esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The Internet of Things (IoT) has vast potential to drive connectivity and automation across various sectors, yet energy inefficiency remains a critical barrier to achieving sustainable, high-performing networks. This study aims to identify and address the primary causes of energy wastage in IoT systems, proposing a framework to optimise energy consumption and improve overall system performance. A comprehensive literature review was conducted, focusing on studies from 2010 onwards across major databases, resulting in the identification of eleven key factors driving energy inefficiency: offloading, scheduling, latency, changing topology, load balancing, node deployment, resource management, congestion, clustering, routing, and limited bandwidth. The impact of each factor on energy usage was analysed, leading to a proposed framework that incorporates optimised communication protocols (such as CoAP and MQTT), adaptive fuzzy logic systems, and bio-inspired algorithms to streamline resource management and enhance network stability. This framework presents actionable strategies to improve IoT energy efficiency, extend device lifespan, and reduce operational costs. By addressing these energy inefficiency challenges, this study provides a path forward for more sustainable IoT systems, emphasising the need for continued research into experimental validations, context-aware solutions, and AI-driven energy management to ensure scalable and resilient IoT deployment.  
Internet of Things; energy efficiency; IoT networks; communication protocols; resource management; fuzzy logic; bio-inspired algorithms; network optimisation; sustainable deployment 2025
## 2 Materials and Methods
### Flow Circuit Setup  
In the imaging experiment of each flow phantom, the inlet of the flow phantom was connected to the output of a gear pump (GA-V21, Micropump, Vancouver, WA, USA).  
Figure 1: The fabricated flow phantom.  
Figure 2: Stenosis shape with degrees of stenosis of 0%, 20%, 40%, and 60%.  
A blood-mimicking fluid (BMF) was prepared to be delivered through the flow loop by the pump. The BMF was stored in a plastic reservoir. Nylon particles with a diameter of 5 \(\upmu\)m were combined with DI water with dextran and surfactant and mixed for 4 h with a magnetic stirrer. The resultant BMF had similar acoustic and viscous properties to those of human blood [33]. The gear pump was controlled by a data acquisition card (National Instruments, Newbury, UK) through a LabVIEW program, which could output arbitrary waveforms within the voltage limitation of the pump. The BMF was fully degassed in a vacuum pump every time before being pumped into the flow circuit. To ensure there were no air bubbles trapped in the flow loop, degassed water was used to run the system for half an hour before pumping the BMF. A TS410 flow meter (Transonic Systems Inc., Ithaca, NY, USA) was connected directly to the outlet of the flow phantom block to measure the flow rate. The data acquisition begins when the flow rates remain stable. A schematic of the experimental setup is presented in Figure 3. For the steady flow, the pump was applied on the square signals with different voltage amplitudes to implement steady flow with a velocity of 13.1 cm\(\cdot\)s\({}^{-1}\), 19.6 cm\(\cdot\)s\({}^{-1}\), 26.1 cm\(\cdot\)s\({}^{-1}\), 32.7 cm\(\cdot\)s\({}^{-1}\), and 39.2 cm\(\cdot\)s\({}^{-1}\) and a pulsatile flow waveform, which has a flow rate of 6 mL\(\cdot\)s\({}^{-1}\) in systole.
## 6 Conclusions  
In this study, a comprehensive, integrated design methodology for multiscale materials and structures was developed specifically for woven-reinforced composites, demonstrated through a case study on a car door system. The main conclusions are as follows:  
(1) A predictive model for the microscale and mesoscale elastic properties of woven-reinforced composites was established. It was observed that as the fiber volume fraction \(V_{\text{f}}\) in the microscale cells increases, \(E_{1}\) linearly increases, whereas \(E_{2}\) and \(G_{12}\) increase nonlinearly with an escalating growth rate. Furthermore, increasing the fiber yarn volume fraction \(V_{\text{y}}\) in mesoscale cells leads to a linear rise in parameters like \(E_{1}\), \(E_{3}\), and \(G_{12}\).  
