esa-sceva/satcom-chunk-collection huggingface dataset:
# An Analysis of ML-Based Outlier Detection from Mobile Phone Trajectories
## 3 Framework for Continuous PoI Detection in Smart Cities
### Proposed Framework Functional Blocks  
Figure 1 provides a functional illustration for the proposed continuous PoI detection framework.  
The presented order of steps is not arbitrary, but some functional blocks may also be placed in a different order. For instance, outliers can be computed before or after the detection of _stop points (SPs)_, i.e., possible Pols in a trajectory. We chose to perform a prior detection of SPs based on _visit time_ (time a user stays stationary in a location), distance travelled, and speed between SPs.  
Once trajectory data is received by the framework, such data is analysed to extract parameters that may assist in a fine-grained detection of Pols, e.g., speed, distance traversed between two readings, and visit time. Additional information may be inferred, e.g., the type of transportation considered by the user may be relevant also to detect similarity in different trajectories and thus assist in the detection of Pols. The next functional block handles the detection of outliers (B), and afterwards, the data without outliers are used to estimate SPs. While outlier detection may also be handled after estimating SPs, in our opinion, outlier detection is the first step to handle. Before the SP inference, the undesirable values (outliers) must be removed so that no erroneous conclusions can be drawn.  
Both the outlier detection and the SP inference require ML application. Therefore, in the context of this paper, we focus on the next sections on the application of ML for outlier detection.  
Still, in the context of SP inference, there is the need to validate next the obtained SPs, by removing SPs that may occur sporadically, for instance.  
For instance, it is feasible to consider the number of times that the detected SPs are visited, and then cross-reference each SP across different individual trajectories. PoIs can then be obtained after validating the number of different trajectories sharing a common SP, and comparing the average visit time against individual visit times of that SP. Then, a final list of PoIs will be created.  
However, this process is continuous, and when repeated for new data sources and respective trajectories, the PoIs already found are automatically removed during the SP detection in order to discover new PoIs.  
The next section provides further detail concerning PoI detection, and Section 3.4 provides more detail concerning outlier detection.
# A Neural Network Approach to a Grayscale Image-Based Multi-File Type Malware Detection System
## 3 Methodology
### Datasets  
Several datasets were utilized in this work to ensure that the developed model possesses good generalization properties. Consequently, we combined multiple datasets comprising malicious and benign files in MS Word documents, PDFs, and Windows PE files. The train-test split ratio that was adopted is 70 to 30. The used dataset in this work is summarized in Table 2. VirusTotal [29] was used to validate and add confidence levels to the obtained results.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Type** & **Year** & **Size (MB)** & **Input Size** & **Depth** & **Parameters** \\ \hline DenseNet [41] & C & 2020 & 44 & 224 \(\times\) 224 & 201 & 20 million \\ \hline EfficientNet [41] & C & 2020 & 20 & 224 \(\times\) 224 & 82 & 5.3 million \\ \hline MobileNet-v2 [42] & C & 2019 & 13 & 224 \(\times\) 224 & 53 & 3.5 million \\ \hline NasNet [43] & R & 2017 & 332 & 331 \(\times\) 331 & - & 88.9 million \\ \hline ShuffleNet [44] & C & 2017 & 5.4 & 224 \(\times\) 224 & 50 & 1.4 million \\ \hline Inception-ResNet [45] & R & 2017 & 209 & 299 \(\times\) 299 & 164 * & 55.9 million \\ \hline Xception [46] & R & 2016 & 85 & 299 \(\times\) 299 & 71 & 22.9 million \\ \hline SqueezeNet [47] & C & 2016 & 5.2 & 227 \(\times\) 227 & 18 & 1.25 million \\ \hline ResNet [48] & R & 2015 & 167 & 224 \(\times\) 224 & 101 * & 25 million \\ \hline GoogleNet [49] & C & 2014 & 27 & 224 \(\times\) 224 & 22 & 4 million \\ \hline VGGNet [50] & R & 2014 & 515 & 224 \(\times\) 224 & 41 * & 138 million \\ \hline AlexNet [51] & R & 2012 & 227 & 227 \(\times\) 227 & 8 & 62.3 million \\ \hline LeNet [52] & R & 1998 & - & 32 \(\times\) 32 & 7 & 60,000 \\ \hline \hline \end{tabular}
* Depth may vary as per the version utilized. Type: R—regular, C—compact.  
