esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Communication Frame
### Power Consumption of Mobile Unit  
The first prototype was built with rmod_1 (Radiometrix RF module, Radiometrix Ltd., Harrow, UK) and a standard 2500 mAh Li-Po (lithium polymer) 3.7 V battery (LP356696, size: 3.5 \(\times\) 66 \(\times\) 81 mm, Batimex Sp. z o.o., Pruszkow, Poland). The power consumption of the prototype submodules is shown in Table 12. The GPS module mod_1 must be supplied with 5 V, so the current consumption is measured at the input of the switching converter 3.3 V \(\rightarrow\) 5 V (\(\sim\)90\(\%\) efficiency)--Table 12.  
The proposed communication protocol and position coding allows 81 requests per second, but the industrial requirement is limited to only 50 requests per second. Therefore, in the worst case, if a single mobile device (out of 50) responds once per second, up to 3\(\%\) of the time is taken up by transmission and the remaining time (i.e., 97\(\%\)) is spent listening and waiting for the request. The average power consumption is therefore \(I_{avg}\) = 161 mA. The tested prototype operated for about 12 h, which fulfills the project requirements (at least 10 h).  
The second prototype used rmod_2 (Adeunis RF module, Adeunis, Crolles, France) and a smaller 1900 mAh Li-Po (lithium polymer) 3.7 V battery (LP476066, 4.7 mm \(\times\) 60 mm \(\times\) 59 mm, Batimex Sp. z o.o., Pruszkow, Poland). Table 13 shows the power consumption for the second prototype modules (all supplied at 3.3 V).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Delay Source** & **Delay [ms]** & **Delay [\%]** \\ \hline Wired communication & 0.5–1 & 4.1–8.1 \\ PC processing & 2.8–3.3 & 22.8-26.8 \\ MU + BS processing & 4.1 & 33.3 \\ Wireless communication & 4.4 & 35.8 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Influence of selected subsystems on total communication delay.  
Using exactly the same timing assumptions (as for prototype 1), the average current consumption is \(I_{avg}=108\) mA. The tested prototype operated for about 14 h, which also meets the project requirements (at least 10 h).  
The third prototype of the mobile unit uses rmod_3 (ChipCon family RF modules: CC1101 and CC1190, PitchBook, Seattle, WA, USA) together with a 1400 mAh Li-Po (lithium polymer) 3.7 V battery (LP465555, 4.6 mm \(\times\) 55 mm \(\times\) 55 mm, Batimex Sp. z o.o., Pruszkow, Poland). Table 14 shows the current consumption of certain modules of the MU (all supplied with 3.3 V).  
In this case, the average current consumption is \(I_{avg}=78\) mA. The tested prototype operates for approx. 15 h, which again meets the project requirements.  
Comparing the three devices (prototypes) presented, the last one has the smallest size and is equipped with a relatively low-capacity battery. Further investigation in the real environment has proven the low power consumption and sufficient accuracy of the device. Moreover, it responds in a reasonable time and the communication range exceeds 1 km, which is sufficient for the early-stage requirements.
# Using Feature Selection with Machine Learning for Generation of Insurance Insights
## 3 Proposed Framework
### Framework Description  
Our proposed framework aims to highlight the importance of employing feature selection algorithms prior to applying machine learning algorithms for insurance data. The steps of the proposed framework are shown in Algorithm 1.  
**Input:**  
* D: A Mixed Insurance Dataset containing \(v\) Mixed features.
* C: List of candidate feature selection algorithms.
* \(\pi\): predefined set of features percentages.  
**Output:**  
* \(S\): The best representative set of features to represent D.  
```
1:for<each feature selection algorithm\(c_{i}\) in C>do
2:for<each each percentage \(\pi_{j}\) of selected feature in \(\pi\)>do
3:D\({}_{ij}\leftarrow\) Identify selected feature by Algorithm \(c_{i}\)
4:Apply intended machine learning algorithm on D\({}_{ij}\)
5:Compute performing measures
6:endfor
7:endfor
8:Identify D\({}_{ij}\) that gets best performance results
9:return D\({}_{ij}\)
```  
**Algorithm 1** Proposed framework for identifying most influential attributes in insurance datasetsThe proposed framework requires the input datasets, the list of numbers of desired features, and the choice of candidate feature selection algorithms. For each pair of feature selection algorithm and percentage of feature selection, the set of classification and clustering-based performance measures is computed. It then returns the set of features of required size that has the best performance measures.
