esa-sceva/satcom-chunk-collection huggingface dataset:
# Indoor Wavelet OFDM VLC-MIMO System: Performance Evaluation
## 2 System Model
### The Receiver
#### 2.3.3 SNR Estimation  
The calculated electrical signals of four SNRs are mentioned in [11]:  
\[\mathrm{SNR}^{\prime}_{\mathrm{i}}=\frac{\left(\mathrm{RP}_{\mathrm{opt}}\ \mathrm{\xi}\mathrm{a}_{\mathrm{i}}\mathrm{h}_{\mathrm{ii}}\right)^{2}}{ \sigma_{\mathrm{hi}}^{2}}=\mathrm{a}_{\mathrm{i}}^{2}\mathrm{SNR}_{\mathrm{i}}, \ \ \ \ \mathrm{i}\ =\ 1,2,\ \cdots,\ \mathrm{N}, \tag{14}\]  
Figure 5: Proposed receiver model of DWT-OFDM.  
where \(\text{SNR}_{\text{i}}=(\text{R}\ \text{P}_{\text{opt}}\ \xi,\text{h}_{\text{ii}})^{2}\ \sigma^{2} _{\text{xi}}\ /\sigma^{2}\text{n}_{\text{ii}}\), i = 1, 2, \(\ldots\).., N. R is the photodiode responsivity, \(\text{P}_{\text{opt}}\) is the LED output power, \(\xi\) is the modulation index, and \(\text{h}_{\text{ii}}\) are the elements of the H matrix.  
Therefore, Pre-SDE could be effectively employed [22]. To guarantee that the four received signals have the same SNR, as per Equation (4), the following requirement must be satisfied:  
\[\text{a}^{2}_{1}\text{SNR}_{1}=\text{a}^{2}_{2}\ \text{SNR}_{2}=\cdots\cdots= \text{a}^{2}_{N}\text{SNR}_{N}. \tag{15}\]  
By solving Equations (2) and (5), the N diagonal elements of matrix A can be obtained by [14; 24]:  
\[\text{a}_{\text{i}}=\sqrt{\frac{\text{N}}{\text{SNR}_{\text{i}}\sum_{\text{i= 1}}^{\text{N}}\frac{1}{\text{SNR}_{\text{i}}}}}. \tag{16}\]  
The diagonal elements of A have been provided based on the SNR value of every channel. So, the evaluated SNR values must be supposed to be transferred back to the ceiling via the uplink channels as feedback information. For the most part, the optical channel remains static, while users of VLC are usually in fixed positions or moving with a slightly slower speed. Hence, the uplink delay will provide a relative effect on Pre-SDE performance. However, it should be observed that the electrical power of every channel ought to be flexible to adapt to a confirmed extent to efficiently excite the Pre-SDE, in the pre-coded VLC-MIMO techniques [31]. Thus, for VLC-MIMO systems, the Pre-SDE can be supposed like a precoding system.  
The SNR calculations are carried out in the wavelet-OFDM receiver (Rx), and the acquired SNR information is back to the wavelet-OFDM transmitter (Tx). By using the returns feedback of the SNR data, a sufficient modulation bandwidth could be offered to the OFDM data [22].
# Statistical Analysis of the Weight and Center-of-Gravity Position of an Empty Container Ship
## 1 Introduction
### The Aim and Scope of the Study
The results of the ongoing research presented in the article are novel in the absence of such regression formulas in the available literature. During preliminary design, the basic characteristic parameters of the ship are determined based on the most important functions of the ship. These functions include: deadweight tonnage, displacement, speed, propulsion power, cruising range. To optimize the ship design that meets the listed functional characteristics, we need to know the weight and position of the center of gravity of the designed ship. Therefore, the main objective of this research was to develop regression formulas in the form of:  
* weight indices for each class (3): \[C_{k}=\frac{M_{k}}{P_{k}}=f(P_{k})\] (6)
* hull weight light ship (4): \[WLS=f(P_{PS})\] (7)  
Figure 4: Percentages of container ships built between 2000 and 2024 [33]. * ship's center of gravity coordinates (5): \[x_{CG}=\frac{\sum_{k=1}^{8}x_{\text{GK}}\cdot M_{k}}{\sum_{k=1}^{8}M_{k}}=\text{ f}(DWT/TEU/LBH)\] (8a) \[y_{CG}=\frac{\sum_{k=1}^{8}y_{\text{GK}}\cdot M_{k}}{\sum_{k=1}^{8}M_{k}}=\text{ f}(DWT/TEU/LBH)\] (8b) \[z_{CG}=\frac{\sum_{k=1}^{8}z_{\text{GK}}\cdot M_{k}}{\sum_{k=1}^{8}M_{k}}=\text{ f}(DWT/TEU/LBH)\] (8c) where \[P_{PS}\] --parameter of the vessel, e.g., _DWT_, _TEU_ or _LBH_.
