esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Deployment of Information Systems
### Benefits of Continuous Deployment  
First of all, instant implementation, often referred to as rapid deployment or quick deployment, involves the swift and immediate execution of a system, software, or process. This approach offers several advantages. Firstly, it significantly reduces the time required to launch a system or deploy a solution, allowing organizations to promptly respond to emerging needs or changing business conditions. Secondly, instant implementation facilitates a faster return on investment, enabling organizations to realize the benefits of their investment in a much shorter timeframe. Thirdly, it enhances organizational agility,empowering businesses to adapt swiftly to market demands, technological changes, or competitive pressures--a crucial aspect in dynamic and fast-paced environments. Lastly, rapid deployment enables the quick testing of new features or functionalities, fostering an iterative approach that allows for prompt adjustments and improvements based on real-world usage and feedback.  
Second, reducing manual work involves the automation of processes to diminish reliance on manual labor, presenting various advantages. Firstly, it enhances overall efficiency by eliminating the need for manual, repetitive tasks, allowing employees to redirect their focus towards more value-added activities. Secondly, automated processes ensure a higher level of consistency and accuracy compared to manual execution, particularly in tasks demanding precision and attention to detail. Thirdly, time savings are realized as employees are freed from routine, manual tasks, enabling them to invest their time in more strategic and creative aspects of their roles, ultimately contributing to heightened productivity. Moreover, this approach often leads to reduced operational costs by streamlining processes and minimizing required labor, thereby positively impacting the organization's bottom line. Simultaneously, minimizing errors is imperative for maintaining data integrity, ensuring quality outcomes, and improving overall organizational performance. The benefits encompass improved data quality, enhanced decision making through access to accurate information, increased customer satisfaction resulting from reliable interactions, and compliance with industry regulations and standards, thereby mitigating risks associated with incorrect data or flawed processes.
# Integrated Spatio-Temporal Graph Neural Network for Traffic Forecasting
###### Abstract  
This research introduces integrated spatio-temporal graph convolutional networks (ISTGCN), designed to capture complex spatiotemporal traffic data patterns. The proposed model integrates multi-layer graph convolutional networks (GCNs) to address dependencies in temporal and spatial traffic dynamics. Specifically, ISTCN integrates graph convolutional layers and convolutional sequence learning layers within multiple spatiotemporal convolutional blocks. For capturing the temporal aspect, predictive graph modeling for road network traffic at particular time stamps is performed. To integrate the spatial information, graph convolution operations are applied. The proposed model was validated on real-life datasets, and the experimental results demonstrate that ISTGCN achieves significantly lower error values across key metrics--RMSE, MAE, and MAPE.  
graph convolutional neural network; spatio-temporal data; time series analysis; traffic forecasting +
Footnote †: journal: applied sciences
## 2 Enabling Technologies in Next-Generation Network Communications: SDN and NFV
NFV entails implementing network functions in software capable of operating on diverse industry-standard server hardware. These functions can be flexibly moved or instantiated at different locations within the network as needed, eliminating the necessity for new equipment installations. The principal attributes of NFV encompass the following:  
* Flexibility: NFV architecture facilitates the swift and straightforward deployment, installation, and provisioning of novel network services, thereby expediting Time-to-Market to meet the demands of businesses and users. * Cost-effectiveness: NFV eliminates the requirement for costly hardware-based appliances by allowing the emulation of these devices via virtualization on standard high-volume servers, which are notably more economical. * Scalability: NFV enables the deployment of new services or machines across multiple servers, obviating the necessity for additional physical space and simplifying network scalability to align with business requirements. * Security: Network operators can administer and oversee the network while permitting their customers to securely manage their own virtual space and firewall within the network. * Ubiquity: NFV facilitates the deployment of network services worldwide through virtualization, ensuring global availability. Frequently, IT experts place SDN and NFV together because they share common objectives, as depicted in Figure 2. The primary aim of both SDN and NFV is to logically manage the network using software, reducing manual interaction with network devices. Hence, SDN and NFV paradigms are closely related [5], and with the efficient integration of both, significant cost savings and greater flexibility in service provisioning could be achieved. The integration of these paradigms into a single environment is known as an SDNFV network. Finally, although both solutions are complementary, they are not mutually dependent and can be implemented separately, allowing for the deployment of SDN, NFV, or a combination of both. According to the NFV white paper [22], the objective of virtualizing network functions is to enable their use without applying SDN mechanisms, relying on the techniques currently prevalent in most data centers. However, approaches based on the separation of the control and forwarding planes, as proposed by SDN, improve performance, simplify compatibility with existing deployments, and facilitate management and maintenance protocols.