## 3 Description of the Measurement System and Experimental Test
### Tests Description  
Purpose of the experimental activities was to compare the sag value, estimated with the proposed method based on the sensor node angular measurements, and the actual reference value, measured through the linear position sensor. Essentially, two test sessions were carried out, decreasing step-by-step the value of the tensile load in the first one and increasing it in the second one (Table 3). For each session, several steps were performed to obtain, as much as possible, a continuous curve of the sag value as a function of the tensile load tested. The scope of the tests was also to verify a possible dependence of the sag estimation on the tensile load, namely, if the estimation was rougher for low tensile load values. The higher tensile load tested (40 kN) corresponds to approximately the 22% of the cable UTS, which represents a common value adopted in operative conditions. The lower value tested (5 kN) was the minimum acceptable before reaching an excessively low sag value (the cable would have touched the ground).  
For each tensile load tested, the following parameters were obtained: the angle measured by the wireless sensor through Equation (21), the tensile load imposed to the cable, and the actual sag at midspan.  
During the tests, a progressive torsion of the cable occurring with the tensile load variation was observed. This behavior justifies the adoption of a MEMS triaxial accelerometer, which allows to compensate for the rotation (through the measurement of the lateral static acceleration a\({}_{y}\)) and to get reliable angular measurement. The results obtained from the experimental tests are presented in the following section.  
Figure 4: Experimental set-up arranged in the Cable Dynamics Laboratory: (**a**) Sensor node positioned at one extremity to carry out angular measurement; (**b**) Linear position sensor placed at midspan to obtain the reference value of maximum sag.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test Number** & **Initial Tensile Load** & **Final Tensile Load** \\ \hline Test 1 & 40 kN (22.2\% UTS) & 5 kN (2.8\% UTS) \\ Test 2 & 6 kN (3.3\% UTS) & 40 kN (22.2\% UTS) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Tests performed in the Cable Dynamics Laboratory.
# A Novel QoS Provisioning Algorithm for Optimal Multicast Routing in WMNs
## 4 The Simulation Experiments
Since the approximation factor \(2(1+\varepsilon)(1-1/q)\) solely does not illustrate the quality of the proposed algorithm, the approximation ratio will be studied in the following simulations, where we pay attention to the approximation ratio as well as its distribution. The inverse of the approximation ratio (IAR) is adopted to evaluate the performance of NMHA [35]. In other words, the ratio equal to 1 means that the solution found is the optimal solution. The ratio of less than 1 means that the solution found is sub-optimal, and the ratio of its objective to the optimal solution is represented by the value of the ratio to the optimal solution, represented by the value of the ratio. The experiments results of approximation ratio for the NMHA algorithm are shown in Figure 7. Different numbers of destination nodes (from 2 to 59) were chosen each time to finish the simulation experiments, which were run to calculate the IAR ten times. From the results, it could be concluded that the lower bound of IAR is close to 0.63, and the upper bound is very close to 1. Over 80% of all scenarios have the approximation factor greater than 0.8, which means that our proposed NMHA algorithm found a solution very close to the optimal multicast routing tree. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline NO. & \(q=2\) & \(q=35\) & \(q=70\) & \(q=105\) & \(q=140\) & \(q=175\) & \(q=210\) & \(q=245\) \\ \hline NMHA & 1009 & 1490 & 1822 & 2201 & 2330 & 2474 & 2819 & 2837 \\ FMPH & 1067 & 1490 & 2230 & 2485 & 2764 & 3310 & 3545 & 3355 \\ \hline NO. & \(q=270\) & \(q=305\) & \(q=340\) & \(q=375\) & \(q=410\) & \(q=445\) & \(q=496\) & \(q=497\) \\ \hline NMHA & 3320 & 3349 & 3610 & 3535 & 3894 & 4041 & 4488 & 4492 \\ FMPH & 4128 & 4129 & 4824 & 4877 & 5139 & 5212 & 5985 & 5989 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: AWO (\(10^{-5}\)) for NMHA and FMPH with 500 nodes. Figure 7: Experiment analysis of the approximation ratio.
