esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Machine Learning and Deep Learning Techniques for Anomaly Detection in IoT Data
## 3 Taxonomy of Anomaly Detection Techniques
### Window Models  
In time window modeling, the data are divided into some basic windows, and these basic windows are utilized as update units. There are three types of window models as listed below [63]:  
* Fading (Damped) window model: a weight is allocated to every data point based on a fading concept, and more weights are allocated to latest data compared to old data. The usage of a damped window models reduces the impact of the old data on the mining performance (Figure 7a).
* Landmark window model: the window is defined by a particular time point called a landmark and a current time point. It is utilized for mining throughout the history of data streams (Figure 7b).
* Sliding window model: data can be counted from a certain range in the past to the present. The concept behind the \"sliding window\" is to do a thorough review of both the recent data points together with the summarization of the old data points (Figure 7c).  
Figure 7: Window models.  
A summary of the window models that includes their pros and cons is given in Table 3. The selection of a window model varies based on the requirements of the domain applications [63].
# CircleVis: A Visualization Tool for Circular Labeling Arrangements and Overlap Removal
## 3 Proposed Approach
### Label Arrangement Formulation  
Let \(T\) be a set of labels. Each label \(t\in T\) is surrounded by a rectangle that describes a bounding box with a length \(l\), height \(h\), and position \(p=(x_{t},y_{t})\) around the circle and an orientation angle \(q\). In circle layout (see Figure 2), two main circles are drawn with the same center position \(c\). The first covers the arcs' \(C_{r}\) border, while the second is used to create gaps between arcs and labels \(C_{p}\). Note that \(C_{r}\) and \(C_{v}\) describe the radius of each circle with \(C_{r}<C_{v}\). The mathematical formulation includes two parts: labels \(t\) and circles \(C\):  
\[t=\{p,q,l,h\},C=\{c,C_{r},C_{v}\}\]  
Each label is associated to an arc on the circle. All arrangements have the same angle, which is calculated according to the associated arc. \(q_{arc_{in}}\) is the initial arc angle and \(q_{arc_{j}}\) is the final arc angle. \(q\) of each label is calculated as follow:  
* \(q=q_{arc_{in}}+((q_{arc_{j}}-q_{arc_{in}})/2)\)
* \(p=C_{v}*(cos(q_{i}),sin(q_{i}))\)
* \(l=|x_{f}|-|x_{i}|\)
* \(h=|y_{f}|-|y_{i}|\)
# Revealing the Spatial Pattern of Weather-Related Road Traffic Crashes in Slovenia
## 2 Materials and Methods
### Weather-Related Road Traffic Crash Hotspot Analysis  
After revealing regional trends in weather-related road traffic crashes, we tried to identify significant spatial clusters of these events. In the first step, we performed a cluster and outlier analysis (Anselin Local Morans I) and continued with the hot spot analysis (Gets-Ord Gi*) by applying the contiguity-edges-corners conceptualization of spatial relationships, since we were operating with polygon features and a standardized dependent variable (AR_P). The Anselin Local Morans I statistics resulted in the following attributes for each municipality: Local Moran's I index, z-score, pseudo \(p\)-value, and cluster/outlier type. By adding the result of the Gets-Ord Gi* function, statistically significant spatial clusters of high values (AR_P hot spots) and low values (AR_P cold spots) were identified. The tool created an output feature class in the ArcGIS environment with a z-score, \(p\)-value, and confidence level bin field (Gi_Bin) for each municipality.
# Video Quality Modelling--Comparison of the Classical and Machine Learning Techniques
## 3 Materials and Methods
### Using a Machine Learning Model
Predictions for new samples are made by averaging (for regression) or majority voting (for classification) the results from individual trees. \[\hat{y}=\frac{1}{M}\sum_{m=1}^{M}h_{m}(x),\] (10) where: \(M\)--number of trees in the forest, \(h_{m}(x)\)--prediction from the m-th tree for sample x. 8. Gradient Boosting Machines (GBMs) iteratively build a sequence of models, each of which corrects the errors of the previous models. Each new model minimises the loss function by adding it to the previous models. \[F_{m}(x)=F_{m-1}(x)+\gamma_{m}h_{m}(x),\] (11) where: \(F_{m}(x)\)--prediction at iteration m, \(\gamma_{m}\)--scaling factor, \(h_{m}(x)\)--new model learning on the residuals (errors) of the previous model. 9. kNN is an algorithm that predicts the outcome based on the k nearest neighbours in the feature space. The prediction for the kNN regression is the average target value of the neighbours. \[\hat{y}=\frac{1}{k}\sum_{i=1}^{k}y_{i}, \tag{12}\]  
where:  
\(y_{i}\)--target value of the \(i\)-th nearest neighbour.