# Advancements in EEG Emotion Recognition: Leveraging Multi-Modal Database Integration
### Emotion Recognition through EEG Signals
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Frequency Band** & **Frequency Range** & **Emotion Recognition Relevance** & **References** \\ \hline Delta & 0.5–4 & Limited direct relevance in emotion recognition; More related to deep sleep and unconsciousness & [18] \\ \hline Theta & 4–8 & Associated with emotional processing and memory; Some studies link theta activity to emotional arousal and valence & [19] \\ \hline Alpha & 8–13 & Lower alpha band (8–10 Hz) has been found to reflect emotionally induced changes in cognitive and affective processing & [19] \\ \hline Beta & 13–30 & Beta band activity has been associated with emotional regulation and processing, especially in frontal and prefrontal brain regions & [20] \\ \hline Gamma & 30–100 & Some studies suggest a role in emotion perception, attention, and high-level cognitive processing related to emotions & [19] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: EEG frequency bands and emotion recognition relevance. effective in capturing the temporal dynamics within EEG data, enhancing the accuracy of emotion detection [33; 34]. One of the key benefits of EEG-based emotion recognition is its ability to track emotional responses in real-time. This opens up potential applications in fields such as neuro-marketing, human-machine interaction, and affective computing [17; 21; 35]. The process typically involves the preprocessing of raw data, extracting features, and classifying emotions, as illustrated in Figure 2.  
EEG signals are increasingly utilized in neuro-feedback applications, enabling individuals to modulate their emotional states through real-time brain activity feedback. However, EEG-based emotion recognition encounters several challenges. The susceptibility of EEG signals to noise and artifacts, along with individual variations in brain structure and neural function, can affect recognition accuracy. Emotions, being complex and multi-dimensional, pose difficulties in precise classification based solely on EEG data. To address these issues and enhance recognition systems' reliability, researchers are exploring the integration of EEG signals with other data sources, such as physiological signals or facial expressions [26; 36]. However, only a few studies have used brain heat map topographies, which display relative gradient colors of brain activity across specific electrodes. The primary advantage of these topographies is their ability to show relative activity levels between different brain areas under varying conditions. Brain heat map representation is suitable for a wide range of subjects, including diverse racial and age groups [21; 37; 38; 39; 40; 41].
# Engineering a Combinatorial Laplacian Solver: Lessons Learned +
## 4 Evaluation
### Results
#### 4.2.2 Convergence  
In Figure 4, we plot the convergence of the residual for different graphs and different algorithm settings. We examine a \(100\times 100\) grid and a Barabasi-Albert graph with 25,000 nodes. While the residuals can increase, they follow a global downward trend. Furthermore, note that the spikes of the residuals are smaller if the convergence is better and that the order (by convergence speed) of the residual curves is the same.  
In all cases, the solver converges exponentially, but the convergence speed crucially depends on the solver settings. If we select cycles by their stretch, the order of the convergence speeds is the same as the order of the stretches of the ST (compare Figure 3), except for the Dijkstra ST and the Kruskal ST on the weighted grid. In particular, on Barabasi-Albert graphs, there is a significant gap between the Elkin ST and the other algorithm settings. On these graphs, the Elkin ST barely converges at all. Thus, low-stretch STs are crucial for convergence. In informal experiments we also saw this behavior for 3D grids and non-synthetic graphs. In contrast, for the uniform cycle selection on the unweighted grid, the special ST is superior over the Kruskal ST, even though its stretch is smaller. This is caused by the fact that the basis cycles with the Kruskal ST are longer than the basis cycles with the special ST. Fixing the shorter basis cycles of the special ST seems to help the algorithm converge quicker. Still, the other curves with uniform cycle selection have the same order as stretch cycle selection.  
Figure 3: Average stretch \(\operatorname{st}(T)/m\) with different ST algorithms.  