# Modeling and Control Simulation of Power Converters in Automotive Applications
## 3 Technical Specifications and Components: Sizing and Selection
In fact, starting from the values obtained, a simulation analysis was carried out to find the values that make the dynamic behavior of the filter connected to the AC/DC converter as satisfactory as possible, in compliance with the specifications. This involved calculating the PF and the ripple (in terms of THD) for each combination of \(L_{f}\) and \(C_{f}\) values. The values chosen at the end of the evaluation were \(L_{f}=190\) uH and \(C_{f}=525\) uF. Regarding the selection of active components using SiC/GaN technology, for each of the three power conversion stages, practical considerations must be made that guarantee the safety of the device itself, and they must be in line with the technical specifications. For the choice of switching devices, the most relevant aspects concern the maximum voltage between the drain and source \(V_{ds}\), the maximum current absorbed by the drain channel \(I_{d}\), and the switching frequency \(f_{sw}\). Once commercial devices that respect these three fundamental aspects are identified, it will be necessary to select the device that can be driven with the gate density that can be provided via a switching system, possibly equipped with a gate driver unit (GDU); this device should also dissipate the lowest possible power per joule effect and have the lowest-declared resistance, \(R_{ds_{(}an)}\). \[V_{ds,max} \geq(1+\alpha_{v})V_{dc,max}\] \[I_{d,max} \geq(1+\alpha_{i})\frac{P_{out,max}}{V_{g,min}} \tag{8}\]  
Referring to the conditions in Equation (8), it is possible to obtain the \"minimum\" values for the search for marketed devices, in terms of the maximum drain source voltage and maximum drain current. Coefficients \(\alpha_{v}\) and \(\alpha_{i}\) represent robustness parameters, and serve to make the selection of components \"oversized\". Obviously, these parameters cannot be too high to avoid excessive sizing compared to the design specifications of the converter. The above choice refers to the devices for the PFC AC/DC (SiC) stage and the DC/AC (GaN) module of the DAB converter. Assuming that the AC/AC conversion by the transformer from galvanic isolation is as ideal as possible, for power conservation, GaN devices could be selected for smaller voltages and higher currents (consistent with the transformer gain), and to meet the input-output voltage specifications of the DAB itself. For the PFC active rectifier and the initial DC/AC conversion phase of the DAB, the minimum considered AC supply voltage is \(V_{g,min}=110V_{rms}\), while the maximum value for the DC-bus voltage is \(V_{dc,max}\). By incorporating safety coefficients of 10%, the minimum thresholds for component selections are derived: \(V_{ds,max}\simeq 900\) V and \(I_{d,max}\simeq 60\) A. These GaN devices are regarded as suitable for the second AC/DC conversion phase of the DAB converter, although in this instance, the \(V_{ds,max}\) constraint may be reduced.
## 2 Materials and Methods
### Boundary Conditions  
A boundary condition represents the type of behavior performed/exhibited by a particular boundary in a problem. This represents a constraint that helps to solve the problem. FLOW 3D incorporated with CFD calculates the values of a particular parameter at nodes of grids by using numerical approximation. Accuracy of numerical approximation depends upon many factors such as mesh size, type of boundary conditions, and model geometry, etc. In this study, a stepped spillway model was considered. As we know, the main function of the spillways is to dissipate the energy coming from upstream; therefore, at the upstream, we need to consider the discharge or the fluid height at the upstream mesh boundary of the model. In our case fluid height was considered as the boundary condition at the upstream. Similarly, as the water flow over the steps, it exits from the outlet boundary. So, the outlet boundary is considered at the x maximum.  
