esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion  
The performances of the CAZAC-TD and CAZAC-FD SNR estimators and the corresponding adaptive modulation scheme with STBC for the \(2\times 2\) and \(4\times 4\) MIMO-OFDM systems in Figure 1 are presented in this section.  
Section 5.1 compares the performance of the SISO-OFDM system that uses the modified CAZAC preamble structure of Figure 3 to that of the Suparna preamble structure of Figure 2 using the autocorrelation plot and the NMSE performance, while the performance of the CAZAC-TD and CAZAC-FD SNR estimators that are invoked in the non-adaptive STBC-assisted \(2\times 2\) and \(4\times 4\) MIMO-OFDM systems are discussed in Section 5.2.  
The performance of the proposed adaptive STBC-assisted MIMO-OFDM system that invokes the CAZAC-TD SNR estimator for transmission over the SUI-5 channel is presented in Section 5.3. Let us refer to this adaptive scheme as the AM-CAZAC-TD-MIMO system. The proposed AM-CAZAZ-TD-MIMO system employing \(M\)-PSK dynamically selects the modulation scheme to match the channel condition.  
Table 3 summarizes the simulation parameters used in this study, which are selected based on the IEEE802.16d standard [46, 47]. Moreover, the modified CAZAC preamble of Figure 3, with a frame length of \(L_{S}=320\) bits, is transmitted on even sub-carriers, while the odd sub-carriers are set to zero values. In this work, the MATLAB \(rand(N,M)\) function is used to generate pseudorandom numbers of length \(N\), producing an \(N-by-M\) matrix. The MATLAB Communications Toolbox is used for STBC encoding and decoding, OFDM modulation and demodulation, and channel definition. Fourier transforms are applied using the FFT and IFFT functions to switch between the frequency and time domains. Autocorrelation for SNR estimation is performed with the \(xcorr(data)\) function. Simulations are run on an HP ProBook 6560b laptop with an Intel(r) Core(tm) i5-2410M processor, 4 GB RAM, 256 GB HDD, AMD Radeon HD 6470M graphics, and Windows 7 Professional.  
Figure 8: The non-adaptive STBC-assisted MIMO-OFDM system’s BER performance when employing \(M\)-PSK modulation for transmission over the SUI-5 channel.  
Meanwhile, the parameters used for the SUI-5 channel simulation are summarized in Table 4. The SUI-5 channel model is a multipath Rician channel that is adopted by the IEEE802.16d standard, comprising three outdoor-terrain categories' real-time data, and the SUI-5 channel is the channel model for hilly terrain with high tree density [48; 49].
## 1 Introduction
Different technologies were combined (i.e., remote chemical detection, remote video surveillance, industrial complex information, and an artificial intelligence (AI) chemical accident detection model) to develop the industrial complex chemical accident monitoring system. The goal is the early detection of chemical accidents that may occur in industrial complexes through large-scale scans; the system can detect and evaluate chemical leakages and fires or smoke. The industrial complex chemical accident monitoring system was applied to the Yeosu National Industrial Complex (YNIC), located in Jeoltanam-do. Since the chemical factories of the YNIC are densely packed without sufficient safe distance from other business sites, accidents, such as chemical leakages, will likely affect the neighboring companies and residents [21]. To create the chemical accident monitoring system, 70m-high steel towers were built and installed at two locations (Jeongnyang and Hwachi) within the YNIC. Moreover, an IR system for remote chemical detection (IR-RCD) and a long-range video surveillance system (LRVS) were installed at the top of each tower to enable the system to monitor the entire industrial complex. The total area of the industrial complex was divided into 50 m \(\times\) 50 m cells, and coordinates and numbers were then assigned to each cell. Subsequently, the monitoring area was divided into 12 \({}^{\circ}\) sections, and the respective cell numbers were assigned to each section. Thus, the cell number is the key to linking information to a specific area in the system. To detect chemicals remotely, substances that are handled in the YNIC, as well as those that are detectable in real time by the IR-RCD, were selected as targets for monitoring. Thereafter, the latitude and longitude information of the facilities handling the target substances was identified and linked to the cell numbers. The IR-RCD and the LRVS each generate chemical accident alerts after analyzing the actual chemical handling information in the section corresponding to a detected anomaly while scanning the industrial complex. These chemical accident alerts will enable the persons in charge to respond more swiftly by identifying the type of chemical accident and its approximate location. The main contributions of this article include the following:  
1. This system can detect and evaluate the leakage of chemical substances and the occurrence of fires or smoke through large-scale scans. 2. The system can be used for early detection of and effective responses to chemical accidents in industrial complexes. 3. The information on chemical substances detected in each section can be used to identify routine emissions of substances from workplaces. 4. Based on this, the government can manage and supervise workplaces in industrial complexes.
