esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Reliable and Efficient Transmission Task Assignment for WSNs
## 5 Reliable and Efficient Task Allocation Algorithm for WSNs
### Distributed Task Assignment
#### 5.2.1 Distributed Reliable and Efficient Task Allocation in a Hierarchical Topology  
A hierarchical topological diagram of a clustered WSN, where the sink is the center of the entire network, is depicted in Figure 4a. The cluster head saves the collected local physical routing topology as well the state information of the member nodes in the local cluster, passing them next to the sink. Then, the sink node can obtain the physical topology of the entire network by merging all the topologies received, so the topology management module receives a two-level global view. Figure 3: Transmission-oriented reliable and energy-efficient task allocation (TRETA) algorithm. Based on the above observations, we present a distributed reliable and efficient task assignment algorithm based on TRETA for clustered WSNs, named TRETA-Cluster, which divides the task allocation into two phases: backbone network and intra-cluster allocation. 1. The sink performs the reliability distribution at the backbone network composed of the cluster heads according to the target reliability, where reliability \(r_{i}\) is obtained at each cluster head, as shown in Figure 3(b). Combined with the target reliability and the task deadline constraint, the sink calls the TRETA algorithm to select the cluster head with the smallest total energy consumption that satisfies the task deadline and the target reliability constraint, and the transmission path of the cluster head to the sink. 2. The cluster head calls the TRETA to select the nodes in the cluster and the intra-cluster paths according to the obtained intra-cluster reliability index and local view to realize the task allocation of the cluster. If the task allocation is successful, the allocation result is returned; otherwise, the cluster head selects the node that satisfies the target reliability according to the local physical topology. In case the node is found, the allocation result is returned. The task allocation fails and returns if otherwise, as shown in Figure 3(c). Figure 4: Reliable and efficient distributed task allocation based on clustering: (**a**) Cluster-based WSNs; (**b**) Sink assigns reliability to the cluster head; (**c**) Cluster head performs local task allocation. As a topology change occurs, the reliability may become lower than the threshold or the cluster head can be interrupted. In this case, the task allocation is re-executed by the sink or the cluster head according to the rule, as seen in Algorithm 1, TRETA-Cluster, Lines 17-20. The reliability allocation [19; 20] is to assign the target reliability of a task to the appropriate subsystems, components, and nodes of the system to determine the reliability of each component. Referring to the reliability allocation algorithm in [21], we assign the target reliability of the task to the two stages and use them as the target reliability of each stage (Algorithm 1, TRETA-Cluster, Lines 5-6; \(RA\) is the reliability distribution function).
# On the Use of a Feedback Tracking Architecture for Satellite Navigation Spoofing Detection
## 7 Conclusions  
In this paper, a spoofing detection algorithm based on the use of ECADLL was introduced. The algorithm is able to identify spoofing attacks when different conditions are met, based on the expected behavior of multipath reflections and on the dynamics of the receiver.  
The ECADLL architecture along with the spoofing detection algorithm were tested against the TEXBAT datasets, demonstrating that the technique is able to detect the spoofing attack scenario under certain conditions and that it is able to provide accurate estimation of the parameters of the spoofing signal. The detection algorithm presented in this work works on a single satellite observation. Looking at more satellites simultaneously, using a parallel architecture, can improve the decisions and help identify more easily the spoofing attack.  
The ECADLL version presented in the paper, including the spoofing detection algorithm and the ratio metrics in the monitoring block, has overall good detection capabilities and low false alarms, while improving over the original ECADLL in the computational burden and on the detection latency. On the other hand, a careful definition of the thresholds for the ratio metric is important, in order to turn on the units when it is needed and obtain optimal performances.  
By only containing two units for each tracked channel, the presented version of the algorithm is only able to provide protection against one spoofing signal at a time. The presence of more than one spoofer competing to gain control of the receiver is quite unrealistic, but other units could be added to the architecture without major modifications, as was presented in [13].  
It is important to notice that the anti-spoofing method proposed here is not able to defend the receiver in the cases of overpower attacks, where the vestigial receiver signal is buried under the noise levels introduced by the spoofer. Fortunately, these types of attacks are easily detectable by using in-band power measurements, given that increments of more than a few dBs in the overall power are suspicious and can be a priori excluded.  
