esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Evaluation and Analysis
### Experimental Results  
#### 5.2.1 Impact of Data Generation Rate  
In this section, we illustrate the impact of the data generation rate to evaluate the performance of the two protocols under different transmission loads. As the data generation rate differs from 100 to 1,000 Round/Packet, the performance of the mentioned protocols will be shown as in Figure 10.  
From Figure 10(a) we can see that SFRRP protocol achieves better performances than the Flooding protocol. The performance of SFRRP is high and efficient at each generation rate. However, as the data generation rate decreases, the performance of the Flooding partly increases. Flooding uses the broadcast method to transmit packets to the base station. Thus, the generated packets are transmitted with more delay, are more copies of the packets are transmitted over the network. This causes additional network traffic and delay time for the delivery of the packets. However, SFRRP uses fuzzy logic to select the best node to forward the packet towards the base station and a smaller number of nodes are involved in the routing process. Moreover, it will not produce any copy of the packet, and only one packet is sent to the selected node, which reduces network traffic and the delay time of transmitted packets. As shown in Figure 10(b), the average delivery of SFRRP is the same, partly because network traffic is low and all generated packets are transmitted to the base station quickly. Nevertheless, the average delivery of the Flooding goes up when the generation rate increases, because network traffic is high and several packets are. Therefore, the generated packets are transmitted to the base station too late. From Figure 10(c), we can demonstrate that the network life of the proposed protocol is higher than with Flooding. The reason is that the redundant packets in the Flooding routing are too many. Thus, the consumption energy of the sensor nodes will be high, which leads to low network life.  
\begin{table}
\begin{tabular}{|l|c|} \hline
**Parameter** & **Default Value** \\ \hline Network size (m\({}^{3}\)) & \(300\times 300\times 300\) \\ \hline Number of sensor nodes & 50 \\ \hline Transmission range of sensor nodes (m) & 75 \\ \hline Initial nodes energy (Joule) & 5 \\ \hline Maximum buffer size of sensor nodes (packet) & 60 \\ \hline Position of base station (m) & (150, 150, 0) \\ \hline \end{tabular}
\end{table}
Table 4: Simulation parameters.
# Flooding Message Mitigation of Wireless Content Centric Networking for Last-Mile Smart-Grid +
## 3 Proposed Scheme
### Extended CCN Packet Formats  
TOP-CCN extends the traditional CCN header formats to include the topology information and application characteristics of the SG application. We define three formats, CA packet, data packet, and the interest packet as shown in Figure 4. CA has ten fields; content prefix, time, ID, type, number of neighbors (# of NBRs), neighbor prefix list (NBR-PL), neighbor list (NBR-L), neighbor type list (NBR-TL), HOP count, cache hit ratio, and CA type. Content prefix is the name of the content provider that is assigned by the network manager or the prefix assignment algorithm [3] as '/region/MeterID/eventType/Information'. Time field represents the available time duration of the stored content that can be assigned by application layer protocol. The ID is a unique identifier of the device such as MAC address or assigned meter device ID. Type attribute represents the type of the relay policy of the device. Its default value is non-MPR, which means that the content and control packets cannot be rebroadcasted. If some device is elected as a MPR by neighbor(s), then the device can rebroadcast the received TOP-CCN packet, and its type attribute is set to MPR notifying the flooding policy of the device. NBR-L is a ID list of the neighbor(s) gathered by the received CA message from neighbor(s). NBR-TL represents the type list of neighbor(s) such as MPR or not. The number of NBR fields represent to included number of neighbor list in the packet to decide the neighbor related field size in the packet. NBR-PL is a list of content prefixes of the entire neighbors. HOP count is the forwarded count of the packet that is increased by each forwarding device. Every device periodically measures a cache hit ratio during a period of time \(\Delta\)t. Cache-hit ratio is shown as the content reply ratio among the entire requested content items. When a device has fresh or popular content items in its CS, then the cache-hit ratio will be measured as a high value. When a device receives a CA packet from one of its neighbors, then the 1-hop neighbor table, the 2-hop neighbor table, and the FIB are sequentially updated. CA type attribute represents the type of the flooding policy of the CA message such as 1-hop limit and multi-hop flooding. Both interest and content packets of the TOP-CCN have three extended fields such as the ID, expected hop, and the hop count. ID is the unique identifier of the sender such as consumer or provider of the packet. The expected hop represents the distance between the consumer and the content provider. The expected hop in the interest packet and the content packet are updated by the sender and relay devices to limit the flooding range of the packet.
