esa-sceva/satcom-chunk-collection huggingface dataset:
# Routing Protocol for Heterogeneous Wireless Sensor Networks Based on a Modified Grey Wolf Optimizer
###### Abstract  
Wireless sensor network (WSN) nodes are devices with limited power, and rational utilization of node energy and prolonging the network lifetime are the main objectives of the WSN's routing protocol. However, irrational considerations of heterogeneity of node energy will lead to an energy imbalance between nodes in heterogeneous WSNs (HWSNs). Therefore, in this paper, a routing protocol for HWSNs based on the modified grey wolf optimizer (HMGWO) is proposed. First, the protocol selects the appropriate initial clusters by defining different fitness functions for heterogeneous energy nodes; the nodes' fitness values are then calculated and treated as initial weights in the GWO. At the same time, the weights are dynamically updated according to the distance between the wolves and their prey and coefficient vectors to improve the GWO's optimization ability and ensure the selection of the optimal cluster heads (CHs). The experimental results indicate that the network lifecycle of the HMGWO protocol improves by 55.7%, 31.9%, 46.3%, and 27.0%, respectively, compared with the stable election protocol (SEP), distributed energy-efficient clustering algorithm (DEEC), modified SEP (M-SEP), and fitness-value-based improved GWO (FIGWO) protocols. In terms of the power consumption and network throughput, the HMGWO is also superior to other protocols.  
heterogeneous wireless sensor networks; grey wolf optimizer; network lifecycle; energy consumption +
Footnote †: journal: Journal of Sensors
# A Comparative Study of Storage Batteries for Electrical Energy Produced by Photovoltaic Panels
###### Abstract  
This article presents a comparative study of the storage of energy produced by photovoltaic panels by means of two types of batteries: Lead-Acid and Lithium-Ion batteries. The work involved the construction of a model in MATLAB-Simulink for controlling the loading/unloading of storage batteries with energy produced by photovoltaic panels through a buck-type DC-DC convertor, controlled by means of the MPPT algorithm implemented through the method of incremental conductance based on a MATLAB function. The program for the MATLAB function was developed by the author in the C++ programming environment. The MPPT algorithm provides maximum energy transfer from the photovoltaic panels to the battery. The electric power taken over at a certain moment by Lithium-Ion batteries in photovoltaic panels is higher than the electric power taken over by Lead-Acid batteries. Two types of batteries were successively used in this model: Lead-Acid and Lithium-Ion batteries. Based on the results being obtained and presented in this work it may be affirmed that the storage battery Lithium-Ion is more performant than the Lead-Acid storage battery. At the Laboratory of Electrical Machinery and Drives of the Engineering Faculty of Bacau, an experimental stand was built for a storing system for electric energy produced by photovoltaic panels. For controlling DC-DC buck-type convertors, a program was developed in the programming environment Arduino IDE for implementing the MPPT algorithm for incremental conductance. The simulation part of this program is similar to that of the program developed in C++. Through conducting experiments, it was observed that, during battery charging, along with an increase in the charging voltage, an increase in the filling factor of the PWM signal controlling the buck DC-DC convertor also occurred. The findings of this study may be applicable to the storage of battery-generated electrical energy used for supplying electrical motors in electric cars.  
photovoltaic systems; the storage battery; incremental conductance k
## 3 System Functional Modules
### Base Station Module Hardware and Software Design
#### 3.3.5 Other Tasks  
(1) Message Loss Handling  
To avoid losing messages, the system establishes message numbering and a response message. All the data messages correspond to a specified response message; when a node is sending messages, it must receive its response message, thus it can be considered as a successful communication; each message has a message numbering; when communication is finished, 1 will be automatically added to the message numbering; when the response is not received within a fixed time interval or an error prompts into the response message, the message will be re-transmitted; if the received message numbering is greater than the previous one, this message will be identified as a valid message processing; if it is determined to be an invalid one, it should send the corresponding response message in order to inform the sending terminal on whether the current communication is normal.  
(2) Delay Processing  
In the network communication, it may occur that multiple nodes simultaneously send data and block the communications. In this study, the nodes will adopt different time delays, so that the above situation can be solved. To ensure a different time delay assigned for each node, the delay time of each node is associated with the node number. The higher priority the response message has, the shorter the time delay is.  
