esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The IEEE 802.15.4e standard proposes Medium Access Control (MAC) to support collision-free wireless channel access mechanisms for industrial, commercial and healthcare applications. However, unnecessary wastage of energy and bandwidth consumption occur due to inefficient backoff management and collisions. In this paper, a new channel access mechanism is designed for the buffer constraint sensor devices to reduce the packet drop rate, energy consumption and collisions. In order to avoid collision due to the hidden terminal problem, a new frame structure is designed for the data transmission. A new superframe structure is proposed to mitigate the problems due to WiFi and ZigBee interference. A modified superframe structure with a new retransmission opportunity for failure devices is proposed to reduce the collisions and retransmission delay with high reliability. Performance evaluation and validation of our scheme indicate that the packet drop rate, throughput, reliability, energy consumption and average delay of the nodes can be improved significantly.  
superframe; IEEE 802.15.4e; wireless sensor networks; performance analysis +
Footnote †: journal: Sensors
## 6 Lightweight IDS for IoT Devices
### Feature Selection Based Lightweight IDSfor IoT
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Category** & **Dataset** & **Total No. of Features** & \begin{tabular}{c} **No. of Selected** \\ **Features** \\ \end{tabular} & \
\begin{tabular}{c} **Feature Selection Algorithm** \\ \end{tabular} & **Paper ID** \\ \hline \multirow{8}{*}{Filter} & \multirow{8}{*}{UNSW-NB15} & \multirow{8}{*}{49} & \multirow{8}{*}{10} & Principal Component Analysis & [114] \\ \cline{3-5}  & & & 3 & Correlation Coefficient & [116] \\ \cline{3-5}  & & & 13 & Information Gain & [123] \\ \cline{3-5}  & & & 7 & Correlation Coefficient & [128] \\ \cline{2-5}  & \multirow{2}{*}{NSL-KDD} & \multirow{2}{*}{41} & \multirow{2}{*}{28} & Variance Inflation Factor & [115] \\ \cline{3-5}  & N-BaloT 2018 & & 12 & Correlation Coefficient & [117] \\ \cline{3-5}  & \multicolumn{2}{c}{IoTID20} & \multirow{2}{*}{86} & \multirow{2}{*}{28} & Correlation Coefficient & [118] \\ \cline{3-5}  & \multicolumn{2}{c}{ADFA-LD} & \multirow{2}{*}{44} & \multirow{2}{*}{4} & Information Gain & [31] \\ \cline{3-5}  & \multicolumn{2}{c}{AWID2} & \multirow{2}{*}{154} & \multirow{2}{*}{50} & Information Gain & [119] \\ \cline{3-5}  & \multicolumn{2}{c}{Network traffic} & \multirow{2}{*}{35} & \multirow{2}{*}{20} & Information Gain & [32] \\ \cline{3-5}  & \multicolumn{2}{c}{(self-generated dataset)} & \multirow{2}{*}{35} & \multirow{2}{*}{20} & \multirow{2}{*}{Information Gain} & [32] \\ \cline{3-5}  & \multicolumn{2}{c}{TON\_IoT} & \multirow{2}{*}{45} & \multirow{2}{*}{7} & Chi-square & [112] \\ \cline{3-5}  & \multicolumn{2}{c}{KDD-CUP-1999} & \multirow{2}{*}{41} & \multirow{2}{*}{6} & Correlation Coefficient & [129] \\ \hline Wrapper & \multirow{2}{*}{UNSW-NB15} & \multirow{2}{*}{49} & \multirow{2}{*}{19} & Recursive Feature Elimination (RFE) & [111] \\ \hline \multirow{8}{*}{Embedded} & \multirow{2}{*}{
\begin{tabular}{c} TON\_IoT \\ and BOT-IoT \\ \end{tabular} } & \multirow{2}{*}{45} & \multirow{2}{*}{20} & Extra Trees Classifier & [113] \\ \cline{3-5}  & \multicolumn{2}{c}{KDDCUP99} & \multirow{2}{*}{44} & \multirow{2}{*}{10} & Random Forest & [124] \\ \cline{3-5}  & \multicolumn{2}{c}{Self-Generated} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & [126] \\ \cline{3-5}  & \multicolumn{2}{c}{CICIDS2017} & \multirow{2}{*}{40} & \multirow{2}{*}{1} & - & [103] \\ \cline{3-5}  & \multicolumn{2}{c}{AWID2} & \multirow{2}{*}{154} & \multirow{2}{*}{49} & Random Forest & [120] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of Feature Selection Techniques for Lightweight IDS.