## 5 Experiments and Analysis
### Sensitivity Analysis
#### 5.3.2 Sensitivity Analysis for Exploration Rates  
Figure 3 presents the convergence performance of the MATD3-TORA algorithm under varying exploration rates (\(\sigma_{explore}\)). The exploration rate was initialized at 1 and exponentially decayed with a fixed decay factor of 0.9997 per iteration until it reached the target value. When \(\sigma_{explore}\) was set to 0.5, the algorithm relied heavily on random action selection for exploration, leading to convergence to a suboptimal solution. At \(\sigma_{explore}\) = 0.25, the algorithm maintained a balance between exploring new actions and exploiting learned knowledge, reducing behavioral randomness and yielding solutions that approached the optimal solution. When \(\sigma_{explore}\) was further reduced to 0.1, the algorithm, after sufficient exploration, predominantly exploited acquired knowledge to make decisions, resulting in convergence to the optimal solution. Notably, setting \(\sigma_{explore}\) to 0.05 yielded results identical to those at 0.1, indicating that sufficient exploration had already been achieved, and further reduction in the exploration rate was unnecessary. This analysis highlights the critical role of the exploration rate in balancing exploration and exploitation, ensuring the algorithm's ability to discover optimal strategies while minimizing unnecessary randomness in decision making.  
Figure 3: The convergence of the MATD3-TORA algorithm under varying exploration rates (\(\sigma_{explore}\)).  
Figure 2: The convergence of the MATD3–TORA algorithm under varying learning rates \(\alpha\).
## 5 Evaluation
### Experimental Methodology
#### 5.2.4 Experimental Design  
**Environment Setup:** Some THUCTF tasks require a containerized environment. We deployed these directly on the local host machine and used prompts to inform the model about how to interact with the environment. Our framework, NYUCTF Agents and HackSynth, supports direct interaction with the container via the terminal. For ChatGPT-4o, we provided similar prompts requesting terminal commands, which were then executed manually on the local machine, with the results fed back to the model.  
**Input Format:** For problems with plain text attachments that can be extracted without loss of essential information, the text along with the prompt was directly fed into the model. For tasks involving multiple files, multimodal content, or requiring terminal interaction, we provided a file list and instructed the model to operate within a terminal environment.  
**Result Evaluation:** Tasks in the GITW dataset typically involve two stages: analysis and question answering. We recorded performance on both parts separately. If the model successfully produced the plaintext, the decryption was considered successful. If the question could be answered through a simple keyword search in the GITW origin over the plaintext without the need for web access or complex reasoning, we considered the task complete. Otherwise, the model was prompted to continue reasoning or search for information until a final answer was produced, at which point the task was marked as fully solved. As previously mentioned, we also tested the effect of human hints on LLM. Detailed rules of the hints can be found in Appendix B. For the THUCTF dataset, successful extraction of the final flag is used as the sole criterion for task completion. If a model solved the task entirely autonomously, the result was marked as Execution Success. Considering the difficulty and complexity of the THUCTF Dataset, we added an observation on our framework's reasoning ability: If the agent gave a right plan to solve the problem but did not execute it successfully due to some ability limits, the result was marked as Planning Success.  
In addition, for the GITW-ZH dataset, which is held as a real competition when published, we compare the performance of our framework with that of the top three human contestants in the competition, in order to analyze the gap between our framework and top-performing human participants.
# Organic Nanostructured Materials for Sustainable Application in Next Generation Solar Cells
## 3 The Performance of Nanostructured Organic Devices  
In the energy conversion processes, the use of nanostructured electrode materials have great potential in improving the overall electrochemical performance of renewable energy sources such as perovskites [57], OPVs and fuel cells [58]. Nanostructured materials demonstrated enhanced mechanical, electrical, and optical performance as a result of decreased dimensions at the nanoscale, which increase charge species mobility [59]. OPVs also employ nanostructured material in their fabrication, which is made up of a crucial component such as bulk heterojunction (BHJ) which was first introduced by [PERSON] et al. in 1995 [3]. [PERSON] et al. [3] also made their input where nanoparticles (NP), ultrathin nanolayers, and graphene oxide were among the nanostructured materials they studied. Their research revealed that there are 3 main types of semiconducting nanocrystals for charge transport layers in OPVs: transition metal oxides (TMOs), alkali-metal compounds, and organic monolayers. Because of their high optical transparency and electrical conductivity, transition metal oxide (TMO) nanoparticles are one of the most popular charge transfer layers in OPVs [60].  