## 2 Related Work
### Trustworthiness Estimation  
Related studies on trustworthiness estimation methods are actively being conducted, and the methods used mainly can be classified into three categories: direct trust, indirect trust, and hybrid trust.  
Direct trust is a trust value calculated based on direct communication between the source node (evaluator) and its direct (immediate) neighbors and between direct nodes and its direct neighbors [11].  
Indirect trust is a trust value of the evaluated node, measured or gained from indirect neighbors of the evaluator. The indirect neighbors of the evaluator are direct neighbors of the evaluated node. The indirect trust is forwarded by the direct node to the source node. When a source node receives an indirect trust value for the target node from indirect neighbors, it can calculate the trust value for the target node without performing a direct trust evaluation [11]. Another trust evaluation method, known as hybrid trust evaluation, measures the reliability of the target node based on both the trust value measured through direct trust evaluation, as well as on the indirect trust evaluation value recommended from indirect neighbors [12]. This method is useful because the accuracy of the trustworthiness may be low if the trust value is considered solely based on direct observation and may improve after the trust value evaluated by other nodes together is also considered. However,this method may result in overhead costs depending on the way both types of trust evaluations are considered and calculated.  
Trust is a relative factor and can be represented as a value either confined in the interval [0, 1] or [\(-\)1,1]. The closer the trust value is to one, the more trustworthy the node, and the closer it is to \(-\)1 or zero, the less reliable the node [12].  
The most commonly used method for measuring the trust value is to check whether the next-hop node has forwarded the packet after receiving the transmission from the sending node and then calculate the packet forwarding ratio (\(PFR\)) [13].
## 3 Results and Discussion
### Phenolic Acids  
HPLC analysis of phenolic acids after 7 days of treatment is reported in Table 2. If compared to the control, some compounds belonging to the classes of hydroxycinnamic acids, benzoic acids and flavonoids were slightly reduced in quantity after treatment, without remarkable differences between \begin{table}
\begin{tabular}{c c c c} \hline  & **C** & **KT** & **KTs** \\ \hline
**pH** & 3.65 \(\pm\) 0.01 b & 3.70 \(\pm\) 0.01 a & 3.71 \(\pm\) 0.01 a \\
**Tittable acidity (g/L)** & 5.89 \(\pm\) 0.13 a & 5.61 \(\pm\) 0.09 b & 5.74 \(\pm\) 0.01 ab \\
**TPI (GAE) (mg/L)** & 1286 \(\pm\) 17.43 a & 1179 \(\pm\) 19.23 b & 1170 \(\pm\) 4.14 b \\
**Abs 420 nm (AU)** & 1.587 \(\pm\) 0.02 a & 1.423 \(\pm\) 0.01 b & 1.421 \(\pm\) 0.01 b \\
**Abs 520 nm (AU)** & 1.690 \(\pm\) 0.02 a & 1.528 \(\pm\) 0.01 b & 1.528 \(\pm\) 0.01 b \\
**Abs 620 nm (AU)** & 0.455 \(\pm\) 0.02 a & 0.376 \(\pm\) 0.03 b & 0.337 \(\pm\) 0.04 b \\
**Hue** & 0.939 \(\pm\) 0.01 a & 0.931 \(\pm\) 0.01 b & 0.930 \(\pm\) 0.01 b \\
**Color density (AU)** & 3.730 \(\pm\) 0.04 a & 3.327 \(\pm\) 0.01 b & 3.386 \(\pm\) 0.02 b \\ \hline \end{tabular}
\end{table} 
