esa-sceva/satcom-chunk-collection huggingface dataset:
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 4 Proposed SLAM Classification Based on UWSNs
### New Classification Based on UWSNs  
Classifying SLAM methods specifically for underwater communication is challenging due to the unique constraints of underwater environments. Given the growing reliance on networked underwater exploration and multi-agent collaboration, we propose a new classification based on the integration with UWSNs, as shown in Figure 8. This classification introduces four categories, each addressing different levels of communication and collaboration in underwater SLAM systems.  
Underwater SLAM systems increasingly operate in networked environments where communication constraints directly impact performance. Our UWSN-based classification addresses this reality by providing the following:  
Figure 8: New proposed classifications for underwater SLAM based on UWSN integration.  
* Deployment Guidance: matches system capabilities to mission requirements, for example, standalone for deep trench exploration and UWSN-integrated for coastal monitoring;
* Resource Optimization: helps balance the computational load between onboard processing and network utilization;
* Adaptive Design Framework: enables dynamic reconfiguration based on changing channel conditions.
## 5 Security Analysis
### Revocation Information Forged  
The revocation information needs to be signed by the initiator. It is proved by cryptography that the revocation signature cannot be forged maliciously by the attacker. The reasons for the user's public key revocation can be divided into two categories. One is its own reasons, such as the private key being stolen, and another is because of security threats to other nodes.  
For the first reason, the purpose of the attacker is to steal the user's identity by stealing the private key, so as to carry out attack activities in the network. At this time, although the attacker can forge the user signature and initiate public key revocation information, this behavior has no benefit to the attacker, but it can help the user revoke their already insecure public keys more quickly.  
For the second reason, the initiator public key of revocation information is within the validity period and has not been embezzled, so the signature of revocation information is valid.  
From the above analysis, we can be sure that when the revocation information appears in the network, it is a valid revocation information.
## 1 Introduction
In [10], the multiuser case is examined in MIMO-WCDMA/HSDPA systems, where an effective user scheduling technique is proposed. In [11], mean BER is improved by applying initial-phase optimized Gold codes in a quasi-static fading channel. In [12], the performance of various beamforming algorithms for CDMA systems is investigated, in terms of achievable algorithmic complexity reduction. In this work, the performance of a novel approach for MIMO-WCDMA networks is investigated, that is based on eigenvalue decomposition of the received signal matrix. This technique is also known as Principal Component Analysis (PCA) [13]. PCA has been used in various scientific applications (e.g., [14; 15]), where the main goal is to reduce signal dimension and consequently algorithmic complexity for signal processing and analysis. Therefore, in this study our main purpose is to reduce signal burden by sending to the transmitter only the primary eigenvector of the corresponding covariance matrix. As in [6], all multipath components contribute to signal estimation at the receiver. This work extends the study initially presented by the authors in the conference paper in [16], as the proposed approached is also evaluated considering a realistic four-element transmit antenna. In the next section, signal transmission and reception in MIMO-WCDMA networks is described, in terms of equivalent signal matrices. In addition, a theoretical analysis for mean BER estimation is provided as well. In Section 3, the proposed approach based on PCA is analyzed, while results are presented in Section 4. Proposals for future work along with concluding remarks are provided in Section 5.
# Surface Acoustic Wave Sensor for C-Reactive Protein Detection
## 2 Materials and Methods  
### Design and Fabrication  
A SAW sensor was designed by two interdigital transducers--one for input and the other for output, as shown in Figure 1. The distance, L, between these two finger electrodes, was 1000 \(\upmu\)m. Each IDT consisted of 100 equal-interval double-finger electrodes. The finger width of the IDTs was 8 \(\upmu\)m with 8 \(\upmu\)m separation, yielding 64 \(\upmu\)m acoustic wavelength or periodicity (\(\lambda\)). The IDT aperture (W) was 3500 \(\upmu\)m and the center-to-center distance, H, of the IDTs was 7392 \(\upmu\)m. The bio-sensing area of SAW sensor consisted of \(503\times 2628\)\(\upmu\)m dimensions. This IDT pattern was fabricated on a single 500-\(\upmu\)m thick, single-side polished 128-degree Y-cut LiNbO\({}_{3}\) (2 \(\times\) 2 cm dimension). The aluminum (Al) electrodes with a thickness of 70 nm were evaporated using thermal evaporation. The patterning of the electrodes for the SAW IDT was performed by a conventional lift-off process. A guiding layer of silicon dioxide (SiO\({}_{2}\)) with 100 nm thickness was deposited on top of the device using electron beam evaporation for the protection of the IDTs or the isolation of the pattern. Finally, the bio-sensing area between two IDTs was deposited with a 10-nm thin layer of gold and 1 nm of chromium (Cr) adhesion layer by thermal evaporation and lift-off. A printed circuit board (PCB) was designed with 4.2 \(\times\) 1.7 cm (chip area 1.2 \(\times\) 1.2 cm) dimensions and the SAW device was connected to the PCB using wire bonding with silver glue, as shown in Figure 2.  
