esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Machine Learning Algorithms and Susceptibility Modeling
#### 2.6.3 Maximum Entropy (maxENT)  
Maximum entropy has been successfully applied to study species distribution and related research [60]. The Bayesian approach of maxENT modeling analyzes the available data for predicting distribution based on responses from influencing factors without relying on the sample size. Maximum entropy is a statistical learning approach that works on the principle of entropy and attempts to fit the model based on given data. Therefore, it also requires parametrization like other ML algorithms to avoid overfitting problems. The parametrization is done before generating the final susceptibility map. Other than the number of background samples, the parameterization and model tuning mainly involve a tradeoff factor known as the regularization parameter [61]. After several iterations with the help of already defined training and validation datasets, the best fit of the model was found with the regularization parameter of 1.0 and background samples of 8000 in this study. We used the 'dismo' package for modeling through the maxENT.
## 5 Results and Discussion
### Routing Overhead (ROH)  
The number of packets to process and route across a network connection is referred to as the ROH. Figures 7 and 8 demonstrate E2E delay and throughput yield optimal outcomes in situations when PDR was greater in Figure 6, and it is thus particularly assessed in these figures. This means that, when compared to the EAODV technique as well as other techniques, the largest number of packets is delivered to the destination with the least amount of latency. Overhead in the proposed model OEAODV is less than the other, which means that detection of Black Holes and Gray Holes is much better, as mean processing of malicious packets is reduced, and as a result, routing overhead is reduced in our technique as matched to our competitor. We have checked it on various speeds and performances of OEAODV (our technique) as starting on 50 m/s was approximately the same as for EAODV, but as we cannot make a decision on a single speed in the vehicular network, especially where nodes are moving, we have verified it on different speeds as we moved from 50 to 60 m/s. On other speeds as well, we can see that our technique is outperforming.  
Figure 6: Number of packets sent by nodes from each technique.  
Figure 7: End-to-end delay for packet sending from nodes of each technique.
# Performance Analysis of On-Demand Scheduling with and without Network Coding in Wireless Broadcast
## 6 Performance Evaluation
### Simulation Results in Stretch Optimal Models
#### 6.3.1 Impact of Workload  
Figure 6 depicts the behavior of the stretch optimal algorithms under different system workloads. The coding versions show a significant better performance than their non-coding versions in terms of minimizing the ART (Figure 5(a)). With respect to the average stretch (AS), a coding version also outperforms the corresponding non-coding version (Figure 5(b)). Both in the non-coding and the coding versions, LTSF and STOBS have similar performance. It is worth mentioning that STOBS schedules the data item with the maximum \(\frac{R\times W}{5}\) value. In contrast, LTSF schedules the data item with the longest total current stretch, which is the total stretches of all the pending requests of that data item. This implies that LTSF also considers both the data item popularity and the request waiting time. Given the broadcast bandwidth of \(1\) unit/tick, the time to broadcast a data item (i.e., the number of broadcast ticks) equals the number of units of the data item (i.e., the item size). On a whole, the attributes of LTSF and STOBS are very similar, which result in their close performance. For the implementation of their coding versions, STOBS_N computes \(\frac{N(d_{s})\times t_{p}}{t_{p}}\) for finding the _selected vertex_\(v_{ps}\), which is the ratio of the data item popularity multiply the maximum waiting time to the data item size. On the other hand, LTSF_N computes \(\left(max\left\{\frac{\Gamma t_{i}}{\frac{\Gamma t_{p}}{\text{p}}}\right\}\right)\) for finding the _selected vertex_\(v_{ps}\), which is the ratio of the sum of the waiting time of requests to the item size of the corresponding data item. Likewise, the attributes of STOBS_N and LTSF_N are similar, which result in their close performance. Moreover, with an increase of clients, the AEL of a coding version increases (Figure 5(c)), and the ABPs of both versions increase. Please note that the ABP of a coding version is much higher than its non-coding version (Figure 5(d)).
