esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Particulate Matter and Carbon Dioxide on Students' Emotions in a Smart Classroom
## 4 Discussion
The regression analysis for sadness revealed a positive relationship with PM\({}_{1}\) levels, with a linear coefficient of 0.961 (\(p<0.001\)), indicating that higher PM\({}_{1}\) levels are initially associated with increased feelings of sadness. The negative quadratic coefficient of \(-0.636\) (\(p<0.001\)) suggests a decrease in sadness at higher PM\({}_{1}\) levels, highlighting a nonlinear relationship. This pattern points to an inverted U-shaped curve, where sadness increases with initial increases in PM\({}_{1}\) but begins to decrease as PM\({}_{1}\) levels continue to rise. While the literature does not specifically address PM\({}_{1}\), our findings are consistent with general research linking particulate matter to negative emotional responses [25; 26; 27], including an increase in mental health emergency visits among young people [28]. In addition to sadness, other emotions such as happiness and surprise also exhibited significant nonlinear relationships with PM\({}_{1}\) levels. For happiness, we found a negative linear coefficient (\(\beta=-0.954\), \(p<0.001\)) and a positive quadratic coefficient (\(\beta=0.552\), \(p<0.001\)), indicating a U-shaped relationship. This suggests that happiness decreases with initial increases in PM\({}_{1}\) but begins to rise again at higher levels of exposure. Similarly, surprise showed a negative linear coefficient (\(\beta=-0.705\), \(p<0.001\)) and a positive quadratic coefficient (\(\beta=0.496\), \(p<0.001\)), reflecting a comparable pattern. These findings imply that the impact of PM\({}_{1}\) on emotional responses is complex and varies across different emotions, necessitating further investigation to understand the underlying mechanisms. Overall, our findings suggest that poor air quality is associated with negative emotional responses. However, more research is necessary to fully understand the nuances of how fluctuations in air quality affect emotions. Constructs like Affective Sensitivity to Air Pollution (ASAP) [29], offer promising frameworks for further exploration of how individual sensitivity to air quality impacts emotional well-being. In addition to the emotional effects, it is important to acknowledge the well-documented cognitive and health impacts of poor air quality. Elevated levels of CO\({}_{2}\) and particulate matter (PM) have been shown to reduce cognitive performance, particularly in tasks requiring concentration and problem-solving [19; 20]. This cognitive decline may also influence emotions, as lower cognitive function can lead to frustration, stress, and disengagement in learning environments. Furthermore, poor IAQ is associated with adverse health outcomes [14]. These health problems can exacerbate emotional distress, particularly in children. The overlap between cognitive, physical, and emotional effects highlights the importance of addressing air quality comprehensively in educational settings.
# Ensemble Deep Learning on Time-Series Representation of Tweets for Rumor Detection in Social Media
## 6 Results and Discussions
### General Observations  
In case of micro-F1 score, the ensembles I-1, I-2, and I-3 obtained their best micro-F1 scores for \(T=60\) min w.r.t the chosen batch input sizes. The only exception is where the micro-F1 score of the ensemble I-3 is lower than its own micro-F1 scores when \(T=2,10\) and 30 min when batch input size set to 16. This means that \(T=60\) min is appropriate for effective detection of rumors. In case of macro-F1 score, the best performances of the ensembles I-1, I-2, and I-3 are varied for each batch input size across \(T\), which means based on the need we can choose an ensemble model and select an appropriate time interval.  
As discussed earlier, we have seen the same behavior for the 10 min time interval, which caused most of the ensemble implementations to perform poorly for both micro and macro averaging schemes. In addition to that, ensemble I-3 again showed low performance in 5 min time intervals under both averaging schemes.  
By observing the above results, varying the hyperparameters batch input size and learning rate resulted in producing similar kinds of behavior in the ensembles. In general, when micro-averaging is used, both hyperparameter variations supported higher time interval values for better performance. In the case where a macro-averaging scheme is employed, time intervals 2 and 60 min helped ensembles I-1 and I-2 to perform well. However, ensemble I-3 still achieved better performance when \(T=60\) min. As all ensembles are performing well with 60 min time interval, it is a good choice to achieve decent performance regardless of variations in chosen batch input sizes and learning rates. For \(T=60\), the generated time-series data will have lesser data sparsity than that of other values of \(T\) that make the feature space short for the conversation samples. This may be the reason for all ensembles to perform better at higher time intervals, especially ensembles with base learners designed using LSTM layers.  
