esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Schiff base ligands with oxygen or nitrogen donor atoms are a good class of organic compounds capable of binding to different metal ions with interesting medical and non-medical properties and very popular in the last decade [1, 2]. These ligands can be easily synthesized by condensation reaction of aldehyde or ketone with a primary amine [3]. The multifarious role of transition complexes of Schiff base ligands in inorganic, metallo-organic and biochemistry have received considerable attention because of their extensive applications in a wide range of areas [4, 5].  
They display diverse chemical, optical and magnetic properties by modifying with different ligands [6, 7, 8]. It has been revealed that Schiff bases play an important role by serving as chelating ligands in the main groups and transition metal coordination chemistry; owing to their stability in different oxidative and reductive conditions [9]. The interaction of these donor ligands and metal ions gives complexes of different geometries and literature survey reveals that these complexes are potentially more biologically active compounds [10] such as anticancer, antifungal, antibacterial, antimalarial, anti-inflammatory, antiviral, and antipyretic properties [11, 12, 13, 14]. It should be noted that metal chelation can tremendously influence the antimicrobial/bioactive behavior of the organic ligands; therefore, the synthesis of various transition metal complexes has been attempted in this field [15].  
In the past few years, bacterial infection and their resistance for many antibacterial agents is a growing problem [16; 17]. While there are already several classes of antibacterial agents, there has been some considerable emerging resistance in most pathogenic bacteria to these drugs [18]. For prevention of this serious medical problem, it is necessary to develop some new antibacterial agents or to expand the bioactivity of the previously used drugs [19; 20]. Metal-based antibacterial compounds seem to be a promising research for designing a novel therapeutic methodology for new antibiotic drugs to control and prevent the growth of bacterial strains [21; 22].  
Herein, we report the synthesis of bidentate Schiff base ligands by the condensation of terephthalaldehyde with _ortho_-aniline derivatives in the presence of \(N\)-propyl-benzoguanamine-SO\({}_{3}\)H MNPs as a catalyst. The cobalt (II) complexes were prepared in methanol as a solvent. The synthesized compounds were characterized with several spectroscopic methods and screened for their antibacterial activity against Gram (+) and Gram (-) bacteria strains.
# Air Conditioning Load Forecasting and Optimal Operation of Water Systems
## 2 Methods
### Load-Forecasting Model
#### 2.2.2 Data Dimensionality Reduction Method  
In order to ensure the high accuracy of load-forecasting results, the historical data derived from the energy management system are preprocessed first. Data preprocessing includes data cleaning, data integration, data dimensionality reduction, and data transfor  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Name** & **Rated Parameters** & **Number** \\ \hline \multirow{3}{*}{1} & Magnetic suspension & Cooling capacity 800 RT, power & \\  & water chiller & 432 kW, rated COP 6.51 & \\  & & Flow rate 200 m\({}^{3}\)/h, power 30 kW, & \\  & & lift 33 m & \\
3 & Cooling water pump (inverter) & Flow rate 300 m\({}^{3}\)/h, power & \\  & & 18.5 kW, lift 16 m & \\
4 & Cooling tower & Air volume 250,000 m\({}^{3}\)/h, water & \\  & & volume 300 m\({}^{3}\)/h, fan power & \\  & & 7.5 kW & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main equipment parameters of hotel’s central air conditioning system.  
mation. Based on the existing measuring points of the hotel energy management system and expert knowledge, we selected the daily type (N1), local dry bulb temperature (N2), local relative humidity (N3), outdoor dry bulb temperature (N4), outdoor wet-bulb temperature (N5), real-time water consumption (N6), water chiller power consumption (N7), machine room power consumption (N8), water chiller COP (N9), refrigeration temperature setting (N10), chilled water header flow (N11), cooling water header flow (N12), chilled water septum header temperature (N13), chilled water supply header temperature (N14), cooling water return header temperature (N15), and cooling water supply header temperature (N16). A total of 16 variables were used as the initial input parameters of the model.  
