esa-sceva/satcom-chunk-collection huggingface dataset:
# Effective Behavioural Dynamic Coupling through Echo State Networks
## 2 Material and Methods
### Sampling  
In this stage the output is 'written' in output units, a procedure referred to as teacher forcing, and the input is provided through the input units. The network is initialised using a zero initial state \(x\).  
The network is driven by the input and output signals for \(n\) times \(n=0,\cdots n_{max}\), at each time step having as input \(u(n)\) and teacher signal \(d(n-1)\), this since there exists the feedback from the output. For the first time step where \(d\) does not exist, it is set to zero.  
For each time step, after the washout period, the extended system states \(z(n)\) and the teacher signal \(d(n)\) are collected. The washout period includes those time steps just after the presentation of an input signal to the network where the systems extended states are discarded and not used in the training. This is to wait for the network to settle and the internal dynamics to stabilise and the network to settle to the input provided.  
The extended states are collected in a matrix \(S\) of size \([n_{max}\times(N+K)]\) and the desired outputs \(d(n)\) in a matrix \(D\) of size \([n_{max}\times L]\).  
Now, the desired output weights \(W^{out}\) can be calculated as follows. First, the correlation matrix of the extended system states is calculated, \(R=S^{\prime}S\). Then, the cross-correlation matrix of the extended states against the desired outputs \(d\), \(P=S^{\prime}D\). Finally, the calculation of the output weights of the network \(W_{out}\) is done by calculating the pseudoinverse of \(S\), \(S^{\dagger}\),  
\[W^{out}=(S^{\dagger}D)^{\prime} \tag{3}\]
## 3 Results
### Seasonal Features  
The first part of this subsection is dedicated to the analysis of the mean values, SDs and frequency distributions from a seasonal perspective. Table 3 shows the seasonal grand-time mean values and SDs for both \(\rm T_{b}\) and CTH. The term grand-time mean was used here to reflect that the calculations were carried out within the entire period of analysis irrespective of the year, incorporating information from December, January and February (summer), March, April and May (autumn), June, July and August (winter), and September, October and November (spring). A general result from Table 3 is that the seasonal grand-time mean values for \(\rm T_{b}\) (CTH) increased (decreased) as the cluster order increased, and that the intra-seasonal variability decreased as the cluster order increased for both \(\rm T_{b}\) and CTH. With the exception of cluster 4, in terms of CTH winter presents the seasonal lowest mean values and autumn had the highest ones; this made the autumn-to-winter transition generally steeper than in any other season-to-season case. As to variability, it was greater in spring for clusters 1 and 2, in autumn for cluster 3 and in summer for cluster 4. The grand-time histograms for the four seasons are presented in Figure 11. They did not differ in a remarkable fashion from the corresponding ones shown in Figure 10.  
The seasonal character of the clusters represented in the columns of Table 3 warrants them to be termed \"cloud regimes\" to parallel similar plots that are present in the literature (75,76). Clouds in cluster 1 represent high clouds including, but not restricted to, cirriform clouds, as this cluster is likely capturing the features of clouds that have a vertical extent as well, such as the cumulonimbus (Cbs). This is the less dominant cluster, representing, on average, 6% of the pixels. On the opposite side of the classification clouds in cluster 4 have their tops below 2 km. According to (77) they are classified as stratiform clouds. This is the second most dominant cluster as it represents, on average, 32% of the pixels. Clusters 2 (49%) and 3 (13%) spanned the middle troposphere. Altostratus and nimobstratus are examples of clouds that may populate them (77). Given that seasonality implies that a number of different physical processes conspire to give the figures shown in Table 3 and Figure 11, it is difficult to draw conclusions about individual processes from these aggregated values. Monthly means were calculated for both the number of pixels that populate each cluster and the entropy. These values are shown in Figure 12. A noticeable feature in this figure is that the entropy closely followed the evolution of cluster 4. It can also be seen that the entropy increased when the number of pixels in clusters 3 and 4 were close to one another, i.e., when these two clusters became less distinguishable from each other. This tended to occur in fall and winter. In an endeavor to provide further insights on the intraseasonal variability and to shed light on the possible physical processes involved the mean values in Figure 12 were correlated with several circulation