Figure 1: Surface acoustic wave (SAW) interdigital transducers (IDT) pattern.  
Figure 2: Fabricated SAW device connected to printed circuit board (PCB).
## 4 Results and Discussion
### Discussion
#### 4.5.3 The Motivation of Extensive Load Scheduling  
In this study, the compensation for the operation of interactive loads is given by the demand response special fund of the Jiangsu power company, which is the main motivation of the schedule  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(S\times M\) & \(\varepsilon\) & **Upper Bound** & **Optimal Value** & **Gap** \\ \hline \(5\times 5\) & 0.2 & $3,292,763.4 & $3,281,729.4 & 0.34\% \\ \(5\times 10\) & 0.2 & $3,263,957.7 & $3,257,231.5 & 0.21\% \\ \(10\times 5\) & 0.2 & $3,260,120.1 & $3,254,598.7 & 0.17\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Cost of system operation of different iteration numbers.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Risk Level \(\varepsilon\)** & **Wind Power Utilization** & **Cost of System Operation** \\ \hline \(0.05\) & 41,708.7 MWh & $3,281,956.1 \\ \(0.1\) & 41,723.5 MWh & $3,281,900.1 \\ \(0.2\) & 41,743.5 MWh & $3,281,823.3 \\ \(0.4\) & 41,756.7 MWh & $3,281,729.4 \\ \(0.5\) & 41,764.2 MWh & $3,281,675.3 \\ \(1\) & 41,770.2 MWh & $3,281,607.5 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results of different risk levels \(\varepsilon\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(S\times M\) & \(\varepsilon\) & **Upper Bound** & **Optimal Value** & **Gap** \\ \hline \(5\times 5\) & 0.2 & 41,997.6 MWh & 41,743.5 MWh & 0.61\% \\ \(5\times 10\) & 0.2 & 41,798.1 MWh & 41,624.5 MWh & 0.42\% \\ \(10\times 5\) & 0.2 & 41,783.3 MWh & 41,653.2 MWh & 0.31\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Wind power utilization of different iteration numbers.  
of extensive load. The focus of this study is to discuss how to increase the wind power utilization through the refined controllable loads. As for the power market mechanism incorporating renewable energy and interactive loads, we take it as our next researcus.
# Effect of IoT Capabilities and Energy Consumption behavior on Green Supply Chain Integration
## 2 Background and Related Work
### Supply Chain Integration  
In supply chain management literature, SCI is a new concept compared to the relationship between a manufacturer, supplier, and customer that is very established. However, this collaborative relationship is found to be unidimensional in the literature [36; 37; 38; 39]. Moreover, a few studies have focused on the dynamic relationship between supply chain partners [40]. Furthermore, a few studies have considered the supply chain as a single system, instead of dividing the supply chain into different subsystems [41; 42; 43; 44; 45]. SCI reflects the flow of material and parts from one supply chain component to another [47]. Some authors suggest that SCI is a flow of cash, resources, and information in supply chain subsystems. However, in the previous literature, the strategic nature of SCI has been ignored. SCI can be divided into two dimensions of internal integration with manufacturers and external integration with suppliers and customers. However, external integration can be in both directions [41].  
Integration can be implemented in the supply chain as the strategic process for collaboration with other supply chain partners to allow the flow of information and resources that can manage inter-organizational and intra-organizational processes and make organizational processes smoother. The ultimate goal of SCI is the flow of information, resources, money, products, and services to serve customers more effectively and efficiently and to result in speedy organizational processes and reduce operational costs [41; 43; 48; 49].  
SCI encourages supply chain partners to strategically collaborate. It can provide operational and strategic benefits [50]. SCI also motivates supply chain partners to share information, reduce supply chain risks, and increase contract duration and trust [51; 52; 53]. SCI highlights the importance of inter- and intra-organizational processes and their outcomes to achieve operational and organizational performance since the primary objective of SCI is customer satisfaction.  