In order to reduce the redundancy between parameters and improve the accuracy of load-forecasting model, principal component analysis (PCA) is used to reduce the dimension of 16 variables in the dataset. After calculation, the variance contribution rates of the first to sixth principal components are 36.90%, 18.76%, 12.07%, 8.92%, 4.82%, and 3.86%, respectively, and their cumulative contribution rate reaches 85.33%. Therefore, the first six principal components can reflect most of the information of the original variables.  
The six principal components can be expressed by the linear combination of 16 variables, and the calculation formula is given by Equation (4).  
\[n^{\prime}_{ij}=\sum_{j=1}^{16}p_{ij}n_{ij} \tag{4}\]  
where \(n_{ij}\) represents the actual value of the \(i\)-th principal component under the \(j\)-th influencing factor, \(n^{\prime}_{ij}\) indicates the value after linear combination, \(p_{ij}\) is the weight in the score matrix, and the score matrix of each principal component is shown in Table 2.
## 4 Proposed Algorithm for Multi-Agent Configuration Change
### Observation and State Space  
The local observations \(O_{i}\) of each agent primarily include the distances and velocity of itself \(i\) and the neighbors \(N_{i}\) rather than the global information. Thus, each agent's policy decision is informed by the current observation as \(O_{i}^{t}\) or the hidden state (RNN) at first in the time step \(T_{i}\), denoted as \(O_{i}^{t}=\Big{\{}(p_{i}^{t},v_{i}^{t},\omega_{i}^{t}),(p_{i}^{t},v_{i}^{t}, \omega_{i}^{t})\Big{\}}\), where \((p_{i}^{t},v_{i}^{t},\omega_{i}^{t})\) is the state itself and \((p_{j}^{t},v_{j}^{t},\omega_{j}^{t})\) is the neighbors' \(N_{i}\) observation. The local observation information \(O_{i}^{t}\) is sent to each agent at the start of each time step \(T_{i}\).  
To enhance the agent's comprehension of the dynamic environment and to acquire the appropriate formation configuration, we delineate the state space into distinct components: the current environmental state and the predefined configuration state. The state space \(S\) can be illustrated as below:  
\[S=\{s,s_{d}(t)\}=\left\{\begin{array}{l}s=\Big{\{}(\mathbf{p}_{i},\mathbf{v}_{i}, \mathbf{\omega}_{i}),(\mathbf{p}_{ij},\mathbf{p}_{id},\mathbf{p}_{i\sigma},\mathbf{v}_{ij},\mathbf{v}_ {id})\Big{\}}\\ s_{d}(t)=\big{\{}\mathbf{p}_{d}^{*}\big{|},[(\mathbf{p}_{d},\mathbf{v}_{d})]\big{|}\mathbf{p}_{ i}^{*}(t)=A(t)r_{i}+b(t)\big{\}}\end{array}\right., \tag{27}\]  
where \((\mathbf{p}_{i},\mathbf{v}_{i},\mathbf{\omega}_{i})\) signifies the agent's position and velocity, while \((\mathbf{p}_{ij},\mathbf{p}_{id},\mathbf{p}_{i\sigma},\mathbf{v}_{ij},\mathbf{v}_{id})\) denotes the distances between agents, the agents and their desired trajectory, and the agents and any obstacles. Furthermore, \((\mathbf{p}_{ij},\mathbf{p}_{id},\mathbf{p}_{i\sigma},\mathbf{v}_{ij},\mathbf{v}_{id})\) also encompasses the differences in velocity between the agent and its neighbors, as well as the discrepancies between the agent's velocity and its desired value.  
Additionally, \(s_{d}(t)\) represents the trajectory reference. One of the desired pieces of information is \(\{\mathbf{p}_{d}^{*}\}\), which denotes a fixed and predefined formation shape sequence. The other reference information \((\mathbf{p}_{d},\mathbf{v}_{d})\) is derived from the DMPC module.
