esa-sceva/satcom-chunk-collection huggingface dataset:
# Applying Machine Learning to DEEC Protocol: Improved Cluster Formation in Wireless Sensor Networks
## 5 Conclusions  
The DEEC-K-NN and DEEC-KM approaches dynamically adjust the number and location of cluster heads, leading to more balanced energy consumption and extended network lifetimes. The K-Means approach optimises spatial distribution and outperforms K-NN and the original DEEC in packet delivery and energy conservation. Selecting cluster heads based on spatial proximity and residual energy (as in K-Means) ensures that no single node is overburdened, reducing the likelihood of early node death and network partitioning. The novelty of this work lies in the integration of unsupervised ML algorithms (K-NN and K-Means) with the DEEC protocol for adaptive cluster head selection in heterogeneous WSNs. Our simulation results show that these methods significantly outperform the original DEEC protocol in terms of packet delivery, network stability, and energy efficiency. Overall, this work highlights the promise of combining ML with traditional clustering protocols to address the persistent challenges of energy efficiency and scalability in WSNs. The proposed methods achieved up to 110% improvements in packet delivery and significant increases in network stability and energy efficiency, as demonstrated by our simulation results. The results suggest that spatially aware, adaptive clustering is a compelling direction for future protocol design, especially in applications demanding high reliability and longevity. Limitations of the proposed methods include increased computational overhead compared with traditional DEEC, the assumption of static node deployment, and potential scalability challenges in very large networks. Addressing these limitations will be the focus of future work, which will also explore hybrid models, real-time adaptation, and the integration of additional ML techniques to unlock even greater efficiencies and resilience in Wireless Sensor Networks.  
Conceptualisation, [PERSON] and L.J.-S.; methodology, [PERSON] and L.J.-S.; software, A.J.; validation, A.J.; formalism analysis, A.J.; investigation, A.J.; resources, A.J.; data curation, A.J.; writing--original draft preparation, A.J.; writing--review and editing, L.J.-S.; visualisation, A.J.; supervision, L.J.-S.; project administration, A.J.; funding acquisition, A.J. All authors have read and agreed to the published version of the manuscript.  
This research work was financed with statutory funds of the Institute of Applied Computer Science, Lodz University of Technology, Poland, No. 501/2-24-1-3.  
The data are not available because they are being used for a PhD thesis that is in progress.  
The authors declare no conflicts of interest.
# A Novel Data Augmentation-Based Brain Tumor Detection Using Convolutional Neural Network
## 1 Introduction
### Contribution  
Convolutional Neural Networks (CNNs) have demonstrated indisputable effectiveness in detecting many diseases and are widely used in medical image analysis. These networks are especially being used for the detection, classification, and segmentation of brain tumors in MRI datasets. The main objectives of our approach are  
* Detecting brain tumors from MRI datasets using deep learning and convolutional neural networks.
* Sometimes, we face issues like limited data, so we are extremely interested in the data augmentation technique. This technique allows us to implement the detection algorithms we plan to develop.
* In our paper, we used data augmentation techniques to improve the detection of brain tumors by using the VGG-16 model.
* Experimental results showed that expanding a dataset by using flipping, rotation, and translation techniques is very useful to train the VGG model.  
In fact, when we have limited data, deep learning algorithms and CNNs should be improved to be more efficient. The data augmentation technique exploits various transformations of the original data, such as affine image transformations, elastic transformations, and pixel-level transformations. In the literature, data augmentation approaches have been applied to enrich the size of training sets to allow developers to benefit from more representative training data.
## 4 Simulation Results and Discussion
### Simulation Results of the Localization Scheme  
Figure 6 compares the cumulative distribution function (CDF) of location errors using the proposed tracking approach and WLAN RSSI-based tracking. As expected, the positioning accuracy achieves significant improvement by fusion of the kinematic tracking and WLAN triangular tracking. In particular, 90% of the location errors are within 2.5 m, while that of RSSI-based tracking stays within 3.5 meters.  