# Effect of DEM Used for Terrain Correction on Forest Windthrow Detection Using COSMO SkyMed Data
## 2 Materials and Methods
### Methods
#### 2.2.3 Windthrow Detection Algorithm  
The windthrow detection methodology used was developed starting from the one proposed by [29] to detect forest fires using ERS-1 SAR data. The algorithm takes in input one pre- and one post-event SAR image already pre-processed with the terrain correction. The strength of this method is that it decomposes the images at different scales. The algorithm has been successfully used in previous studies [18], and we refer the reader to [18; 29; 30] for a detailed description of it.  
The algorithm is organised into four steps: (i) creation of the log ratio image: the logarithm of the ratio between the pre-event image and the post-event image in linear scale. The ratio is used instead of the difference due to speckle noise present in these images: the statistical distribution of the data changes and cannot be compared by standard subtraction; (ii) multiscale decomposition/reconstruction: this step is done using a 2D stationary wavelet transform (2D-SWT) using a Daubechies filter of 8. As windthrow in each area have different sizes, they cannot be compared on the same scale, otherwise smaller or larger windthrows would be missed (iii) Otsu thresholding of each multiscale component: given the analysis at different scales, separate thresholds are required in order to improve detection capability; and (iv) final decision using a majority rule for each pixel: a given pixel could be observed more than once at different scales, reporting different results, thus a majority rule helps to assign the best label. Afterwards, an additional spatial filtering of 3 \(\times\) 3 pixels based on a majority rule is applied to the final map.  
In the case of the use of both ascending and descending images, the algorithm could be applied either to the mosaic or to each couple separately, and the windthrow maps merged. In this study we decided to use this second option: any pixel detected as windthrow in the ascending and/or descending map was considered as windthrow in the final map.
## 2 Materials and Methods
### Proposed Modification in LEACH
#### 2.6.1 Dynamic Topology Modeling  
The simulation of the dynamic topology model is signed with the changing node location on the field during operation. This reflects the phenomenon that the micro-sensors exposed to the real environment show movements. As round (_r_) in Equation (10) increase, the position of the node (\(X_{r}\), \(Y_{r}\)),  
\[X_{r}=\ X_{r+1}+\ R_{random}Y_{r}=\ Y_{r+1}+\ R_{random}-m_{random}\leq R_{random }\leq m_{random} \tag{10}\]  
every node updates its location as (\(X_{r+1}\), \(Y_{r+1}\)), while \(R_{random}\) is a random number between \(-m_{random}\) and \(+m_{random}\). In the proposed method, one step is inserted before the network moves to the steady state step. After cluster formation is complete, the cluster topology is improved using the various methods (Q-LEACH: reinforcement learning, F-LEACH: IT2 PFCM). The aim of the improvement is to still maintain network connectivity with minimal energy loss in the network. All sensors set the transmit power to the minimum level but keep the received signal power of CH above the \(E_{th}/l_{o}\) (Threshold) value so that the sensor transmits with an acceptable error rate.  
In order to improve the topology of the cluster, the simulation is set up as follows in Equation (11). \(E_{t}\) is the total energy consumed in the network, \(E_{tx,i}\) is the energy of the sensor \(i\) transmitting  
\[E_{t}\ =\ min(\sum_{i=1}^{N}E_{tx,i}\ +\ \sum_{k=1}^{n}E_{CH,k}) \tag{11}\]  
data to the CH, and \(k\) is the energy of the CH receiving data from the sensor.  
