esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Cross-Layer Routing Protocol Based on Network Coding for Underwater Sensor Networks
## 6 Conclusions  
In this paper, we propose NCRP, a cross-layer routing protocol based on network coding for UWSNs. NCRP takes full use of multi-cast feature in underwater wireless networks and designs an efficient way to find a reliable data transmission link. The process of NCRP can be divided into two parts: initial routing construction and route maintenance. Beacon messages are only used in initial routing construction and route can be updated along with data packets transmissions. The long propagation delay for control messages is then saved and the demand for neighborhood nodes information is also decreased. Meanwhile, as we use network coding method for multiple nodes transmission, NCRP does not limit the transmission range for one hop transmission and the receivers can jointly decode messages with received packets from several neighboring nodes. So data packets received from unstable links are used effectively without any waste. The update of selecting next hop relay is based on the current channel quality and this can make the data transmissions more reliable and avoid the occurrence of void areas. The channel utilization and packet delivery ratio are also improved by real-time transmission rate control and route maintenance. Extensive simulations based on NS-3 show that NCRP outperforms VBF, HHVBE,DBR and VAPR in terms of packet delivery ratio, end to end delay and average energy tax. In future work, we would like to explore the real implementation of NCRP and improve the performance by introducing optimal node deployment technique into the design.  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 14: AET performance with varying node numbers.
## 2 T* Revise Attenuated Travel Time Inversion
The logarithm ratio of amplitude spectra of reflections A\({}_{1}\) in the overburden and A\({}_{2}\) in the second layer are used for attenuated travel time (\(t^{*}_{2}-\)\({}^{*}\)1) calculation; also, reflections \(A_{1}\) and \(A_{3}\) in the overburden are used for attenuated travel time (\(t^{*}_{3}-t^{*}\)1) calculation. Generally, reflection \(A_{2n-1}\) in the overburden and \(A_{2n}\) in the second layer are used for attenuated travel time (\(t^{*}_{2n}-t^{*}_{2n-1}\)) calculation; also, reflections \(A_{2n+1}\), \(A_{2n-1}\) in the overburden are used for attenuated travel time (\(t^{*}_{2n+1}-t^{*}_{2n-1}\)) calculation:  
\[\left\{ \begin{array}{c}b_{1}=ln(\frac{A_{2}}{A_{1}})=B_{1}-\pi f \big{(}t_{2}^{*}-t_{1}^{*}\big{)},\\ b_{2}=ln(\frac{A_{3}}{A_{1}})=B_{2}-\pi f\big{(}t_{3}^{*}-t_{1}^{*}\big{)},\\ b_{3}=ln(\frac{A_{3}}{A_{3}})=B_{3}-\pi f\big{(}t_{3}^{*}-t_{3}^{*}\big{)},\\ b_{4}=ln(\frac{A_{5}}{A_{3}})=B_{4}-\pi f(t_{5}^{*}-t_{3}^{*}),\\ \vdots\\ b_{2n-1}=ln(\frac{A_{2n}}{A_{2n-1}})=B_{2n-1}-\pi f\big{(}t_{2n}^{*}-t_{2n-1}^ {*}\big{)},\\ b_{2n}=ln(\frac{A_{2n+1}}{A_{2n-1}})=B_{2n}-\pi f\big{(}t_{2n+1}^{*}-t_{2n-1}^ {*}\big{)},\end{array} \right. \tag{4}\]  
where \(b_{1}\), \(b_{2}\), \(b_{2n}\) are spectra ratios calculated from the reflection pair of \(A_{1}\) and \(A_{2}\), pair \(A_{1}\) and \(A_{3}\), pair \(A_{2n+1}\) and \(A_{2n-1}\), respectively. Then we modify and re-write Equation (4) as:  
\[\left( \begin{array}{c}b_{1}\\ b_{2}\\ \vdots\\ b_{2n}\end{array} \right)=\left( \begin{array}{ccccccccccccc}v&z&\cdots&z&z&- \pi F&-\pi F&z&\cdots&z&z&z\\ z&v&\cdots&z&z&-\pi F&z&-\pi F&\cdots&z&z&z\\ \vdots&\vdots&\ddots&\vdots&\vdots&\vdots&\vdots&\ddots&\vdots&\vdots& \vdots\\ z&z&\cdots&v&z&z&z&z&\cdots&-\pi F&-\pi F&z\\ z&z&\cdots&z&v&z&z&z&\cdots&-\pi F&z&-\pi F\\ \end{array} \right), \tag{5}\]  
where vectors \(v=\) (\(1\)\(\ldots\)\(1\))\({}^{T}\), \(F=\) (\(f_{1}\)\(f_{2}\)\(\ldots\)\(f_{n}\))\({}^{T}\), and \(z=\) (\(0\)\(\ldots\)\(0\))\({}^{T}\). By solving Equation (5), the terms B and Q\({}^{-1}\) are inverted. To update \(\delta Q^{-1}\), the inverted attenuated travel times are then used in Equation (1):  
\[\delta t_{12}{}^{*}=(t_{r1}^{*}-t_{r2}^{*})-(t_{m1}^{*}-t_{m2}^{*})=\sum(t_{1} -t_{2})\cdot\delta Q^{-1}, \tag{6}\]  
where \(t^{*}_{r1}\) and \(t^{*}_{r2}\) are the calculated attenuated travel times from real data, and \(t^{*}_{m1}\) and \(t^{*}_{m2}\) are calculated attenuated travel times from synthetic data. The final Q can be derived when the error in \(\delta t_{12}{}^{*}\) is acceptable. Figure 1: The reference and target reflections in a two-layer model. In this model, the overburden is the first layer, which is the layer above the target layer, that is the second layer.
