esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Sampling Procedure  
Participants for this study were identified using a purposive sampling technique. The researchers specifically targeted small-holder cattle farmers residing in rural communities in the Eastern Cape Province. The use of purposive sampling allowed for the selection of participants who were most relevant to the research objectives and could provide valuable insights into drought-related issues and adaptive measures. The criteria for inclusion in the study were that participants had to be small-holder cattle farmers and residents of rural communities in the Eastern Cape Province. This ensured that the sample represented the target population of interest. The researchers aimed to capture the experiences and perspectives of farmers who were directly involved in cattle farming and were likely to be affected by drought events. To ensure diversity within the sample, farmers from different geographic locations within the Eastern Cape Province were included. This helped to capture variations in environmental conditions, access to resources, and experiences with drought. Additionally, farmers from different socioeconomic backgrounds were selected to account for potential variations in adaptive capacities and perspectives. The sample size for the study was determined based on the principle of data saturation. Data saturation occurs when the researchers have collected sufficient information to address the research objectives and further data collection is unlikely to yield substantially new insights. The researchers continued the sampling and data collection until they felt that they had reached data saturation, ensuring that they obtained a comprehensive understanding of farmers' perceptions and willingness regarding drought-related issues. By using purposive sampling and including farmers from diverse geographic locations and socioeconomic backgrounds, the study aimed to capture a range of perspectives and experiences. This approach increased the representativeness and richness of the data, enabling a more comprehensive analysis and interpretation of the findings.
## 4 Discussion
### Deep Learning Network and Volume Estimation  
This study shows that automatic segmentations with deep learning networks and using portal-venous CT images are reliable methods for the automatic tumor volumetric measurements of RPS. The best performance for automatic segmentation was reached by ENet, with the VD between the automatic and manual segmentation at 2.11% for ENet and at \(-\)14.80% for ERFNet. Furthermore, we observed a VOE value of 16.87% for ENet and of 36.85% for ERFNet. These results indicate a low volumetric overlap error between the segmentation results and the manual segmentation, using ENet. To our knowledge, no other studies investigated the role of a deep learning network for RPS segmentation. Our results are concordant with prior investigations exploring the ENet for automatic segmentation in other organs. In a prior study, [PERSON] et al. [38] presented the FastVentric, an ENet variation, with skip connections for cardiac segmentation. They compared their results to that of the DeepVentric, the architecture previously cleared by the FDA for clinical use. Both automatic segmentation methods had a median relative absolute error between 5% and 7%. A study involving 103 patients imaged with a prostate MRI [39] used ENet and UNet for the prostate gland segmentation, in comparison with the manual segmentation, which reported a VD of 6.85% and \(-\)3.11%. [PERSON] et al. [40] tested the ENet, UNet, and ERFNet for prostate gland segmentation, which reported mean VDs of 4.53%, 3.16%, and 5.70%, respectively. They obtained the best VOE value with ENet (16.50%), which is similar to the UNet and ERFNet (17.66% and 22.18%, respectively). The DSC is a widely used measure for evaluating medical image segmentation algorithms. It offers a standardized measure of segmentation accuracy, which has proven useful [41]. In the current study, the obtained DCS was 90.52% for ENet and 74.85% for ERFNet. The higher DSC value obtained in our dataset, using ENet, demonstrates a greater similarity to the manual segmentation contour and high-segmentation accuracy.
