esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Real-Time Traffic Map for Geographic Routing in VANETs
## 4 The Proposed Solutions
### CRT Map Construction and Message Exchanges
#### 4.1.1 Traffic Density Collection  
Vehicles periodically broadcast beacon messages comprised of their current driving information. Each vehicle is aware of other vehicles' existence via the received beacon messages. The beacon message is with a Veh. ID field, so beacons from different sources can be distinguished. A vehicle should be able to encounter all the vehicles driving in the opposite direction on the same road and receive their beacons, regardless of any collisions and/or losses of said beacons. However, for vehicles moving in the same direction, many of them may be invisible due to the limitation of the communication range. To deal with this issue, we let the vehicle, which is about to leave a road, announce a list of Veh. IDs that it has encountered approaching from the opposite direction. With this list, a newcomer, who just enters this road, is aware of vehicles in the same direction. Using this list as a base and attaching vehicles it encounters, the number of vehicles and the traffic density of this road can be derived. Subsequently, the traffic density of this road is merged with the existing record in its CRT Map by Equations (1) and (2).
# Search and Rescue in a Maze-like Environment with Ant and Dijkstra Algorithms
## 2 Background
### Wireless Sensor Networks (WSN) in SAR for Swarms of Drones
#### 2.2.3 UAVs as Nodes in Mobile Networks  
The use of UAVs seems a straightforward consideration, and indeed, there is a host of literature and surveys [49] on the use of UAVs as mobile nodes in communication networks [50], or as parts of dynamic networks [51]. Practitioners in the field have identified UAVs, used as mobile sensing devices, as having great potential when it comes to  
Figure 2: An indoor Search and Rescue (SAR) scenario where the victim sends out a distress signal to guide a search conducted by autonomous robotic agents. On discovery of the victim, the agents create an optimal relay link (by optimizing various aspects such as time, cost, length of the link, etc.) to connect the victim to the base station. The other scattered agents not involved in the relay can be called back to the base.  
providing communication support [35; 52] and mobile communication infrastructure [9; 18], such as a drone-based (mobile) wireless sensor network (WSN) [53] or a wireless local area network (WLAN) in which communication is realized node to node (N2N) using ad hoc [54] routing [55] to exchange messages between nodes. Examples for the use of drone-based wireless sensors networks (WSN) [56; 57; 58] are, e.g., healthcare and environmental monitoring [11; 59; 60; 61]) as well as reconnaissance in the aftermath of a disaster [10], and general applications where the topology of the network is dynamic and adaptive [62].
## 5 Experiments and Assumptions on Abnormal Behavior Detection and Noise Removal Performance
### Parameters Used in Ensemble Model for Attack Detection and Noise Reduction
#### 5.3.4 Naive-Bayes  
On the dataset used in this experiment, all 77 features had real type values, and no unusual distribution was identified in the dataset. Thus, we constructed an ensemble model using the Gaussian Naive-Bayes model to enhance the coverage of natural events.
# Enhancing Machine Learning Prediction in Cybersecurity Using Dynamic Feature Selector
## 3 Algorithms Used for Dimensionality Reduction
### Correlated Feature Elimination  
Another feature engineering method applied to reduce the dimensionality of the dataset was the elimination of highly correlated features. Consider two features a and b where a \(=x_{1}\ldots,x_{n}\) and b \(=y_{1}\ldots\),\(y_{n}\). The degree of similarity between these two features can be represented using a correlation coefficient. The Pearson Correlation coefficient was used to express the correlation between features. It was the most logical choice due to two reasons. The complexity of Pearson Correlation is linear making it efficient [33; 34]. Furthermore, the features on which Pearson Correlation was applied were mostly binary which reduces any drawbacks of having outliers. It is obtained as shown in Equation (1).  
\[r=\frac{\sum_{i=1}^{n}(a_{i}-\overline{a})(b_{i}-\overline{b})}{\sqrt{\sum_{i= 1}^{n}(a_{i}-\overline{a})^{2}(b_{i}-\overline{b})^{2}}}. \tag{1}\]  
Here, \(\overline{a}\) and \(\overline{b}\) denotes the mean of all records in features a and b respectively. The Pearson Correlation returns a value from \(-1\) to 1. Values closer to 1 denote a high degree of correlation between the two features. In this research certain features which have a degree of collinearity greater than 0.8 with several other unique features were potential candidates for being dropped.
