esa-sceva/satcom-chunk-collection huggingface dataset:
# Node-Based QoS-Aware Security Framework for Sinkhole Attacks in Mobile Ad-Hoc Networks
## 5 Results and Discussion
### Simulation
#### 5.1.3 Jitter Performance Analysis  
The proposed QoS framework outperformed the malicious scenario again in terms of jitter recording an average of about 6500 microseconds as compared to the malicious scenario which had margins of about 35,000 microseconds. In the sinkhole scenario, the first node had a delay of 26,543.9 microseconds. Nodes 2-5 showed a constant jitter of 26,468.72 microseconds but node 6 and node 12 both had 26,400.87 microseconds. This was an expected margin based on the malicious activity within this broadcast session. As shown in Figure 12, the framework significantly lowered the levels of jitter among all the nodes. The framework proved to be effective in low-density node scenarios.
# Design and Implementation of a Multi-Modal Biometric System for Company Access Control +
## 3 Results
### Iris Verification  
The assessment of the iris verification procedure started from the exhaustive analysis of the impostors: over the 40 sets of five samples from UBIRIS, each set being associated to a single subject. For the iris verification phase, only two thresholds are sufficient, because the only countermeasure when the score of a comparison is in the interval [\(t_{1}\), \(t_{2}\)] asks for the repetition of the acquisition of the iris (Figure 3). The thresholds \(t_{1}\) and \(t_{2}\) were set to 0.35 and 0.4, respectively. Using a brute force approach, all the combinations of couples (trials) of samples from different subjects were presented to the iris verification procedure. For all the tests, the score was always above the \(t_{2}\) threshold, resulting in the correct identification of an impostor. Because it appears that there is no chance of errors with in this kind of comparison, its FAR is zero (i.e., all the impostors are blocked).  
The second step in the assessment involved the testing of all the intra-user couples of samples. Because there are 40 different users and five images for each user, there are 400 intra-user couples of samples, 10 couples for each subject. As shown in Table 5, almost three-quarters of the comparisons succeeded in finding a genuine user (the obtained score was below \(t_{1}\)). Moreover, only a few comparisons raised an Undecided output, mainly because the distance between \(t_{2}\) and \(t_{1}\) is small. In this case, the verification needs to be repeated. The 22.5% genuine user rejection rate looks quite high, but a deeper analysis of the actual iris images under test led us to the discovery that almost half of the samples showed a very bad quality, mainly because they were out of focus.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Input Set** & **Accepted Impostors** & **Rejected Impostors** & **Unsolved** \\ \hline face94, (160 images) & 0 (0\%) & 112 (70\%) & 48 (30\%) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Solving rates for impostors, in unsolvable state.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Input Set** & **Forwarded to Structured Face Acquisition** & **Forwarded to Iris Verification** \\ \hline \(T1\), (160 images) & 46 (28.75\%) & 23 (14.37\%) \\ \(T2\), (120 images) & 0\% & 20 (16.7\%) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Solving rates for genuine subjects, in unsolvable state.  
Figure 3: Iris verification thresholds.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input Set** & **Accepted Genuine Users** & **Rejected Genuine Users** & **Undecided** \\ \hline UBIRIS, (400 images) & 290 (72.5\%) & 90 (22.5\%) & 20 (5\%) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Iris recognition acceptance rates for genuine subjects.
# Power System Reliability Assessment Considering Coal-Fired Unit Peaking Characteristics
## 4 PPS Considering the Load-Following Characteristics of Coal-Fired Units
### UGF Model of Coal-Fired Unit  
Based on the three unit models described in Section 2.2, in actual power system operation, units must flexibly adjust their output levels in response to load demand to accommodate instantaneous load fluctuations while meeting generation requirements as well as facilitating the integration of wind and photovoltaic power. This process requires careful consideration of the unit's peak shaving capacity and output range to ensure the stable operation of the system.  
Furthermore, to ensure the long-term reliability of the units, maintenance personnel typically develop detailed maintenance plans based on the operational status of the units, which is also one of the key factors in ensuring stable unit operation. Therefore, when performing system simulations, the maintenance schedule must be incorporated into the model to enhance the accuracy of the simulation results.  