# A Comparative Study of 3D UE Positioning in 5G New Radio with a Single Station
## 4 Performance Comparison  
### Simulation Setting and Examples  
#### 4.1.1 Simulation Setting  
The positioning performance evaluation was carried out using simulations and the system parameters. The SRS configuration and positioning algorithm parameters are listed in Tables 1-3.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline Carrier frequency & The n78 3.5 GHz in Frequency Range 1 (FR1, sub-6 GHz) frequency band, the most common 5G band in deployed networks. Furthermore, this band is more suitable for the outdoor scenarios concerned in this work. \\ \hline Receiving array & Square shaped, half wavelength spacing URA is used. The \(4\times 4\), \(8\times 8\), and \(16\times 16\) configurations are used to test the array scale impact on the angle estimation performance. \\ \hline Bandwidth & Though the supported channel bandwidth of n78 band is from 10 to 100 MHz, different values are used in this paper to test the bandwidth impact on the delay estimation performance. \\ \hline Sub-carrier interval & The default subcarrier spaces 15 kHz, 30 kHz, 60 kHz, and 120 kHz are used to test the bandwidth impact on the positioning performance. \\ \hline Modulation & The SRS is an OFDM signal. However, it is not a data payload. Thus, the symbols allocated to OFDM subcarriers are not Zadoff–Chu code elements, which do not have any modulation. \\ \hline Channel model & The additive white Gaussian noise (AWGN) complex channel is used. \\ \hline Duplex mode & Time-division duplexing (TDD) is the most used duplex mode in the n78 band. The positioning methods in this work are neutral to the duplex mode. \\ \hline \hline \end{tabular}
\end{table}
Table 2: SRS configuration. RB, resource block; RRC, radio resource control.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline \(C_{\mathit{Srs}}\) & 42 & The total bandwidth configuration index to the maximum available RBs can be used. \\ \hline \(B_{\mathit{Srs}}\) & 2 & The transmission bandwidth selecting index, used with \(C_{\mathit{Srs}}\) for selecting the RBs for the SRS. \\ \hline \(K_{\mathit{TC}}\) & 2 & The comb structure, which indicates the number of subcarrier gaps between two SRS subcarriers. \\ \hline \(l_{0}\) & 0 & The symbols’ start position index carried in the RRC message. Controls the SRS starting position range from the 8 th to 13 th OFDM symbol. \\ \hline \(k_{0}\) & 0 & The SRS subcarrier starting position. Frequency hopping and sequence hopping are disabled in the simulations. The subcarrier mapping starts from the first subcarrier. \\ \hline \hline \end{tabular}
\end{table}
Table 2: SRS configuration. RB, resource block; RRC, radio resource control.
# EL-MTSA: Stock Prediction Model Based on Ensemble Learning and Multimodal Time Series Analysis
## 4 Experimental Results and Discussion
### Training Parameter Setting  
In this model, the mean squared error (MSE) is selected as the loss function, and its mathematical formulation is provided in Equation (40). The Adam optimizer is employed for training due to its efficiency and adaptability. The model is implemented in a Python 3.8 environment, utilizing the TensorFlow framework to construct the LSTM architecture for time series prediction tasks.  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}\left(Y_{i}-\hat{Y}_{i}\right)^{2} \tag{40}\]  
where \(n\) represents the number of samples, \(Y_{i}\) represents the real value, and \(\hat{Y}_{i}\) represents the predicted value.  
The model's training parameters are configured as follows: the number of epochs is set to 200, the learning rate is initialized to 0.0001, and the Adam optimizer is employed for training. The model involves several hyperparameters, among which the number of hidden layer units and the batch size are the most sensitive, significantly impacting the model's performance. Other tunable hyperparameters include the batch size, the number of layers, and the number of hidden units. A comprehensive summary of the hyperparameter settings is provided in Table 1.
