esa-sceva/satcom-chunk-collection huggingface dataset:
# Environmental Footprint Assessment of a Cleanup at Hypothetical Contaminated Site
## 6 Conclusions and Future Work  
The environmental footprint of a cleanup at a hypothetical contaminated site is analyzed by using EPA's Spreadsheet for Environmental Footprint Assessment (SEFA). The effect of renewable energy integration into the remedial system is studied by considering three scenarios: cleanup without any renewable energy sources at all, cleanup with a small share of renewable energy sources, and cleanup with a large share of renewable energy sources. It is observed that the greatest contributor in the overall environmental footprint for this cleanup activity is grid electricity due to the fuel mix used in Pakistan. However, integration of renewable energy systems onsite and voluntary purchase of renewable energy can reduce the CO\({}_{2}\), NOx, SOx, PM and HAP emissions up to 29% if done on a small scale, and up to 68% if done on a large scale.  
The study presented here can be extended further by taking an actual contaminated site and applying this methodology to analyze the environmental footprint of remediation with possible shares of renewable energy resources. Owing to abundant solar resources and excellent wind power class in Pakistan, there is an immense need to incorporate these renewable energy sources in the remediation and to accurately predict the realistic reduction in CO\({}_{2}\), NOx, SOx, PM and HAP emissions, thereby promoting green remediation across the country.  
Conceptualization, M.A.A.K., Z.Q., and M.A.; methodology M.A.A.K., M.A. and soft-ware, M.A.A.K., Z.Q.; validation, M.A.; formal analysis, M.A., Z.Q. investigation, M.A.A.K., Z.Q. and M.A. resources, A.Z.K.; data curation, M.A.P.M.; writing--original draft preparation, M.A.A.K., Z.Q. and M.A. funding acquisition, M.A.P.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 9: Wind energy potential of Pakistan [28].
# Multivariate Regression Modeling for Coastal Urban Air Quality Estimates
## 3 Results
### Scatter Plot and Empirical Regression Formula  
Figures 6-8 show scatter plots to display the comparison between the predicted and measured concentrations of PM\({}_{10}\), PM\({}_{2.5}\), NO\({}_{2}\), SO\({}_{2}\), O\({}_{3}\) and CO calculated by multivariate predictive regression models in Table 2. The scatter plots to test the goodness of the estimations of each variable present a model fitting with the calculated vs. the measured values.  
Figure 6: Scatter plots between the measured and predicted concentrations of PM\({}_{10}\) (**a**), PM\({}_{2.5}\) (**b**) (\(\mathrm{ug/m^{3}}\)) and NO\({}_{2}\) (\(\times\)1000) (**c**), SO\({}_{2}\) (\(\times\)1000) (**d**), O\({}_{3}\) (\(\times\)1000) (**e**), and CO (\(\times\)100) (**f**) (ppm) before the yellow sand event from 21 March to 21 March 2015 in Kangnung, Korea.  
The predicted values for the six hourly variables and their measured values in Figure 6 (as shown in Tables 2-4) are very highly correlated before (0.957, 0.906, 0.886, 0.795, 0.864 and 0.932), during (0.936, 0.982, 0.866, 0.917, 0.887 and 0.916) and after the yellow sand period (0.919, 0.945, 0.902, 0.857, 0.887 and 0.892), respectively. The linear regression equations in the scatter plots can also be easily used to calculate the predicted concentration of each variable from both the three-hours-earlier measured values in Kangnung and two-days-earlier measured values in Beijing.  
The scatter plots to test the goodness of the estimations of each variable present a model fitting with the calculated vs. the measured values. The predicted values for the hourly PM\({}_{10}\), PM\({}_{2.5}\), NO\({}_{2}\), SO\({}_{2}\), O\({}_{3}\) and CO concentrations and their measured values in Figure 6, as shown in Table 2, are very highly correlated before (0.957, 0.906, 0.886, 0.795, 0.864 and 0.932), during (0.936, 0.982, 0.866, 0.917, 0.887 and 0.916) and after the yellow sand period (0.919, 0.945, 0.902, 0.857, 0.887 and 0.892), respectively. The linear regression equations given in the scatter plots can be easily used to calculate the predicted concentration of each variable from both the three-hours-earlier measured values in Kangnung and two-days-earlier measured values in Beijing.  
