esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Description of the Datasets
#### 4.1.2. UNSW-NB15 Datasets  
UNSW-NB15 is a sophisticated dataset used in IDS research and is highly referenced in the literature. The IXIA Storm tool in the Cyber Range Laboratory of the Australian Centre for Cybersecurity (ACCS) produced the raw packets (network traces) that make up the UNSW-NB15 dataset. The dataset is simulated over 2.5 million network packets [44]. Nine different attack types, namely, exploit, reconnaissance, denial-of-service, shellcode, generic, backdoors, worms, fuzzers, and analysis attacks, as well as non-anomalous packets, are included in this dataset. The dataset is highly skewed since over 87% of the packets are non-anomalous. The dataset features and descriptions are provided in Table 3. The protocol feature, which identifies the protocols used by the hosts, such as TCP or UDP, is included in the first group (flow). The essential connection data, including the length of time and number of packets exchanged between the hosts, is represented by the second group (basic). Fourteen features are grouped. Content information, including base sequence numbers and window advertisement values, is sent by the third group (content) over the TCP. Additionally, it offers certain details about HTTP connections, including the amount of data sent through the HTTP service. There are eight characteristics in this group. Eight features, including packet arrival time and jitter, are included in the fourth group (time). Table 4 describes the attacks in the dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group** & **Feature Numbers** & **Count** \\ \hline Basic feature & 1, 2, 3, 4, 5, 6, 7, 8, 9 & 9 \\ Content feature & 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 & 13 \\ Time feature & 23, 24, 25, 26, 27, 28, 29, 30, 31 & 9 \\ Host feature & 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: NSL-KDD dataset groups of features.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Training Dataset** & \multicolumn{2}{c}{**Testing Dataset**} \\ \hline  & **KDD\_Train** & **KDD\_Train\_20 percent** & **KDD\_Test** & **KDDTest-21** \\ \hline Normal & 67,343 & 13,449 & 9711 & 2152 \\ Probe & 11,656 & 2289 & 2421 & 2402 \\ DoS & 45,927 & 9234 & 7458 & 4343 \\ U2R & 52 & 11 & 200 & 200 \\ R2L & 995 & 209 & 2751 & 2754 \\ Total & 125,973 & 25,192 & 22,544 & 11,850 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details of attack type for the NSL-KDD dataset.
# Analytical-Numerical Model for Temperature Prediction of a Serpentine Belt Drive System
## 5 Conclusions  
This paper presents a novel thermal model to enable the temperature distribution prediction of a serpentine belt drive system in an accurate and efficient manner. The model uses an analytical-numerical method, which can promptly provide precise temperature results pertaining to pulley irregular structures under abundant belt drive load cases. In this study, a general set of equations was developed according to the flow behaviors within the belt drive system. In addition, the coefficient \(\lambda_{pn}\) and function \(S_{n}(\omega\text{R}_{pn})\) were investigated by performing numerical simulations. By solving this set of equations, the belt temperature and thermal flux flowing into each pulley was obtained. Then, utilizing this information, together with the baseline results obtained from the numerical simulations in the previous step, the model outputs the temperature distribution of each pulley on the designed operating conditions. To validate the proposed model, experiments were performed under various operating conditions, and the experimental and calculated results were compared. The good agreements noted in these comparisons prove the accuracy and reliability of the model. In summary, the proposed model can successfully predict the temperature distribution of a belt drive system under several operating conditions in only a few seconds, not accounting for the time taken to establish the database of the baseline numerical simulations in advance.  
Furthermore, the model provides considerable avenues for further development of the belt drive system. The time-efficient and accurate nature of this model can avoid the thermal failure of the belt drive system in the early design stages. In addition, the model can be applied to the thermal and fatigue analyses of the materials of pulleys and belts. Moreover, because this model involves a numerical method, it can be applied to any type of pulley geometry and belt transmission.  
Data curation: X.L.; Formal analysis: X.L.; Investigation: X.L.; Methodology: X.L.; Resources: X.L.; Software: X.L.; Validation: X.L.; Visualization: X.L.; Writing--original draft: X.L.; Funding acquisition: K.B.; Supervision: K.B.; Writing--review & editing: K.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Advanced Research Laboratory for Multifunctional Lightweight Structures and the Litems Automotive Group is gratefully acknowledged.  
The authors declare no conflict of interest.
