esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Viscoelastic Properties of Rubber Compounds for Ballistic Applications
## 1 Introduction
They also reported that the frequency of the shockwave of ballistic impact is around \(10^{5}\) Hz, which according to time-temperature superposition corresponds to a glass transition temperature of around \(-50\ ^{\circ}\)C. In a different work from the same group [9], it was examined that by coating steel with a single layer of polyurea, the ballistic limit \(v_{50}\) can be improved by \(\sim\)25% in comparison to the noncoated steel. Besides the observation of rubber improving ballistic properties, no additional effort was put into further investigating the influence of different material properties. In this work, improvements of rubber-interlayered armor systems are targeted by selecting an optimized rubber compound for the interlayer with a focus on the influence of damping, hardness and the glass transition temperature on the protective properties of the multilayered armor. An appropriate material is preselected by analyzing the temperature and frequency dependency of the viscoelastic properties using dynamic mechanical analysis (DMA). Furthermore, the selection is validated by different ballistic tests that are used for observing the material response of the rubber layer during the ballistic impact and to determine the energy absorption of the armor configuration. The results are then compared to a reference configuration without rubber and for configurations with different rubbers that are not optimized for the application.
## 3 Results
### Linewidth Measurement  
To measure the spectral linewidth of the pulsed laser, the output was sent via an optical isolator to a scanning FPI (Toptica FPI 100) with a specified FSR of 1 GHz. A scan generator applied a saw-tooth voltage ramp to the FPI piezo covering a scan-range of about two FSR. As seen in Figure 8, a fairly slow scan was performed and the delay between two succeeding transmission peaks was measured. Following this, a single transmission peak was observed in higher resolution as revealed in the inset. The peak consists of several short pulses spaced according to the PRR of 5 kHz of the laser. The signal amplitude at each point in time is the overlap of the spectral distribution of the Ti:sapphire laser pulse with the Airy transmission peak of the interferometer. From the shape of the envelope of pulses the spectral distribution can be assumed to be approximately Gaussian. Additionally, as the pulse-to-pulse amplitude fluctuations are small, the pulse-to-pulse frequency jitter must be smaller than the linewidth. Based on the ratio of the FSR to the FWHM of the pulse envelope a linewidth of 11(3) MHz is estimated, with the error dominated by the unmeasured nonlinear expansion of the piezo actuator in the FPI. Typically a relative nonlinearity of <20% can be assumed for common piezo materials.
# Heuristic Routing Algorithms for Time-Sensitive Networks in Smart Factories
## 4 Improved Genetic Algorithm to Solve TT Flow Routing Problem
### Definition of the Optimization Problem in TT Routing  
Based on the distributed traversal approach for TT communication that was proposed above, the TT routing problem in TSNs could be transferred into the following: the central control node \(v_{1}\) was directly connected to \(m\) SW nodes and periodically sent \(m\) control-type data frames (noted as \(a_{1},a_{2},\cdots,a_{m}\)) to \(n\) destination subnetworks at the same time. After each subcontrol system finished receiving and processing, it overwrote the control data field at the corresponding position within the frame with feedback data containing operating status and then forwarded it downward. Finally, all frames converged at \(v_{1}\) after the traversal. Every subnetwork that was connected to a SW node could be merged into  
Figure 3: Schematic of TT control flow network traversal.  
Figure 2: Aggregated forwarding frame-based TT communication.  
the model. Considered as a single node, the processing delay of the node equaled the total traversal time inside the node. We let the number of ES nodes inside subnetwork \(v_{i}\) be \(o_{i}\) and the time granularity of traversing a single ES node be \(\delta\), then the total time a single common traversal cycle. Constraint (c2) ensured that every node \(v_{i}\), except for \(v_{1}\), was visited only one time because a node being accessed two or more times within one cycle would break the transmission periodicity and thus, cause chaos in the whole network. Constraint (c3) ensured that each route included at least one node in addition to the central control node. Constraint (c4) ensured that the final result \(T\) was less than the preset threshold \(MaxTime\), otherwise its real-time performance could not be guaranteed.
