esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Optimization Methods and Results
### Hardware System Optimization
#### 3.2.2 Memory Access Optimization Method 2: Task Pipelining  
To further improve the performance of the accelerator, we analyze the memory access mechanism. According to the description in Section 2.4.3, the operation procedures of memory access are writing RAF (marked as task 1), reading RAF and writing RDF (marked as task 2), reading RDF and writing WDF (marked as task 3), and writing DDR (marked as task 4) eventually. The scheme is shown in Figure 10, where the arrows mean the data stream's flow direction. However, the interval between two adjacent data streams cannot be neglected, which adds unnecessary latency in the whole process.  
To reduce the interval, we reschedule the process using the task pipeline, as shown in Figure 10. The FSM described in Figure 8b is divided into four independent FSMs. Therefore, tasks 1, 2, and 3 can run in parallel without interference and the latency of a complete memory access process is reduced remarkably.
## 2 Materials and Methods
### Proposed Methodology
#### 2.1.3 Hybrid Model for Missing Value Estimation  
In the proposed hybrid model, the spatial kriging estimate and the MLP estimate are integrated with optimal weights. The optimal weights must be estimated using a sample of artificially created missing cases. Suppose the spatial kriging estimate of that sample is \(\hat{y}_{k}\) (which was produced as \(\hat{Z}(X_{0})\) in Section 2.1.2) and the MLP estimate is \(\hat{y}_{m}\) (which was  
Figure 2: The neighboring observed locations and the location of interest, \(X_{0}\).  
produced as \(\hat{y}_{j}\) in Section 2.1.1), weights from each estimate are \(w_{k}\) and \(w_{m}\), respectively, the actual value is \(y_{a}\), and the final estimate is \(\hat{y}_{f}\). In order to find the final prediction closer to the actual value, the optimal weights from each prediction should be found such that the prediction error is minimized. Therefore, the optimization problem can be defined as mean squared error (MSE) loss function (\(f_{loss}\)) between predicted and actual values:  
\[f_{loss}(w_{k},\ w_{m})=\sum_{i=1}^{n}(w_{k}\hat{y}_{ki}+w_{m}\hat{y}_{mi}-y_{ ai})^{2} \tag{7}\]  
with respect to the following constraints that provide a convex combination of two edge points \(\hat{y}_{ki}\) and \(\hat{y}_{mi}\):  
\[w_{k}+w_{m}=1,\ 0\leq w_{k}\ \leq 1,\ 0\leq w_{m}\leq 1 \tag{8}\]  
Following the study [27], it would be logical to consider the case when the weights \(w_{k}\) and \(w_{m}\) are greater than 1 or less than 0. Considering this assumption and denoting \(w=w_{k}\), Equations (7) and (8) can be formulated as an unconstrained optimization problem with respect to \(w\) in the form:  
\[\text{Minimize}:\ f_{loss}(w)=\sum_{i=1}^{n}(w\ \hat{y}_{ki}+(1-w)\ \hat{y}_{mi}-y_{ ai})^{2} \tag{9}\]  
The objective function \(f_{loss}(w)\) in this problem is convex and smooth; therefore, optimization Equation (9) can be easily solved. We have:  
\[\frac{1}{dw}\ f_{loss}(w)=\sum_{i=1}^{n}2(w\ \hat{y}_{ki}+(1-w)\ \hat{y}_{mi}-y_{ ai})(\hat{y}_{ki}-\hat{y}_{mi})=0 \tag{10}\]  
Solving Equation (10) with respect to \(w\), we find optimal weight \(w\) and, therefore, weights \(w_{k}\ =w\) and \(w_{m}\) in the form:  
\[w_{k}=(\sum_{i=1}^{n}(\hat{y}_{mi}^{2}+\hat{y}_{ki}y_{ai}-\hat{y}_{mi}y_{ai}- \hat{y}_{ki}\hat{y}_{mi}))/(\sum_{i=1}^{n}(\hat{y}_{ki}^{2}+\hat{y}_{mi}^{2}-2 \hat{y}_{ki}\hat{y}_{mi})) \tag{11}\]  
\[\text{and}\ w_{m}=1-w_{k} \tag{12}\]  
The found optimal weights were used to obtain the final prediction \(\hat{y}_{f}\) at the point \(i\) where the particular value is missing:  
\[\hat{y}_{f}=w_{m}*\ \hat{y}_{m}+w_{k}*\ \hat{y}_{k} \tag{13}\]  
The imputed missing values using the proposed method were compared with those obtained from spatial kriging, MLP as well as spatiotemporal kriging. The reason for considering spatiotemporal kriging is that it is a widely used spatiotemporal missing value imputation method in past studies.