Based on the above factors, assume that coal-fired unit \(i\) has \(k\) states, and its UGF model is as follows:  
\[u_{i,k}(z,t)=(1-M_{i}(t))\sum_{j_{i}=1}^{k}p_{j_{i}}^{i}(t)z^{p_{j_{i}}^{i}}+ M_{i}(t)z^{0} \tag{30}\]  
where \(u_{i,k}(z,t)\) represents the UGF model of unit \(i\), \(P_{j_{i}}^{i}(t)\) and \(p_{j_{i}}^{i}(t)\) represent the output and corresponding probability of unit \(i\) at time \(t\), respectively. \(M_{i}(t)\) is the parameter reflecting the planned maintenance of the unit. If the unit is in maintenance at time \(t\), then \(M_{i}(t)=1\); otherwise, \(M_{i}(t)=0\).
## 6 Experiment and Analysis
### Performance Evaluation
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Score \(\uparrow\)**} & \multicolumn{4}{c}{**Cost \(\downarrow\)**} \\ \cline{2-7}  & **Gain** & **Benefit** & **Wait** & **Travel** & **Charging** & \
\begin{tabular}{c} **Travel** \\ **Max [min]** \\ \end{tabular} &
\begin{tabular}{c} **Wait Max** \\ **[min]** \\ \end{tabular} \\ \hline \multicolumn{7}{c}{Stanford} \\ \hline Existing Charging & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 & 4.02 & 15.47 \\ GA & 228.09 & 142.71 & 57.06 & 77.93 & 72.33 & 3.20 & 8.77 \\ Greedy-Benefit, first & 329.98 & 183.08 & 35.84 & 64.76 & 66.41 & 2.37 & 7.17 \\ Greedy-Denamid, first & 200.70 & 136.21 & 67.68 & 97.73 & 79.89 & 4.03 & 15.47 \\ PPO-Attention (Ours) & **426.38** & **223.10** & **19.19** & **58.43** & **62.46** & **2.16** & **1.98** \\ \hline \multicolumn{7}{c}{Quenstown, Central, Singapore} \\ \hline Existing Charging & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 & 8.35 & - \\ GA & 225.86 & 144.17 & 44.04 & **75.65** & 99.37 & **7.73** & - \\ Greedy-Benefit, first & 192.42 & 132.39 & 60.02 & 88.01 & 94.28 & 8.32 & - \\ Greedy-Denamid, first & 192.42 & 132.39 & 60.02 & 88.01 & 94.28 & 8.32 & - \\ PPO-Attention (Ours) & **253.10** & **133.87** & **14.85** & 97.97 & **13.45** & 8.29 & **19.47** \\ \hline \multicolumn{7}{c}{Cambridge, UK} \\ \hline Existing Charging & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 & 5.18 & 819.75 \\ GA & 104.63 & 102.41 & 100.11 & 100.00 & 101.17 & 5.18 & 819.76 \\ Greedy-Benefit, first & 106.60 & 101.91 & 96.97 & 98.26 & 98.33 & 4.71 & 819.76 \\ Greedy-Denamid, first & 106.60 & 101.87 & 96.90 & 98.31 & 98.18 & 4.70 & 819.76 \\ PPO-Attention (Ours) & **141.00** & **113.84** & **84.34** & **89.34** & **96.31** & **4.17** & **796.26** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results are presented for the Stanford, Queenstown, and Cambridge datasets from the United Kingdom. Evaluation metrics favoring higher values are indicated with \(\uparrow\), while those favoring lower values are labeled with \(\downarrow\). The best scores are highlighted in bold.
## 5 Leishmanases
The isoprenoid pathway is initiated with the conversion of mevalonate into IPP (isopentenyl diphosphate) by two phosphorylation reactions followed by one decarboxylation. Afterward, dimethylallyl diphosphate (DMAPP) is obtained from the isomerization of IPP by isopentenyl diphosphate isomerase. Furthermore, condensation of IPP with DMAPP and geranyl diphosphate (GPP) generates longer chains of isoprenoids (15 carbons), namely, farnesyl diphosphate (FPP), in two successive reactions catalyzed by farnesyl diphosphate synthase (FPPS). Farnesyl diphosphate is the direct precursor for the synthesis of sterols, di-chols, preynlated proteins, etc. It is noteworthy that the enzymes required for the isoprenoid pathway are located in the cytosol (_L. major_), peroxisome (animals), and mitochondria, among others [33,35]. After the isoprenoid pathway, the next two reactions comprise the first committed step in sterol biosynthesis. Next to the isoprenoid pathway, there is a condensation of two molecules of farnesyl diphosphate that generate squalene in the presence of the enzyme squalene synthase. The plausible localization of squalene synthase in _T. cruzi_ and _Leishmania_ spp. is either in mitochondria, microsomes, or glycosomes [33]. Afterward, squalene epoxidase catalyzes the conversion of squalene to squalene epoxide, and this reaction mostly occurs in microsomes. Next, a 2,2-oxidosqualene cyclase cyclase the 2,3-oxidosqualene to afford lanosterol, the initial precursor of all steroid structures formed  
Figure 2: The mevalonate pathway to obtain mevalonic acid from acetyl-SCoA. by trypanosomatids, mammals, etc. This reaction is followed by a series of transformations to form cholesterol in mammals and ergosterol in trypanosomatids (Figure 3) [33]. The addition of a methyl group at the C24 position in the sterol side chain is one of the crucial stages of ergosterol biosynthesis that does not exist in the synthesis of cholesterol.