In Equation (12), Signal Interference Noise Rate (SINR) is the received signal-to-noise-noise ratio, where \(P\) is the transmit power of the sensor to CH to transmit synchro data, \(I\) is the interference power, and N is the noise density. As the distance between nodes gets closer, I and P values increase, and, as the distance between nodes increases, \(I\) and \(P\) values decrease. The energy the sensor consumes to send data to the cluster head depends on the distance between the sensor and the CH. If the distance is less than \(d_{o}\), the energy depends on the free space. However, if the distance is greater than d\({}_{0}\), the energy is dependent on multipath fading. In addition to the transmission energy, the sensor consumes energy to process data inside the hardware (\(E_{elc}\)) in Equation (13).  
\[SINR\ =\ \frac{P}{I+N}, \tag{12}\]  
\[E_{tx}=\left\{\begin{array}{ll}LE_{elc}+LE_{EFS}d^{2},&d\ \leq\ d_{0}\\ LE_{elc}+LE_{EM}pd^{4},&d\ >\ d_{0}\end{array},\right. \tag{13}\]  
\[E_{CH}{=}L(E_{RX}{+}E_{elc}{+}E_{DA}){+}LE_{MP}d^{2}. \tag{14}\]  
The CH receives L-bit data from the sensor with the energy \(E_{RX}\), processes the data inside the hardware with the energy \(E_{elc}\), aggregates the data with the energy \(E_{DA}\), and transmits the data to the sink with the energy \(E_{MP}\) in Equation (14).
## 2 Materials and Methods
### Transcriptome Analysis  
The TRIzol Reagent (Life Technologies, Carlsbad, CA, USA) was used for the total RNA extraction of each sample. Using the Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA), checked RNA integrity and concentration. The mRNA was isolated by the NEBNext Poly (A) mRNA Magnetic Isolation Module (NEB, E7490). The Illumina NEBNext Ultra RNA Library Prep Kit (NEB, E7530) and NEBNext Multiplex Oligos (NEB, E7500) were used in the cDNA library construction [41]. Finally, the constructed cDNA libraries were sequenced on a flow cell on the Illumina HiSeq(tm) sequencing platform.  
Low quality and shorter reads were removed by the Perl script. The clean reads that were filtered from the raw reads were mapped to the _Dendrobium_ genome (OGSv3.2) using Tophat2 software. The transcriptome data have been deposited into the NCBI Sequence Read Archive with the identifier PRJNA976822. Then gene expression levels were calculated using FPKM values (fragments per kilobase of exon per million fragments mapped) by the Cufflinks software. DESeq and Q-value were employed and used to evaluate differential expression genes (DEGs) between _D._ 'H1' and _D. moniliforme_. After that, gene abundance differences between those samples were calculated based on the ratio of the FPKM values [42].  
To compute the significance of the differences, only genes with a _p_-value of log2 ratio \(\geq\) 2 and a false discovery rate (FDR) significance score < 0.01 were used for subsequent analysis. With a cut-off E-value of 10\({}^{-5}\), genes were compared against various protein databases by BLASTX, using the National Center for Biotechnology Information (NCBI) non-redundant protein (Nr) database and Swiss-Prot database [43]. Furthermore, the Nr BLAST results were imported into the Blast2 GO program to obtain the gene ontology (GO) term annotation genes [44]. Then, a Perl script was used to plot GO functional classification, and KEGG pathways were assigned to the assembled sequences for the unigenes [45]. The resulting comments were enriched and refined using TopGo (the R package) [46]. Gene sequences were also compared with the Cluster Homologous Group (COG) database to predict and classify function [47].
## 3 Results and Discussion
### Optimization of the Extraction Conditions
[69] obtained above 51 mg GAE/g CS, which is a significantly higher yield in comparison to conventional methods. Figure 4 shows the Pareto charts describing the effects of the variables analyzed in the second stage of the research. In most cases, the SMR at both the linear and quadratic levels was the most significant effect. However, the smallest effect was observed in the interaction between the studied variables. This interaction was not statistically significant for all analyses. Figure 4: Pareto charts showing the effects of the tested extraction factors (ethanol concentration and SMR) and their interaction during the second stage of extraction optimization experimental design on the antioxidant activity measured by the ABTS (**a**) and FRAP (**b**) assays, TPC determination (**c**), BI (browning index) absorbance measurements (**d**), chromatographic analysis of caffeine (**e**) and chlorogenic acid, 3\(-\)CQA (**f**) contents. (L) and (Q) correspond to the effects at linear and quadratic levels, respectively. In order to validate the mathematical models, new ethanol extracts were made under the following conditions: extraction solvent concentration 50%, SMR 45 cm\({}^{3}\)/g, extraction time 30 min, with the use of laboratory shaker with water bath. The values approximated on the basis of models and obtained during validation are presented in Table 6.  