Using the results of all our experiments, we are not able to detect any correlation between the improvement made by a cycle repair and the stretch of the cycle. Therefore, we cannot fully explain the different speeds with uniform cycle selection and stretch cycle selection. For the grid, the stretch cycle selection wins, while Barabasi-Albert graphs favor uniform cycle selection. Another interesting observation is that most of the convergence speeds stay constant after an initial fast improvement at the start to about a residual of one. That is, there is no significant change of behavior or periodicity. Even though we can hugely improve convergence by choosing the right settings, even the best convergence is still very slow, e.g., we need about six million iterations (\(\approx\)3000 sparse matrix-vector multiplications (SpMVs) in time comparison) on a Barabasi-Albert graph with 25,000 nodes and 100,000 edges in order to reach a residual of \(10^{-4}\). In contrast, conjugate gradient (CG) without preconditioning only needs 204 SpMVs for this graph (preconditioning is explained in the corresponding subsection below).
## 4 Results
### Real Data Study
#### 4.3.1 CAT Process in Application Study  
The application study was conducted with 120 learners. All learners took the same statistical assessment as was used in the simulation study. Table 1 displays the descriptive statistics of the subjects in the application study. Half of them were given all of the items from the item bank, meaning there was no stopping rule, while the rest of them took adaptively chosen items where the stopping rule (SE) was less than 0.3. After this, the abilities estimated by CAFT, with a stopping rule of 0.3, were compared with the abilities estimated by CAFT with a no stopping rule. The average number of items used with the 0.3 stopping rule in CAFT was 21.3 items, while all items in the item bank (40 items) were answered with a no stopping rule. The Pearson's correlation between the learners' estimated abilities with a stopping rule of 0.3 and a rule of none was 0.97. This means that the CAFT assessment provides efficient and accurate ability estimates while using approximately half as many items in this case. Figure 5 shows the adaptively used items in terms of a learner's ability change. The X-axis indicates the estimated abilities of the different learners, and the Y-axis indicates the number of items used. Since the items were adaptively selected, as Figure 5 shows, the number of items used was different in terms of a learner's ability. Moreover, the number of times a particular item was used was not the same for all items. Figures 6 and 7 show two examples of the estimation of an examinee's ability. Figure 6A indicates the estimated ability change by administering different items, while Figure 6B shows the final estimated ability of the examinee. Figure 7 contains the same information as Figure 6; however, the examinee in Figure 7 was given more items than the examinee in Figure 6. In Figure 7, the examinee had 25 items in order to estimate their final ability, while in Figure 6, the examinee had 10 items. This means that each examinee received adaptively chosen items from an item bank in order to estimate their ability accurately and efficiently. Therefore, we can confirm CAFT functioned well with this information.
# Decentralization of Virtual Linkage in Formation Control of Multi-Agents via Consensus Strategies
## 3 Decentralization of Virtual Linkage Approach
### Consensus Control
#### 3.2.2 Consensus Module  
With the previous section, each agent now is initialized with a \(\xi_{i}=(r_{i},q_{i},\theta_{i},\lambda_{i})\). This part aims to design a consensus law and drive \(\xi_{i}\) to the desired value \({}^{r}\xi_{contr}\). Here, the consensus tracking algorithm in Reference [22]  
\[\dot{\xi}_{i}=\frac{1}{\eta_{i}}\sum\limits_{j=1}^{n}a_{ij}\left[\xi_{j}-\kappa \big{(}\xi_{i}-\xi_{j}\big{)}\right]+\frac{1}{\eta_{i}}a_{i(n+1)}\left[\dot{ \xi}^{r}_{contr}-\kappa\big{(}\xi_{i}-\xi_{contr}\big{)}\right] \tag{4}\]  
is directly used, where \(a_{ij}\) are the elements of the adjacency matrix \(A^{c}=\left[a^{c}_{ij}\right]\in R^{n+1\times n+1}\) and \(\eta_{i}=\sum\limits_{j=1}^{n+1}a^{c}_{ij}\). The consensus law consists of two parts. The first term uses the information of its neighbors to make all the \(\xi_{i}\) converge into a common value which leads to the desired formation shape. The existence of the second term is to make formation move along the desired path \(\xi_{contr}\). For a connected graph, consensus to the reference value is guaranteed [22].