## 5 LoRaWAN Simulation Tools
### Ns-3  
Ns-3 is an open-source discrete-event network simulator that was initiated in mid-2006 [64; 65] and is still under heavy development now, written in C++ and Python. The ns-3 simulator supports a wide variety of protocols such as Wi-Fi, LTE, IEEE 802.15.4, SigFox, LoRa, and further networks. It also implements IP networking, supporting both simulation and emulation using sockets that aim at academics and research [66]. The ns-3 can be executed with pure C++, and some simulation components can be written with Python. It is modular and can function in graphical and command-line interfaces NetAnim for C++ and PyViz for Python. It also produces Pcap tracks that can be used for debugging. Standard software such as Wireshark [67] can be used to read the trace files for network traffic analysis. The ns-3 simulator offers a practical and well-structured setting with animation support using NetAnim, as shown in Figure 10.  
Figure 10: Ns-3 NetAnim environment.  
Figure 9: LoRaSim network topology deployment.  
A LoRaWAN module was developed and implemented in ns-3 to provide a powerful tool for enabling the simulation of a real LoRaWAN network instead of simulating a simplified MAC protocol. This add-on module allows the research community and developers to achieve a greater understanding of the behavior of the physical and MAC layers in LoRa networks, credited to the LoRaSim that allows the users to test a network with varying SFs based on gateway feedbacks. The LoRaWAN ns-3 module may be used to simulate LoRaWAN networks. Beyond the models created to represent different components of the network and the integrated helpers used to set them up, the ns-3 LoRaWAN module includes a packet tracker that can be used to monitor a network's behavior and analyze its performance. It also includes facilities for storing the network topology in a file to debug and monitor. It also provides many scenarios as examples of simple to complex network use cases. The integrated LoRaWAN module meets the requirement of Class A devices. That means it can simulate the use cases where devices send uplinks and receive downlinks from the server. In comparison with LoRa's two other available classes (Class B and Class C), the most power-efficient end devices are of this class. To deliver a highly configurable and agile solution, the physical layer, MAC layer, and transport and use are built. Concerning the LoRaWAN-based ns-3 module's primary features, they are installations of the network server, ADR, confirmed messages, and support for multi-GW. Configurability of the LoRaWAN ns-3 module was proposed and allowed new algorithms to be implemented on the server side in [68]. In addition, investigational evaluation of LoRaWAN was conducted by ns-3 in [69], as well as improving LoRa performance with CSMA [70], the power consumption model [71], the scalability analysis model for a significant scale [66], and several models in [72; 73; 74; 44; 75].
## 5 Conclusions  
The user-side energy storage providing auxiliary services has drawn widely attention. On the basis of the full life cycle economic analysis, optimization configuration model for the energy storage providing different auxiliary services is proposed in this paper. Taking the maximum total net revenue over the full life cycle as the objective, the model comprehensively considers the delayed transformation income, the government subsidy income, the auxiliary service income and the life cycle cost factor. The established model is solved by the Cplex solver in this paper. To assess the profit of the configuration results and necessity of the energy storage installation, four indexes are adopted including NPV, energy storage recovery period, IRR and profit index. Finally, four kinds of batteries, VRLA, NAS, LFP and V-redox, are simulated to verify the effectiveness of the proposed model.  
Figure 14: IRR index of users participating in load shaving service under different energy storage costs.  
Figure 15: IRR of users participating in load peak shaving service under different peak-to-valley electricity price.  
According to comparison of evaluation indexes of different types of battery, it is most economical for users to adopt VRLA battery storage to participate in demand management services. In addition, through sensitivity analysis, the economic conditions of the user-side energy storage to participate in the commercial applications of load peak shaving services are proposed. What is more, in the reference of PV industry investment boundary, the worthwhile investment situation of energy storage is discussed.  
Conceptualization, L.C.; Formal analysis, T.W.; Methodology, T.W.; Resources, X.X.; Writing original draft, T.W.; Writing review and editing, L.C.  
This research was funded by State Grid Corporation of China in the support of the Scientific Project (Research on Key Technologies for Flexible Coordinated Control and Joint Operation of Distributed Energy Storage on User Side).  