Customer and supplier integrations are typically considered as external integrations. CI and SI both can develop the strategic relationship between a manufacturer, supplier, and customer to develop inter-organizational strategies, processes, practices, and collaboration [54]. CI involves the competencies, practices, and coordination of critical customers, whereas, SI is related to competencies, practices, and integration with critical suppliers [48]. Both CI and SI can interact with customers and suppliers whereas a manufacturer can interact efficiently and synchronize processes to fulfill the requirements of their customers [55; 56; 41; 57].
## 2 Materials and Methods
### Methods
#### 2.3.2 Correlation Analysis and Root Mean Square Error Calculation  
Correlation analysis is to reveal the closeness of the interrelationship between elements. In this paper, the coefficient of determination \(R^{2}\) is mainly used to judge the degree of fit and correlation of different data, and the correlation between vegetation SIF, EVI, NDVI and drought index sc_PDSI is calculated separately with the following equations:  
\[R^{2}=1-\frac{\sum_{i}(y_{i}-f_{i})^{2}}{\sum_{i}(y_{i}-\overline{y})^{2}} \tag{8}\]  
where \(y\) is the actual value, \(f\) is the predicted value, \(\overline{y}\) is the average of the actual value \(y\), and \(y_{i}\) and \(f_{i}\) denote the values of variables \(y\) and \(f\) in year \(i\), respectively.  
In addition, the correlation analysis based on image elements [25] was used in this paper to determine the correlation between the values of GPP response to drought and the drought index with the following equation:\[R_{xy}=\frac{\sum_{i=1}^{n}\left[\left(x_{i}-\overline{x}\right)\times\left(y_{i}- \overline{y}\right)\right]}{\sqrt{\sum_{i=1}^{n}\left(x_{i}-\overline{x}\right) ^{2}-\sum_{i=1}^{n}\left(y_{i}-\overline{y}\right)^{2}}} \tag{9}\]  
where, \(R_{xy}\) is the correlation coefficient of \(x\), \(y\) variables; \(x_{i}\), and \(y_{i}\) represent the values of \(x\) and \(y\) variables in the year \(i\), respectively, \(n\) is the sample size; \(\overline{x}\) denotes the mean value of variable \(x\); and \(\overline{y}\) denotes the mean value of variable \(y\). The root means square error (\(RMSE\)) is calculated as follows:  
\[RMSE=\sqrt{\frac{1}{m}\sum_{i=1}^{m}\left(x_{i}-y_{i}\right)^{2}} \tag{10}\]  
The \(RMSE\) measures the deviation between the observed GPP fluxes and the GPP simulation values.
## 2 Results and Discussion
### Synthesis of Compounds
#### 2.1.2 Synthesis and Characterization of Bis-Triazoles 1**a-d**
The IR spectrum of 1,2-bis-(4\(H\)-4-amino-5-mercapto-1,2,4-triazol-3-yl)ethane (**1c**) shows an absorption band at 3287 cm\({}^{-1}\), indicating the presence of the primary amino group  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Characterization of Compound**} \\ \hline \multirow{3}{*}{**Structure of Triazole**} & **Molecular Formula,** & **Helting** & **FTIR Bands** \\  & **Malar Mass, Aspect** & **Point (\({}^{\circ}\)C)** & **(KBr, cm\({}^{-1}\)):** & **1H-NMR \(\mathrm{\delta_{H}}\) (DMSO-\(d_{6}\), 200 MHz)** \\ \cline{3-4}  & & & **13C-NMR \(\mathrm{\delta_{C}}\) (DMSO-\(d_{6}\), 50 MHz)** \\ \hline \multirow{3}{*}{**H-SH**} & & & C-20.86; H-2-63; N-48.66/ \\  & & & C-20.49; H-2-48; N-48.14 \\ \cline{1-1}  & & & 11,96 (s, 2H, NH); 5,92 (s, 4H, NH\({}_{2}\)) \\ \cline{1-1}  & & \(>\)350 * & 1590; 1508; \\ \cline{1-1}  & & & 1449; 1251; 991 \\ \cline{1-1}  
(-NH\({}_{2}\)) in the molecule. The stretching vibrations of the aromatic C-H bonds are observed at 3159 cm\({}^{-1}\), while the stretching vibrations of the C=N and C=S bonds appear at 1615 and 1248 cm\({}^{-1}\), respectively, as medium-intense bands. The symmetric and asymmetric stretching vibrations for the CH\({}_{2}\) groups are represented by the bands seen at 2941 and 2755 cm\({}^{-1}\). In the case of 1,4-bis-(4\(H\)-4-amino-5-mercapto-1,2,4-triazol-5-yl)butane (**1d**), the stretching vibrations (N-H) for the amino group are observed at 3284 cm\({}^{-1}\), while the bands at 3135 and 3043 cm\({}^{-1}\) characterize the stretching vibrations of the aromatic C-H bond. The bands at 2951 