In physical modeling, stepped spillway model made with plexiglass is placed into a flume. The flume presents the constraints/obstructions on the sides. Therefore, in our study, the wall is considered on the sides and bottom of the model. Figures 3 and 4 present the boundary conditions for Model 2 and Model, 1 respectively. On the other side, at the top of stepped spillway, atmospheric pressure is considered as the boundary condition. Moreover, fluid fraction is put equal to zero as at the top boundary of the mesh has no fluid. The commercial code FLOW 3D was used to perform this study. It is the commercial code that can solve 3D Reynolds-averaged Navier-Stokes (RANS) equations for one fluid  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sr No** & **Part** & **Model 2** & **Model 1 (Validation Model)** \\ \hline
1 & Total Height & 1.06 m & 78.9 cm \\
2 & Total Width & 30 cm & 30 cm \\
3 & No of Steps & 14 & 13 \\ \hline
4 & Riser Height & 6 cm & First 5 steps (2, 2, 4, 3, 4, and 5 cm) others 6 cm \\
5 & Tread length & 5 cm & First 5 steps (1.5, 1.8, 2.25, 3, and 3.75 cm) others 6 cm \\ \hline
6 & Equation of Crest & y = 2.632x\({}^{1.64}\) & y = 3.632x\({}^{1.85}\) \\
7 & Tread Shape & Horizontal & Horizontal \\
8 & Riser Shape & Vertical/Curved & Vertical \\
9 & Slope of spillway & 1 V:0.80 H & 1 V: 0.75 H \\
10 & Radius of Bottom Curve & 116 mm & 28 cm \\ \hline \hline \end{tabular}
\end{table}
Table 4: Geometrical and physical properties of both models.  
Figure 2: (**a**). Pictorial View of Model 1; (**b**) Pictorial View of Model 2.  
including the RNG k-\(\varepsilon\) turbulence model numerically. FLOW 3D uses TurVOF method for interface tracking [27].
## 4 Case Study
### Final Comparative Analysis of Results  
In this study, the F1-score was calculated to compare the noise reduction performance of the two approaches. The F1-scores for each case are shown in Tables 4-6. As previously discussed, the fault detection rate for the kurtosis feature differed significantly between the models, resulting in a higher F1-score for the high-pass noise-based model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**High-Pass Noise**} & \multicolumn{4}{c}{**Actual Answer**} & \multirow{2}{*}{**Running Rate**} & \multirow{2}{*}{**Failure Rate**} \\ \cline{3-4} \cline{6-7}  & & & **Normal** & & **Failure** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Classification \\ results by \\ feature \\ \end{tabular} } & \multirow{2}{*}{Mean} & Normal & 7331 & 469 & \multirow{4}{*}{94\%} & \multirow{2}{*}{100\%} \\ \cline{3-4} \cline{6-7}  & & Failure & 0 & 7800 & & \\ \cline{3-4} \cline{6-7}  & & Normal & 7173 & 627 & \multirow{3}{*}{92\%} & \multirow{3}{*}{100\%} \\ \cline{3-4} \cline{6-7}  & & Failure & 0 & 7800 & & \\ \cline{3-4} \cline{6-7} Classification & \multirow{2}{*}{Standard} & Normal & 7029 & 771 & \multirow{3}{*}{90.1\%} & \multirow{3}{*}{100\%} \\ \cline{3-4} \cline{6-7}  & & Deviation & Failure & 0 & 7800 & \\ \cline{3-4} \cline{6-7}  & & Normal & 7331 & 469 & \multirow{3}{*}{94\%} & \multirow{3}{*}{68.6\%} \\ \cline{3-4} \cline{6-7}  & & Failure & 2453 & 5347 & & \\ \cline{3-4} \cline{6-7}  & & Normal & 7411 & 389 & & \\ \cline{3-4} \cline{6-7}  & & Failure & 37 & 7763 & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Result of classification (High-pass).  
The average F1-scores obtained were 0.6456 for the Wavelet-based method, 0.9074 for the Gaussian noise-based DAE, and 0.9367 for the proposed high-frequency noise-based DAE. The proposed method outperformed the Wavelet approach by a margin of 0.2911 and exceeded the Gaussian noise-based model by 0.0293. Except for the skewness feature, which showed a slightly lower F1-score compared to the Gaussian noise case, the high-pass noise model achieved higher average fault detection rates and F1-scores across all features.  
