esa-sceva/satcom-chunk-collection huggingface dataset:
# Reducing the False Negative Rate in Deep Learning Based Network Intrusion Detection Systems
## 5 Experiments
### Experiments on the NSL-KDD Dataset
#### 5.1.4 NSL-KDD Binary--Class Weights  
The fourth binary classification experiment included the use of class weights. When dealing with an imbalanced dataset, assigning weights to different classes can help the model make more accurate predictions. For our research, we consider the false negatives  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Predicted** & \\ \cline{2-3}  & & **Normal** & **Attack** \\ \hline \multirow{2}{*}{\(\text{NSL}\)} & Normal & 15,371 & 52 \\  & Attack & 32 & 14,249 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Confusion matrix for NSL-KDD ”modified distribution” binary classification experiment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Predicted**} \\ \cline{3-4}  & & **Normal** & **Attack** \\ \hline \multirow{2}{*}{\(\text{NSL}\)} & Normal & 15,185 & 215 \\  & Attack & 147 & 14,157 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Confusion matrix for NSL-KDD ”reduced features” binary classification experiment.  
more dangerous than false positives. Hence, we needed a way to make the model penalize the false negatives by assigning different class weights. We assigned a weight of 1 for the normal class (which has the label 0) and 2 for the attack class (which has the label 1). Aside from assigning weights to the classes, this experiment uses the same hyperparameters as the first and the second. The input dimension is equal to the second experiment since we considered the same 41 features of the feature selection phase. We used early Stopping in this experiment as well. The confusion matrix for this experiment can be seen in Table 9. From the confusion matrix it can be seen that the number of false negatives is 115, which is slightly lower than in the previous experiment, but still higher than in the \"NSL-KDD binary--modified distribution\" experiment. The number of false positives is higher than in the previous experiment.
# Review of Fast Computation Methods for Finite-State Predictive Control of Multi-Phase Drives
## 2 MPC for Current Control of MPS
### Predictive Model  
In standard FSMPC, the control action is defined by the state of the VSI, denoted as \(u\). In multi-vector approaches, the control action consists of a sequence of different VSI configurations applied during a sample period. For example, in the case of Virtual Voltage Vector (VVV) techniques, each control action corresponds to a specific VVV. For the following discussion, it may be simpler to focus on the single-vector approach.  
FSMPC employs a predictive model to relate predicted stator currents to potential control actions. This model is typically represented by a set of discrete-time state-space equations, as \(\hat{i}(k+1)=(C(\omega)+G)i(k)+BV(k)\), where \(i\) contains the \(\alpha\), \(\beta\), \(x\), and \(y\) stator currents, \(\hat{i}\) is the prediction of \(i\), and \(\omega\) is the angular speed. Matrices \(C\) and \(B\) are  
Figure 2: Voltage vectors for a five-phase VSI.  
obtained from first principles applying time discretization with sampling time \(T_{s}\). This gives \(C=(I+A_{c}T_{s})\) and \(B=T_{s}B_{c}\), where  
\[A_{c}=\begin{pmatrix}a_{2}&-a_{4}&0&0\\ a_{4}&a_{2}&0&0\\ 0&0&a_{3}&0\\ 0&0&0&a_{3}\end{pmatrix},B_{c}=\begin{pmatrix}c_{2}&0&0&0\\ 0&c_{2}&0&0\\ 0&0&c_{3}&0\\ 0&0&0&c_{3}\end{pmatrix} \tag{3}\]  
In the previous expressions, the coefficients used are \(a_{2}=-R_{s}c_{2}\), \(a_{3}=-R_{s}c_{3}\), \(a_{4}=-L_{M}c_{4}a_{r}\), which depend on machine parameters such as \(c_{1}=L_{s}L_{r}-L_{M}^{2}\), \(c_{2}=L_{r}/c_{1}\), \(c_{3}=1/L_{ls}\), \(c_{4}=L_{M}/c_{1}\). Furthermore, term \(G\) represents the effect of the rotor currents, which are typically unmeasured. Various methods for estimating these currents have been proposed to improve predictions [20; 21].  