## 5 Results and Discussion
### Packet Delivery Ratio (PDR)  
The PDR is the percentage of actual data packets received to total data packets sent. Figure 6 shows how the PDR changes as the number of nodes increases, with the greatest value occurring when there are 60 nodes. The OEAODV method, however, provides a  
Figure 4: Proposed architecture.  
Figure 5: Proposed model.  
higher PDR rate for each node check than EAODV as well as other techniques. Variations in PDR values are the result of changes in the network environment. Pre-defined criteria were employed in our method in order to determine whether or not a path to the destination was free of attack activity for the packets that were to be sent to the destination when compared to other methods.
# How 5G Wireless (and Concomitant Technologies) Will Revolutionize Healthcare?
## 3 How Technologies Can Fix the Current Healthcare System
### (Wireless) Connectivity  
Wireless technologies due to the untethered nature and inexpensive deployment cost are ideally suited to provide the necessary connectivity that is needed between the various stakeholders of the healthcare system. The use of wireless technology for healthcare has been explored in various studies [24; 25]. The advancements in wearable computing, bio-engineering and mobile devices have enabled a dramatic increase in the exploitation of ubiquitous and pervasive wireless technologies in the healthcare system. This trend will likely continue and further be strengthened by the development of 5G wireless technology, which will provide much better performance in terms of throughput and latency compared to pre-5G technologies (as is illustrated in Figure 3).  
One of the most prominent e-Health application that wireless technology can enable is telemedicine, or the provision of expert-based medical care from a distance (see Figure 4). In particular, wireless technologies can be used to facilitate an electronic exchange of data, voice, text, images and other health-rated files. [PERSON] et al. [26] present how wireless technologies and medical services can be integrated to provide flexible, convenient and economical telemedicine. Nowadays, the rapid growth of wireless sensor networks, wireless communications, wearable sensors and especially smartphones  
Figure 3: The performance trends (throughput and latency) of 2G, 3G, 4G and 5G wireless standards.  
Figure 2: Patient-centric big data-enabled healthcare ecosystem.  
has paved the path to new e-Health systems, which are superior to traditional telemedicine systems. Future technology like Ultra-Reliable Low Latency Communications (URLLC) enabled by 5G will make health communications more resilient and will open many new healthcare opportunities like remote surgery and remote diagnosis with haptic feedback. Moreover, Simultaneous Wireless Information and Power Transfer (SWIPT) can result in a significant gain in the performance of medical implants.  
Another promising application of 5G is bio-connectivity, which will decentralize hospital services and enable the provisioning of medical care on the move (i.e., for emergency response in ambulances) [27]. This will open a new era of advantages from enhancing performance in hospitals to new ways of monitoring the patients' health, disease progression and individualized pharmaceutical analysis. We will discuss more details of 5G-related healthcare and applications in Section 4.
## 2 Materials and Methods
### Multi-Sensor Drought Indices
#### 2.1.3 Water Budget-Based Drought Index (WBDI)  
The water budget-based drought index (WBDI), which was proposed by [PERSON] et al. [18], was developed by adopting the water balance perspective and by using precipitation and evaporation as input variables. The evaporation of water balance is caused primarily by changing the state of water, which is achieved by changing the temperature [31]. The WBDIis defined as the difference between precipitation and evaporation as surface runoff and sub-surface runoff in the water budget equation, as given below (Equation (6)):  
\[P-E=dS+R, \tag{6}\]  
where \(P\) is the precipitation (mm), \(E\) is actual evaporation (mm), \(dS\) is soil moisture change (mm), and \(R\) is the potential runoff (mm). The above results are treated as possible runoff in the basin and expressed in an index, as given below (Equation (6)):  
\[\text{WBDI}=z(P-E), \tag{7}\]  
where \(z\) denotes the standardization. Instead of monitoring the current precipitation and drought conditions through evaporation, the WBDI, estimated by using the water balance formula, defines a hydrological drought through potential (near future) runoff, thereby adopting a short-term prognosis approach.
