esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluation and Obtained Results
### Obtained Results, Analysis and Discussion
#### 4.3.2 Network Reliability Performance Evaluation Results  
The average rate of successful up-link transmissions is assessed for comparison of the proposed N-MANC to the other approaches (N-AFHSS, N-LEACH, and the N-MANNC). This average rate of  
Figure 4: Average energy consumption versus network scalability.  
successful transmissions is computed as the various number of nodes per cell is increased to assess the impact of the network scalability on the network transmission reliability of the network. The obtained results are illustrated in Figure 5.  
It can be observed from the bar graph in Figure 5 that the rate of successful transmissions drops as the number of NB-IoT nodes increases in the cell or all the assessed approaches. However, the proposed mixed approach (N-MANC) maintains the highest probability of successful transmission as compared to the N-MANNC, the N-AFHSS, and the N-LEACH respectively. On average the proposed N-MANC maintains a 5.86% higher transmission success rate as compared to the N-MANNC, 10.83% higher than the N-AFHSS, and 55.84% higher than the N-LEACH respectively. It is important to notice that the N-AFHSS approach outperforms the N-LEACH in terms of uplink transmission reliability. This is because the N-AFHSS unlike the N-LEACH is primarily designed to mitigate the inter-link interference which is one of the main causes of unsuccessful up-link transmissions; unlike the N-LEACH which is mainly focused on reducing the network communication traffic to primarily enhance its energy efficiency. It is however important to note the N-LEACH approach also contributes towards improving the network communication reliability and, therefore, the proposed mixed N-MANC demonstrates way better transmission success rate as its taps into the benefits of the N-AFHSS as well as the N-LEACH approach. On the other hand, the improved transmission reliability enhances the RLNC performance in terms of its decoding process. This results in the RLNC further contributing towards enhanced network transmission reliability as compared to the mixed approach with no network coding (N-MANNC) despite the increase in the number of nodes within the cell.
## 2 Theory and Methodology
### Dataset Generation  
In the case of a vessel with forward speed, our approach is designed to estimate the wave spectrum from vessel motion sensor signals through the Kalman filter method in the encounter-frequency domain [11; 12; 15]. In that case, as in the previous section, it was clearly explained that the conversion of the wave spectrum from the encounter-frequency axis to the original-frequency axis may not be unique in the case of following waves and certain vessel speeds. When the spectral conversion from \(S_{e}(\omega_{e})\) to \(S(\omega)\) cannot be uniquely determined, we can utilize the present ANN-based ML method, which is demonstrated in the following sections. In that regard, a pair of wave spectra for the original and encounter frequencies are generated using Equations (4), (9), and (10) for various wave parameters and vessel speeds. To demonstrate the workability of the proposed method, the JONSWAP wave parameters and vessel speeds used for the dataset are presented in Table 1. The original and encounter wave spectra are generated with 300 discretized frequency components (\(\Delta\omega\) = 0.01 rad/s) in the range of angular frequencies of 0 to 3 rad/s. The combination of the wave parameters and vessel speeds generated a total of 231,660 data points.  
Figure 6 shows the resulting encounter-frequency spectra generated from the original wave-amplitude spectrum. Various forward speeds and wave headings are considered to provide a comprehensive illustration of the encounter wave characteristics. The various \(S_{e}(\omega_{e})\) values correspond to the same original wave spectrum, which demonstrates why there exists no unique solution in the inverse problem.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**JONSWAP Wave Parameter** & **Range of Values** & **Interval** \\ \hline \(Hs\), significant wave height (m) & [1, 2, \(\ldots\), 9] & 1 m \\ \(Tp\), peak period (s) & [6, 7, \(\ldots\), 18] & 1 s \\ \(\gamma\), peak enhancement factor * & [1, 12, \(\ldots\), 3] & 0.2 \\ \(\beta\), wave heading (deg) ** & [0, 10, \(\ldots\), 80] & 10\({}^{\circ}\) \\ \(V\), forward speed (knots) & [1, 2, \(\ldots\), 20] & 1 m/s \\ \hline \hline \end{tabular}  
* For ML Model 1, the peak enhancement factor \(\gamma\) is fixed to 2. ** A wave heading of 0 degrees represents the following sea, while a heading of 90 degrees means the beam sea.  
\end{table}
Table 1: Wave parameters and vessel forward speeds for dataset.  
Figure 5: Estimated original wave spectrum from encounter wave spectrum with and without singularity.