(2) The particle generation strategy in the MOPSO algorithm was refined to apply the layup process constraints for composite materials. The optimization showed that the enhanced algorithm effectively generates Pareto solution sets meeting process constraints, thereby boosting the multiscale optimization method's applicability in engineering.  
(3) Applying the proposed integrated optimization approach to the design of plain, twill, and satin weave composite car doors resulted in significant achievements. Post-optimization, the CFRP car doors showed a weight reduction of 15.85%, 14.54%, and 15.35% compared to traditional metal doors, meeting all performance and layup process constraints. These results underscore the efficacy of the developed universal design method in facilitating lightweight automotive design.  
Conceptualization, H.Z. and Z.S.; methodology, H.Z.; software, H.Z.; validation, H.Z., Z.S. and P.Z.; formal analysis, P.Z.; investigation, H.Z.; resources, W.W.; data curation, H.Z.; writing--original draft preparation, H.Z.; writing--review and editing, H.Z.; visualization, Z.W.; supervision, Z.W.; project administration, H.Z.; funding acquisition, Z.S. All authors have read and agreed to the published version of the manuscript.  
This research work was supported by the Open Fund Project of the Shanghai Key Laboratory of Lightweight Structural Composites (Grant No. 2232021A4-06) and the S&T Special Program of Huzhou (Grant No. 2023 GZ09).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Performance Indicators** & **Threshold** & **Initial Metal** & **Plain Weave** & **Twill Weave** & **Satin Weave** \\ \hline First-order natural frequency (Hz) & \textgreater{}35 & 36.6 & 37 & 38 & 36 \\ Vertical sag & \textless{}4.5 & 9.85 & 3.55 & 3.72 & 4.4 \\ Upper lateral \(d_{\text{U}}\) (mm) & \textless{}10 & 6.63 & 7.55 & 7.15 & 8.83 \\ Lower lateral & \textless{}7 & 38 & 5.48 & 5.31 & 6.79 \\ \(d_{\text{L}}\) (mm) & & 15.96 & 13.43 & 13.48 & 13.51 \\ Mass \(M\) (kg) & - & - & 15.85 & 14.54 & 15.35 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of performance indicators between CFRP and metal car doors.
## 2 Background: Existing Space-Based Monitoring Systems  
The majority of existing volcano monitoring systems are set up and operated by national monitoring agencies, and consequently focus on volcanoes located within their own political boundaries or limited regions of interest. The U.S. Geological Survey (USGS) for example, provides earthquake information on a global scale, but their Volcano Hazards Program monitors volcanoes on U.S. territory only ([[https://volcanoes.usgs.gov](https://volcanoes.usgs.gov)]([https://volcanoes.usgs.gov](https://volcanoes.usgs.gov))). Each monitored volcano has a dedicated webpage, where a standardized red-yellow-green Volcano Alert Level System (VALS) informs on the state of volcanic activity [39]. Similar systems are operated by several other countries in charge of monitoring active volcanoes on their territory. However, many volcanoes are located on political boundaries between neighboring countries and states, and therefore leave uncertainties concerning the responsibilities during eruptive crisis. In this regard, only a few satellite-based platforms have the scope to monitor volcanoes on a global scale. Such platforms are often operated by scientists at research institutions and universities, and have proven to be particularly important for volcano observatories which do not have the resources to operate a monitoring network. These however, usually focus on the analysis of one type of data: spectroradiometric imagery (IR and UV), or SAR. We hereafter briefly describe a selection of relevant operating satellite-based platforms which provide open data access on a global scale (Table 1), and expose afterwards the rationale behind MOUNTS.
# Development of Traffic Scheduling Based on TSN in Smart Substation Devices
## 3 TSN End-Side Equipment Design Scheme  
To ensure that the substation network achieves low latency and predictability, the TSN switches at each layer utilize strict prioritization and the TAS scheduling mechanism. Furthermore, IEEE 802.1 AS is employed to synchronize the clocks of all the devices to form a global clock and drive the switch GCL gating table to run periodically. To ensure the predictable end-to-end delay and jitter of time-sensitive streams, the time at which an end device sends messages must match the forwarding schedule of the TSN switch. The end device needs to create a sending schedule that is coordinated with the TSN switch gating cycle to ensure that real-time periodic data are sent accurately and on time.