### Analysis of the Problem of Disease Management
* _Research and development_: (_a_) _accelerated discovery_ of new treatments, drugs, and therapies due to innovations in research, potentially leading to breakthroughs in disease management; (_b_) _collaboration_ among researchers and healthcare professionals globally fosters collaboration, accelerating the pace of discoveries and advancements [59]. * _Behavioural change and adherence_: _(a) encouraging patients to follow their treatment regimens_ and take up better lifestyles through the use of motivating techniques like gamification; (_b_) _better illness management results_ can be obtained from people being motivated to make beneficial behavioural changes by personalized feedback provided by digital health platforms [60]. * _Education and awareness_: _Patients_ who are better informed about the necessity of managing their diseases and who have access to educational materials _are more proactive in their pursuit of health_[61]. * _Community support and peer interaction_: _Social components added to healthcare applications or platforms_ can help patients with comparable diseases feel more connected to one another. Shared experiences and peer support can be effective sources of motivation [62]. * _Self-monitoring and goal setting_: _Motivation can be increased by using interactive tools_ that let patients create and monitor individual health goals. Monitoring progress and acknowledging accomplishments create a positive feedback loop that promotes ongoing participation [63]. * _Feedback and positive reinforcement_: Encouraging patients to adhere to their health management regimens by giving them constructive, real-time feedback on their health measurements or accomplishments [60]. * _Integration of mental health support_: It is critical to _address the psychological and emotional components of managing a condition_. Including mental health services, such as virtual therapy or mindfulness applications, can encourage people to manage their illness more skifully [64]. * _Personalized health coaching_: _AI-driven personalized recommendations_ can give patients individualized advice and inspiration based on their particular requirements and advancement [65]. * _Patient-reported outcomes (PROs)_: Incorporating patient-reported outcomes into illness management strategies and having _patients actively participate in their healthcare plans_ can have a good effect on motivation [66]. Novel strategies for managing diseases establish a more comprehensive and patient-focused environment, encouraging sustained involvement and favourable health consequences. These strategies have the potential to revolutionize healthcare systems, enhance patient outcomes, boost accessibility, reduce expenses, and eventually contribute to a healthier global population. Innovative approaches in disease management hold the promise of transforming healthcare systems, improving patient outcomes, increasing accessibility, and driving down costs, ultimately, leading to a healthier population worldwide.
###### Abstract  
In order to further explore the technical difficulties involved in orienting the sparse-aperture imaging system towards practical applications, this paper proposes an optical-mechanical structure design scheme and performance simulation, as well as actual alignment on the beam steering and adjustment structure, which is the core component of the Golay3 sparse-aperture imaging system. The beam steering and adjustment structure corrects the cophasing error by using a combination of mechanical rough adjustment and piezoelectric ceramic precision adjustment. The beam adjustment capability of the beam steering and adjustment structure is analyzed by simulation when the system contains the piston error and the tilt error. The piston error is controlled within 120 \(\upmu\)rad through the mechanical rough adjustment light path using a large-diameter collimation as the point light source, which realizes the confocality of three beams and mutual interference between two beams. With the USAF1951 resolution test panel display in the distance as the surface target, the adjustable piezoelectric ceramics control the piston error within 55 nm and the tilt error within 0.25 \(\upmu\)rad, obtaining the surface target imaging result of the sparse aperture.  
Galaxy3; sparse aperture; optical-mechanical design; system alignment +
Footnote †: journal: Applied
# Blending Sea Surface Winds from the HY-2 Satellite Scatterometers Based on a 2D-Var Method
## 4 Results
### Test Case  
Figure 5a shows the HSCATs winds collocated with the ERA5 background wind field (Figure 5b) on January 15 th, 2022, at UTC 00:00. The 2D-Var analysis wind fields (i.e., the blended winds), based on the configurations of GSF and EBEC, are shown in Figure 5c,d, respectively. For the sake of comparison, the wind speed difference between the blended winds and the background field is shown in Figure 5e,f. It is clear that the differences are observed globally, notably over the tropics (e.g., the intertropical convergence zone, ITCZ), the western boundary ocean current systems (WBC), and the high latitudes (e.g., lat < \(55^{\circ}\)S). Such differences, on the one hand, are due to the misrepresented physical processes in the ERA5 model (e.g, ITCZ and WBC). On the other hand, they may be attributed to the persistent wind speed bias in the HSCATs data (e.g., high latitudes), because the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Scattermeter**} & \multicolumn{4}{c}{**\(\boldsymbol{\mathit{\boldsymbol{\mathit{\boldsymbol{\mathit{\boldsymbol{\mathit{\boldsymbol{\mathit{\boldsymbol{\mathit{\boldsymbol{ \boldsymbol{\mathitmathit{\boldsymbol{    }}}}}}}}}}}}\)**} & **\(\boldsymbol{\mathit{\boldsymbol{\mathit{\boldsymbol{\mathit{\boldsymbol{ \boldsymbol{\mathit{\  
current HSCATs wind inversion does not take the effects of sea surface temperature into account [20]. Furthermore, Figure 5f contains more small-scale variance than Figure 5e, probably due to that the used EBEC is with larger length scales than the GSF. Having different correlation length scales means they are filtering different-scale analysis increments. One could change the parameters of GSF, letting it have a similar length scale as EBEC in order to produce a finer structure than Figure 5e, but this could be tested further.