## 3 Results and Discussion
### PST Removal from Bivalves  
Boosted by the previous encouraging results, the toxin removal experiments were replicated (with appropriate adjustments) on live mussels. On day 6, immediately before adding the resin, the collected water samples did not show any cells of _G. catenatum_ or cysts of the algae, implying that the bivalves on the tanks consumed all available algae. Similarly, the HPLC-FLD analysis of toxins from water samples collected from different reservoirs revealed no quantifiable toxins. For those reasons, the toxin concentration at time 0 (day 6) is assumed as the starting point for assessing mussel PST clearance. In both experiments involving contaminated mussels by _G. catenatum_, the toxins GTX2&3 and STX were undetectable throughout all experiments. The toxins C3&4, GTX1&4 and NEO were not quantifiable over the course of the experiments. dcNEO was also unquantifiable at time zero and undetectable at the remaining times. In the second set of experiments, dcGTX2&3 was only quantified in tank 1 at zero time (34 \(\upmu\)g STX.2 HCl eqv/Kg) and after 24 h, without resin (24 \(\upmu\)g STX.2 HCl eqv/Kg). In the remaining tanks and times, it was unquantifiable. The results of the PST clearance from mussels, with or without resin, are shown in Figure 8, Figures S4 and S5 (individually) and Table 2.  
Figure 7: Molecular structures of some PST, under acidic conditions [2].
Influences of Hyperbranched Polyester Modification on the Crystallization Kinetics of Isotactic Polypropylene/Graphene Oxide Composites  
[PERSON]  
1 Chongqing Zhixiang Paving Technology Engineering Co., Ltd., Chongqing 401336, China; [EMAIL_ADDRESS] (Z.H.); [EMAIL_ADDRESS] (X.S.); hele  
Introduction of inorganic fillers has been found to be an efficient way to improve the strength of iPP without sacrificing other properties. Graphene is a two-dimensional atomic crystal composed of a single atomic layer of carbon atoms connected by \(sp^{2}\) hybridization, which has a thickness of only 0.34 nm, known as the thinnest material [23]. This special structure provides graphene excellent properties such as outstanding electrical conductivity, thermal conductivity, high elastic modulus, larger specific surface area, and fractional quantum hall effect [24; 25; 26]. However, the strong Van der Waal force of graphene results in the characteristics of hydrophobicity and easy agglomeration, and the application of graphene is greatly limited [27]. Graphene oxide (GO) is a precursor of graphene, which has similar structure to graphene and can be used as the preferred choice for reinforcing polymers because of its carboxyl groups and carbonyl groups at the edge and hydroxyl groups and epoxides groups on the matrix [28; 29]. The existence of these functional groups improves the hydrophilicity of GO, which can also react with polar polymers. For nonpolar polymers, functional groups are introduced to modify the surface of GO. Surface-functionalized GO exhibits good interfacial compatibility in nonpolar polymers such as iPP and polystyrene [13; 30]. Composite modification of iPP with GO is an important way to make high-performance iPP materials.  
Hyperbranched polymers have a lower branching efficiency than dendrimers while enjoying many useful properties [31; 32; 33; 34]. The highly branched architecture minimizes chain-chain ability and low melt viscosity of these polymers [35], which is compatible with iPP. The presence of numerous functional groups (-OH) provides high reaction activity with inorganic filler, providing it the possibility to be desirable macromolecular surface modifier for GO to further enhance the compatibility and dispersion in iPP.  