Using the above expressions, the two-step-ahead prediction of the stator currents can be expressed as  
\[\hat{i}(k+2)=A(\omega)\hat{i}(k+1)+BV(k+1) \tag{4}\]  
where \(A=C+G\) is introduced for clarity. The predicted control error is then given by  
\[\hat{e}(k+2)=I_{s}^{*}(k+2)-\big{(}A(\omega)\hat{i}(k+1)+BV(k+1)\big{)} \tag{5}\]
# Recent Studies on the Healing Properties of Eicosapentaenoic Acid
## 2 Skin and Its Healing Mechanisms
### Sources of Eicosapentaenoic Acid, Structure, and Physico-Chemical Properties  
The polyunsaturated fatty acid (PUFA) class of omega-3 fatty acids includes eicosapentaenoic acid (EPA) (Figure 1). Humans can consume EPA through their diets by eating fatty fish, different kinds of edible algae, or by supplementation with fish oil or algal oil. Cod and glacier oil are the fish oils with the greatest concentrations of EPA (Table 1). Alpha linolenic acid (ALA), which is absorbed by the body, is partially converted into EPA. Humans require a sufficient amount of ALA because it is a necessary fatty acid. However, the efficiency of converting ALA to EPA is significantly less than the absorption of EPA from food intake [11].  
The membranes of cells contain EPA. EPA forms eicosanoids during an inflammatory reaction by being metabolized by the cyclooxygenase and lipoxygenase enzymes. Because EPA is used to metabolize docosahexaenoic acid (DHA) as its precursor, which requires additional metabolic energy to produce, it is important to ensure that there is an adequate amount of EPA in the diet. Eicosanoids produced from EPA are generally strong inducing agents of vasoconstriction, inflammation, and blood coagulation [12].  
Thromboxane (TX)A\({}_{3}\) derived from EPA is weakly proaggregatory when compared with the arachidonic acid (AA)-derived TX\({}_{2}\). Also, the influence of pro-inflammatory PGE\({}_{2}\) and the leukotriene (LT)B\({}_{4}\), which is a chemoattractant and activator of neutrophils, is affected by the presence of EPA. Although PGE\({}_{3}\) has a similar action as PGE\({}_{2}\), it is produced with low efficiency. LTB\({}_{5}\) has little inflammatory activity when compared with LTB\({}_{4}\).  
Of particular interest are findings on the highly inducible COX-2 which is involved in the overproduction of prostaglandandins in inflammatory sites. EPA greatly decreased the interleukin-(IL-)1-induced COX-2 expression in endothelial cells. Although it is well documented that \(\omega\)-3 fatty acids suppress cytokine production such as tumor necrosis  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Foods** & **EPA [g/100 g]** \\ \hline Salmon & 13.20 \\ Sardine & 10.14 \\ Cod liver & 9.90 \\ Herring & 6.27 \\ Cooked beef & 0.15 \\ \hline \hline \end{tabular}
\end{table}
Table 1: EPA SOURCES. Foods and the grams of EPA they contain per 100 g [PERSON] and [PERSON] 2018 [11].  
Figure 1: Two-dimensional and three-dimensional molecular structure of EPA [12].  
factor-(TNF-)\(\alpha\) and IL-1, the underlying mechanisms still remain unclear. It appears that interfering with the synthesis of TXA\({}_{2}\) and PGE\({}_{2}\) also affects cytokine production (Figure 2) [13].