Table 1: Oenological parameters of red wines after 7 days of treatments. In the same row, different letters indicate significant differences according to [PERSON]’s test (\(p<0.05\)). \(n=3\). C: no addition; KT: 0.5 g/L of solid chitosan; KTS: 0.5 g/L of chitosan in solution. TPI: total phenolic index. GAE: gallic acid equivalent. AU: absorbance units. solved (KTS) or solid chitosan (KT). As also mentioned previously, the presence of different functional groups in the chitosan backbone such as hydroxy (-OH), amino (-NH\({}_{2}\)) and acetamido (-CONH\({}_{2}\)) allow the absorption of phenols through weak interactions such as hydrogen bonding and van der Waals forces [23; 24; 25]. Moreover, at wine pH, phenolic acids are present in their dissociated phenolate anionic form, and may therefore, be absorbed onto chitosan surface by electrostatic interaction with protonate amino groups [25]. Our results revealed that the presence of chitosan affected to a greater extent the amount of hydroxycinnamic acids by reducing preferentially the concentration of caffe acid derivatives (caftaric acid, GRP and caffe acid) and coumaric acid, reaching removal levels up to 1.22 mg/L and 0.89 mg/L for caftaric acid and GRP respectively (Table 2). The interaction of chitosan with this class of compounds was found by [PERSON] et al. [25] to follow a typical \"cooperative adsorption\" scheme where hydroxycinnamates could set up forces with molecules already adsorbed on the polysaccharide
## 3 Results and Discussion
### Cluster Analysis  
Hierarchical clustering analysis (HCA) using [PERSON]'s linkage was also carried out in order to determine the relationship between the VOs on the basis of their FA compounds. The dendrograms classified the species into two groups (Figure 4). The first group was characterized by higher linoleic and oleic acid contents. The second group, including linseed oil, was characterized by higher concentrations of linolenic acid. Safflower, sunflower, maize, cottonseed and soybean oils were determined as the most similar species with the highest linoleic acid content. Linseed oil had the highest level of linolenic acid; it was different among the studied species in terms of its FA composition. The proportion of \(\alpha\)-linolenic acid C18:3n3 in the FA composition of linseed oil reached 56.01%, while two VOs (safflower and maize) had less than 1%, and another two VOs had none at all. Rapeseed oil had a higher oleic acid C18:1n9c content of 66.31%; at the same time, other VOs had contents lower than 25%.  
The Euclidean distances of the seven FAME clusters were compared within each of the oil types, as presented in Table 5. As shown in Table 5, satflower, sunflower and maize oils were very closely related to each other, with Euclidean distances of 6.6-14.4. Cottonseed oil had a slight similarity to sunflower and maize oils, with an average distance between 16.2 and 18.1. Linseed oil was more distinct, with an Euclidean distance of 70.9-81.1, and rapeseed oils had a distance of 52.8-75.7 from other oils. CA was used to study the influence of FA composition on the clustering variation as well as other data processing parameters, such as sterols,tocopherol and oil stability index [64,67,68].
## 3 Materials and Methods
### Vehicular CO Prediction Model
#### 3.4.2 Correlation-Based Feature Selection (CFS) Model  
The CFS algorithm is one of the machine learning algorithms which is considered as a filter algorithm that choose the features based on correlation concepts [37]. A major characteristic of the correlation-based function is the ability to choose sub-groups that include features that are unusually correlated to the targeted class but unassociated with each other. On the other hand, this algorithm neglects the features with low correlation with the targeted class, and this algorithm is used to delete the duplicated features because they will be correlated with one of the rest of the features at least. The acknowledgement of a feature will rely on the degree to which it predicts classes in territories of the instance space not currently anticipated by different features.  