Correlation coefficients between the predicted values and the measured concentrations were very high, over 0.864 for all periods, except for SO\({}_{2}\) (0.795, before), NO\({}_{2}\) (0.866, during) and SO\({}_{2}\) (0.857, after). These empirical regression equations to calculate the predicted values of PM\({}_{10}\), PM\({}_{2.5}\), NO\({}_{2}\), SO\({}_{2}\), O\({}_{3}\) and CO are very useful and convenient practically and can be used instead of using the multivariate regression equations in Tables 2-4 with three-hours-earlier measured values for each original PM, gas and meteorological element.
## 3 Network Model
### Energy Cost  
We consider the energy model proposed by [PERSON] (2008) to simulate the energy cost of sensors during operation time. Note that sensors are the only devices which are subject to energy limitations in our model. In this formulation, a sensor \(i=(x,y)\in S_{s}(t)\) where \(x\in[0,d_{x}]\) and \(y\in[0,d_{y}]\) sends a number of packets \(P_{i}(t)\) at time \(t>0\) given by  
\[P_{i}(t)=1+Rp_{i}(t). \tag{1}\]  
This formulation considers the number of packets captured by \(i\), a packet per instant time, and the number of packets relayed by the sensor \(i\), \(R_{p_{i}}(t)\), because of the multi-hop routing protocol, which is given by  
\[Rp_{i}(t)=\sum_{j\in\{S_{s}(t)-i\}}z_{j,i}^{c}(t), \tag{2}\]  
where \(z_{j,i}^{c}(t)\) is a variable assuming 1 if \(i\in S_{s}(t)\) is in the minimum path between \(j\in S_{s}(t)\) and the sink node \(c\) at \(t>0\), and 0 otherwise.  
The energy cost \(Ee_{i}(t)\) of a sensor \(i\) at time \(t\) is given by  
\[Ee_{i}(t)=P_{i}(t)\ \beta\ amp\ k\ (||i-w_{i}^{c}(t)||_{d})^{\alpha}, \tag{3}\]where \(||\cdot||_{d}\) is the Euclidean distance between two points, \(\beta>0\) is the transmission quality parameter, \(amp>0\) is the energy cost per bit of the power amplifier, \(k>0\) is the information packet size in bits, \(w_{i}^{c}(t)\) is the variable providing the next device in the minimum path between \(i\in S_{s}(t)\) and the sink node at time \(t>0\), and \(\alpha\in[2,4]\) is the path loss exponent. Thus, the energy charge \(Ec_{i}(t)\) of a sensor \(i\) at time \(t\) is given by  
\[Ec_{i}(t)=\left\{\begin{array}{ll}Ec_{i}(t-1)-Ec_{i}(t)&\quad\text{if $t>0$}\\ ice&\quad\text{if $t=0$}\end{array}\right., \tag{4}\]  
where \(iec\) denotes the initial energy charge of the sensors, for \(iec>0\). Hence, if \(Ec_{i}(t)\) equals zero, the sensor is out of energy, and then it cannot be linked. Otherwise, it is active.
## 4 Results and Discussion
### Estimation of Elapsed Time since the Last Earthquake (t) and Average Recurrence Interval (R)
#### 4.2.2 Maq Segment  
The seismic activity along the Maqu fault is clearly weaker than that along the Maqin fault. Ancient earthquake data of this period were also analyzed intensively by predecessors (Figure 9). There are two trenches near the Xike River and four near Maqu County [2; 25; 26]. In this paper, we compare the research results of previous studies and derive the elapsed time of earthquakes and the average recurrence interval of earthquakes. By comparing the three profiles of ancient earthquakes near Maqu County, [PERSON] (2006) used the successive restriction method proposed by [PERSON] (1996) for ancient earthquake events to obtain comparatively accurate events. Four events occurred during the Holocene Epoch, considering that the first and second times of ancient earthquakes are relatively reliable [12; 25]. However, because the number of prospecting trenches is small, the record of ancient earthquakes is still incomplete. [PERSON] (2008) recognized five ancient earthquake events by lamination and sampling of three natural outcrops and two trench sections in the Maqu segment [26]. The location of trench sections among them matches the [PERSON] (2006) research result, while the obtained age result is also similar. In this paper, by comparing the research results of previous studies, we further combine the sampling positions of outcrops and trench  
Figure 8: **(A)** Distribution map of the Maqin segment and adjacent segments of the Eastern Kunlun fault. **(B)** The occurrence ages of paleoearthquakes since 12 ka are compiled [25; 26; 27].  
profiles and dating methods to describe seven ancient earthquake events that are relatively reliable: the first is 1210 \(\pm\) 40 Cal a BP; the second is 1730 \(\pm\) 50 to 2530 \(\pm\) 40 Cal a BP; the third is 3736 \(\pm\) 57 Cal a BP; the fourth is 4850 \(\pm\) 40 Cal a BP; the fifth is 6100-6700 Cal a BP; the sixth is 8590 \(\pm\) 70 Cal a BP; and the seventh is 9000-10,000 Cal a BP. We can determine that the past event of the most recent ancient earthquake is 1210 \(\pm\) 40 Cal a BP, and the average recurrence time period of ancient earthquakes is approximately 1465 a.
