esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Resolution Approaches  
### Exact Approach  
We present, in this subsection, the mixed-integer linear program (MILP) proposed for solving the presented problem. The variables of the model are presented in Table 9.  
The MILP can be formulated as follows:  
\[\max \sum_{j=1}^{n}p_{j}X_{j}-\sum_{j=1}^{n}fc_{j}X_{j}-\sum_{j=1}^{n} tc_{j}T_{j}-\sum_{j=1}^{n}pc_{j}(1-X_{j})-\] (1) \[v\sum_{i=1}^{m}rcm_{i}RTM_{i}-\sum_{r=1}^{k}\sum_{q=1}^{l_{r}}rcr _{r}(FTR_{rq}-STR_{rq})-\] \[\sum_{j=1}^{n}\sum_{i=1}^{m}rcj_{ij}Y_{ij}-\sum_{r=1}^{k}\sum_{q= 1}^{l_{r}}rcr_{r}NTR_{rq}\] s.t. : \begin{table}
\begin{tabular}{l l l l} \hline \(X_{j}\) & _binary_ & : & 1 if job / is selected and 0 otherwise, \(\forall j\in 1\ldots n\) \\ \(T_{j}\) & _continuous_ & : & tardiness of job \(j\), \(\forall j\in 1\ldots n\) \\ \(W_{i}\) & _binary_ & : & 1 if machine \(i\) is used for some jobs and 0 otherwise, \(\forall i\in 1\ldots m\) \\ \(Y_{ij}\) & _binary_ & : & 1 if job / is processed on machine \(i\) and 0 otherwise, \\  & & \(\forall i\in 1\ldots m\), \(\forall j\in 1\ldots n\) \\ \(C_{ij}\) & _continuous_ & : & completion time of job \(j\) on machine \(i\), \(\forall i\in 1\ldots m\), \(\forall j\in 1\ldots n\) \\ \(STM_{i}\) & _continuous_ & : & starting time of renting machine \(i\), \(\forall i\in 1\ldots m\) \\ \(FTM_{i}\) & _continuous_ & : & finishing time of renting machine \(i\), \(\forall i\in 1\ldots m\) \\ \(RTM_{i}\) & _continuous_ & : & renting time of machine \(i\), \(\forall i\in 1\ldots m\) \\ \(STR_{rq}\) & _continuous_ & : & starting time of renting resource \(q\) of type \(r\), \(\forall r\in 1\ldots k\), \(\forall q\in 1\ldots l_{r}\) \\ \(STR_{rq}\) & _continuous_ & : & finishing time of renting resource \(q\) of type \(r\), \(\forall r\in 1\ldots k\), \(\forall q\in 1\ldots l_{r}\) \\ \(NTR_{rq}\) & _continuous_ & : & number of transfers of resource \(q\) of type \(r\) between machines, \\  & & \(\forall r\in 1\ldots k\), \(\forall q\in 1\ldots l_{r}\) \\ \(Z_{irq}\) & _binary_ & : & 1 if machine \(i\) uses resource \(q\) of type \(r\) to process some jobs \\  & & & & 0 otherwise, \(\forall i\in 1\ldots m\), \(\forall r\in 1\ldots k\), \(\forall q\in 1\ldots l_{r}\) \\ \(V_{ij^{\prime}}\) & _binary_ & : & 1 if job \(j\) precedes job \(j^{\prime}\) when both use the same machine \\  & & & & and 0 otherwise, \(\forall j\), \(j^{\prime}\in 1\ldots n\), \(j<j^{\prime}\) \\ \(U_{ii^{\prime}rq}\) & _binary_ & : & 1 if resource \(q\) of type \(r\) is used by machine \(i\) before machine \(i^{\prime}\), \\  & & & & and 0 otherwise \\  & & & \(\forall i\), \(i^{\prime}\in 1\ldots m\), \(i<i^{\prime}\), \(\forall r\in 1\ldots k\), \(\forall q\in 1\ldots l_{r}\) \\ \(G_{jpq}\) & _binary_ & : & 1 if job / uses resource \(q\) of type \(r\) and 0 otherwise \\  & & & \(\forall j\in 1\ldots n\), \(\forall r\in 1\ldots k\), \(\forall q\in 1\ldots l_{r}\) \\ \hline \end{tabular}
\end{table} 
Table 9: Variables of the MILP.