Figure 13: Implementation of data processing tasks flow.  
(3) Time Synchronization of Data Collecting by Nodes  
To ensure accuracy and consistency in the data collection time of nodes, a precise time source is used in the system for calibrating the time of the various network nodes. Because a star network is used and there are no repeaters in the system, the time for the communication from the base station to the nodes is very short, but relatively stable and fixed. The system is connected to a GPS, using GPS satellite timing to calibrate the time of the base station. Then the time of nodes is corrected through the base station when the node is online for the first time. This ensures that the error between the various nodes and the error between the node time and real time are kept within the ms range.
## 2 Materials and Methods
### Datasets
#### 2.1.2 Land Cover Data  
Land cover information is sourced from the high-resolution (300 m spatial resolution) dataset provided by the Copernicus Climate Change Service [57]. Considering the specific vegetation characteristics of the study area, similar land cover types within the original classification system were aggregated, leading to the establishment of four primary vegetation cover categories: cropland, forest, grassland, and shrubland. The data, based on advanced remote sensing technologies and algorithms, provide a spatial resolution of 300 m, allowing for a more detailed description of the spatial distribution of land cover types with high scientific credibility. Additionally, in its original classification system, similar land cover types are aggregated into four major vegetation cover categories (cropland, forest, grassland, and shrubland), which facilitates integration with research objectives. Therefore, this study selects this dataset to categorize NDVI into different vegetation cover types.
## 4 Adaptive Fuzzy Sliding Mode Control
### Adaptive Fuzzy Sliding Mode Control  
Since it is difficult to measure the upper bound of nonlinearities in practical applications, a fuzzy system is proposed to adaptively estimate the optimal upper bound of the nonlinearities.  
The tracking error \(e\) is selected as the input of the fuzzy controller, and the upper bound of the uncertainties is its output.  
According to the universal approximation theory, there exists an optimal parameter satisfying \(g^{*}{}_{E}=g_{E}+\varepsilon=a^{*T}\xi\), where \(a\) is an adjustable parameter, \(a^{*}\) is an optimal parameter, \(\xi\) is a fuzzy basis function vector, and \(\xi_{i}=\frac{w_{i}}{\sum w_{i}}\), \(i=1,2,3\)...... \(m\), \(\varepsilon\) is the approximation error bounded by \(|\varepsilon|<E\), where \(E\) is a positive constant.  
Figure 6: Block diagram of AFSMC.  
A fuzzy system \(\hat{g}_{E}\) is employed to approximate the upper bound of system nonlinearities as:  
\[\hat{g}_{E}=\hat{a}^{T}\xi \tag{16}\]  
where \(\hat{a}\) is the estimation of \(a^{*}\).  
Replacing \(g_{E}\) in Equation (15) by \(\hat{g}_{E}\) and combining Equations (13) and (14) yields the new controller as Equation (17):  
\[D=0.5[1+\frac{L_{ac}C_{ac}}{u_{dc}}(\frac{1}{R_{L}C_{ac}}\bar{u}_{ac}+\frac{1}{ L_{ac}C_{ac}}u_{ac}+\bar{u}_{ac}-k_{1}\hat{e}-k_{2}e-\hat{g}_{E}\text{sgn}(s))] \tag{17}\]  
Substituting Equation (17) into Equation (12) obtains:  
\[\dot{s}=g-\hat{g}_{E}\text{sgn}(s) \tag{18}\]  
Define \(\widetilde{a}=\hat{a}-a^{*}\) as the estimation error.  
Selecting a Lyapunov function as:  
\[V=\frac{1}{2}s^{2}+\frac{1}{2\eta}\widetilde{a}^{T}\widetilde{a} \tag{19}\]  
where \(\eta\) is a positive constant.  
