esa-sceva/satcom-chunk-collection huggingface dataset:
# Offloaded Computation for QoS Routing in Wireless Sensor Networks
## 5 Theoretical and Experimental Evaluation
### Complexity of Algorithm 2: Computation of the DODAG(s)  
This algorithm computes DODAGs from QoS paths. The worst-case complexity is derived by analyzing the key operations.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Worst-Case Complexity** \\ \hline Algorithm 1 (Non-Dominated QoS Paths) & \(O(|V|^{3}k^{2})\) \\ \hline Algorithm 2 (DODAG Computation) & \(O(|V|^{3})\) \\ \hline Algorithm 3 (DODAG Redundancy) & \(O(|V|^{2})\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of algorithm complexities.
## 4 Results and Discussion
### Tangential Velocity Distributions
V\({}_{t1}\), V\({}_{t2}\), V\({}_{t3}\), and V\({}_{t4}\) are the tangential velocities corresponding to the guide cone with \(d\) of 708, 808, 758, and 658 mm. (**a**) Tangential velocities at points Pi along the rotor inlet. (**b**) Tangential velocities at points Pi along middle of classification chamber. (**c**) Tangential velocities at points Pi near the wall. Figure 7: Distributions of air tangential velocities at section Z = 310 mm. V\({}_{t1}\), V\({}_{t2}\), V\({}_{t3}\), and V\({}_{t4}\) are the tangential velocities corresponding to the guide cone with \(d\) of 708, 808, 758, and 658 mm. \(\mathrm{V}_{t1}\), \(\mathrm{V}_{t3}\), and \(\mathrm{V}_{t4}\) at the lower part and became the smallest. Similarly, near the wall of the classification chamber, the tangential velocity decreased slightly from the top to the bottom surface of the rotor. However, \(\mathrm{V}_{t2}\) was more stable in the left half of the separation chamber. Additionally, a trend was found in the near-wall region presented in Figure 8c, where the larger the material recirculation gap, the higher the tangential velocity, with the exception of the guide cone with a diameter of 808 mm. \(V_{t2}\) has the largest value on the right half and the smallest value on the left half of the separation chamber. Moreover, the distribution of the tangential velocity was not symmetrical. This is due to the fact that there was only one inlet, causing asymmetry.
# BICM-ID with Physical Layer Network Coding in TWR Free Space Optical Communication Links +
## 3 ExIT Charts Analysis
### Setup ExT Function  
The BICM-ID code is constructed from a serial arrangement of inner and outer codes. At the receiver, there is an iterative process between the inner code, the demapper \(D_{m}\) and the outer code, forming the decoder \(D_{c}\) to improve the link performance. However, the receiver for each constituent code is an SISO module, and the conditional pdf of the input LLRs to the SISO module is not known. Hence, the ExIT function setup for the BICM-ID system is simulated for the demapper and the decoder separately. Firstly, we investigate the transfer characteristic for the inner demapper, where the users' symbols are summed following QPSK mapping and then transmitted over the FSO link. The noisy signal \(\mathbf{r}\) is captured by the PNC demapping scheme to generate the bit sequence \(\mathbf{b}_{R}\) as can be observed in Figure 3.  
The bit sequence produced from the PNC demapping is multiplied by the independent samples taken from a Gaussian distribution with mean value \(\sigma_{L}^{2}/2\) and variance \(\sigma_{L}^{2}\) forming a priori information \(\mathbf{L}_{n}^{m}\). Terms \(\mathbf{L}_{a}^{m}\) and \(\mathbf{y}\) are applied to the soft demapper, which has a transfer characteristic \(I_{(E,\,D_{m})}\) of:  
\[I_{(E,\,D_{m})}=\mathcal{T}_{I}(I_{(A,\,D_{m})},\,E_{b}/N_{0}), \tag{20}\]  
Figure 3: The demapper setup for TWR-FSO PNC system employing BICM-ID code to generate the ExIT function.  