\end{table}
Table 1: Summary of the comparison between pre-trained CNN models.
## 3 System Implementation Based on LPA Synchronization Algorithm
### Multi-h CPM Receiver Implementation  
The received signal is sampled and returned to the baseband signal by the digital down conversion (DDC) module. The baseband signal is fed to the MF banks, and the outputs are used for the Viterbi detector. The Viterbi algorithm detects the transmitted sequence and provides information about the surviving path index vector \(M_{n}\) and global winning state index \(G_{n}\). The proposed synchronization algorithm estimates the phase and timing error signals (\(e_{\varphi}(n),e_{\tau}(n)\)) from the PED and TED utilizing the same matched filter. The second-order loop filters are implemented to update the estimated timing and carrier phase. Finally, the synchronized local carrier signal is feedback to DDC. Some of the primary blocks of the receiver are considered, and efficient implementations of these blocks are described. Optimization details are discussed to achieve high throughput, as follows.
# Moving Towards Body-to-Body Sensor Networks for Ubiquitous Applications: A Survey
## 2 From WBANs to BBNs
### Overview of WBAN
#### 2.1.2 Standards  
There are different standards that are used in WBANs in order to permit the communications between the different sensor nodes and between the WBAN coordinator and external devices. The most known and used standard in the industry is IEEE 802.15.6 [41; 42], which is a low-power wireless technology intended for short-range transmissions of WBANs, with around 10 Mbps of data rates. This standard provides capabilities to reduce the signal radiation absorption rate (SAR), caused by body movements. Then, IEEE 802.15.4 (ZigBee) [43] is a well-known standard for WBANs, that deals with the interconnection of low-data rate communication devices, with low-power and long battery life, which could provide low-complexity short-range radio frequency (RF) transmissions. Furthermore, other standards are also used in WBANs, like the Bluetooth Low Energy (BLE) standard [44], that operates on center frequencies that allows it to reduce the overlapping with channels 1, 6 and 11 of the IEEE 802.11 standard, and thus mitigate interference with them.
# A Review of the Optimal Design of Neural Networks Based on FPGA
## 5 Design of the DNN Accelerator and Acceleration Framework Based on FPGA
### FPGA Accelerator for Optimization Strategy
#### 5.3.1 Optimization for Calculation  
In neural networks, the convolution layer and convolution operation are usually the focus of our optimization, especially in convolutional neural networks applied to image processing. The convolutional operation is more intensive, the processing time is longer, and the resources are more occupied, which limits the deployment of convolutional neural networks in edge platforms such as FPGA.  
The usual optimization direction is to increase the parallelism between different layers of the neural network model, the parallelism between different output feature maps, the parallelism between different pixels, and the parallelism between different pixels in order to shorten the processing time and improve the network performance. It can also use circular flow and loop unfolding to build a deeper pipeline to shorten the overall execution time of the network and reduce the time overhead. The matrix cycle is divided into smaller modules by cyclic block technology, and each module can be computed in parallel to improve computing parallelism. Moreover, through data reuse, the number of memory accesses can be reduced, so as to improve the computational efficiency. Reference [159] used data reuse and task mapping technology in order to improve memory bandwidth and throughput, so as to improve design efficiency. In [160], the parallelism between channels was utilized through cyclic unrolling, so as to improve the network running speed and network performance. On the FPGA operating frequency of 110 MHz, the peak performance of 213.7 GOP/s and top-5 accuracy of 79.05% were achieved, and the energy efficiency ratio was at least 4.3 times that of other CPU and GPU platforms.