# EconLedger: A Proof-of-ENF Consensus Based Lightweight Distributed Ledger for IoVT Networks
## 4 PoENF: A Proof-of-ENF Consensus Protocol
### PoENF-Based Block Proposal Mechanism
#### 4.3.3 Chain Extension Policies  
In a PoENF block generation round, validators extend the local chain based on a \"_largest height of confirmed block_\" rule, which requires that new blocks \(B_{i+1}\) are only appended to \(head(\mathcal{C})\) by letting \(B_{i+1}[pre\_hash]=\mathcal{H}(head(\mathcal{C}))\). The PoENF process allows that the probability of a block generated by validator \(v_{i}\) is related to the rank of its ENF proof \(E_{i}\) among the global ENF proof vectors \(G\). However, \(G_{i}\) observed by validator \(v_{i}\) may vary due to network latency or misbehavior of Byzantine nodes. Thus, it is hard to guarantee that only one block is proposed during each slot round, and the amount of candidate blocks could be between zero and the committee size \(K\). Given the candidate blocks number \(b\in[0,K]\), the chain extension rules are described as follows:  
1. \(b=1\): If there is only one proposed candidate block \(B_{i+1}\), then the block is accepted as confirmed status and updates the chain head as \(head(\mathcal{C})=B_{i+1}\).
2. \(b>1\): If more than one candidate blocks are proposed, then all blocks are accepted as confirmed status. The \(head(\mathcal{C})\) update follows two sub-rules: 1. Chain head points to a block that records most ENF proof transactions among other blocks; 2. For the candidate blocks having ENF proof transactions of the same size, the block generated by validator that has the largest credit value becomes the chain head.
3. \(b=0\): If none of the validators proposes a block at the end of current slot round, block generation follows a spin manner. As validators of current committee can be sorted by account address, we can calculate \(ind=height\) (mod \(K\)). Thus, a validator at rank \(ind\) can also propose a candidate block in the current round. The chain head update process follows the rule i) \(b=1\).  
Rule (i) covers a basic scenario to ensure that a new blocks is extended on the chain head. Rule (ii) handles the conflicting chain head update scenario when multiple validators propose valid blocks when they have different global ENF proof vectors \(G\). It also discourages dishonest behaviors by using a smaller \(G\) to win the right to block proposal. Rule (iii) guarantees liveness in PoENF consensus process such that at least one uniform block is generated to ensure chain extension even if a leader cannot propose a new block due to crash failures or attacks.
# Real-Time Indoor Positioning Based on BLE Beacons and Pedestrian Dead Reckoning for Smartphones
## 3 Methodology
### Particle Filter
#### 3.4.2 Particle Position Update  
The position of each particle is updated using the PDR method discussed in Section 3.3. Suppose the position of particle \(i\) at the \(k\)-th step is \(\mathbf{X}_{k}^{(i)}\). Once a new step is detected, according to the estimated step length \(l_{k}\) and the heading direction \(\phi_{k}\), without considering the system noise, the particle state transition follows  
\[\mathbf{X}_{k+1}^{(i)}=\mathbf{X}_{k}^{(i)}+l_{k}\begin{bmatrix}\cos\varphi_{ k}\\ \sin\varphi_{k}\end{bmatrix}, \tag{17}\]  
where \(\varphi_{k}\) and \(l_{k}\) are the heading direction and step length of \(k\)-th step towards \(k+1\)-th step, respectively, which can be obtained by the methods introduced in Sections 3.3.3 and 3.3.2, respectively.