## 6 Earth Observation, Archaeological Research and Spatial Privacy
The particular 'Development Centres' created and provided for by law include- the (a) National Centre for Remote Sensing, Jos; (b) Centre for Space Science and Technology Education, Ile Ile; (c) Centre for Satellite Technology Development, Abuja; (e) Centre for Geodesy and Geodynamics, Toro; and the (j) Centre for Basic Space Science and Astronomy, Nsukka. The existence of NASRDA is clearly of value in developing expertise in all the areas of geospatial sciences discussed in this work. Particular relevance lies in the obligations of NASRDA to (a) develop satellite technology for various applications and operationalize indigenous space systems for providing space services and shall be the government agency charged with the responsibility for building and launching satellites; (b) enhance the development and entrenchment of research, development and production tradition in the Agency, so as to achieve a high output and make the desired impact on national economic and social development; (c) promote the co-ordination of space application programmes, for the purpose of optimizing resources and develop space technologies of direct relevance to national objectives; The work of the Centre for Geodesy and Geodynamics, Toro, in Bauchi State is particularly of value to surveying tasks that would rely on space applications given its mandate to (d) facilitate and sustain the growth in capacity for geodesy surveying and mapping; (e) monitor crustal detonation and subsidence, due to excessive oil and gas exploitation, global mean sea level rise and other related seismic and geodynamic phenomena; (f) implement international agreements with regard to- (i) Satellite Laser Ranging (SLR), (ii) very Long Baseline Interferometry (VLB), and (iii) Co-operative International Geo-Physical Survey Network.
###### Abstract  
Federated Learning is a widely adopted method for training neural networks over distributed data. One main limitation is the performance degradation that occurs when data are heterogeneously distributed. While many studies have attempted to address this problem, a more recent understanding of neural networks provides insight to an alternative approach. In this study, we show that only certain important layers in a neural network require regularization for effective training. We additionally verify that Centered Kernel Alignment (CKA) most accurately calculates similarities between layers of neural networks trained on different data. By applying CKA-based regularization to important layers during training, we significantly improved performances in heterogeneous settings. We present FedCKA, a simple framework that outperforms previous state-of-the-art methods on various deep learning tasks while also improving efficiency and scalability.  
federated learning; heterogeneity; non-IID; regularization; layer-wise similarity +
Footnote †: journal: Medical  
studies have attempted to address this problem by regularizing the entire model during the training process, we argue that a more recent understanding of neural networks suggests regularizing every layer may limit performance.  
In this study, we present FedCKA to address these limitations. First, we show that regularizing the first two naturally similar layers is most important for improving performance in non-IID settings. Previous studies had regularized each individual layers. Not only is this ineffective for training, it also limits scalability as the number of layers in a model increases. By regularizing only these important layers, performance improves beyond previous studies. Efficiency and scalability also improved, as we do not need to calculate regularization terms for every layer. Second, we show that Centered Kernel Alignment (CKA) is most suitable when comparing the representational similarity between layers of neural networks. Previous studies added a regularization term by comparing the representation of neural networks with the l2-distance (FedProx) or cosine similarity (MOON). By using CKA, we improve performances, as representations between important layers can accurately be regularized regardless of dimension or rank [10]; hence, the name FedCKA. Our contributions are summarized as follows:  
* We improve performances in heterogeneous settings. By building on the most up-to-date understanding of neural networks, we apply layer-wise regularization to only important layers.
* We improve the efficiency and scalability of regularization. By regularizing only important layers, we exclusively show training times that are comparable to FedAvg.
# Intelligent TCP Congestion Control Policy Optimization
###### Abstract  
Network congestion control is an important means to improve network throughput and reduce data transmission delay. To further optimize the network data transmission capability, this research suggests a proximal policy optimization-based intelligent TCP congestion management method, creates a proxy that can communicate with the real-time network environment, and abstracts the TCP congestion control mechanism into a partially observable Markov decision process. Changes in the real-time state of the network are fed back to the agent, and the agent makes action commands to control the size of the congestion window, which will produce a new network state, and the agent will immediately receive a feedback reward value. To guarantee that the actions taken are optimum, the agent's goal is to obtain the highest feedback reward value. The state space of network characteristics should be designed so that agents can observe enough information to make appropriate decisions. The reward function is designed through a weighted algorithm that enables the agent to balance and optimize throughput and latency. The model parameters of the agent are updated by the proximal policy optimization algorithm, and the truncation function keeps the parameters within a certain range, reducing the possibility of oscillation during gradient descent and ensuring that the training process can converge quickly. Compared to the traditional CUBIC control method, the results show that the TCP-PPO\({}_{2}\) policy reduces latency by 11.7-87.5%.  
network congestion; congestion control; internet; proximal policy optimization
# Voice Communication Systems Impact on Navigating Officers
## 3 Results
### Satellite Phone Calls
The data from the satellite log that refer to call duration fit in with the answers gathered from the interviews, in which almost all participants answered that most calls had lasted from 1 to 5 min.