Another key observation is that, for all ensembles, 2 and 60 min time intervals are shown to have good performance. However, there is no sweet spot for the ensembles for other values of \(T\). This observation is critical in applying the proposed model depending on the goal. For instance, if early detection is needed we can pick a small time interval value such as \(T=2\) min by sacrificing a little amount of prediction performance. In case of effective prediction is important, we can set time interval to a higher value, for example, \(T=60\) min.
## 3 Results and Discussion
### Method Validation
#### 3.2.1 System Suitability  
Suitability control parameters depend on the procedure being validated and are intended to ensure that system performance is adequate for the proposed analysis. The main parameters tested, based on the USP pharmacopoeia, are listed in Table 2.  
According to the APIs retention times (tr), ferulic acid is the first eluted tr = 4.4, followed by resveratrol and quercetin and then by retinol and \(\alpha\)-tocopherol. Considering the logP values of the analytes, this order was expected, as ferulic acid is the most polar compound, while \(\alpha\)-tocopherol is the most lipophilic. Their tailing coefficient values (Tf) were generally acceptable and within the range of 0.8-1.8.
## 3 Proposed Methodology
### Data Acquisition  
The significant number of threats against network-based information services and the resilience of critical infrastructures constantly overlap. Profiling suspicious activities and taking prompt actions to evade or mitigate the possibility of an impact on security are a still big challenge. In an effort to provide robust and acute-sensing ML-based implementation, a meaningful portion of features must be directly reflected on the size and diversity of the collected observations. SL algorithms aim to make sense of the information provided by an external source; in this regard, a proper collection of samples is required to generalize the most important aspects to which data are exposed. In particular, a dataset extracted from real networks must reflect the innate behavior of adversaries attempting an attack. Therefore, it is not affordable to simulate the attack surface using simulated or mathematical models that can directly influence the usual state of the environment [17; 44]. In this work, data acquisition was performed by examining specific datasets that reported new threats, authentic botnet and intruder footprints, regular traffic generated by benign applications alongside genuine network packets mirroring working-hour scenarios. The datasets used to accomplish the proposed methodology are described below:  
* ISCX-Bot-2014: Provided by The Canadian Institute for Cybersecurity in which 16 different types of botnets are reported. These are, Neris, Rbot, Menti, Sogou, Murlo, Virut, NSIS, Zeus, SMTP Spam, UDP Storm, Tbot, Zero Access, Weasel, Smoke Bot, Zeus Control (C and C), and ISCX IRC bot. A total size of 5.3 GB of packet captures is formed by benign and malicious network traffic. As advised, 43.92% of overall network captures were identified as potentially malware-based. ISCX-Bot-2014 was built upon the consolidation of three main collections: 1. ISOT dataset [46]: integrates various projects, particularly the Honeynet Project [47], Ericsson Research [48], and Lawrence Berkeley National Laboratory Research [49], each containing malicious traces from well-identified botnets like Flow Storm and Zeus. As for non-malicious connections, sources primarily came from game packages, HTTP traffic, and P2P applications. 2. ISCX 2012 IDS Dataset [50]: crafted to be realistic, i.e., to include benign (HTTP, SMTP, SSH, IMAP, POP3, and FTP) and malicious (Botnet ISCX IRC) traffic produced by real devices. 3. Malware Capture Facility Project [51]: the design of this dataset aims to generate and capture various traces of botnets, and it incorporates eight different kind of botnets (Neris, Rbot, Virut, NSIS, Menti, Sogou, and Murlo).
* Coburg Intrusion Detection: provided by the University of Coburg in Germany, it was designed to gather real evidence of network intruders on the basis of anomaly-detection records and logs. Data mostly comprise network flows from small-business environments, including several email clients between rogue web-based modules and abnormal responses from multiple attacks, such as ping scanning, port scanning, brute force, and DoS.