## 2 Related Work
### Cyber Resilience Frameworks  
Numerous frameworks have been developed to assess and enhance cyber resilience across critical infrastructures and organizations. The Cybersecurity and Infrastructure Security Agency (CISA) offers the Cyber Resilience Review (CRR), an interview-based assessment to evaluate operational resilience and cybersecurity practices [6]. The National Institute of Standards and Technology (NIST) provides SP 800-160 Volume 2 Revision 1, focusing on engineering survivable systems through systematic security methods [7]. The European Union's Network and Information Security (NIS) Directive promotes risk assessments and security measures across member states [8]. Canada's Regional Resilience Assessment Program (RRAP) evaluates vulnerabilities in critical facilities [9], while the UK's National Cyber Security Centre (NCSC) provides the Cyber Assessment Framework (CAF) for critical national infrastructure [10]. The EU's Cyber Resilience Act (CRA) [11] sets cybersecurity standards for digital products, and Australia's 2023 Critical Infrastructure Resilience Strategy outlines a national approach to security and resilience [12]. In China, the GB/T 44862-2024 standard offers criteria for evaluating cyber resilience, focusing on prevention, endurance, recovery, and adaptation [13]. Additionally, frameworks like the NIST Cybersecurity Framework 2.0 [2] provide a de facto industry baseline. Standards such as ISO/IEC 27035-1:2023 [14] on incident management directly inform the processes within our Adaptive Recovery (A) dimension. Foundational texts on Resilience Engineering by [PERSON], [PERSON], and [PERSON] [15] help ground our model in established systemsengineering theory. Finally, frameworks like ISO/IEC 27001 emphasize information security management [16], and the MITRE ATT&CK framework maps adversary tactics for resilience planning [17].  
While these frameworks provide valuable guidance on \"what\" to do to enhance resilience, they often lack specificity in \"how\" to quantitatively measure the effectiveness of implemented measures.
# Compacted Area with Effective Links (CAEL) for Data Dissemination in VANETs
## 1 Introduction
The data packets will be sent from the source to target node using the available intermediate vehicles. A high density of vehicles is not the only issue to address in the routing process, whereas crossing and traffic lights may generate a rupture in the network. In VANETs, routing protocol properties such as mobility restrictions and limited road patterns can be utilized using the Global Positioning System (GPS) coordinates and city maps to design an effective routing protocol. The protocol proposed in the present study is an advancement of our previously published protocols, i.e., Reliable group of Vehicles(RGoV) [9] and Dynamic Trilateral Enrollment (DyTE) [10] through which the most reliable network pathways are taken into consideration while establishing a route. The proposed protocol Compacted Area with Effective Links (CAEL) also uses the concept of minimizing the communication region as applied in DyTE and RGoV protocols. However, at the same time, CAEL calculates the expiration time for every connected link between the nodes to enhance the packet delivery rate (PDR) and reduce the routing overhead while keeping the latency time intact (if not minimized as compared to our previously published protocols). DyTE is based on minimizing the communication region of a node, only due to which the packet delivery performance increases; however, if the nodes get out of the minimized zone very quickly due to its high speed, then the performance of DyTE is affected--whereas RGoV first uses the suppression of the communication zone and then creates multiple clusters and each cluster contains a group of vehicles and selects only one cluster which is relatively closer to the destination vehicle. The shortcoming of RGoV lies in the selection process of a cluster group of vehicles that affects the network performance when it selects that cluster which is having few vehicles. The remainder of this article is organized as follows: Section 2 contains a description of related work; Section 3 provides extensive information of the reliability model related to VANETs. The proposed routing method CAEL is discussed in Section 4; the performance of the proposed protocol CAEL is thoroughly evaluated as outlined in Section 5. Finally, Section 6 brings the paper to a conclusion.
## 3 Analysis of the Dual-Peak Resonance Restructured by Thermal Strains  
In this article, \(\alpha_{i}\) is used for thermal expansion coefficient, \(E_{i}\) for [PERSON]'s modulus and \(\mu_{i}\) for Poisson ratio. The model is based on the following assumptions: (1) Thermal expansion coefficients of FBG and the metal coating and thermo-optic coefficient of FBG are constants, independent of temperature changes; (2) The inner metal coating is made on the FBG tightly and; (3) The outer stepped-metal coating and the inner metal coating are attached tightly and there is no relative displacement between them. Three distinct models were developed corresponding to the type of SMC-FBG.  