and 2771 cm\({}^{-1}\) can be assigned to the symmetric and asymmetric vibrations of the CH\({}_{2}\) groups, and the stretching vibrations of the C=N and C=S bonds can be seen at 1615 and 1235 cm\({}^{-1}\) as medium-intense bands. The \({}^{13}\)C-NMR spectra of compounds **1a-d** highlights the presence of only the tautomeric form (C=S) through the signals \(\delta\) = 165.63-167.07 ppm, corresponding to the C=S exocyclic bonds. The \({}^{1}\)H-NMR spectra of compounds 1a-d reveal a singlet in \(\delta\) = 11.96-13.30 ppm region due to the triazole protons (NH), confirming the closure of the triazole rings. The protons of the NH\({}_{2}\) groups appear between \(\delta\) = 5.51-5.92 ppm, demonstrating the presence of the free amino groups on the triazole ring, while the signals from the alkyl chain of compounds 1b-d are observed at \(\delta\) = 1.70-4.14 ppm. As further proof, FTIR, NMR 2D COSY \({}^{1}\)H-\({}^{13}\)C, \({}^{13}\)C-NMR DEPT-135 and 2D HMBC spectra were recorded, as seen in Supplementary Material, Figures S1-S18. Also, the other signals observed in both the \({}^{13}\)C-NMR and \({}^{1}\)H-NMR spectra are presented in Table 2, confirming the identity, structure and purity of the synthesized compounds.
# Strategies for Scaleable Communication and Coordination in Multi-Agent (UAV) Systems
## 3 Multi-Agent Planning and Control
### Performance Metrics  
A low-complexity upper-bound on the performance of \(\pi^{*}(s)\) can be derived by computing in each time-slot, the distribution of agents that matches the distribution of interest-points. Let \(P(t)=\{(x_{n},y_{n}):n\in\mathcal{N}\}\) denote the robot positions, let \(\mathcal{I}(t)\) denote the set of interest-points and let \(I(t)=\{(u_{i},v_{i}):i\in\mathcal{I}(t)\}\) denote the interest-point positions all at some fixed time-slot \(t\). The optimal distribution \(P^{*}(t)\) minimizes the distance between the agents and interest-points:  
\[P^{*}(t):=\arg\min_{P(t)}\sum_{n\in\mathcal{N}}\sum_{i\in\mathcal{I}(t)}||(x_{n },y_{n})-(u_{i},v_{i})||, \tag{15}\]  
where \(||\cdot||\) is the Euclidean distance. Given robot positions \(P(t)\), define the distance from agent \(n\) to its closest interest-point:  
\[d_{P(t)}(n)=\min_{i\in\mathcal{I}(t)}||(x_{n},y_{n})-(u_{i},v_{i})||. \tag{16}\]  
Let \(d^{*}\) denote the average minimum distance over all robots:  
\[d^{*}=\frac{1}{|\mathcal{N}|}\sum_{n\in\mathcal{N}}\frac{1}{T}\int_{0}^{T}d_{ P^{*}(t)}(n)dt, \tag{17}\]  
where \(T\) is the operating lifetime. The minimum average time elapsing between two sequentially visited interest-points is \(\frac{d^{*}}{p_{max}}\) where \(v_{max}\) is the maximum velocity of the agent. An upper bound on the maximum number of interest-points visited by \(\pi^{*}(f(s))\) is given by:  
\[\frac{v_{max}T}{d^{*}}|\mathcal{N}|. \tag{18}\]  
Equations (15)-(18) give an approximately optimal performance metric, apart from the multi-agent TSP, that can be evaluated during simulation. They show whether or not \(\pi^{*}(f(s))\) satisfies (13). Alternatives to (15) for measuring the distance between distributions include the Kullback-Leibler divergence (or mutual information).  
The convergence properties of multi-agent RL with state-space compression, is an ongoing area of research. One sub-topic of interest is mean-field RL, which explores the asymptotic behavior of the system as the number of agents goes to infinity. Each agent  
Figure 3: Soft locally interactive structure in the global state space. (**a**) The normalized distribution of agents and interest-points are approximately equal at the resolution of regions \(\{1,2,3,4\}\), \(\{5\}\), \(\{6\}\), \(\{7\}\), so the optimal agent activity in regions \(\{1,2,3,4\}\) is independent of regions \(5\), \(6\) and \(7\). (**b**) Interest-points drift into region \(6\), destabilizing the equilibrium between the normalized distributions. Options or “macro-actions” move agents across regions to restore this equilibrium and “localize” the optimal activity.  
observes the sum of the local states, which is a form of distributed state-space compression. It has been shown that a Nash equilibrium in the value function exists [21].