Table 2 investigates the performance of the proposed handover scheme over various localization errors. It can be seen that within tolerable error range the proposed handover performance changes  
Figure 6: Empirical CDF comparison of Kalman filter output and RSSI-based tracking.  
slightly with location errors. This is because the proposed scheme is mainly based on the movement trend and user profile, which is extracted from a large volume of historical data.
# Sensor-Data-Driven Prognosis Approach of Liquefied Natural Gas Satellite Plant
## 4 Methodology  
### Sensor-Data Validation and Reconstruction  
Collected data in satellite terminals are sent to a remote central server where they are stored. These data are useful for remote meter reading, monitoring consumption, and controlling. Unfortunately, the stored data suffer from problems such as missing and spurious values, and duplicate records. For decreasing the volume of transmitted data, the monitoring strategy is an event- and time-based method (with a sample time of 1 h), leading to asynchronous and nonuniform sampled signals. Before use, it is necessary to to ensure data quality. Our methodology works offline and, following the method proposed in [35], it combines signal processing techniques with spatial models allowing to validate and reconstruct missing and false data. It consists of the following steps:  
* Step 0 (obtain variable values): Data are exported from the plant database as a.txt file. Each text line includes metadata such as event date and time, satellite-plant identification, and variable name and value. The initial step is to obtain the value of the variables, and to convert date-time information to an ISO format. Each variable is stored in a separate file.
* Step 1 (duplicated-record detection): duplicated records are detected using time information and removed.
* Step 2 (physical-range limits and equipment state): This step checks whether data are within the physical range of the sensor acquiring corresponding measurement. The expected range of the measurements may be obtained from sensor specifications or historical data records. It also allows to check the consistency of the variables in a given equipment unit, i.e., sensor or actuator
* Step 3 (resampling data): As data are asymmetric, resampling allows to construct a periodical series of new samples on the basis of the original dataset. Resampling is performed by using linear interpolation between nearest-neighbor data. Sampling time is selected on the basis of the minimal time between two consecutive events.
* Step 4 (trend level): Checks whether data are derivative, i.e., if the magnitude change of the data in consecutive sample times is within their expected rate. This allows detecting unexpected and possibly undesired sudden changes in the data.
* Step 5 (spatial model): Checks the consistency of collected data by a certain sensor with its spatial model, i.e., correlation between data coming from spatially related sensors. This spatial model is obtained from the physical relations among the plant variables.
## 3 Literature Reviews
### Identity-Based Cryptography
#### 3.5.1 Identity-Based Non-Interactive Key Agreement  
[PERSON] _et al._[23] proposed an identity-based non-interactive key agreement scheme based on bilinear pairings. This scheme is used to bootstrap the security in WSNs. Each node gets a unique id and a secret that is not shared with any other node. Two such nodes use other node's id and derive a mutual secret without even communicating. This mutual secret, unknown to any other nodes, can be used to generate a cryptographic key for secure communication between the nodes under consideration. Such a scheme is referred as identity-based non-interactive key agreement scheme. Because of its non-interactivity, this approach is very suitable for establishing secret keys among resource-constrained nodes.
# A New Approach to Defining Uncertainties for MODIS Land Surface Temperature
## 2 Framework
### Output Product  
The GlobTemperature MODIS products (GT_MOD_2P and GT_MYD_2P) provide data on LST and its associated uncertainty. Auxiliary information used in the LST retrieval, such as surface emissivity, is also output together with indicators of overall quality as a set of flags. The Level-2 output data are produced on the same swath grid and at five-minute granules consistent with the input MODIS Level-1b data. Full-resolution geolocation and viewing geometry data are also provided in the output of Level-2 products. In addition to the individual pixel uncertainty components, the output product defines a total pixel uncertainty. Since the different random, locally correlated and large-scale systematic components can be assumed to be mutually exclusive they can be added together in quadrature:  
\[u_{LST}=\sqrt{u_{ran}^{2}+u_{atm}^{2}+u_{sfc}^{2}+u_{sys}^{2}} \tag{14}\]  
Here we illustrate the contributions of the different components of the uncertainty budget by way of a single GT_MYD 2P five-minute granule (Figure 2). The granule in question is from 1 June 2010 with a timestamp of 10:50 UTC. Higher locally correlated atmospheric uncertainties are evident as you move towards the edges of the swath corresponding to larger errors in estimating atmospheric water vapour. The locally correlated surface uncertainty is larger at the horn of Africa than over much of the rest of the granule. This corresponds to larger errors in estimating emissivity for this primarily bare soil region compared with more vegetated areas. For the lakes of Eastern Africa, the locally correlated surface uncertainty is relatively small, which is consistent with the better knowledge and less variability of lake surface water emissivity.