## 4 Results and Discussion  
### Single-Leaf Resonant Absorber  
Only the larger tube was used and, consequently, the frequencies of interest were limited to the one-third octave bands from 100 Hz to 1600 Hz in order to validate the prediction models. Figure 5a shows the predicted and experimental values of sound absorption coefficient for single-leaf perforated panels backed by a 20 mm thick cavity. It was observed that the measured sound absorption coefficients for panel A, B, and C were in a reasonable agreement with their predictions in terms of the maximum absorption coefficient and the resonant frequency. In particular, while using actual perforation rates instead of the infinite-pattern values yielded better results and the agreement was even better for panel C, the one with hole distribution closer to the \"ideal\" one, showing the smallest differences between the resonant frequencies, as well as the peak sound absorption coefficient value. In fact, the measured peak frequency was at 630 Hz (\(\alpha=0.92\)), while the predicted peak was at 620 Hz with \(\alpha=0.93\) when the actual perforation rate was used (while it moved to 660 Hz with \(\alpha=0.89\) when the \"infinite\" pattern value was used). The deviations of sound absorption coefficient values at the remaining frequencies were almost everywhere below 0.05. For panel A and panel B, the resonant frequency that was measured in laboratory occurred at a frequency a little lower than that predicted, while the peak absorption value was somewhat higher than the corresponding predictions, especially for panel B. The misalignment that was observed between measurements and predictions could be attributed to the influence of further border effects, as the cavity cell behind the holes near the sample edge (close to the boundary) might show a different behavior than the cavity cells of the other holes (close to the center).  
Figure 5b presents the validation of single layer micro-perforated membranes, showing the sound absorption coefficient of sample M4 as a function of varying cavity thickness. The measured resonant frequencies of membrane cases were in general agreement with those that were given by the analytical method and calculated while assuming a hole diameter of 0.267 mm. Some measured results had lower absorption than the corresponding predicted values. This deviation could be explained as a consequence of the small variations in hole diameters, as well as of the possible vibration of the membrane, thus modifying the actual behavior of the panel. As expected, when compared with perforated panels, the micro-perforated membranes showed an improvement of sound absorption, with broader bandwidth, higher absorption peak, and extended high frequency response.  
Overall, for single-leaf resonant absorbers, the results that were measured in experiments showed good agreement with those that were predicted in terms of resonant frequency and peak sound absorption coefficient, thus confirming that the prediction method in this case was reliable and robust.
## 3 Simulation Results
### Design of the Input/Output Cavities
In this step, a rectangular port (port 1) on the output waveguide coupler and a set of discrete ports (port 2 to 9) at the gap centers of different beamlets have been considered, as shown in Figure 10. The time-domain simulation has been carried out using a CST studio time-domain solver, and the obtained reflection and transmission coefficients are shown in Figure 11. The almost identical transmission parameters in Figure 11 clearly show a symmetric field distribution among the four beamlets' gaps. The reflection parameter dip suggests a significant coupling of the electromagnetic energy through the coupling slot at the operating frequency.
# AdaptiveConv2d: A Novel Convolutional Module for Medical Image Segmentation
## 2 Materials and Methods
### Introduction of a Second Optimization Objective  
As shown in Figure 3, in traditional convolutional neural networks, channel weights are often generated through global average pooling and simple linear transformations. However, especially in medical imaging contexts, where data may exhibit complex structures, subtle textural details, and significant global context, such basic channel weighting can fail to fully capture the intricacies of feature distributions. To address this limitation and improve the adaptability and expressiveness of the model, we introduce a secondary optimization objective for channel weight generation [35].  