# Unsupervised Method Based on Adversarial Domain Adaptation for Bearing Fault Diagnosis
## 4 Experiment Result Analysis
### Transferability Estimation  
This paper compares the transferability of different models, such as the baseline model based on random forest, logistic regression with cross-validation, deep autoencoder sparse network (DASN), support vector machine (SVM), DANN, and MCD. The details are shown below.  
Table 4 shows the testing accuracy of different models based on CWRU and XJTU data sets [40]. The baseline model achieves the lowest accuracy in this table, only 52.13%. DASN achieves 69.30%. The accuracies for DANN and MCD with an old feature extractor are about 94.40% and 98.17%, respectively. The accuracies for DANN and MCD with an improved feature extractor in this project achieve 96.84% and 100%, respectively.  
To train the baseline model, only the source data is needed, and the model is then applied to the target data to conduct binary classification. This baseline model is based on logistic regression CV. Logistic regression CV is a method for training a logistic regression model, a classification algorithm. It is similar to regular logistic regression but with an additional step of performing cross-validation to tune the regularization parameter. This allows the model to find a balance between model complexity and overfitting, resulting in improved performance compared to regular logistic regression. The trained model can then be used to make predictions on new data.  
The MCD model achieves the best accuracy compared with other methods. The DANN model ranks second place, but the accuracy curve of this model is violently oscillating. When the MCD model utilizes pseudo-label semi-supervised learning, it achieves a better average correct rate, and the correct rate curve is smoother. To evaluate the transferability of different models, this project utilized fault diagnosis data sets to train several popular models and compare their classification accuracy. The lowest requirement for the results of the DANN and MCD models is that they are higher than those of the baseline model. Otherwise, these models will have negative transferability and will be unfeasible. Finally, these revised models should achieve high enough average accuracy on the test data set.
## 5 Influence of Spatial Step in EMR Methods
### Memory Burden on Data Storage Devices  
The results reported in the previous subsection show that the correlation estimator has a few advantages over the EMR energy metric. However, in order to improve the fault detection accuracy further, the fault estimation stage should use the smallest spatial step possible. As highlighted in Table 4, it is quite clear that this was only possible when \(\Delta\)g = 10 m; only then, the fault position was estimated accurately. However, this would increase the volume of data (number of \(H_{f\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\  
It is clear that gigabytes (GB) of storage space are required to obtain accurate results. To address this issue and further improve the correlation estimator method, this work proposes a hybrid approach, which combines a correlation estimator with regression analysis.
# Optimization of Carsharing Fleet Placement in Round-Trip Carsharing Service
## 2 Related Work
### Shared Fleet Placement  
Shared fleet placement can be formulated into MCLP or FLP (especially in round-trip carsharing service). However, there are other factors to be considered. In previous works on MCLP and FLP, they already have a list of preferred locations. These possible sites are evaluated by considering convenience factors such as parking cost, the proximity to essential facilities, and accessing time as presented in [19; 20]. The solution was then a combination of selected sites to maximize user coverage. In fact, they are very similar to the facility location problem. [PERSON] and [PERSON] evaluated potential stations by the distance between the station and other facilities such as hospitals and train stations. They also had access to historical data to make a decision on where to place the station, which is not available in most cases [20]. Another popular approach is to locate the fleet by user demands [21; 22; 23]. [PERSON] et al. proposed an optimization model to maximize the user coverage based on the demand and predicted destinations [21]. On the contrary, [PERSON] et al. studied a method to identify the potential of city districts in a station-based one-way tripscenario where the demands were estimated from the taxi trips and customer profiles in Sao Paulo, Brazil [22]. Lastly, [PERSON] and [PERSON] proposed an idea for locating the fleet in round-trip carsharing by combining both user demands and proximity to other mean of transportation and other facilities into a model and utilized the open source data available from the government [23].  