# Distributed Online Multi-Label Learning with Privacy Protection in Internet of Things
## 5 Our Proposed Algorithm  
Once the network connection between nodes is set up, each node needs an iterative update, as shown in Figure 2 and Algorithm 1. The steps in Algorithm 1 will be explained in detail in the next paragraph. Followed by the update, each node would periodically broadcast the local \(P_{i}\) and \(B_{i}\) to all neighbour nodes and continuously listen for neighbouring nodes' broadcasted \(P_{i}\) and \(B_{i}\). There are no strict requirements for broadcast frequency.  
In Algorithm 1, the algorithm requires pre-defined balanced parameters and weighted parameters with positive real numbers, one by default. The maximum instance capacity \(s\) depends on the memory of the node device and will define the maximum number of instances that could be cached on this edge device. In line 2, \(X_{i}\) and \(Y_{i}\) denote the instances accumulated by the current node. Their initial values could be empty and represent that the node has not yet cached any instances. In line 4, the current node receives the \(P_{j}\) and \(\beta_{j}\) transmitted from the neighbouring nodes. Lines 5 through 6 indicate that the end device of the current node fetched a set of pairwise instances \((x_{t},y_{t})\) at time \(t\) and added them to the cache. After this, each node will calculate the corresponding \(E_{i}\) and \(F_{i}\) based on Equations (11) and (12), with \(i\) denoting the number of the current node. If the memory requirements of \(X_{i}\) and \(Y_{i}\) exceed the capacity of node \(i\), \(X_{i}^{s}\) and \(Y_{i}^{s}\) will be employed to replace \(X_{i}\) and \(Y_{i}\) by discarding some of the old data. After that, referring to line 16, the algorithm updates and finally broadcasts the updated results to all neighbouring nodes according to Equation (10), ending the current iteration.  
Figure 2: The workflow of the DOML algorithm.  
```
1:Input: weighted parameters \(\{w_{ij}\in\mathbb{R}|w_{ij}>0\}_{(i,j)\in G}\), maximum instance capacity \(s\);
2:Initialize:\(X_{i}=Y_{i}=\varnothing\);
3:for\(t=1,2,\ldots,\)do
4: Receive \(\left[\begin{array}{c}P_{j}(t)\\ \beta_{j}(t)\end{array}\right]\) pairs for all neighbours \(j\in\mathcal{N}_{i}\)
5: Receive pairwise instances: \((x_{t},y_{t})\)
6: Append \(x_{t}\) to \(X_{i}\) and \(y_{t}\) to \(Y_{i}\)
7:if\(n_{i}<s\)then
8: Calculate \(E_{i}(X_{i})\) by Equation (11)
9: Calculate \(F_{i}(X_{i},Y_{i},P_{j}(t),\beta_{j}(t))\) by Equation (12)
10:else
11:\(X_{i}^{s}\gets X_{i}\) [select \(s\) newest instance from \(X_{i}\) to \(X_{i}^{s}\)]
12:\(Y_{i}^{s}\gets Y_{i}\) [select \(s\) newest instance from \(Y_{i}\) to \(Y_{i}^{s}\)]
13: Calculate \(E_{i}(X_{i}^{s})\) by Equation (11)
14: Calculate \(F_{i}(X_{i}^{s},Y_{i}^{s},P_{j}(t),\beta_{j}(t))\) by Equation (12)
15:endif
16: Update \(\left[\begin{array}{c}P_{i}(t+1)\\ \beta_{j}(t+1)\end{array}\right]\) pair by Equation (10)
17: Broad cast \(\left[\begin{array}{c}P_{i}(t+1)\\ \beta_{i}(t+1)\end{array}\right]\) to all neighbours \(j\in\mathcal{N}_{i}\)
18:endfor
```  
**Algorithm 1** Single node update of the distributed OMLC algorithm
## 1 Introduction
Even so, in some cases these mechanisms may cause the sudden death of critical nodes that lead an entire network to malfunction. This is because critical nodes experiencing heavy traffic load deplete their energy fast and die out, whilst nodes in sparse regions in terms of data traffic continue to enjoy high energy levels. Some solutions have been introduced for these problems but they still suffer from the quick draining of the critical sensor nodes and do not utilize the other sensors that still have near full power