## 3 Results
### Identification of VOCs as Basis to Substitute Rhizomes with Leaves  
In line with the objective of the study, to evaluate whether leaves can be used as substitutes for rhizomes, field data were prioritized and used to identify the volatiles with similar compositions in both tissues. This is because the previously reported information from SIMCA(r) and the log\({}_{2}\)-transformed values explained that tissues from the field contain significantly higher numbers and relative abundances of VOCs. Moreover, _K. parviflora_ plants are normally cultivated in field conditions for commercial purposes.  
Based on the log\({}_{2}\)-transformed values, it was already mentioned that a total of 11 leaf volatiles had similar relative abundances of VOCs with those of the rhizomes in the field conditions (\(p\)-value \(>\) 0.05). These are humulene, caryophyllene, 1,4-dichlorobenzene, mmentha-1(7),8-diene, carved, nerolidol, bicyclo [4.2.0]-oct-1-ene, 7-endo-ethenyl-, methyl palmitate, \(\beta\)-myrcene, allo-aromadendrene epoxide, and caryophyllene oxide. Box plot diagrams for these compounds were created based on the normalized peak intensities (Figure 6). However, most of these compounds have low peak intensities of <30 (Figure 6a). Only \(\beta\)-myrcene and caryophyllene have high peak intensities of >100 (Figure 6b). There is a possibility that any of these 11 compounds could be used as a basis to substitute rhizomes with leaves.
# Safety Analysis of Secondary Lining of Yulinzi Tunnel Based on Field Monitoring
## 5 Numerical Simulation Analysis of Long-Term Stress on Secondary Lining
### Surrounding Rock Constitutive Model and Parameters  
The constitutive model used for the surrounding rock was cvisc, which is an integrated model in the FLAC3D software. This model combines the creep constitutive model, Burgers model, with the plastic constitutive model, Mohr-Coulomb model. It can reflect both creep behavior and plastic behavior. The components of the cvisc model are shown in Figure 19.  
Based on the physical and mechanical experimental data of laboratory soil and the calculation method of creep parameters in reference [28], the mechanical parameters of the surrounding rock were obtained, as shown in Table 6.  
The process of tunnel excavation can be divided into five stages: upper bench excavation, middle bench excavation, lower bench excavation, invert excavation, and secondary lining construction.
# Recovery of Polyphenols from Agri-Food By-Products: The Olive Oil and Winery Industries Cases
## 2 Polyphenols from Olive Oil Production Wastes: Source and Applications
Olive pomace is rich in hydroxytrosol but also has a significant content in oleuppein, tyrosol, caffeic acid, \(p\)-coumaric acid, vanillic acid, verbascoside, elenolic acid, catechol, and rutin [18]. It is estimated that olive pomace production is higher than 2.8 million tons/year worldwide [18; 19]. Olive mill wastewater is the liquid residue composed by the olive washing waters, olive pulp water, water added to olive paste in the centrifugation step of the three-phase scheme, and water from the washing of extraction plants [20]. The production of olive mill wastewater is estimated between 7 and more than 30 million m\({}^{3}\) per year worldwide [12]. This waste is constituted by sugars, polyalcohols, lipids, pectins, polyphenols, etc. The main phenolic compound is hydroxytrosol but also is rich in tyrosol, oleuppein, caffeic acid, vanillic acid, gallic acid, luteolin, and verbacoside, among others [13; 21].
# Propagation Attenuation Maps Based on Parabolic Equation Method +
## 2 Radio Environment Maps
The selection of an appropriate method is also dependent on the availability of specific data, e.g., the TX location and parameters, measurement results of signal levels in selected locations, variability of parameters over time, etc. Propagation methods allow for the creation of more accurate RF-REMs [38]. The developed solution based on the PEM can be used in indirect methods of creating the RF-REM.
# Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey
## 3 Survey of State-Of-The-Art MAC Protocols
### Time-Slotted Protocols
#### 3.2.5 A MAC Protocol for Cognitive Radio Wireless Ad Hoc Networks  
A collision-free multichannel protocol is proposed in [77] for wireless ad hoc networks. It considers QoS as a main parameter for single-hop scenarios. The resource sharing technique approves the optimized circulation of time slots between nodes. The technique is capable of improving the assignment of communication resources by considering the QoS requirements. This assignment gives more data transfer capacity and decreases delay to access resources for users with more execution necessities. The proposed protocol uses time slots and impact free, as an entrance window is given in each slot. Slots are divided into mini-slots where the nodes exploit them by using reservation with a specific end goal to attempt each one in turn to get to the accessible channel.  