# Modelling of Failure Behaviour of 3D-Printed Composite Parts
## 2 Materials and Methods
### Computational Modelling
#### 2.2.2 Computational Methodology-2  
In the second computational method (CM-2), the tensile testing data of 3D-printed test coupons are utilized for modelling. The tensile test data of unidirectionally 3D-printed parts are shown in Figure 4. The FE models of RVEs for this computational method are shown in Figure 5a for the parts made of ABS material and Figure 5b for 3D-printed parts made of ABS + sCF composite material.  
Figure 4: Tensile test data of uniaxially 3D-printed parts: (**a**) for ABS polymer [6], (**b**) ABS + sCF composite material [16].  
Figure 3: Finite element models of RVEs of 3D-printed parts for computational methodology-1 (CM-1): (**a**) ABS polymer, (**b**) ABS + sCF composite material.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Property** & **E\({}_{1}\)**, in GPa** & **E\({}_{2}\)**, in GPa** & **G\({}_{12}\)**, in GPa** & **G\({}_{23}\)**, in GPa** & **v\({}_{12}\)** \\ \hline Value & 225 & 15 & 15 & 7 & 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Material properties of short Carbon fiber (sCF) reinforcements for damage modelling [38].  
The main differences between the previous computational method and this method are the FE modelling of RVEs and nonlinear material data. In this case, the FE models of RVE have a different material (red region) for the interface of extrudates, and this considers the debonding failure mode. For nonlinear material data, it employs tensile testing data of unidirectionally 3D-printed parts (with 0\({}^{\circ}\) printing direction) for extrudates and the test data of printed parts (with 90\({}^{\circ}\) printing direction) for the interface material. The tensile testing data is considered to account for the predominant failure modes, debonding and breakage of extrudates in 3D-printed parts. 3D-printed parts with 0\({}^{\circ}\) printing direction experienced only breakage of extrudates, and in the case of the parts with 90\({}^{\circ}\) printing direction, they experienced only debonding at the interface of extrudates. The tensile test data of the above two cases can capture the actual failure behaviour of 3D-printed parts. The tensile testing data of unidirectionally 3D-printed parts from Figure 4a is utilized for computational modelling of the parts made of ABS polymer, and Figure 4b for modelling the parts made of composite material. Furthermore, implementing actual test data in the computational models eliminates several assumptions made in the previous case, including imperfect bonding at the interface and no pores within extrudates. This procedure makes the computational model realistic. Further, the method allows for the prediction of the nonlinear behaviour of 3D-printed parts accurately.
# Assessment of Spatial Variability in Ground Models Using Mini-Cone Penetration Testing
## 3 Experimental Program
### Ground Models  
A soil tank with dimensions of 2000 \(\times\) 600 \(\times\) 600 mm was used to construct the ground model, as shown in Figure 2. The experiment was conducted using silica sand and weathered soil samples, as mentioned in Section 3.1. Each soil sample had a water content of 0%. The ground model was designed considering the penetration depth of the cone. Penetration was conducted at a total of 18 locations, with the 30 cm point designated as A and the 45 cm point as B. The spatial variability in the ground model was evaluated using the cone penetration resistance data from all 18 locations through calculations of CV and CL.  
Figure 1: Mini-Cone Penetrometer.  
Figure 2: Test model. (**a**) Case 1 (silica sand ground model); (**b**) Case 2 (two-layer ground model).  
Figure 2a consists of a single ground layer of silica sand with a relative density of 75%, and the composition was made homogeneously to validate the results of spatial variability in the ground model through the coefficient of variance and vertical CL. Figure 2b shows a two-layer ground composed of silica sand and weathered soil. For the weathered soil layer, the composition was conducted with a relative density of 95%, and for the silica sand layer, with a relative density of 75%. The depth of the silica sand layer was set to 15 cm considering the cone penetration depth.  
The construction of the sandy grounds for Case 1 and Case 2 was carried out as uniformly as possible by dropping the sand through the pluviation system.  
Accounting for the considerations for the Cone Penetration Tests mentioned in Section 2.4, the penetration points were determined. The Cone Penetration Tests were conducted outside the influence range of the walls and bottom surface, with a penetration speed of 20 mm/s. The cone penetration points were designated as point A at the 30 cm location and point B at the 45 cm location.