A similar perspective is noticed for the snap-on button and conductive Velcro components that could act both as antenna parts and clothing items at the same time. Furthermore, the CCs can contribute to the textile antenna integration inside the garments providing a stable and user-friendly environment and minimizing potential distortion of the antenna performance. The second target of this article is to record, review and describe a specific design procedure from scratch to reach a final operating prototype of a wearable textile antenna that uses CCs. The presented procedure is addressed through research studies conducted by the authors of this article and research works found in the literature. Through this procedure, challenges that are linked to the materials and geometry of the CCs are presented. Furthermore, the experimental investigation (antenna parameters, evaluation scenarios, and measurement techniques) of wearable textile antennas with emphasis on the nature of CC is reviewed and described. Finally, the potentials of the textile technology by implementing CCswithin wearable antennas are described. Potentials such as the textile market expansion into antenna technology, special manufacture of CCs that are appropriate for wearable applications, low profile and user-friendly wearable textile antennas (WTA), and smart clothes designs for various applications are discussed. The paper is structured as follows: In Section 2, CCs that operate as wearable textile antennas (WTA) or as parts of the antennas are extensively described. Regular buttons, snap-on buttons, Velcro tapes, and zip fasteners are reported in terms of geometry and performance properties. Section 3 outlines the design procedure of WTA using CCs (i.e., concept selection, textile materials, design of a reference antenna model and a CC textile antenna, and CC antenna design validation). Section 4 describes the experimental evaluation of WTA in terms of parameters, scenarios, techniques, and special experiments/measurements on the evaluation of a CC textile antenna. Section 5 presents the textiles technology potentials within wearable antennas. Finally, in Section 6 the conclusions are drawn.
# Short-Term Power Load Forecasting Using an Improved Model Integrating GCN and Transformer
## 4 Analysis of Experimental Results  
### Data Source  
A total of three datasets were used in the experiments of this paper, namely, the ETTh1 and ETTm1 sub-datasets of the ETT dataset and the public power load dataset of a certain region in Australia.  
ETT Dataset ([[https://github.com/zhouhaoyi/ETDataset](https://github.com/zhouhaoyi/ETDataset)]([https://github.com/zhouhaoyi/ETDataset](https://github.com/zhouhaoyi/ETDataset)), accessed on 1 April 2025): This dataset was collected from a certain area in Xinjiang, China, with a time period spanning from July 2016 to July 2018, and contains information such as power load and oil temperature. There are two time granularity levels for sub-datasets in the dataset: hourly ETTh1 and ETTh2 and minute-level ETTm1 and ETTm2. The hourly sub-datasets all contain 17,420 pieces of data, while the minute-level sub-datasets contain 69,680 pieces of data. In this study, we selected ETTh1 and ETTm1 as the experimental datasets.  
Figure 2: MSGNet-MLLA-Transformer method flow.  
Australian Electricity Load Dataset ([[https://gitcode.com/qq_42998340/Australia](https://gitcode.com/qq_42998340/Australia)]([https://gitcode.com/qq_42998340/Australia](https://gitcode.com/qq_42998340/Australia)), accessed on 1 April 2025): This dataset contains the electricity load of a certain region in Australia. It contains six variable sequences (dry bulb temperature, dew point temperature, wet bulb temperature, temperature, electricity price, and electricity load). The time span of this dataset is from 1 January 2006 to 1 January 2011. It is recorded once every 0.5 h, and there are a total of 87,648 pieces of data.  
For the three datasets in this paper, the training set, validation set, and test set are divided in a ratio of 7:1:2.  
Table 1 presents each data source, including the prediction targets and the specific division of the dataset.
# Process Mineralogy of Micro/Nano Silicon-Carbon Ore Obtained from Jiangxi, China
## 1 Introduction  
Process mineralogy is an integrated discipline that combines quantitative (and qualitative) mineralogical techniques with metallurgical test work [1]. It plays a fundamental role in the planning, optimization, and monitoring stages for different types of ores [2]. The exploitation of new mineral resources and the reuse of tailings both depend on research into the various process mineralogy parameters of different ores [3, 4, 5, 6]. However, different ores can have differing compositions, morphologies, particle size distributions, mineral liberation degrees, and textural relationships. Owing to these features, it is necessary to use different complementary analytical techniques, such as X-ray diffraction (XRD), X-ray fluorescence spectrometry (XRF), scanning electron microscopy (SEM), Fourier-transform infrared (FTIR) spectrometry, optical microscopy, and inductively coupled plasma mass spectrometry (ICP-MS) [7, 8, 9, 10, 11]. To date, various process mineralogy research methods have been used to analyze a variety of ores, and a range of different beneficiation methods have been designed [12, 13, 14, 15].  