## 3 NCF Federated Recommendation Based on DP Results
### LSH Models in Federated Recommendation  
To improve the efficiency and level of privacy protection, we recommend combining NCF with LSH. The NCF can be used to make personalized recommendations. Meanwhile, LSH can be used to accelerate the approximate nearest neighbor search, thereby reducing the computational complexity of the recommendation system and improving the efficiency of the recommendation.  
The specific implementation method is as follows:  
The NCF model is used to represent the data: first, the data are analyzed using the neural network model. These represent the complex relationships therein, thereby improving the accuracy of the recommendations.  
Accelerate nearest neighbor search with LSH: LSH hash the resulting data processed, mapping similar users and items to the same hash bucket. In the recommendation process, only the users or items in the same hash bucket need to be considered, thus reducing the size of the candidate set and improving the recommendation efficiency.  
Protecting user privacy: In federated learning, use local hash technology to protect the model parameters uploaded by clients. By hashing the local parameters of the model, we ensure that even if the parameters are intercepted or leaked, the user's specific information cannot be directly obtained, so as to protect the user's privacy.  
Comparative differential privacy techniques: Local hash techniques may be more suitable to solve communication costs and privacy issues than the differential privacy technologies using Laplace noise. Partial hashing technology can reduce traffic, reduce communication costs, and provide stronger privacy protection.  
By combining NCF with LSH and adopting local hashing technology to protect user privacy, we can improve the efficiency and privacy protection level of the recommendation system at the same time, so as to provide a better recommendation experience for users' hash family of functions.
## 3 Results and Discussion
### Construction of Quality Score Prediction Model
#### 3.3.1 Selection of Characteristic Spectral Intervals via the siPLS Method
In conclusion, the characteristic spectral intervals identified through the siPLS method provide an accurate reflection of YCXY tea quality and are consistent with the sensory evaluation results.
# Improving Spaceborne GNSS-R Algal Bloom Meteorological Data
###### Abstract  
Algal bloom has become a serious environmental problem caused by the overgrowth of plankton in many waterbodies, and effective remote sensing methods for monitoring it are urgently needed. Global navigation satellite system-reflectometry (GNSS-R) has been developed rapidly in recent years, which offers a new perspective on algal bloom detection. When algal bloom emerges, the water surface will turn smoother, which can be detected by GNSS-R. In addition, meteorological parameters, such as temperature, wind speed and solar radiation, are generally regarded as the key factors in the formation of algal bloom. In this article, a new algal bloom detection method aided by machine learning and auxiliary meteorological data is established. This work employs the Cyclone GNSS (CYGNSS) data and the fifth generation European Reanalysis (ERA-5) data with the application of the random under sampling boost (RUSBoost) algorithm. Experiments were carried out for Taihu Lake, China, over the period of August 2018 to May 2022. During the evaluation stage, the test true positive rate (TPR) of 81.9%, true negative rate (TNR) of 82.9%, overall accuracy (OA) of 82.9% and the area under (receiver operating characteristic) curve (AUC) of 0.88 were achieved, with all the GNSS-R observables and meteorological factors being involved. Meanwhile, the contribution of each meteorological factor and the error sources were assessed, and the results indicate that temperature and solar radiation play a prominent role among other meteorological factors in this research. This work demonstrates the capability of CYGNSS as an effective tool for algal bloom detection and the inclusion of meteorological data for further enhanced performance.  