# Post-Quantum Key Exchange and Subscriber Identity Encryption in 5G Using ML-KEM (Kyber) 1
## 6 Experimentation and Performance Analysis
### Byte Overhead Analysis  
We also compare the protection scheme output byte size analysis in our experimentation. The protection scheme output consists of an encrypted public key or cipher text, MAC code, and the encrypted SUPI. Table 4 summarizes the protection scheme output among all three ciphers considering the 64-bytes MAC size and 15-bytes SUPI size. The Kyber-512 has a greater scheme output size than the ECIES ciphers because of the ciphertext output size (768 bytes). Since the protection scheme output is transmitted to the CN from the UE, the communication networking overhead will increase significantly.  
However, in our future work, we experiment with the impact of the increased byte size in networking while prototyping our implementation using open-source software. In terms of the storage requirement of the public keys in the USIM (refer to Section 5), the Kyber-512 consumes (\(\frac{800}{32}=25\)) magnitudes more memory size than the ECIES ciphers. The memory size of a standard USIM card is 256 KB [(49)]; hence, the USIM can store the public keys of the Kyber-512 algorithm and is feasible to use.  
**Summary:** Table 4 describes protection scheme output sizes among cryptographic ciphers, with Kyber-512 having a larger output due to its ciphertext size. While this may increase communication overhead, it remains feasible for the storage of public keys within a standard USIM card (with 256 KB memory), facilitating its practical usage in mobile devices.  
Figure 5: Power (measured) and energy (calculated) consumption on Raspberry Pi and Mini-PC.
## 5 Experiments and Evaluation
### Experimental Setting  
#### 5.1.1 Metrics  
We adopt three prevalent metrics in recommender systems, i.e., _TopK Hitrate_, _TopK Accuracy_, and _nDCG\({}_{K}\)_ to evaluate the recommending performances. We use different @\(N\) values to evaluate two different datasets because the number of samples in TalkingData is significantly smaller than that of Telecom Data.  
\(TopK\)\(Hitrate\) measures the successful proportion of users whose top-k recommended locations are predicted correctly for at least one location that hits the ground truth. The formula is expressed as follows.  
\[TopK\ Hitrate=\frac{\sum_{i=1}^{N}(|L_{i}^{p}\cap L_{i}^{t}|\geq 1)}{N}, \tag{16}\]  
where \(L_{i}^{p}\) denotes the list of top-k recommended locations by the \(i\)th user in the test set. \(L_{i}^{t}\) denotes the \(K\) locations that are most frequently visited by the user \(u_{i}\), for each user \(u_{i}\in U\). \(N\) is the number of users in test set.  
\(TopK\)\(Accuracy\) is used to measure the prediction accuracy on the top-k predictions of all users. The formula is  
\[TopK\ Accuracy=\frac{\sum_{i=1}^{N}(|L_{i}^{p}\cap L_{i}^{t}|/K)}{N}. \tag{17}\]  
\(nDCG_{K}\) (Normalized Discounted Cumulative Gain) is commonly used to measure the ranking quality of the top-k predicted results. The computation is expressed as follows  
\[\begin{split} nDCG_{K}=\frac{DCG_{K}}{IDCG_{K}}\\ =\sum_{i=1}^{N}\frac{\sum_{j=1}^{K}rel_{j}^{p(i)}/log_{2}(j+1)}{N \sum_{j=1}^{K}rel_{j}^{t(i)}/log_{2}(j+1)},\end{split} \tag{18}\]  
where \(rel_{k}^{p}\) represents the prediction of the \(k\)th app's usage frequency of the \(i\)th user in the \(j\)th place, and \(rel_{k}^{t}\) represents the corresponding groundtruth. Higher value of \(nDCG_{K}\) means better ranking quality.
## 3 Results  
### Extraction of Herb-Derived Essential Oils  
The tested essential oils were extracted by steam distillation, and the final polyphenol content was determined by ELISA to be 1.0 GAE mg/kg for lavender oil (essential), 1.9 GAE mg/kg for lennograss (essential) oil, 0.5 GAE mg/kg for rosmenary oil, and 17.9 GAE mg/kg for chamomile oil (essential). According to mass spectrometric analysis, the primary phytochemicals in lavender oil were ascorbic acid and retinol; in lennograss oil, they were retinoic acid, retinol, and beta-carotene; in rosmenary oil, they were retinoic acid, retinol, and retinal; and in chamomile oil, it was \(\beta\)-carotene [31].  
In preliminary experiments, an MTT assay was performed to determine the optimal concentration of each essential oil. These results indicated that the optimal dose ranges without cytotoxicity were 0.0025-0.01% (vol/vol) for lavender oil, 0.0012-0.005% for lennograss oil, 0.005-0.02% for rosmenary oil, and 0.005-0.02% for chamomile oil (data not shown). The following experiments were performed with essential oils at three concentrations within these ranges.  