## 6 Demonstration
### Visualization of RGN Prediction Accuracy  
The CASP7 data are divided into FM and TBM. There are 61 chains in the test set (provided by RGN), TBM accounts for about 80%, and FM accounts for about 20%. According to the Table 2, the predicted similarity of RGN under TBM data is much better than that of FM. In the TBM data, about 10% of the protein chains have a _dRMSD_ lower than 3 A, 35% of the protein chains have a _TM-Score_ higher than 0.5, and 35% of the protein chains have a _GDT_TS_ higher than 50. In the TBM data, the average value of _dRMSD_ is 5.32 A, the average value of _TM-Score_ is 0.4, and the average value of _GDT_TS_ is 40. However, for no protein chain in FM class data, _dRMSD_ is lower than 3 A, _TM-Score_ is higher than 0.5 and _GDT_TS_ is higher than 50. In the TBM data, the average value of _dRMSD_ is 9.8 A, the average value of _TM-Score_ is 0.15, and the average value of _GDT_TS_ is 18. It can be seen that for the TBM prediction method, about 30% of protein chains have _GDT_TS_ above 60 (Figure 12b), _TM-Score_ above 0.6, and _dRMSD_ below 4A. For FM, which is relatively difficult to predict, we can see that the _GDT_TS_ prediction results of the RGN are basically below 40, the _TM-Score_ is basically below 0.3, and the _dRMSD_ is mostly above 10A, and the overall similarity is relatively low (Figure 12e).  
Because some actual structures in these protein chains have missed coordinates, resulting in lower _GDT_TS_ and larger _dRMSD_. For example, we can see from Figure 13 that for the protein chains T0308 and T0315. From the _TM-Score_ standard, T0315 is similar to T0308. However, due to the abnormal coordinates of the ends of the T0315 chain (Figure 13d), resulting in its _dRMSD_ value of 8.098 A, which is judged to be dissimilar. The _GDT_TS_ can weaken the impact of this lack of coordinates and the final result is 71.98.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Percentage of Similarity Standards/Average**} \\
**Data Prediction Direction** & _dRMSD_ **(<3 Å)** & _TM-Score_ **(>0.5)** & _GDT_TS_ **(>50)** \\ \hline TBM & 10\%/5.32 Å & 35\%/0.4 & 35\%/40 \\ FM & 0\%/9.8 Å & 0\%/0.15 & 0\%/18 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Prediction accuracy of RGN.  
Figure 12: The prediction accuracy of the RGN. The charts (**a–c**) represent the prediction results of the RGN on the TBM data, and (**d–f**) represent the prediction results of the FM data. Pie charts (**a,d**) show the distribution of _dRMSD_, and pie charts (**c,f**) show the distribution of _TM-Score_. The mixed line and bar chart (**b,e**) show the comparison of _GDT_TS_ and the other two evaluation standards.
# Node Depth Adjustment Based Target Tracking in UWSNs Using Improved Harmony Search
## 5 Improved Harmony Search for Node Depth Adjustment  
As mentioned before, this paper adopts the harmony search algorithm (HS) and modifies it to improve searching speed. HS is a meta-heuristic algorithm that mimics the improvisation process of music players. In recent years, HS has been successfully used in a wide variety of optimization problems. Compared with traditional optimization algorithms, HS requires fewer mathematical computations and does not require setting initial values of decision variables. In addition, a new vector is generated after considering all existing vectors, which is different from genetic algorithms (GAs). These features increase the flexibility of HS and lead to better performance. This section presents how to modify the HS algorithm to improve tracking accuracy with adjusting node depth to the optimal depth.  
The HS algorithm begins with a population of vectors, which is the Harmony Memory (HM). Each of these vectors stands for a combination of all activated sensors' moving distance. In the initial step, these vectors are randomly generated within the moving range. Then, the evolving process of HS will be started with the improvisation process of the new solution vector. At each iteration, a new vector can be generated in two ways. The new solution vector may be selected from HM solutions or generated randomly as initialization to diversify HM. Then, we will use pitch adjustment to locally adjust the new solution vector. The new vector will be compared with the worst vector in HM, the better one will be left in HM, and another will be removed. After reaching the maximum iteration, the best vector will be selected as a final solution. In addition, to improve searching speed, we adjust the probability of a new harmony to be generated as increasing as the distance to the target decreases. The reason is that some vectors far from targets may be not that useful, and it will waste many resources and much time to adjust these vectors. In addition, the pitch adjustment rate and bandwidth dynamically changes during iteration to improve performance. Furthermore, the objective function is one of the important factors to all optimization problems, and a solution with the best function value is selected as the final result. As shown before, the determinant of FIM in Equation (14) is used as an objective function to evaluate solution vectors. All details of the improved HS based depth adjustment method are presented in the following five steps.
