esa-sceva/satcom-chunk-collection huggingface dataset:
# Considerations about Parameters Estimation into a Minimum Variance Control System
## 3 Case Studies for Validation
### Case 2. Mechanical Torque Variation
In these new conditions and for the adequate operating point transition P\({}_{0}\)-> P\({}_{1}\)-> P\({}_{0}\), Figure 6a shows the open-loop process parameters estimates, respectively Figure 6b shows the process gain computed based on them. Figure 4: Operational characteristics of induction generator. Comparing the results from Figures 3d and 6a, it can be clearly seen that the values of the parameters estimates in open-loop, respectively in closed-loop are different. Instead, it can also be seen very clearly (and this must be highlighted in context of the paper goal) that in steady-state, although they were calculated based on different parameters estimates, the closed-loop process gain estimate (Figure 3e) is similar to the computed gain in open-loop (Figure 6b). The conclusion that can be formulated is the following: in the steady state, (although in the closed-loop), the estimator does not estimate the real parameters of the linearized process model (accurately identified in open-loop), however, based on them, the real process gain is correctly computed. The difference between the open-loop gain estimate (Figure 6b) and the closed-loop gain estimate (Figure 3e) is depicted in Figure 6d. The gain error is very small and one can notice a very good similarity of the two gain estimates. In the steady-state regime, the process gain can be also directly calculated by dividing the process output with the process input (Figure 6c), its value being very similar to the estimated one based on parameter estimates (\(k_{o}=0.8645\), \(k_{1}=0.8407\)- see comparatively the steady-state gains values in Figure 6b,c). A more pronounced effect of the stochastic noise (small oscillations) can be noticed in the case of direct calculation (instead attenuated, when estimates are used). Figure 6: (**a**) Open-loop process parameters estimates-polynomials \(A\big{(}z^{-1}\big{)}\) and \(B\big{(}z^{-1}\big{)}\) (functioning points: P0\(>\) P1\(>\) P0); (**b**) open-loop process gain \(k_{gain}=\frac{\text{B}\big{(}z^{-1}\big{)}}{\text{A}\big{(}z^{-1}\big{)}}\) (functioning points: P0\(>\) P1\(>\) P0); (**c**) open-loop gain calculated by dividing process output with process input: \(k_{gain}=\frac{y_{i}}{u_{i}}\); (**d**) gain error (difference between open-loop and closed-loop estimated gain).
###### Abstract  
Information-Centric Networking (ICN) is the emerging next-generation internet paradigm. The Low Earth Orbit (LEO) satellite mega-constellation based on ICN can achieve seamless global coverage and provide excellent support for Internet of Things (IoT) services. Additionally, in-network caching, typically characteristic of ICN, plays a paramount role in network performance. Therefore, the in-network caching policy is one of the hotspot problems. Especially, compared to caching traditional internet content, in-networking caching IoT content is more challenging, since the IoT content lifetime is small and transient. In this paper, firstly, the framework of the LEO satellite mega-constellation Information-Centric Networking for IoT (LEO-SMC-ICN-IoT) is proposed. Then, introducing the concept of \"viscosity\", the proposed Caching Algorithm based on the Random Forest (CARF) policy of satellite nodes combines both content popularity prediction and satellite nodes location prediction, for achieving good cache matching between the satellite nodes and content. And using the matching rule, the Random Forest (RF) algorithm is adopted to predict the matching relationship among satellite nodes and content for guiding the deployment of caches. Especially, the content is cached in advance at the future satellite to maintain communication with the current ground segment at the time of satellite switchover. Additionally, the policy considers both the IoT content lifetime and the freshness. Finally, a simulation platform with LEO satellite mega-constellation based on ICN is developed in Network Simulator 3 (NS-3). The simulation results show that the proposed caching policy compared with the Leave Copy Everywhere (LCE), the opportunistic (OPP), the Leave Copy down (LCD), and the probabilistic algorithm which caches each content with probability 0.5 (prob 0.5) yield a significant performance improvement, such as the average number of hops, i.e., delay, cache hit rate, and throughput.  
low earth orbit (LEO) satellite mega-constellation; information-centric network; caching policy; random forest; network simulator 3 (NS-3) +
Footnote †: journal: Sensors  
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Footnote 0: Copyright** © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 3 Results and Discussion
### Untargeted Analysis
#### 3.1.1 Hydrocarbons
Analogously to the first monitoring, a general decrease was observed for each analyte, especially for short-chain compounds: C12 and C14 hydrocarbons presented the most abundant decrease (84% and 69%, respectively). As a final remark for hydrocarbons, it is worth highlighting that the profile of supply water presented the highest peaks in the diesel range (C8-C23) [23,24] both in the first and in the second monitoring.
