esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Experiment Materials and Setup
#### 4.1.3 Training and Evaluation Setup  
During the training process, we set the default length of the observed series \(O\) to 96 (equivalent to 16 h) and set the length of the future series \(P\) as \(\{96,192,336,720\}\), which means predicting the weather for the next 16, 32, 56, and 120 h.  
Although our PCC module is simple and lightweight compared to the backbone models, it introduces additional complexity as a supplementary component, which may introduce additional risk of unstable training. We carefully selected the hyperparameters of the PCC module as follows:  
1. The hidden dimension for the two submodule networks was set as 128, balancing performance and computational complexity.
2. Tanh was chosen as the activation function \(\sigma\) due to its symmetry around zero and smooth gradient properties. These characteristics ensure training stability, which is  
Figure 4: Trends of four meteorological variables from the Max-Planck-Institut dataset for the same time period. The trends of air temperature and specific humidity show obvious similarity, while the trend of wind velocity carries dense noise and fluctuations and is relatively independent. There are also some time slots with zero precipitation.  
particularly important because weather variables often contain extreme values and rapid fluctuations.
3. The dropout rate was set to 0.3, representing an optimal tradeoff between regularization to prevent overfitting and maintaining sufficient network capacity.
4. The learnable matrices \(\lambda_{1}\), \(\lambda_{2}\) were both initialized to 1, ensuring that the differential terms start with equivalent contribution weights. This helps to prevent initial bias that may cause unstable parameter updates during the early training stages.  
We evaluated PCC's sensitivity to the settings of the hidden dimension, dropout rate, and initialization of the differential matrix in Appendix C.  
Our PCC module functions as a plug-and-play component that can be integrated into backbone models by simply connecting it at the feature dimension. This integration requires no modifications to the backbone architecture itself nor additional data preprocessing steps such as normalization. Therefore, in our experiments we followed the same settings for each model as in their original publications. In addition, we used the official code implementations, including data processing methods, normalization methods, hyperparameters, learning rate, etc., ensuring fairness of the comparison and reproducibility of the results. The detailed training configurations of all the backbone models are provided in Appendix D.  
We evaluated the models' performance on the test set using the metrics of mean absolute error (MAE) and mean squared error (MSE). To ensure reliability, each numerical result was the average of five runs with different random seeds.  
The experiments were conducted on a server with an Intel Xeon Platinum 8474C CPU, 80 GB memory, and an NVIDIA RTX 4090D GPU with 24 GB memory. The PCC module and backbone models were implemented with PyTorch 2.0.0 and CUDA 11.8 with Python 3.8 on Ubuntu 20.04.
## 2 Modeling of USV  
### Kinematics Equation  
The corresponding relationship between the body-fixed frame and the Earth-fixed inertial frame is shown in Figure 1; where \(o-x_{0}y_{0}z_{0}\) is the body-fixed frame and \(O-X_{0}Y_{0}Z_{0}\) is the Earth-fixed inertial frame.  
Figure 1: The Earth-fixed inertial and body-fixed frame.  
In practice, USV has six DOFs, including: surge velocity \(u\), sway velocity \(v\), yaw rate \(r\), heave velocity \(w\), rolling rate \(p\) and pitching rate \(q\). Since path following is a planar motion task, only \(w\), \(p\) and \(q\) need to be taken into account. \((x,y)\) represents the position of the USV, and \(\delta\) is the propulsion angle. The kinematics equation transformation between the body-fixed frame and the Earth-fixed inertial frame can be expressed as:  
\[\dot{\zeta}=J(\psi)v \tag{1}\]  
where \(\psi\) is the heading angle, \(\zeta=[x,y,\psi]^{T}\), \(v=[u,v,r]^{T}\) and \(J(\psi)\) is the transition matrix.  
\[J(\psi)=\left[\begin{array}{ccc}\cos(\psi)&-\sin(\psi)&0\\ \sin(\psi)&\cos(\psi)&0\\ 0&0&1\end{array}\right] \tag{2}\]  
Then, we can get the kinematics equation of USV [26].  
\[\left\{\begin{array}{l}\dot{x}=u\cos\psi-v\sin\psi\\ \dot{y}=u\sin\psi+v\cos\psi\\ \dot{\psi}=r\end{array}\right. \tag{3}\]
# ACE: A Routing Algorithm Based on Autonomous Channel Scheduling for Bluetooth Mesh Network
## 6 Evaluation
### Route Setup Time and Route Recovery Time  
In this section, we evaluate the ACE performance in a dynamic scenario with different network sizes. The network size is defined by varying amounts of hops, as shown in Figure 10. For example, nodes 1-9 form a three-hop network. We evaluate the topology setup time with different network sizes. In addition, We use a movable node and enable the source node to transmit packets to the movable constantly. We change the movable node position around the testbed and measure the time of receiving packets again since each movement, that is, the time of route recovery.  