It is assumed that all of the nodes are synchronized, and the time is partitioned into periods comprising control time, sensing period and time slots for transmission. The control time frame is utilized to exchange notification in the network about nodes that are leaving or joining, so that every node can overhaul, as its ID needs to be. IDs determine the node access request to the network. In particular, the access window is separated into mini slots, and the highest number of mini-slots is compelled by the length of the time slot and the entrance window.
## 3 Model
### Answer Decoder
#### 3.3.4 Fine-Tuning on Dense Annotations  
In the discriminative setting, MVAN mainly focuses on finding the one-hot ground truth answer. However, many candidates may be semantically relevant to the ground truth so that they also have to be predicted with a high ranking. For example, more than one answer to Q6 in Figure 1 exists in the candidate pool (\"no\", \"no\", \"nope\", \"i don't think so\", and \"not that i can see\"). To evaluate how well the model is capable of generalizing, visual dialog challenge ([[https://visualdialiolog.org/challenge/2018](https://visualdialiolog.org/challenge/2018)]([https://visualdialiolog.org/challenge/2018](https://visualdialiolog.org/challenge/2018)) (accessed on 26 March 2021)) organizers released relevance scores (i.e., dense annotations) for each candidate answer, denoted as \(\{d_{i}\}_{i=1}^{100}\), where \(d_{i}\in[0,1]\). Once MVAN is trained based on a discriminative decoder, we fine-tune the model on dense annotations (we utilize both training and validation set for fine-tuning the model since only 2k cases are provided for each dataset) to predict more semantically relevant answers with higher rankings. Most existing works [17; 18; 22; 24] have shown fine-tuning on dense annotations significantly boosts performance in NDCG but simultaneously deteriorates performance in non-NDCG metrics. To alleviate the trade-off between NDCG and non-NDCG metrics during the fine-tuning period, we design joint loss function by combining relevance loss and discriminative loss as follows:  
\[\mathcal{L}_{Rel} =-\sum_{i=1}^{100}d_{i}\text{log}p_{i}, \tag{15a}\] \[\mathcal{L}_{Dense} =\alpha\mathcal{L}_{Disc}+\mathcal{L}_{Rel}. \tag{15b}\]  
In order to examine the effect of the discriminative loss, we conduct various experiments by increasing the \(\alpha\) from 0 to 2 in 0.2 increments (see Figure 3). We decide the final \(\alpha\) as 1 to obtain the most balanced performance.
## 3 Industrial Applications
### Tools and Moulds  
Metal AM is gradually being adopted by mould and tool makers. In this industry, metal AM is used to make tooling such as mould inserts, jigs, fixtures, and gauges ([PERSON] et al., 2014; [PERSON] et al., 2014). Figure 10 shows an AM manufactured tool with conformal-cooling channels, which would  
Figure 9: Siemens Oil and Gas AM components ([PERSON] et al., 2014): (**a**) burner head, (**b**) turbine blade, and (**c**) sealing ring.  
be difficult to manufacture by conventional manufacturing techniques. This optimised component offers minimal warpage and reduced cycle time, and hence, improves the part quality and cooling process [111]. Another example is a 3D printed mould, which has a skeletal form including lattice-shell and rib reinforced structures. This AM mould provides fast and uniform cooling processes. Results showed that the new AM mould is 30% more efficient than the original mould and has less deformation, residual stress, and casting defects [109].
## 3 Discussion  
### Improvement of the Regional Vegetation NPP Simulation  
The regional NPP was quantified over Northeast China using the BEPS model in this study, where the model's input data were improved using GLASS _LAI_. As a key variable in the model, _LAI_ directly reflects the carbon content of vegetation leaves, even the vegetation growth and development. Currently, GLASS _LAI_ products have been validated well against various sites around the world. However, overestimation remained for regional forest NPP during the validation against tree ring data, indicating that uncertainties caused by that were model parameters require further optimization. Some parameters in the biological functions of the BEPS model usually varied significantly at seasonal and interannual scales; for example, the foliage clumping index (\(C_{f}\)), the slope of stomatal conductance to the net photosynthetic rate (m), the maximum photosynthetic carboxylation rate (\(V_{cmax}\)), and the electron transport rate (\(J_{max}\)). Therefore, sensitivity analysis is necessary, which calculates the main contributors among the parameters and provides potential prerequisites for model parameter optimization [22; 23]. Data assimilation has also shown itself to be a potential technique for adjusting model parameters and updating model state variables, and even improving modeling accuracy [24]. This strategy considers the errors of input data, parameters, and model structure, providing an effective scheme for carbon and water fluxes.  