# T-Type Vertical Wall for Decoupling and Pattern Correction of Patch Antenna
## 2 Antenna Configuration and Parametric Analysis
### Configuration  
The configuration of the proposed vertical wall is shown in Figure 1, where Figure 1a,b correspond to the whole structure and its side view, and Figure 1c,d indicate the details of the designed decoupling wall. The patch antennas are parallel along their H-plane with an inner edge-to-edge spacing of 2.58 mm, which is about \(\lambda_{0}/20\). The two symmetric T-type metal strips are etched on a dielectric substrate wall and inserted between the patch elements forming the vertical wall in the middle. A rectangular hole is cut on the substrate of the radiating patches to accommodate the vertical wall exactly. It should be clear that the vertical metal wall is connected to the ground plane of the radiating patches.  
The patch antenna is a conventional rectangular that is fed by microstrip lines, as shown in Figure 1a. A quarter-wave transformer is employed between the feeding strip and patch to adjust the impedance matching. The substrate is F4 BTM440 with a thickness of 1.6 mm, and the corresponding dielectric constant and loss tangent are 4.4 and 0.0033, respectively. The vertical wall substrate has a higher loss whose loss tangent is 0.025 from the FR4 to increase the dielectric loss on the wall. As mentioned above, the two symmetrical T-shape metals are etched on both sides of the FR4.  
Figure 1: Antenna configuration and decoupling wall: (**a**) The whole structure, (**b**) side view of the proposed antenna, (**c**) front view of the wall, and (**d**) detailed wall.  
Depending on the above structure, we obtained the final values for the related variables shown in Figure 1 by Ansys HFSS 2019, as listed in Table 1. The value of \(H_{2}\) is 1.9 mm, which is a little higher than the thickness of 1.6 mm of the antenna substrate. This means that the parallel strip of the T-type metal is not immersed in the substrate, so its primary function is to suppress direct radiation to the adjacent element.
# Knowledge Reasoning via Jointly Modeling Knowledge Graphs and Soft Rules
## 4 Experiments and Results
### Influence of Confidence Levels  
Additionally, we evaluate the effect of the rules' confidence thresholds on FB15K. Since there are different rules and ComplEx does not merge rules, we refer only to their fixed results on FB15K. We set all hyperparameters to their optimum values and change the confidence threshold within [0.5, 1] in 0.05-step increments. Both SoLE and Iterlogic-E use rules with confidence levels greater than this threshold. Figure 4 displays the MRR and HITS@1 values obtained by Iterlogic-E and other baselines on the FB15K test set. We make the following observations: 1) Iterlogic-E beats both ComplEx and IterE at varying confidence levels. This demonstrates that Iterlogic-E is sufficiently robust to deal with uncertain soft rules.  
Runtime  
At last, we compared the running time of Iterlogic-E(ComplEx) and ComplEx methods. In general, the running time of the hybrid reasoning model is longer than its basic embedding model because of the addition of rule mining and the introduction of new facts (Notably, running times cannot be directly compared with other hybrid reasoning methods due to differences in code languages and deep learning frameworks. For example, SLRE is implemented in Java and only has a CPU-running version). Iterlogic-E (ComplEx) took an average of 4.6 s and 4.2 s per 100 batches on the FB15K dataset and DB100K dataset, while ComplEx took 4.6 s and 4.2 s on average. The times are very close, mainly because the method does not introduce new model parameters and only slightly increases the calculation time. Rule mining took 7.97 s and 3.63 s on those two datasets, and rule reasoning took 8.6 s and 7.98 s, respectively. Note that rule mining will only be executed once, while rule reasoning will only be executed two to five times, which is negligible compared to the total time spent training over 100,000 batches.
## 3 Method
### Experimental Framework  
#### 3.4.1 Dataset Preparation and Labeling Methodology  
The experimental dataset comprised 48,000 gesture samples collected from 32 participants (18 males, 14 females, age: 22-45 years, \(\mu\) = 31.2, \(\sigma\) = 6.8). Each participant performed 15 distinct gesture types across three environmental conditions, yielding a balanced dataset with 3200 samples per gesture class. The labeling process employed a dual-verification protocol: (1) automated temporal segmentation using 60 GHz radar velocity thresholds (vthreshold = 0.3 m/s), followed by (2) manual validation by two independent annotators achieving 94.7% inter-rater reliability ([PERSON]'s \(\kappa\) = 0.892, 95% CI [0.878, 0.906]).  
