esa-sceva/satcom-chunk-collection huggingface dataset:
# LoRaWAN Network for Fire Monitoring in Rural Environments
## 3 LoRa Technology Overview
### LoRa Modulation  
Modulation is the way in which information (digital or analog) is encoded in the carrier signal that will be responsible for transmitting it. LoRa modulation is based on CSS (Chirp Spread Spectrum) which is a spread spectrum technique that uses linear frequency modulation chirp pulses with high bandwidth to encode the information. A chirp pulse is nothing more than a sinusoidal signal in which the frequency increases (up-chirp) or decreases (down-chirp) over time. A chirp determines a symbol.  
The chirps change frequency for a certain amount of time, called symbol time (\(T_{s}\)). These \"jumps\" of frequency determine how the information is encoded. The number of bits that can be encoded in each symbol is given by the spreading factor (\(SF\)), so a symbol can have \(2^{SF}\) values to jump to. These values are called chirps (e.g., if \(SF\) it is 7, the number of bits that the symbol can encode is also 7, and it has \(2^{7}\) chirps). LoRa admits a range of integer values of \(SF\) between 7 and 12 [22].  
The symbol rate \(R_{s}\) is defined, as indicated in [23], as:  
\[R_{s}(symbol/s)=\frac{BW}{2^{SF}} \tag{1}\]  
Since the chirp rate is constant for a given bandwidth (\(R_{c}=BW\)), (1) can be rewritten as (2):  
\[R_{s}=\frac{R_{c}}{2^{SF}} \tag{2}\]  
Bit rate (\(R_{b}\)) is defined as:  
\[R_{b}(bps)=\frac{SF\cdot BW}{2^{SF}}\frac{4}{4+CR} \tag{3}\]  
where \(CR\) (Coding Rate) is the proportion of non-redundant bits for Forward Error Correction (\(FEC\)). LoRa modulation allows different values for this:  
\[CR=\frac{4}{4+n} \tag{4}\]  
where \(n=1\), \(2\), \(3\) and \(4\), so the allowed values are 4/5, 4/6, 4/7 and 4/8.  
The duration (in seconds) can be calculated as \(T_{c}(s)=\frac{1}{BW}\), since, as we previously stated, \(R_{c}=BW\). The duration of the symbols is then defined by:  
\[T_{s}(s)=\frac{2^{SF}}{BW} \tag{5}\]  
As equations show, the bigger the SF value, the lower the bit rate and, the greater the symbol time. Therefore, it will increase the message transmission time or Time on Air (_ToA_), and the power consumption in the device transmitter. On the other hand, the coverage range will be larger at a higher SF value due to the resulting greater robustness against noise [24].  
Regarding the use of different channels, signals received with different SFs are generally considered purely orthogonal. However, it is not true under certain power level conditions. The end node parameters (SF and transmit power) can be adjusted based on the distance from the gateway and, it offers the possibility to run networks with multiple gateways [25; 26; 27].  
Finally, another characteristic of the LoRa modulation is its immunity against the Doppler Effect. The displacement caused by the Doppler Effect causes a small frequency shift to the modulated signal that hardly affects the baseband signal in the time domain.
# A Review of Machine Learning and Deep Learning Techniques for Anomaly Detection in IoT Data
###### Abstract
To have good quality anomaly detection, algorithms must have the ability to handle the following challenges before they can be used effectively:  
* Ability to handle fast data--the anomaly detection algorithm must be able to handle and process data in real-time when data points from the data source come constantly, as data streams could be huge and should be handled in one pass. * Ability to process data in given memory--the massive amount of data should not influence the data stream's processing capabilities. Thus, the data stream algorithm should not require unlimited memory for the unlimited data points arriving in the system, and it should be able to process the data within the available memory. Figure 1: Outliers in the data stream. * Ability to handle dimensionality--high-dimensional data have the additional problem of selecting the proper feature vectors or dimensions that could create better clusters. High-dimensional data also require additional calculation and additional processing. Thus, the algorithm should consider such factors in which the clusters' quality will not be affected. * Ability to handle evolving data streams--data sources are numerous and moving over time; they grow continuously. Therefore, data produced would change, and the algorithm outcomes can change considerably. The activities of streams are known to evolve over time and need a special method to handle them. Existing research has mostly analyzed anomalies based on machine learning techniques primarily focused only on batch processing. In contrast, this paper focuses on machine learning techniques for anomaly detection in data streams, more specifically on evolving data streams. The major contributions of this research are as follows:  
1. Examination of state-of-the-art studies centered on machine learning and deep learning techniques for anomaly detection in data streams;
2. Taxonomy that defines current literature based on the nature of the data, anomaly types, detection learning modes, window model, dataset, and evaluation criteria;
3. Analysis of the existing techniques based on the proposed taxonomy;
4. Highlighting challenges that form the future research direction. The rest of the paper is organized as follows: Section 2 provides a brief background of the study, which includes the definition of the main terms in the review paper. Section 3 presents a taxonomy of anomaly detection techniques for IoT data stream that includes the machine learning and deep learning techniques used, nature of data, anomaly types, detection learning mode, window models, datasets, and the evaluation criteria. Section 4 discusses the research challenges and the potential future directions. Section 5 presents the research results. Finally, the conclusion is given in Section 6.