capacity inside the network; this shortens the life time of network since it is determined by the energy of the sensor nodes. Thus the motivation of this study was to propose a decentralized energy balancing method that is generic and applicable to most of WSN applications that require reduction of energy consumption and this will therefore extend a network's life time. This is achieved by considering the energy of the sensor nodes as the most critical factor to prevent fast energy depletion. In this method the traffic load are regularly and slowly distributed over all the sensor nodes during routing which will minimize their energy usage averaged over the network; the sensors die almost all at the same time which will in turn optimize the overall network life time. The contributions of the paper are: firstly, in developing a routing algorithm whose main consideration is to balance the energy consumption in order to maximize the sensor network lifetime. Secondly, combining the simplicity of BI with the efficiency of diffusion scheme in routing to introduce a SensorAnt mechanism based on Ant Colony Optimization (ACO), which uses two quality assessment functions for hop and path to select the best next hop and update the routing tables of nodes efficiently. Finally, presenting a new hop function assessment based on multi-criteria metrics such as the minimum residual battery power, hop count and average energy of both route and network, to contribute to the path function choosing the optimal route to the destination. We compared their performance and show that it gives a better performance compared with the Energy Efficient Ant-Based Routing algorithm (EEABR) [8]. The EEABR is a well-known WSN algorithm that essentially uses an ACO-based system. The authors of the EEABR protocol compare it with another two algorithms; the basic ant based routing (BABR) and the improved ant based routing (IABR) and both use the ACO metaheuristic. In this way the EEABR is considered a good benchmark in most of WSN routing protocols based on ACO. Simulation results show that our proposed model reduces energy consumption in WSNs and optimizes the number of packet drops efficiently which extends the lifespan of the network. The rest of this paper organized as follows: Section 2 reviews some of the related work, Section 3 describes in detail our proposed method, while Section 4 presents the performance evaluation of our method. The last section summarizes our work.
# Brazing Ti-48 Al-2 Nb-2 Cr Alloys with Cu-Based Amorphous Alloy Filler
## 2 Materials and Methods  
### Materials  
TiAl alloy with a nominal composition of Ti-48 Al-2 Nb-2 Cr (at. %) was prepared by arc melting with high purity Ti, Al, and Cr metals, and an Nb-Al intermediate alloy. To ensure a compositional homogeneity, the alloy ingots were re-melted at least four times, and the electromagnetic stirring system was introduced to the equipment by adding a current loop under the water-cooled copper plate. Subsequently, the as-cast alloy was treated at 1653 K for 1800 s in Ar atmosphere to eliminate the residual stress and shrinkage porosity. The composition of the amorphous foils is Cu\({}_{41.83}\)Ti\({}_{30.21}\)Zr\({}_{19.76}\)Ni\({}_{8.19}\) (at. %), which were prepared by re-melting the alloy ingot in quartz tubes and ejecting through a nozzle onto a copper wheel rotating at a velocity of 40 m/s in a purified argon atmosphere. The thickness of the foil was about 30 \(\upmu\)m. The solidus and liquidus temperature of the foil were 1103 K and 1143 K, respectively [27]. The amorphous structure of the brazing foil was examined by an X-ray diffraction (XRD, D8, Bruker, Germany) using Cu K\(\alpha\) radiation.