# Impact of Direction Parameter in Performance of Modified AODV in VANET
## Nomenclature  
ACC Automatic cruise control  
AODV-L AODV link expire time  
AODV Ad hoc On-demand Distance Vector  
DSN Destination sequence number  
E2E End-to-end  
FCAR Fuzzy control-based AODV routing  
GBAODV Global positioning system (GPS)-based AODV  
GPS Global positioning system  
ITS Intelligent traffic system  
IVC Intervehicle communication  
JOSM Java OpenStreetMap  
LLAKS Link-layer acknowledgments  
LOS Line-of-sight  
MAC Medium access control  
MANET Mobile ad hoc network  
NED Network description  
NIC Network interface card  
OFDMA Orthogonal frequency division multiple access  
OMNeT++ Objective Modular Network NESTbed in C++  
OSM OpenStreetMap  
PDR Packet delivery ratio
# Blockchain-Assisted Adaptive Reconfiguration Method for Trusted UAV Network
## 5 Scheme Feasibility Analysis
### Security Analysis  
Security is necessary for UAV networks in insecure environments. The blockchain system in this solution provides the function of node authentication, and each node is assigned public and private keys, a unique identity ID, and an identity witness of the existence network to prevent unauthorized access by external nodes. At the same time, the blockchain keeps updating the trustworthiness of the network nodes and isolates the network from legitimate but untrustworthy nodes to maximize resistance to black  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Consensus Algorithm**} & \multicolumn{2}{c}{**Complexity**} & \multirow{2}{*}{**Deterministic Consensus**} \\ \cline{2-3} \cline{3-4}  & **Computation** & & **Communication** \\ \hline POW & \(O(N\log N)\) & \(O(1)\) & No \\ \hline POS & \(O(C)\) & \(O(1)\) & No \\ \hline PBFT & \(O(C)\) & \(O(|m|N^{3})\) & Yes \\ \hline Proposed Scheme & \(O(1)\) & \(O(M|m|M^{2}/N)\) & Yes \\ \hline \hline \end{tabular} Note: POW and POS are probabilistic consensuses, and the determination of transactions requires multiple rounds of consensus, with POW requiring 6 rounds of consensus and POS requiring more. \(M\) is the number of proxy agents, \(N\) is the number of nodes in the whole network, \(M<<N\).  
\end{table}
Table 4: Consensus methods performance comparison.  
hole attacks, gray hole attacks, impersonation attacks, multiple identity attacks, etc. The statistical analysis of the global trustworthiness of block nodes can also detect selfish nodes and malicious lying nodes and prevent collusion attacks by giving high scores to each other. All of the above enables the BC_TZRP scheme to ensure the trustworthiness of participating network nodes through adaptive reconfiguration of the network, which in turn guarantees the security of the UAV network during the entire mission.  
The UAV network uses a fuzzy K-Modes clustering approach to cluster the list of neighbor IDs provided by the Neighbor Node Detection Subprotocol (NDP) to select the trusted central nodes in the partition. These central nodes constitute the upper logical network for consensus operations and for completing inter-partition routing. Through each consensus of the blockchain, the members of the upper logical network are updated periodically. This not only increases the scalability, but also allocates resources rationally, improves the lifetime of the whole network, and ensures the safe use of the UAV network.