The authors declare no conflict of interest.
# Computational Accountability in MAS Organizations with ADOPT
## 4 The ADOPT Accountability Protocol  
ADOPT is a protocol that allows the realization of accountable MAS organizations. Agents and organization will, thus, share the relevant information by exchanging messages, whose structure follows the FIPA ACL specification [45]. In the protocol, the organization is considered as a persona juris, a principal as any other principal, on which mutual expectations can be put. The protocol is divided into two main phases, a role adoption phase and a goal agreement one (explained in Sections 4.1 and 4.2, respectively), which are shown in Figures 1 and 2 as UML sequence diagrams. Such diagrams provide a sequencing of the messages, but what produces the accountability is the set of commitments that is created and that evolves with the messages, of both the role-adoption and the goal-agreement phases. Along with the message exchanges, in fact, the protocol records and tracks the evolution of accountability relationships between the parties, that we represent by way of social commitments [19; 46]. In principle, other sequencings can be allowed as long as the the commitments are satisfied.  
A social commitment is formally specified as \(C(x,y,p,q)\), where \(x\) is the debtor, who commits to the creditor \(y\) to bring about the consequent condition \(q\) should the antecedent condition \(p\) hold. Social commitments embody the capacity of an agent to take responsibilities autonomously towards bringing about some conditions. They can be manipulated by the agents through the standard operations create, cancel, release, discharge, assign, delegate [19]. Commitment evolution follows the life-cycle formalized in [47]. A commitment is _Violated_ either when its antecedent is true but its consequent is false, or when it is canceled when detached. It is _Satisfied_ when the engagement is accomplished. It is _Expired_ when it is no longer in effect. A commitment should be _Active_ when it is initially created. Active has two sub-states: _Conditional_ as long as the antecedent does not occur,and _Detached_ when the antecedent has occurred. A commitment is autonomously taken by a debtor towards a creditor on its own initiative. This preserves the autonomy of the agents and is fundamental to harmonize deliberation with goal achievement. An agent will create engagements towards other agents while it is trying to achieve its goals or to the aim of achieving them. Commitments concern the observable behavior of the agents and have a normative value, meaning that debtors are expected to satisfy their engagements, otherwise a violation will occur. Commitment-based approaches assume that a (notional) social state is available and inspectable by all the involved agents. The social state traces which commitments currently exist and the states of these commitments according to the commitments life-cycle. By relying on the social state, an agent can deliberate to create further commitments, or to bring about a condition involved in some existing commitment.  
Figure 1: The first phase of the accountability protocol (role adoption).
## 3 Floquet Analysis
### Analysis of the IEP Effect on Stopband

Table 1: Design parameters and corresponding values for the dispersion analysis. Figure 4: Analysis of the IEP effects on the low cut-off frequency \(f_{L}\) with various \(L_{\mathit{eff}}\) values. Instead of these former methods, we increased \(L_{\textit{eff}}\) using the IEP techniques without increasing the UC size, PCB thickness, or cost. To further examine the IEP effects on the stopband, the high cut-off frequency \(f_{H}\) was obtained by applying the condition of \(\beta_{\textit{eff}}\) = 0 to Equation (5) as follows:  
\[\tan\left(\beta_{\textit{eff}}d_{c}/2\right)\approx Z_{oc}\left(4\pi f_{H}L_{ \textit{eff}}\right) \tag{9}\]  
In Equation (9), it was approximated by assuming that the resonance frequency defined by the product \(L_{\textit{eff}}\)-\(C_{p}\) was widely separated from the \(f_{H}\). To analyze the IEP effect on the \(f_{H}\), a graphical analysis was adopted by plotting the left- and right-hand sides of Equation (9) as shown in Figure 5. The tangent function of the left-hand side is shown as a blue curve. Five fractional functions of the right-hand side with respect to the various \(L_{\textit{eff}}\) values are represented. The \(L_{\textit{eff}}\) values of 0.6 nH, 0.8 nH, 1.0 nH, 1.2 nH, and 1.4 nH were used. The point where the tangent function curve and the fractional function curve intersect indicates the approximate value of the \(f_{H}\). As shown in Figure 5, the \(f_{\text{H}}\) decreased as the \(L_{\textit{eff}}\) increased. Consequently, it is shown that the IEP technique reduced the \(f_{L}\) and \(f_{H}\), thus shifting down the stopband in the low frequency range, which resulted in downsizing the UC in the IEP-EBG structure.