The derivative of \(V\) becomes:  
\[\begin{array}{ll}\dot{V}&=s\dot{s}+\frac{1}{\eta}\widetilde{a}^{T}\dot{ \widetilde{a}}\\ &=s(g-\hat{g}_{E}\text{sgn}(s))+\frac{1}{\eta}\widetilde{a}^{T}\dot{ \widetilde{a}}\\ &=s(g-\hat{a}^{T}\xi\text{sgn}(s))+\frac{1}{\eta}(\hat{a}-a^{*})^{T}\dot{ \widetilde{a}}\\ &=sg-\hat{a}^{T}\xi|s|+\frac{1}{\eta}(\hat{a}-a^{*})^{T}\dot{\widetilde{a}} \end{array} \tag{20}\]  
An adaptive law can be obtained as:  
\[\dot{\hat{a}}=\dot{\widetilde{a}}=\eta|s|\xi \tag{21}\]  
Substituting Equation (21) into Equation (20) yields:  
\[\begin{array}{ll}\dot{V}&=sg-|s|a^{*T}\xi\\ &\leq|s|g-|s|a^{*T}\xi\\ &=-(g_{E}-g+\epsilon)|s|\\ &\leq 0\end{array} \tag{22}\]  
\(\dot{V}\) is negative semi-definite, which implies that the closed-loop system is asymptotically stable, where \(V\to 0\) as \(t\rightarrow\infty\), and therefore \(e\to 0\) as \(t\rightarrow\infty\), meaning the output of inverter can track the grid reference voltage.
# Effective Assessment of Inelastic Torsional Deformation of Plan-Asymmetric Shear Wall Systems
## 3 Inelastic Torsional Response of Plan-Asymmetric Structures  
The torsional response of plan-asymmetric structures in the inelastic range becomes quite different from that in the linear elastic range. In the linear elastic range, the rotation of the asymmetric structure increases in proportion to the level of the lateral load applied, but if any of the lateral force resisting walls start to yield, there is change in the overall torsional stiffness of the structure, which leads to a change in the eccentricity value as well. This again affects the torsional moment induced in the asymmetric structure, and thus the rotation of the structure increases in a nonlinear fashion in the inelastic range.  
To accurately assess the inelastic torsional response of the structure, we derived the generalized form of the inelastic lateral displacement-rotation relation of the structure by following a step by step procedure, and we represent this relation in the lateral displacement-rotation coordinate system in this section. According to the authors' knowledge, this coordinate system has never been introduced before and is very convenient to describe the inelastic torsional behavior of the asymmetric structure. In addition, we discuss the effects of transverse wall stiffness on the yielding procedure of asymmetric structures.
## 3 Testing Results
### AE Characteristics
#### 3.2.2 AE Energy Count  
Figure 6 shows the results of the AE energy count of the No. 4 coal specimen. The evolution characteristics of AE energy count are the same as that of AE ring count; just in the unloading phase, the AE energy count is very lower and close to 0. Further, the evolution laws of cumulative ring count are different from the cumulative energy count. The cumulative AE ring count basically increases linearly. The cumulative AE energy count increases in the loading phase of each cycle and keeps stable in the unloading phase of each cycle; it presents a stepped increase with the cycle number.
## 7 Simulation Results and Analysis
### Service Request SR on Two Network Topologies
#### 7.2.2 Service Request SR of the RAWC Scheme in Different Resource Cases  
In this paper, we consider the following three resource cases: 1 Case 1: the initial number of wavelength channels \(\lambda_{ini}\) in the network is set to 20, and the initial number of quantum keys \(K_{ini}\) stored in QKP corresponding to each link is set to 80; 2 Case 2: \(\lambda_{ini}=30,K_{ini}=90\); 3 Case 3: \(\lambda_{ini}=40,K_{ini}=100\). The SRs of the RAWC scheme in the three cases on the six-node network topology and the NSFNET topology are shown in Figure 8,b, respectively. It is clear that with a fixed traffic load, the more initial resources are in QKDON, the higher the service request success rate is. This indicates that initial resources in the network should be supplemented as much as possible for QKP to ensure more secure service transmission.
###### Abstract  
Standalone Name Resolution (SNR) is an essential component of many Information-Centric Networking (ICN) infrastructures that maps and stores the mappings of IDs and locators. The delivery of data can be realized only when the name resolution process is completed correctly. It also makes the SNR become the key target of network attackers. In this paper, our research focuses on the more covert and complex Content Pollution Attack (CPA). By continuously sending invalid content to the network at a low speed, attackers will consume a lot of the resources and time of the SNR system, resulting in a serious increase in the resolution delay of normal users and further cache pollution in ICN. It is difficult to be quickly detected because the characteristics of attack are inconspicuous. To address the challenge, a register access control scheme for an SNR system based on a malicious user blacklist query is proposed. A neighbor voting algorithm is designed to discover possible attacks in the network quickly and build a blacklist of malicious users reasonably. Users on the blacklist will be restricted from accessing the ICN network during the registration phase with the resolution system. Incentives and punishments for network users are introduced to automate responses about the potential malicious behavior reports. Our scheme is more efficient as users do not have to wait for an additional system component to perform operations. In addition, our algorithm can better solve the collusion problem in the voting process when compared with the others. We experimentally evaluate our protocol to demonstrate that the probability of successful collusion attack can be reduced to less than 0.1 when the attacker ratio is 0.5.  