# A New Individual-Based Model to Simulate Malware Propagation in Wireless Sensor Networks
## 1 Introduction
Also, the verification and validation phases of the model must be taken into account and it may make the process longer. The nature of mathematical epidemiology has been considered as the basis for studies of malware propagation over different types of computer networks, including WSN. Moreover, in the last years, epidemiological problems are being modelled using the ABM paradigm due to the significant advantages of this model, compared with traditional mathematical modelling [17; 18]. Consequently, in this research, the malware propagation in WSN will be analysed, identifying the individual characteristics of the agents involved, as well as the agent-agent and agent-environment interactions. The ABM proposed in this research will use mathematical epidemiology to determine the states of agents in each period of time. In previously proposed models, system characteristics such as the different types of sensor nodes, the malware and, in some recent, cases the topology have been considered as actors [19]. The SEIRS-D agent-based model proposed in this study takes into account other essential characteristics of these actors and new actors that have still not considered. The rest of the paper is organised as follows--Section 2 gives an overview of recent research in the field of mathematical models that have been proposed to model the malware propagation in WSN. In Section 3 the SEIRS-D agent-based model to simulate malware spreading over WSN is presented. The proposed model parameters for simulations are given in Section 4, the simulation results are discussed in Section 5, and finally, in Section 6 the conclusions are showed.
# An Aeromagnetic Compensation Algorithm Based on a Residual Neural Network
## 2 Compensation Model and Method
### Primitive Neural Model  
The neural network structure for aeromagnetic compensation includes an input, hidden, and output layer. The input training samples of the network are \(X_{nxm}\). The nodes of the neural network are connected by \((W,\ b)=(W^{1},\ b^{1},\ W^{2},\ b^{2},\ldots,\ W^{l},\ b^{l}\)), where \(b^{l}\) is the bias term of layer \(l\), and \(W^{l}\) is the weight between layers \(l\) and \(l\ -\ 1\).  
In the process of forward propagation, the features of layer \(l=2\) are:  
\[Z_{i}^{(2)}=f\left(\sum_{j}^{m}W_{ij}^{1}X_{j}+b_{i}^{1}\right), \tag{10}\]where \(i\) is the number of nodes in layer 2, and \(j\) is the number of nodes in the input layer, \(f\)\((\cdot)\) is the activation function, and the features of layer \(\ l+1\)\((l\ >\ 2)\) are:  
\[Z_{i}^{(l+1)}{=}\ f\left(\sum_{j}^{h}W_{ij}^{l}Z_{j}^{(l)}{+}b_{i}^{l}\right), \tag{11}\]  
where \(Z_{j}^{(l)}\) is feature \(j\) of layer \(l\), and \(W_{ij}^{l}\) is the weight between node \(j\) of layer \(l\) and node \(i\) of layer \(\ l+1\).  
To improve the speed of training convergence, in this paper, we select a ReLU activation function and MSE as the loss function, expressed as:  
\[f(a)=max(0,a)=\left\{\begin{array}{cc}&a\ \ a>0\\ &0\ \ a\leq 0\end{array}\right\} \tag{12}\]  
\[\text{Loss}=\frac{1}{n}\sum_{k=1}^{n}\ (y_{k}-\hat{y}_{k})^{2}, \tag{13}\]  
where \(y_{k}\) is the true value, and \(\hat{y}_{k}\) is the predicted value.  
In the process of backpropagation, the gradient descent algorithm is used to calculate the gradient of loss function on each parameter through the derivative chain rule, the global optimal parameters are obtained, and the parameters are updated according to the gradient [17]. Taking two hidden layers as an example, the gradient process of loss function to \(W^{1}\) is:  
\[Z^{1}{=}\ f\ \left(W^{1}X+b^{1}\right)\] \[Z^{2}{=}\ f\ \left(W^{2}Z^{1}{+}b^{2}\right) \tag{14}\] \[\hat{y}{=}\ f\ \left(W^{3}Z^{2}{+}b^{3}\right)\]  
\[\frac{\mathit{aloss}}{\alpha W^{1}}\ =\ \frac{\mathit{aloss}}{\alpha\hat{y}} \ \frac{\alpha\hat{y}}{\alpha Z^{2}}\frac{\alpha Z^{2}}{\alpha Z^{1}} \frac{\alpha Z^{1}}{\alpha W^{1}} \tag{15}\]  
When one of the terms in Equation (15) is less than 1, with an increase in the number of layers, the gradient update information decays exponentially, so the shallow network parameters cannot be updated. Therefore, the neural network exhibits the phenomenon of gradient disappearance, which leads to an insufficient fitting ability of the model, thus affecting the accuracy of aeromagnetic compensation.