## 1 Introduction  
For optimization of computationally hard problems and of problems that are mathematically intractable, machine-learning-based strategies such as evolutionary computation (EC) [1] and artificial neural network (ANN) [2] have seen significant success in numerous application areas. The \"no-free-lunch theorem\" [3] tells us that, theoretically, over all possible optimization functions, all algorithms perform equally well. In practice, however, for specific problems (particularly, hard problems), the need for better and still better algorithms (and heuristics) remains.  
The Jaya algorithm [4], one of the newest members of the evolutionary computation family, has seen remarkable success across a wide variety of applications in continuous optimization. Jaya's success can arguably be attributed to the following two features: (a) it requires very few algorithm parameters, and (b) compared to most of its EC-cousins, Jaya is extremely simple to implement. A user of the Jaya algorithm has to decide on suitable values for only two parameters--population size and the number of iterations (generations). Because any population-based algorithm (or heuristic) must have a population size, and because the user of any algorithm/heuristic must have an idea of when to stop the process, it can be argued that the population size and the stopping condition are two fundamental attributes of any population-based heuristic and that the Jaya algorithm is parameterless. In this paper, we present an algorithm that improves over the Jaya algorithm by modifying the search strategy, without compromising on the above two qualities. The improved algorithm uses new update strategies for the best and the worst members in the population. The comparative performance of Jaya and the proposed method is studied empirically on a twelve-function benchmark test-suite as well as on a real-world problem from fuel cell stack design optimization. The improvement in performance afforded by the proposed algorithm is validated with statistical tests of significance. (Technically, Jaya is not an algorithm; it is a heuristic. However, following common practice in the evolutionary computation community, we continue to refer to it as an algorithm in this paper.)  
The remainder of this paper is organized as follows. A very brief outline of some of the most interesting previous work on the Jaya algorithm is presented in Section 2. Section 3 presents the proposed algorithm. Simulation results and statistical tests for performance analysis are presented in Section 4. Finally, conclusions are drawn in Section 5.
## 3 Results and Discussion
### Flory-Rehner Crosslink Density  
In VNR samples and VNR-based composites, crosslink density describes the number of chemical bonds formed between polymer chains during vulcanization. Typically, crosslinks are covalent bonds formed between polymer chains, transforming rubber from a viscoelastic material into one that is more elastic and resistant [57]. The degree of crosslinking has a direct impact on the mechanical properties of vulcanized rubber as well as VNR-based nanocomposites. Generally, a higher crosslink density translates into greater stiffness, tensile strength, and elasticity. It is important to note, however, that if the density of the rubber is too high, it can become very stiff and brittle [58]. The swelling analysis of rubber involves determining how much rubber swells in a solvent. An increase in crosslink density is accompanied by a decrease in swelling. It is possible to calculate the crosslink density using the Flory-Rehner theory, according to which swelling volume is related to the number of crosslinks [59]. As shown in Table 3, the crosslink density of the NR/MWCNT nanocomposites was determined. The gradual addition of MWCNTs to the VNR matrix increases the crosslinking density, which can improve the mechanical properties of vulcanized composites.