{ij}/\sum_{i=1}^{m}a^{*}_{ij^{*}}\ j=1,2,\ldots,n, \tag{5}\]  
where, \(0<p_{ij}<\)1.  
2 The entropy value of the \(j\)th index is:  
\[e_{j}=-\frac{1}{ln(m)}\sum_{i=1}^{m}p_{ij}ln(p_{ij}),\ j=1,2,\ldots,n, \tag{6}\]  
where \(p_{ij}ln(p_{ij})\to 0\) for \(p_{ij}\to 0\).  
3 Difference coefficient of the \(j\)th index can be obtained as follows:  
\[g_{j}=1-e_{j},\ j=1,2,\ldots,n. \tag{7}\]  
4 The entropy weight \(\beta_{j}\) of the \(j\)th index can be obtained as follows:  
\[\beta_{j}=g_{j}/\sum_{j=1}^{n}g_{j},\ j=1,2,\ldots,n. \tag{8}\]  
According to Figure 5, we can understand the relationship between entropy value and entropy weight intuitively, and how the objective weight is obtained from the original data.
## 6 Results and Discussion
### User-Based Features  
As discussed earlier, the statistical features are changed over time, but this cannot prevent their abilities to detect spammers' actions with high accuracy and precision. Therefore, we attempt to find new user-based features. SVM and random forest are compared to get the best algorithm to be part of our detection framework. As shown in Tables 5 and 6, random forest achieves the best results in terms of precision and recall where trained with 6 million-tweet dataset [40] to get the user-based statistical features.
# Real UAV-Bird Image Classification Using CNN with a Synthetic Dataset
## 4 Result and Discussion
### Training and Test Results
The success of the test results shows that the CDNTS network transforms the images while preserving their structural properties. The results of experiments 1 and 2, in which we observed the effect of the CDNTS layer on AlexNet, Squeezenet, and VGG16 networks, showed that DL networks trained with fully synthetic and segmented data are not effective when faced with synthetic images and real images that could be encountered in daily life. On the other hand, DL networks in combination with CDNTS trained with fully synthetic and segmented data are effective for synthetic and real images that could be encountered in daily life. The results show that the CDNTS layer has a significant effect on test accuracy results and increases the performance of DL networks. The images that were wrongly predicted by the most successful networks were separated and analyzed. The common point of these images is that the parts containing RW-UAV and birds are small. Trained networks cannot define the image parts between 8 \(\times\) 8 pixels and 20 \(\times\) 20 pixels. Therefore, the proposed method is limited for RW-UAV and other image fragments smaller than 20 \(\times\) 20 pixels.
## 3 Methodology and Simulation Conditions
### Tlm03e Prototype
#### 3.1.1 Batteries  
The selected accumulator has to ensure an appropriate energy capacity for the purposes of the competition. If the capacity is too low, either lower power modes have to be used, which does not bode well for a racing scenario, or there might even be a risk of not finishing the race. If, however, the installed capacity is too high, there are a few disadvantages to be considered: increased cost, which is a make-or-break factor in a project with limited funding and increased volume and weight, which contribute to an inferior on-track performance.  
Since the purpose of all the team's prototypes is to race in the MotoStudent competition in the Aragon circuit, the team calculates the energy required to complete a race, then adds a safety factor. A team-developed software optimizes the number of parallel and series connections in the battery pack.  
The TLM03e prototype has 754 Sony US18650 VTC6 cylindrical rechargeable lithium-ion batteries with the properties presented in Table 2, which combine into the total energy (\(E\)) that is calculated in Equation (2) by multiplying the nominal voltage (\(V\)), the nominal capacity (\(C\)), the number of cells in series (\(N_{series}\)) and the number of cells in parallel (\(N_{parallel}\)):  
\[E=V\times C\times N_{series}\times N_{parallel}=8.5\;\text{kWh} \tag{2}\]  
The recommended depth of discharge given in the manufacturer's datasheet is between 50% and 80% for long-term storage. This work uses the value of DoD = 80%, from a state of charge (SOC) of 90% (=7650 Wh) to SOC = 10% (=850 Wh). From Equation (1), it is concluded that a higher DoD means a lower number of cycles and a lower discharge capacity for the batteries. This can be explained since high performance is required at all times, which surpasses sustainability purposes.  
Energy storage units can be used in significantly different ways, according to the power mode of the motorcycle (100% power is not always the case, particularly for a better endurance performance). Hence, manufacturer datasheets do not contain efficiency values, as they are not constant.  
Figure 4: TLM03e prototype’s simplified powertrain diagram.  
Not having the charge/discharge efficiency of the selected lithium-ion batteries would diminish the quality of the achieved results. Therefore, this paper uses a simplified constant efficiency of 98%, as reported by [PERSON] et al. [19], even though in the most realistic scenario possible, the charge/discharge efficiency values vary.