Figure 1: The architecture of the AdaptiveConv2d module. The proposed module starts by processing the input feature map \(X\) through a standard convolutional operation Conv\({}_{1\times 1}\)(X)), followed by global average pooling to extract spatially aggregated features. These features are used to compute the energy function (\(\mathcal{L}_{\text{energy}}\)), which guides the generation of channel-wise attention weights (\(a_{c}\)) through an optimization process. The channel weights are activated using a sigmoid function and are applied to adaptively modulate the feature map. The modulated feature map is then combined with the output of a \(1\times 1\) convolutional layer, Conv\({}_{1\times 1}\)(X), to produce the final enhanced output. This module is designed to improve feature representation in medical image segmentation tasks by dynamically adjusting channel importance based on energy optimization.  
Figure 2: Global average pooling. This figure illustrates the procedure of global average pooling (GAP). GAP operates by computing the average value of each feature channel independently, reducing the spatial dimensions of the input feature map into a single scalar per channel. This operation retains the essential global spatial information while significantly reducing computational complexity, making it highly effective for tasks such as feature aggregation and attention mechanism generation.
## 4 Conclusions  
This study successfully produces PP/GNs and PP/CF conductive composites by cooperating with a melt compounding method. The combination of these two filler types as the nucleating agent results in a greater crystallization temperature of PP. With the same content, GNs demonstrate a greater influence over the crystallization temperature than CF does. The combination of GNs retards the crystallization rate of PP, while the combination of CF does the opposite. The tensile modulus of both PP/GNs and PP/CF conductive composites is considerably proportional to the loading level of the conductive fillers. The tensile strength of PP/GNs conductive composites decreases with an increasing content of GNs, while that of PP/CF conductive composites first decreases then increases as a result of the increasing CF. The optimal conductivity of 0.36 S/m and the optimal EMI SE of 13 dB occurs when the PP/GNs conductive composites contain 20 wt% of GNs. By contrast, the optimal conductivity of \(10^{-6}\) S/m occurs when the PP/CF conductive composites contain more than 3 wt% of CF, while the optimal EMI SE occurs when containing 20 wt% of CF. Therefore, possible applications in domestic appliances of the composites in terms of electrical and mechanical properties can thus be attained by adjusting different fillers and different amounts of them.
# Assessment of Drought Severity and Vulnerability in the Lam Phaniang River Basin, Thailand
###### Abstract
The main findings indicated that the total yield losses due to past climate trends are rather low, by less than 50 kg/ha per decade (3% of actual average yields). However, the future yield reduction is likely to be more serious if the observed trends in temperature and rainfall remain the same. That is to say, the increasing trend of maximum temperature, especially reaching more than 35 \({}^{\circ}\)C in October, may lead to spikleet sterility and severe yield losses. Likewise, the increasing trend of rainfall and its intensity occurring in September may also lead to more severe yield losses. Due to the above shortcoming, this study was then highlighted on the assessment of drought severity and vulnerability in the Lam Phaniang River Basin, where the pattern and consequences of recurrent drought was found to be a prominent event in this study area. According to the Department of Water Resources [9], it was revealed that the Lam Phaniang River Basin is highly prone to drought compared to other sub-basins, with about 55.7% of its total area under extreme drought conditions, whereas approximately 27.4% fall in moderate drought conditions. In this study, three indicators, i.e., meteorological drought index, spatial sensitivity of drought, and adaptive capacity to climate change, were used to evaluate the drought severity. Moreover, additional recurrent and intense droughts were also investigated under climate and land use changes, including mapping of drought risk areas at the sub-basin scale. Eventually, the main findings from this study might be beneficial for developing frameworks and plans regarding equity, efficiency and sustainability in water allocation in the Lam Phaniang River Basin.