Class distribution maintained strict balance through stratified sampling:  
* Emergency gestures (Class 3): 3200 samples (6.67%);
* Navigation commands (Classes 1-2): 6400 samples (13.33% each);
* Non-command motions (Class 0): 38,400 samples (80%).
# Electromagnetic Field-Aware Radio Resource Management for 5G and Beyond: A Survey
## 5 Challenges and Open Research Topics
### Open Research Topics
#### 5.2.5 Joint Base Station and Active RIS Precoding Optimization  
Future research should develop joint optimization algorithms for base station and active RIS precoding while addressing both EMF exposure and power constraints [75; 76; 77]. Techniques like fractional programming and deep reinforcement learning can be employed to enhance efficiency.
# Secure Quantum Communication Technologies and Systems: From Labs to Markets
## 3 Devices for QKD Systems
### Quantum Random Number Generators
Photonic chip-based implementations have high potential to reduce cost and footprint and improve scalability for mass production. Although generation rates at the Gbit/s scale have been demonstrated in laboratory environments, real-world implementation is still constrained by practical challenges in the speed of the electronic subsystems and post-processing methods.
# A Twitter-Based Comparative Analysis of Emotions and
## 5 Discussion
### Emotion Analysis  
5.1.1. Individualism vs. Collectivism  
According to [PERSON] [23], the values that differentiate nations can be statistically categorized into five clusters: power distance (PDI), individualism versus collectivism (IDV), masculinity versus femininity (MAS), uncertainty avoidance (UAI), and long-term orientation (LTO) (LTO). Of these aspects, we consider individualism vs. collectivism (IDV) to draw a comparison between Arab and Hispanic football fans and ignore other aspects, as IDV is the most frequently studied [PERSON] dimension to date [24], especially in development communities. Individualist (IDV) cultures, according to this model, are distinguished by their emphasis on personal liberties and accomplishments. People are typically socialized to be independent, autonomous, and competitive in such communities. On the other hand, individuals raised in very collectivist societies typically place more value on interdependence than independence, emphasizing communal harmony over individual success. Personal interest and psychological autonomy are generally put aside in favor of the cohesion and welfare of the larger social group. We should notice that a low IDV characterizes societies of a more collectivist nature with close ties between individuals.  
According to [PERSON]'s model, the Arab world has an IDV score of 38. Latin America does not have an IDV score, since [PERSON] did not calculate the cultural dimensions of Latin America as a distinct entity, as in the Arab world. Therefore, we used Argentina's score to represent the Hispanics, as the competition was between Argentina and Saudi Arabia in the first place. Consequently, we deemed Argentina to be the representative of Latin America. Argentina's IDV score was 46. Thus, we noticed that the IDV score of the Arabic-speaking fans was lower than that of the Spanish-speaking fans (38 vs. 46). Accordingly, as a high IDV suggests a low amount of societal interdependence, whereas a low IDV suggests a more collectivist outlook, Arabs are more collectivistic than Hispanics.  
Additionally, according to [25], Arabic- and Spanish-speaking individuals both live in collectivistic cultures. Nonetheless, there may be some cultural variances between them, as Latin America has low collectivism scores while the Arab world has moderate scores. In the same vein [26], a comparison between nations in terms of collectivism revealed that, in general, those from Arab countries, Eastern Europe, Africa, and to a lesser extent, Latin Americans, accept collectivist beliefs more strongly. Even while the differences are moderate, they are more obvious than in the case of individualist ideas.  
As a result, it appears, from the literature, that Arab nations and, to a lesser extent Latin Americans, accept collectivism. Arabs are therefore more collectivistic than Latin Americans. Figure 5 depicts the relationship between individualism and collectivism and how Arabs and Hispanics fit within it.
