esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 WPAN MAC: Designing and Analysis
### Performance Evaluation
Along with an optimum payload size it is also important for a WPAN designer to find an optimum K burst size for frame aggregation policy, so to find the effect of K-Dly-ACK on bandwidth utilization as well as to find the optimal value of burst size for K-Dly-ACK and K-Dly-ACK-AGG policies, we define the MEB metric in (7). Here, Imm-ACK is a special case of K-Dly-ACK (where K = 1) so we do not need to define it separately. Figures 16 and 17 shows the MEB for different burst values for a given BER value in CTA and CAP, respectively. From the figures we see that when the burst size increases, bandwidth utilization can be increased initially, but the BER probability also increases and so the bandwidth utilization. From the figures we can find the optimum value for K for different payload sizes under a given BER value, From these figures we can observe that burst size K = 4 gives good results in fairly all payload values. So we select K = 4 and obtain the results for a range of BER values _versus_ MEB as shown in Figure 18. As  
Figure 8:0 Throughput _versus_ BER value with different ACK policies. shown in Figure 18, we compare two sets of results: one with PAM and second without PAM. As we mentioned earlier, payload adjustment mechanism (PAM) can adjust the payload size according to the observed value of BER. After a certain threshold BER value PAM can set the payload size to a smaller value. In our simulations we set several threshold BER values for PAM to adjust the payload size which might not be optimized values. However, our results show that the PAM can have noticeable impact on MEB. In the future version of this paper we want to consider dynamic payload adjustment mechanism. Figure 8:1 MEB _versus_ burst size without aggregation. Figure 8:2 MEB _versus_ burst size with aggregation. Here, the aggregation method clearly shows its advantage over the non aggregation method even at higher values of BER. Again it is an open tradeoff between MEB and payload size that a WPAN designer will have to decide according to his application requirements. This result also supports the need for PAM in WPAN MAC design. Figure 19 shows the access delay performance for different burst sizes with aggregation method in CTA and CAP, respectively. Here, we define the access delay as the time from the moment a packet is ready to be transmitted to the moment the packet starts its successful transmission. For a WPAN designer it is very important to know the maximum possible delay limit for a given network. _K_-Dly-ACK-AGG policy gives the maximum delay limit compared to other ACK policies as it transmit large payload size with aggregation. Figure 19(a) shows access delay during CTA period where we do not need to consider any backoff and channel access delay, however, during CAP period, the obtained results [Figure 19(b)] are largely depends on fixed back off window size of the IB. As the number of burst size increases, access delay also increases linearly with it. Figure 8:3 Access delay _versus_ burst size. Figure 8:4 MEB _versus_ BER value.
## 3 Simulation Analysis and Results
### Results and Discussion  
#### 3.3.1 Received Optical Power Distribution  
The underwater spatial irradiance characteristics of the different LED light source layouts are simulated and analyzed using MATLAB 9.6 software. First, we compare the performance of the received optical power after it is transmitted over different underwater depths with three LED array layouts. Taking the coastal seawater environment as an example, the power distribution performance for the receiving planes is measured with the transmission depths of 5 m, 7 m, and 9 m, respectively, as shown in Figure 5.  