# Distributed Information Compression for Target Tracking in Cluster-Based Wireless Sensor Networks
## 3 Data Processing Schemes
### Network Configuration and TCAT  
This section describes the fundamental system configuration of CTCI. To achieve distributed scheduling target detection and tracking, a two-level clustering (_i.e._, a hybrid static/dynamic clustering scheme) is applied to deal with the control of network operations and to handle the tracking task, which is based on our previous works, the Clustering Algorithm via Waiting Timer (CAWT) scheme [18] and the TCAT scheme [13]. During the network initialization phase, sensors may apply the CAWT to establish a Level 1 network architecture (Figure 3). In order to handle the impact of the target movement, the TCAT scheme may be applied to form a sub-cluster with tracking responsibility and allow each sub-cluster member to locally locate the target in a distributed manner. Figure 4 shows an example of leader and sub-cluster member selection. Therefore, the TCAT scheme may be applied to select the sensors with tracking responsibility and build a Level 2 network architecture. Notice that the information flow is bi-directional (_i.e._, it goes through the sub-cluster members to the leader and then to the clusterhead, and vice versa).  
Figure 5: Illustration of the block diagram for the CTCI method.
# Minimal Collagen-Binding Epitope of Glycoprotein VI in Human and Mouse Platelets
## 1 Introduction
### Collagen-Binding Sites of GPVI Receptor  
GPVI interaction with collagen regulates platelet adhesion, activation, aggregation, and procaagulant activity that is essential for thrombus growth [9; 10]. GPVI belongs to the immunoglobulin superfamily, building a complex with the immunoreceptor tyrosine-based activation motif (ITAM)-subunit FcRy-chain. GPVI has two extracellular C2-type Ig-like domains (D1 and D2 domains) which are linked to a transmembrane (TM) domain and a short cytoplasmic tail, interacting with Src family kinases, protein kinase C (PKC), and calmodulin [10; 11]. The first crystal structure of the D1 and D2 domains of the human GPVI proposed the dimerization site between two \(\beta\)-strands of D2 domains [12]. The amino acid residue in the TM domain of GPVI forms a salt-bridge with the FcRy-chain, triggering ITAM-mediated signals to the linker adaptor for the T cell (LAT) complex. Interestingly, GPVI dimers bind with high affinity to tandem GPO sequences on the collagen fiber, while GPVI monomers had a lower affinity, indicating that mainly GPVI dimers are involved in collagen-dependent platelet adhesion. Therefore, it was proposed that a functional unit of GPVI is a dimer [13; 14; 15]. In line with this result, most GPVI receptors on the surface of resting platelets form dimers, and the number of dimers is further increased during platelet activation, promoting collagen-mediated platelet adhesion [13; 14].