## 3 Results and Discussion
### Fundamental Analysis of the SC Ni-Mn-Ga Alloy/Silicone Rubber Composite
In addition to the above-mentioned one-particle/epoxy composite, the 20 vol.% SC Ni-Mn-Ga particles/silicone composite was further synthesized. The \(\upmu\)CT images of the 20 vol.% SC Ni-Mn-Ga particles/silicone composite are shown in Figure 8. Similarly, all the particles with comparatively small dimensions compared to the relatively large particle, as shown in Figure 7, can also be clearly observed by using the \(\upmu\)CT observation. The SC Ni-Mn-Ga particles are pointed-out by a white arrow in the upper left corner. The dimensions of the SC Ni-Mn-Ga particles that were utilized to fabricate this composite were in the range of 100-160 \(\upmu\)m. The silicone matrix, which is excluded from the \(\upmu\)CT image, is approximately portrayed by using a yellow cube. The dimensions of the overall composite of the 20 vol.% SC Ni-Mn-Ga particles/silicone were approximately 2 mm (length) \(\times\) 2 mm (width) \(\times\) 2 mm (height). Judging from the \(\upmu\)CT image, it is found that, generally, the SC Ni-Mn-Ga particles were homogeneously distributed in the cubic silicone matrix.  
Consequently, previous descriptions and explanations indicated that composites were successfully fabricated using the processes described in the experimental section and their 3D microstructures were detected very well by using \(\upmu\)CT. In addition, it is necessary to mention that it was found that with the integration of SC Ni-Mn-Ga particles (a relatively stiff material) with the silicone rubber (a relatively soft material), the effective Young's modulus of the composites can be enhanced compared to the pure silicone [32].  
Figure 7: The \(\upmu\)CT image of SC Ni-Mn-Ga particles/epoxy composite (i.e., one-particle/epoxy composite). The relatively large particle is the target particle, while the relatively small particle is the marker for the compression tests. The regime of the epoxy matrix, which is excluded in the \(\upmu\)CT image, is approximately portrayed by using a yellow cube.
# Detecting Cyber Threats in UWF-ZeekDataFall22 Using K-Means Clustering in the Big Data Environment
## 4 Experimental Setup
### Evaluation  
Labeled attack data was used to evaluate the effectiveness of our unsupervised K-means clustering model. The labels, which indicate whether or not an attack occurred, were applied only during the evaluation phase to calculate the key performance metrics: accuracy, precision, recall, F1 score, AUC, and Confusion Matrix. These metrics provided a benchmark to prove that our clustering approach successfully distinguished between normal and attack data. That is, actual labels from the 'label' column were used to classifyclusters as attack clusters or normal clusters. Once classified, a 'prediction' column was created, where all data points within the attack clusters were labeled as attacks, while those in normal clusters were labeled as normal data. Then the classification was evaluated by comparing the 'prediction' column against the actual labels, computing accuracy, precision, recall, etc. Hence, by comparing the clusters produced by K-means with the known attack labels, we were able to verify the model's accuracy in identifying cyber threats.  
The key statistical metrics are defined as:  
* Accuracy offers an overall assessment of the model's performance, representing the proportion of total correct predictions.
* Precision indicates the accuracy of the model's positive predictions, reflecting how often these predictions are correct.
* Recall measures the model's ability to capture all actual positive instances, showing its sensitivity to detecting attacks.
* F1 Score balances precision and recall, offering a single metric that reflects the model's effectiveness in handling positive cases.
* AUC evaluates the model's ability to distinguish between positive and negative cases.
* The confusion matrix provides a summary of the model's correct and incorrect predictions for both classes, highlighting where the model succeeded and where it faltered.
## 2 Methodology
### Available Safe Egress Time (ASET)-Required Safe Egress Time (RSET) Analysis  
Available safe egress time (ASET) is defined as the time between fire detection and the onset of conditions which is hazardous to continued human occupancy. This time is a function of visibility, temperature, and fractional effective doses. The time can be estimated using a fire modelling simulation tool B-Risk. Required safe egress time (RSET) is the amount of time required after a fire ignition for occupants to evacuate a building or space and reach the building exterior or a protected exit enclosure. RSET is the sum of the detection time, the evacuation delay time (sometimes called the pre-movement time), and the movement time. Detection time is the time at which occupants first become aware of a fire through a building's fire alarm system. The pre-movement time is the time that elapses between activation of the occupant notification system and the time at which occupants make the decision to begin evacuating. Pre-movement time includes the search for family and friends and to check whether the fire is real or not. The movement time is calculated by applying empirical relations for walking speed through egress elements such as doors, stairs, and corridors or using B-risk simulations. Evacuation time calculations were performed using the hydraulics methods outlined in Society of Fire Protection Engineering (SFPE) Handbook [43] and Pathfinder evacuation modelling software from Thunderhead Engineering.  