Figure 10 shows the results. Route setup can be done more quickly than the route recovery, it takes 31 s to set up the routing table for the entire testbed size (time of the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Table Size** & **10** & **100** & **500** & **1000** & **5000** & **10,000** \\ \hline Searching Time (us) & 18 & 165 & 822 & 1642 & 8205 & 16,408 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Routing table searching time. The search time is linearly proportional to the size of the routing table.  
Figure 10: Performance of route setup time and route recovery time using a varying of network size. The recovery time of ACE is shorter than Bluetooth Mesh.  
Figure 9: Grid topology used in simulation.  
last node to set up a stable route table), but the average time is only 14 s. As shown in Figure 10, the increasing route setup time is acceptable with the growth of the network size. As for route recovery time, it is increasing more significantly as the network size grows. The reason is that route recovery need to wait for each node's distributed routing update cycle; therefore, more nodes will lead to a longer time for the whole mesh network to finish updating routes process. We use linear regression to fit the route recovery time and route setup time. There is an obvious linear relationship between the scale of the network and the route setup and recovery time, and the variances are 3.64 and 3.83, respectively.
# Low Complexity DSP for High Speed Optical Access Networking +
## 3 Experimental Results at 20 km Transmission
This is identified by comparing the histograms shown in Figure 4c of the equalized signals of linear and nonlinear equalization cases under the same optical power: nonlinear equalization reduces the frequency of amplitudes distributed in the threshold regimes, leading to reduced errors on the following hard decisions. Moreover, Figure 4c also indicates that the nonlinear equalized PAM-4 signal shows even more amplitude space between each level. The NS filter not only significantly suppresses the noise distributed in the frequency range beyond 10 GHz but also whitens the noise power spectral density. It is also shown in Figure 4a that it is possible to significantly reduce the FFE/DFE and NS filter taps, and hence, the filter complexity, without sacrificing the performance of 20 km SMF transmission. This is achieved by disabling the taps whose values are below the average of the corresponding tap category. The pre-amplified receiver could potentially be replaced by an avalanche photodiode (APD) in the downlink system for cost-efficiency reasons, but the corresponding sensitivity penalty will typically rise to ~5 dB in this case [7; 8; 12]. However, if dynamic biasing of the APD is used, it is expected that the sensitivity can be reasonably improved [28].
## 2 Structures, Synthesis, and Magnetic Properties of Fe\({}_{3}\)O\({}_{4}\) Nanoparticles
### Fe\({}_{3}\)O\({}_{4}\) Cubic Nanoparticles (CNPs)
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Edge (nm)** & **Precursor and Reagents** & **Solvent(s)** & **Size Control** & **M\({}_{\text{S}}\) (emu/g)** & **H\({}_{\text{C}}\) (Oe)** & **Ref.** \\ \hline
78, 87, 101, 130 & Fe(acac)\({}_{3}\) & Benzyl ether & Concentration, Reaction time & 90, 95, 95, 80 & 139, 165, 177, 80 & [42] \\ \hline
10–80 & Fe(acac)\({}_{3}\) & Benzyl ether & Concentration, Degassing temperature, Ramping rate & 84 & SPM (for 15.3 nm) & [74] \\  & Oleic acid & 1-octadec & temperature, Ramping rate & (for 15.3 nm) & (for 15.3 nm) & [74] \\ \hline
80 & FeSO\({}_{4}\)-7H\({}_{2}\)O & H\({}_{2}\)O & None & 85.8 emu/g & UN & [75] \\  & NaOH & & & & \\ \hline
26 & F(acac)\({}_{3}\) & & & & \\  & Trimethylamine N-oxide & Benzyl ether & None & 51.8 & 262 & [71] \\  & Mercaptoethanol & & & & \\  & \(\beta\)-amyrin & & & & \\ \hline \hline \end{tabular} UN denotes unknown, as the information was not provided in the literature. SPM denotes superparamagnetic, H\({}_{\text{C}}\) = M\({}_{\text{R}}\) = 0. M\({}_{\text{S}}\) and H\({}_{\text{C}}\) at 300 K. Values in italics indicate that these data were estimated from figures provided in the references.  