## 3 Materials and Methods
### Pilots Training and in-Classroom Experience  
Before implementation, a two-step preliminary plan was developed for training teachers and the executive board of each school. This training consists in introducing the methodology and usage know-how of the Smile and Learn's platform.  
The first step addressed a general training period, coordinated by the government of the involved Spanish regions, together with the educational team. This training consists in explaining the implementation and technical characteristics, how to download the platform, or how to work with the school's free user. Additionally, they were exposed the platform's management and the methodologies they were able to apply should they wanted to continue with the project further on. The use format in these schools came through a'superuser' for the school, that managed the different classes and users, and that was able to create unlimited profiles. Each teacher also had a free access account to all the content in order to manage their students and see their progress.  
The second step addressed a more specific training for each collaborating school, allocated in the different provinces within the participating regions of the country. These partnered with the team in the agreement for explaining all the resources needed and detailing how to manage the platform's activities. For that training, members from Smile and Learn's educational team were prepared for explaining and resolving any questions that may appear, creating the lists of users for each school, and for sharing the material content, the catalogues of contents and tutorial videos supplementing the training process. During the entire academic year technical support was provided to any teachers that required so. Likewise, didactic guides offered examples of methodologies to apply in class, explaining several ways for accommodating content and complementary activities through the platform.  
As a result, these games can be used by teachers in their lessons under several teaching methods. For example, as the platform is developed in five languages by natives, it can be used for CLILmethodology [34]. Activities like 'Experiments' can be worked at classroom to learn expressions and verbal tenses used for researching goals within thought experiments. Meanwhile, students will also be learning scientific concepts. As an example of a Flipped Classroom [33], teachers can schedule home readings of the tales incorporated in the 'Literacy' world of activities, to be worked at class the next day by a discussion, a topic presentation, some group activities, etc. As a different example, if teachers want to develop an environmental project by groups, they can use activities like 'Green City' (a game in which children need to save a polluted city) and 'Recycle' (to learn how to separate waste). The possibility of having more than 4500 activities, with combined methodologies, gives a wide range of opportunities to adapt and support teachers' lessons with active learning.
# Hyperband-Optimized CNN-BiLSTM with Attention Mechanism for Corporate Financial Distress Prediction
## 3 Methodology
### Hyperband Algorithm  
The Hyperband algorithm, proposed by [PERSON] and [PERSON] in 2016 [21], eliminates the underperforming hyperparameter configurations in each round by combining the Successive Halving algorithm in the multi-armed bandit strategy and efficiently selects the best-performing hyperparameter configurations from the final configuration to optimize the model training process.  
1. Initialization parameters. Set the resource budget \(R\) (the total number of training rounds), set the bandwidth factor \(\eta\) (a parameter controlling the elimination speed of hyper-parameter configurations; the default is 3), and set the initial amount of resources \(r_{0}\) (the amount of resources used by each configuration in the initial evaluation). (2) Calculate maximum rounds. \(s_{max}\) is the maximum number of rounds that can be performed with the current resource budget \(R\), and initial resource amount \(r_{0}\). \[s_{max}=\lfloor log_{\eta}\frac{R}{r_{0}}\rfloor.\] (17) where \(\lfloor\rfloor\) stands for the downward rounding sign. (3) Perform multiple rounds of hyperparameter configuration and the elimination process. Multiple rounds are performed from \(s=0\) to \(s=max\). For each round \(s\), the number of configurations and the amount of resources are initialized, and \(n\) hyperparameter configurations are generated, each of which receives a training resource amount \(r\), at the beginning. \[n=\lceil\frac{R}{r_{0}(s+1)\eta^{s}}\rceil,r=r_{0}\cdot\eta^{s}.\] (18) In each round \(s\), obtain the number of hyperparameter configurations \(n_{i}\), and the amount of resources \(r_{i}\), used for each configuration in the ith subphase (from 0 to \(s\)). \[n_{i}=\lceil\frac{n}{\eta^{i}}\rceil,r=r_{0}\cdot\eta^{i}.\] (19) Allocate resources evenly to all hyperparameter configurations and perform initial training. According to the Successive Halving algorithm, eliminate the underperforming configurations and concentrate the resources on the best-performing configurations, at which point the elimination ratio is \(1-\frac{1}{\eta}\), and the best-performing \(\frac{1}{\eta}\) configurations are retained. (4) Selection of the optimal configuration. At the end of all rounds, the best-performing hyperparameter configuration is selected from the final configuration. The Hyperband CNN-BiLSTM-AT Flowchart is shown in Figure 2.