Different TMO NPs can be employed as the electron or hole transfer layer depending on their functionality. To decrease energy loss during transportation inside the charge transfer layer, efficient charge transfer layers require strong electronic characteristics such as superior electrical conductivity or low series resistance, as well as high charge carrier mobility. [PERSON] et al. have produced ultraflexible OPVs with low incident light angle dependency and a PCE of 10.5% using ultrathin nanograting polymer substrates [61]. They point out that the optimized PCE of the OPV with an ultrathin nanograting polymer substrate was 71.3% at a 50\({}^{\circ}\) incident light angle, whereas the PCE of the OPV with a nonpatterned substrate was 65.6%. The nanograting-patterned fluorinated polymer enabled the development of periodic nanograting frameworks onto the back surface of a 1 m thick polymer substrate with a pitch of 760 nm and a height of 100 nm, while the reverse surface remained flat after the configuration of the planarization layer, according to their explanation. As a result, as compared to conventional active layer patterning methods, the proposed patterning approach is successful in preventing yield loss owing to active layer contamination.
## 5 Research and Development Challenges
### Joint Routing and Charging Strategy  
Drone route planning is commonly studied problems in drone-based delivery. Although existing literature addresses many issues in drone routing to minimize the average delivery time and maximize the delivery coverage, this area still requires further study. Here, owing to the limited payload capacity, limited battery, environmental effect (i.e., wind, surrounding dynamic, and static obstacles), and restricted flying zone, drone routing  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **Evaluated Metrics** & **Performance Objective** & **Innovative Features** \\ \hline \multirow{3}{*}{Covadel [56]} & Transaction time, mining time, & \multirow{3}{*}{Provides secure and quality of service (QoS) to users of drone-based delivery} & \multirow{3}{*}{Decoupled blockchain-based secure delivery mechanism.} \\  & communication cost, computation cost, average network throughput, & & \\  & and end-to-end delay. & & \\ \hline PA-NOP [57] & Execution time, validation time, and service delivery time. & Maximizing the scalability of the system. & Jointly considered security and collision avoidance. \\ \hline \multirow{3}{*}{GaRuDa [58]} & Storage cost, computation cost, & \multirow{3}{*}{Minimize the blockchain storage cost.} & Latency issue has been considered by considering the 5G-enabled tactile internet. \\  & and communication cost. & & \\ \hline \multirow{3}{*}{Delivery-Coin [60]} & Latency of blockchain consensus, & \multirow{3}{*}{Minimizing the overall latency of blockchain consensus and accuracy.} & Combined intrusion system and blockchain technology to secure customer and product data. \\  & communication overhead, attack & & \\ \cline{1-1}  & classification accuracy. & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of security algorithms in drone routing in terms of evaluated metrics, performance objective, and innovative features.  
problem becomes extremely challenging. With the increased number of depots, number of trucks, number of customers or delivery points, and coverage area, long-range multi-drone routing requires a suitable charging strategy to extend the drone flight time, and enable it to visit multiple nearby delivery points within a single mission [61]. Each drone requires a suitable energy consumption model according to its type, and propulsion power consumption to accurately estimate its flight time based on its traveled trajectory. Thus, the joint consideration of drone routing and charging strategy requires further research by adopting a realistic energy consumption model. Additionally, the placement of optimal number of CSs according to the optimal clustering of delivery locations considering several practical constraints such as restriction in drone payload, placement of CSs, deployment cost, optimizing the number of CSs, and their resources (spare batteries, or charging pad), and congestion at CSs, needs to be considered to address the existing research gap [12].