\end{table} 
Table 3: _Cont._acid and sodium oleate in mixtures of three solvents (1-octadecene, dibenzyl ether, and 1-tetradene) to obtain nanocubes with uniform sizes ranging from 9 to 80 nm [74]. Benzyl ether was again identified as a good solvent for the dispersion of Fe(acac)\({}_{3}\) precursors; however, it is not stable at high temperatures, producing volatile products such as benzyl aldehyde or benzyl benzoate [44; 76]. Thus, the combination of 1-octadecene, dibenzyl ether, and 1-tetradecene was utilized to compromise between the good dispersion of precursors and maintain a stable temperature during synthesis. Figure 5a presents the synthetic strategy using a combination of three solvents and two surfactants to synthesize Fe\({}_{3}\)O\({}_{4}\) nanocubes in a 9-80 nm size range. The growth mechanism is proposed in Figure 5b, starting from the nucleus to the truncated octahedron, then the tetradecahedron, and finally the nanocubes. In this figure, the chemical potentials of crystal facets are presented from low to high, as indicated by a green-to-red color scale. In addition to thermal decomposition, magnetite nanocubes have also been synthesized using sonochemistry [75; 77], precipitation [78], and solvothermal green synthesis [71]. However, thermal decomposition at high temperatures assisted by fatty acid ligands is the most efficient method that provides uniform cubic geometries, a narrow size distribution, and tunable sizes in a wide range from approximately 10 to 180 nm.
## 5 Conclusions  
A staple inserting apparatus was developed and mounted on a 3D printing system, with which 3D-printed and staple-reinforced cementitious specimens were automatically fabricated therein to investigate the mechanical improvement when the staple inserted inner- and inter-strips. The conclusions follow:  
* The staples were distributed and aligned as designed entirely using automatic equipment with a success ratio higher than 98%.
* The inner-inserted staples (in IR- samples) showed no noticeable improvement to the flexural strengths because the inserted staples were distributed without overlap and worked alone to lock the composite within the staple pins. They improved the compressive strengths by an average of 5%. Samples with a staple density of 0.25 staples per cubic centimeter had the maximum improvement in compressive strength, which was 25%.
* The inter-inserted staples (in IL- samples) improved the flexural stress by 46-120% by locking the strips. Further analysis showed that the printing parameters can affect the mechanical properties and the improving rate.
* The staple inserting operation introduced voids as defects in the composite properties, and a high staple density decreased the overall mechanical properties. Thus, the inserting technique should be further studied to minimize the voids.
* Future studies should be conducted on reinforcements with high staple density, via an upgraded printing-inserting system which can print cement mortar and insert staples at the same time.  
The staples worked alone; thus, the overall improvement of the mechanical properties was not as high as that of long reinforcements such as meshes, cables, and rebar. However, the staple inserting mechanism is simpler than the meshes/wires/cables. Each staple (or inserting operation) is independent of other staples. The 3 DCP-SIT system has more prospects in engineering applications. The staples can be considered as fibers to reinforce the printed outputs and have proved certain material locking effects in printed outputs. The proposed 3 DCP-SIT system fabricates fiber-reinforced printed concrete structures with controlled fiber distribution by the inserting technique. Further investigations are worth pursuing.  
Figure 17: Illustration of the 3 DCP-SIA system, reinforcing the extruded filaments.  
**Author Contributions:** Conceptualization, X.C.; data curation, S.Y.; formal analysis, X.C. and S.Y.; funding acquisition, H.C.; investigation, X.C. and S.Y.; methodology, X.C.; resources, S.Y.; supervision, H.C.; writing--original draft, X.C.; writing--review and editing, H.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Shenzhen Research Grant (No. KQTD20200909113951005).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article.  
**Acknowledgments:** The work in this study was funded by the Shenzhen Research Grant (No. KQTD20200909113951005).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Fuzzy Logic-Based Geographic Routing Protocol for Dynamic Wireless Sensor Networks
## 5 Conclusions  
The geographic routing protocol can establish routing quickly and reliably based on the location information of neighbor nodes and destination nodes. However, additional routing overhead is incurred, because the location information of destination nodes needs to be obtained in real-time through the location service algorithm. In addition, the existing geographic routing protocols also have the problem of routing void and inaccurate positioning.  
To solve all the above problems, based on the fuzzy logic system, the FLGR protocol has been put forward without the real-time location information of the destination node. Aiming for fewer hops and a higher packet delivery rate, the selection criteria and parameters for assessment have also been proposed.  
In addition, the routing void problem was too considered in the FLGR protocol. The routing void avoidance scheme was proposed to reduce the rate of routing failure by adjusting the CNR and discarding the void node. The forwarding node can be selected through the fuzzification, fuzzy reasoning, and defuzzification of node parameters.  
Finally, the feasibility of the FLGR forwarding mode was verified and the performance of FLGR protocol analyzed via simulation. Simulation results show that the proposed FLGR forwarding mode is able to effectively avoid the routing void problem. Compared with existing protocols, the FLGR protocol has lower routing overhead and a higher packet delivery rate in a sparse network.  
Figure 12: Impact of node number on routing overhead ratio with \(v_{\max}=20\).  
**Author Contributions:** [PERSON] initiated the idea of the paper and performed the simulation; [PERSON] led the research; [PERSON] and [PERSON] contributed to the development of processing tool; [PERSON], [PERSON], [PERSON] and [PERSON] contributed to discussions and revisions.  