The CFS's feature subset assessment function is presented in Equation (1):  
\[Ms=k\overline{r_{cf}}/\sqrt{k+k\left(k-1\right)\overline{r_{ff}}} \tag{1}\]  
where \(Ms\) is the heuristic \"merit\" of a feature subset S containing \(k\) features, \(\overline{r_{cf}}\) is the mean of the feature-class correlation (\(f\in\) S), and \(\overline{r_{ff}}\) is the average of the feature-feature intercorrelation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Average** & **Minimum** & **Maximum** \\ \hline Number of vehicles (per 15 min) & 1172 & 126 & 2762 \\ Number of heavy vehicles (per 15 min) & 78 & 16 & 325 \\ Number of motorbikes (per 15 min) & 112 & 9 & 489 \\ Temperature (\({}^{\circ}\)C) & 29.9 & 25.6 & 37.7 \\ Humidity (\%) & 73.5 & 54.3 & 94.5 \\ Wind Speed (mph) & 16.87 & 16 & 18.20 \\ Wind Direction (angle) & 247.1 & 0 & 350 \\ DSM (m) & 25.7 & 10.03 & 129.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary statistics of traffic CO predictors.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Time**} & \multicolumn{3}{c}{**Weekend**} & \multicolumn{3}{c}{**Weekday**} \\ \cline{2-5}  & \multicolumn{3}{c}{**Average CO Concentration (per 15 min) (ppm)**} & \multicolumn{3}{c}{**Average CO Concentration (per 15 min) (ppm)**} \\ \cline{2-5}  & **Min** & **Max** & **Mean** & **Min** & **Max** & **Mean** \\ \hline Morning & 0 & 8 & 2.36 & 0 & 30.5 & 8.5 \\ Afternoon & 0 & 14.5 & 3.5 & 0 & 12.8 & 4.5 \\ Evening & 0 & 9.3 & 3.92 & 0 & 27.3 & 5.84 \\ Night & 0 & 3.6 & 1.47 & 0 & 5.6 & 1.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary statistics of traffic CO measurements.
# Hybrid Window Decoding for Joint Source Channel Anytime Coding System
## 2 Preliminaries
### Source Model  
Let \(\mathbf{s}=(s_{1},\ldots,s_{2})\in\{0,1\}^{n}\) be a binary sequence of length \(n\) generated by an independent identically distributed (i.i.d) nonuniform memory-less source with probability distribution \((p,1-p)\), where \(p\) is the probability of the binary bit \"1\" in a source sequence (named as source probability), and \(p=P_{r}(s_{i}=1)\
eq 0.5\). The initial log-likelihood-ratio (LLR) of each source bit is denoted by \(z^{sc}=\log(\frac{(1-p)}{p})\); and the initial LLRs of the received code word \(\mathbf{y}\) after the binary-phase-shift-keying modulation (BPSK) and over the additive white Gaussian noise (AWGN) channel can be represented as \(\mathbf{z}^{cc}=\frac{2\mathbf{y}}{\sigma^{2}}\), where \(\sigma^{2}\) is the variance of channel noise.
## 7 Results and Discussions
### The Algorithms' Effectiveness in Achieving the Global Best Optimal Solutions  
Figure 9 presents the convergence curves of the various algorithms used to analyze nickel-iron-based IRES@80% DOD with an HPRALC-based scenario. The lowest possible LCC is achieved by GA, PSO, GWO, ALO, DA, MFO, GOA, and SSA algorithms at the 28 th, 48 th, 69 th, 36 th, 63 rd, 61 st, 94 th, and 15 th iterations, respectively.  