# Dynamic Allocation of SDN Controllers in NFV-Based MEC for the Internet of Vehicles
## 2 Related Work
### Mobile Networks
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Reference** & **Objective** & **Modeling** & **Strategy** & **Scenario** & **Evaluation** \\ \hline [PERSON] et al. [36] & Minimize flow setup time & Single Source Unsplittable & Integer Linear Program & WAN & Simulation \\  & and communication overhead & Flow Problem & & & \\ \hline [PERSON] et al. [37] & Minimize the total cost & Stable matching problems & - & DC & Simulation \\  & caused by response time and & with transfers & & & \\  & maintenance on the cluster of & & & & \\  & controllers & & & & \\ \hline [PERSON] et al. [38] & Minimize the average flow & - & Integer Nonlinear & WAN & Simulation \\  & setup latency & Programming & & & \\ \hline [PERSON] et al. [99] & Minimize flow setup time & - & - & Traditional Network & Numerical analysis \\ \hline [PERSON] et al. [40] & Maximize control resource utilization & Switch Migration Problem & Markov Chain & Traditional Network & Prototype \\ \hline [PERSON] et al. [41] & Maximise security & - & - & Traditional Network & Simulation \\ [PERSON] et al. [42] & - & - & Linear Programming & DC & Simulation \\ \hline [PERSON] et al. [43] & Maximize the service rate for virtual machine placement & Bin packing problem & - & DC & Simulation \\ \hline [PERSON] et al. [45] & Minimize control resource consumption and control traffic overhead & Resource optimization & Graph Theory & Traditional Network & Simulation \\ \hline [PERSON] et al. [46] & - & Multi Knapsack Problem & Markov Chain & Software-Defined Mobile & Simulation \\ \hline [PERSON] et al. [47] & Minimize the overall & VNF placement and resource allocation problem & Mixed Integer Program & MEC Network & Simulation \\ \hline [PERSON] et al. [48] & - & - & - & 5G & Testbed \\ \hline [PERSON] et al. [49] & - & - & - & 5G-MEC & Testbed \\ \hline [PERSON] et al. [50] & Maximizing bandwidth efficiency and enhancing system scalability & Coding-Assisted Broadcast & Memetic Algorithm & SDN Vehicular Networks & Simulation \\ \hline [PERSON] et al. [51] & - & Cooperative Temporal Data & Classical Knapsack Problem & SDN Heterogeneous & Simulation \\  & Dissemination Problem & & Vehicular Networks & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Some of the Dynamic SDN controller assignment efforts in the literature. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Objective** & **Modeling** & **Strategy** & **Scenario** & **Evaluation** \\ \hline Proposed solution & Minimize wasted resources considering bad balancing between controllers, management complexity, and scalability & Bin Packing Problem & Linear Programming & IoV & Simulation \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._
## 5 Practical Dependability Assessment  
In this section it is presented the dependability assessment of an industrial application in order to show the applicability of the proposed methodology. Actually, for this task, the methodology parameters could be empirically set. However, in order to facilitate the usage of the methodology in real scenarios, some approaches are enhanced in relation of previous works, and employed to determine most of these parameters.  
There are many possibilities to determine each parameter. Of course, each approach will be as accurate as the considered detailing level. In fact, we are not focused on strictly determining the parameters, but providing possible solutions when defining them. With a realistic parameters setting, we can more confidently evaluate the impact of visual coverage failures on the system dependability.  