Solutions for reducing and mitigating the effects of the spoofing attacks in the receiver remain to be explored. This feature could be achieved by switching the navigation solution algorithm to get its ranging information from the unit tracking the satellite signal on a channel under spoofing influence. In order to do this, a powerful and trusted detection algorithm, as the one presented during this work, is a good initial step.  
All authors contributed equally to the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Formal Modeling of IoT and Drone-Based Forest Fire Detection and Counteraction System
###### Abstract  
Forests are an enduring component of the natural world and perform a vital role in protecting the environment. Forests are valuable resources to control global warming and provide oxygen for the survival of human life, including wood for households. Forest fires have recently emerged as a major threat to biological processes and the ecosystem. Unfortunately, almost every year, fire damages millions of hectares of forest land due to late and inefficient detection of fire. However, it is important to identify the forest fire at the initial level before it spreads to vast areas and destroys natural resources. In this paper, a formal model of the Internet of Things (IoT) and drone-based forest fire detection and counteraction system is presented. The proposed system comprises network maintenance. Sensor deployment is on trees, the ground, and animals in the form of subnets to transmit sensed data to the control room. All subnets are connected to the control room through gateway nodes. Alarms are being used to alert human beings and animals to save their lives, which will help to initially protect them from fire. The embedded sensors collect the information and transfer it to the gateways. Drones are being used for real-time visualization of fire-affected areas and to perform actions to control fires because they play a vital role in disasters. Graph theory is used to construct an efficient model and to show the connectivity of the network. To identify failures and develop recovery procedures, the algorithm is designed through the graph-based model. The model is developed by the Vienna Development Method-Specification Language (VDM-SL), and the correctness of the model is ensured using various VDM-SL toolbox facilities.  
forest fire detection; Internet of Things (IoT); drones; failure recovery; formal specification; correctness +
Footnote †: journal: Electronics Electronics
## 4 Methodology
### Designed Model MFA Methods  
In our designed model, there are three main methods configured to prove authentication [42]. Multifactor authentication process defined in Figure 4 below.  
**SMS:** The user receives an SMS on a given cell number and put this SMS OTP into a required field. We do not use this option as Meraki Client firewall servers no longer support SMS-based authentication.  
**Call on Phone:** Users receive a call on their given number and prove authorization by pressing the numeric digit on a cell phone keypad. It is a beneficial and effective method in our case.  
**App approval:** Users need to configure the Microsoft authenticator on their cell phones, and authority will be approved by assigning access on request.  
In this figure, we divided the topology into two sections: customer network and multi-cloud services zone. Complete configuration steps of the given model are as follows:  
**Step 1:** When a user tries to utilize services, an automatic message request is sent to the Meraki MX 84 firewall virtual private network (VPN) server. Users must connect to the VPN client via a username and password.  
Figure 4: MFA implementation with azure and cisco. NPS, network policy server.  
**Step 2:** The Meraki firewall VPN server is directly connected to the central radius server called a network policy server (NPS). Therefore, it forwards the client request to the NPS. The NPS has central position radius servers connected on both ends for primary authentication via Azure active directory and secondarily via multi-cloud MFA. The NPS is also responsible for replying to the required request code to the Meraki firewall server.  
**Step 3, 4:** In these steps, the NPS will shake hands with the Azure active directory to prove primary authentication. In return, the Azure active directory sends a token back to the NPS server. Hence, the first authentication will be confirmed; it will also count attempts per device and per user as accountability as per login based.  
**Step 5, 6:** For the secondary authentication, the NPS forwards the token request to multi-cloud MFA, where users need to configure their desired authentication choice, e.g., call, SMS, or app authentication. In case in which we are using Meraki, we are limited to using the call or app authentication option. The SMS option is not supported by Meraki firewall.  
**Step 7:** In this step, the user will receive any of his/her configuration options and receive a call or approve access from the Microsoft authenticator application. Hence, secondary authentication is proved, and authorization and availability are confirmed--availability in the sense that users have multiple options to verify their secondary authentication.  
**Step 8, 9:** These steps involve handshaking to ensure two-way connectivity between the user VPN client, the Meraki firewall server, and the NPS.