# Sustainable Operation of Active Distribution Networks
## 2 Description of the Suggested Multi-Agent System Structure  
The layout of the examined ADN and the structure of the hierarchical MAS developed in this work are shown in Figure 1. In the examined MAS, the agents used were classified into three major types: (1) local agents; (2) cluster agents; and (3) the active distribution network management agent. Local agents are accountable to model an individual module of the ADN, and cluster agents are accountable to gather and properly handle the responses of a cluster of local agents and forward information back to them. All cluster agents are supervised and managed by the active distribution network management agent (ADNM/A). ADNM/A is accountable for the optimal coordination of the MAS according to the responses received by cluster agents and possesses a higher place in the MAS hierarchy, as depicted in Figure 1. It can be concluded by Figure 1 that a bidirectional communication system should be available to each agent. A variety of communication technologies (i.e., DSL, Power Line Communication, Wi-Fi, GPRS, etc.) can be exploited. Their pros and cons can be found in [29]. Usual communication protocols include: AMI, SCADA, cellular communications, and other wireless communication technologies. Smart meters and PMUs can be installed at carefully selected points of the network. A low-cost PMU for smart distribution networks was proposed in [30]. Next, a brief explanation of each type of agent is provided, while their detailed operation is presented in the following section.
## Materials and Methods  
### Yeast Strain, Media, and Transformation  
The yeast strains used in this study were constructed from the haploid strain of _S. cerevisiae_ BCC39850 and strain WWY005 [15] (Table 1). The CRISPR-Cas9 plasmids used in this study for genome editing were generated from pRPR1-gRNA-handle and p414-TEFlp-Cas9-CYC1t [16]. The yeast expression plasmids used in this study were generated from pRSII426 and pRSII416 [17]. The primers used for plasmid construction, strain verification, and qPCR are listed in Table S1. A detailed description of plasmid and strain construction is also provided in the Supplementary Materials (Supplementary File S1). Yeast and bacterial strains were stored in 25% glycerol at \(-80\)\({}^{\circ}\)C. _E. coli_ was grown in a Luria-Bertani medium. Ampicillin at 100 ug/mL was added to the medium when required. Yeast strain without plasmid was cultivated in YPD medium (10 g/L yeast extract, 20 g/L Bacto Peptone, and 20 g/L glucose). Yeast cells were transformed using the previously described LiAc/SS Carrier DNA/PEG method [18; 19]. Selection of yeast transformants was done on a yeast minimal medium (6.7 g/L of Yeast Nitrogen Base (Difco), 20 g/L glucose, and a mixture of appropriate nucleotide bases and amino acids with dropouts (i.e., CSM-HIS for HIS3 selection and CSM-URA for URA3 selection).  
\begin{table}
\begin{tabular}{c c c} \hline
**Strain Name** & **Genotype** & **Description** \\ \hline BCC39850 hlu & _Mata; his3\(\Delta\); leu2\(\Delta\); ura3\(\Delta\)_ & BCC39850 with triple deletions (_his3\(\Delta\), leu2\(\Delta\)_ and _urn3\(\Delta\)_) \\ \hline FPPY001\_39850 & BCC39850 hlu & BCC39850 hlu overexpressing _ERG10_ and _YPRC\(\Delta\)15c:_:P\({}_{\text{GAL1/10}}\)-_ERG10-ERG20_ & _ERG20_ from _S. cerevisiae_ \\ \hline FPPY002\_39850 & FPPY001\_39850 & FPPY001\_39850 overexpressing _tHMG1_ and _ARS308a:_P\({}_{\text{GAL1/10}}\)-_tHMG1-ERG8_ & _ERG8_ from _S. cerevisiae_ \\ \hline FPPY003\_39850 & FPPY002\_39850 & FPPY002\_39850 overexpressing _ERG13_ and _ARS1021_:P\({}_{\text{GAL1/10}}\)-_ERG13-IDI1_ & _ID11_ from _S. cerevisiae_ \\ \hline FPPY004\_39850 & FPPY003\_39850 & FPPY003\_39850 overexpressing _tHMG1_ and _ERG19_ from _S. cerevisiae_ \\ \hline FPPY005\_39850 & FPPY004\_39850 & FPPY004\_39850 overexpressing _tHMG1_ and _ARS1309:_P\({}_{\text{GAL1/10}}\)-_tHMG1-ERG12_ & _ERG12_ from _S. cerevisiae_ \\ \hline FPPY005\_39850 & FPPY005\_39850 & FPPY005\_39850 with the endogenous promoter of _ERG9_ replaced with the L-methionine _repressible promoter_ (P\({}_{\text{MET3}}\)) \\ \hline WWY005 & WWY005 _ERG9:_P\({}_{\text{MET3}}\)-_ERG9_ & WWY005 with the endogenous promoter of _ERG9_ replaced with the L-methionine _repressible promoter_ (P\({}_{\text{MET3}}\)) \\ \hline \end{tabular}
\end{table}
Table 1: Engineered strains generated in this study.