# Fog Computing for Smart Cities' Big Data Management and Analytics: A Review
## 6 Fog Computing and Data Management Use Cases in Smart Cities
### Fog Computing in Smart Grid Architectures
The data pipeline relies on a private cloud infrastructure to ensure on-demand resource elasticity and to visualize current and historical energy consumption patterns. The authors used public cloud platforms, which provide powerful computing resources for analytics and reliable data hosting solutions suitable for distributed access. Private clouds offer the advantage of providing physical data security, but hardware management remains a concern. They are essential when data transfer latency is a concern. As they are expected to be generated by smart city grids, multi-terabyte data sets would favor private clouds due to the high costs of transferring and storing data in public clouds. These studies show the need for fog-cloud-based platforms for smart grids, which are highly reliable and can ensure the efficient management of resources to support consumer and business operations. The last few years witnessed a growing trend in using fog computing in smart grids to address several of the concerns involved when the smart grid relies only on cloud servers for data storage and analysis [69; 70; 71; 72; 73; 74]. [PERSON] et al. [74] examined the cloud-based smart grid architectures' current state and highlighted the motivation to adopt fog computing as a technology catalyst for real-time smart grid analysis. They proposed a three-tier fog-based solution for a smart grid and described a use case scenario for the proposed model. [PERSON] et al. [72] also proposed a three-layer fog computing based smart grid framework. They characterized its features with regard to the integration of a massive number of IoT devices. Their evaluation of the framework on real-world parameters showed that for a network with approximately 50% time-critical applications, the overall service latency obtained using fog computing is nearly half that obtained when using a cloud-only solution. They also found that fog computing reduces the generic cloud computing model's aggregated power consumption by more than 44%. [PERSON] et al. [71] investigated resource management in smart grids and proposed a fog-cloud-based model. They used five load-balancing techniques to evaluate its performance enhancement over a cloud computing model. These five algorithms are Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Round-Robin, and Throttled. Furthermore, the authors proposed a hybrid approach, which combines ABC and ACO. The results of their simulations revealed that their technique outperformed the other techniques. Figure 6 depicts the typical components of a fog-cloud-based platform for smart grids. Several fog nodes can be deployed at various city facilities, including power generation facilities, transmission and distribution stations, and near power consumers (smart buildings and offices, smart homes, and manufacturing factories).
## 4 Challenges and Open Research Directions
### Economic Efficiency
In reality, service providers may commercially exploit the information received (\(V_{info}\)) in the period of providing services for farms, aiming to encourage the deployment of IoT applications in smart agriculture. Nowadays, several IoT platform providers allow free registration and use of services with some limitation conditions regarding services' functionality and ability processing; the number of connected IoT devices; and the number of data stored while premium functions and services charge users a fee. In addition, one of the significant factors slowing down IoT adoption in agriculture is farmers' knowledge and ability to use IoT devices. In developed countries, this issue can be easily solved due to the accessibility of new technologies of farmers. Otherwise, in developing countries, where the majority of farmers in rural areas have very limited access to advanced technologies, this issue is a significant challenge [118; 119].
## 6 Conclusions  
This investigation introduces an electro-optical fusion system employing the standard modulation dependent on the SOA-MZI at an exceptionally high frequency range, extending up to 195.5 GHz. The architecture efficiency is evaluated based on optical and electrical conversion gains. With QPSK data, the system exhibits achievable EVMs for very high bit transmission rates, up to 5 Gbit/s. Specifically, when the mixed QPSK field is generated at 5 Gbit/s, the EVM reaches 24% at 195.5 GHz. A comparative analysis between the simulated findings and real-world experiments reveals similar outcomes. Moreover, by increasing the control frequency to 100 GHz, it becomes possible to generate terahertz 4096-QAM up-mixing replicas up to 1 THz, with 120 Gbit/s, while maintaining excellent performance characteristics.  
[PERSON] performed the principle, experiments, and simulations and prepared the manuscript; [PERSON] reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
The authors declare no conflicts of interest.