## 2 Experiment
### Experimental Method and Theoretical Calculation  
Tert-butyl chloride and its isotopologues are the main products of a chemical reaction between tert-butyl alcohol and hydrogen chloride. The products were formed by flowing a mixed carrier gas (0.5% HCl + 99.5% Ar/V) through a U-tube with tert-butyl alcohol (99%, purchased from Sigma-Aldrich) inside at room temperature. At 60 psi back pressure, the sample gas was supersonic expanded into the vacuum chamber through the solenoid valve nozzle (general valve series 9, diameter 0.9 mm) to bring the rotational temperature of the sample molecules down to 1-10 Kelvin, so as to optimize the molecular population on the rotational energy levels. The gas pulse frequency was 1 Hz, and the jet duration was 500 us. After each injection, the oscilloscope collected 23 FID signals and cut them evenly and average, that was our newly developed broadband technology of multiple free induction decay. In the final spectrum, 10,000-20,000 FID signals were averaged to obtain better spectral signal-to-noise ratio compared with the previous work in [26]. Thus, rotational transitions of isotopologues (\({}^{13}\)C, \({}^{37}\)Cl) could be detected in their natural abundance.  
The ab initio electronic structure of tert-butyl chloride, as shown in Table 1, was calculated and optimized by Gaussian03 software at the MP2/6-311++G (D, P) theory level [27] to provide structural information (three-dimensional coordinates, bond length, bond angle) and rotational constants. Figure 2 is the calculated three-dimensional structure diagram and plane projection diagram of tert-butyl chloride. It is obvious that the molecule is highly symmetrical. The moment of inertia and rotational angular momentum of tert-butyl chloride and its \({}^{13}\)C, \({}^{37}\)Cl substituted isotopologues could be predicted by using PMIFST (Principal Moments of Inertia from Structure) [28] combined with Gaussian03 calculated three-dimensional coordinate matrix, to guide our initial spectroscopic assignment and fitting procedures.  
Figure 1: Schematic diagram of broadband cp-FTMW spectrometer.
# An Affinity Propagation-Based Self-Adaptive Clustering Method for Wireless Sensor Networks
## 4 The Proposed Affinity Propagation-Based Self-Adaptive (APSA) Algorithm
### Set-Up Phase
In order to minimize the criterion of the absolute error \(\sigma\), greedy method is adopted to achieve this object. A set of nodes of a cluster is represented as \(T=\left\{\tau_{1},\tau_{2},\ldots,\tau_{i},\ldots,\tau_{k-1},\tau_{k}\right\}\). Then a node \(\tau_{random}\) is randomly selected in the network to replace the node in set \(T\), meanwhile, the residual energy of \(\tau_{random}\) which is randomly selected must be richer than other nodes in set \(T\). Formula (11) describes the replacement method. \[T^{(t+1)}=\left\{ \begin{array}{ll}T^{*}&,\sigma^{*}-\sigma^{(t)}<0\\ T^{(t)}&,otherwise\end{array} \right. \tag{11}\]  
where \(T^{*}=\left\{\tau_{1},\tau_{2},\ldots,\tau_{random},\ldots,\tau_{k-1},\tau_{k}\right\}\). Then, the network is temporarily reclassified into \(k\) clusters. The new absolute error criterion \(\sigma^{*}\) can be calculated by Formula (10). Compared with original \(\sigma^{(t)}\) in the \(t\)-th time iteration, if \(\sigma^{(t)}\) is greater than \(\sigma^{*}\), \(T^{(t+1)}\) will be replaced by \(T^{*}\). In the process of iteration, we focus on the remaining energy of each CH. In each iteration, once the average residual energy of all sensor nodes is greater than that of a CH, the CH must give up the election and become a member node. By repeating Formula (11), the final clustering results can be obtained. The pseudocode of the modified K-medoids is described as Algorithm 2. ```