In this study, the GO grafted with hyperbranched polyester was prepared to enhance the interaction between GO and iPP matrix. Then, PP/GO composites were prepared to study the impacts of raw GO and hyperbranched polymer ester grafted GO on the detailed isothermal crystallization kinetics of the composites, which is an important issue that has not been reported before.
# Carrot Pomace Characterization for Application in Cereal-Based Products
## 3 Results  
### Carrot Pomace Powder of Different Varieties Chemical Properties  
The chemical composition of carrot pomace powders is presented in Table 1. Significant differences (\(p<0.05\)) between the analyzed carrot pomace varieties were observed regarding the fat, ash, fiber, and carbohydrates values.  
The Niagara variety presented the highest content of carbohydrates and the lowest fat and fiber content compared to the other varieties. The Belgrade carrot pomace showed the greatest fiber content and the smallest moisture. The richest in fat, protein, and ash contents was the Sirkana carrot pomace with this variety having the lowest carbohydrates content.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Variety** & **Protein (\%)** & **Fat (\%)** & **Ash (\%)** & **Fiber (\%)** & **Moisture (\%)** & **Carbohydrates** & **Energetic Value** \\  & & & & & & **(\%)** & **(kcal/100 g)** \\ \hline Baltimore & 6.87 \(\pm\) 0.06 \({}^{\rm c}\) & 1.00 \(\pm\) 0.02 \({}^{\rm b}\) & 5.29 \(\pm\) 0.04 \({}^{\rm c}\) & 28.69 \(\pm\) 0.58 \({}^{\rm c}\) & 4.04 \(\pm\) 0.02 \({}^{\rm b}\) & 54.13 \(\pm\) 0.52 \({}^{\rm b}\) & 339.40 \(\pm\) 40.18 \({}^{\rm b}\) \\ Belgrade & 8.01 \(\pm\) 0.06 \({}^{\rm b}\) & 1.01 \(\pm\) 0.03 \({}^{\rm b}\) & 5.89 \(\pm\) 0.02 \({}^{\rm a}\) & 33.34 \(\pm\) 0.25 \({}^{\rm a}\) & 3.78 \(\pm\) 0.01 \({}^{\rm b}\) & 48.00 \(\pm\) 0.24 \({}^{\rm c}\) & 333.96 \(\pm\) 47.87 \({}^{\rm b}\) \\ Niagara & 8.84 \(\pm\) 0.12 \({}^{\rm a}\) & 0.70 \(\pm\) 0.02 \({}^{\rm c}\) & 5.56 \(\pm\) 0.03 \({}^{\rm b}\) & 20.09 \(\pm\) 0.08 \({}^{\rm d}\) & 5.88 \(\pm\) 0.46 \({}^{\rm a}\) & 58.95 \(\pm\) 0.65 \({}^{\rm a}\) & 343.32 \(\pm\) 34.64 \({}^{\rm a}\) \\ Sirkana & 9.14 \(\pm\) 0.06 \({}^{\rm a}\) & 1.13 \(\pm\) 0.03 \({}^{\rm a}\) & 5.89 \(\pm\) 0.01 \({}^{\rm a}\) & 31.40 \(\pm\) 0.70 \({}^{\rm b}\) & 5.91 \(\pm\) 0.15 \({}^{\rm a}\) & 46.55 \(\pm\) 0.64 \({}^{\rm c}\) & 332.00 \(\pm\) 35.81 \({}^{\rm b}\) \\ \hline \hline \end{tabular}  
* Mean values (n = 2) followed by different superscripts in the same column are significantly different between varieties (\(p<0.05\)).  
\end{table}
Table 1: Chemical composition of carrot pomace powders.  
The Baltimore variety presented the lowest ash content. With respect to the energetic value, only Niagara exhibited a significantly higher value compared to the other varieties.