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Specimens** & \begin{tabular}{c} **M\({}_{\text{L}}\)** \\ **(dNm)** \\ \end{tabular} & \begin{tabular}{c} **M\({}_{\text{H}}\)** \\ **(dNm)** \\ \end{tabular} & \begin{tabular}{c} **\(\Delta\)M = (M\({}_{\text{H}}-{\text{M}}_{\text{L}}\))** \\ **(dNm)** \\ \end{tabular} & \
\begin{tabular}{c} **t\({}_{\text{S1}}\)** \\ **(min)** \\ \end{tabular} &
\begin{tabular}{c} **t\({}_{\text{90}}\)** \\ **(min)** \\ \end{tabular} \\ \hline VNR & 1.40 \(\pm\) 0.08 & 27.69 \(\pm\) 1.93 & 26.29 \(\pm\) 1.85 & 2.22 \(\pm\) 0.03 & 3.25 \(\pm\) 0.14 \\ VNR/MWCNT1 & 1.14 \(\pm\) 0.07 & 28.46 \(\pm\) 0.75 & 27.32 \(\pm\) 0.78 & 2.27 \(\pm\) 0.02 & 3.30 \(\pm\) 0.18 \\ VNR/MWCNT2 & 1.40 \(\pm\) 0.05 & 31.01 \(\pm\) 0.95 & 29.61 \(\pm\) 1.02 & 2.13 \(\pm\) 0.05 & 3.10 \(\pm\) 0.21 \\ VNR/MWCNT3 & 1.40 \(\pm\) 0.03 & 32.68 \(\pm\) 0.91 & 31.27 \(\pm\) 1.01 & 2.07 \(\pm\) 0.03 & 3.10 \(\pm\) 0.22 \\ VNR/MWCNT4 & 1.53 \(\pm\) 0.05 & 35.35 \(\pm\) 0.54 & 33.83 \(\pm\) 0.63 & 2.00 \(\pm\) 0.04 & 3.07 \(\pm\) 0.28 \\ VNR/MWCNT5 & 1.79 \(\pm\) 0.90 & 37.01 \(\pm\) 0.80 & 35.23 \(\pm\) 0.89 & 1.95 \(\pm\) 0.05 & 3.05 \(\pm\) 0.31 \\ \hline \hline \end{tabular}
\end{table}
Table 2: VNR/MWCNT nanocomposite’s rheometric parameters.
## 8 Rotor Blades Frequencies for Non-Nominal Conditions  
For the highest and lowest condenser pressures in measurement 1, the frequencies were obtained and compared with [27,28].  
The differences between the L-M and [27,28] frequencies were below 1% (Figure 15) because one mode in fitting was assumed in the L-M and [27,28]. The amplitudes were compared in Figure 4.  
Figure 14: The amplitudes of 53 blades for condenser pressures 5.875 kPa and 18.7 kPa (Measurement 3).  
The blade frequencies for measurements 2 and 3 were similar to those of measurement 1 (Figures 16-18).  
Figure 16: The frequencies of 53 blades for condenser pressures 10.67 kPa and 23.5 kPa (Measurement 1).  
Figure 15: The frequencies of 53 blades for 23.5 kPa condenser pressures for the L–M algorithm and one mode algorithms [27,28], Measurement 1.  
The results show that the higher condenser pressure increased blade amplitudes, but they did not increase their frequencies.  
In [27], blade vibrations were analyzed for condenser pressures ranging from 10.4 to 23.6 kPa (368.7-365 MW). In this paper, blade vibrations were analyzed with condenser pressure ranging from 7.4 to 19 kPa (299.1-300 MW) and from 5.9 to 18.7 kPa (219.5-219 MW). This covers the entire non-nominal work range of a 380 MW steam turbine LP last stage, which is a novelty in the literature.  
Figures 19 and 20 present the rotor blade amplitudes for various turbine powers. The maximal blade amplitude for 365 MW was 1.56 mm, whereas for 300 MW it was 1.03 mm. The average blade amplitude was 0.81 mm for AVG (365 MW) (Figure 19) and 0.72 mm for AVG (300 MW) (Figure 20). This shows that for 365 MW (Figure 19), the average rotor blade amplitudes were higher than for 300 MW.  
Figure 17: The frequencies of 53 blades for condenser pressures 7.4 kPa and 19 kPa (Measurement 2).  
Figure 18: The frequencies of 53 blades for condenser pressures 5.87 kPa and 18.7 kPa (Measurement 3).  
Figure 19: Blade amplitudes of 53 blades at various powers (measurements 1 and 2).  