content pollution attacks; self-certifying naming; decentralization; Standalone Name Resolution (SNR); voting mechanism +
Footnote †: journal:  
_Future Internet_  
Footnote †: journal:  
**Keywords:** content pollution attacks; self-certifying naming; decentralization; Standalone Name Resolution (SNR); voting mechanism
# Al-Fe-Ni Metallic Glasses via Mechanical Alloying and Its Consolidation
## 1 Introduction
There are several compaction techniques to produce bulk amorphous alloys from amorphous powders samples, such as static hot-pressing, warm extrusion, hot pressing and spark plasma sintering [19]. The analysis of the literature in [33] shows that spark plasma sintering (SPS) has many significant advantages over the conventional consolidation technique, such as a lower sintering temperature, faster heating rates and a shorter holding time [34; 35; 36; 37; 38]. In SPS, the spark discharges appear in the gap between the particles of a material, and a local high temperature occurs at the contact zones of the powder particles. There is a mass transport from the surface with a high temperature and the internal parts of the powder particles. Together with heating the sample, external pressure is applied to make full-density bulk samples. Nowadays, SPS is widely used to consolidate nanostructured/amorphous bulk samples. Until now, few works investigated the effect of milling speed on the amorphization and the thermal stability of Al-based amorphous alloys. Nowadays, SPS is widely used to consolidate nanostructured/amorphous bulk samples. There is an effort to produce a bulk amorphous alloy sample using spark plasma sintering to obtain full density and high strength. In this article, the fully amorphous Al\({}_{82}\)Fe\({}_{14}\)Ni\({}_{4}\) alloys were synthesized by mechanical alloying, and the influence of ball milling intensities on the microstructural evolution, thermal stability and magnetic properties was investigated. The as-milled amorphous powders were consolidated by SPS to produce bulk samples. The densification behavior and compression tests followed by a failure analysis of the sintered samples were also investigated.
## 3 Proposed Technique
### Watermark Embedding Process  
In this subsection, we discussed the procedure for embedding a watermark (illustrated by the Figure 2. The method for embedding the watermark is given in Algorithm 2. The watermark embedding algorithms take two inputs viz a colour medical image (called the host image) and a binary watermark. In the first stage, the host image is separated from its colour components \((R,G,B)\). In the second stage, the green channel of the host image is transformed using LWT to acquire the low-frequency sub-bands (i.e., \(LL\), \(LH\), \(HL\), \(HH\)). In the third stage, low-frequency sub-bands \((LL)\) are converted into \(3\times 3\) block. As shown in Figure 3, the blocks are chosen in a zigzag pattern. In the fourth stage, each block is decomposed using the Hessenberg decomposition to obtain the \(Q\) matrix. In the fifth stage, the Arnold cat map encryption method is used to encrypt the watermark bits. In the sixth stage, the marking bits are multiplied with \(\Delta\) and added to the central element of \(Q\) matrix to obtain the \(Q^{\prime}\) matrix. Here, the value of \(\Delta\) is optimized by the PSO algorithm. The range of \(\Delta\) is between 0.020 and 0.040. In the seventh stage, modified blocks are obtained via inverse Hessenberg decomposition. In the eighth stage, the modified blocks are rearranged into the \(LL\) sub-band. In the ninth stage, the \(H_{g}^{\prime}\) modified green channel of the host image is transformed using inverse LWT. Finally, all colour channels are combined to produce the watermarked image.  
Figure 2: Block Diagram: Watermark Embedding Process.