## 2 Design of Path Tracking Controller with Constraints on Tire Slip Angles
### Constraint on Tire Slip Angles  
Generally, there are physical limits on the front and rear steering angles, \(\delta_{f}\) and \(\delta_{r}\). For example, \(\delta_{f}\) and \(\delta_{r}\) are limited to \(30^{\circ}\) and \(5^{\circ}\), respectively. Under the limitations, \(\delta_{f}\) is easily saturated on a low-friction road surface [33; 34]. Moreover, as shown in Figure 1, \(F_{W}\) is also saturated and decreased as \(\alpha_{f}\) is over \(\alpha_{m}\) where \(F_{y,max}\) is generated. In case of that condition, a path tracking controller cannot achieve the maximum performance. For the reason, \(\alpha\) should be limited to \(\alpha_{m}\), as given in Equation (16). By combining Equation (2) and Equation (16), Equation (17) is obtained. From Equation (17), the constraints on \(\alpha_{f}\) and \(\alpha_{r}\) are obtained as Equation (18). Let this denote the constraint on the tire slip angle, Equation (18), as CTSA.  
\[\left|\alpha_{f}(t)\right|\leq\alpha_{m},\ \left|\alpha_{r}(t)\right|\leq\alpha_{m} \tag{16}\]  
\[\left|\alpha_{f}(t)\right|=\left|\delta_{f}(t)-\beta(t)-\frac{l_{f}\gamma(t)}{v _{x}}\right|\leq\alpha_{m},\ \left|\alpha_{r}(t)\right|=\left|\delta_{r}(t)-\beta(t)+\frac{l_{r}\gamma(t)}{v _{x}}\right|\leq\alpha_{m} \tag{17}\]\[\left\{\begin{array}{l}-\alpha_{m}+\beta(t)+\frac{l_{T}(t)}{\
u_{x}}\leq\delta_{ f}(t)\leq\alpha_{m}+\beta(t)+\frac{l_{T}(t)}{\
u_{x}}\\ -\alpha_{m}+\beta(t)-\frac{l_{T}(t)}{\
u_{x}}\leq\delta_{r}(t)\leq\alpha_{m}+ \beta(t)-\frac{l_{T}(t)}{\
u_{x}}\end{array}\right. \tag{18}\]  
In order to calculate the lower and upper bounds of Equation (18), it is essential to measure or estimate \(\beta\). However, \(\beta\) is hard to measure with an on-board sensor. For this reason, \(\beta\) should be estimated with an observer. For the purpose, an extended Kalman Filter is employed in this paper [47].  
From the point of view of LQR, Equation (18) is the constraint on the control inputs, \(\delta_{f}\) and \(\delta_{r}\). For this reason, it is necessary to design LQR with the consideration of the constraints. The simplest way to impose the constraints, Equation (18), is to clip the control inputs obtained from LQR with the constraints. This does not need any additional procedures. The second method is to design with LQR under the constraints, Equation (18). The third method is to use MPC because it can explicitly handle any constraints in optimization procedure. The second method is not considered in this paper. Let this denote LQR without and with the CTSA as LQRN and LQRC, respectively.
# Analysis of Distributed Measurement Method for Array Antenna Position
## 2 Scheme and Principle
### Error Analysis of Distributed POS Technology  
#### 2.3.1 Lever Arm Effect Error  
The main and sub-systems are installed at different positions on the carrier, when the carrier moves in different maneuvers, the dynamic changes of lever arm between the main and sub-systems affect the transfer alignment convergence time and alignment accuracy. The compensation method of arm error is usually to estimate the arm length by filtering, and then to modify the expression of velocity error directly by calculating the compensation method. In the airborne distributed measurement system, the deflection of the carrier is similar to the lever arm effect. However, compared with the compensation of arm error, the establishment of the deflection model and error compensation is more complex.  
In order to reduce the influence of bending deformation on transfer alignment accuracy, [PERSON] et al. used second-order Markov to simulate the dynamic bending deformation angle, and took the bending deformation angle as the state quantity of the Kalman filter [24]. The method effectively compensated the error introduced by the bending deformation. [PERSON] et al. decomposed the flexure motion into quasi-static flexure motion and high-frequency flexure motion [25]. The accuracy of this method is high, but the real-time performance of the algorithm is affected. [PERSON] et al. divided the dynamic flexure movement into two parts, the first is high-frequency and low-amplitude vibration, and the second is low-frequency and high-amplitude bending [26]. The experimental results show that this method has both accuracy and real-time performance. In this paper, the vibration is regarded as white noise, and the second-order Markov model is used to reduce the influence of bending deformation on transfer alignment accuracy.