In building safety design, it is considered acceptable if the ASET is greater than the RSET, after applying an appropriate safety factor. The ASET can be increased by limiting combustibles, providing large separation distances between fuel drums (if any), providing fire suppression systems to suppress developing fires, or provide active or passive smoke detection systems. Live voice messages help in reducing the pre-movement time. Proper location of exit signs helps in reducing the movement time.
Influence of Low Yield Point Reinforcement and High Toughness Concrete on Seismic Performance of the Frame  
Kaihua Cheng  
College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou 225009, China  
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with traditional steel, the inherent mechanical properties of low yield point steel make it an ideal material for energy dissipation components [10]. At present, typical applications of low yield point steel include buckling restrained brace (BRB), steel plate shear wall, coupling beam, and various damper types [11; 12]. The research combines the latest research results of low yield point metal energy dissipation technology and high toughness concrete. A new reinforced concrete frame structure with different yield points is developed. It has a strong energy dissipation ability and a large range of elastic working sections. Frame structural members are composed of ordinary steel bars, low yield point steel bars, and high toughness concrete materials.  
The energy consumption mechanism was expounded, and a reasonable mechanical model was established in this paper. In order to provide a reference for engineering design, the seismic damping capacity of RC frame with low yield point reinforcement is explored by means of a test. The arrangement of different low yield steel bars in the frame structure is equivalent to a built-in damper [13]. Its working principle under the action of an earthquake is that when the ordinary reinforcement and high toughness concrete are still in the linear elastic state, the low yield point reinforcement enters into the plastic deformation earlier because of the lower yield point so that the whole structure can consume the seismic energy in the online elastic working state.
## 4 Results and Discussion
### Measurement Results and Analysis
The reason for such a distribution is the handover and cellular network architecture in the considered measurement area, which is in line with the standard architecture of the cellular network in urban and suburban areas. For a closer look, Figure 10 depicts the box plots of all collected data. From the results of Figure 10c,d, it can be seen that 75% of RSRP and RSRQ are in the range of \(-\)59 dBm and \(-\)79 dBm, and \(-\)5 to \(-\)14 dB, respectively. Based on the results of Figure 10a,b and Table 3, it can be revealed that within a distance of 320 m and an elevation angle of 30 degrees, most probably a terrestrial BS can serve a drone with good or medium signal strength and quality. The hollow circles also show the outliers, which the boxplot function of the _matplotlib_ library has identified. This data mainly belongs to the measurements at the heights of 105 and 125 m, where the adjacent BS is in the drone's line of sight and receives service for a short period. However, since these samples are reliable and represent the signal condition over a long distance, we keep and consider these samples for developing the prediction models. In conclusion, the data analysis revealed that the reliability and performance of a terrestrial cellular link in aerial communication is a function of distance and elevation angle. Enhancing the distance destructing factors such as path loss and shadowing attenuate the energy of receive signals, RSRP, at the drone side. In contrast, the degradation of RSRQ is mainly affected by the elevation angle enhancement. The main reasons behind this phenomenon are reduced antenna gain and enhanced signal interference at higher altitudes, resulting in a lower signal-to-interference plus noise ratio (SINR). The reason for the desired signal level degradation is that the BSs antennas are down tilted and, based on the antenna radiation pattern, by increasing the flight height, the chance of serving the drone with the main lobe decreases, which consequently degrades the received signal energy at the drone side. The reason for interference enhancement is because, in higher altitudes, a drone can see a larger number of BSs. Therefore, the  
Figure 9: (**a**) Data distribution of RSRQ versus angle and distance, (**b**) RSRQ versus distance, and (**c**) RSRQ versus angle. probability of receiving signals from adjacent/interfering BSs increases. Consequently, the drone receives higher interference energy at higher altitudes.