The ROS-Savenging Activity of Herb-Derived Essential Oils in UVB-Irradiated DPCs. DCF-DA Signals Are Reduced to Confirm Anti-Oxidant Activity  
In UVB 50 mJ/cm\({}^{2}\)-irradiated DPCs, increased DCF-DA signal indicating ROS level increased significantly (370% of the non-irradiated control). UVB-induced ROS production was blocked by essential oils with different potencies of ROS-scavenging activity as follows: 41.3% \(\pm\) 5.1% reduction by 0.01% lavender oil, 19.2% \(\pm\) 3.3% reduction by 0.005% lennograss oil, 50.4% \(\pm\) 1.2% reduction by 0.01% rosmenary oil, and 51.6% \(\pm\) 3.9% reduction by 0.01% chamomile oil, respectively (Figure 1).  
Figure 1: Effect of essential oils on ROS production in UVB-irradiated DPCs. FACS analysis to detect the DCF-DA-positive cells was performed. (**a**) Control DPC. (**b**) UVB-irradiated DPC; and (**c**) Lavender oil-treated, UVB-irradiated DPC. (**d**) Lennograss oil-treated, UVB-irradiated DPC. (**e**) Rosemary oil-treated, UVB-irradiated DPC. (**f**) Chamomile oil-treated, UVB-irradiated DPC. The average number of DCF-DA-positive cells shown in (**a**) is designated as 100%.
## 3 Methodology
GNSS networks are highlighted as the best geodetic technique [129] to study regional active surface deformations on a millimetre scale [130; 131]. The geodetic data included GNSS data taken from both continuous and survey mode networks (Figure 5). The continuous sites of the central Betic Cordillera (NEVA, LOJA, PALM, and MALA) are part of the Iberian and North Morocco regional network deployed by the Topo Iberia project [31]. The survey-mode GNSS data are part of the Padul Fault network [132], Campo de Dalias network [101], and central Betic Cordillera Extensional network [133]. Since the Padul network is highly localized, the mean value for the sites located within the hanging wall block of the Padul Fault was considered [132]. In this study, the Sierra Nevada was considered a relatively stable block of the central Betic Cordillera due to its limited seismic activity and minimal affection by major faults. Consequently, to assess recent surface deformation above the Nevada-Filabride Complex, the velocity vectors with respect to the NEVA geodetic site were computed. Figure 5: GNSS velocity field with respect to the fixed NEVA. The numbers show the velocity rate in mm/year. Blue arrows belong to the Iberia–North Morocco continuous GNSS network [31]. The orange arrow is the mean value of the sites located in the hanging wall block of the Padul Fault [132]. Red arrows belong to the Campo de Dalias survey-mode network [101]. Green arrows belong to the central Betic Cordillera extensional survey-mode network [133]. Each GNSS site maintains its original name as a reference. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **S1** & **S2** & **S3** & **R** & **Reference** \\ \hline Eastern Granada Basin & 78/240 & 01/336 & 12/067 & 0.31 & [104] \\
2021 Santa Fe seismic series & 74/277 & 12/141 & 11/049 & 0.28 & [119] \\ External Arc & 37/147 & 27/035 & 41/278 & 0.58 & [88] \\ Internal arc & 33/316 & 02/047 & 57/139 & 0.61 & [88] \\ \hline \end{tabular} R indicates the axial ratio.  
\end{table} 
Table 3: Reduced stress tensors of the central Betic Cordillera.
## 5 Conclusions  
In this research, several data mining techniques including continuous and binary classification techniques were applied to the prediction of base oil biodegradability using three of their most significant predictive characteristics, namely PC, KV and NV. Following suggestions from research practice and clustering results, all the tested techniques were evaluated for their performance at three different partitions of the biodegradability spectrum in an attempt to uncover any significant differences in their predictive accuracy over these biodegradability partitions. Thus, in addition to the whole data set, two biodegradability classes are considered: low biodegradable (less than 50%), and high biodegradable (50% or higher) for model evaluation. Most continuous methods tended to be more accurate with the high end for which MLR is the best performer (6.10% error), closely followed by NN-MLR (6.60% error). Classification techniques had a mixed of performance. The best classification techniques resulted in misclassification errors in the 3.2% to 6.5% range. These include Logistic Regression, the simple K-Means clustering, and binary methods derived from continuous models. The best derived binary results were achieved by NN-MLR and ANN for the overall set with a 4.76% error. Both favored the low end with 0% and 3.23% error respectively. The best derived binary results were achieved by NN-MLR and ANN for the overall set with a 4.76% error. Both favored the low end with 0% and 3.23% error respectively.  