Figure 10 shows the converging curves of all algorithms for the LA-based IRES at 80% DOD with the HPRALC-based scenario. The GA, PSO, GWO, ALO, DA, MFO, GOA, and SSA algorithms yielded the least LCC values on the 38 th, 52 nd, 61 st, 33 rd, 66 th, 56 th, 93 rd, and 25 th iterations, respectively.  
The convergence curves of all algorithms used for an HPRALC-based scenario on the lithium-ion IRES at 50% DOD are presented in Figure 11. The GA, PSO, GWO, MFO, GOA, and SSA algorithms achieved the lowest LCCs at the 61 st, 54 th, 93 rd, 42 nd, 82 nd, and 29 th iterations, respectively.  
The convergence curves of all techniques for a nickel-iron-battery-based IRES operating at 80% DOD with an MPRAMC-based scenario are depicted in Figure 12. The GA, PSO, GWO, ALO, DA, MFO, and SSA algorithms had the lowest LCC at the 28 th, 59 th, 95 th, 30 th, 81 st, 93 rd, and 23 rd iterations, respectively.  
Figure 13 shows the convergence curves of the nickel-iron-battery-based IRES with an LPRAHC-based situation at 80% DOD. The minimum LCC of this configuration was obtained at the 32 nd, 29 th, 96 th, 59 th, 64 th, 26 th, 91 st, and 21 st iterations using the GA, PSO, GWO, ALO, DA, MFO, GOA, and SSA algorithms, respectively.  
Figure 11: Curves of convergence for the IRES powered by Li-Ion batteries in the HPRALC scenario.  
Figure 10: Curves of convergence for the lead–acid-battery-based IRES under the conditions of HPRALC.  
The proposed SSA algorithm has been demonstrated to be reliable and efficient in achieving the global best optimal values for all efficiency-based scenarios, as evidenced in Sections 7.6 and 7.7. Consequently, it is recommended for use in addressing all microgrid size issues.
## 6 Conclusions  
In this paper, a serial code acquisition method based on segment correlation is proposed. The proposed code synchronization algorithm can deal with misjudgment caused by the unreasonable data acquisition time effectively. An overall communication system based on the proposed code synchronization algorithm is presented. The M-sequence, BPSK modulation and demodulation, NCO, low-pass filter, carrier synchronization and code synchronization modules were simulated using Matlab. Simulation results show the feasibility of the DSSS transceiver based on the proposed code synchronization algorithm. Each module was then coded and debugged using Verilog. Then, the relevant code was loaded on a FPGA system to verify the overall function of the DSSS transceiver. Finally, a PCB board was designed to verify the overall design.
# Chloride Resistance of Assembled Bridge Piers Reinforced with Epoxy-Coated Steel Bars
## 2 Materials, Specimens, and Methods
### Experimental Method
#### 2.3.3 Chloride Ion Content Testing Method  
Before testing, the NaCl solution in the container was emptied. A drill with a diameter of 8 mm was used to extract powder at the pier and interface locations after the surface of the specimen was dry. The positions and depths of the powder drilling are illustrated in Figure 7. At each location of Joint 1 and pier segment of Joint 2, samples of at least 5 g were collected every 5 mm, drilling to a depth of 50 mm. Along the interface of Joint 2, samples were collected every 10 mm, drilling to a depth of 150 mm to ensure sufficient powder collection. After drilling, the collected powder samples were bagged and labeled, and the holes were filled with cement paste.  
Figure 5: Schematic diagram of electrochemical test.  
Figure 6: Schematic diagram of potentiodynamic polarization curve fitting.  