Figure 20: Blade amplitudes of 53 blades at various powers (measurements 2 and 3).  
Figure 20 shows that the rotor blade amplitude was higher at 300 than at 219 MW. The maximal blade amplitude for 300 MW was 1.09 mm, whereas it was 0.98 mm for 219 MW. The average blade amplitude for 300 MW was 0.7 mm (AVG (300 MW), Figure 20), and 0.68 mm for 219 MW (AVG(219 MW) (Figure 20).  
The rotor blade displacement was also higher for higher turbine powers, although this dependence was nonlinear.
# An Ensemble Extreme Learning Machine for Data Stream Classification
## 3 The Basic Principles of CELM
### The Data Stream Classification and Concept Drift Detection of CELM
_{\mathbf{N}_{k+1}\times m}^{T} \tag{24}\]  
The \(\mathbf{K}_{k+1}\) and \(\mathbf{\beta}^{(k+1)}\) are updated as  
\[\mathbf{K}_{k+1}=\mathbf{K}_{k}+\mathbf{H}_{k+1}^{T}\mathbf{H}_{k+1} \tag{25}\]  
\[\mathbf{\beta}^{(k+1)}=\mathbf{\beta}^{(k)}+\mathbf{K}_{k+1}^{-1}\mathbf{H}_{k+1}^{T}(\mathbf{T}_{k +1}-\mathbf{H}_{k+1}\mathbf{\beta}^{(k)}) \tag{26}\]  
when calculating the output weight matrix \(\mathbf{\beta}\), it needs to perform a matrix inversion, but the calculated amount of the pseudo inverse is very large, so Woodbury formula is often used to diminish the computation [37] and the formula is as  
\[\mathbf{K}_{k+1}^{-1} =(\mathbf{K}_{k}+\mathbf{H}_{k+1}^{T}\mathbf{H}_{k+1})^{-1} \tag{27}\] \[=\mathbf{K}_{k}^{-1}-\mathbf{K}_{k}^{-1}\mathbf{H}_{k+1}^{T}(\mathbf{I}+\mathbf{H}_{k +1}\mathbf{K}_{k}^{-1}\mathbf{H}_{k+1}^{T})^{-1}\mathbf{H}_{k+1}\mathbf{K}_{k}^{-1}\]By the online sequential learning mechanism, when the change of concept in data stream is small, CELM can update classifiers to adapt to the change of concept which is also effective for gradual concept drift. If \(p_{i}+s_{i}\geq p_{min}+2s_{min}\) or \(p_{i}\geq\varepsilon\), it indicates that the change of data stream is too large or the performance of classifiers is in low level. The classification model is not fit for the current data stream, so all classifiers must be deleted and retrain a series of classifiers. The steps of CELM are summarized in Algorithm 3. ```
Data stream \(\boldsymbol{S}\), the size of data block \(\boldsymbol{B}_{i}\): winsize, \(k\) and \(d\), \(\varepsilon\), \(K\) classifiers; An ensemble classifiers system. while\(\boldsymbol{S}\
eq NULL\)do  Get a data \(\boldsymbol{B}_{i}\) from sliding window;  Use Algorithm 2 to descend dimension for \(\boldsymbol{B}_{i}\); if\(p_{i}+s_{i}<p_{min}+2s_{min}\)&&\(p_{i}<\varepsilon\)then  The data stream is stable and directly uses classifier to finish classification task; else if\(p_{i}+s_{i}\geq p_{min}+2s_{min}\)&&\(p_{i}<\varepsilon\)then  Uses online learning mechanism to update classifiers as Equations (21)-(27); else if\(p_{i}+s_{i}\geq p_{min}+2s_{min}||p_{i}\geq\varepsilon\)then  Concept drift has happened;  Delete all classifiers and retrain each classifier as Algorithm 1;
```  
**Algorithm 3** CELM. From the steps of CELM, it is known that, when the change of data stream is small, CELM uses online sequential learning mechanism to update classifiers which ensures the classifiers can utilize the last model and do not need to be retrained again and again; in other words, the method also gives a way to handle gradual concept drift. In addition, the dimension-reduction algorithm which preprocesses data blocks and the advantages of ELM makes CELM keep a good performance and have a fast speed.