Although the powerful technique of Decision Trees did not perform as well, it provided a simple but valuable classification rule using only the KV variable. Based on the available data, the quick test defined by the derived using a threshold KV value (KV = 96), can provide an efficient prediction accuracy, which is second only to Logistics Regression with three variables. The discrete methods resulted in significantly better accuracy than QSARs methods with misclassification errors in the 3.2%to 6.25% range compared to QSAR's 40% to 90% range. The application of Rule 1 enables effective classification of base oils into the slowly/highly biodegradationity classes. A higher accuracy can be pursued for oils predicted to have a high biodegradability (\(\mathrm{KV}<96\)), using the continuous predictive methods of MLR or NN-MLR.
# Geophysical Monitoring of Leachate Injection in Pretreated Waste Landfill
## 2 Materials and Methods
### Monitoring of Biogas and Leachate  
After the landfill's definitive covering, the biogas extraction and leachate collection plants started to operate. At the same time, biogas and leachate monitoring started.  
The landfill was equipped with a system for online monitoring of the extracted biogas (temperature; flow rate; composition by methane, carbon dioxide, oxygen and hydrogen sulphide). Concerning leachate, the following parameters were analyzed weekly on samples: temperature, pH, chemical oxygen demand (COD) and ammonium and chloride concentration.  
The zone managed as a bioreactor was equipped with a system to monitor the leachate recirculation and collect data about temperature and electrical resistivity. The measurement of these parameters at different landfill depths can be good indicators of spatial and temporal changes in the liquid contained within the bioreactor as a result of the infiltration process.  
To study the effect of leachate recirculation on biogas flow rate and composition, the tests were carried out for more than two years.  
Two biogas wells were monitored with and without leachate recirculation in their influence zone (green circle in Figure 1). The recirculation tests were done with the same flow rate as the infiltration tests, about 3 m\({}^{3}\)/h for a daily cumulative volume equal to 35-38 m\({}^{3}\), in different seasonal periods to study the influence on biogas generation.  
At last, a long-term test (4 months) was carried out in which the leachate flow rate was split around the two tested wells.
## 2 Data and Preprocessing
### MODIS Data  
The MODIS surface spectral reflectance (MOD09A1 V6), FPAR, and leaf area index (LAI) products (MCD15A3H V6) are accessible at a spatial resolution of 500 m on the Google Earth Engine (GEE) platform and were used in the study. The MOD09A1 and MCD15A3H products are 8-day and 4-day composite data which choose the best pixels filtered by their quality labels. We used red (620-670 nm), blue (459-479 nm), near infrared (NIR, 841-876 nm), and shortwave infrared (SWIR, 2105-2155 nm) bands to calculate NDVI, EVI, and LSWI (land surface water index) [48; 49; 50] in the following equations:  
\[\mathrm{NDVI}=\frac{\rho_{\mathrm{NIR}}-\rho_{\mathrm{Red}}}{\rho_{\mathrm{NIR }}+\rho_{\mathrm{Red}}} \tag{2}\]  
\[\mathrm{EVI}=\mathrm{G}\times\frac{\rho_{\mathrm{NIR}}-\rho_{\mathrm{Red}}}{ \rho_{\mathrm{NIR}}+(\mathrm{C}_{1}\times\rho_{\mathrm{Red}}-\mathrm{C}_{2} \times\rho_{\mathrm{Blue}})+\mathrm{L}} \tag{3}\]  
\[\mathrm{LSWI}=\frac{\rho_{\mathrm{NIR}}-\rho_{\mathrm{SWIR}}}{\rho_{\mathrm{ NIR}}+\rho_{\mathrm{SWIR}}} \tag{4}\]  
where \(\rho_{\mathrm{Blue}}\), \(\rho_{\mathrm{Red}}\), \(\rho_{\mathrm{NIR}}\), and \(\rho_{\mathrm{SWIR}}\) are the spectral reflectance of the blue, red, NIR, and SWIR bands, respectively. G is set to 2.5, \(\mathrm{C}_{1}\) is set to 6, \(\mathrm{C}_{2}\) is set to 7.5, and \(\mathrm{L}\) is set to 1 in Equation (3). The MODIS sensor may have a small amount of abnormal data or missing data due to its internal components or external solar radiation, whose time series data need to be gap-filled and smoothed through interpolation and filter.  
The Savitzky-Golay (SG) filter employs a locally weighted regression to fit the data within the window as a polynomial. It replaces the original value at the center point of the window with the polynomial value. The degree of fitting of the SG filter depends on the window size and the order of the polynomial. The sliding window sizes for SG filtering of NDVI, EVI, LSWI, and LAI time series data were set to 11 (for a few sites where data anomalies still existed after processing, the window sizes were set to 15), and the order of the polynomial was set to 3. The 4-day LAI products were averaged every two observations to match the time resolution of MOD09A1. Likewise, we filtered daily GCC data and took the maximum value of GCC within every 8-day period as the value for that period.  