## 7 Observations and Recommendations
### Recommendations and Research Challenges
#### 7.3.3 Data Recovery  
In a practical setup, sensor readings are inherently unreliable and inaccurate [46]. This means that nodes will frequently produce erroneous readings even though there is nothing wrong in the system. Anomaly detection schemes may falsely classify these random errors as anomalous (which they are) thus increasing the false alarm rate. Pairing anomaly detection algorithms with data recovery models or developing schemes with inbuilt predictive models would help alleviate this problem. Doing this helps in two ways, firstly the faulty readings can be replaced with the predicted values thus improving system performance, and secondly the overall accuracy of the detection scheme is improved. This often overlooked topic will be central in improving the feasibility of anomaly detection schemes in practice.
# Digital Twins, Virtual Devices, and Augmentations for Self-Organising Cyber-Physical Collectives +
## 5 Evaluation
### Experiment Setup  
In order to exercise the idea on a challenging environment, we setup our experiments to run in the city map of Urbino in central Italy. We picked the city as it features a medieval, irregular road structure with sharp turns, U-shaped roads, and considerable altimetric changes; making exploration harder and naturally producing the potential problems that the system would encounter in a real-world situation.  
\(N_{r}\) explorers, equipped with a physical device, are deployed at random location in the urban area. These are instructed to explore the area by generating random destinations and renewing them every time they are reached (or, if they are unreachable, the closest point on a reachable road has been reached).  
Explorers move at a mean speed of 1.4 ms, which is a reasonable approximation of the average walking speed of a pedestrian [48] (although people in charge of exploring would probably walk faster than the average person, the terrain in a post-disaster situation is often rough and attention is advised, we thus think that the mean speed should not differ too much). Devices, both physical and virtual, run rounds asynchronously with an average frequency of 0.5 Hz. We assume message delivery to be instantaneous, namely, we do not simulate network delays. This choice was made, as reasonable network delay modelling would require assumptions on the specific deployment (including networking technologies and protocols and actual execution location of pulverised logical components) that we do not want to investigate in the current work, whose focus is on evaluating the approach regardless of the actual deployment details. We simulate one hour of area exploration for every run (3600 s). Figure 5 depicts subsequent snapshots of a simulation run, providing an idea of the correspondence between deployed virtual devices and road shapes, as well as showing the reachability map formation with time.  
Figure 5: Snapshot of the simulation. Initially, (**top left**) explorers (red dots) are deployed in random positions in the simulation arena. A PoI (yellow square) is generated randomly as a reference point for route computation. When the simulation begins (**top right**), explorers begin wandering the map along the available routes, and when conditions are appropriate, they spawn virtual devices (blue dots). With time, explorers indirectly increase the knowledge of the actual road structure (**bottom**).
## 2 Materials and Methods
### Determination of In Vitro Antimicrobial Activity
#### 2.6.2 Agar Diffusion  
A semi-quantitative determination of the antimicrobial activity of nutraceutical extracts was conducted by the agar diffusion method against pathogenic strains of _Escherichia coli_ (ATCC 8739), _Escherichia coli_ (O157:H7), _Enterococcus faecalis_, _Enterobacter cloacae_, and _Listeria innocua_. These strains were selected because they are representative of different bacterial groups and are relevant to food safety. Strains originated from the Hungarian University of Agriculture and Life Sciences, except for _Escherichia coli_ (ATCC 8739), which originated from the American Type Culture Collection. Bacterial strains were cultured in tryptic soy broth (TSB) culture medium and incubated at 37 \({}^{\circ}\)C for 24 h. Following that, 200 \(\mu\)L of freshly grown culture was pipetted into the Petri dishes and mixed well with TSB agar made by adding 30 g of tryptic soy broth to 1000 mL of distilled water and 1% of bacteriological agar sterilized at 121 \({}^{\circ}\)C for 15 min. An 8 mm sterile punch was used to create wells in the agar, and then 150 \(\mu\)L of each extract was pipetted into the wells. An ethanol:distilled water (60:40) mixture was used as the control. Following that, Petri dishes were placed in a refrigerator (4 \({}^{\circ}\)C) for 1 h and incubated using a thermostat (37 \({}^{\circ}\)C) for 24 h. After incubation, diameters of the inhibitory zones were measured using a ruler, and the results were divided into four categories as follows: no activity (zone of inhibition is not visible), small activity (<2 mm), medium activity (2-4 mm), and high activity (>4 mm).