# Comparison of ML/DL Approaches for Detecting DDoS Attacks in SDN
## 3 Method
### ML/DL Classifiers
#### 3.3.3 Decision Tree (DT)  
The decision tree algorithm is a tree structure that follows a set of rules to determine a class or value, where the root node represents the strongest predictor, and subsequent predictors are reached through stem nodes. The algorithm ultimately arrives at leaf nodes, which typically represent a decision or classification [26]. Despite their usefulness, decision trees have several drawbacks, including instability resulting from even small changes in the data, which can lead to a significant change in the structure of the tree. Additionally, when compared to other algorithms, decision tree calculations can occasionally become significantly more complicated.
# Simulation of Wind Speeds with Spatio-Temporal Correlation
## 2 Methodology
### Modeling
#### 2.1.4 Approximation Weibull to Normal (0,1)  
Values belonging to sequences that fit a Weibull distribution can be converted into values of sequences that follow a standardized Normal one, i.e., a Normal distribution with mean 0 and standard deviation 1, denoted as Normal (0,1). This is the next step, and the reason for pursuing it is that, as is explained in Section 2.2.2, the use of Normal (0,1) distributions makes it possible the induction of given correlations to the random sequences generated directly and keeping all the features of the distributions [18]. In other words, in statistics, it is a well-known fact that it is possible to get every number of normally distributed sequences satisfying a given correlation matrix. Normal (0,1) data can be obtained from Weibull ones by matching both cumulative distribution functions (CDF), as can be seen in Equation (5), where Weibull parameters obtained for each place must be dealt with:  
\[v_{N}=\sqrt{2}erfinv\left(1-2e^{\left(-v_{N}/c\right)^{k}}\right) \tag{5}\]  
where \(v_{N}\) is the wind speed belonging to a Normal (0,1) distributed sequence, \(v_{W}\) is the wind speed belonging to a Weibull distributed one, and \(erfinv(\ldots)\) is the inverse of the error function, where the error function is given by Equation (6):  
\[erf(x)=\frac{2}{\sqrt{\pi}}\int_{0}^{x}e^{-t^{2}}dt \tag{6}\]  
\(t\) is the time, and \(x\) is a symbolic number.  
Both directions of this conversion, from Weibull to Normal (0,1) and from Normal (0,1) to Weibull, are shown in Figure 2.