It can be seen from Figure 5 that the maximum received optical power of the three different LED array layouts, affected by the underwater channel environments, decreases from 27.42 dBm to 18.60 dBm, 28.93 dBm to 19.54 dBm, and 27.96 dBm to 18.77 dBm, respectively, as the transmission depth increases. The maximum received optical power of the ring-compensated layout is the largest, since the LED light sources are relatively concentrated in the central area of the transmitter. At the same time, the maximum received optical power of this layout also decreases the fastest after underwater transmission. However, considering the complex characteristics of the underwater environment, to further simplify the alignment operation of the transceivers and improve the dynamic response capability of the UOWC system, a smaller fluctuation of the received optical power is more important to ensure the reliability of the communication link. Therefore, we define the peak power deviation (PPD) index to evaluate the fluctuation of the received optical signals, and the PPD is given by the ratio of the maximum received optical power to the minimum received optical power. The PPD results of the three LED layouts at different coastal seawater depths are shown in Table 3. It can be seen that the PPD of the lemniscate-compensated layout is 0.79, which is 0.02 less than the ellipse-compensated layout and 0.1 less than the ring-compensated layout at a depth of 5 m in coastal seawater. At a depth of 7 m in coastal seawater, the PPD of the lemniscate-compensated layout is 0.66, which is 0.02 less than the ellipse-compensated layout and 0.09 less than the ring-compensated layout. At a depth of 9 m in coastal seawater, the PPD of the lemniscate-compensated layout is the lowest compared with the ellipse-compensated layout and the ring-compensated layout. The receiving power fluctuation of the lemniscate-compensated layout is lower than that of the other two layouts when propagating 5 m, 7 m, and 9 m in coastal seawater, which means that the uniformity of the received optical power is improved in the proposed UOWC system.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline quantity of LED array & 16 \\ divergence angle & \(20^{\circ}\) \\ pattern length & \(2^{20}-1\) \\ receiver field of view & \(180^{\circ}\) \\ size of receiving plane & \(10\) m \(\times\) 10 m \\ data rate & 0.1 Gbps \\ \hline \hline \end{tabular}
\end{table}
Table 2: System simulation parameters [28].
## 5 Policy Implications
Alongside this, organizing regular health check-ups and immunization drives helps prevent illnesses and ensures overall well-being. Additionally, educating parents on the importance of maintaining a healthy diet and hygiene creates a strong foundation for sustained child health;
* **Parenting Support:** Workshops should be conducted to raise awareness of early childhood care, provide counseling for behavioral issues, and encourage activities that strengthen parent-child bonds;
* **Education Enhancements:** Remedial or special education programs should be implemented to address the individual learning needs of children. At the same time, teachers should be trained in inclusive education practices and early detection of learning challenges, ensuring that resources are accessible for children with special needs;  
Figure 15: A data-driven framework for early childhood learning difficulties prediction and prevention policy implications. * **Socio-economic Support:** Scholarships or financial aid should be provided to under-privileged families, and they should be linked to social welfare schemes to improve their living standards. This would be complemented by ensuring access to technology and learning aids in under-resourced communities, promoting equal opportunities for all children. Generalizability and LimitationsThis study, which uses the MICS 2019 dataset, provides useful insights into predicting early learning issues in children under the age of five years old. However, there are some limitations to consider. The use of self-reported data presents potential reporting biases. Guardians and parents might understate developmental issues due to cultural shame, a lack of understanding, or subjective impressions, hampering the dataset's credibility. Also, the dataset does not include all the important factors that can indicate learning difficulties, like family history, prenatal conditions, or socio-environmental factors. Furthermore, while the MICS dataset represents the Bangladeship population, the socioeconomic and cultural diversity of other regions may restrict the findings' applicability to other populations. Despite these limitations, the proposed work demonstrates substantial potential for adaptation and application across other similar contexts (ASD, ADHD, etc.). By retraining and validating the models with region-specific data, the approach could be extended to address early learning challenges in diverse populations.