GNSS-R; CYGNSS; algal bloom detection; meteorological data; RUSBoost Article
# Penetration Overload Prediction Method Based on a Deep Neural Network with Multiple Inputs
## 1 Introduction  
With the intensification of battlefield confrontation situations, the functional requirements of penetration ammunition must be improved to efficiently damage targets. The process of a projectile penetrating a concrete target at a high speed is very complex; it includes the friction effects of materials under high temperature and pressure conditions, the crack propagation of concrete, the strain rate effect and the stress wave propagation process [1]. The main problems of layer count initiation control include the following: (1) as the speed of a penetrating washed increases, the fuze bears a higher impact overload, and the interlayer overload changes dramatically at the same time; (2) during the process of high-speed penetration, the vibration of the projectile body leads to the mixing of the penetration overload during the warhead penetration process [2]; and (3) sufficient adaptive detonation control cannot be achieved for targets with different hardness levels, thicknesses, falling speeds and attack angles [3]. The main problems concerning thick target initiation control are (1) the deflection of the missile body attitude, which leads to initiation control failure [4], and (2) the irregular overload changes caused by the uneven densities and material differences of the thick target penetrating the target medium [5]. The above problems make it difficult for a fuze to accurately identify the target layer under the layer count initiation control mode and make the accuracy of layer count initiation decrease or even cause the process to fail. Under the thick target initiation control mode, the attitude deflection of the projectile cannot be detected in real time, and the penetration state of the thick target cannot be judged normally through the real-time penetration overload.  
With the development of modern computer technology, the application of artificial intelligence in the military field is exhibiting a rapidly rising trend. At the same time, inthe fields of target recognition, trajectory correction, etc., there are cases in which artificial intelligence technology is superior to traditional algorithms. In machine learning, to solve the problems of time series prediction and classification, many deep learning-based neural networks such as long short-time memory (LSTM) networks [6] and gate recurrent units (GRUs) [7] have been proposed. These models are called traditional cyclic neural networks [8]. Google proposed the transformer architecture [9] in 2017 to solve the problem of translating between different languages. This model has good performance in time series prediction problems.
# Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing
## 1 Introduction
### Summary  
This paper designs and implement the tire error segmentation system. To this end, after acquiring a depth image of the tire surface through a 3D camera, deep learning is performed by using the concept of anomaly detection. For tires, defects, such as bulges, dents, and scratches, are detected using visual inspection [54]. Therefore, there is a need to detect errors through a depth image that can express the height of the tire surface more precisely than the existing RGB camera. Since the number of bad tires in a manufacturing plant is low per day, it takes a significant amount of time to collect enough data to ensure the reliability of the training results. Therefore, before other tire error segmentation, we aim to detect vent spew errors that can obtain the most data when a bad tire occurs. The trained model using the tire depth images detects the area of fault on the tire surface. Since the tire detection system must have the robustness to distinguish various tire errors, it aims to derive the maximum classification result, without losing the original data as much as possible. Therefore, in this paper, the deep learning network uses preprocessed tire depth images that apply anomaly detection concepts to train images.  
This paper is structured as follows. Section 2 describes the acquiring of tire data, and the data preprocessing process and architecture for more effective training. Section 3 compares the training result through preprocessing presented in this paper with the training result without the same model. And analyzes the points to be improved in this study through the results and suggests future research. Section 4 draws our conclusion.
# NewApproach to Predict the Motion Characteristics of Single Bubbles in Still Water
## 1 Introduction
### Research Objectives  
The major aim of this research is, through a machine learning approach, to propose machine learning models which can provide a basis for the single bubble motion and transport mechanism under breaking wave conditions. The proposed models were developed based on the experimental data obtained from laboratory works. During the training process, Reynold (Re) number, Eotvos (Eo) number, and Weber (We) number, all of which are the critical elements influencing motion characteristics of single bubbles, were kept as the input parameters. This study is innovative in its use of the machine learning approach to predict the final velocity, drag coefficient, and shape of single bubbles in still water.