## 3 Results
### Ndvi  
#### 3.3.1 The Temporal Variation Characteristics  
The interannual variation of the NDVI, as depicted in Figure 7, demonstrates that the annual average NDVI for each study area oscillated within a specific range and exhibited consistent temporal trends. The average annual NDVI inside and outside MdfNNR and ZINNR ranged from 0.436-0.712, 0.439-0.690, 0.221-0.452, and 0.183-0.370, respectively. The annual average NDVI has been higher inside ZINNR than outside over a period of nineteen years. The difference in NDVI between the inside and outside of MdfNNR is relatively small, but, overall, the inside has slightly higher NDVI values. The annual average NDVI of the MdfNNR was higher than that of the ZINNR, primarily due to the predominance of forest land inside the MdfNNR. More specifically, the annual average NDVI of the MdfNNR experienced a steady decline from 2000 to 2003, although the rate of decline diminished each year. In 2010, the MdfNNR's annual average NDVI peaked. From 2018 to 2020, the annual average NDVI of the MdfNNR consistently increased, with a more rapid increase observed inside than outside MdfNNR. The annual average NDVI of the ZINNR consistently increased from 2003 to 2007, with a more rapid increase observed inside than outside ZINNR. In 2007, the ZINNR's annual average NDVI reached its peak. Overall, both NNRs exhibited an upward trend in their annual average NDVI.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Inside and Outside** & **Year** & **PD\({}_{\text{L}}\)** & **LPI\({}_{\text{L}}\)** & **MPS\({}_{\text{L}}\)** & **ED\({}_{\text{L}}\)** & **LSI\({}_{\text{L}}\)** & **CONTAG\({}_{\text{L}}\)** & **SHDI\({}_{\text{L}}\)** & **SHEI\({}_{\text{L}}\)** \\ \hline \multirow{4}{*}{Inside} & 2000 & 0.4228 & 83.5368 & 236.5390 & 9.6841 & 5.1603 & 85.3014 & 0.4011 & 0.2492 \\  & 2010 & 0.4284 & 50.9712 & 233.4431 & 7.8288 & 5.0759 & 73.0289 & 0.9025 & 0.5037 \\  & 2020 & 0.2006 & 40.4178 & 498.3910 & 7.1997 & 4.7459 & 73.2023 & 0.9002 & 0.5024 \\  & 2000 & 0.7387 & 67.9482 & 135.3763 & 16.8821 & 9.7542 & 75.9286 & 0.6538 & 0.4062 \\ \multirow{4}{*}{Outside} & 2010 & 0.3589 & 29.6175 & 278.6023 & 8.3511 & 6.5954 & 74.6234 & 0.8411 & 0.4694 \\  & 2020 & 0.3210 & 36.3360 & 311.5052 & 8.3445 & 6.5939 & 74.4851 & 0.8460 & 0.4722 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Landscape index of landscape metrics MdfNNR in 2000, 2010, and 2020.  
Figure 5: Changes in landscape pattern in of ZINNR (the line in each graph is the border of different functional zone, the same below). **(a–h)**: 2000 year; **(i–p)**: 2010 year; **(q–x)**: 2020 year.  
Figure 6: Changes in landscape pattern in MdfNNR. (**a**-**h**): 2000 year; (**i**-**p**): 2010 year; (**q**-**x**): 2020 year.
## 2 Preliminary Knowledge: The Inverter Model and Voltage Source Inverter (VSI) Controller
### Necessity of VSI Controller Parameter Tuning  
According to the model above, the output control module of VSIs in the primary control level contains many control loops, which are composed of multiple PI controllers with parallel and serial forms [30]. It is clear that the different parameters have different response performances to the PI controller (as Figure 3 shows). Figure 4 shows the step-response curves of the cascade control loop form, which includes two PI controllers. The response curves have shown great differences when one of the parameters changes, and the remaining three parameters remain unchanged. Comparing Figures 3 and 4, it can be seen that the response curves of the PI control loop in the series have obvious overshooting and oscillation. Furthermore, the rising time, overshoot, and oscillation appear largely different when the proportionality coefficient parameters change, as shown in Figure 4. This situation also applies to changes in the remaining three parameters.  
Figure 3: The step–response curves of a single PI controller with different proportional coefficients.  
The inverter-output control module is composed of an outer voltage control loop and inner current control loop [9]. The power sharing control stage mainly divides into the master-slave control strategy and the peer-to-peer control strategy [30]. The master-slave control strategy, such as the voltage-frequency (V-f) control method and active-reactive power (P-Q) control method, is usually used in a centralized micro-grid control system [15]. The V-f control method has a single closed voltage control loop with DC voltage compensation and two PI controllers [30]. The typical P-Q control structure is contained in a cascade control, with an inner current control loop and outer voltage control loop. There are four PI controllers in the cascade control structure, which are parallel in the first part and cascaded in the second. The peer-to-peer control strategy, such as the droop control method, is usually adopted in a decentralized control system structure [31,32]. The droop control method contains more than four PI controllers, which are distributed in the voltage control loop and frequency control loop [33].  
The PI controller has been adopted in all of the above control methods. It is easy to see that the micro-grid hierarchical control system is actually a complex control network with a large numbers of PI controllers, if the control loop has been introduced into the control system and topology of the control network. Parameter tuning of the PI controllers will have a huge impact on the output response performance. Therefore, it is necessary to study the control parameter tuning method for VSI control networks.