let T as the set of initial cluster centers; calculate the number of initial cluster centers \(k=\|T\|\) Repeat  assign each remaining common node to the cluster with the nearest medoid; randomly select a common sensor node \(\tau_{random}\); calculate the cost function \(S(S=\sigma^{*}-\sigma)\) of swapping node \(\tau_{j}\) with \(\tau_{random}\); ifS<0 then  swap \(\tau_{j}\) with \(\tau_{random}\) to form the new set of \(k\) clusters; Until no change Output: a set of \(k\) clusters. ```  
**Algorithm 2**The method for clustering
# Machine Learning Applications for Short Reach Optical Communication
## 5 Conclusions
\begin{tabular}{l l} DBN & Deep Belief Network \\ DCI & Data-center Inter Connection Link \\ DCN & Data-center Network \\ DFE & Decision-Feedback Linear Equalizer \\ DIFF & Difference between Pairs of Signal Strength \\ DNN & Deep Neural Network \\ DNN-LMS & Deep Neural Network-Least Mean Square \\ DPD & Digital Predistortion \\ DSO & Digital Storage Oscilloscope \\ DSP & Digital Signal Processing \\ DT & Decision Tree \\ DU & Digital Unit \\ EDFA & Erbium-Doped Fiber Amplifier \\ EEPN & Equalization Enhanced Phase Noise \\ ELM & Extreme Learning Machine \\ EON & Elastic Optical Network \\ FC & Feature Classifier \\ FC-NN & Fully Convolutional Neural Network \\ FEC & Forward Error Correction \\ FFE & Feed-Forward Equalizer \\ FFNN & Feed-Forward Neural Network \\ FFT & Fast Fourier Transform \\ FIR & Finite Impulse Response \\ FOV & Field of View \\ FPGA & Field Programmable Gate Array \\ FITx & Fiber to the x, x = Home, Building, Curb or Node \\ FWM & Four-Wave mixing \\ HLF & Hyperbolic Location Fingerprint \\ IM/DD & Intensity Modulation/Direct Detection \\ IMU & Inertial Measurement Unit \\ ISI & Inter-symbol Interference \\ KNN & K-Nearest Neighbor \\ LD & Laser Diode \\ LDBP & Learned Digital Back Propagation \\ LED-ID & LED Identity \\ LOS & Line-of-Sight \\ LP & Label Predictor \\ LRPON & Long-Reach Passive Optical Network \\ LSTM & Long Short-Term Memory \\ MFI & Modulation Format Identification \\ ML & Machine Learning \\ MLP & Multi-Layer Perception \\ MLSE & Maximum Likelihood Sequence Estimation \\ MMF & Multi-Mode Fiber \\ MSE & Mean Squared Error \\ MT-DNN & Multi-Task Deep Neural Network \\ MUX & Multiplexer \\ MZM & Mach-Zehnder Modulator \\ NGFI & Next-Generation Fronthaul Interface \\ NLMS & Normalized Least-Mean-Square \\ NLOS & Non-Line-of-Sight \\ NRZ & Non-Return-to-Zero \\ OCC & Optical Camera Communication \\ OFDM & Orthogonal Frequency Division Multiplexing \\ OLT & Optical Line Terminal \\ ONU & Optical Network Unit \\ OOK & On-Off Keying \\ OPM & Optical Performance Monitoring \\ \end{tabular}
## 3 Results  
Out of 1191 evaluations conducted, 237 were excluded for not meeting the established reliability standards. Analysis of the remaining 954 answers from ChatGPT-4 and 954 answers from Bard revealed that the ones generated by ChatGPT-4+context were more accurate and complete compared to those generated by Bard, as shown in Table 2.  
For completeness, [PERSON]'s responses averaged 3.47 \(\pm\) 0.042, while ChatGPT-4+context achieved a higher average of 3.68 \(\pm\) 0.041 (\(p\) < 0.05). ChatGPT-4+context reached the maximum completeness score of 5 in 30.3% of cases, compared to 26.5% for Bard. In terms of minimum scores, Bard received the lowest rating in 10.5% of cases, slightly more than ChatGPT-4+context at 10.3%. Effect size calculations indicated that the influence of the sample on completeness was negligible ([PERSON]'s d = 0.16; r = 0.08).  
Regarding accuracy, Bard scored an average of 3.14 \(\pm\) 0.043, while ChatGPT-4+context performed better, with an average score of 3.67 \(\pm\) 0.043 (\(p\) < 0.05). Bard achieved the maximum Likert scale score for accuracy in 19.2% of cases, compared to 33.4% for ChatGPT-4+context. The lowest score of 1 was assigned to Bard in 14.9% of cases, compared to 11.3% for ChatGPT-4+context. Effect size calculations showed a moderate sample influence for accuracy ([PERSON]'s d = 0.40; r = 0.20).  