# The Use of Chalcogenite as a Biosorption Bed in the Treatment of Groundwater
## 5 Conclusions  
1. The removal of ammonium nitrogen, iron, and manganese in the biofiltration process on chalcogenite beds is an alternative method to conventional processes used in water treatment plants-filtration on quartz beds. Chalcedonite has greater porosity and a more proper surface than quartz sand. It is a better substrate for immobilization and development of microorganisms involved in the biodegradation of pollutants, which allows for simultaneous removal of NH\({}_{4}\)\({}^{+}\), Mn, and Fe according to different types of mechanisms.
2. Among the analyzed systems, the best efficiency of removing ammonium nitrogen, iron, and manganese from groundwater was demonstrated by two-stage biofiltration on chalcedonite beds, with initial chemical activation at the first stage of filtration.
3. Chemical activation of the chalcogenite bed had an effect on the efficiency of removal of manganese (II) ions but did not affect the efficiency of iron removal.
4. In the pre-activated bed system, a longer biofilm formation time (start-up of the bed) was observed, which resulted in a longer waiting period for obtaining the required concentration of ammonium nitrogen in the treated water.
5. Activation of the bed with potassium manganese (VII) reduces the oxygen consumption in the biofiltration process as compared with the non-activated bed. The oxygen deficiency can inhibit the ammonium nitrogen removal process.
6. The sorption properties of chalcedonite and the ability to retain water borne microorganisms that cause the biofilm formed on its surface takes over the sorbent functions and retained ammonium ions processing the construction of new bacterial cells. The creation of biofilms and the creation of manganese oxide coatings results in continuous filter operation. Filling the biofilter does not require regeneration.  
Conceptualization, D.P., A.P., J.K. and A.D; methodology, D.P., A.P. and J.K.; validation, D.P., A.P. and J.K.; formal analysis, D.P. and A.D.; writing--original draft preparation, D.P., A.P. and J.K.; writing--review and editing, A.D. and D.P.; visualization, A.D.; supervision, D.P.; project administration, D.P.; funding acquisition, D.P., A.P., A.D and J.K.  
This research was funded by subsidies for statutory activity (number: DS.BO.17.001).  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Uncertainty Analysis of the Proposed Method  
Since the method is based mostly on numerical simulations, the impact of the simulation parameters on the SAR distribution was assessed. Therefore, the uncertainty budget was evaluated for three numerical processes: (a) SAR distribution calculation, (b) TF calculation and (c) estimation of scaling factors A\({}_{\text{ij}}\). To determine the uncertainty contribution of a single simulation parameter, a sensitivity analysis has been performed using numerical simulations and varying the parameter under consideration.  
In particular, the impacts of the grid resolution, using mesh steps between 0.1 mm and 0.5 mm, of the simulation time and of the absorbing boundary conditions were considered. The resulting tolerance of the deposited power was assessed by comparing the calculated values from the proposed method to the obtained ones from the simulations. Furthermore, the contribution to the uncertainty budget of the different placement of the excitation current source and of the different calculation positions of the electrical current was considered, varying the corresponding distances by 0.5 mm and 1 mm, respectively. The contribution of the volume of the SAR integration to the calculation of the power was also assessed, considering the volume containing the \(-40\) dB iso-contour relative to the maximum SAR value.
# Void Hole Avoidance for Reliable Data Delivery in IoT Enabled Underwater Wireless Sensor Networks
###### Abstract  
Due to the limited availability of battery power of the acoustic node, an efficient utilization is desired. Additionally, the aquatic environment is harsh; therefore, the battery cannot be replaced, which leaves the network prone to sudden failures. Thus, an efficient node battery dissipation is required to prolong the network lifespan and optimize the available resources. In this paper, we propose four schemes: Adaptive transmission range in WDFAD-Depth-Based Routing (DBR) (A-DBR), Cluster-based WDFAD-DBR (C-DBR), Backward transmission-based WDFAD-DBR (B-DBR) and Collision Avoidance-based WDFAD-DBR (CA-DBR) for Internet of Things-enabled Underwater Wireless Sensor Networks (IoT, UWSNs). A-DBR adaptively adjusts its transmission range to avoid the void node for forwarding data packets at the sink, while C-DBR minimizes end-to-end delay along with energy consumption by making small clusters of nodes gather data. In continuous transmission range adjustment, energy consumption increases exponentially; thus, in B-DBR, a fall back recovery mechanism is used to find an alternative route to deliver the data packet at the destination node with minimal energy dissipation; whereas, CA-DBR uses a fall back mechanism along with the selection of the potential node that has the minimum number of neighbors to minimize collision on the acoustic channel. Simulation results show that our schemes outperform the baseline solution in terms of average packet delivery ratio, energy tax, end-to-end delay and accumulated propagation distance.  