Under strain and temperature, the SMC-FBG's Bragg wavelength shifts by \(\Delta\lambda_{B}\) can be expressed as [19]:  
\[\Delta\lambda_{B}=2n\Lambda\left(\left\{1-\left(\frac{n^{2}}{2}\right)[p_{12} -\mu(p_{11}+p_{12})]\right\}\epsilon+[\alpha+\frac{\left(\frac{dt}{dT}\right)} {n}]\Delta T\right)\] (1a) where \[n\] is the effective index of the fiber core, \[\Lambda\] is the grating pitch, \[\alpha\] the coefficient of linear thermal expansion of the fiber, \[p_{i,j}\] are the [PERSON]'s coefficient of the stress-optic tensor, \[\mu\] is the Poisson's ration, and \[\Delta T\] is the temperature change, \[\epsilon\] is the applied axial strain. The factor \[2n\Lambda\] is the resonance condition of a Bragg grating and expressed as \[\lambda_{B}\], the Bragg wavelength. The factor \[\{(n^{2}/2)[p-\mu\ (p_{11}+p_{12})]\}\] is usually expressed as \[p_{e}\], the Pockels constant. The factor \[[(dn/dT)/n]\] is usually expressed as \[\xi\], the thermo-optic coefficients. Equation ( 1a ) can be given simply by Equation ( 1b ). \[\Delta\lambda_{B}=\lambda_{B}(\alpha+\xi)\Delta T+\lambda_{B}(1-\mathrm{p_{e} })\epsilon \tag{1b}\]  
For an SMC-FBG sensor, the Bragg grating is divided into two sections: double-layer metal coating and single-layer metal coating. Each layer tends to expand or contract when the temperature fluctuates. However, the expansions and contractions generally cannot occur freely due to the boundary limits. Because the restrictions lead to two different thermal strains of the fiber Bragg grating sensor, the dual-peak resonance is induced.
## 2 Mathematical Model of Reservoir Heating by E-CSS in Horizontal Wells
### Synergistic Heating Model of Electric Heating and Steam Injection
According to the principle of heat superposition, the increased temperature of the reservoir at any point \(x\) on the two-dimensional plane is affected by three main control factors: the heat of the output fluid exchange, the heat of the outward diffusion of the reservoir at this point and the heat compensated by the electric heater. Formula (8) is used to represent the temperature of the reservoir at any point \(x\) on the two-dimensional plane. \[\Delta T_{x3}=T-T_{2}=\left(\frac{\phi_{l}}{4\pi k}\right)\text{Ei}\left(- \frac{r^{2}}{4\lambda\alpha t_{4}}\right)+\left(\frac{\phi_{\rm d3}}{4\pi k} \right)\text{Ei}\left(-\frac{r^{2}}{4\lambda\alpha t_{4}}\right)-\left(\frac{ \phi_{\rm e3}}{4\pi k}\right)\text{Ei}\left(-\frac{r^{2}}{4\lambda\alpha t_{4 }}\right) \tag{8}\]  
\(T_{2}\) is the reservoir temperature at the end of the soak phase, calculated by Formula (7), \(\phi_{l}\) is the heat transfer rate of the produced fluid per unit of horizontal section length, W/m; \(\phi_{\rm d3}\) is the heat diffusion rate of per unit horizontal length of this point, W/m; \(\phi_{\rm e3}\) is the heat input rate of electric heating for the unit horizontal length of the point during the production phase, W/m. In sight of the pore space of the porous medium of the output fluid being occupied by the low-temperature fluid from peripheral reservoir, based on the temperature difference method, the point of fluid exchange and heat transfer temperature difference is expressed as follows:  
\[\phi_{l}=(q_{w}C_{w}+q_{o}C_{o})\Delta T_{x3} \tag{9}\]The heat diffusion rate per unit in horizontal length of this point is expressed by the following formula:  
\[\phi_{\text{d3}}=\frac{2\pi\Delta T_{x3}}{\frac{1}{k}ln\frac{r}{T_{w}}} \tag{10}\]  
In view of the continuous change in the speed of water and oil production during the production process over time, the next time step \(T_{n+1}\) is calculated iteratively according to the temperature result \(T_{n}\) of the previous time step:  
\[T_{x1}=-\left(\frac{\phi_{\text{s1}}}{4\pi k}\right)\text{Ei}\left(-\frac{r^{ 2}}{4\lambda\alpha t_{1}}\right)-\left(\frac{\phi_{\text{e1}}}{4\pi k}\right) \text{Ei}\left(-\frac{r^{2}}{4\lambda\alpha t_{2}}\right) \tag{3}\]0  
\[T_{x1}=-\left(\frac{\phi_{\text{s1}}}{4\pi k}\right)\text{Ei}\left(-\frac{r^{ 2}}{4\lambda\alpha t_{1}}\right)-\left(\frac{\phi_{\text{e1}}}{4\pi k}\right) \text{Ei}\left(-\frac{r^{2}}{4\lambda\alpha t_{2}}\right) \tag{3}\]1  
The initial condition is when \(n=0\), \(T_{n}=T_{2}\). According to Formulas (3), (7) and (12), combined with the thermophysical parameters of the reservoir and the operating parameters of electric heating and steam injection, the reservoir temperatures of the soak phase and the production phase can be calculated under E-CSS in horizontal well. Reservoir heating characteristics of E-CSS in horizontal well therefore can be revealed.