## 1 Introduction
Afterwards, to further eliminate non-informative features, PCA [19] was used for feature dimension reduction. Finally, the extracted features were classified using each of the four tree ensemble learning methods. To conduct a comparative evaluation, two benchmark methods, NN [20] and SVM [21], were also used to classify the same features. To achieve a fair comparison, all the classification methods were tested based on the same training and test sets determined using a ten-fold cross-validation method. Before obtaining the PI accuracies, the hyper-parameters for all the classification methods tested were tuned using the simulated annealing method [22]. The main contribution of this paper is the provision of quantitative comparison results that can provide important guidance when employing EN systems in the perfume industry. The combination of six empirically selected equations for feature extraction represents a further contribution of this paper. The experimental setup and methods are presented in Section 2. For clarity and coherent explanation, the principles underpinning PCA and the four tree ensemble learning methods are briefly introduced in Sections 2.3.2 and 2.3.3, respectively. In Section 3, the experimental results obtained using the tree ensemble learning and benchmark methods are compared and discussed. Finally, the conclusions and suggestions for future work are presented in Section 4.
# IAACaaS: IoT Application-Scoped Access Control as a Service +
## 4 IoT Application-Scoped Access Control as a Service
### Sample Authorization Flow
#### 4.6.2 Securing Requests  
Once the environment is configured, the process to send a publish request from the _Device_ to the _Service_ consists of the following interactions and is illustrated in Figure 3:  
1. The _Device_ sends a request to the _IdP_ in order to create an OAuth 2.0 access token. The application credentials that were obtained during the registration are needed here.
2. The _Device_ sends the publish request to the _Service_, which includes in the authentication header the OAuth 2.0 token that was created in the previous step.
3. The _PEP_ module intercepts the request and extracts the token from the header. After that, it sends a validation request to the _IdP_ in order to check whether the token corresponds to a registered device in the platform. The _PEP_ is allowed to perform this validation in the _IdP_ because it authenticated previously, using the _PEP_ credentials obtained by the _Service Admin_ at registration time. If the validation is rejected, the _PEP_ sends an Unauthorized response to the _Device_. In case  
Figure 2: Registration and configuration of the _Service_ and the _Device_.  
the validation succeeds, the _IdP_ adds the public information of the _Device_ to the response, which includes the roles that it has in the scope of the application in which the token was created.
4. The _PEP_ then sends an authorization request to the _PDP_, which includes the retrieved roles, the action that the _Device_ is trying to perform and the resource that it is trying to access. Based on the policies that are stored in the _Policies DB_, the _PDP_ makes the decision of either allowing or denying the access to the resource and returns the verdict to the _PEP_.
5. If the _PDP_ has denied the access, the _PEP_ will finally send an Unauthorized response to the _Device_. Otherwise, it will forward the request of the _Device_ to the _Service_, including the public information, which was previously obtained from the _IdP_. This information can be useful for the _Service_ when performing the needed actions.