**Funding:** This work has been supported by the Nature Science Foundation of China (Grant No.61701524) and The State Key Laboratory of Xidian University (ISN15-13).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Related Work
### \(2^{k}\) Factorial Analysis in Wireless Networks  
In the networking literature we can find several works that adopted the \(2^{k}\) factorial approach to discriminate among the many available parameters so as to determine the most relevant ones.  
[PERSON] _et al_. [15] studied Distributed Network Control Systems (D-NCS), a network structure and components that are capable of integrating sensors, actuators, communication, and control algorithms to suit real-time applications. Standard statistical approaches, such as \(2^{k}\) factorial experiment design, analysis of variance, and hypothesis testing, were used to study and estimate the effect of each factor on the system performance.  
[PERSON] _et al_. [16] studied the use of multipath routes to improve throughput, end-to-end delay, and the reliability of data transport in Wireless Sensor Networks (WSNs). They reported the results of a series of simulations based on a factorial experimental design. Results showed that both the congestion window size and the retry limit are key factors. [PERSON] _et al_. [17] studied how different WSNs can cooperate in order to reduce the total energy consumption. Simulation results revealed that different densities and data collecting rates among WSNs, the routing algorithm, and the path loss exponent had a major impact in the establishment of cooperation. The initial assessment of the impact of these factors was made through a \(2^{k}\) factorial experimental analysis.  
[PERSON] _et al_. [18] studied and quantified the effects of various factors and their two-way interactions on the overall performance of MANETs. Using \(2^{k}\) factorial experimental design, they isolated and quantified the effects of five factors: (i) node speed; (ii) pause-time; (iii) network size; (iv) number of traffic sources; and (v) type of routing. They evaluated the impact that these factors have over the throughput, routing overhead, and power consumption. In [19], they investigated the impact of some characteristics on the performance of TCP in MANETs. Moreover, a factorial design experiment was conducted to quantify the effects and interactions that node speed and node pause time have over the TCP throughput.  
Although the use of standard statistical approaches such as the \(2^{k}\) factorial analysis is found in many other fields, it is not so frequently used in _Ad Hoc_ network communications. Specifically, the \(2^{k}\) factorial approach has been adopted to discriminate among the many available parameters so as to determine the most relevant ones. As the number of different parameters in vehicular communications is very high, we consider that this method can also be applied in VANETs [20]. As shown in Section 5.1, the two extreme values used in our \(2^{k}\) factorial analysis are chosen among representative extreme values, within the bounds of applicability and technical feasibility. Additionally, in Sections 5.2-5.4 we confirmed the outcome of the \(2^{k}\) factorial analysis by performing a sensibility analysis when varying the values of the key factors in simulations.
# A Review of Industry 4.0 Manufacturing Process Security Risks
## 4 Solutions
### Securing the Manufacturing Information Architecture
#### 4.1.2 Architectural Approach to System Design and Integration  
As briefly introduced in Section 3.4.3, system safety is also of considerable concern in highly integrated and automated systems, particularly with respect to the risk of cyber-physical attacks compromising the safety of such equipment. In order to address this issue, the same basic approach for the inclusion of security measures can be applied to an inclusion of safety measures in the design of such systems.  
[PERSON] et al. [39; 40] discuss an architectural approach to the integration of both security and safety aspects in automated and integrated systems. Although this approach also considers the inclusion of system safety in system design (which should be of equal importance in the broader perspective), it particularly introduces the concept of dividing the signal flow within a cyber-physical system into individual layers. These signals that pass through each layer can then be analyzed to determine the authenticity thereof. For example, when system commands must be executed, each layer can verify that the applicable signals are authentic, by making use of measurements from various interconnected sensors to determine if the combination of measurements is in accord with the signals received from interfacing subsystems.  
The architectural approach for the integration of safety and security measures within an integrated system relies on layers of security being implemented in every subsequent subsystem to perform the localized (and unique) analysis of data, instead of relying on a single, all-inclusive security measure. This approach presents a considerable advantage, in that security measures can be enforced on multiple functional layers, providing a multi-layered security framework against cyber-attacks. For example, a cyber-attack could make use of a certain combination of known attack techniques to break an individual layer of security, but is prevented by a subsequent layer of security performing more rigorous analysis of data and signal flow. On the contrary, a single dedicated security measure could present a single point of failure in the system's security if it has been breached [39].  
Another added benefit of dividing security measures in multiple layers is that a more robust overall security solution is obtained by providing localized protection against multiple attack vectors over the broader scope of the entire system. Furthermore, it is even possible to provide protection against attacks that are not yet known through rigorous and interleaved signal and data analysis that eventually block an attack due to, e.g., a combination of measurements that are not in accord with a certain combination of system commands.