## 3 Model
### Answer Decoder
#### 3.3.2 Generative Decoder  
Unlike most previous approaches, which take only a discriminative approach, we also train our model in a generative manner [7]. During the training phase, we use a two-layer LSTM to predict the next token given the previous tokens in the ground truth answer. Specifically, the hidden state \(u_{gt,k}^{a}\in\mathbb{R}^{d_{k}}\) at \(k\)-th time step is computed given current word embedding \(w_{k}^{a}\) and previous hidden state \(u_{gt,k-1}^{a}\) as follows:  
\[u_{a}^{gt,k}=\text{LSTM}(w_{k}^{a},u_{gt,k-1}^{a}). \tag{12}\]  
The initial hidden state of the LSTM \(u_{a}^{gt,0}\) is initialized with \(m^{enc}\) (i.e., output representation of the encoder). When generating each token, we compute the likelihood of the ground truth of each token \(\{p_{k}\}_{k=1}^{n_{a}}\) as follows:  
\[p_{k}=\text{softmax}_{j}(\mathbf{W}_{k}^{\top}u_{gt,k}^{a}+b_{k}), \tag{13}\]  
where \(\mathbf{W}_{k}\in\mathbb{R}^{d_{k}\times|V|}\) is a projection matrix and \(|V|\) is the size of vocabulary. Note that \(j\) is the index of \(k\)-th word in the vocabulary and \(p_{k}\in\mathbb{R}^{|V|}\) is the probability score of \(k\)-th ground truth token. In the training phase, the model is optimized by minimizing the summation of the negative log-likelihood as follows:  
\[\mathcal{L}_{Gen}=-\sum_{k=1}^{n_{a}}\text{log}p_{k}. \tag{14}\]
# Water Pipeline Leakage Detection Based on Coherent \(\varphi\)-OTDR and Deep Learning Technology
## 4 Deep Learning Training  
### CNN with Mel Spectrograms as Input  
When dealing with vibration audio signals, more complex feature representations are often required. Simple one-dimensional data input may not suffice, leading to suboptimal model performance. Therefore, as described in this section, Mel spectrograms were trans  
Figure 7: Mel spectrum diagrams of different events: (**a–e**) no leakage; (**f–j**) leakage.  
formed from the one-dimensional phase data, then the Mel spectrograms that served as the input for the CNN model.  
After generating Mel spectrograms, original phase data were transferred into two-dimensional images, where the horizontal axes represented time and the vertical axes represented energy at different frequencies. This allowed us to input them into the CNN model, capturing audio signal features more effectively. The model was built and trained using TensorFlow 2.6 GPU and Python 3.8. When using Mel spectrograms as input data, the corresponding model employs two-dimensional convolutional layers with a kernel size of 3 \(\times\) 3 and a pooling layer window size of 2 \(\times\) 2. Other basic settings remain unchanged, and the neural network architecture has been appropriately optimized, as illustrated in the diagram shown in Figure 8:  
In the process of model training, we continued to utilize the Adam optimizer, with batch size, learning rate, and training epoch values set at 32, 0.001, and 200. The corresponding training curve is depicted in Figure 9a. Using the model, we identified the test dataset and obtained a confusion matrix as shown in Figure 9b. The achieved accuracy was 99.7%.
## 3 Results and Discussion
### Implications of LAI as a Habitat Index for Reptiles
Moreover, earlier studies have shown that the surveyed reptiles are associated with habitats with diverse landscapes [31] and that when in environments dominated by monocultures, they search for such places [32]. It has also been found that some particular vegetation mosaics have a favorable impact on the diversity and abundance of reptiles [33]. The analysis of the variability of LAI distributions over months and for various species reveals another aspect of our results (Figure 8). No significant seasonal changes in LAI distributions were found in smooth snake areas or in the areas of the three other species for which a significant difference was stated between the \"observed\" and \"not observed\" factor (_i.e._, common viper, grass snake, blind worm, Figure 7). This suggests that they use habitats or complexes of habitats that guarantee similar, steady LAI levels from April until August. Apparently, these habitats are of the dry and coniferous forest type (Frz, BM, Wbag) as well as open land used by man, systematically reaped or mown (_Leucobryo-Pinetum_ community; peripheral subatlantical coniferous forest with an admixture of meadow species and _Piceo-Vaccinienion_ community). Frequently mown xerothermic grasses, farm meadows or kitchen gardens, which are present at Roj, Krl and Dom sites, are good examples of such open land habitats. Seasonal variability of LAI distributions can be seen at sites with sand and common lizards. The latter species were frequently sighted at sites with open, unknown areas (Joz, ZP, Bor, Buko) and with lush herbaceous vegetation (_Artemisietea_--rudimentary xerophylous and xerothermic grassland and _Leucobryo-Pinetum_ community). This supports the fact that despite the above-mentioned lack of significant difference between the \"observed\" and \"not observed\" factors, these two species have an association with sites with variable LAI distributions in the period April-August. Therefore, one specific LAI value during the whole year may be inappropriate to characterize the habitat of these lizards. Figure 7: LAI distributions per reptile species observed (obs.) or not observed (not. obs) in the surveys at our Poland study sites. Significant differences in LAI distributions in each month (see Figure 8) are important for the creation of models simulating species occurrence areas. In these models, a generalized variable for the period of a year or a number of years is frequently used as an independent variable [2,5,7]. This type of annual analysis provides good results when some response metrics are analyzed; however, our reptile study seems to indicate an opportunity for further improvement by taking into account seasonal variability of the studied indicators. From our analyses, reptiles appear to need variable habitats in terms of LAI (Figure 7) and this fact, in turn, suggests that for the modelling of habitat extent, information about the LAI's variability within the studied area or the analysis of its vicinity is required.