# Labeled Multi-Bernoulli Filter Joint Detection and Tracking of Radar Targets
## 2 Background  
### Lmb Rfs  
The label space denoted as \(\mathbb{L}\) contains a countable number of distinct labels. The unlabeled target state space is denoted as \(\mathbb{X}\). The labeled single target state is denoted as \(\mathbf{x}=(x,\ell)\), where \(x\in\mathbb{X}\) denotes the target state and \(\ell\in\mathbb{L}\) denotes the label. The labeled multi-target state is denoted as \(\mathbf{X}=\{\mathbf{x}_{1},\ldots,\mathbf{x}_{i},\ldots,\mathbf{x}_{| \mathbf{X}|}\}\), where \(|\mathbf{X}|\) denotes the cardinality of the multi-target, \(\mathbf{x}_{i}\) denotes a labeled single target state and \(\mathbf{X}\subset\mathbb{X}\times\mathbb{L}\). The generalized Kronecker delta function and the inclusion function can be defined as:  
\[\delta_{\mathbf{Y}}(\mathbf{X})\triangleq\left\{\begin{array}{ll}1,& \mathbf{X}=\mathbf{Y}\\ 0,&\text{otherwise},\end{array}\right.\mathbf{1}_{\mathbf{Y}}(\mathbf{X}) \triangleq\left\{\begin{array}{ll}1,&\mathbf{X}\subset\mathbf{Y}\\ 0,&\text{otherwise}.\end{array}\right. \tag{1}\]  
Define a projection \(L:\mathbb{X}\times\mathbb{L}\rightarrow\mathbb{L}\) to make \(L(\mathbf{x})=\ell\) and \(L(\mathbf{X})=\{L(\mathbf{x}):\mathbf{x}\in\mathbf{X}\}\). \(\Delta(\mathbf{X})=\delta_{|\mathbf{X}|}(|L(\mathbf{X})|)\) is the indicator function of distinct labels. When \(\mathbf{X}\) has distinct labels, \(\Delta(\mathbf{X})=1\). The inner product of \(f\left(\mathbf{x}\right)\) and \(g\left(\mathbf{x}\right)\) on \(\mathbb{X}\times\mathbb{L}\) is defined as \(\left\langle f,g\right\rangle\triangleq\int f\left(\mathbf{x}\right)g\left( \mathbf{x}\right)d\mathbf{x}\). The multi-target exponential function is defined as  
\[h^{\mathbf{X}}\triangleq\left\{\begin{array}{ll}1,&\mathbf{X}=\varnothing\\ \prod_{\mathbf{x}\in\mathbf{X}}h(\mathbf{x}),&\mathbf{X}\
eq\varnothing.\end{array}\right. \tag{2}\]The LMB distribution of an LMB RFS with the parameter form \(\pi=\left\{\left(r^{\ell},p^{\ell}\right)\right\}_{\ell\in\mathbb{L}}\) is  
\[\pi\left(\mathbf{X}\right)=\Delta\left(\mathbf{X}\right)\omega\left(L\left( \mathbf{X}\right)\right)p^{\mathbf{X}} \tag{3}\]  
where \(r^{\ell}\) is the existence probability of the target \(\mathbf{x}\) and \(p^{\ell}=p\left(\mathbf{x}\right)\) is the probability density of the target \(\mathbf{x}\). The weight \(\omega(\mathbf{I})\) of the hypothesis \(\mathbf{I}=\left\{\ell_{1},...,\ell_{|\mathbf{I}|}\right\}\) is  
\[\omega(\mathbf{I})=\prod_{i\in\mathbb{L}}(1-r^{i})\prod_{\ell\in\mathbf{I}} \frac{1_{\mathbb{L}}(\ell)r^{\ell}}{1-r^{\ell}}. \tag{4}\]
## 2 UAS Platform Types and Characteristics
### Characteristics and Examples of UAS Categories
#### 2.2.3. Lase/lae Uas  
Low-altitude, Short Endurance and Low-Altitude, Long Endurance UAS occur in an array of sizes and configurations, from back-packable, hand-launched platforms to catapult-launch platforms (Figure 7). Some of the more common, low-cost LASE/LALE platforms in use today are those that are lightweight and can be hand-launched, allowing versatile field condition operations in areas without solid surface runways [9]. The disadvantage of hand-launch platforms is their relative short-duration operational capabilities of 45 min-2 h, and a reduced payload capacity.  
The LALE/LASE UAS are relatively simple to operate, with flight controls similar to RC models and simplified ground-control stations that allow for small crews. Most of the remote sensing systems are small, simplified cameras or streaming video cameras in either daylight (color or B/W) or infrared (B/W) video provided of surface objects being imaged, although increased capabilities are enabling direct image georeferencing in some of these systems.  
Figure 6: The Yamaha RMAX VTOL aircraft, shown with a pesticide/herbicide application sprayer payload used in precision agriculture. Image courtesy of Yamaha Motor Australia.