# Experimental Study of 6 LoPLC for Home Energy Management Systems
## 6 Conclusions  
An empirical assessment of 6 LoPLC for HEMS has been presented in this paper. A model was developed in NS-3 and validated with measurement results from Hanadu 6 LoPLC evaluation kits. It was shown that 6 LoPLC could introduce additional one-way delay, as high as 131.7512 ms to control and 108.8314 ms to monitoring applications. Based on the parameters employed in this work, in order to guarantee 99% minimum system reliability, it was shown that the application data must not exceed 64 bytes. These results could provide some useful insights for system designers and application developers. The results also reveal that with the current system configurations, latencies of 238.117 ms and 248.959 ms are achievable for monitoring and control applications, respectively, provided the data size is 64 bytes or less. Finally, it was shown that, with the CoAP payload of 128 bytes, impulsive noise can further degrade the latency by an additional 32.759 ms.  
We further showed that larger application data sizes are more affected by impulsive noise and 6 LoPLC fragmentation/reassembly processes. Beyond HEMS, the model presented in this paper is also feasible for smart grid applications and other cyber-physical systems where high reliability and low cost are of higher priority than high throughput. Future work in this area will include system optimisation and miniaturisation of the hardware.
# MINC-NRL: An Information-Based Approach for Community Detection
## 4 Community Detection Based on MINC  
### Community Evolution Process  
Apart from \(MINC\) as a community evaluation index, a community transformer is still needed to constantly transform the community structure, find the peak value of the evaluation index and finally carry out a solution of community detection. Here, we introduce the community evolution process, which will play a role as a community transformer in the MINC-NRL approach.  
Definition 1: Community evolution process  
A community evolution process \(P\) is an ordered set consist of a series of state, defined as:  
\[P=\{P_{0},P_{1},P_{2},\cdots,P_{N}\}\]  
where each state \(P_{n}\) is essentially a community partition of the same network.  
Definition 2: Community evolution process of bottom-up cohesion  
If adjacent states \(P_{n}\) and \(P_{n+1}\) in community evolution process \(P\) satisfy the following:  
\[\forall Y_{j  
1. Each state of the generated process is itself a high-quality partition of the network, and there would be no unreasonable case where a node tries to pull adjacent nodes into its community without discrimination.
2. The number of states of the generated process is less, which reduces the calculation time to evaluate the merits of each state.
3. For such community evolution process, a stable and unique result can be obtained by finding the maximum of community evaluation index, or by filtering out the states with larger evaluation index, result partitions with different resolution can be output as well.  
To generated a community evolution process for given network, network representation learning (NRL) and hierarchical clustering are used in the approach, which will be illustrated in detail in the next subsection.