# Natural Compounds in the Battle against Microorganisms--Linalool
## 3 Antimicrobial Activity
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Strain** & **Activity** & **Reference** \\ \hline Gram-Positive Bacteria & & \\ _Microoccus flavus_ ATCC 9341 & 4 \(\upmu\)g/mL & [61] \\ _Basillus subtilis_ ATCC 10707 & 4 \(\upmu\)g/mL & [61] \\ _Staphylococcus epidermidis_ ATCC 12228 & 4 \(\upmu\)g/mL & [61] \\ _Staphylococcus aureus_ ATCC 25923 & 5 \(\upmu\)g/mL & [61] \\ _Staphylococcus aureus_ NCTC 10788 & 13 mm & [63] \\ _Listeria monocytogenes_ & 5 \(\upmu\)g/mL & [61] \\ _Listeria innocua_ NRRLB-33314 & 1\% & [55] \\ \multicolumn{3}{c}{Gram-Negative Bacteria} \\ _Salmonella enterica_ subsp. _enterica_ serovar Enteritidis ATCC 13076 & 5 \(\upmu\)g/mL & [61] \\ _Salmonella enterica_ subsp. _enterica_ serovar Typhimurium ATCC 13311 & 5 \(\upmu\)g/mL & [61] \\ _Salmonella enterica_ subsp. _enterica_ serovar Typhimurium CCM 5445 & 0.7\% & [55] \\ _Vibrio fluvialis_ L-15318 & 32 mM & [57] \\ _Salmonella enterica_ subsp. _enterica_ serovar Typhi ATCC 14901 & 7.5 mM & [57] \\ _E. coli_ ATCC 25922 & 212 \(\upmu\)M & [57] \\ _E. coli_ NCTC 12923 & 21 mm & [63] \\ _E. coli_ ATCC O157:H7 & 6 \(\upmu\)g/mL (0.6\%) & [55; 61] \\ _Pseudomonas aeruginosa_ NCTC 12924 & 8 mm & [63] \\ _Pseudomonas aeruginosa_ ATCC 27853 & 7 \(\upmu\)g/mL & [61] \\ _Pseudomonas aeruginosa_ ATCC 9027 & 431 \(\upmu\)g/mL & [56] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Antimicrobial activity of linalool against bacteria. Linalool as a component of many essential oils (EO) is used not only in disentology but also in medicine. It has been shown that its addition to EO, i.e., _Thymus vulgaris_ (thyme), _Juniperus communis_ (juniper), _Pelargonium gravevelens_ (geranium), _Citrus bergamia_ (bergamot), _Citrus paradise_ (grapefruit), _Lavandula angustifolia_ (lavender), _Cinnamomum xylanicum_ (cinnamon), _Melaleuca alternifolia_ (tea tree) and _Syzgium aromatum_ (clove) increase antimicrobial activity against both Gram-positive bacteria (_S. aureus_, _L. monocytogenes_) and fungi (_Candida albicans_, _Aspergillus brasiliensis_), and Gram-negative bacteria (_E. coli_, _P. aeruginosa_, _S. Typhimurium
# Video Description Model Based on Temporal-Spatial and Channel Multi-Attention Mechanisms
## 3 Multi-Attention Video Description Model
### Attention Visualization  
This paper attempts to adopt a top-down analysis on the model with the foundation of existing video description models and proposes significance analysis based on the visual video description. In detail, in an existing video description model, when we get a video and the corresponding description, we want to use the model directly to establish the correspondence between the input object and the words in the sentence, then output the saliency map of the video. The basic \"encoding-decoding\" model is used in this paper. As shown in Figure 3, the front h\({}_{1}\) to h\({}_{m}\) is the encoding network, which is the encoding network of the attention model proposed in the paper. Its main principle is to measure the significance level by figuring out the loss of information when using local visual inputs to approximate the entire input sequence.  
Figure 3: Saliency analysis flowchart based on the video description model.  