# Multiple Observations for Secret-Key Binding with SRAM PUFs
## 1 Introduction  
The The Internet of Things (IoT) makes it possible to connect and share information between many different devices through the Internet. This sharing of information is beneficial for many applications, e.g., in healthcare or consumer electronics. At the same time, the information may be sensitive and should not fall into the wrong hands or be tampered with. Therefore, security is one of the main challenges of the IoT devices. Since the IoT devices are often small and low cost, securing the devices should come at a low price.  
Secure communication is often achieved through cryptographic protocols that rely on secret keys. A low-cost alternative for secure storage of the keys is enabled by Static Random-Access Memory Physical Unclonable Functions (SRAM PUFs). A PUF is a physical object or device that responds to a challenge with a response that is unique and unpredictable [1, 2]. The SRAM-PUF functionality is based on the uninitialized values of the SRAM. These are the values that appear in the memory cells directly after power up of the SRAM. The corresponding binary vector is unique for each SRAM and it is the result of small variations in the silicon material. It can be considered to be a noisy fingerprint of the device and can be used to generate and bind secret keys [3, 4].  
Since the SRAM-PUF observations are noisy, additional processing is required to ensure reliable reconstruction of the key. This can be achieved through a so-called key binding scheme, see Figure 1. The scheme considers two phases: an enrollment phase during which a uniformly generated key is bound to a first SRAM-PUF observation; and a reconstruction phase during which the key is reconstructed from an additional observationof the SRAM-PUF. Please note that enrollment is usually performed only once, whereas reconstruction can be repeated many times. During enrollment, besides the key \(s\), also some helper data \(w\) is generated. This helper data \(w\) ensures that the key can be reliably reconstructed even though \(y^{n}\) is a noisy version of \(x^{n}\). The helper data are considered public, and therefore should not reveal information about the key \(s\) to an attacker.  
In classic helper data schemes, a single SRAM-PUF observation is used for enrollment and a single observation is used for reconstruction. However, it has been shown that the mutual information between the encoder and decoder observations increases when more observations are considered [5]. Since the secret-key capacity is equal to the mutual information (see Section 3), it follows that the achievable secret-key rate is increased when multiple SRAM-PUF observations are used instead of a single observation.  
We introduce the Multiple-Observations (MO) helper data scheme, which enrolls a single key after processing multiple SRAM-PUF observation vectors. The scheme is based on a linear error-correcting code and can be seen as an extension of the fuzzy commitment scheme [6]. Any number of enrollment observations can be used. Furthermore, the performance of the scheme increases when more observations are used.
## 6 Conclusions  
This paper proposes a hybrid dual path SSPLL, including a P-path and an I-path, with 0.8 V supply voltage which is fabricated in the 40-nm CMOS technology. A differential MSSF, replacing the SSCP, composed the P-path to avoid the degraded feature caused by the decreasing of the supply voltage. The I-path is built by a rail-to-rail SSCP to suppress the phase noise of the VCO and avoid the trouble of locking at the non-zero phase offset (as in type-I PLL). The measurement results indicate that the SSPLL can operate at the 5.3 to 5.9 GHz frequency range, 196.5 fs RMS integrated jitter and 1.8 mW power dissipation with \(-\)251.6 dB FoM. Compared with prior art PLLs, this SSPLL reaches a good phase noise performance with low power dissipation.  
Conceptualization, S.Z.; formal analysis, S.Z. and J.Z.; investigation, S.Z.; resources, S.Z.; data curation, S.Z.; writing--original draft preparation, S.Z.; writing--review and editing, S.Z., J.Z. and Y.Z.; visualization, S.Z.; supervision, J.Z. and Y.Z.; project administration, Y.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 15: (**a**) phase noise curves and (**b**) integrated jitters covering the frequency range from 5.3 GHz to 5.9 GHz.