## 3 LDCP Problem
### Problem Statement  
We state the LDCP problem in the following way. Given network topology, traffic demands, routing paths connecting network nodes, capacities of PP nodes and network links, latencies introduced in network elements, and latency limits for flows, we find for all demands a feasible placement of the DU and CU processing entities in the PP nodes under constraints:  
1. _Clustering of RUs_: the DUs associated with the RUs that belong to the same cluster are placed in the same PP node;
2. _PP node assignment for DU processing_: for each demand, locate the DU in the PP node that has been assigned to its cluster (i.e., to which its RU belongs to);
3. _PP node selection for CU processing_: a PP node is selected for the CU processing of the demand;
4. _PP node capacity_: the overall DU and CU processing load of all demands processed in each PP node does not exceed the node processing capacity;
5. _Traffic flows_: the traffic flows (FH, MH, and BH) are terminated in the PP nodes in which DU and CU entities are placed; if DU and CU are located in the same PP node, then flow MH is not realized in the network;
6. _Capacity of link_: the overall bit-rates of all flows going through a link must be lower or equal to the link capacity;
7. _Latency of flow_: the latency of a flow cannot be greater than the maximum latency that is allowable for this flow.  
The optimization objective considered in this work is to minimize the amount of active PPs and the sum of latencies of all flows in the network. We assume that the former objective is a primary goal and the latter is a secondary goal.  
Note that in the problem considered, a single path is provided for each pair of network nodes. Further extensions of the LDCP problem might assume the availability of candidate paths between pairs of network nodes. We will address such a scenario in future work.
## 2 Related Works and Contribution
### Our Contribution  
The proposed approach is based on a different concept than the above-discussed state-of-the-art methods. According to this approach, the sensor nodes use a classifier to decide whether the data readings have to be transmitted. The classifier is trained in advance to select the useful data, which are necessary for a target application of the wearable sensor network, e.g., for recognition of human activities.  
The state-of-the-art methods are aimed at transmitting the reduced data, which allow the receiver node to reconstruct the original series of sensor readings. In contrast, the new method presented in this paper is designed to transmit the selected data that are sufficient for the receiver node to made accurate decisions with use of embedded machine learning algorithms. The motivation for this work was that in many situations only a subset of the data from sensor nodes is required by the machine learning algorithms to make correct decisions. For instance, the activity or position of a monitored person in some cases can be recognized based on data from a single wearable sensor, while in other circumstances the data from several nodes are necessary for correct recognition.  
Unlike the data aggregation and data compression approaches, the proposed method does not involve the buffering of sensor readings before transmission, which results in additional delay. Thus, the presented solution is more suitable for real-time applications. Moreover, our method is suitable for networks with different topologies, including those without multi-hop transmissions.  
The contribution of this work also includes experimental evaluation of the transmission reduction methods with use of real wireless wearable sensor network. Comparison of the proposed method, with prediction-based approaches, was conducted for physical prototypes of wearable nodes. Results of this work include analysis of the wearable sensor network lifetime in real-world application. In contrast, the results presented in many previous works were obtained with use of simulation techniques.  
The main contributions of this paper are summarized as follows:  
* A new concept using embedded classifiers for data transmission reduction in sensor networks is presented. According to the best authors' knowledge the embedded machine learning algorithms have not been used so far in this context. Existing works are limited to simpler case, where individual IoT devices are considered.
* An algorithm is introduced for preparing a data set, which facilitate training of the classifiers designed to eliminate unnecessary data transmissions.
* Feasibility and effectiveness of the proposed approach was confirmed in experiments with wearable sensor network for human activity monitoring.
# Research and Development of Inland Green and Smart Ship Technologies in China
## 3 Current Inland Green and Smart Ship Technology in China
### Smart Ship Technology
#### 3.3.2 Smart Engine  
Through sensing technology, it is possible to remotely control the operating state of the equipment in the engine room in real time and provide early warning information and maintenance plans in a timely manner, eliminating the need for crew members to monitor the equipment operation in the engine room. The horizontal, longitudinal, and torsional vibrations of propulsion shafting can directly reflect the health of the propulsion and transmission system. The system's shafting power can be used to monitor the transmission efficiency of the engine, gearbox, and shafting in real time. A multi-dimensional vibration and shafting power smart sensing system was established based on data mining to evaluate the operating state of the shafting [46].  