# Synthetic Approaches to 1,3,4-Oxadiazole-Containing Boronic Derivatives
## 3 Synthesis of Boronic Derivatives from Reagents Containing 1,3,4-Oxadiazole Moiety
### Indirect Connection of Boron with 1,3,4-Oxadiazole Ring via Heteracyclic Linker  
Beyond phenyl derivatives, the linkers connecting 1,3,4-oxadiazol and boronic acid units also include a diverse range of heteroaromatic compounds. A literature review demonstrates that, among heterocyclic linker-containing scaffolds, pyridine derivatives are the most commonly reported (48%), followed by indoles (24%) and thiophene rings (12%) (Figure 7).  
Figure 7: Frequency of papers containing heteroaromatic bridging units linking 1,3,4-oxadiazol to boronic acids or esters as determined via the SciFinder database (accessed on 3 March 2025).
# Experimental Study on Rock-Like Specimens with Single Flaw under Hydro-Mechanical Coupling
## 2 Specim and Testing Procedure  
### Specim Preparation  
Rock-like materials are widely used to study jointed rock masses. In this study, specimens were made of a mixture of high-strength gypsum powder, water, and retarder at a ratio of 1:0.35:0.005 by weight. The underlying parameters of the material were determined: the uniaxial compressive strength was 27.3 MPa, the tensile strength was 1.4 MPa, Poisson's ratio was 0.24, and elastic modulus was 19 GPa. The cylindrical specimens were \(\varphi\)50 mm \(\times\) 100 mm, which were made by mold. A pre-existing flaw was fabricated by inserting a piece of mica sheet that remained in the specimen, resulting in a single flaw with a width of 14 mm and thickness of 0.7 mm. The orientations of the mica sheet were predetermined by the mold to make specimens with different flaw inclination angles, and the inclination angle (\(\alpha\)) varied from 0\({}^{\circ}\) to 90\({}^{\circ}\) in 15\({}^{\circ}\) increments. As shown in Figure 1a, in order to fill the flaws with water pressure, a water injection channel was drilled after curing two weeks. In order to control the variables, all specimens including intact specimens had the same water injection channel. The diameter of the water injection channel was 8 mm. In this paper, \"intact specimen\" denotes specimens without pre-existing flaws; \"jointed specimen\" denotes specimens with pre-existing flaws; \"60\({}^{\circ}\) specimen\" denotes jointed specimens with a 60\({}^{\circ}\) pre-existing flaw.  
In order to evaluate the consistency of specimen preparation, two of the intact specimens and 15\({}^{\circ}\) specimens were randomly selected for a conventional triaxial test under confining pressure of 2 MPa and 6 MPa, respectively. As the stress-strain curves show in Figure 1b, the dispersion degrees of peak deviator stress, peak axial strain, peak annular strain, and elastic modulus were within 1.5%, indicating that these specimens have a good consistency.
## 6 Testing and Measuring Performance: Analysis of PSO, GWO, and Hybrid PSO-GWO Methods
### Results
#### 6.4.7 Cumulative Traffic Movement over Time  
The chart below shows the volume of traffic as it changes over time after implementing the optimal traffic signal schedule specific to each road.  
The PSO method produces a steady increase in traffic over time. The GWO route follows a similar trend to PSO but features a slightly smaller traffic flow. The hybrid PSO-GWO method maintains the most smooth and continuous flow of traffic over time. Figure 9 shows the cumulative movement of traffic over time.  
The PSO method starts with a fitness value of about \(-0.7\) and gradually improves, converging to a value of about \(-0.2\). This indicates a rapid improvement in the quality of the solution. The GWO method starts at \(-0.5\) and shows a significant difference every time it is used, eventually settling at \(-0.4\). This shows that GWO can explore a large breadth of information but is intelligently integrated. The PSO-GWO hybrid method combines the initial convergence speed of PSO with the higher search capabilities of GWO. The fit value of the hybrid system initially follows a similar path to PSO but also benefits from GWO analysis, finally resulting in a fit value of around \(-0.35\), which indicates a process of consistent improvement.