## 2 A New Transient Numerical Model
### Momentum Conservation  
The momentum equation of the liquid slug is as follows:  
\[\frac{dm_{l}v_{l}}{dt}=F_{p}-F_{f}-F_{\sigma}+F_{g} \tag{25}\]  
where \(F_{p},F_{f},F_{\sigma}\), and \(F_{g}\) represent the driving force, the frictional resistance, the capillary resistance, and the gravity of the liquid slug, respectively. The driving force of the liquid slug is calculated based on the pressure difference between the two adjacent vapor plugs:  
\[F_{p}=(P_{i-1}-P_{i})A_{c} \tag{26}\]  
The frictional resistance of the liquid slug is calculated by:  
\[F_{f}=f_{\tau}\frac{\rho_{l}Le_{l}u^{2}}{2d_{i}}A_{c} \tag{27}\]  
where \(f_{\tau}\) is the friction factor. For the straight section in the PHP, the friction factor is:  
\[f_{\tau}=\left\{\begin{array}{ll}64/\text{Re}&\text{Re}<2000\\ 0.316\text{Re}^{-1/4}&\text{Re}\geq 2000\end{array}\right. \tag{28}\]  
The turns of the PHP will cause the additional pressure loss of the working fluid, and the friction factor in the turns can be calculated by:  
\[\Delta p_{f}=\frac{\rho_{l}u^{2}}{2}\bigg{(}\frac{f_{\tau}Le_{l}}{d_{i}}+K \bigg{)} \tag{29}\]  
where the loss coefficient (K) of the turn is calculated as follows:  
\[K=\left[0.131+0.1632\bigg{(}\frac{d_{i}}{R_{turn}}\bigg{)}^{3.5}\bigg{]}\bigg{(} \frac{\theta}{90^{\circ}}\bigg{)}^{0.5} \tag{30}\]  
The capillary resistance of the working fluid cab be calculated by:  
\[F_{\sigma}=\pi d_{i}\sigma(\cos\theta_{r}-\cos\theta_{a}) \tag{31}\]The advancing contact angle of the working fluid is related to the static contact angle and the capillary number (\(Ca\)):  
\[\cos\theta_{a}=\cos\theta_{e}-2(1+\cos\theta_{e})Ca^{0.5} \tag{32}\]  
where \(Ca=\mu_{l}u/\sigma\). According to [PERSON] et al. [19], the receding contact angle can be calculated by:  
\[\theta_{r}=\theta_{e}-(1-\eta)\times(\theta_{a}-\theta_{r}) \tag{33}\]  
where \(f\) is set to 0.74 according to the visual experiment by [PERSON] [29], and the static contact angle \(\theta_{e}\) is 52\({}^{\circ}\)[30]. The gravity force on the liquid slug is calculated by:  
\[F_{g}=\sum_{i}\rho gA_{c}dx\cos\beta \tag{34}\]  
where \(\beta\) represents the angle between the gravity direction and the flow direction.
## 4 Engineering Application  
We applied the AD_SI model on the K section of a subway tunnel construction project in Nanjing and analyzed its application performance. The AD_SI model allows for dynamic and continuous model training and testing during the shield construction process and continuously adjusts the model's training data according to the anomaly detection results. Therefore, this model can adapt to the dynamically changing external environment.
# Characterizing the Impact of Data-Damaged Models on Generalization Strength in Intrusion Detection
## 4 Results of Global Two-Class Models
### Exposure to Unseen Data
#### 4.2.3 Other Classifiers  
The metrics for a logistic regression show that the features must be normalized to be a decent classifier. It has an upward trend with respect to the training volume, but it is not steep. Generally speaking, this upward trend stays intact when removing features. As expected, the absolute values for the metrics are lowered when reducing features, albeit not by much. At 30% training volume and top-5 features being removed (among others contaminating timestamp being removed) on CIC-IDS2017, it manages to classify the samples of CSE-CIC-IDS2018 with a recall score of 90.3%, precision of 53.8% and balanced accuracy of 77%. The class separation is well above chance, but still not sufficiently high to be able to recommend the method as a reliable classifier.  
A support vector machine with a linear kernel has results similar to those of the logistic regression, but there is almost no upward trend and its classification performance is damaged more by feature removal. Its best result is obtained with normalized features, the top-5 of which have been removed and at training volumes of between 0.1 and 1% (recall: 90.5%, precision: 51.5%, balanced accuracy: 77%).  
Switching the kernel to the radial basis function has the interesting property of topping out higher, but only for min-max-scaled features. Recall and precision move in opposite directions to one another with regard to the amount of data used for training, regardless of feature removal. With 25% training volume on CIC-IDS2017, recall climbs from 92.2 (top-0 features removed) to 99.45% (top-10 features removed), while precision at the same points drops from 60.5% to 50.3%. A minority of result points have not been collected for this algorithm due to the excessive run time of the algorithm (>1 day per run, caused by the implementation that locks execution to a single core). This classifier could benefit from feature selection in the standard manner (with the removal of poor features instead of the removal of top features). It scores the highest overall, but the time required to train and subsequently evaluate samples holds this algorithm back.