## 2 Biodiesel Synthesis Processes
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Biodiesel Methods** & **Merits** & **Demerits** \\ \hline \multirow{6}{*}{Direct use of oil and blends} & Liquid based-portability, renewable, readily available, Heat content & Low cetane, high viscosity, natural gum in oils, low flash point, low volatility Reaction of unsaturated chains Gumming and Plugging of, lines, filters and injectors \\  & No major operational difficulty on engine, on engine, & Extensive deposits in exhaust valve of engine. \\  & Isotropic fluid, stable and clear with aqueous phase, an oil phase and surfactant. & Amass of carbon all over the orifice of injector outlet \\  & & Partial combustion in 200 h lab screening \\  & & endurance test. \\  & & Irregular needle sticking of injector \\ \hline \multirow{6}{*}{Pyrolysis} & Pyrolyzed oil has satisfactory sulphur amount, sediment, water and copper corrosion value. Undesirable carbon residue amounts, ash, pour point. Short term engine tests. & Equipment is expensive Low moisture is required Products are similar to petroleum diesel fuel. Deoxygenation during pyrolysis removes eco-friendly aspects. Production of low graded materials and less diesel as compared to gasoline. \\ \hline \multirow{3}{*}{Dilution} & Low viscosity of oil makes it efficient for short term use. & Not recommended for long term usage Severe sticking and cooking of injector nozzle Heavy carbon deposits on intake valve and appearance of top ring wear. \\ \hline \multirow{6}{*}{Transesterification} & Fuel properties almost similar to petroleum diesel Low cost High conversion yield efficiency & Low water and FFA content of oil is required (Base catalyzed transesterification) \\  & High conversion yield efficiency Suitable for large scale production of biodiesel & Washing of biodiesel is required that produces pollutants Accompanied by side products \\ \hline \multirow{2}{*}{a.} & Transesterification based on Homogeneous catalysis & Adequate reaction time Efficient conversion and easy to perform Inexpensive & Catalyst recovery not possible Saponification Low quality glycerol is produced \\ \hline \multirow{2}{*}{b.} & Transesterification based on Heterogeneous catalysis & High biodiesel yield Eco-friendly as washing is not required No saponification and hydrolysis & Expensive as compared to Homogeneous catalyzed transesterification Longer reaction time \\ \hline \multirow{2}{*}{c.} & Enzyme catalyzed transesterification & Tolerance to water content in oil No FFA saponification Low energy consumption/input Enzymes absorb glycerol on its surface \\ \hline \multirow{2}{*}{d.} & \multirow{2}{*}{Supercritical/Non catalyszed transesterification} & No catalyst required Esier product purification Fast reaction time No effect of high FFA and Water & Need of High reaction temperature and pressure Large amount of methanol High energy consumption High capita cost \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Merits and demerits of few biodiesel Synthesis processes [8].
## 3 Materials and Methods
### Proposed Approach  
Three modules have been integrated into the proposed approach to solve the multi-period SSOA problem while considering the data acquisition and order emission trade-off: a predictive module, a prescriptive module, and a continuous training module. An illustration of how these modules are integrated is provided in Figure 1. Moreover, the pseudocode of the proposed approach and of its main module is reported in Appendix A in Figures A1-A4.  
According to Figure 1, past historical data on suppliers' delivery punctuality are provided as inputs to train the predictive module.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Study**} & **Model** & **Ordering** & **Purchasing** & **Shortage** & **Holding** & **Data Acquisition** \\  & **Type** & **Cost** & **Cost** & **Cost** & **Cost** & **Cost** & **Benefit** \\ \hline
[27] & DP & ✔ & ✔ & & & ✔ \\
[28] & SP & ✔ & ✔ & & ✔ & \\
[29] & SP & ✔ & ✔ & & & ✔ \\
[30] & SP & ✔ & ✔ & & & ✔ \\
[12] & DLM & & & & & \\
[13] & DLM & ✔ & ✔ & & ✔ & \\
[14] & DLM & ✔ & ✔ & & ✔ & \\
[15] & DLM & ✔ & ✔ & & & ✔ \\
[16] & DLM & ✔ & ✔ & & & ✔ \\
[17] & DLM & ✔ & & & & ✔ \\ This & & & & & & ✔ \\ study & & & & & & \\ \hline \hline \end{tabular}  
* DP: deterministic programming, SP: stochastic programming, DLM: direct lookahead model, PDLM: parametrized direct lookahead model.  
\end{table}
Table 1: _Cont._  
Figure 1: Integration of modules composing the proposed approach.  
Predictions related to the suppliers' future delivery punctuality provided by the predictive module will thus be adopted as inputs by the prescriptive module. Here, decisions about which supplier to select and how to allocate orders to suppliers over future periods are made. Moreover, considerations related to the advantages of data acquisition over order allocation cost are modelled to solve the problem while considering this trade-off.  