# Towards Lightweight URL-Based Phishing Detection
###### Abstract
Given that the reported median phishing webpage lifespan is of less than twenty-four hours [6], synchronisation speed and frequency of denylist updates are crucial factors in defending against this type of threat. For this reason web browsers synchronise their denylist frequently in an attempt to have the most up-to-date threat information. Nonetheless, previous work has uncovered that the aforementioned technologies provide a sub-optimal update and classification process in terms of execution time and thus limited phishing protection to its users [7; 8; 9; 10]. For this reason, the relevant literature includes security controls that use machine learning in order to defend against phishing [11]. In this work, we propose and evaluate a phishing detection engine, which uses supervised machine learning in order to detect phishing attacks based on a novel combination features that are extracted from the URL. This allows us to avoid any delays which stem from the computation of features that need access to third-party resources, such as access to WHOIS records. In summary, our work makes the following contributions:  
* We train, optimise and evaluate a phishing detection engine which relies on supervised machine learning, based on features that stem from the URL. Our feature selection process includes features that have been proven suitable by the literature, coupled with new ones that we propose and evaluate. To the best of our knowledge, we are the first to use the Levenshtein distance as a similarity index feature for training a range of machine learning algorithms in this domain. We also, revisit the use of suggestive vocabulary, which was used in the past [12], as one of our features. * We evaluate the performance of our phishing detection engine over time by classifying active phishing attacks that were reported on PhishTank, without model retraining. We find that the performance of the classification is not affected by time, as well as it significantly outperforms the protection that is offered by GSB. The rest of the paper is structured as follows. Section 2 discusses related work and Section 3 presents our methodology with regards to feature selection, model selection and tuning. Section 4 shows the evaluation of our model before the paper's conclusion in Section 5.
# Estimating Global Gross Primary Production Using an Improved MODIS Leaf Area Index Dataset
## 3 Results
### The Dynamic of Global GPP from 2000 to 2022  
The long-term trend of annual global summed GPP simulated by the proposed model, BEPS [71; 72; 73], MODIS [27], and VPM [3] shows an increase over the time series (Figure 4). The proposed model quantified the annual global GPP between 116.4 PgC year\({}^{-1}\) and 133.94 PgC year\({}^{-1}\), averaging 125.93 PgC year\({}^{-1}\) from 2000 to 2022. In contrast, the BEPS model produced annual global summed GPP estimates ranging from 117.04 PgC year\({}^{-1}\) to 129.92 PgC year\({}^{-1}\), averaging 122.98 PgC year\({}^{-1}\) from 2000 to 2019. Additionally, the MODIS model simulated annual global summed GPP ranging from 95.28 PgC year\({}^{-1}\) to 103.92 PgC year\({}^{-1}\), averaging 99.1 PgC year\({}^{-1}\) from 2003 to 2022. Lastly, another VPM model simulation of annual global summed GPP spanned 116.67 PgC year\({}^{-1}\) to 126.29 PgC year\({}^{-1}\), averaging 121.09 PgC year\({}^{-1}\) from 2000 to 2017. We found a significant increasing trend for the proposed model, BEPS, MODIS, and VPM, with average increase rates of 0.548 PgC year\({}^{-1}\), 0.685 PgC year\({}^{-1}\), 0.34 PgC year\({}^{-1}\), and 0.53 PgC year\({}^{-1}\) globally, respectively. A significance test of the increasing trend showed that all p values were below 0.01. The GPP values estimated by the proposed model showed correlations of 0.767, 0.765, and 0.657 with those of the BEPS, VPM, and MODIS models, respectively. All significance test \(p\) values were below 0.01 (Table 3).