As vegetation productivity was simulated based on a 1 km scale, a problem of mixed pixels occurred, which is a common problem within this study area due to the heterogeneity of regional landscapes. Accommodating the decomposition of mixed pixels using high-resolution remote sensing data is a crucial issue for the process-based model. Additionally, the high-resolution remote sensing data could improve the accuracy of the land classification map and provide more specific vegetation information, which are important input data for the BEPS model.  
Figure 5: Distribution of the vegetation types and forest plots in Northeast China (DNF: deciduous needleleaf forest, ENF: evergreen needleleaf forest, DBF: deciduous broadleaf forest).
# A Nonlinear Optimization Design Algorithm for Nearly Linear-Phase 2D IIR Digital Filters
## 6 Experimental Results
### 2D Bandpass Filter  
In this example, a 2D IIR bandpass filter with the design specifications shown in Figure 12 is presented. It is also required that the order of this filter remain low, specifically \((4,4)\) as in [8]. In addition to these design specifications and to assess the performance of our filter and the one proposed in [8], we have included the transition band constraint in our design. As a result, we calculated the maximum transfer function magnitude \(H_{\max}^{t}\) for both filters, as presented in Table 3. The algorithm is implemented with various values of \(\Gamma_{g}\), starting from 0.1 to 4, and the resulting filter with \(\Gamma_{g}=1.6\) has the best quality.  
Figure 11: First quadrant group delays of the lowpass in [8].  
Figure 10: Group delays in the passband of the lowpass filter in [8].  
Figure 9: First quadrant group delays for lowpass (\(\Gamma_{g}=2.35\)).  
The magnitude response of the obtained 2D IIR bandpass filter and the filter designed in [8] are shown in Figure 13. The group delays \(\tau_{1}\) and \(\tau_{2}\) of the designed 2D bandpass filter are illustrated in Figure 14. Figure 15 shows the group delays of the same filter in the first quadrant. The group delays of the 2D bandpass filter presented in [8] are illustrated in Figure 16. The first quadrant plot of the group delays \(\tau_{1}\) and \(\tau_{2}\) of the same filter are shown in Figure 17. Table 3 compares the results of the bandpass filter designed by the proposed algorithm and the bandpass filter obtained in [8]. The quality measure of the magnitude response in the passband, defined by Equation (15), of the 2D IIR bandpass filter obtained by the proposed method is \(Q_{h}=19.09\), where \(H_{min}=0.809\) and \(H_{max}=1.191\). In the same manner, the quality measure of the magnitude in the passband region of the 2D IIR bandpass filter presented in [8] is \(Q_{h}=29.667\), where \(H_{min}=0.633\) and \(H_{max}=1.167\). The quality of the magnitude response in the stopband defined by Equation (16) is \(Q_{s}=0.668\) for the filter obtained by the proposed method, and \(Q_{s}=0.734\) for the filter presented in [8]. The quality of the group delay characteristic, defined by Equation (14), of the 2D IIR bandpass filter obtained by the proposed method is \(Q_{\tau}=47.150\), where \(\tau_{min}=1.822\) and \(\tau_{max}=5.073\). The same measure is applied to the 2D IIR bandpass filter designed in [8], which gives \(Q_{\tau}=57.315\), where \(\tau_{min}=1.828\) and \(\tau_{max}=6.737\).  
In this example, the filter obtained with the proposed algorithm has a better passband quality factor, \(Q_{h}\), stopband quality factor, \(Q_{s}\), and group delay quality factor, \(Q_{\tau}\), than the filter presented in [8].  
Figure 16: Group delays in the passband of the bandpass filter in [8].  
Figure 14: Group delays in the passband of the obtained bandpass filter.  
Figure 17: Group delays of the bandpass filter in [8] in the first quadrant.  
Figure 15: Group delays of the obtained bandpass filter in the first quadrant.