The deviations of the actual values obtained during validation tests and values approximated by the fitted models were all below 5%. This close agreement between experimental results and those predicted by the equations proves the model viability for the rapid prediction of the extraction results in the studied range. Figure 5: Surface response plots and the corresponding contour plots representing the antioxidant activity measured by ABTS (**a**) and FRAP (**b**) assays, the TPC determination (**c**), BI (browning index) absorbance measurements (**d**), chromatographic analysis of caffeine (**e**) and chlorogenic acid, 3\(-\)CQA (**f**) contents, used to assess CS extracts characteristics obtained in the second stage of extraction optimization experimental design. Figure 6: Optimum region by overlay plots of the six analytical RSM responses (ABTS and FRAP assays, TPC determination, BI (browning index) absorbance measurements, chromatographic analysis of caffeine and chlorogenic acid (3\(-\)CQA) as a function of the ethanol concentration and SMR applied in the second stage of extraction optimization experimental design. The variables are shown in their original levels.
## 4 Discussion
### Regional Brain Volume Changes Associated with Blood Dixin Levels  
We showed that high TCDD and TEQ-PCDD levels in the blood were significantly associated with low gray matter volume in the left medial temporal pole and fusiform gyrus. Conversely, high 1,2,3,4,7,8-HxCDD exposure was associated with gray matter volume in the left cerebellum lobule VII and the right middle frontal gyrus. However, no significant correlations between blood dioxin levels and global volumes were found. These results suggest that dioxin exposure in adulthood might affect regional brain structure to alter brain volumes.  
Previously, a follow-up study performed on workers in the Czech Republic 35 years after exposure to high levels of TCDD reported alteration of EEG and visual evoked potentials, and reduction of perfusion in various locations in the brain [21]. However, the effects of dioxin exposure on alterations to regional brain volumes are unknown. To our knowledge, this is the first study reporting morphological brain alterations associated with dioxin exposure. We found that opposing effects were associated with high dioxin levels, observing both volume increases and volume decreases. Previous animal studies suggested that the effects of persistent organic pollutants (POPs), including dioxins, might be non-monotonic: POPs exert both promoting and suppressing effects on neuronal dendritic growth, depending on their dose and the brain region, which may lead to brain volume increases and decreases, respectively [22; 23; 24].  
We found that the gray matter volume in the fusiform gyrus was significantly inversely correlated with blood TCDD level. The fusiform gyrus is a large region in the temporal cortex implicated in high-level visual functions such as face perception, object recognition, and reading [25]. [PERSON] et al. (2016) reported that some brain regions such as the middle frontal gyrus, fusiform gyrus, and cerebellum exhibited functional and structural abnormalities in patients with ASD in comparison with controls, referring to these regions as an autism-specific structural network [26]. The cerebellum is active in cognitive functions (including language) and executive functions [27], which might be disturbed in ASD. Another finding of our study is the association between low gray matter volume in the medial temporal pole and high exposure to TCDD and TEQ-PCDDs. The medial temporal pole may be involved in olfactory processing and connects with the orbitofrontal cortex and other emotion-related areas [28]. These results indicated that dioxin exposure in adulthood, including exposure to TCDD and 1,2,3,4,7,8-HxCDD, induced brain structural alterations in the areas involved in cognitive and emotional functions. Further following-up studies of our subjects with an assessment of cognitive and emotional functions are required to determine if these structural changes induce cognitive and emotional impairments.
## 3 Methods
### XGBoost Model  
The XGBoost model is a representative algorithm of the boosting method in ensemble algorithms. By incorporating a regularization term into the objective function, it significantly enhances the generalization ability of the model and helps prevent overfitting.  
Figure 2: Technical workflow.  
areas compared to others. Notably, the region with higher WUE in the southwest shows a decreasing trend, while the area with lower WUE in the central region exhibits an increasing trend. This phenomenon suggests that the WUE variation trends in different regions are closely related to local climate, vegetation, and ecological changes, potentially influenced by multiple factors.  