This study focuses on a black powdery potential ore that was originally obtained from Fengcheng, Jiangxi, China. This ore was found in a mining area named Shilukeng. Clay minerals, such as montmorillonite, kaolin, illite, and pyrite, have also been mined in Shilukeng. The ore is found in an area of 1.83 km\({}^{2}\), and the average thickness is 30.53 m. This study details how process mineralogy methods were applied to characterizethe species, characteristics, and mineral composition of this ore, which were previously unknown. Herein, ICP-MS, elemental analysis (EA), differential scanning calorimetry (DSC), FTIR, and XRD were used to determine the mineral composition of the ore. SEM and laser particle size analysis were used to explore its morphology and particle size. The genesis and application potential of the ore were then discussed based on these test results. We analyzed the genesis and application field of the unknown ore by studying its mineral composition and morphology. Moreover, we aimed to provide a theoretical basis for the investigation, purification, and utilization of the ore in question.
# Neural Network-Based Cost-Effective Estimation of Useful Variables to Improve Wind Turbine Control
## 3 Neural Network Based Estimators
### Estimation Results
#### 3.3.2 Wind Speed  
Figures 12 and 13 show (in black) the wind speed estimated by the NN-based estimator for Scenario 4 in both the time (left column) and frequency (right column) domains compared to that estimated by the conventional wind speed estimator (Section 3.1) (in blue) and the actual measurements (in red). Figures 12 and 13 are for Turbines 1 and 5, respectively, and were selected randomly because Turbines 2 to 4 produced similar results. In real world scenarios, the measurements would only be available from Turbine 1, that is, the turbine with a LiDAR system; however, the measurements from Turbine 5 are included for comparison purposes. Note that this applies to Figures 12 and 13 for Scenario 4 and Figures 14 and 15 for Scenario 5.  
Figure 14: Scenario 5: Wind speed estimation from Turbine 1.  
Figure 13: Scenario 4: Wind speed estimation from Turbine 5.  
In Scenario 4, for both turbines, the time and frequency results demonstrate that the wind speed estimated by the NN-based estimator tracks the measurements more closely than that estimated by the conventional wind speed estimator. For Scenario 5, Figures 14 and 15 demonstrate that the wind speed estimated by the NN-based estimator tracks the measurements more closely than that estimated by the conventional wind speed estimator for both turbines in the time and frequency domains. Both the time plots and power spectra also clearly demonstrate improved results compared to those obtained in Scenario 4, which indicates that the combination of variables used to train the NN in Scenario 5 is better than that used in Scenario 4.  
In summary, the NN-based estimator performs best in Scenario 5 (between Scenarios 4 and 5), that is, when rotor speed, fore-aft acceleration and TBM are used as the inputs for training the NN. The NN-based estimator outperforms the conventional wind speed estimator in each scenario, which is partly due to the limited accuracy of the conventional wind speed estimator model.