# DAB Signal Preprocessing for Passive Coherent Location
## 5 Conclusions  
The proposed modification of the received DAB signals at the transmission-frame level has very little computational complexity since it employs only two moving-average computations, thresholding and synchronous copying of parts of the two input signals. Thus, it may be introduced at the beginning of the signal processing pipeline designed for the DAB-illuminated passive bistatic radar, as shown in Figure 13, with a minimal computational cost. Such an improvement allows extended integration time (i.e., much longer than the DAB transmission frame duration). This, in turn, provides increased Doppler resolution (desired in the presence of a broadband sea-clutter) without periodical artifacts in range/Doppler plots caused by the time structure of the DAB signal, and with around 3 dB of gain in SNR, when compared to the processing of raw DAB signals.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/s22010378/s1](https://www.mdpi.com/article/10.3390/s22010378/s1)]([https://www.mdpi.com/article/10.3390/s22010378/s1](https://www.mdpi.com/article/10.3390/s22010378/s1)), Videos S1 and S2: Subsequent PCL detection results without modifications in DAB signal, Figures S3 and S4: Subsequent PCL detection results with MSC symbols copied in place of Null, PR, FIC symbols in DAB frame.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Operation** & **Number of Operations** \\ \hline Real multiplications & 3 \\ Real divisions 1  & 2 \\ Real additions & 3 \\ Real subtractions & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Computation costs (per sample) of the proposed algorithm.
## 2 Network Model
### Traffic Flows  
The network supports three different types of flows, namely fronthaul, midhaul, and backhaul flows. The FH flow corresponds to the radio data transmitted between a RU and a DU, the MH flow carries the radio data between a DU and a CU, and the BH flow is the flow of traffic between a CU and the DC. In this work, we assume that one DC supports all CUs. The flows are realized in two directions, namely in an uplink direction (RU\(\rightarrow\)DU\(\rightarrow\)CU\(\rightarrow\)DC) and in a downlink direction (DC\(\rightarrow\)CU\(\rightarrow\)DU\(\rightarrow\)RU). We consider that traffic flows have diversified latency requirements. In particular, the one-way latency limits are 100 \(\upmu\)s, 1 ms, and 2 ms, respectively, for FH, MH, and BH flows. Note that depending on the particular network and service scenario, other latency limits may be applied.
## 3 Results and Discussions
### NH\({}_{3}\)-TPD Analysis  
Acidity of the catalysts is crucial for efficiency NH\({}_{3}\) adsorption during the SCR process. The acidity and the relative strength of all the acid sites on the surfaces of the Ce-W-Ti catalysts were analyzed using NH\({}_{3}\)-TPD measurement. A broad desorption peak spanned between 100 \({}^{\circ}\)C and 400 \({}^{\circ}\)C for all samples in Figure 5, which could be attributed to the desorption of NH\({}_{3}\) molecules on the weak and medium acid sites [3; 37]. It is reported that the thermal stability of restrained NH\({}_{4}\)\({}^{+}\) ions on the Lewis acid sites were stronger than those on the Brensted acid sites. The NH\({}_{3}\) desorption peaks below 200 \({}^{\circ}\)C belonged to the Brensted acid sites, while the desorption peaks at higher temperatures were attributed to the Lewis acid sites [35]. For Ce\({}_{0.4}\)Ti and W\({}_{0.4}\)Ti, the strong desorption peaks at 150 \({}^{\circ}\)C and 181 \({}^{\circ}\)C belonged to the NH\({}_{4}\)\({}^{+}\) ions adsorbed on the Brensted acid sites, while the peaks located  
Figure 4: X-ray photoelectron spectrometry (XPS) spectra of O 1s for the Ce-W-Ti catalyst.  