# Parameter Estimation of a Class of Neural Systems with Limit Cycles
## 3 The Identification Model of Spiking Neurons  
Usually, the neuron membrane and the inactivation of the sodium channels are discretely sampled in experiment. Hence, let us consider the discretized system of the FHN model using the explicit forward Euler method with step size \(T\) as follows  
\[\frac{x(k+1)-x(k)}{T}=f(x(k))+\xi(k), \tag{3}\]  
where \(T\) is the sampling period which is sufficiently small, \(k\) represents the \(kT\) time instant and  
\[f(x(k))=\begin{bmatrix}\mu\big{(}v(k)(v(k)+a)(b-v(k))-w(k)\big{)}+J\\ c_{1}v(k)-c_{2}w(k)\end{bmatrix}.\]  
Denote \(v_{k,m}:=v(k-m)\), \(w_{k,m}:=w(k-m)\) and \(\xi_{k,m}:=\xi(k-m)\) for \(m=1,2,\cdots.\) Then, we have  
\[\begin{bmatrix}\frac{v(k)-v(k-1)}{T}\\ \frac{w(k)-v(k-1)}{T}\end{bmatrix}=\begin{bmatrix}\mu\big{(}v_{k,1}(v_{k,1}+a) (b-v_{k,1})-w_{k,1})+J\\ c_{1}v_{k,1}-c_{2}w_{k,1}\end{bmatrix}+\xi_{k,1} \tag{4}\]  
Define \(y(k)=[y_{1}(k),y_{2}(k)]^{\top}\), \(y_{1}(k)=(v(k)-v(k-1))/T\), \(y_{2}(k)=(w(k)-w(k-1))/T\), \(\theta(k)=\begin{bmatrix}\mu&(a+b)\mu&ab\mu&J&c_{1}&c_{2}\end{bmatrix}^{\top}\) and  
\[\phi(k)=\begin{bmatrix}-v_{k,1}^{3}-w_{k,1}&v_{k,1}^{2}&-v_{k,1}&1&0&0\\ 0&0&0&0&v_{k,1}&-w_{k,1}\end{bmatrix}^{\top}.\]  
Therefore, Equation (4) can be rewritten as  
\[y(k)=\phi^{\top}(k)\theta(k)+\xi_{k,1} \tag{5}\]  
which is called as the identification model. Apparently, to estimate the parameters \(a,b,\mu,J,c_{1},c_{2}\), it is equivalent to estimate the parameter vector \(\theta\).
# An Evaluation of Optimization Algorithms for the Optimal Selection of GNSS Satellite Subsets
## 2 GNSS Satellite Constellation Determination
### Optimization Algorithms
#### 2.3.2 Ant Colony Optimization (ACO)  
ACO is an algorithm developed by [PERSON] et al. in 1996 [40]. The algorithm simulates the behavior of ants in finding the shortest path between food and their nest. This population-based algorithm generates a number of artificial ants to solve the problem. In ACO, the optimal solution can be found after a number of iterations. At each iteration, each ant creates a new solution (path) that is evaluated by the pheromone rate, which rises as the quality of the solution improves. The pheromone rates on the paths influence the ants' choice in finding the best path (solution) with the highest pheromone rate, as shown in Figure 2. ACO is a robust and flexible algorithm [41; 42] suitable for the path planning problem [42; 43], but it has a slow convergence rate [41].
## 2 Design of the Solar-Powered ECG Device
### Hardware Design
#### 2.1.3 Optimization Power Consumption  
As we presented in the previous section, the voltage applied to the main components was reduced to very low levels to allow the smallest current to be consumed. However, as PIC16 LF18196 and RN4020 Bluetooth modules are the two major energy consuming components in this design, they need to be configured to operate in energy-saving modes to optimize power usage. The Bluetooth module is configured with the smallest transmission power (TX Power \(-\)19.1 dBm).  
Figure 6 presents the operation of the main components in the firmware of our ECG device [18,19]. In the normal operation mode, MCU operates in energy-saving mode. When the data is ready and there is a signal from ADS1293, these parts go into operation mode and read data and buffer the Bluetooth module data into the active mode to transfer data. Then, the module goes into energy-saving mode.  