Figure 4: Temporal variation in WUE from 2003 to 2020 (**a**), annual average spatial distribution (**b**), and trend of change (**c**).  
Figure 3: Annual average spatial distribution of GPP from 2003 to 2020 (**a**) and temporal variation (**b**); annual average spatial distribution of ET (**c**) and temporal variation (**d**).  
Next, based on the Theil-Sen slope results and combined with the Mann-Kendall significance analysis method, the spatial distribution map of significant changes in WUE in the study area was generated (Figure 5a), and the area proportions of each change type were calculated (Figure 5b). From the spatial distribution map of significant changes, it can be seen that WUE changes in the study area exhibit four types: significant increase, weak increase, weak decrease, and significant decrease. The regions with a significant increase are mainly concentrated in the central area, covering 6.68% of the total area; other WUE increase areas are predominantly characterized by weak increases, which account for 50.80% of the area. The regions with a significant decrease are primarily located in the southwest, covering 4.60% of the area, while the remaining WUE decrease areas show weak decreases, accounting for 37.92% of the area.
## 4 Analysis
### Other Potential Uses of Entropy in Anomaly Detection  
In the literature, there are studies citing the use of entropy in anomaly detection, and these methods might also be used to characterise and fingerprint a particular infrastructure. For example, entropy can be used to characterise the use of IP addresses, and TCP and UDP Port ranges. This may give valuable insights.  
For example, we can use the same technique described in Section 3 to estimate entropy for features such as the following:  
* Packet attributes over time;
* IP Addresses and IP Address Pairs;
* Port ID and Port ID Pairs;
* Timing intervals;
* Packet classification;
* Flow composition changes across time.  
The entropy of a set number of attributes with packets can be tracked to assess changes in entropy over time, as described in [15].  
Address and port number entropy (calculated individually or as flow pairs) may give some insights on whether the allocation process for such values appears to be synthetic (or has bugs in the implementation). Entropy in these identifiers may also be used to draw conclusions about the variety of endpoints and services within a packet trace or live network.  
Timing (such as packet intervals) can also be a strong indicator of synthetic behaviour. For example, in a denial of service (DOS) attack or brute force password attack, regular packet intervals may be an indicator that the attack is scripted. Even where some random ness has been introduced by the adversary, it may be possible to infer a higher predictability than expected (for example, where a weak random number generator has been used).  
Packets may be classified as encrypted on unencrypted using entropy estimates, for example, as described in [41]. This may be problematic if only the first packet payload is used (as in [41]), since early-stage protocol interactions (such as key exchange) may not reflect subsequent higher entropy values.  
As discussed earlier, by measuring entropy deviations across the lifecycle of a flow, by flow direction, we may be able to indicate that a flow has been compromised (for example, during a masquerade attack, or where a particular encryption method has been subverted [6]).  
Finally, we should keep in mind that skilled malware authors may attempt and hinder entropy-based detection by building synthetic randomness into malware, although it seems promising that weighted or conditional entropy could be deployed across several features to identify outliers.
# The Polar Code Construction Method in Free Space Optical Communication
###### Abstract  
Abstract: Abstract: Abstractin order to solve the problem of the high complexity of polarization code construction under free-space optical communication, this paper proposes a segmented turbulent partial order construction method, which effectively reduces the complexity of polarization code construction while obtaining the highest possible quality of communication performance. The method introduces the generalized partial order technique into the atmospheric strong turbulent channel, determines the reliability relationship between each sub-channel in the strong turbulence using Monte Carlo simulation, and combines it with the polarization weight formula to find the optimal parameter values in different signal-to-noise ratio ranges to construct the polarization code. The simulation results showed that the method had a similar communication performance compared with the conventional Monte Carlo construction method for different turbulence intensities, code rates, and code lengths, and its construction complexity was negligible due to its offline operation nature. For different code rate code lengths and BER of 10-4, this method generated about 0.03-0.17 dB loss compared with the Monte Carlo method; for different turbulence strengths and BER of 10-4, only a loss of about 0.05-0.07 dB was generated. It provides a solution for the efficient application of polarization codes under free-space optical communication.  
Keywords:atmospheric turbulence; free space optical communication; polar code construction; segmental turbulence partial order; strong turbulence channel; bit error rate +
Footnote †: journal:  
_Photonics_