The proposed scenario used as case study is a merging of specifications of two real industrial shop floors into a single realistic model. This industrial scenario is similar to a paper mill proposed in Reference [8] (Iggesund Paperboard), which contains machinery such as boilers, mixing and storage tanks, conveyors and storage racks. We assume that the factory occupy an area \(\mathbb{A}\) of 250 m \(\times\) 200 m, where the monitored area \(MA\) is 240 m \(\times\) 180 m. Over this \(MA\) are deployed 11 visual sensor nodes and one gateway node, according to the industrial wireless network presented in Reference [9], implementing a network topology according to the WirelessHART standard (one of the most adopted wireless communication protocol in industrial applications [30]). The visual nodes are oriented in a way to monitor regions of production more susceptible to failures, accidents and intrusion. Figure 9 shows the shop floor layout and Figure 10 presents the network topology. The percentage on the edges of the graph are the packet reception rates (PRR), which are link quality metrics that will be used to determine the link failure rates.  
Next we show how to obtain the dependability methodology parameters from the component specifications. The interested reader is referred to Reference [10] for more details on the parameters meaning.  
Figure 9: Shop floor layout.
## 3 Results
From the results it can be seen that with the increase of the pump power, the performance firstly improves due to the enhanced OSNR as shown in Figure 2. The optimum was found at around 550 mW in all cases. Figure 5 shows the constellations of DP-16 QAM with the bidirectional pumping scheme for different pump powers. It is noted that in the constellations with a high pump power, nonlinear distortions are not clearly observed. The signal suffers from the induced amplitude and phase noise as shown in Figure 3, indicating that the induced noise dominates the performance despite the high OSNR values at the higher pump power. Consequently, unrepeetered coherent transmission should be optimized by balancing the signal OSNR and the Raman pump-induced impairments. By comparing the backward and bidirectional pumping schemes, we could observe a clear performance improvement around the optimum pump power, enabling the bidirectional pumping scheme to achieve a Q factor above the 8.5 dB HD-FEC limit after 200 km SSMF unrepeetered transmission. Moreover, it was observed that the BPS slightly outperformed DDPLL and QPSKP in mitigating the Raman-induced impairments, thanks to the accurate phase tracking capability at the high OSNR [27]. One should note that due to the experimental constraints, the power ratio between the forward and backward pump was fixed during this work; however, it is worth further investigation as an important aspect to draw a more generic conclusion. Additionally, a longer fiber span, higher modulation orders, as well as higher-order pumping schemes are to be investigated in our further studies. Figure 4: Q factor versus pump power for both (**a**) backward and (**b**) bidirectional pumping schemes with DDPLL, QPSKP and BPS algorithms, respectively, for launched signal powers of 5 and 2 dBm. Figure 5: Constellations of DP-16 QAM versus pump power for bidirectional pumping scheme for launched signal power of 5 dBm.
### Innovative and Sustainable Extraction Methods
#### 2.2.3 Enzyme-Assisted Extraction (EAE)  
The search for more and more environmentally friendly extraction methods has been recently focused on alternative methods such enzyme-assisted extraction (EAE). Based on the principle that enzymes are able to catalyze reactions with high precision, specificity, selectivity, and conversion, EAE has the potential to improve solvent extraction toward a greener path. One of the principles of EAE is that enzymes can break organic membranes and cell walls, facilitating the diffusion of the compounds of interest into the medium. A positive feature of EAE is the possibility to work with mild extraction conditions [61; 62]. Enzymes are usually added to aqueous mixtures of byproduct at appropriate buffer conditions and temperature. After extraction, enzymes can be inactivated by heating and removed by centrifugation [61; 62].  
Studies in the literature have shown that EAE methods are able to achieve considerably high-yield extracts, especially for anthocyanin [63; 64], carotenoids [65], lipids [66], proteins [67], and a wide variety of phenolic compounds [68]. A multistep process for the recovery of the lipophilic fractions of blackcurrant pomace after extraction with supercritical CO2 showed some very positive results on the isolation of substances with higher polarity with pressurized liquid and EAE extractions. PLE and EAE yielded an additional 12.3-39.9% of extracts [69].  