Figure 1: Distribution of flux sites.
## 2 Literature Review
### Order Releasing and Scheduling Using Heuristic Based on Drum Buffer (DRB)
The analysis, conducted longitudinally through a case study, utilised data envelopment analysis (DEA), the Wilcoxon test, and analysis of variance (ANOVA) to assess the impact of DBR. The findings revealed a significant efficiency increase of up to 19% following the implementation of DBR. [PERSON] et al. [54] incorporated the theory of constraints and leveraged the DBR mechanism to develop multi-level planning and scheduling strategies in the context of mixed-model production. Additionally, the researchers propose a multi-level planning heuristic (MLPH) that employs DBR and priority rules to achieve efficient planning and scheduling. The approach considers material requirement planning and optimally utilises capacity-constrained resources (CCR) to the fullest extent.
## 2 Governing Equations and Boundary Conditions
### Artificial Neural Network (ANN)  
The back propagation (_BP_) _ANN_ is a multilayer feedforward ANN used for benchmarking prediction performance [24; 25; 26]. There are three layers in _BP ANN_: input, hidden, and output. Each hidden layer receives the input signal through the input layer, and finally, the output layer receives it. Error signals are sent back to the hidden layer and the input layer. After that, the gradient descent algorithm is applied to adjust the weight and threshold of each neuron so that the _BP_ analogy output is close to the expected value (See Figure 2). The number of neurons in the hidden layer is selected by repeated experiments to optimize the performance of the neural network.  
As ANN with many neurons can be used only as the \"black box\", the approach may be inconvenient for submarine designers, who are usually not familiar with _ANN_. Therefore, it is of great practical interest to develop design equations for practical engineering usage. Since the equation derived from a neural network with many neurons would be very long and unusable, two _ANN_ structures with one and two neurons in the hidden layer are considered (See Figures 3 and 4).  
Figure 3: _ANN_ structures with one neuron in the hidden layer.  
Figure 2: Structure of _BP ANN_ (taken from [27]).  
Convolution produces the following output for the \(ANN\) shown in Figures 3 and 4, respectively:  
\[C_{R}=f_{2}\bigg{(}b_{2}+w_{3}f_{1}\bigg{(}w_{1}\frac{L}{D_{s}}+w_{2}log(R_{e})+b _{1}\bigg{)}\bigg{)} \tag{7}\]  
\[C_{R}=f_{2}\bigg{(}b_{3}+w_{5}f_{1}\bigg{(}w_{1}\frac{L}{D_{s}}+w_{3}log(R_{e})+b _{1}\bigg{)}+w_{6}f_{1}\bigg{(}w_{2}\frac{L}{D_{s}}+w_{4}log(R_{e})+b_{2}\bigg{)} \bigg{)} \tag{8}\]  
Activation functions introduce non-linearity into neural networks and help to capture the non-linear characteristics of input data [28; 29]. There are different activation functions including Sigmoid, hyperbolic tangent, and Identity used in the neurons of the hidden and output layers of \(ANN\)[27]. According to the preliminary results of this study, \(ANN\)s with hyperbolic tangents \((f_{1}(x)=tanh(x))\) and Identity \((f_{2}(x)=x)\) activation functions in the hidden and output layers provides credible results.
# Rapid-DEM: Rapid Topographic Updates through Satellite Change Detection and UAS Data Fusion
## 2 Methods
### Rapid-DEM
#### 2.1.4 UAS Data Acquisition and Processing  
A flight plan of a selected high priority area is generated with a priority queue defined polygon or a manually defined area determined by the UAS pilot using a flight planning and control application such as Drone Deploy [44]. However, other UAS flight planning and mapping software solutions would work to capture the updates. Additionally, to ensure safety and compliance with local UAS regulations, a qualified UAS pilot should review all flight plans generated using priority queue-defined polygons. The use of GCPs is recommended to improve horizontal and vertical accuracy of the flight data, but is not necessary to produce a DEM with acceptable vertical accuracy for fusion in all locations [45; 46]. The collected UAS data is processed using the open-source photogrammetry software WebODM [24], which provides a web user interface to OpenDroneMap's (ODM) [47] implementation of SfM. SfM processes images with embedded positional information from the UAS GNSS receiver and outputs an orthomosaic and DEM(s), e.g., bare ground digital terrain model (DTM) and a digital surface model (DSM) with above-ground features.