## 6 The Local Scheduler  
Within a single invoker, our scheduling strategy aims to maximize resource utilization while ensuring that SLA latency requirements are met. The key to achieving this is the aggressive reuse of available containers within the invoker, allowing a greater number of diverse incoming tasks to be processed concurrently without breaching SLA constraints. Pre-warming a set number of containers based on forecasted demand is an effective method for significantly reducing cold starts. However, this approach can increase the total number of active containers, potentially leading to underutilization of the invoker's resources. In real-world serverless environments, application execution times vary widely, whereas SLA thresholds are fixed and based on latency constraints perceptible to users. Given that these applications often execute in just a few milliseconds, a substantial buffer (or slack) typically remains. The concept of slack, introduced by [31], refers to the difference between a function's actual runtime and its response latency. As shown in Figure 6, if this available buffer can be accurately calculated, it can be leveraged to reuse an existing container instead of pre-warming a new one. To exploit this, we designed a buffer-aware greedy scheduling algorithm that utilizes both forecasting data and the currently available slack to determine which containers can be reused without violating response latency constraints.  
In our approach, each container maintains a status: either busy or idle. When a request is assigned to a container, that container becomes busy for the duration of the function's execution time. Knowing the execution time of a function task \(i\) on container \(j\), we can calculate \(rt_{ij}\) (the expected ready time of container \(R_{j}\)). Based on this, the buffer-aware scheduler calculates the buffer vector \(VR_{j}\) as:  
\[VR_{j}=SLA-rt_{ij} \tag{11}\]  
Figure 6: Illustration of the buffer-aware scheduling algorithm.  
Based on the forecast provided by the hybrid predictor, the scheduler must determine the estimated execution time \(\widehat{Rt_{ij}}\) for each predicted upcoming function \(\hat{i}\) The scheduler then assigns the function with the shortest expected execution time to the container with the longest available buffer vector--if and only if the total time \(\widehat{Rt_{ij}}+rt_{ij}<SLA\) threshold (the assigning condition). If no container has sufficient buffer to satisfy this condition, a new container is pre-warmed to serve the incoming function \(\hat{i}\). This scheduling strategy (as shown in Algorithm 1) optimizes invoker resource usage by aggressively reusing current containers. Assigning the function with the shortest runtime to the container with the maximum buffer allows a single container to sequentially handle multiple functions. At the same time, enforcing the assigning condition helps minimize SLA violations. The assumption that SLA response constraints are typically greater than function runtime in serverless environments effectively transforms the scheduling challenge into an optimization problem.
# A Digital Twin-Based Approach for Detecting Cyber-Physical Attacks in ICS Using Knowledge Discovery
## 2 Background
### Process Mining  
Process mining is a data mining technique that allows organizations to improve their business processes by analyzing data from event logs that are made available by today's information systems [16]. Event logs are records of all the activities that have been carried out within a system. By analyzing these event logs, traditional industries, organizations, and companies specialized in the production of software can identify bottlenecks and inefficiencies in their processes and take steps to improve them.  
Process discovery is a prominent task in process mining involving the derivation of process models from data captured during the execution of real-world processes. The main of process discovery goal is to analyze event logs in order to extract structures, control flows, and characteristic paths that accurately represent the actual processes. These event logs are crucial, as they provide the concrete evidence necessary to map the real-world process executions to the theoretical models originally conceived for these processes.  
Numerous techniques for process model discovery can be found in the literature, mainly differing in terms of their modeling language. For example, in [17; 18; 19] a simple directed graph was used to model the process; other works have adopted much more expressive specification languages, ranging from the use of tree-structured [20] or block-structured workflow models [21; 22] to the use of special classes of Petri nets [23; 24]. Despite the considerable variety and differences among process discovery approaches, they share a common denominator in the definition of techniques for discovering causal relationships between process activities, known as the problem of dependency-graph mining.