It is also possible to evaluate the air compressor state based on the vibration method, the multi-parameter gearbox state, and the pump state based on vibration and thermal parameters. An evaluation method of the health of the ship's main engine, auxiliary engine, and main equipment was also established to ensure that the equipment in the engine room operates safely and reliably. The system includes smart decision-making methods for fault diagnosis of the engine room equipment, spare part trend analysis, and predictive evaluation methods based on spare part consumption statistics [47]. Dynamic inventory management software was developed based on handheld terminals and radio-frequency identification (RFID) electronic tags for spare part warehousing, storage, retrieval, early warning, and purchase reminders. Furthermore, spare part prediction and assessment methods were developed based on the analysis of engine room equipment health management data, as was a dynamic spare part management module based on intelligent data analysis. By studying the analysis of monitoring data and the prediction and evaluation of the main equipment in the engine room, basic parameters, such as the health state, fault diagnosis, and maintenance information for each piece of equipment can be assessed, allowing for a condition-based equipment maintenance strategy to achieve the dynamic management of spare parts, maintenance support, and efficient equipment operation.
# Performance Analysis of Latency-Aware Data Management in Industrial IoT Networks +
## 5 Implementation and Experimental Evaluation
### Experimental Strategy
Both the WG1 of Plattform Industrie 4.0 (reference architectures, standards and norms) [34] and the Expert Committee 7.2 of ITG (radio systems) set the latency requirements for condition monitoring applications to 100 ms. However, in order to provide a complete and diverse set of results, we also measure the performance of our method for different values of \(L_{\max}\). Performance benchmarks: In order to measure the performance of the DML with respect to traditional industrial IoT alternatives, we implement two additional data delivery strategies. The first method is the most traditional data delivery strategy in current industrial IoT environments and imposes that all data requests and data deliveries are being routed through the controller \(C\). More specifically, the request is routed from consumer \(u\) to \(C\) and then from \(C\) to producer \(v\). At the next step, the data are routed from \(v\) again to \(C\) and then from \(C\) to \(u\). We call this mode of operation non-storing mode, and it is obvious that it is completely centralized and not cooperative. Note that this would be the simplest data management approach that can be implemented in relevant routing  
Figure 4: Experimental setup. mechanisms like RPL (IPv6 Routing Protocol for Low-Power and Lossy Networks [35]), where intermediate nodes are not allowed to cache data (thus, the RPL terminology non-storing mode). The second method is another, less commonly used in industrial IoT settings, but nevertheless useful alternative. It imposes that all data requests and data deliveries are being routed through the Lowest Common Ancestor (LCA) of the routing tree, routed at the controller \(C\). The LCA of two nodes \(u\) and \(v\) in the routing tree is the lowest (i.e., deepest) node that has both \(u\) and \(v\) as descendants. We call this mode of operation storing mode, because the LCAs should store additional information about their descendants, and it is obvious that it is a distributed alternative. Again, this is the simplest method that one would implement with routing mechanisms like RPL in storing mode, i.e., when intermediate nodes between communication endpoints are allowed to cache content. Storing mode thus provides a distributed method. We made those choices after careful consideration of the current realistic industrial networking status-quo. The selected protocols are standardized components of a reference and well-established communications and data management stack. In fact, they are considered the state-of-the-art, for current and future wireless industrial applications, as discussed extensively in [15; 16]. More specifically, the stack is presented in detail in [15] and is considered as the de facto standard for future industrial networks. In the following, for convenience, we use the term \"special nodes\" when we refer to the network controller, the proxies or the LCAs.