# Variance-Triggered Two-Step GPS Acquisition
## 3 Variance-Triggered Two-Step Acquisition
### The Coarse Search Step
```
Data:\(S\),\(N\),\(f_{c}\),\(\delta_{\text{CS}}\),\(N_{b}^{\text{CS}}\),\(\delta_{\text{FS}}\),\(N_{b}^{\text{FS}}\),\(\lambda_{\text{CS}}\) Result: [Acquired, Not Acquired]
1\(R_{C}=[\text{zeros}]\)\(P=\frac{N}{1023}\)\(higherLowerPeaks[0]=1\)for\(d=0\)to\(P-1\)do
2 Reset ListOfPeaks, LowestPeak
3 for\(n=d\)to\(N-1\)step\(P\)do
4 for\(\hat{f}_{\text{index}}=0\)to\(N_{b}^{\text{CS}}-1\)do
5\(\hat{f}=f_{c}+\delta_{\text{CS}}\left(\hat{f}_{\text{index}}-\frac{N_{b}^{ \text{CS}}}{2}\right)\)for\(\hat{\phi}=0\)to\(1022\)do
6\(R_{\text{C}}[\hat{f}_{\text{index}}][\hat{\phi}]=R_{\text{C}}[\hat{f}_{\text{ index}}][\hat{\phi}]+\left(C_{a}\left(\text{mod}\left(\frac{n}{P}+\hat{\phi},1023 \right)\right)S(n)\cos\left(2\pi\frac{\hat{f}}{f_{s}}n\right)\right)^{2}+\left( C_{a}\left(\text{mod}\left(\frac{n}{P}+\hat{\phi},1023\right)\right)S(n)\sin \left(2\pi\frac{\hat{f}}{f_{s}}n\right)\right)^{2}\)if\(R_{\text{C}}[\hat{f}_{\text{index}}][\hat{\phi}]>ListOfPeaks(Shostest Peak)then Insert([\(\hat{f}\),\(\hat{\phi}\),\(R_{\text{C}}[\hat{f}_{\text{index}}][\hat{\phi}]\), ListOfPeaks) ifSizeOf(ListOfPeaks)>MaxNumPeaksthen Delete(\(LowestPeak\), \(ListOfPeaks\))\(LowestPeak=\)GetLowest(\(ListOfPeaks\))



eturnNotAcquired
```  
**Algorithm 1**Coarse Search  
The pseudo code of the CS procedure is listed in Algorithm 1. After 1023 interleaved samples are accounted in the second loop, an attempt is made to detect a peak. When a peak is detected, the FS step is triggered. If the satellite is not acquired in the fine search, the next 1023 consecutive interleaved samples are then used to accumulate more samples to the correlation values before the next peak detection. This process is repeated until the satellite is acquired; or until all samples per chip are used, with the given satellite not acquired. Due to the incremental use of samples, this two-step approach does not have a fixed computational complexity. Each signal requires a different number of operations depending on the quality of the signal. Naturally, low C/N0 signals need more processing.