## 2 Materials
### Preparation of OGFC Containing OSW Filler  
In a previous study, the preparation process of OGFC containing OSW filler (COF-OGFC) was optimized based on response surface methodology. The whole preparation procedure of OGFC containing OSW filler can generally be divided into five stages, as follows [38].  
Step 1: Since the oil shale waste has a high moisture absorption rate, the oil shale waste should be pretreated before utilization. The pretreatment procedure of oil shale waste includes two steps of drying and smashing.  
Step 2: According to the gradation, the oil shale waste with a particle size of 0.075-4.75 mm and the coarse aggregate with a particle size of 4.75-19 mm are mixed at 153 \({}^{\circ}\)C for the first mixing. The gradation of OGFC used in this study is shown in Figure 2.  
Step 3: The base asphalt is heated to about 135 \({}^{\circ}\)C to ensure that the asphalt is completely melted and in a flowing state, and 2.5 wt% silane coupling agent stock solution (KH550, Shanghai Yiyan Co., Ltd., China) of asphalt is directly mixed into the base asphalt and manually stirred for 3 min.  
Step 4: The silane coupling agent modified asphalt and the mixtures containing oil shale waste and coarse aggregate are mixed at 153 \({}^{\circ}\)C for the second mixing. The optimized bitumen-aggregate ratio is 5.18%.  
Step 5: The mineral powder and the mixtures containing modified asphalt, oil shale waste and coarse aggregate are mixed at 153 \({}^{\circ}\)C for the final mixing. Then, the prepared mixture is processed into Marshall test pieces with 50 double-sided compactions according to GB/T 0709-2011.  
Moreover, two types of OGFC were used for the comparison: a conventional OGFC mixture with \"Pan Jin\" base asphalt (OGFC), and a modified OGFC with Styrene-Butadiene-Styrene modified asphalt (SBS-OGFC). All asphalt mixtures were specified based on the Technical Specifications for the Construction of Highway Asphalt Pavement in China (JTG F40-2004), and the optimum asphalt content of OGFC and SBS-OGFC was found to be 4.8% and 4.1% for the control mixture.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Item** & **Unit** & **Test Results** & **Specification Requirement** & **Test Procedure** \\ \hline Apparent specific gravity & g-cm\({}^{-3}\) & 2.799 & \(\geq\)2.5 & T 0352 \\ Water absorption & \% & 0.3 & \(\leq\)1 & T 0103 \\ Granular & \textless{}0.6 mm & \% & 100 & 100 \\ composition & \textless{}0.15 mm & \% & 99.2 & 90–100 & T 0351 \\  & \textless{}0.075 mm & \% & 97.9 & 75–100 & \\ Hydrophilic coefficient & N/A & 0.6 & \(\leq\)1 & T 0353 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Technical parameters of mineral powder.  
Figure 2: Gradation of open graded friction course (OGFC)-16 in the test.
# Energy Harvesting Techniques for Wireless Sensor Networks/Radio-Frequency Identification: A Review
## 1 Introduction
### Energy Harvesting and Wireless Charging Techniques
Certain works have suggested using a wireless energy charger application for WSNs because it supports mobility [84; 85]. A practical, real-time wireless recharging protocol for dynamic wireless recharging in sensor networks is proposed in [86]. The real-time recharging framework supports single or multiple mobile vehicles. Similarly, a collaborative wireless charging scheme has been presented in [87]. Recharging selected nodes in remote areas by using unmanned aerial vehicles can address the issue of recharging a single node. [PERSON] et al. [88] simulated a limited recharging system and verified the effectiveness of recharging a single node. However, their work does not describe a robust system model and is limited to simulations. A \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Technique** & **Advantages** & & **Disadvantages** & **Charging Distance** \\ \hline Magnetic inductive coupling & \(\bullet\) & Simple implementation. Safe for human. & \(\bullet\) & Short charging distance. Needs tight alignment between chargers and charging devices. Heating effect. & From a few millimetres to a few centimeters. \\ \hline Magnetic resonance coupling & \(\bullet\) & Loose alignment. Nonlinear-of-sight charging. Charging multiple devices simultaneously on different power. High charging efficiency. & \(\bullet\) & Limited charging distance. Complex implementation. & From a few centimeters to a few meters. \\ \hline Non-directive RF radiation & \(\bullet\) & Long effective charging distance. & \(\bullet\) & Line of-sight charging. Low charging efficiency. Not safe when the RF density exposure is high. & Typically, within several tens of meters, up to several kilometers. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of the advantages, disadvantages, and effective charging distance of the energy transfer techniques. prototype-based implementation for similar EH architectures is provided in [89]. Further research must be conducted to select a proper energy storage mechanism, which is directly related to efficiency and long operational lifetime of sensors.