## 4 Design of the Sliding-Mode Controller
### Closed-Loop Dynamics of the PV Voltage  
The closed-loop dynamics are calculated by assuming a correct operation of the SMC; thus, the magnetizing current is defined by \(\psi_{x}=0\), as given in (28), which compensates for the changes on the PV current.  
\[i_{pv}-k_{2}\cdot i_{m}=-k_{1}\cdot\left(v_{pv}-v_{mpt}\right) \tag{28}\]  
From the averaged differential equation of the PV voltage, given in (6), it is observed that the PV voltage dynamic behavior is determined by the relation between the PV current, the duty cycle, and the magnetizing current as \(i_{pv}-i_{m}\cdot d\). Comparing this expression with the left term of (28) shows that making \(k_{2}\) equal to the duty cycle \(d\) transforms the PV voltage differential (6) into:\[\frac{dv_{pv}}{dt}=\frac{-k_{1}\cdot\left(v_{pv}-v_{mppt}\right)}{C_{pv}}\quad \texttt{where}\quad k_{2}=d>0 \tag{29}\]  
Applying the Laplace transformation to (29) leads to the PV voltage transfer function reported in (30), which ensures a null steady-state error between the PV voltage \(v_{pv}\) and the optimal MPPT reference \(v_{mppt}\). Moreover, such an expression also confirms that the PV current does not introduce any error into the PV voltage; thus, the reference value can be accurately tracked, and the PV power could be maximized. Finally, the \(k_{1}\) value must be positive to ensure a stable transfer function.  
\[V_{pv}(s)=\frac{k_{1}}{C_{pv}\cdot s+k_{1}}\cdot V_{mppt}(s) \tag{30}\]  
This closed-loop transfer function will be used in the next section to calculate the value of \(k_{1}\) in agreement with the MPPT algorithm.
## 3 Simulation Study  
The simulation study is carried out to provide numerical support for the proposed \(PLS\)-based variable selection methods, where we know truly important variables. The data is generated by simulating a known model. \(\mathbf{y}=\mathbf{X}\beta+\epsilon\), where \(\mathbf{y}\) is response and \(\mathbf{x}\) is data matrix assumed to follow multivariate normally distribution with mean-vector \(\mu=0\) and covariance matrix \(\Sigma\)[25].  
\[\mathbf{X}\sim MVN(0,\Sigma).\]  
Because the variances of all variables are assumed to be one, \(\Sigma\) is the correlation matrix. Furthermore, \(H\) groups of correlated \(\mathbf{x}\) variables are extracted by applying the group diagonal structure to \(\Sigma\), that is:  
\[\Sigma=\begin{bmatrix}\Sigma_{1}&0&\dots&0\\ 0&\Sigma_{2}&\dots&0\\ \vdots&\vdots&\ddots&\vdots\\ 0&0&\dots&\Sigma_{L}.\end{bmatrix}\]  
We have used \(H=10\) groups of the equal number of variables. Data are simulated with \(n=100\), with four levels of the number of variables \(p=(100,500,1000)\), with two levels of within-group correlation \(R_{1}=(0.35,0.15,0.2,0,0,0,0,0,0,0)\), \(R_{2}=(0.75,0.95,0.50,0,0,0,0,0,0)\) and with the correlation between \(x\) and \(y\)\(C_{xy}=(0.6,-0.5,0.2,0,0,0,0,0,0,0)\). For each simulated data set, the first 30 (percentage) variables are considered important. For example, with (\(p=100\)), the variables 1 to 30 are considered important, while the rest are considered unimportant. The first 30 variables will be influential. This results in six different parameter combinations for the simulation. For tuning the \(PLS\) model's parameters, 100 simulation runs are used, with each run data divided into 70% training and 30% test. The simulated data set is used to fit the proposed \(PLS\)-based methods for variable selection and reference method. \(C_{xy}\) indicates that the first two groups of simulated variables are significant. Different levels of (R) and (p) produce6 simulation designs. The behaviour of measures used in reference methods and proposed methods can be seen in Figure 1. This indicates the proposed methods have higher specificity and Johnsen index with \(W,V\) has higher sensitivity. Because sensitivity indicates the proportion of true variable selection. Table 1 shows the results of a sensitivity, specificity, and accuracy comparison of proposed and reference \(PLS\) models against the simulation design. This means that in the majority of simulation designs, our proposed algorithms are more sensitive and accurate, whereas the reference methods are more specific.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(p\) & **Rohs** & **Method** & **Sensitivity (\%)** & **Specificity(\%)** & **Accuracy (\%)** \\ \hline
100 & R1 & \(W\) & 0.86 & 0.50 & 0.79 \\  & & \(V\) & 0.81 & 0.90 & 0.83 \\  & & \(C\) &
## 3 Petri Nets
### Basic Definition  
A Petri net, see Figure 2, is a specific type of graph comprising three kinds of objects: _places_, represented by circles; _transitions_, represented by bars, and _directed arcs_, which connect places and transitions. The dynamic nature of the system is represented by the movement of _entities_, in a Petri net, and this can be represented as tokens (drawn as dots) that are dynamically created and destroyed through the net.  