where \(I_{(A,\;D_{m})}\) is the mutual information between transmitted sequence and a priori LLR. Hence, the inner demapper system is affected by the channel information as it depends on \(E_{b}/N_{0}\) caused by the LLR of \(\mathbf{L}_{a}^{m}\) and \(\mathbf{y}\), whereas the SISO decoder of PNC with BICM-ID starts with a block of independent message bits \(\mathbf{b}_{k}\) from each user and is encoded by an NSC encoder to obtain \(\mathbf{c}_{k}\). The outer decoder system setup is shown in Figure 4. The modulo-two sum code bits of users is represented by \(\mathbf{c}\), which are modelled as random independent Gaussian distributed variables with a mean value of \(\sigma_{L}^{2}/2\) and a variance of \(\sigma_{L}^{2}\) to form the decoder input in terms of LLRs, \(\mathbf{L}_{a}^{c}\)[31]. The decoder employs the maximum a posteriori algorithm in the logarithm domain (Log-MAP) to provide information about the source bits. Therefore, the decoder input is a priori information coming from the decoders \(\mathbf{L}_{a}^{c}\) in terms of LLRs, where the outputs are extrinsic information of coded bits \(\mathbf{L}_{e}^{c}\), which are in turn fed back for the next iteration, and with the \(\mathbf{b}_{1}\oplus\mathbf{b}_{2}\) operation to estimate source bits. Consequently, the decoder transfer characteristic \(I_{(E,\;D_{c})}\) is the transfer function of a priori mutual information \(I_{(A,\;D_{c})}\), which can be written as:  
\[I_{(E,\;D_{c})}=\mathcal{T}_{I}(I_{(A,\;D_{c})}). \tag{21}\]
## 4 Analysis of the Empirical Results
### Analysis of Preliminary Goodness-of-Fit  
The results of this analysis are summarized in Table 9. The preliminary goodness-of-fit of this model is described as follows. The results of the global model analysis in Table 8 indicated that for the preliminary goodness-of-fit the three constituent elements of career counseling including self-awareness, discussion of future career direction, and career planning all reached the significance level (\(t>1.96\), \(p<0.05\)). For the explained results of the two constituent elements of vocational self-concept including clarity and certainty, both reached the significance level (\(t>1.96\), \(p<0.05\)). For the three constituent elements of employment competencies including characteristics and attitude, knowledge and skills, and career planning knowledge, the explained results reached the significance level (\(t>1.96\), \(p<0.05\)). Therefore, the overall model has a favorable preliminary goodness-of-fit.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Evaluation Item** & **Parameter/Evaluation Criteria** & **Result** & \(p\) **Value** \\ \hline \multirow{3}{*}{Global goodness-of-fit} & X2/Df & 1.822 \\ \cline{2-3}  & GFI & 0.969 \\ \cline{2-3}  & AGFI & 0.911 \\ \cline{2-3}  & RMR & 0.005 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results of global goodness-of-fit analysis.
## 2 Models and Methods
### Real Driving Emission Modeling
The relative error between the interpolated total trip cumulative NO\({}_{\rm x}\) emissions and those measured in the real driving emission test is 30.6%. As can be seen, the transient pollutant emissions caused by engine start-stop account for a considerable share of the total pollutant emissions during the actual use of EREVs. Therefore, the severe pollutant emissions from engine start-stops in EREVs should not be ignored. Because engine speed is decoupled from vehicle speed during EREV driving, the engine operating conditions do not vary much under non-start-stop conditions. In this case, engine operating dynamics have little effect on real driving emissions. Thus, the transient operation of the engine in non-start-stop conditions can be approximated as steady-state  
Figure 6: Real-driving NO\({}_{\rm x}\) emissions associated with the engine’s start–stop events. operation, and the real driving pollutant emissions under non-start-stop conditions can be calculated by interpolating in the engine steady-state performance data table. Meanwhile, the emission increment of the engine start-stop events is statistically inferred from the emission data samples of the engine start-stop events in the two real driving emission tests. However, the start-up emissions in the two real driving emission tests exhibit strong randomness, and the correlation between the engine start-up emissions and the start-stop event characteristics (e.g., speed, torque, and power during start-stop) is not well defined. In this regard, the statistical mean of the abnormal pollutant emissions associated with the engine start-stop events is considered approximatively as the incremental emissions resulting from a single engine start-stop event. In the real driving emission calculation for the EREV, one start-up emission increment is added when an engine start-stop event is recognized. In contrast, the real driving emissions at the no-start-stop operating points are calculated by interpolating in the engine steady-state performance data tables. Therefore, a new real driving emission modeling method, the interpolated-startup-corrected method, is proposed. It should be noted that since the abnormal NOx emissions due to the engine start-stop events are mainly manifested in the motorway trip, the above interpolated-startup-corrected NOx emission models do not work for engine start-up emissions in rural and motorway driving conditions. As shown in Figures 4-6, the relative errors between the total trip real driving CO, PN, and NOx emissions obtained by the interpolated-startup-corrected models and those obtained by the integration of the instantaneous emissions in the real driving emission test are 2.2%, 1.8%, and 13.1%, respectively.