There are also works which focus on electric carsharing fleets, reflecting the increasing awareness of environmental issues and benefiting from governmental support. The electric carsharing fleet is more complicated than its fossil-fuel counterpart considering the additional constraints for battery/electrical load management of the car. [PERSON] and [PERSON] proposed a model for a one-way electric carsharing service which considered previously mentioned factors [24]. Since charging is very important in electric carsharing service, [PERSON] et al. [25] formulated their model to consider a situation where the user changes the drop off station. The charging station location optimization was presented by [PERSON] et al. [26] where the authors based the location on the source and destination of trips in both simulation and Vienna. Another example was proposed by [PERSON] et al. [27] which consider a more realistic case where demand was stochastic and capacitated charging stations. In addition, the shared fleet placement is also studied in the bikesharing community where station locations and bike stocking are highly important as well [28; 29; 30; 31; 32].
# KQI Performance Evaluation of 3 GPP LBT Priorities for Indoor Unlicensed Coexistence Scenarios
## 2 Unlicensed Medium Access Mechanisms
### 3 GPP Standards  
As described in the description of the shared channel download of the 3 GPP standards [5] in 5-GHz unlicensed bands (including eLAA, FeLAA and NR-U) the use of Listen Before Talk is mandatory, although it is allowed to include or not the use of the licensed band for signaling in the uplink. To this end, several variants of LBT mechanism have been considered for DSCH unlicensed channel access.  
LBT in this context is a contention mechanism where the transmitter has to check the channel state before using it. It relies mainly on two basic processes: the clear channel assessment procedure (CCA) with energy detection (ED) threshold to sense the channel state for a defer period whether any signal (regardless of its kind) is present above a certain power value. As shown in Figure 2, when the channel is detected free, then the LAA station is allowed to transmit for a Maximum Channel Occupancy Time (MCOT). Otherwise enhanced CCA (eCCA) is activated and has to wait for a backoff period of time determined by a Contention Window (CW) measured as a number of time slots.  
Before standardization by 3 GPP, several variants or categories (CAT) of LBT were considered [14] for its implementation:  
* Category 1: No LBT procedure is performed* Category 2: LBT without random backoff with deterministic waiting time when the channel is found free
* Category 3: LBT with random backoff and fixed contention window size
* Category 4: LBT with random backoff and variable contention window size  
In the above mentioned study of coexistence [15] made by 3 GPP, it is concluded that the LBT category allowing a better coexistence with respect to WiFi due to the configuration flexibility of its parameters is Category 4 (CAT4). Under this category, which has finally been standardized, different priorities are defined. Each priority represents a predetermined set of values for the parameters implicated in the LBT process. As shown in Table 1, within this category, four priority classes [5] have been established according to MCOT and C\({}_{\text{w}}\) size values combinations.  
As observed, each of the priorities defines a combination of backoff time (given by C\({}_{\text{w}}\)) and transmission time. Taking into account this principle, Priority 1, having a lower backoff time, will have a higher probability of transmitting, although each transmission will have less time available. Thus, according to theoretical models proposed [16], Priority 1 should offer a higher performance than Priority 3 or 4.
## 5 Experimental Results
### Experiment 1: Intrusion Detection without Feature Filtration  
In this experiment, forty-one features were used with GA and CS. Several parameters must be set and the optimal settings must be specified. These parameters include the following.  
* Crossover probability (Pc).
* Mutation probability (Pm).
* Number of generations (G).
* Number of solutions (Nests).
* Abandoned probability (Pa).  
After studying various combinations of parameters and parameter settings, we determined the best values for these parameters that could produce the best DR and AC results (Table 1).  
\begin{table}
\begin{tabular}{c c c c c c c}
**Pm** & **Pc** & **(G)** & **(Nests)** & **(Pa)** & **DR\%** & **AC\%** & **FAR\%** \\
0.1 & 0.7 & 1000 & 50 & 0.3 & 93.70 & 84.33 & 6.3 \\ \end{tabular}
\end{table}
Table 1: Parameter settings and the associated detection rate and accuracy values.  
Using all 41 features of the KDD99 dataset, we found that the best values for Pm, Pc, G, Pa, and Nests were 0.1, 0.7, 1000, 50, and 0.3, respectively (Table 1). The associated DR, AC and FAR values were 93.70%, 84.33% and 6.3, respectively. Although these DR and AC values are somewhat good and the FAR needs an adjustment to gain better results. The researchers considered that reducing the number of features to process by identifying the most meaningful features and excluding the unimportant features can lead to ID results and minimize the FAR result.