Before testing, the powder samples were dried, passed through a 200-mesh sieve, and weighed. Then, the powder was transferred into a centrifuge tube containing 30 mL of deionized water. After vigorous shaking, the suspension was stood for 24 h before filtering. The rapid chloride ion concentration tester (RCT) was used to measure the chloride ion concentration in the filtrate. Finally, Equation (1) was adopted to calculate the chloride ion content in the concrete (in mass percentage).  
\[W_{\text{CI}^{-}}=\frac{m_{\text{CI}^{-}}}{m_{\text{s}}}\times 100\%=\frac{C_{ \text{CI}^{-}}V_{0}M_{\text{CI}^{-}}}{m_{\text{s}}}\times 100\% \tag{1}\]  
where \(W_{\text{CI}^{-}}\) is the chloride ion content in the concrete (%); \(m_{\text{CI}^{-}}\) and \(m_{\text{s}}\) are the masses of chloride ions and concrete powder in the measured sample (g), respectively; \(C_{\text{CI}^{-}}\) is the concentration of chloride ions in the filtrate (mol/L); \(V_{0}\) is the volume of the solution (0.03 L); \(M_{\text{CI}^{-}}\) is the molar mass of chloride ions (35.45 g/mol).
## 4 Proposed Approach
### Design Methodology
#### 4.2.10 Authenticity and Key Material Verification  
To ensure trust and secure access control, the service provider (or relying party) must receive formally provable guarantees that the attributes revealed by the user have not been altered. Acting as a verifier, the relying party checks the signed access form to confirm the user's authorization to access specific resources or services. This process relies on the user's agreement with the access policy and explicit consent to disclose the necessary attributes or proofs.  
Equally important is the authenticity of the cryptographic key material used in credential issuance. In real-world deployments, it must be guaranteed that keys belong to legitimate issuers to prevent adversaries from issuing credentials under unauthorized or uncertified keys. In our system, user credentials are constructed through trusted processes--either physical (e.g., visiting a government authority) or digital (e.g., leveraging existing government-issued eIDs)--to ensure the binding of keys to verified identities.
# Asymmetric Propagation Delay-Aware TDMA MAC Protocol for Mobile Underwater Acoustic Sensor Networks
## 1 Introduction
However, the travel time from node N\({}_{1}\) to node N\({}_{2}\) is different with the travel time from node N\({}_{2}\) to node N\({}_{1}\), because the positions of nodes are changed continuously due to mobility of nodes. This asymmetry damages the collision avoidance mechanism of spatial reuse MAC protocols. In this paper, we propose an asymmetric propagation delay-aware TDMA (APD-TDMA) for a MUASN, in which a number of nodes periodically send data to a SN. In order to avoid collisions at the SN, data packet transmission at each UN is deferred by a certain amount of time after reception of a beacon packet from the SN. The time-offset, which is the time for a node to wait before the transmission of a data packet after reception of a beacon packet, is determined by estimating the propagation delay over two consecutive cycles such that the idle interval at the SN is minimized, and data packets arrive at the SN in a packet-train manner without collision. The transmission schedule is specified by the time difference, not the time itself, and thus, time synchronization is not required in the proposed APD-TDMA. The proposed scheduling mechanism is an extension of the TDA-MAC [40] to a dynamic topology that allows mobile nodes. Despite asynchronous behavior, the TDA-MAC provides performance similar to synchronized TDMA. However, the TDA-MAC assumes a static topology, although the effect of surface waves and ocean currents is compensated for by a guard time. Therefore, the TDA-MAC does not work in a mobile scenario. On the other hand, the proposed APD-TDMA can track the propagation delay in a cycle-by-cycle manner. In addition, the APD-TDMA estimates the initial propagation delay by exploiting the natural random backoff effect resulting from the slow propagation speed of sound waves, which can decrease the time required for the estimation of the initial propagation delay between the SN and a node in a sparse network. The remainder of this paper is organized as follows. Section 2 describes the target network model for MUASNs and the proposed APD-TDMA that includes the initialization procedure and the transmission scheduling algorithm. In Section 3, the network performance of the proposed APD-TDMAis evaluated and compared with that of the BTB-TDMA, and finally, concluding remarks are given in Section 4.