Lastly, the continuous training module will adopt new collected supplier delivery punctuality data to update the forecasts released from the predictive module over time. Indeed, as time passes, order decisions translate into real purchasing orders and consequently into new supplier deliveries whose punctuality can be recorded. Suppose orders will be allocated to a specific supplier. In that case, the continuous training module will update the predictive model related to the specific supplier with the new data, and new forecasts related to the supplier's future delivery performance will be provided as new input to the prescriptive module. On the other hand, if no order is allocated to a specific supplier, its forecast will not be updated.
## 2 Distributed Fair Rate Allocation Algorithm
### Differences with Classical Decompositions  
From the point of view of the algorithm, two of the building blocks in the proposed method are the same as in the classical decompositions, that is, the definition of the primal and the dual subproblems has not been modified. On the contrary, the primal and the dual projections differ from the primal and dual master problems as far as subgradients are no longer used. Notwithstanding, the primal projection resembles the projection that appears in the primal master problem. The difference is that we force the constraints in \(\mathbf{A}\mathbf{y}\preceq\mathbf{c}\) to be attained with equality when the corresponding dual variable in \(\mathbf{\mu}\) is not zero. Finally, the dual projection is completely different from the master dual problem and is indeed the key block in our proposal.  
From the point of view of signalling, one iteration in traditional primal decomposition algorithms is equivalent to the first two steps in the proposed method. In other words, from \(y_{j}^{k}\) the nodes compute the primal subproblems and calculate \(y_{j}^{k+1}=y_{j}^{k}+\alpha_{p}^{k}\lambda_{j}^{k}\), which requires no signalling. Thereafter, the projection \([\cdot]^{\dagger}\) in traditional primal decomposition algorithms requires the same amount of signalling as in the worst-case of the primal projection at the CDM, that is, when \([\mathbf{A}]_{j}\mathbf{y}\preceq c_{j},\forall j\). Similarly, one iteration in the traditional dual decomposition is equivalent to the last two steps of the CDM. That is, from \(\mathbf{\mu}^{k}\), the nodes implementing the traditional dual decomposition approach obtain \(y_{j}^{k}\), which requires no signalling, and afterwards send these values to the parent nodes. Using that information the parent nodes can extract the values of \(\mathbf{\mu}^{k+1}\) and send the required linear combination of the values in \(\mathbf{\mu}^{k+1}\) back to the child nodes. In this case, the total amount of signalling is exactly the same as in the dual projection of the  
Figure 7: A complete iteration of the CDM from the point of view of signalling.  
CDM, where the nodes send the current allocation, the parent nodes compute the required subgradient and send the updates of the values in \(\mathbf{\lambda}\) afterwards.  
In summary, the key step that allows us to combine traditional primal and dual decompositions in a single algorithm, the CDM, is the dual projection. Conceptually, the big difference is to interpret the primal variables \(\mathbf{y}^{k},\hat{\mathbf{y}}^{k}\) and the dual variables \(\mathbf{\lambda}^{k},\hat{\mathbf{\lambda}}^{k}\) as candidates to the optimal solution that must accomplish the well-known KKT optimality conditions. In terms of signalling, one iteration of the CDM is equivalent to two iterations of the traditional decomposition algorithms.
## 4 Results
### Experimentation on DS1
#### 4.2.2 Comparison between MZS and Triangulation  
The ground truth from **DS1** allows us to compute an accuracy indicator. The accuracy parameter is computed by calculating the Euclidean distance from the returned zone to all verified position points each 15 s, as we can have several points of this type per interval of time. If the zone includes the point, the precision is set to 0.  
Table 4 shows the accuracy parameter computed for triangulation and MZS, the average and the standard deviation of size of calculated areas. The \"frames computed\" column of Table 4 refers to the number of position estimation that could be computed.  
We can deduce that a typical \"grid positional\" arrangement of sensors is more suitable for the triangulation as the accuracy is better than MZS.  
But we will see later that this method is vulnerable when the position of the sensors are not ideal. This validation process shows that our algorithm still gives a pretty good idea of the position of the user, even with an open access dataset.