Figure 7 describes the signal type when MCU reads 9 bytes of data from ADS1293 and the communication frequency of the SPI module is 2 MHz. The time PIC16 LF19186 receives the data ready signal to read is 0.25 ms. During this time, PIC16 LF19186 operates at a maximum frequency of 32 MHz and the current consumption is 1.5 mA.  
Figure 6: Mode of main components MCU, ADS1293, and RN4020.  
The total number of analog channels of the ECG device is 3 but due to the data transfer speed limit between the MCU and Bluetooth module (max speed 115,200 bps) [18], with the high sampling frequency, the device cannot transmit all data through all three channels. The details of the relationship between the sampling frequency and the number of channels are described in Table 4.
## 3 Optimization Model and Evaluation Indexes
### Evaluation Indexes  
In addition, we selected several evaluation indicators, including the root mean square error (RMSE), mean absolute error (MAE), mean absolute error percentage (MAPE), and  
Figure 4: Flow chart of DEGWO-BPNN prediction.  
determination coefficient (R\({}^{2}\)), to evaluate the prediction performance of the target model and the comparative models, and the evaluation indexes are calculated using Equation (7).  
\[\left\{\begin{array}{l} RMSE=\sqrt{\frac{\sum\limits_{i}{{{{\left({y_{i}-{{ \hat{y_{i}}}\right)}^{2}}}}\right)}^{2}}}}\\ MAE=\frac{\sum\limits_{i}{{{\left|{{y_{i}-{{\hat{y_{i}}}}}\right|}}}}}{N}\\ MAPE=\frac{\sum\limits_{i}{{{\left|{{\frac{{{y_{i}-{{\hat{y_{i}}}}}}}}{{{y_{i} }}}}}\right|}}}{N}\times 100\%\\ R^{2}=1-\frac{\sum\limits_{i}{{{\left({{y_{i}-{{\overline{y_{i}}}}}}\right)}^{2 }}}}{\sum\limits_{i}{{{\left({{y_{i}-{{\hat{y_{i}}}}}}\right)}^{2}}}}}\end{array}\right. \tag{7}\]  
where \(y_{i}\), \({{\hat{y_{i}}}}\), and \({{\overline{y_{i}}}}\) are the actual value, the prediction value, and the actual mean value; \(N\) is the number of data samples.  
Moreover, the prediction accuracy of the models is calculated by (\(1-\text{MAPE}\)) \(\times\) 100%, when the MAPE value is determined using Equation (7).
## 4 Results and Discussion
### Temperature Distribution of the Jets  
Figure 10a,b show the theoretically calculated and numerically simulated temperature distribution patterns of the OFHC-Cu and Mo jets at 40 \(\upmu\)s, respectively. Similar to its strain, the temperature of each jet also exhibited a stratified distribution pattern. The temperature of the material gradually decreased from the center along the radial direction of each jet and was the highest at the center of the jet.  
To facilitate comparative analysis, the temperatures of the material at 40 \(\upmu\)s were extracted from a total of 10 test points, forming the array composed of five columns and two rows along the movement and radial directions of each jet, and were subsequently averaged and compared with the theoretical calculation results. Table 4 summarizes the results. Evidently, the temperatures of the Mo jet were appreciably higher than those of the OFHC-Cu jet. The numerical simulations yielded average temperatures of 873.2 and 2826.9 K for the OFHC-Cu and Mo jets, respectively. The theoretical model predicted average temperatures of 804 and 2277.8 K for the two jets at the same time point, respectively. The theoretical predictions did not differ significantly from the numerical simulation results.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multicolumn{5}{c}{**Numerically Simulated Torperature Distribution of the Jet (K)**} & \multicolumn{5}{c}{**Average Temperature (K)**} \\ \cline{3-8}  & **13 cm** & **15 cm** & **17 cm** & **19 cm** & **21 cm** & **24 cm** & **Numerical Simulation** & **Theoretical Calculation** \\ \hline OFHC-Cu & \(4.5\times 10^{4}\) & \(4.4\times 10^{4}\) & \(3.6\times 10^{4}\) & \(5.1\times 10^{4}\) & \(3.1\times 10^{4}\) & \(3.0\times 10^{4}\) & \(4.0\times 10^{4}\) & \(6.2\times 10^{4}\) \\ Mo & \(4.8\times 10^{4}\) & \(9.2\times 10^{4}\) & \(2.8\times 10^{4}\) & \(2.8\times 10^{4}\) & \(3.6\times 10^{4}\) & \(5.0\times 10^{4}\) & \(4.7\times 10^{4}\) & \(6.0\times 10^{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Distribution of the deformation strain rate of each jet at 40 \(\upmu\)s and data comparison.  