Figure 2 shows a comparison of median, 25 th, 50 th, and 75 th percentiles for the accuracy and completeness of the responses from Bard and ChatGPT-4+context (abbreviated as ChatGPT4 in the charts on the right).
## 3 Applications to Communication Network Design
### Background Concepts
The routing methods with built-in reliability requirements are designated in general as _resilient routing_ methods. Another class is designated as _multicast routing_ for which a set of paths has to be calculated from the originating node to a set of destination nodes--point-to-multipoint routing (this is the situation typical of the distributional services supplied by a certain service provider)--or interconnecting two subsets of network nodes--a multipoint-to-multipoint routing model (for example in teleconferencing services in Internet). If all the nodes have to be interconnected, it is designated as _broadcast routing_ and is formulated as a spanning tree problem. If the set of destination nodes is a proper subset of the set of network nodes, the associated multicast routing problem can be formulated as a Steiner tree problem, where the destination nodes and the originating node are the terminal nodes. These types of models using spanning trees or Steiner tree problem formulations are out of the scope of the present work. Reference books focusing on concepts, techniques, models and algorithms concerning communication network routing methods and protocols for key types of telecommunication network technologies and architectures are [143; 144].
## 2 Background
### Convolutional Neural Network  
As a classical and widely used structure of deep learning, CNN overcomes some problems that were difficult to be solved by artificial intelligence in the past. The local connections, shared weights and down sampling operation of CNN can effectively reduce the complexity of the network and the number of training parameters [27; 28; 29; 30].  
There are three major layers in CNN:  
1. convolutional layer,
2. pooling layer,
3. fully connected layer.  
The convolution layer applies a certain number of filters to obtain the feature map of the input signal. Concretely, the convolution is to take a dot product between a kernel \(\omega_{c}\in R^{m}\) and the \(j\)th segmented signal \(s^{i}_{j-m+1;j}\in R^{m}\) to obtain features  
\[c_{j}=\text{Relu}(\sum_{i=1}^{n}w_{c}*s^{i}_{j-m+1;j}+b_{c}), \tag{1}\]where \(*\) is convolution operator, \(\omega_{c}\) is referred to as the convolution kernel, \(b_{c}\) is the corresponding bias, \(n\) is the number of kernels, and \(c_{j}\) is the \(j\)th output point of the convolutional layer. Relu is activation function. The pooling layer is the down sampling layer reducing the input feature dimension. In this paper, the Max pooling function is used, and the maximum value within a certain sub-region is returned as follows:  
\[p_{j}=\max\{c_{j\times k(j+1)\times k}\}, \tag{2}\]  
where \(k\) is the pooling length, and \(p_{j}\) is the pooling output of the \(j\)th point. After several alternating convolution layers and pooling layers, a fully connected layer is flattened from the output of the upper layer. Based on fully connected (FC) layer output \(f\), the health conditions of machines are estimated in the health condition output layer through the softmax regression,  
\[y=\frac{1}{\sum\limits_{i=1}^{K}e^{((w_{i})^{T}f+b)}}\left[\begin{array}{c}e ^{((w_{1})^{T}f+b)}\\ e^{((w_{2})^{T}f+b)}\\ \vdots\\ e^{((w_{k})^{T}f+b)}\end{array}\right], \tag{3}\]  
where \(w_{i}\) is the weight matrix connecting to the \(i\)th output neuron, \(b\) is the corresponding bias vector, and \(K\) denotes the health condition categories.  
The typical structure of the classic LeNet-5 CNN model [31] is shown in Figure 1. LeNet-5 was applied to recognize the handwritten digit characters and computer printed characters. There are two convolutions layers, two pooling layers and two fully connected layers in this model. In this research, a modified LeNet-5 CNN is designed to solve the classification task on the fault diagnosis.  
The design CNN structure is shown in Figure 2, which is like a LeNet-5 CNN model. The detail definitions would be discussed in Section 3, and the CNN is developed based on TensorFlow.