## 2 The Proposed Image Fusion Method
### Implementation of Fused Technique
0: Fused image, difference image and objective evaluation indexes
1: Image registration to obtain overlapped area image \(I_{1}\), \(I_{2}\);
2: Acquisition of low and high frequency image matrix \(C^{\prime\prime B}\), \(d_{ij}^{\prime\prime B}\) with NSCT transform;
3: Calculate the SML energy of low frequency sub-band coefficient by Equation (1);
4: Relate SML energy with visual adjustment value to obtain RLV parameter values based on Equation (2);
5: Decision map is obtained by the guided filter and multiple filters based on Equation (3);
6: Determine the coefficient source of low frequency sub-band by using Equation (4);
7:while (iteration number \(n\leq\) maximum iteration \(Kn\))
8: Average gradient \(\overline{d}\) (\(x\), \(y\)) and \(NSML\) (\(x\), \(y\)) is obtained by Equations (9) and (10);
9: Get \(F_{ij}^{k}\) (\(n\)), \(L_{ij}\) (\(n\)), \(L_{ij}^{k}\) (\(n\)) of each neuron in the receiving domain by Equation (5);
10: Judge whether the pulse signal is activated by Equation (8);
11:if Amplitude value \(Re\) (\(i,j\)) \(\leq\) 0.5
12: The neuron (\(i,j\)) is activated, \(Y_{ij}=1\). Record the ignition time of each iteration \(T_{ij}^{k}\) (\(n\)), and update dynamic threshold \(\theta_{ij}\) (\(n\)) by Equation (7);
13:endif
1:n = n + 1;
14:endwhile
15: After \(Kn\) iterations, high frequency sub-band coefficients are selected according to the total ignition time;
16: Sub-band coefficients of low and high frequency pass the window verification to ensure the consistency of adjacent coefficient sources;
17: The fused image of overlapping area is reconstructed by NSCT inverse transform;
18:return Fused image is subtracted from overlapping image to obtain the difference image, and the objective evaluation indexes of fused image are obtained by Table 1.
```  
**Algorithm 1** Sonar Image Fusion Based on SMLF energy and IDPCNN Model
## 2 Review of Conventional Path Loss Models  
### Walfisch-Ikegami Model  
The Walfisch-Ikegami model computes the path loss by considering both LOS and NLOS conditions. The path loss in decibels is given by  
\[PL^{WI}=L_{FS}+L_{rts}+L_{msd} \tag{1}\]  
where \(L_{FS}\) is the free space path loss, \(L_{rts}\) is the roof-top-to-street diffraction and scatter loss, and \(L_{msd}\) is the multiscreen diffraction loss [16; 17]. These loss terms are all functions of the carrier frequency. In addition, they relate to some geometric parameters. For instance, \(L_{FS}\) relates to the range between the transmitter (Tx) and receiver (Rx) pair; \(L_{rts}\) relates to the average street width, the road orientation angle, and the average building height; and \(L_{msd}\) relates to the average building separation, the height from the average building level to the Tx antenna, and a factor associated with diffraction loss. Note that the operating frequency for the Walfisch-Ikegami model ranges from 800 MHz to 2 GHz, although most of the 5G frequency bands are out of this range.
## 3 Materials and Methods
### Data Source  
The Ecological Monitoring Centre (CSE) in Senegal supplied shapefile data delineating the administrative commune boundaries. For this research, all 31 administrative communes were considered to explore the features of forested areas from 2005 to 2020. The remote sensing data used in the study were collected from various satellites, namely Landsat 7 ETM + C1, Landsat 5, and Landsat 8 OLI. These satellite imageries were sourced from the United States Geological Survey (USGS) website, boasting a spatial resolution of 30 m ([[http://eartheplorer.usgs.gov/](http://eartheplorer.usgs.gov/)]([http://eartheplorer.usgs.gov/](http://eartheplorer.usgs.gov/)), accessed between July and August 2022). Furthermore, socio-economic data, including population statistics and climatic data such as rainfall, were gathered in November 2022. ANSD, the World Bank (WB), and the ANACIM of Senegal provided the respective datasets for this research.  
Figure 2: The localization of the study area: (**a**) regional location of the area; (**b**) Senegal, and (**c**) African continental; (**d,e**) land use features of the study area.  
Accurately determining forested areas in Senegal hinges on the crucial consideration of the collection time for satellite images. Senegal experiences two primary seasons that define its weather trends: a dry season extending from November to April or May and a rainy season from May or June to October, contingent on specific geographical location [44]. The Thies region's rainy season extends from July to October [45], coinciding with the rainfall in our research area. However, to optimize the identification of features related to land use types, we opted for the rainy months to mitigate the impact of clouds and seasonal variations. Thus, following [PERSON] et al., the choice of Landsat images primarily relied on factors such as the availability of cloud cover percentages [46]. Due to these constraints, the collection of Landsat data was confined to the period from September through to November.