## 4 Results of Experiment 1: Recall/Detection Rate
### Cooperative Decision Performance  
Now we investigate if it is possible to improve the detection capability by making a cooperative decision using the alarms generated by each SIDS. For the sake of simplicity and clarity, we only consider the same configurations in Inspector Log and ModSecurity (i.e., low, med., max.). Please note that Nemesida only offers a default configuration. We investigate three possible cooperative decisions:  
* Union. Alarm is produced if an attack is detected by at least one SIDS.
* Voting majority. Alarm is produced if an attack is detected by at least two SIDS.
* Intersection. Alarm is produced only if an attack is detected by the three SIDS.  
The results are shown in Table 4. An interesting point is that the percentage of known attacks identified by the three systems is low (ranging from 0 to 48% depending on the configuration), which suggests that the detection capabilities of these detectors may be complementary. Indeed, only one attack out of the 60,585 was jointly signaled by the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**Inspectorlog**} & \multicolumn{3}{c}{**ModSecurity**} & \multicolumn{2}{c}{**Nemesida**} \\ \cline{2-7}  & **low** & **med.** & **max.** & **low** & **med.** & **max.** & **Default** \\ \hline attacks-800 & 0.48 & 87.14 & 92.07 & 8.41 & 42.55 & 70.43 & 68.03 \\ \hline attacks-1100 & 0.60 & 89.37 & 93.71 & 5.70 & 51.96 & 75.17 & 72.45 \\ \hline fwaf-bad & 0.99 & 61.12 & 66.16 & 8.63 & 30.59 & 55.80 & 52.37 \\ \hline fwaf-2200 & 2.55 & 86.73 & 91.23 & 6.36 & 63.86 & 89.18 & 96.59 \\ \hline rdb & 0.11 & 88.22 & 91.01 & 10.49 & 39.40 & 66.17 & 64.24 \\ \hline osvdb & 5.87 & 58.43 & 66.96 & 2.64 & 65.67 & 80.37 & 54.72 \\ \hline a-420 & 47.14 & 81.19 & 81.67 & 0.71 & 68.1 & 79.29 & 38.81 \\ \hline AVERAGE & 8.25 & 78.89 & 83.26 & 6.13 & 51.73 & 73.77 & 63.89 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Detection rate or recall (percentage of attacks detected).  
three detectors using the least sensitive configuration. The union of the least sensitive configuration of InspectorLog and ModSecurity (low) increased the individual detection rate from 6-8% (InspectorLog, ModSecurity) or 63% Nemesida to 73%. The union of the most sensitive configurations (max.) led to a stunning recall of 96.7%.  
Then, one could take advantage of running various SIDS simultaneously to increase the detection capability (note that running various SIDS also impacts computational resources). However, it is apparent that either individually or cooperatively, the detection rate has to be put into perspective in light of the false alarm rate generated by the detectors. This is addressed in the next section.
## 4 Public Health Epidemiology of COVID-19  
COVID-19, also known as SARS-CoV-2, belongs to the human coronavirus family of viruses, sharing genome similarities with SARS (Severe Acute Respiratory Syndrome, SARS-CoV) and MERS (Middle East Respiratory Syndrome, MERS-CoV). The transmission of SARS-CoV-2 occurs through droplet transmission (when droplets are >5-10 um in size), contact transmission (physical contact with contaminated surfaces and objects, followed by touching the mouth, nose, or eyes), and aerosol transmission (cloud of droplet particles <5 um in size). Both droplet and aerosol transmissions occur when an infected person coughs or sneezes and an uninfected person inhales the droplets. The virus can remain active on various surfaces for several days, and the contaminated droplets can stay airborne for up to an hour, particularly in indoor spaces [26]. Consequently, the risk of transmission increases in enclosed environments, such as shopping malls, restaurants, and cafes.  
Public health agencies play a crucial role in providing the population with essential pandemic-related information. However, many individuals may struggle to understand and interpret the overwhelming volume of released and sometimes conflicting information. Therefore, the presence of an intelligent, user-centric device capable of interpreting and aligning this information with the user's demographic details becomes vital.  