From a numerical perspective, an improvement of approximately 0.03 in F1-score is generally considered meaningful. In fields such as manufacturing, healthcare, and security--where accurate fault diagnosis or anomaly detection is critical--even a small gain in F1-score can provide tangible benefits. For instance, in a manufacturing setting, an improvement of 0.02 to 0.03 in F1-score can help reduce missed detections of defective products and minimize unnecessary re-inspections, thereby enhancing overall production efficiency [24].
## 4 SDR Implementation of FM-ACSK System Prototype
### Hamming Code  
Hamming codes are error-correcting codes that can detect and correct a single error in the encoded binary data string. These codes were first presented in [43]. In this prototype communication system, a (1023, 1013) Hamming code is used. This code has 1013 data bits and 10 parity bits with a total bit number of 1023, which comes from these two equations:  
\[k=2^{m}-m-1\,, \tag{11}\]  
\[n=2^{m}-1\,, \tag{12}\]  
where \(k\) is the number of data bits, \(m\) is the number of parity bits and \(n\) is the total bits after the addition of parity bits \(m\) to the data. Lower (\(n\), \(k\)) Hamming codes provide better coverage of data bits, raising the probability of right error correction, at the expense of higher transmitted bit count for the same binary string's length, lowering transmission speed.
# AI Chatots for Mental Health: A Scoping Review of Effectiveness, Feasibility, and Applications
## 3 Results
### Mental Health Allies during COVID-19  
The COVID-19 pandemic has had a significant impact on mental health, particularly among young people. Two studies have demonstrated the potential effectiveness of AI chatbots as interventions for mental health issues during this time. [PERSON] et al. (2018) conducted a randomized controlled trial to assess the impact of an AI chatbot on depressive symptoms in college students; the chatbot, named XiaoE, was employed as a standalone intervention in this study. Their randomized controlled trial found that using the CBT-based AI chatbot, XiaoE, for one week significantly reduced depressive symptoms in college students compared to control groups that read an e-book about depression or used a general chatbot, with a moderate effect size post-intervention and a small effect size at 1-month follow-up. XiaoE had high engagement, acceptability, and working alliance ratings. Qualitative analysis showed participants valued XiaoE's ability to provide an emotional relationship, promote emotional expression, give personalized responses, and offer practical advice, although some criticized its inflexible content and technical glitches. The authors conclude that while XiaoE is a feasible and effective digital intervention, further research is needed on its long-term efficacy compared to other active treatments, as mental health chatbots may be best used as an adjunct to human therapists rather than a full replacement. Supporting these findings, [PERSON] et al. (2019) evaluated the #LIFEGOALS mobile health intervention, which also included an AI chatbot component. The latter was not a standalone program, but rather offered as one component of a multi-component mHealth intervention. Their cluster-controlled trial with process evaluation interviews showed positive effects on physical activity, sleep quality, and positive moods. Interestingly, the pandemic-related restrictions moderated these effects, with in-person schooling enhancing the mental health benefits. Engagement was a challenge in this study as well, but users highlighted the importance of gamification, self-regulation techniques, and personalized information from the chatbot in facilitating behavior change. These insights provide valuable directions for developing effective AI-based mental health tools for adolescents. Together, these studies suggest that AI chatbots can be a valuable component of mental health interventions, particularly during times of crisis like the COVID-19 pandemic.
# Encapsulated Probiotics: Potential Techniques and Coating Materials for Non-Dairy Food Applications
## 4 Biomaterials Utilized for Probiotics Encapsulation
### Carbohydrate Polymers
#### 4.1.5 Cellulose and Cellulose Derivatives  
Cellulose is the most abundant biopolymer found in nature. Cellulose and its derivatives have been extensively used in probiotic encapsulation owing to their non-toxic character, biocompatibility, tunable surface properties, and pH-controlled release ability. Cellulose is insoluble at pH \(\leq\) 5 but soluble at pH \(\geq\) 6. Hence, it is effective in delivering probiotics to the colon. Common celluloses used in probiotic encapsulation are carboxymethyl cellulose, methyl cellulose, hydroxyethyl cellulose, hydroxypropyl cellulose, hydroxypropyl methyl cellulose, and microcrystalline cellulose. An optimized delivery with a sustained and slow release of probiotics to the targeted region (intestine tracts) has been developed on cellulose-based gel beads [77]. [PERSON] et al. [94] reported that the viability of _L. salivarius_ subsp. _salivarius_ CGMCC No. 1.1881 encapsulated in alginate and coated with cellulose (carboxymethyl cellulose) was higher than the probiotics encapsulated in alginate without coating under thermal treatment, storage, and simulated gastrointestinal conditions. The main limitation of celluloses is that they cannot form gel beads using the extrusion technique [77].