# Intersection Routing Based on Fuzzy Multi-Factor Decision for VANETs
## 3 Intersection Routing Based on Fuzzy Multi-Factor Decision
### Vehicle Decision
#### 3.2.2 Neighbor Quantity  
Collisions may be introduced if there are many neighboring vehicles around the current vehicle. Also, the current vehicle can choose a much better neighbor as next-hop from these candidates to keep the routing connectivity. As presented previously [23], the road is crowded when the traffic flow reaches 133 vehicles/km/lane. However, in a common urban scenario, the vehicle speed is less than 70 km/h, and the vehicle density is about 20 vehicles/km/lane. We assume that the range of the radio is 250 m and the message can be forwarded within 200 m. We set the period of communication information from an SN to be less than 1 s. In such situations, we can calculate the density within a 6 vehicle/km/lane. In most situations, there is more than one lane on the road, thus we assume the fuzzy number of density factors using the following formula:  
\[\text{N}=\left\{\begin{array}{ll}\overline{2},&\text{if neighbors }\leq 3\\ \overline{4},&\text{if }3<\text{neighbors }\leq 6\\ \overline{6},&\text{if }6<\text{neighbors }\leq 9\\ \overline{8},&\text{if neighbors }\geq 10\end{array}\right. \tag{3}\]
# Multi-Objective Task Scheduling Optimization in Spatial Crowdsourcing
## 2 Related Work
### Task Scheduling Problem in SC  
The task scheduling problem in SC has been addressed by some researchers based on several objectives [2; 7; 10; 11]. For instance, the researchers in [11] were the first to formulate the task scheduling problem in SC within the WST model. Through the proposed maximum task scheduling problem, the number of completed tasks is maximized considering the expiration time and travel costs. Meanwhile, the authors in [7] addressed an oriented online route recommendation for WST. The research in [7] was an extension of [11], considering an online model, i.e., they updated the route of the worker when new tasks appeared under the expiration time. To solve the proposed problem in [7], the authors introduced two approaches: The first was the get-next task, which selects the next task greedily in order to reduce the response time, and the other is the re-route approach, which conducts a full search and finds an optimal route with newly released tasks.  
Regarding the SAT, the authors of [10] mixed task matching and task scheduling to solve maximum task scheduling in order to improve the number of completed tasks and the travel costs under the expiration time. However, the authors of [10] did not consider the workload between workers, which means that the workload is still imbalanced between workers, leading to work overload.  
Meanwhile, [PERSON] et al. [2] proposed load balancing-based spatial task scheduling to minimize the waiting time for the user by avoiding overloading a worker. They did so by devising the load balancing algorithm (LBA) based on two strategies: the imbalance ratio and location entropy to find the task schedule plan greedily.  
All of the abovementioned research addressed the task scheduling problem in SC but optimized only one main objective; they did not consider the MOO problem, and real applications require the optimization of many objectives at the same time to utilize the SC better and to gain more of its advantages. In this work, we optimized three objectives that had the same importance, i.e., maximizing the number of completed tasks (\(\left\lvert V\right\rvert\)), minimizing the TTCs, and ensuring a balance of the workload between workers to avoid overloading said workers. More details for optimizing two objectives are discussed in Section 2.3.
## 1 Introduction  
Cataract surgery is one of the most commonly performed surgical procedures worldwide, and phacoemulsification has enabled surgeons to achieve promising results with no major complications [1]. One of the recent changes in cataract surgery is the introduction of femtosecond laser-assisted cataract surgery (FLACS), allowing safer and more efficient surgery. This new technology uses a photodissection laser and has the advantages of executing precise corneal incisions, arcuate keratotomy, circular anterior capsulotomy, and lens nucleus fragmentations [2; 3; 4; 5]. Despite these advanced technologies and excellent postoperative visual acuity results for most patients, many patients complain of dry eye symptoms, such as dryness, burning sensation, foreign body sensation, and pain [6; 7; 8; 9].  
Recent development of point-of-care diagnostics such as tear osmolarity and matrix metalloproteinase-9 (MMP-9) testing provide high sensitivity and specificity in diagnosing dry eye disease [10; 11; 12; 13]. Recently, the American Society of Cataract and Refractive Surgery (ASCRS) Cornea Clinical Committee proposed an algorithm for preoperative diagnosis and treatment of ocular surface disease, including preoperative osmolarity and MMP-9 tests as essential screening tests [14]. However, very few studies have analyzed tear osmolarity in patients after cataract surgery and they have yet to reach a consensus [15; 16; 17]. Additionally, none have investigated MMP-9 in patients after cataract surgery. Hence, we evaluated the changes in tear osmolarity and MMP-9, and their relationship with ocular surface disease index (OSDI) in patients undergoing FLACS.