## 2 Results and Discussion
### Volatile Organic Compounds
This compound, as well as hexanal, nonanal and benzaldehyde, has a low odour threshold and contributes to not intensive pasta flavour [43]. Notably, limonene was detected in higher concentrations in cooked pasta than in BLP. Because terpen are known to have distinctive aromatic properties [35], limonene could also contribute to the flavour of pasta. (_E_)-2-nonenal and (_E,E_)-2,4-decadienal, both with a fatty odour, have been reported as the most potent odours in bread crumb [36]. In the present study, these compounds were detected in all types of cooked pasta but not in BLP, which may suggest that these aldehydes are volatiles associated with cereal products. [PERSON] et al. [45] concluded that pasta of high quality should contain higher content of hexanal, nonanal and 2-nonenal compared to 2-hexanal and pentenal, as the latter are typical of low-quality pasta. This suggests that the pasta obtained in the present study was of high quality. In pasta fortified with BLP, compounds described as fruity and green were most frequently detected, confirming the contribution of BLP to the overall aroma. The profile of VOCs was strongly affected by the incorporation of BLP, as 30 VOCs derived exclusively from BLP were detected in fortified pasta. These compounds contributed to the appearance of odours, such as cabbage-like, sulphur, pungent, rancid, malty and spicy. Moreover,as shown in Table 3, the abundance of many compounds increased proportionally to the percentage of BLP. Notably, some of compounds were detected at higher concentrations in B2.5 than in B5, which can be partly explained by the difficulties in analysis of food matrices containing high concentrations of non-polar VOCs, which can result in a displacement effect [44]. Multivariate analysis confirmed that the most VOCs were associated with BLP (Figure 3A). The following 15 compounds contributed most to the separation between groups: 2-octanone, linalool, 2-pentylfuran, octanal, propanal, hexanal, 1-octen-3-ol, (_E_)-2-nonenal, (_E_)-2-octenal, (_E_)-2-octen-1-ol, (_E_,_E_)-2,4-nonnadien, nonanal, 1-hexanol and (_Z_)-3-hexen-1-ol (Figure 3B). Compounds associated with BLP and experimental pasta are presented in Figure S1. Figure 3: Orthogonal partial least-squares discriminant analysis (OPLS-DA) of experimental pasta and BLP (\(R^{2}X=0.966\); \(R^{2}Y=0.960\); \(Q^{2}=0.810\)). **(A)** biplot presenting the association between VOCs and samples; **(B)** variable importance in projection (VIP) score. A VIP value > 1.00 indicates the contribution of VOCs in separating the groups (blue bars).
# An Integrated Optimization Method of High-Speed Railway Rescheduling Problem at the Network Level
## 4 NSGA-III Algorithm
### Representation Scheme  
In this algorithm, we represent solutions indirectly by parameters that are later used to obtain a solution by a special decoding procedure. Each chromosome is made of four gene fragments, as Figure 7 shows.  
Gene fragment I: The first gene fragment represents the operating scheme of the standby train sets. As shown in Figure 7, \(b_{1}-b_{6}\) are 6 standby train sets, and number 1 represents the corresponding standby train sets is operated, while number 0 represents the corresponding standby train sets is not operated. Therefore, \(b_{2}\) and \(b_{6}\) are operated, and other standby train sets are not operated.  
Gene fragment II: The second gene fragment represents the cancelation scheme of the trains. Number 1 represents the corresponding train is cancelled while number 0 represents not. As shown in Figure 7, \(t_{1}-t_{6}\) are 6 trains and we can see that \(t_{4}\) is cancelled.  
Gene fragment III: The third gene fragment represents the sequence of the trains in the sections. This fragment is a two-dimensional matrix; the abscissa represents trains, and the ordinate represents sections, as shown in Figure 7. Figure 8 is the simple timetable of Figure 7. The colorful lines represent different trains. For example, we can know that the operation sequence of train \(t_{5}\) in section \(S_{5}\) is 4. If one train does not pass through one section, the value will be 0.  