# Deployment Optimization of Defense Stations in an Attack-Defense Aerial War Game
## 4 Inspection of Some Interesting Outlier Cases
### Case 2: On \(\zeta_{1}\) against Slant Formation, Low Score on North Post  
In this case, an optimal deployment plan based on \(\zeta_{1}\) inflics an unexpected low score on the north post. The nearest defense station to the north post is placed marginally within 40 km and west to the latter. If an attacker, marked by only one interceptor, flies within 50 km of the north post but farther than 40 km away, it will be hunted by an interceptor fired from this defense station. In addition, the defense station also attracts attackers to itself.  
Figure 13 shows some sequential footages in part of the deployment area to demonstrate the progress of game. Figure 13a shows that, at \(t=580\) s, five attackers, A1, A2, A3, A4, and A5, are dispatched to attack the north post and are marked by interceptors B1, B2, B3, B4, and B5, respectively. Attacker A6 aims for the defense station closest to the north post and is marked by B6 and B7, and B7 is fired when A6 reaches within 50 km of the defense station. A4 and A5 are marked by additional interceptors B8 and B9, respectively, fired from the defense station closest to the north post as the former fly within 50 km but farther than 40 km of the latter.  
Figure 13b shows that, at \(t=600\) s, A4 and A5 are hit by B8 and B9, respectively, then B4 and B5 detour to intercept [PERSON]. A6 is hit by B5 later, causing B4, B6, and B7 detour to hunt the nearest attacker A3.  
Figure 13c shows that, at \(t=640\) s, A3 finds itself marked by more than one interceptor and detours to attack a defense station. An additional interceptor B10 hunts for [PERSON] after the previous target of B10 disappeared. [PERSON] is hit by B4 later, releasing B3, B6, and B7 to hunt for the nearest attacker A2.  
Figure 13d shows that, at \(t=680\) s, A2 finds itself marked by more than one interceptor and detours to attack a defense station. [PERSON] is hit by B3 later, releasing B2, B6, B7, and B10 to hunt for the nearest attacker A1.  
Figure 13e shows that, at \(t=720\) s, A1 finds itself marked by more than one interceptor and detours to attack a defense station. A1 is hit by B10 at \(t=760\) s.
# Laminar _Ulva_ Species: A Multi-Tool for Humankind?
## 4 _Ulva_ Compounds of Interest
### Pigments
#### 4.2.2 Carotenoids  
Carotenoids are natural pigments originating from five-carbon isoprene units, which enzymatically polymerize to create regular, extensively conjugated 40-carbon structures containing up to 15 double conjugated bonds [130; 131]. They are the most prevalent pigments found in nature, existing in all algae, higher plants, and numerous photosynthetic bacteria [130; 131]. They represent photosynthetic pigments in red, orange, and yellow wavelengths. Green seaweed species are characterized as containing \(\beta\)-carotene, lutein, violaxanthin, neoxanthin, and zeaxanthin [131]. _Ulva_ spp. have been reported to contain various caroten, including 9-cis-\(\beta\)-carotene, all-trans-\(\beta\)-carotene, \(\alpha\)-carotene, and other caroten isomers. Additionally, reported xanthophylls include lutein, violaxanthin, antheraxanthin, zeaxanthin, neoxanthin, and \(\beta\)-cryptoxanthin [128].  
In terms of commercial production, carotenoids are widely recognized for their antioxidative properties, which play a crucial role in counteracting the detrimental effects of free radicals, thereby mitigating oxidative stress and safeguarding cellular and tissue integrity [128]. For that, they have an important role as food additives, animal feed, colorants, medications, and nutraceuticals. For example, according to [PERSON] et al., 2021 [138], beta-carotene is a precursor to vitamin A and is pivotal in preserving optimal vision and reinforcing the immune system. [PERSON] et al., 2018 [139] noted that Lutein and zeaxanthin have a crucial role in sustaining optimal eye health by accumulating within the retina and offering protection against age-related macular degeneration and various other vision-related disorders [128].