# Hemodynamic Analysis on the Anastomosis Angle in Arteriovenous Graft Using Multiphase Blood Model
## 3 Numerical Details
### Grid Independence Test  
The computational grid consists of hexahedron meshes. The number of grids was selected at about 410,683 through a grid independence test based on the flow velocity at Line (D), as shown in Figure 5. Grid independence was performed at a steady flow. An important factor in the analysis of this study, such as WSS, is dominantly influenced by the flow near the wall. For the number of grids, the smallest number of grids in which the flow velocity (Y \(\sim\)\(-0.9\) and \(0.9\)) near the wall differs by less than 5% from the result of the maximum number of grids was selected.  
\begin{table}
\begin{tabular}{l c c c c c} \hline  & **Patient 1** & **Patient 2** & **Patient 3** & **Patient 4** & **Mean** \\ \hline Graft diameter (mm) & 6.0 & 6.0 & 6.0 & 6.0 & 6.0 \\ Vein diameter (mm) & 7.0 & 8.7 & 7.1 & 9.2 & 8.0 \\ Artery diameter (mm) & 4.0 & 3.0 & 3.9 & 4.1 & 3.75 \\ Anastomosis angle & 15 & 18 & 21 & 30 & \\ \hline \end{tabular}
\end{table}
Table 1: Diameter and anastomosis angle of the patients.  
Figure 4: Velocity profile throughout the cardiac cycle at the graft and vein.
# Assessment of Strength Development at Hardened Stage on High-Strength Concrete Using NDT
## 2 Experimental Procedure  
### Materials  
Table 1 shows the physical properties of the used materials. The cement used was ASTM type I ordinary Portland cement (OPC, Yeongwol, Ssangyong Cement Industrial, Korea). This cement has a density of 3150 kg/m\({}^{3}\) and a fineness of 320 m\({}^{2}\)/kg. Table 2 shows the chemical compositions of the cement used.  
Figure 1 shows the gradation sieve analysis curves of the aggregates. Washed sea sand was used as the fine aggregate (fineness modulus: 2.90, density of 2590 kg/m\({}^{3}\), and absorption of 1.00%).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Material** & **Property** \\ \hline Ordinary Portland cement (OPC) & ASTM Type I OPC \\  & Fineness: 320 m\({}^{2}\)/kg, density: 3150 kg/m\({}^{3}\) \\ \hline \multirow{2}{*}{Fine aggregate} & Washed sea sand \\  & Fineness modulus: 2.90, absorption: 1.00\%, density: 2590 kg/m\({}^{3}\) \\ \hline \multirow{2}{*}{Coarse aggregate} & Crushed granite aggregate \\  & Size: 25 mm, absorption: 0.90\%, density: 2600 kg/m\({}^{3}\) \\ \hline \multirow{2}{*}{Chemical admixture} & Polycarboxylate (PC)-based superplasticizer with retarder \\  & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical properties of materials used in study.  
For the coarse aggregate, crushed granite aggregate was used, with a size of 25 mm, density of 2600 kg/m\({}^{3}\), and absorption of 0.90%. To improve the workability of concrete, a polycarboxylate (PC)-based superplasticizer with retarder was used as the chemical admixture.
## 1 Introduction and Background
### Solutions?  
What do researchers and practitioners do when they face low-bandwidth large-data scenarios? Despite the \"bandwidth crunch\" worries, it is likely that in the short term we can expect further development in the technology. That is, the bandwidth itself may get \"larger\" and cheaper. However, the rate of data production always competes with the availability of computational resources. Sensors get better, faster and cheaper, leading to high quality data in large quantities. Additionally, with Web 2.0 approaches more people create and publish new kinds of information as more data becomes available. There is a strong interdisciplinary effort in dealing with the problem of \"large data sets\" from various different aspects and there will always be an interest in intelligent data management in scientific discourse; particularly in fields such as landscape visualization and other domains where geographic data is used. As demonstrated, geographic data tend to be large, and since the amount of transferred geographic data available to transfer constantly increases, solutions need to be provided _beyond_ the hope for increasing the bandwidth. In the remaining sections of this paper, we systematically review the current hardware, software, and user-centric approaches to tackle the bandwidth and resource issues when working with large data sets.