Suppose that the probability distribution of words \(p(y_{t})\) is predicted by LSTM at each moment in the prediction process. We assume that the probability distribution is a \"true\" distribution. To measure how much information the \(i\)-th local description feature of the video brings to the word at time \(t\), we only use the \(i\)-th local description feature input encoder and calculate the probability distribution \(q_{i}(y_{t})\) at time \(t\) after encoding and decoding. Then, we calculate its KL divergence as a measure of information loss:  
\[p(y_{t})=P(y_{t}|y_{1:t-1},v_{1:m}) \tag{22}\]  
\[q_{i}(y_{t})=P(y_{t}|y_{1:t-1},v_{i}) \tag{23}\]  
\[Loss(t,i)=D_{KL}(p(y_{t})\big{\|}q_{i}(y_{t})) \tag{24}\]  
In the input sentence \(S\), because what we input is the true distribution of words, the probability distribution of words should satisfy \"one-hot\" distribution. The above equation can be simplified as follows:  
\[Loss(t,i,w)=\sum_{k:w}p(y_{t}=k)\log\frac{p(y_{t}=k)}{q_{i}(y_{t}=k)}=\log\frac {1}{q_{i}(y_{t}=w)} \tag{25}\]  
Therefore, we get the information loss of the predicted word \(w\) in the \(i\)-th local feature description of the video at time \(t\), and this value represents the connection between the visual features and the words. Hence, we can use it as a saliency value:  
\[e_{it}=scale(-Loss(t,i,w)) \tag{26}\]  
The range of the value is 0-1. When the value is higher, the saliency is stronger.
## 3 Results
### Terpenoid Backbone Biosynthesis Pathway
The two genes in comparisons of DK with FB-A (_Her010901_ and _Her001132_) were found to encode 4-hydroxybenzoateQUFpprenyltransferase (ubiA)/4-hydroxybenzoateQUFpprenyltransferase (COQ2) and 3-demethoxyyubiquinol 3-hydroxylase (ubiF)/3-demethoxyyubiquinol 3-hydroxylase (COQ7); the three genes in comparisons of DK with FB-B (_Her010901_; _Her009959_; and _Her001132_) were found to encode ubiA/COQ2 and ubiF/COQ7, and the three genes in comparisons of DK with FB-C (_Her006816_; _Her010901_; and _Her009959_) were found to encode tyrosine aminotransferase (TAT)/aromatic amino acid aminotransferase I/2-aminoadipate transaminase (ARO8), and ubiF/COQ7, ubiA/COQ2 and TAT/ARO8, enzymes involved in the synthesis of ubiquinone and vitamin E. COQ2, a polymeric membrane protein of the UbiA terpenoid cyclase superfamily, was found to be essential for the biosynthesis of ubiquinone (COXQ) in Saccharomyces cerevisiae, as this enzyme catalyzed the pentenylation of p-hydroxybenzoate and all-trans polypentenyls; making terpenoid cyclase (COXQ2) was crucial for the biosynthesis of ubiquinone (COXQ) [39]. Genes encoding COQ2 and TAT were found to participate in the regulation of terpenoids and to be core genes in terpenoid biosynthesis pathways [40], suggesting that the COQ2 and TAT genes were related to the synthesis of terpenoids. Comparisons of MK-1 and MK-2 showed that FDPS, GGPPS, and iSPE genes were significantly up-regulated in MK-1. Comparisons of DK with FB-A, FB-B, and FB-C showed that the FDPS, GGPPS, iSPE, COQ2, COQ7, and TAT genes were usually up-regulated in DK. These findings indicated that terpenoids may be more abundant in MK-1 than in MK-2 and may be more abundant in DK than in FB-A, FB-B, and FB-C.
## 3 Theoretical Background and Problem Formulation
### STPA as Component of a Decision Making Tool  
In our approach, we consider as unsafe control actions (UCAs) the actions that lead to a hazardous situation, and have been defined during the STPA analysis, while we consider as safe control actions (CAs) the nominal actions that occur when the system operation is in normal and safe conditions. We incorporate UCAs and resulting hazardous system states into the operational phase by holistically interpreting all the possible states to which the system can transit with respect to the executed control actions--either safe or unsafe ones. Thus, we handle the safety control structure as a self-organized decision-making problem upon which the system can select automatically the next best control action that will accommodate the transition from an unsafe to a safe state. This extends the usability of the STPA analysis since it turns it from a tool utilized during the system design phase into an active tool suitable for the operational phase, that can help controllers to infer selected actions to prompt the system into balanced safety levels. This allows us to establish an efficient method in reflecting dynamics features of system behavior in real-time.  
Towards this direction, we seek to transform the STPA control structure into a policy-driven action selection mechanism able to bring the system into a safety equilibrium. When we cope with a decision, there are several different actions that we could select. Choosing the best action requires reasoning about more than just the immediate effects of this action. The immediate effects are often easy to see, but the long term effects are less obvious [29]. In safety control structure, we are interested in selecting an action that will lead the system from an unsafe to a safe state. However, the future impact of an action currently appears as a wise selection could lead to hazard. Therefore, a trade-off between the immediate rewards and the future gains is required to yield the best possible solution.