# Historic Built Environment Assessment and Management by Deep Learning Techniques: A Scoping Review
## 3 Results
### Post-Disaster Damage at District Scale
#### 3.3.1 Binary Classification  
According to the binary classification method, [PERSON] et al. [53] presented a method for the early detection of the HBE damage caused by disasters, specifically earthquakes,  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Reference**} & **DL Algorithm** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Post-Disaster** \\  & **(Architecture)** & **mPA [\%]** & **mAP [\%]** & **[\%]** & **[\%]** & **Damage Levels** \\ \hline \multicolumn{5}{l}{BINARY CLASSIFICATION} \\ \hline
[53] & Logistic Regression & & 81/94 & 87/92 & 84/93 & damage/no damage \\  & (DenseNet121) & & & & \\ \hline \multicolumn{5}{l}{MULTI-CLASS CLASSIFICATION} \\ \hline
[54] & VAE + GMM (ResNet50) & 78.20–94.30 & & 76.80–94.20 & damage/no damage/other \\ \hline
[55] & CNN (VGG-OR) & 74 & & & & \\  & & & & & \\ \hline
[56] & CNN (ResNet50) & 73/87/90/96 & 69/79/90/98 & 71/83/90/97 & no damage/major \\ \hline \multicolumn{5}{l}{MULTI-LABEL CLASSIFICATION} \\ \hline
[57] & CNN (ResNet50) & \
\begin{tabular}{l} MSE = 1.1282–1.3893 * \\ MAE = 0.9266–1.1235 * \\ \end{tabular} & & & &
\begin{tabular}{l} no damage/affected/ \\ minor damage/major \\ damage/destroyed \\ \end{tabular} \\ \hline
[58] & CNN with \
\begin{tabular}{l} incremental learning \\ \end{tabular} & 99.55 & 99.53 &
\begin{tabular}{l} no damage/minor \\ damage/major damage \\ \end{tabular} \\ \hline \hline \end{tabular}  
* MSE = Mean Square Error; MAE = Mean Absolute Error.  
\end{table}
Table 4: Review of image-based DL models applied for post-disaster damage assessment at district scale, with accuracy values obtained.  
using data from social media, a timely and large-scale source. Images depicting historic built sites affected by post-disaster damage were collected and annotated according to two classes: Damage/No Damage. Several convolutional neural networks (VGG16, ResNet50, DenseNet121, InceptionResNetV2, Xception, and NASNetLarge), in combination with a variety of classification algorithms (Logistic Regression, Support Vector Machines, Random Forests, and AdaBoost), were tested and compared. For this specific problem, the model trained using the logistic regression algorithm on DenseNet121 featured performed the best, achieving classification accuracy values of 81% for the \"Damage\" class and 94% for the \"No damage\" class. This disparity is likely related to the imbalanced distribution of damage and no-damage images in the dataset.
## 4 Simulation Results
### Towards Realistic Experimental Scenarios  
To conclude this study, we perform a realistic simulated scenario using a fleet of drones and a set of fixed anchors to evaluate the efficiency of solving the localization problem by multilateration of anchors and drones based on distance measurements. In this scenario (Figure 8), we consider 10 drones starting from different locations on the map. They are each equipped with an inertial measurement unit allowing us to obtain an odometry subject to significant errors, and an ultra-wideband sensor that can serve either as a track or as an anchor. The map is made up of 200 anchors of unknown positions scattered randomly across the map. Each drone executes a local trajectory and will therefore have the possibility of being within the range of certain anchors. The goal is to solve the twofold problem of locating as many anchors as possible, then to use the anchors located, to improve the odometric localization of the drones which is only given by inertial measurements. Figure 9 shows the results of the estimated positions of the anchors detected by each drone, superimposed on the real positions (ground truth) on the initial map. One can observe a very small number of outliers compared to the number of well-located anchors. Some anchors have been located by different drones with small differences in their estimates. Interesting future work will be to merge and filter these different estimates to further improve the localization accuracy. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{5}{c}{**Localization Results Based on 9 Relative Measurements**} \\ \hline
**Method** & **Convergence Rate** & **Localization Error (m)** & **Number of Iterations** & **Computation Time (ms)** \\ \hline Fixed-step GD/Equation (3) (w/o errors) & **100.0\%** & 0.005 \(\pm\) 0.003 & 28.0 \(\pm\) 15.4 & **0.066 \(\pm\)** 0.036 \\ \hline Variable-step GD/Equation (3) (w/o errors) & 95.5\% & 0.005 \(\pm\) 0.003 & 249 \(\pm\) 218 & 3.3 \(\pm\) 2.8 \\ \hline Fixed-step GD/Equation (4) (w/o errors) & **100.0\%** & **0.0000 \(\pm\) 0.0000 & 1801 \(\pm\) 1296 & 7.7 \(\pm\) 5.8 \\ \hline Variable-step GD/Equation (4) (w/o errors) & 91.5\% & **0.0000 \(\pm\) 0.0000 & 779 \(\pm\) 1263 & 3.5\(\pm\) 5.6 \\ \hline Fixed-step GD/Equation (5) (w/errors) & **97.2\%** & **0.108 \(\pm\) 0.035** & 27.7 \(\pm\) 15.3 & **0.095 \(\pm\) 0.054** \\ \hline Variable-step GD/Equation (5) (w/errors) & 93.4\% & **0.108 \(\pm\) 0.035 & 247 \(\pm\) 212 & 1.6 \(\pm\) 1.4 \\ \hline Fixed-step GD/Equation (6) (w/errors) & 92.0\% & 0.113 \(\pm\) 0.041 & 1715 \(\pm\) 1237 & 5.9 \(\pm\) 5.1 \\ \hline Variable-step GD/Equation (6) (w/errors) & 84.6\% & 0.113 \(\pm\) 0.041 & 692 \(\pm\) 1112 & 6.0 \(\pm\) 9.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparative localization results using the different formulations of the estimation problem based on 9 relative measurements, with and without taking into account uncertainty models.