Recently, it was also reported that EAE helped to reduce the extraction time and enhance the quality of the extracts while reducing the amount of solvent necessary. The recovery of essential fatty acids, tocopherols, phytosterols, ellagitannins, and polyphenolics from raspberry pomace industrial waste using EAE is an exemplification of the sustainable potential that this technique can offer. In the work reported by [PERSON] et al., the efficacies of the enzymes carbohydrates and proteases were evaluated, allowing the recovery of polyphenols and, consequently, antioxidant activity to reach 48% and 25%, respectively, higher than what was observed when the extractions were made with traditional extraction methods using solvents such as methanol, acetone, and water mixtures [70].
# Tightly-Coupled GNSS/Vision Using a Sky-Pointing Camera for Vehicle Navigation in Urban Areas
## 3 Vehicle Motion Estimation Using an Upward-Facing Camera
### Feature Detection, Description and Matching  
Feature extraction and matching constitute essential processes for the platform (robot, vehicle) motion estimation. In this work, we use ORB (Oriented FAST and Rotated BRIEF) [48] as the feature detector and descriptor algorithm. ORB combines an improved version of the Feature from the Accelerated Segment Test (FAST) algorithm [49] for feature detection, with the Binary Robust Independent Elementary Features (BRIEF) [50] descriptor extraction process. Its main advantages are its low run-time and its ability to detect a relatively large number of features.
## 2 Method  
### Model Formulation  
We consider a two-link transportation network connecting a single Origin-Destination (O-D) pair and assume a total travel demand of 1 unit. Hence, the flow pattern is uniquely determined by the value of a single variable, and in the following analyses we denote by \(F\) the flow on link 1 and by (\(1-F\)) the flow on link 2. The day-to-day evolution of the network state is modeled by means of a discrete-time deterministic process whose generic period is denoted by \(t\). Adopting a general approach which allows consideration of the effects of both memory and inertia upon the dynamics of travelers' route choice behavior [2], the network dynamics can be described by the following recursive expressions:  
\[C_{1}^{t}=\beta K_{1}\Big{(}F^{t-1}\Big{)}+(1-\beta)C_{1}^{t-1} \tag{1}\]  
\[C_{2}^{t}=\beta K_{2}\Big{(}1-F^{t-1}\Big{)}+(1-\beta)C_{2}^{t-1} \tag{2}\]  
\[F^{t}=\alpha S\big{(}C_{1}^{t}-C_{2}^{t}\big{)}+(1-\alpha)F^{t-1} \tag{3}\]  
where:  
\(C_{i}^{t}\) (\(i=1,2\)) is the mean perceived cost of route \(i\) at the start of day \(t\);  
\(K_{i}\) (\(i=1,2\)) is the average flow-dependent cost actually experienced by users on route \(i\);  
\(S\) is the probability that a user who reconsiders their previous day's choice will choose link 1 on day \(t\), expressed as a function of the difference between day \(t\)'s perceived costs;  
\(\beta\) (\(0<\beta\leq 1\)) is a parameter quantifying the extent to which the most recent travel experience (\"yesterday's trip\") contributes to the formation of the cost perceived by users at the start of day \(t\);  
\(\alpha\) (\(0<\alpha\leq 1\)) is a parameter representing the fraction of travelers who actually reconsider their previous day's choice. Expressions (1) and (2) provide a simplified but effective representation of the learning process by which network users develop their cost forecasts on the basis of past travel experiences. More specifically, \(\beta\) can be interpreted as a memory depth parameter: in the limit, when \(\beta=1\) travelers are \"memoryless\" as they base their next choice solely on the most recently experienced costs. Note that \(\beta=0\) is excluded from the feasible range of the parameter because it would \"freeze\" the system's dynamics by keeping mean perceived costs constant over time. On the other hand, expression (3) allows the incorporation of inertia and habitual behavior into the route choice process, as (\(1-\alpha\)) represents the fraction of users who repeat their previous day's choice regardless of the cost actually experienced. In the limit, when \(\alpha=1\) inertia and habit effects are completely absent, while the value \(\alpha=0\) is excluded from the feasible range of the parameter because it would preempt the system's dynamics by keeping flows constant over time. Note that the unit of measurement of the travel cost variables appearing in Equations (1)-(3) and in all subsequent expressions is not specified because it does not affect the interpretation of the results nor the ensuing conclusions.