## 4 Implementation of ACO Algorithm  
Because of space constraints on the sensor nodes, designers should consider the space requirements of the hardware components they use. A small sized chip having the routing capability mentioned so far offers advantageous solutions for the design stage. By using a separate chip, the full computational power of the main processor could be used for sensor node tasks (e.g. sensing, data processing) specific to itself instead of allocating some of that power to relatively complex routing tasks. Moreover, it is not required that node designers code routing protocols which need detailed knowledge of the issue. Instead, a hardware component ready to perform dynamic routing operations is proposed. Routing tasks are performed under the guidance of the router chip embedded a novel routing protocol using ACO as designers have no need to know any routing considerations. The chip uses a simple communication protocol illustrated in detail in Figure 8. In Table 2, the main specifications of this router chip are given.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter Name** & **Value** \\ \hline Power Up & 64 ms \\ Initial Configuration & 139\(\upmu\)s \\ Neighbor Update & 7.5 ms \\ CPU Speed & 5 MIPS \\ Operating Temperature & -40\({}^{\circ}\)C to 125\({}^{\circ}\)C \\ Operating Voltage & 2V to 5.5V \\ Pin Count & 8 \\ Package Type & Soic-8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of the router chip.  
Figure 7: Average residual energy after 256 packets are received.  
The proposed ACO routing protocol is embedded on a programmable device which has only four pins, requiring minimum connections and smaller space of only 30 mm\({}^{2}\) of SOIC footprint, suitable for tiny node designs. The router chip has two pins for RX and TX of the UART terminal (9,600 baud rate) used to communicate with the master processor of the node. The circuit using the proposed router chip was tested in the Proteus simulation platform [22] and the performance results given in Table 3 were obtained.  
As mentioned in Section 2.2, protocol operations are described in three groups which are disseminating data, acknowledging and informing of energy levels of neighbor nodes. All of these tasks are performed under the guidance of the router chip as the main processor of the node communicates with this chip. The chip has a ROM to hold the ACO algorithm, a processor to run the algorithm, and RAM memory space to hold the ACO parameters. The signals (used for communication to router chip) and their descriptions are given in Table 4.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**State** & **Response Time** \\ \hline Form Package Header (by running ACO) & 25 ms \\ Update Package Header (by running ACO) & 25 ms \\ Acknowledge (From the Base) & 625 μs \\ Acknowledge (From the Others) & \textless{}5 μs \\ Ignore & 140 μs \\ \hline \hline \end{tabular}
\end{table}
Table 3: Response times of the router chip.  
Figure 8: State Diagram of Communication Protocol.