# A Next-Generation Core Network Architecture for Mobile Networks
## 10 Other Advantages  
Let us summarize some of the non-security-related advantages the new architecture brings.  
By assigning a VCN to each UE, we can now assign network resources at the _user_ level. In particular, not all users need all the capabilities a cellular network has to offer. For example, some users are static or nomadic while others are highly mobile. This means that a UE may have no need for high mobility management and, as such, this functionality may be stripped out of its VCN. Similarly, a user may have no need for paging as, perhaps, it is not supposed to receive notifications or calls (e.g., possible in some machine-to-machine scenarios). In such a case, the paging functionality could also be stripped out of the UE's VCN, further reducing its complexity. Another UE may still not need VoLTE services but just Internet access: this would allow the removal of all IMS-related functionalities from the UE's VCN. In other words, the proposed architecture allows for customization of the cellular core network at the UE level, giving a much more personalized experience and services. This is beneficial for both the user and the network operator as it allows for new business models and, at the same time, simplifies operations. In addition, it reduces VCN complexity, simultaneously reducing the VCN's attack surface.  
As mentioned earlier, in today's cellular networks, when a user travels from New York to Los Angeles, for example, whenever he/she makes a call, the signaling has to go all the way to New York and back to Los Angeles, introducing greater latency and delays. To make things worse, as discussed in Section 6, each UE establishes multiple IP-in-IP tunnels with the core network, further adding to RTT and delays. By using the proposed architecture, these two sources of delay are no longer present. In particular, as discussed in Section 6, by removing all bearers in the core network and simplifying the core network into one single MPLS network, there are no more multiple fine-grained tunnels to be established by the UE. In addition, as discussed in Section 7, by having a VCN per UE, we can \"move\" the VCN as close to a UE as possible, further reducing latency and delay due to physical distance.  
The current core network architecture, virtualized or not, has a bottleneck at the datacenter where all traffic converges with consequent load peaks and, potentially, service degradation. On the other hand, as we show in Figure 19, our core network architecture is distributed and very dynamic, as VCNs can be deployed in different hosting infrastructures and datacenters and can be moved as needed. This minimizes load peaks and avoids bottlenecks turning VCNs placement into an optimization problem with many parameters to consider (e.g., distance from UE, current load at hosting infrastructure, security requirements).  
Finally, a per-UE customization of the core network also helps in Operations and Management thanks to easier access to single-user traffic, easier billing, easier policy enforcement and more.  
Figure 19: New hosting architecture.
# Prediction Model for Compaction Quality of Earth-Rock Dams Based on IFA-RF Model
## 1 Introduction
Nevertheless, the parameter selection process in RF still depends on manual tuning, which may result in unstable model performance and limit its precision and generalization ability [19; 20]. To address this issue, researchers have recently introduced intelligent optimization algorithms to improve the RF. [PERSON] et al. [21] proposed a dam deformation prediction model based on an improved particle swarm optimization algorithm, which enhanced the model's prediction performance by optimizing the RF parameters. This study proposes IFA, which introduces dynamic inertia weight, sine function adaptive factors, and differential evolution strategies to strengthen the algorithm's search capability while avoiding local optima entrapment. By hybridizing IFA with the RF algorithm, an IFA-RF model is developed to adaptively optimize the RF parameters, thereby systematically improving the prediction accuracy, robustness, and generalization ability. Ultimately, this study integrates real-time compaction monitoring systems, test pit experiment data, and meteorological data to propose an IFA-RF compaction quality evaluation model, providing a new technical approach and theoretical support for the construction quality control of earth-rock dams.