Gene fragment IV: The last gene fragment represents the coupling scheme of the trains. This fragment is also a two-dimensional matrix; the abscissa and the ordinate both represent trains. Number 1 means the corresponding two trains are coupled, while number 0 means they are not. As shown in Figure 7, \(t_{1}\) and \(t_{3}\) are coupled, while \(t_{2}\) and \(t_{5}\) are coupled, too.  
Figure 7: Gene fragments.
# Advanced AI and Machine Learning Techniques for Time Series Analysis and Pattern Recognition
## 2 Machine Learning Techniques for Time Series Analysis  
### Recurrent Neural Networks (RNNs) and LSTMs  
Long Short-Term Memory (LSTM) networks have transformed how we understand and predict patterns in sequential information, enabling breakthroughs that touch our daily lives in countless ways. These sophisticated neural networks possess what amounts to a digital memory system, allowing them to recognize meaningful connections in data that unfolds over time. As noted in recent research by [PERSON] [25], this capability has proven remarkably effective for predicting energy demands and many other applications. What makes LSTMs truly revolutionary is their elegant solution to a fundamental challenge in machine learning: maintaining important information over extended sequences. Through specialized internal mechanisms, these networks can selectively remember critical patterns while discarding irrelevant noise, much like how human memory works, but with mathematical precision. Despite requiring significant computational resources, LSTMs have earned their place as essential tools in our predictive arsenal. Their ability to model complex, non-linear relationships in time-dependent data continues to open new frontiers in fields ranging from climate science to healthcare.
## 2 Radiative Transfer
### Pseudo-Spherical Geometry  
Due to the spherical geometry of the Earth's atmosphere (Figure 2), the (solar and satellite) zenith angle changes with height \(z\).  
\[\theta(z)=\arcsin\left(\frac{r_{e}}{r_{e}+z}\ \sin\ \theta\right), \tag{26}\]  
with \(r_{e}\) being the Earth's radius and \(\theta\) the (solar or satellite) zenith angle at the surface.  
Correspondingly, also the light path extensions \(\zeta\) and \(\zeta_{0}\) become height dependent. In the following, \(\theta\), \(\theta_{0}\), \(\zeta\), and \(\zeta_{0}\) shall refer to values defined at the surface. \(\theta(z)\), \(\theta_{0}(z)\), \(\zeta(z)\), and \(\zeta_{0}(z)\)shall refer to height \(z\) (Equation (26)) and \(\theta^{s}\), \(\theta^{s}_{0}\), \(\zeta^{s}\), and \(\zeta^{s}_{0}\) shall refer to the scattering layer. This has implications for Equation (2) which now becomes  
\[T^{g}_{I}(K(z),\zeta(z))=e^{-\int K(z)\;\zeta(z)\;dz}. \tag{27}\]  
Additionally, \(\zeta\) in Equations (3), (4), (5), (22) and (24) has to be replaced with the corresponding value at the scattering layer \(\zeta^{s}\).  
In order to keep the integral in Equation (6) simple, we do not account for the spherical geometry for the transmission of the diffuse fluxes contributing to multiple scattering. For this reason, we consider this approach a pseudo-spherical approximation.
## 3 Material and Methods
### Data Pre-Processing
#### 3.7.2 Encoding  
Categorical variables are those that do not have any natural order. The UNSW-NB15 dataset has three variables that have categorical values. These variables are proto, service, and state. The first variable carries the name of the transport layer protocol, the second variable keeps an application layer service name, and the third variable contains TCP connection state information. Since this data does not have any natural order, the One-Hot encoding is applied to transform them into a numeric form.
# Offloaded Computation for QoS Routing in Wireless Sensor Networks
## 5 Theoretical and Experimental Evaluation
### Experimental Evaluation
#### 5.4.2 Impact of QoS Constraint Tightness  
This subsection evaluates the impact of the end-to-end QoS constraint threshold \(L\) on routing performance. The experiments are conducted on randomly generated WSNs within a \(100\times 100\) square area, with each node having a communication range of 20 m. Each network comprises 60 nodes, including a single BR located at the center. (See Table 4).
## 6 Challenges and Opportunities
### Future Research Directions
#### 6.4.2 Next-Generation AI and Machine Learning  
Many HAR algorithms are trained on standardized datasets that do not fully capture the complexity of real-world interactions [69; 126]. Future research may need to focus on situation-specific AI systems capable of adapting to the demographic diversity of climate, geography, culture, and social factors. For example, what qualifies as \"abnormal activity\" in a crowded urban setting could differ significantly from that in a sparsely populated rural area. Adapting models to local conditions can reduce false positives, making actual safety threats harder to overlook.  