## 5 Experiment
### Experimental Results and Analysis
#### 5.4.1 Comparative Experiment
The STSeq2 Seq model dynamically adjusts the weights of adjacency matrices in graph convolution based on the dynamic temporal information of input sequences, while the DGCRN model generates dynamic graphs through Hyper Network modules and node embedding features. Experimental results indicate that the STSeq2 Seq model outperforms DGCRN, possibly because of its adoption of spatiotemporal graph convolution modules in the encoder, benefiting from the good performance of STGCN in single-step prediction, allowing it to better capture spatiotemporal correlations. It is also possible that the dynamic graph generation approach of the STSeq2 Seq model is more suitable for bike-sharing station demand prediction than that of the DGCRN model. 5. The LGDMGCN model exhibits excellent performance across all prediction time steps, with lower MAE and RMSE compared to other models. This indicates the effectiveness of the LGDMGCN model in multi-step bike-sharing demand prediction. Moreover, the LGDMGCN model demonstrates relative stability in performance across different prediction time steps, indicating good robustness. It effectively captures complex rela \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Pred\_Step** & **4 h** & & **8 h** & & **12 h** & & **16 h** \\ \hline
**Model** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** \\ \hline LSTM & 0.4099 & 0.4525 & 0.4141 & 0.5410 & 0.4534 & 0.5983 & 0.5167 & 0.7246 \\ STGCN & 0.0380 & 0.2342 & 0.0506 & 0.2564 & 0.0560 & 0.2675 & 0.0765 & 0.2760 \\ DGCRN & 0.0915 & 0.2679 & 0.0951 & 0.3322 & 0.0976 & 0.3520 & 0.0985 & 0.3718 \\ AttConvLSTM & 0.0959 & 0.2548 & 0.1016 & 0.2682 & 0.1044 & 0.2690 & 0.1069 & 0.2702 \\ STSeq2 Seq & 0.0398 & 0.2474 & 0.0408 & 0.2629 & 0.0432 & 0.2686 & 0.0498 & 0.2761 \\ LGDMGCN & 0.0350 & 0.2476 & 0.0367 & 0.2628 & 0.0370 & 0.2629 & 0.0371 & 0.2679 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative experimental results. tionships between spatiotemporal data, thereby improving prediction accuracy and reliability. In addition to considering spatiotemporal correlations, the LGDMGCN model also accounts for spatiotemporal heterogeneity, resulting in further performance improvements compared to the STSeq2 Seq model.
## 4 Discussion
NS alone disrupted the bacterial cell surface and therefore caused destruction, which was evident in the bacterial growth inhibition. However, in the combinations of NS with OXA and AUG, where there was marked cellular aggregation/cell destruction, we can point to the possible synergistic/additive effects of NS, as shown in the FIC results in Tables 2 and 3, all of which created significant changes in the molecular ultrastructure that Gram-positive bacteria such as MRSA use for survival. It could also mean that binding of \(\beta\)-lactam antibiotics to penicillin binding protein (PBP) sites was significantly enhanced. Note that the isolates used in this study exhibited marked antimicrobial resistance. The TEM micrograph analysis also confirmed this view. It showed cell death and cell surface disruption, similar to the ultrastructural changes in the MRSA isolates from a previous study with NS alone [32]. The molecular structural changes seen in the TEM micrographs could also be due to interference in the transportation or the synthesis of PBP by NS. Such cell wall destruction, either by NS or in combination with \(\beta\)-lactam antibiotics, would be expected to remove the bacterial protective barriers, thus leading to cell lysis, as shown by the TEM micrograph results. Generally, the mode of action of augmentin as a \(\beta\)-lactam is that it leads to the inhibition of bacterial cell wall peptidoglycan by binding and inhibiting PBPs [50]. It is documented that antimicrobial resistance results from the activation of the Mec A gene and its variants, which leads to the formation of penicillin-binding protein 2A (PBP2a), which binds to \(\beta\)-lactams, thus creating resistance [50]. However, we noted that alkaloid plant components, alone or when combined with conventional antibiotics, could have an effect on bacterial acquired resistance, which could explain the NS and antibiotic combined effects seen in this investigation [3]. _N. sativa_ oil is rich in diverse volatile compounds involved in various biological activities [51]. The GC-MS results confirmed that _N. sativa_ essential oil is a good source of bioactive components such as p-cymene, linalool, thymoquinone, transanethole, and m-thymol, which have been evaluated by various reports and are considered to possess good antimicrobial activity [51; 52; 53; 54]. Earlier reports documented synergistic effects of the extracts of black cumin with streptomycin and gentamicin against antibiotic sensitive \(S\). _aureus_ and other bacterial isolates [55; 56]. Thymoquinone was indicated to exert synergism with the antibiotics against \(S\). _aureus_[56]. Thus, thymoquinone an abundant component of _N. sativa_ is reported to be the bioactive compound with multiple pharmacological actions including being a strong antimicrobial [55; 57]. Additionally, as other reports show synergistic effects when thymoquinone is combined with synthetic and natural compound, the present study further confirms that the contents of _N. sativa_ essential oil are potent at inhibiting bacterial activity [54; 58].