# Lyapunov-Guided Energy Scheduling and Computation Offloading for Solar-Powered WSN
## 5 Performance Analysis of Algorithms
At time period B, we use iterative expectation and scaling summation to obtain\[ \begin{split}&\mathbb{E}\Big{\{}L[\mathbf{G}^{T}]-L[\mathbf{G}^{0}] \Big{\}}-V\sum\limits_{t=1}^{T}\mathbb{E}\Big{\{}\sum\limits_{k=1}^{K}\Phi_{k} ^{t}-\eta_{EE}^{t}E_{total}^{t*}\Big{\}}\leq CT+\varepsilon\sum\limits_{t=1}^{ T}\Big{(}E_{S}^{t-1}+\sum\limits_{k=1}^{K}E_{k}^{t-1}+\sum\limits_{k=1}^{K}Q_{k}^{t-1 }\Big{)}\\ -2\varepsilon TB^{\text{max}}-V\sum\limits_{t=1}^{T}\big{(}\eta_{ EE}^{t*}-\mathbb{E}\{\eta_{EE}^{t}\}\big{)}\mathbb{E}\Big{\{}E_{total}^{t, \text{max}}\Big{\}}\end{split}  \tag{37}\]  
We divide by both sides of Equation (37) simultaneously to obtain  
\[ \begin{split}&\frac{\mathbb{E}\big{\{}L[\mathbf{G}^{T}]-L[\mathbf{G}^{0}] \big{\}}}{VT}-\frac{1}{T}\sum\limits_{t=1}^{T}\mathbb{E}\Big{\{}\sum\limits_{ k=1}^{K}\Phi_{k}^{t}-\eta_{EE}^{t}E_{total}^{t*}\Big{\}}\leq\frac{C}{V}+\frac{1}{V \tau}\varepsilon\sum\limits_{t=1}^{T}\Big{(}E_{S}^{t-1}+\sum\limits_{k=1}^{ K}E_{k}^{t-1}+\sum\limits_{k=1}^{K}Q_{k}^{t-1}\Big{)}\\ -&\frac{2\varepsilon}{V}B^{\text{max}}-\frac{1}{T} \sum\limits_{t=1}^{T}\big{(}\eta_{EE}^{t*}-\mathbb{E}\{\eta_{EE}^{t}\}\big{)} \mathbb{E}\Big{\{}E_{total}^{t,\text{max}}\Big{\}}\end{split}  \tag{38}\]  
\[\text{When }\ T\rightarrow\infty,\lim\limits_{T\rightarrow\infty}\frac{ \mathbb{E}\big{\{}L[\mathbf{G}^{T}]-L[\mathbf{G}^{0}]\big{\}}}{T}=0,\text{ we appropriately ignore the non-negative terms and the formula can be transformed as  
\[\frac{C}{V}-\bigg{(}\eta_{EE}^{t*}-\lim\limits_{T\rightarrow\infty}\frac{1}{T} \sum\limits_{t=1}^{T}\mathbb{E}\big{\{}\eta_{EE}^{t}\big{\}}\bigg{)}\mathbb{E }\Big{\{}E_{total}^{t,\text{max}}\Big{\}}\geq 0 \tag{39}\]  
Then, the network energy efficiency can be expressed as  
\[\eta_{EE}^{t}=\lim\limits_{T\rightarrow\infty}\frac{1}{T}\sum\limits_{t=1}^{ T}\mathbb{E}\{\eta_{EE}^{t}\}\geq\eta_{EE}^{t*}-\frac{C}{V\mathbb{E}\Big{\{}E_{ total}^{t,\text{max}}\Big{\}}} \tag{40}\]  
Similarly, dividing both sides of the equation by A simultaneously, the network queue stability can be expressed as  
\[\lim\limits_{T\rightarrow\infty}\frac{1}{T}\sum\limits_{t=1}^{T}\bigg{(}E_{S }^{t-1}+\sum\limits_{k=1}^{K}E_{k}^{t-1}+\sum\limits_{k=1}^{K}Q_{k}^{t-1}\bigg{)} \geq 2B^{\text{max}}+\frac{V\Big{(}\eta_{EE}^{t*}\mathbb{E}\Big{\{}E_{ total}^{t,\text{max}}\Big{\}}-\mathbb{E}\Big{\{}\eta_{EE}^{t}E_{total}^{t,\text{max}} \Big{\}}\Big{)}-C}{\varepsilon}. \tag{41}\]  
\(\square\)