Another well-recognized open problem for centralized data processing is the challenge of bandwidth, latency, and privacy. Federated learning offers a valuable approach to addressing this issue by enabling local devices and edge nodes to collaboratively train a model without sending the original data to the cloud [127; 128]. This can enhance privacy and alleviate network constraints. Moreover, on-device analytics, including partial or complete inference on edge hardware, can improve responsiveness, which is especially beneficial in time-sensitive scenarios like accident detection or emergency alerts.  
Alongside these advancements, privacy-preserving AI/ML techniques should be prioritized in future development and research [129]. As HAR models become capable of recognizing fine-grained behaviors, they also become more likely to capture sensitive personal information. Using techniques such as differential privacy, secure enclaves, or encrypted computation, we can preserve user identity and mitigate data leakage so that the use of such systems does not result in a lack of control for the user to whom such learning pertains. More generally, establishing robust data governance frameworks, providing clarity around consent handling, and implementing role-based access protocols can help protect against improper use or sharing of sensitive data. This approach promotes ethics while contributing to the diverse data necessary for cutting-edge models and insights in next-generation AI and machine learning.
# Review of Fast Computation Methods for Finite-State Predictive Control of Multi-Phase Drives
## 3 Fast Computation Strategies
### Dead-Beat Approach  
Dead-beat control is enabled by direct digital control of dynamical systems. The basic idea is to apply a control action that, after one sampling period, will drive the error to zero. Its application to drives can be traced back to the 1980s [46]. In the case of FSMPC, using Equation (5), one can derive the voltage that, applied during the \(k+1\) interval, would drive the error to zero at \(k+2\). Denoting it by \(V^{*}\), it turns out to be  
\[V^{*}=B^{-1}\big{(}i^{*}(k+2)-A(\omega)\hat{i}(k+1)\big{)}. \tag{8}\]  
Voltage \(V^{*}\) might not be one of the 32 VVs that the VSI can produce. Then it must be synthesized using various basic VVs applied at different times and for different subperiods during the whole sampling period. The basic VVs that need be applied can be found using trigonometric rules and lookup tables [47; 48]. Different approaches have appeared using the DB concept. They have been applied to different types of drives and numbers of phases.
## 2 Related Research
### Utilization of the Stretch Sensor to Estimate the Contraction Force of Muscle  
Recent studies have used stretch sensors to estimate muscle contraction force. One developed a fabric stretch sensor to monitor muscle activity during leg motions [31]. Another demonstrated a wearable soft strain sensor sensitive to isometric contractions, tracking changes in muscle deformation [32]. A further study introduced Motion Tape, a self-adhesive, elastic fabric skin-strain sensor that monitors muscle engagement during activities such as biceps curls [33]. Additionally, a handgrip strength estimation system was created using a flexible deformation sensor, achieving good accuracy and stability [34]. However, these studies do not provide necessary finger-kinematic information for finger activity, especially cylinder grasping. This limitation suggests that they focus on normal muscle conditions without considering finger shape, which is crucial for accurate monitoring as it indicates the hand's actual kinematic information.  
Subsequently, several studies have demonstrated the outstanding performance of stretch sensors attached to the finger and glove in providing shape and kinematic information about the finger [13; 35; 36; 37]. The studies demonstrated the feasibility of the stretch sensor for monitoring cylindrical grasping by tracking joint angles and finger movements. Nevertheless, the studies only provide kinematic information about the finger, which is a peripheral outcome of the primary source, the muscle contraction force.  
Regarding the aforementioned studies, although there is a fundamental discrepancy between the EMG and stretch sensor approaches, they share a particular relationship. EMG is directly correlated with muscle information, while the stretch sensor is limited to kinematic information only, which is considered insufficient; however, it provides the actual condition of finger shape while grasping a cylinder. Therefore, the proposed method attempted to utilize the stretch sensor to estimate the force of the forearm muscle contraction, especially in cylindrical grasping. As part of the proposed method, we utilized a stretch sensor attached to each finger, enabling the observation of finger movement and the contraction force produced on the forearm muscle while holding various cylinders. Using a stretch-sensor glove, our proposed method employed an approach to address the practicality issue of monitoring in home-based rehabilitation.