###### Abstract  
Power systems have been going through a barrage of transformations due to the recent developments in the field, such as deregulation and restructuring of the electric power supply chain, the proliferation of distributed generation (DG), and advancements in information and communications technologies. These have significantly impacted the approach to the planning, design, and operation of active distribution networks or systems. Due to this constant change, the system has become more complex to plan, maintain, and control. In this paper, the benefits and challenges of active distribution systems relative to traditional passive and active distribution systems are evaluated and investigated while the management and operational characteristics of demand-side resources in active distribution systems (ADS) are studied. In a typical ADS, there exist several vulnerabilities and threats that eventually pose a challenge in the control and automation of substations. These vulnerabilities and threats are reviewed, and potential mitigation measures are suggested. Also in this paper, the communication technologies and their implementation in terms of control and automation capabilities in active distribution networks are also studied. From this work, it is concluded that communication technologies play an integral role in the realization of more active distribution networks and that the Internet of Energy (IoE) is a major player in ADS in the reduction of faults due to human error, fast responses, and improving the stability of power supply. Cyber threats are also and will still be a continuous challenge in smart metering technologies and in substation automation systems (SAS), which will require frequent evaluation and mitigation measures so as not to prevent the power supply system from collapsing.  
active distribution systems (ADS); demand-side resources; IEC61850; smart metering; control and automation in ADS; optimization techniques in ADS
# Analyzing Meta-Heuristic Algorithms for Task Scheduling in a Fog-Based IoT Application
## 2 Related Work
### Hybrid Heuristic Algorithms  
[PERSON] et al. [43] presented a new bio-inspired algorithm (BIA) for job scheduling and resource management in the cloud computing environment. The proposed algorithm is a combination of modified PSO and CSO. The simulation was performed using PySim tools, and the results show that the proposed algorithm reduces the average response time and reduces resource utilization.  
[PERSON] et al. [44] offered a mix of GA, best fit, and Round Robin (RR) algorithms to reduce the number of algorithm iterations. An optimal initial population is obtained by integrating the best Fit and RR algorithms. They optimized makespan, load balancing, and speed.  
[PERSON] et al. [45] offered a mix of ACO and MAX-MIN algorithms (MMACO) to optimize load balancing and makespan. In the MAX-MIN algorithm, big tasks have higher priority than smaller tasks. For their proposed method, the waiting time is reduced for smaller tasks because small jobs run parallel on the fastest machines. The results of simulation in CloudSim show improved performance of the proposed algorithm.  
Researchers in [46] provide a Hybrid Flamingo Search with a Genetic Algorithm (HFSGA) for cost-efficient QoS-aware task scheduling in a fog-cloud environment. This strategy improved the makespan and cost compared to ACO, PSO, GA, Min-CCV, Min-V, and RR algorithms.  
A group of researchers in [47] worked on task scheduling in a cloud environment by an improved pathfinder algorithm using opposition-based learning (OBLPFA). Their approach improved total execution time, cost, and resource utilization compared to PSO, dragonfly, the Arithmetic Optimization Technique, the Reptile Search technique, the Aquila Optimization method, and Lion Optimization methods.  
The hybrid strategy has also been used in [48] by combining a neural network-based method with heuristic policy (JNNHSP) to present an optimized task scheduling algorithm in a hierarchical edge cloud environment. The authors improved the scheduling error ratio, average service latency, and execution efficiency.
## 2 Methodology
### Load Consumption Profile  
This research case is an industrial user located in northern Taiwan, and the statistical period for the load data sampled every 5 min is from January 2019 to December 2019. The annual electricity consumption is 30.331 \(\times\) 10\({}^{3}\) MWh/year, and the average monthly electricity consumption is 2.527 \(\times\) 10\({}^{3}\) MWh/month. The monthly electricity consumption is differently related to the monthly production volume of the factory as shown in Figure 4, the highest electricity consumption of 2.791 \(\times\) 10\({}^{3}\) MWh/month is in May. However, the lowest electricity consumption is 2.003 \(\times\) 10\({}^{3}\) MWh/month in February, because of the Chinese New Year in Taiwan.  
The average daily load profile of annual power consumption shows the periods of peak power consumption from 8:00 to 12:00 and from 13:00 to 16:00, as shown in Figure 5, and the daily power consumption of annual average is 83.1 MWh/day. However, because most of the high power consumption is mainly during the daytime, a PV system with daytime power generation characteristics can reduce the electricity expense (energy purchased from utility electricity).  
The load profile shows results based on the power data of users in 2019. The power data imported into the software is raw data, with each recorded every 5 min. The load power in the 24 h in Figure 5 is the average value of 365 days in each period. The peak power of 7378 kW is the highest load power ever experienced in the year. The user schedule of load consumption power shows an average of 3.463 MW. As shown in Figure 6, the frequency distribution of load consumption power can be accounted for by 60.13%, the most in this range between 1.75 MW and 3.5 MW. The frequencies locate the highest at 2.75 MW with accounting for 13.09%, the proportion of 7.11% at 6 MW or more, and the proportion of 0.1% at 7 MW or more. Therefore, if the energy storage system (PV/BESS) can provide a short time of 6 MW or more electricity demand, it can reduce electricity expenses for the contract capacity.  
Figure 4: Monthly electricity consumption distribution.  
Figure 5: The load profile for the average daily power consumption of annual energy.