# Investigating Master-Slave Architecture for Underwater Wireless Sensor Network
## 4 System Model
### Holding Time and Activation Time Calculation  
When a node transmits data, it must allocate two types of timer slots to the receiving masters i.e., activation time and holding time. Activation time and holding times are associated with the control packet and data packet, respectively. The control packet is released after completion of Activation time. Similarly, the data packet broadcasts right after the last tick of the holding time clock. These timers are allocated based on the MSF values of master nodes. Master node with small-time span is the nodes, whose depth is low compared to others and when its next-hop master lies in its PFZ1, while having more energy than others.  
Furthermore, the timer is separately allocated to the three of the masters in that PFN region. If in case the source node does not find any master in PFZ1, forwarding will be formed in PFZ2, and their timer allocation will be of different magnitude.
## 4 Discussion  
The test results presented in Table 4 for the compressive and flexural strengths of the mortars showed the properties of the mortars and the differences between them. The cement-based recipes with the highest cement-to-wool-content ratio showed the highest compressive strength. The combination of high cement content and low wool content resulted in the samples presenting the highest compressive strength. A logical conclusion is that the lower the amount of cement and the higher the amount of mineral wool, the lower the compressive strength. A similar trend can be observed with geopolymer mortars.  
Recipes in which mineral wool dominated over aluminosilicate (MK) had lower compressive strengths. It is also important to note the relation in which geopolymer mortars have a better flexural strength compared with cement mortars. The durability of the geopolymer is also influenced by the homogenisation and granulation of the wool [37]. The purity of the wool waste used for recycling is also a significant factor; it is crucial that it is uncontaminated by gypsum, adhesive mortar, or other construction contaminants [38]. Emphasis should also be placed on balancing the aluminium/silicon ratio. Therefore, aluminium oxide was added to the mineral wool sample to raise the proportion of aluminium to silicon. The results of leaching tests on cement mortar samples in Table 6 showed that waste from these materials cannot be disposed of in inert landfills. Nevertheless, they can be disposed of in landfills for non-hazardous and neutral waste, as can mineral wool [15]. In the case of geopolymer mortars, the concentrations of copper, nickel, lead, and fluoride were exceeded in relation to the limits set out in the regulation on landfill of inert waste. In addition, the dissolved organic carbon limit was exceeded in relation to the limits set out in the regulation on landfills for non-hazardous and inert waste [26; 27]. The comparison made in Table 8 is the most important observation resulting from the study. By comparing the leaching of formaldehyde and the phenolic index from mineral wool and geopolymer mortars, it is concluded that the geopolymer mortar effectively inhibits the leaching of hazardous substances from the wool contained in the mortar. The table below shows the effect of the amount of wool on the formaldehyde content of the water extract.  
The above results indicate a correlation between the amount of wool and the amount of formaldehyde in the water extract. On the basis of the tests described in the article, it is not possible to conclude whether the geopolymer or cement mortar is a better solution for formaldehyde immobilisation. The leaching of formaldehyde with both geopolymer and cement mortar is inhibited to a comparable, impressive significant level.
## 4 Results
### Dataset Characteristics and Preprocessing  
The unified dataset, combining student-mat.csv and student-por.csv, consists of **395** unique student records after removing duplicates. For the predictive modeling, this dataset was split into a training set (\(n=316\)) and a test set (\(n=79\)), maintaining an approximate 80/20 ratio. Categorical features were one-hot-encoded, and numerical values were standardized using z-score normalization. To address class imbalance (271 \"pass\"; 124 \"fail\") in the training set, SMOTE (Synthetic Minority Oversampling Technique) was employed, generating synthetic samples for the minority class. This process resulted in a balanced 1:1 class ratio within the training data. Table 5 illustrates the class distributions before and after SMOTE application.  
The dataset includes a variety of features categorized into student demographics (e.g., age, gender, address, famsize, and Pstatus), academic history (Medu, Fedu, traveltime, studytime, failures, famrel, freetime, gout, Dalc, Walc, health, and absences), and past grades (G1 and G2). For instance, studytime is an ordinal variable where 1 represents less than 2 h, 2 represents 2 to 5 h, 3 represents 5 to 10 h, and 4 represents more than 10 h. The failures feature represents the number of past class failures.