A Petri net is formally defined as a 5-tuple N = (P, T, A, W, M0), where:  
* P = [P1, P2, P3,....., Pnp] is a finite set of places;
* T = [T1, T2, T3,....., Tne] is a finite set of transitions;
* A = {A1, A2, A3,....., Ana} is a finite set of arcs that connect places to transitions and vice versa;
* W: Ai \(\rightarrow\) {1, 2, 3,...} \(\forall\) Ai is the weight associated with each arc;
* M0: Pi \(\rightarrow\) {1, 2, 3,...} \(\forall\) Pi is the initial number of entities in each place (initial marking).  
The current location and distribution of entities in a Petri net is called a marking.  
Figure 2: Example of a basic Petri net.  
A transition can be fired if each transition input has the required number of entities specified by the weight associated with the arc from the place to the transition. Firing the transitions (that represent the simulation events) removes entities from the input places and adds entities to the output places. The number of entities removed or added equals the weight of the associated arc [3].
## 4 Mechanism
### Narrowing the Band Gap  
Combining TiO\({}_{2}\) with carbonaceous nanomaterials may result in the band gap narrowing. Some ways were summarized below.  
(i) Ti-O-C bonding bridge. [PERSON] et al. [83] investigated that carbon can act as a dopant to replace O and Ti by using density functional theory (DFT) calculations. The calculated structures suggested it was easier to replace O when the amount of doped carbon is low and in the oxygen-poor environment, whereas, under oxygen-rich condition, substitution to Ti is preferred. The chemically bonded Ti-O-C plays a significant role in narrowing the band gap, thus promoting the photocatalytic ability [84; 85; 86]. The spectral response range can be broadened to the visible region due to the forming of Ti-O-C bonds [87; 88]. [PERSON] et al. [89] synthesized TiO\({}_{2}\)-olive pits lignocellulosic biochar photocatalysts via ultrasonic-assisted sol-gel method. TiO\({}_{2}\) clusters were successfully bonded to BC functional groups through Ti-O-C bridge (Figure 4A). The Ti-O-C was more pronounced in the samples of lower BC content, while the light absorbance increased with the BC content. Additionally, the optical absorption edge of TiO\({}_{2}\) was shifted towards the visible-light region due to the introduction of BC. Ti-O-C bonds exhibited a remarkable role in band gap narrowing. The band gap energy varied from 3.02 to 2.63 eV depending on the TiO\({}_{2}\)/BC ratio.  
(ii) Carbonaceous materials can act as photosensizers and charges receiver to narrow the band gap when combined with other semiconductors [89; 90]. [PERSON] et al. [89] demonstrated that the BC displayed an organic structure, which induced the visible-light absorption. The photosensitization of TiO\({}_{2}\) by BC polymer is shown in Figure 4B. Although the BCPs with higher bi  
Figure 4: Schematic illustration of the proposed mechanism of narrowing the band gap induced by C-O-Ti bond (**A**) and photosensitization (**B**). Reproduced with permission from reference [89].  
obvious visible-light response and narrow band gap, the photocatalytic reduction rate of Cr(VI) was the lowest. It was because the higher content of biochar may block the light absorption and affect the electrons generating. [PERSON] et al. [55] reported similar photosensitization effects by BCPs.
# Future Industrial Networks in Process Automation: Goals, Challenges, and Future Directions
## 4 Evolution Challenges
### Synchronization  
Synchronization of industrial devices and systems with adequate accuracy and precision is an essential part of monitoring and control functions of automation systems. Different synchronization requirements per application, harsh environment, and nondeterministic networks make the synchronization in industrial systems challenging.  
With TSN, the IEEE802.1 AS standard is introduced. The revision of this standard, IEEE802.1 AS-rev, is under discussion. It is envisioned to provide fault tolerance and highly accurate time synchronization. The green-field installations would get benefited by implementing this feature-rich synchronization profile. However, the comprehensive functional and security performance of a new profile in the industrial environment has yet to be assessed.  
In the case of brown-field installations, the automation systems typically require one to a few thousands of milliseconds of synchronization accuracy for most of their applications. Since the TSN networks operate at the synchronization accuracy of nanoseconds order, integrating legacy industrial devices to the TSN network and thereby achieving deterministic data delivery of critical messages is technically challenging.