# A Node Placement Algorithm Utilizing Mobile Nodes in WSN and IoT Networks +
## 5 The Energy Node Placement Algorithm
### Sink Node
As a result, we can define the total computational delay or delay for short, as the sum of the communication delay (links a and b) and the moving delay (link c), i.e.,  
\[delay=communication\_delay+moving\_delay.\]  
The \(communication\_delay\) is calculated as \(r*dist\_hops\), where \(r\) is the _rate_ of the packet per hop and \(dist\_hops\) is the _distance_ in hops, from the problematic area (N) to the sink (S) and from the sink to the mobile node (MN). For the no reuse case, in the worst case,  
Figure 3: Delay Scenarios. (**a**) Delay without Reuse, (**b**) Delay with Reuse. dist_hops_\(=D\), where \(D\) is the diameter of the network (and \(b\) is almost zero), whereas, for the reuse case, in the worst case, \(dist\_hops=2D\); however, the closer the in-use mobile node is to the problematic area, the smaller the moving delay will be. The _moving_delay_ is calculated as _speed_\(*\)_distance_, where _speed_ is the speed of the mobile node and _distance_ is the distance the mobile node must move from its current position to its new position. As the algorithm attempts to find the mobile node that is closer to the position to be moved (X), which could be a mobile node at the sink or an in-use mobile node and given that in general, the moving delay is longer than the communication delay (unless an auxiliary device is used to move the mobile node, e.g., a drone), the algorithm using reuse can significantly reduce the moving delay, and hence the total delay (cf. Section 6.3.2).
## 4 Discussion and Conclusions
Compared with previous studies, the categorization identification in this study clarifies the features of each category and highlights the efficacy of the hybrid methods in gauging and characterizing the type and weathering status of glass relics. Despite the positive outcomes of this study, certain limitations should be acknowledged. Firstly, the dataset utilized may not encompass all possible types of glass and weathering conditions, potentially restricting the model's generalizability. Secondly, biases and noise in the data collection process could affect the accuracy of the model. Additionally, this study primarily focused on the impact of chemical composition on glass type and weathering status, with insufficient consideration given to external environmental factors, such as soil type and climatic conditions. To address these limitations, future research could delve into the following directions: 1. Collect data on glass artifacts from various regions, historical periods, and cultural backgrounds to enhance the model's generalizability and applicability. 2. Investigate the effects of external factors, such as soil type, climatic conditions, and burial environments, on glass weathering and how these factors interact with the chemical composition of glass. 3. Explore new machine learning algorithms to improve the model's robustness against data noise and outliers, thereby further enhancing classification and identification accuracy. 4. Conduct a more comprehensive study on the classification and weathering mechanisms of glass artifacts by integrating knowledge from fields such as materials science, chemistry, and archaeology. Through these future research directions, we hope to gain a deeper understanding of the chemical composition, type classification, and weathering processes of ancient glass artifacts, providing more scientific and systematic methods for the conservation and restoration of cultural heritage. Conceptualization and writing--original draft preparation, Z.C. ; investigation, Y.X. ; writing--review and editing, M.T. ; supervision and funding acquisition, C.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by National Natural Science Foundation of China (52379049, 52209071), the China Postdoctoral Science Foundation (2023T160552, 2020M671623), \"Chunhui Plan\" Cooperative Scientific Research Project of Ministry of Education of China (HZKY20220115) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).
## Appendix B Measurement of fluid material parameters for experiments.  
For the high-viscosity metal slurry fluids used in this research, the fluids were scanned with a low shear rate of 0-13 s\({}^{-1}\) using a Haake Mars III rotational rheometer at 25\({}^{\circ}\), and the relationship between the shear stress and shear rate of the fluids was obtained. Processing the experimentally measured data, as shown in Figure 11, Herschel-Bulkley fluids model equations best fit the data.  
According to the curve fitting, three fluid material parameters of Herschel-Bulkley fluids are shown in Table A1.  
The equation for Herschel-Bulkley fluids used in this research is given by:  
\[\tau=10+63\dot{\gamma}^{0.42} \tag{10}\]
## 4 Conclusions
### Limitations Section  
* Comparisons of LCA are complicated, even when using the same methodology, as results can differ with particular assumptions in each study.
* The results of an LCA study with national- or regional-level approaches may not be accurate for local applications, or vice versa. This study has an important geographical limitation to consider. The studied farm is located in the main egg-producing zone in Mexico (Jalisco State), which has favourable weather conditions for this activity. Other important egg-producing farms in Mexico are located in hot-dry or hot-humid climates. To provide thermal comfort to laying hens, technified farms are likely to use different technologies than those reported here. Different, additional, newer, or more modern equipment would imply variations of energy consumption as well. Therefore, the results from this semi-technified farm may not be suitable for small farms that employ manual handling.
* The VSM results are also related to the limitations described for LCA.