## 3 Results
### Genus Level and Metagenomics Analysis of Indigenous Bacterial Communities in OMW and OMP Samples  
The Bar Chart (Figure 5a) presents the bacterial genera within the OMP and OMW. Results showed a remarkable difference in the bacterial genera distribution and diversification within the tested samples. The metagenomics analysis of OMP display _Acetobacter_ as the most abundant genus (49.7%), followed by _Glucobacter_ (17.36%), _Gortzia_ (13.77%) and _Nardonalla_ (5.38%) genera. However, the OMW sample was massively dominated by _Prevotella_ (82.8%) succeeded by _Spiroplasma_ (6.75%), _Olsenella_ (2.73%) and _Lactobacillus_ (2.16%) genera (Figure 5a). The heat map in Figure 5b contains the bacterial genus level of OMW with both _Prevotella_ and _Spiroplasma_ being the most abundant genera and in OMP with _Acetobacter_ as the most abundant genus. Furthermore, the bacterial genera within the OMW were far more abundant than those existing in OMP. Six bacterial genera named _Acetobacter_, _Glucobacter_, _Fusobacterium_, _Catenibacterium_, _Lactobacillus_ and _Prevotella_ existed in common between OMW and OMP samples (Figure 5b).  
Figure 4: Heat map resulting from ICP-OES analysis and describing heavy metal composition of 11 samples (A1, A2, A3, A4, A5, C1, C2, C3, C5, C6 and C7) collected from different spots situated within and surrounding OMW evaporation ponds of Agareb and Cherarda. (Original data (Table S3) unit is ”ppm’ but was transformed by Primer-e program with Euclidean similarity and forth root transformation during heat-map producing).  
Supplementary Material S1 contains heat-maps of the detailed bacterial classification of OMW and OMP including the phylum, class, order, family, genus and species of bacteria. Indeed, 4 _Prevotella_ species (_Prevotella oris_, _Prevotella stercorea_, _Prevotella genomo_ sp. and _Prevotella alhensis_) and 1 _Spiroplasma_ sp. prevailed the OMW. Other than that, _Acetobacter ghanensis_ and _Gortzia infectious_ species prevailed the OMP (Supplementary Material S1).  
Bacterial functions in OMW are very similar to those of OMP (Figure 5c). They are divided into 7 categories: cellular processes, organismal systems, metabolism, genetic information processing, environmental information processing, human diseases and few unclassified functions. The main bacterial functions were dedicated to the carbohydrate metabolism (10.7%), the amino acid metabolism (9.6%), and the energy metabolism (6.6%). Otherwise, a considerable percentage (12.5%) was attributed to the categories of environmental information processing and human diseases (Figure 5c).  
Figure 5: (**a**) Bar-chart (percentages) and (**b**) heat map (abundance) presenting uncultivable bacterial genera abundance within the OMP and OMW samples. Original data (Supplementary Material S3) unit is the number of bacterial genera in to OMW and OMP samples but was transformed by Primer-e program with Euclidean similarity and forth root transformation during heat-map producing. (**c**) PICRUSt classification of OMW and OMP functional category abundance.