## 4 Post-Sustained Loading Bond Behaviour of NSM CFRP-Concrete Specimens
### Effect on the Stiffness  
The PSL load-slip load is illustrated in Figure 10 (black curve) together with the load-slip curves obtained in the instantaneous characterization (blue curve) for specimens PSL-225-10-30 from each series (S1, S2, S3-B and S4). Note that in Figure 10a, the specimen experienced a premature failure in the CFRP strip (marked with an X in Figure 10). A reduction in the initial stiffness was observed in all specimens, but there was an overall recovery as the load in the instantaneous test surpassed the value of the sustained loading. This effect is attributed to the unloading-loading path as a combination of damage and creep effects. During the sustained loading tests, the activated length was lower than the total bonded length (\(L_{b}\)) and, consequently, the effects of sustained loading affected only part of \(L_{b}\). Subsequently, as the load was increased in the monotonic PSL test, as the load increased, the activated length increased and moved towards the free end and joint stiffness was recovered because this zone had not been activated during the sustained loading test.  
When the observed values of all the specimens are considered, the global overall reduction of the stiffness was about 14% (see Table 3). This reduction was more noticeable for specimens loaded under 30% of the initial ultimate load, and, in particular, those from series S2, thus showing that the higher the sustained load level and average temperature are, the greater the decrease in stiffness is. These observations agree with the results presented in [PERSON] et al. [7], where a significant decrease in the stiffness of the bonded joint was observed in specimens under cyclic environmental conditions.  
Figure 9: (**a**) S3-B-PSL-225-10-30 failure in the A-C interface; (**b**) S3-B-PSL-225-7.5-30 cohesive failure in the concrete.
## 3 Methodology
### Generating Forecasting Models  
Long short-term memory (LSTM) is used to predict two future outputs (i.e., network normal and attack events). LSTM is a type of recurrent neural network (RNN) architecture that is frequently used in time-series analysis [36]. It addresses the vanishing gradient problem in RNN by providing longer-lived short-term memory to preserve information across timesteps [37]. It includes four components: a memory cell, an input gate, an output gate, and a forget gate. The memory cell serves as an information store, while the gates regulate the flow of that information [38]. We used a traditional LSTM model with four components to forecast network normal and attack events (see Figure 1). It has three layers to extract temporal features from the data. The model was built with two hidden layers, dense and dropout layers, and used the rectified linear unit (ReLU) activation function to forecast network attacks. The mean squared error (MSE) is used for loss function in model training and validation. Adaptive moment estimation (ADAM) is also used as a model optimizer to compute adaptive learning rates. The root mean square error (RMSE) is computed to evaluate forecasting performances.
## 4 Conclusions  
This paper conducted uniaxial compression tests on basalt rock and granite while also collecting AE data throughout the entire loading process. Based on AE analysis, the distribution patterns of \(b\)-value, \(\gamma\)-value, and \(\beta_{l}\)-value during the rock-loading process were investigated, and their relationships were explored. The main conclusions obtained are as follows:  
1. AE \(b\)-value, \(\gamma\)-value, and \(\beta_{l}\)-values monitored during the uniaxial compression process of the rock can effectively reflect the evolution of rock damage. These three values can be used to assess whether the rock is approaching catastrophic failure.
2. There is a negative correlation between the \(b\)-value and the \(\gamma\)-value, as well as between the \(b\)-value and the \(\beta_{l}\)-value.
3. The \(b\)-value, \(\gamma\)-value, and \(\beta_{l}\)-value, as precursory indicators of rock instability and catastrophic collapse, provide a theoretical and experimental foundation for the prevention and control of dynamic hazards in deep mining.  
G.J.: Writing, Software, Formal Analysis, Investigation, Writing--Original Draft. P.M.M.: Visualization, Investigation. G.L.: Investigation, Data Curation, Writing--Review and Editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available on request from the corresponding author.  
The authors would like to express sincere gratitude to the China Scholarship Council (CSC) for providing financial support for this study. The sponsorship guaranteed with basic research funds provided by Politecnico di Torino, Italy, for its financial aid in this work is also acknowledged.  
The authors declare no conflicts of interest.