Figure 10: Temperature distribution of the OFHC-Cu and Mo jets (at 40 \(\upmu\)s). (a) Theoretical predictions; (b) Numerical simulations.
# Synchronizing TSN Devices via 802.1 AS over 5G Networks
## 2 Time-Sensitive Networking  
Time-Sensitive Networking (TSN) standards enable determinism, ultra-reliable and low latency communication in Ethernet networks and being the industry standard ensures interoperability between manufacturers. The TSN standards encompass various extensions for the IEEE 802.1Q standard [1] for VLANs in bridged networks. As TSN mainly focuses on real-time communication in bridged networks, its standards are separated into four major categories, each aiming to guarantee quality of service to specific real-time traffic. These main categories are Time Synchronization to synchronize all network devices to a network master, Bounded Low Latency for deterministic end-to-end latency in the network, Ultra-Reliability for data prioritization with end-to-end route reservation in addition to path redundancy, and Resource Management for managing the network devices through a network devices coordinator and configurator. Table 1 gives an overview of TSN standards in each of the mentioned categories.  
Each TSN component is briefly described in the next subsections. Time synchronization is discussed in detail as it is crucial for understanding the simulation results presented later in Section 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**TSN Standard** & **Category** & \\ \hline IEEE 802.1 Qat & & Stream Reservation Protocol and Enhancements (802.1 Qcc) \\ \hline IEEE 802.1 Qcp & & YANG Model for Bridging \\ \hline IEEE 802.1 Qcw & Resource Management & YANG Model for Traffic Scheduling \\  & & and Stream Filtering \\ \hline IEEE 802.1 Qcbcv & & YANG Model for Frame Replication \\  & & and Elimination \\ \hline IEEE 802.1 AS & Time Synchronization & Precision Time Protocol for Audio \\  & & Video Bridging and TSN \\ \hline IEEE 802.1 Qbv * & & Time Aware Shaping \\ \hline IEEE 802.1 Qav * & & Credit-Based Shaping \\ \hline IEEE 802.1 Qbu * & & Frame Preemption \\ \hline IEEE 802.1 Qch * & & Cyclic Queueing and Forwarding \\ \hline IEEE 802.1 Qca * & & Path Control and Reservation \\ \hline IEEE 802.1 Qci * & Ultra-Reliability & Per-Stream Filtering and Policing \\ \hline IEEE 802.1 CB & & Frame Replication and Elimination \\ \hline \hline \end{tabular}  
* These standards are already incorporated in IEEE 802.1Q Standard.  
\end{table}
Table 1: TSN standards.
## 4 Case Study
### PV Installation  
The facilities are equipped with a PV installation, located on a pergola covering one of the parking spaces. The PV installation has a maximum peak power output of 333.72 kWp and is conformed by three inverters of 100 kW each, resulting in a total nominal power of 300 kW. The estimated annual energy generation ranges between 310 and 400 MWh/year. The PV inverters, according to the information provided by the manufacturer, Huawei, would consume around 84 Wh/day and 30,660 Wh/year. According to the expected energy generated per year (407,490 kWh/year), the energy consumed by the PV inverters would represent just 0.0075% of total generation.  
The generation varies depending on the season. In general, the highest energy production occurs during May, June, and July, whereas the lowest production is observed in January and December. During peak irradiation hours in high-production periods, the system generates more energy than the industry necessitates. The facility can feed energy into the grid; therefore, when surplus energy is generated, it is either stored in the SESS or sold.