# Real-Time Monitoring of Indoor Air Quality with Internet of Things-Based E-Nose
## 4 Results and Discussion
Furthermore, there was also a positive correlation between time-CO, time-NO\({}_{2}\), time-CO\({}_{2}\) and time-PM\({}_{10}\) at 0.968, 0.846, 0.622 and 0.407, respectively. We can conclude that the cost of the proposed e-nose measurement system is very low compared to commercial products sold on the market. Moreover, the majority of domestic air quality meters available for commercial use measure only a restricted amount of air quality and climate parameters. In this system, CO, CO\({}_{2}\), NO\({}_{2}\), PM\({}_{10}\), and six parameters of temperature and humidity can be evaluated. Another significant advantage is that our measuring system is IoT-based and provides users with real-time data transfer through its mobile interface. The major drawback of the proposed e-nose system is that no comparison and calibration is conducted with any conventional measuring system. The measuring precision of the system is equivalent to the measuring precision indicated by manufacturers. To rectify this, in our future studies the measuring system will be calibrated first, then the sensor node created and longer-term readings made. Thus, the change in IAQ through individual activities will be disclosed more obviously. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Time** & **CO** & **NO\({}_{2}\)** & **CO\({}_{2}\)** & **Temp** & **Humidity PM\({}_{10}\)** & **1** \\ \hline
**Time** & 1 & 0.844 & 0.869 & 0.762 & 0.640 & 0.735 & \(-0.242\) \\
**CO** & & 1 & 0.841 & 0.869 & \(-0.266\) & 0.415 & 0.014 \\
**NO\({}_{2}\)** & & & 1.851 & 0.86 & \(-0.098\) & 0.254 & \(-0.092\) \\
**CO\({}_{2}\)** & & & 1 & 0.186 & 0.314 & \(-0.020\) \\
**Temp.** & & & & & & & 0.327 \\
**Humidity PM\({}_{10}\)** & & & & & & & 0.317 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Linear correlation matrix for period 2. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Time** & **CO** & **NO\({}_{2}\)** & **CO\({}_{2}\)** & **Temp** & **Humidity PM\({}_{10}\)** & **1** \\ \hline
**Time** & 1 & 0.846 & 0.622 & 0.234 & 0.304 & 0.407 \\
**CO** & & 1 & 0.841 & 0.622 & 0.234 & 0.304 & 0.407 \\
**CO** & & 1 & 0.831 & 0.671 & 0.213 & 0.296 & 0.379 \\
**NO\({}_{2}\)** & & & 1 & 0.474 & 0.213 & 0.328 & 0.347 \\
**CO\({}_{2}\)** & & & 1 & 0.051 & 0.001 & 0.192 \\
**Temp.** & & & & & & 0.629 & 0.230 \\
**Humidity PM\({}_{10}\)** & & & & & & 1 & 0.169 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Linear correlation matrix for period 3.
## 4 MAC Layer in WMSN
### Scheduling and Admission Control
It is shown that by using priority queuing (PQ) scheduling mechanism for EF traffic class and weighted round robin (WRR) scheduler for non real time traffic classes, low delay bound and guaranteed network bandwidth for high priority real time traffic can be provided. This work, as it simply considers a scheduling system, does not provide any insight on the physical and MAC layers of WMSNs. The main drawback of this work, however, is that it demonstrates that the proposed system can provide differentiated services but it does not study signaling overheads or energy consumption. In concordance with [24], the work in [16] shows also that UWB characteristics should be taken into account in the MAC layer for channel access, scheduling, and error control wherein the low duty cycle and low power transmission reduce the probability of collisions between pulses and interference. A theoretic work done in [30] tries to give insights in where and how to deploy sensor nodes (and how many of them) so that all the nodes can be supported by the limited communication resources in WMSNs. It assumes low-mobility or a static network, flat topology, and a single-channel TDMA-based communication to present a cross-layer design model where node admission in WMSNs and its interaction with resource management and link scheduling is investigated. The interaction is formulated as a two-stage optimization problem: first stage is to maximize the number of admitted sensor nodes, and the second stage is to maximize the network life time. It is shown also that this optimization problem can be presented in equivalent one-stage optimization problem with more compact mathematical form. Note that a common characteristic in all the presented proposals is that some kind of service differentiation (Figure 2) is provided: it seems clear that without this feature it is not possible to guarantee the QoS needed by WMSNs.