Keywords:underwater wireless sensor networks; adaptive transmission range; residual energy; clustering; void hole; collision +
Footnote †: journal: Sensors
## 4 Results and Discussions
### Goodness of Fit of Model (GoF)  
GoF was devised as a comprehensive indicator of the adequacy of model fit for SEM. Nevertheless, the GoF is unable to accurately differentiate between valid and invalid models and its applicability to specific model configurations is restricted. A very good model fit was demonstrated by the calculation, which yielded a GoF index value of 0.628. It can be concluded that both the measurement and structural models were in agreement. The structural model results for this study are shown in Figure 7.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Variables** & **Indicator** & **Standardized Estimates** & **T-Value** & **VIF** \\ \hline \multirow{3}{*}{Road accident} & Accident rate & 0.617 & 5.052 & 1.175 \\  & Severity index & 0.435 & 4.324 & 2.167 \\ \cline{1-1}  & Number of fatalities & 0.699 & 8.402 & 1.937 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The results of the measurement model for formative construct.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Relationship** & **Beta** & **Std Error** & **T-Value** & \(p\)**-Value** & **Decision** \\ \hline Horizontal alignment Road accident & \(-0.316\) & 0.104 & 2.922 & 0.004 & Supported ** \\ \hline Vertical alignment Road accident & \(-0.118\) & 0.090 & 1.311 & 0.190 & Not Supported \\ \hline Intersection Road accident & 0.597 & 0.057 & 10.570 & 0.000 & Supported ** \\ \hline \hline \end{tabular} (**\({}^{**}\)_p_-value \(<0.01\)).  
\end{table}
Table 7: Path coefficients of research hypotheses.
## 4 Discussion  
This study investigated the determinants of bicycle use among the student population in the city of Maribor, whereas no comparable studies on students cycling in Slovenia were performed before. Exploratory research of social and infrastructural factors investigated which determinants influenced students to cycle more or less. The research provided a variety of interesting insights. Most of the results were not expected at all, especially regarding, for instance, the ecological orientation that proved to be statistically insignificant. This is particularly interesting in the light of environmental protests inspired by the young climate activist [PERSON] where the youth are finding their way to communicate their \"green mindset\". Part of the explanation maybe lies in the convenient distance between crucial institutions that allows students to walk. Note that we did not include 'walking' in our questionnaire, however, short distances might affect students' perception that walking is not as unpleasant and time-consuming to reconsider alternative ways of commuting. Additionally, in the past few years, Slovenian students have been encouraged to use the IJPP card (integrated public transport car) that allows combining buses and trains. Therefore, the youth already use two means of transport that are promoting sustainable mobility; however, short distances seem to be managed without a bike. The frequency of cycling was quite low, and therefore motivation for cycling of those who already cycle probably lies somewhere else. We can assume that they are particularly internally (or externally) motivated by some other factors (not having a car, genuine enjoyment, etc.), and thus they use the approach that best suits them 'as they go'. If there are lanes, they will use them, and if there are not, they will find another way to get to the desired location. However, we show that there exists a generally positive attitude towards bikers in Maribor, also at locations where cycling infrastructure is of poor quality or non-existent. On the other hand, people who do not want to cycle will not cycle, no matter the quality of infrastructure, as [PERSON] et al. state [23].  
Although this study contributes to broader research field with valuable data that was unavailable before, it is difficult to draw clear conclusions because there was no explicit statistical result. We cannot conclude that the rather average representation of active cycling among students of the University of Maribor is the result of imperfect infrastructure or lack of cycling culture and other social factors, but it is somehow evident that both social and infrastructural factors deeply intertwine, that is, the fear of bicycle theft is connected to the quality and safety of bicycle parking. The general notion that infrastructure presents a larger impact on a person's decision to cycle was not exposed as more significant as some other social determinants.