A fundamental epidemiological concept, known as the reproduction number, or \(R_{0}\), is utilized to assess the contagiousness of COVID-19. \(R_{0}\) represents the average number of people that can be infected by a single sick individual. An \(R_{0}\) value of 1 suggests that one infected person can spread the virus to one other person, resulting in a relatively stable number of infections. An \(R_{0}\) value less than 1 indicates that an infected person, on average, transmits the virus to fewer than one person, leading to a gradual decline in the disease's spread. Conversely, an \(R_{0}\) value greater than 1 signifies that an infected person, on average, transmits the virus to more than one person, resulting in a rapid spread of the disease, depending on the magnitude of \(R_{0}\). Initially, the estimated \(R_{0}\) for COVID-19 ranged from 2.2 to 3.58 [27]. However, its contagiousness had increased, with \(R_{0}\) values estimated to have been around 5.08 (95% CI 3.8-8.9) for the Delta variant, and with a 3-to-6-fold increase to 10-18.6 for the Omicron variant (BA.7 and XBB) (references [28; 29; 30]). In comparison, the flu has an \(R_{0}\) of 1.4-1.6 [31; 32].  
The mPOC system utilizes the \(R_{0}\) value to calculate the Predictive Exposure Index (PEI) and Deterioration Risk Index (DRI), which measure the likelihood of users infecting others based on sensed CPPs and other demographic factors. A higher PEI value indicates a higher likelihood of users exposing others to the virus, while a higher DRI value indicates an increased risk of the individual's health deterioration once exposure occurs.
# Hyperband-Optimized CNN-BiLSTM with Attention Mechanism for Corporate Financial Distress Prediction
## 3 Methodology
### Cnn  
The CNN (convolutional neural network) was proposed by [PERSON] in 1980, who introduced the concept of hierarchical structure and local connectivity, and something that is good at dealing with data with a grid structure [16]. [PERSON] et al. in 1998 described in detail the structure of convolutional neural networks (CNNs) and the application of handwritten digit recognition, which contains convolutional, pooling, and fully connected layers, etc., as well as how to use the backpropagation algorithm for training and prediction [17].  
1. The convolutional layer operation extracts the local features of the input data \(x_{i+m-1,j+n-1}\) by convolution operation, i.e., the convolution kernel sliding dot product operation on the input data. \[h_{ij}=f(\sum_{m=1}^{M}\sum_{n=1}^{N}x_{i+m-1,j+n-1}\cdot w_{m,n}+b),\] (1) where \(h_{ij}\) is the output of the convolutional layer, \(x_{i+m-1,j+n-1}\) is the input data, \(w_{m,n}\) is the weight of the convolutional kernel, \(b\) is the bias of the convolutional kernel, and \(f\) is the \(ReLU\) activation function.
2. Pooling layer operation uses a maximum pooling operation to divide the input data into non-overlapping rectangular regions. It selects the maximum value within each rectangular region as the representative value for that region. \[p_{ij}=max(h_{2,2j},h_{2,2j+1},h_{2i+1,2j},h_{2i+1,2j+1})\] (2) where the above formula is for a maximum pooling window of \(2\times 2\) and a step size of 2, indicating the value of the position in the output region after the pooling operation.
3. Fully connected layer operations, where the multi-dimensional feature maps output from the convolutional or pooling layers are spread into one-dimensional vectors and fed into one or more fully connected layers, and the classification results are outputted using the softmax activation function. \[y_{k}=\sigma(\sum_{i=1}^{N}w_{k,i}\cdot p_{i}+b_{k}),\] (3)where \(w_{k,i}\) is the weight of the fully connected layer, \(p_{i}\) is the output of the last pooling layer, \(b_{k}\) is the bias of the fully connected layer, and \(\sigma\) is the activation function.