at 295 \({}^{\circ}\)C and 311 \({}^{\circ}\)C could be ascribed to the desorption of NH\({}_{3}\) species adsorbed on the Lewis acid sites. For the Ce-W-Ti catalysts, the NH\({}_{3}\) desorption peaks shifted to higher temperatures (between 158 \({}^{\circ}\)C and 167 \({}^{\circ}\)C) compared to the Ce\({}_{0.4}\)Ti catalyst, while the intensities of the desorption peaks were significantly increased. Moreover, the intensities of the desorption peaks above 200 \({}^{\circ}\)C were also enhanced over the Ce-W-Ti catalysts, indicating the existence of stronger Lewis acid sites [38]. This phenomenon could be attributed to the synergistic effect between the cerium and tungsten species. The amounts of NH\({}_{3}\) uptake of all the catalysts were given in Table 1 as evidence for the total acidity on the catalysts. The Ce-W-Ti catalysts show stronger acidity compared to the Ce\({}_{0.4}\)Ti and W\({}_{0.4}\)Ti catalysts, while the Ce\({}_{0.2}\)W\({}_{0.2}\)Ti catalyst possessed the largest NH\({}_{3}\) uptake of 193.9 \(\upmu\)mol\(\cdot\)g\({}^{-1}\). Further increases or decreases in the Ce/W molar ratio decreases the acidity on the catalysts. It was well acknowledged that the acidity of the catalysts played a significant role in the process of NH\({}_{3}\)-SCR, and more acid sites on the surface Ce\({}_{0.2}\)W\({}_{0.2}\)Ti catalysts could be favorable for the conversion of NO in the low temperature range [3].
# Novel Equalization Techniques for Space Division Multiplexing Based on Stokes Space Update Rule
## 5 Results
### Step Size Optimization  
Since the MMF transmission generally results in longer impulse response, we evaluate our system with 3 different equalizer lengths: 64-taps, 512-taps, and 1024-taps, corresponding to 32-taps, 256-taps, and 512-taps of a TD equalizer, respectively.  
Figure 5 shows the optimization of the step-size for the considered algorithms: (a) single-mode equalization and (b) multi-mode equalization. It can be observed that the optimum step-size decreases with the number of taps considered.  
Table 1 sums up the optimum step-sizes for FD-SSA and FD-LMS for the 2 \(\times\) 2 and 6 \(\times\) 6 case.  
Figure 4: Full digital signal processing (DSP) stack of the single- and multi-mode DSP. Orth: orthonormalization, Intp: Interpolation.
# A Multinomial DGA Classifier for Incipient Fault Detection in Oil-Impregnated Power Transformers
## 3 Methods and Modeling
### Analysis of Dissolved Gases
In the presence of high temperatures, paper insulation, and other solid insulating materials are known to produce carbon monoxide. At temperatures lower than 300 \({}^{\circ}\)C, carbon dioxide is produced. Gas chromatography isolates each dissolved gas from all others making it possible to analyze and measure their individual concentrations. [41] provides standard procedures for collecting samples of oil from transformers and [42] details the analytical laboratory processes that separate and measure gas concentrations. The IEEE [12], Conseil International des Grands Reseaux Electriques (CIGRE) [43] and The International Electrotechnical Commission (IEC) [40] are reputable international organizations that have separately drawn technical guidelines used to interpret DGA results. As seen in [12; 40; 41] each fault has a distinct signature in terms of the quantity and combination of the various fault gases. This distinction makes it possible to detect impending or active faults. Further to this, the particular combination of gases generated depends on the energy level and temperature at the location of the fault. High energy levels tend to produce higher temperatures. High temperatures in turn result in an accelerated rate of production of gases. Figure 3 shows how the temperature influences the production of gases at various temperatures and the faults they are likely to suggest.