# Time Domain NMR in Polymer Science: From the Laboratory to the Industry
## 2 Sequences
### Data Processing  
Other than the application of fitting functions, such as the ones described in Section 2.1, on the magnetization evolution graphs, there are more sophisticated procedures for data handling. The information is extracted relying on algorithms.  
One of the most widely applied is the inverse Laplace transform (ILT). It is based on the description of the NMR decay signal as an integral superposition of exponentials, weighed by their probability density [39]. The integral equation that has to be solved is known as a Fredholm integral equation of the first kind. Applying a numerical inversion procedure, like the Fourier Transform, the probability density functions are obtained. They are generally represented as peak distribution functions of different T\({}_{2}\)[40]. The number of peaks and their distributions are linked to the degree of heterogeneity of the material under analysis [41].  
Generally, to stabilize the outcome of the integral solution and avoid the noise contribution, regularization methods are implemented. The most common in NMR is the Tikhonov regularization [42], especially concerning the extraction of D\({}_{\mathrm{res}}\) from the curves of MQ experiments [23]. It consists in finding the optimum parameter that minimizes the fitting error, applying a minimization procedure on the fitting function chosen as the Kernel function.
## 2 Dynamics of Quadruped Robots
### Gait Planning  
To keep the quadruped robot balanced during a trot, the center of gravity should be placed on the diagonal of the support phase. Due to errors in the trot movement, the center of gravity cannot be kept on the diagonal at all times. To make the robot move steadily, it is necessary to reasonably plan the foot coordinate trajectory when the swinging phase is transformed into the supporting phase. The two legs on the diagonal are called diagonal legs, and the quadruped robot has two groups of diagonal legs. To simplify the control algorithm of quadruped movement, we introduce the concept of a virtual leg. In trot gait, the diagonal legs have the same movement form and the hip joint exerts similar force on the trunk, so the two legs on the diagonal can be equivalent to a virtual leg [18; 19]. After equivalence, the two virtual legs are also on the diagonal and can be further equivalent to a virtual leg, as shown in Figure 3:  
To balance the trunk posture, PD (proportional-differential) control of trunk posture is added to the foot-end planning of the supporting phase. Based on the kinematics model of the robot, the trot trajectory of the quadruped robot is proposed. During the motion of the robot, each foot periodically sways relative to the trunk of the quadruped robot. The quadruped robot can move at a steady and uniform speed and can pass through the slightly undulating ground. The characteristics of the robot's foot trajectory during the motion are analyzed:  
(1) The speed of the swing leg decreases to zero when it falls on the ground.  
(2) Keep the supporting legs moving at a constant speed during the retraction process.  
Figure 3: Virtual leg equivalent model of the quadruped robot.  
(3) There should be no abrupt change in the speed and acceleration of the swing leg and supporting leg during movement.  
In order to ensure the continuity of the body in the process of movement, the method of combining a cubic spline and a straight line is used to plan the foot trajectory. It is assumed that the step length of the foot target trajectory is S, the step height is H, the initial position of the foot end is \((-20,0,0)\), the unit is mm, the velocity and acceleration at the beginning and end are 0, and the movement period is T. The fitted curve of foot end trajectory of the quadruped robot is realized.  
The trajectory of the robot's supporting leg is defined as:  
\[x(t) =S\times\left(3-\tfrac{4t}{T}\right)\quad\left(\tfrac{T}{2}<t\leq T\right) \tag{4}\] \[z(t) =0\quad\quad\left(\tfrac{T}{2}<t\leq T\right)\]  
The trajectory of robot's swinging leg is defined as:  
\[x(t) =S\times\left(-64\times\tfrac{t^{3}}{T^{3}}+48\times\tfrac{t^{2}}{ T^{2}}-4\times\tfrac{t}{T}-1\right)\quad\quad\quad(0<t\leq\tfrac{T}{2}) \tag{5}\] \[z(t) =\left\{\begin{array}{ll}H\times\left(-128\times\tfrac{t^{3}}{T^ {3}}+48\times\tfrac{t^{2}}{T^{2}}\right)&0<t\leq\tfrac{4}{T}\\ H\times\left(128\times\left(\tfrac{t}{T}-\tfrac{T}{4}\right)^{3}-48\times \left(\tfrac{t}{T}-\tfrac{T}{4}\right)^{2}+1\right)&\tfrac{T}{4}<t\leq\tfrac{T }{2}\end{array}\right.\]  
where, \(t\) represents the sampling time of gait trajectory.