esa-sceva/satcom-chunk-collection huggingface dataset:
# Spectral Data-Driven Prediction of Soil Properties Using LSTM-CNN-Attention Model
## 2 Materials and Methods
### Model and Methodology
#### 2.5.3 Self-Attention Mechanism  
When processing large datasets, the attention mechanism plays a pivotal role in selectively emphasizing the most relevant information. It functions by selecting the most suitable input from multiple alternatives based on observed environmental data [46]. The self-attention mechanism, as illustrated in Figure 4, is a computational process that assigns adaptive weights to elements of the input data based on their relevance to the task. By dynamically identifying and prioritizing important features, this mechanism enables the model to focus on the most critical spectral bands, thereby enhancing the accuracy of soil property prediction.  
The self-attention mechanism operates through the following steps:  
Figure 4: Self-attention mechanism.  
1. Projection of Inputs: The input data matrix (\(X\in\mathbb{R}^{N\times D_{x}}\)) is first transformed into the following three separate representations: \[Q =XW_{q}\] (7) \[K =XW_{k}\] (8) \[V =XW_{v}\] (9) where \(Q\), \(K\), and \(V\) represent the Query, Key, and Value matrices, respectively. \(W_{q}\in\mathbb{R}^{D_{x}\times D_{t}}\), \(W_{k}\in\mathbb{R}^{D_{x}\times D_{k}}\), and \(W_{v}\in\mathbb{R}^{D_{x}\times D_{v}}\) are learnable weight matrices. \(N\) denotes the sequence length, and \(D_{x}\), \(D_{k}\), and \(D_{v}\) represent the dimensions of the input, key, and value spaces.
2. Similarity Calculation: The similarity between Query and Key is measured using the following scaled dot product: \[\text{Score}(Q,K)=\frac{QK^{T}}{\sqrt{D_{k}}}\] (10) This calculation measures the compatibility between each Query and Key element. The scaling factor \(\sqrt{D_{k}}\) normalizes the dot product to stabilize gradient updates during training.
3. Attention Weights: The computed similarity scores are passed through a softmax function to transform them into the following probability distribution: \[A=\text{Softmax}\bigg{(}\frac{QK^{T}}{\sqrt{D_{k}}}\bigg{)}\] (11) The resulting weights determine the importance of each Key in relation to a given Query. These weights are subsequently applied to the Value matrix.
4. Weighted Aggregation: The attention weights are applied to the Value matrix to compute the following final attention-enhanced output: \[\text{Attention}(Q,K,V)=\text{Softmax}\bigg{(}\frac{QK^{T}}{\sqrt{D_{k}}}\bigg{)}V\] (12) This process ensures that features most relevant to the predictive task receive higher weights, effectively highlighting key spectral bands while suppressing less informative or noisy data. By incorporating the self-attention mechanism, the model dynamically adapts to the unique characteristics of the input data, enabling more accurate and interpretable predictions.
# A Heuristic Approach to the Decision-Making Process of Energy Prosumers in a Circular Economy
## 3 Case Study and Method  
### Methodology  
The research design hinges on a new dataset built through a semi-structured interview of companies and individual users who have already adopted PV self-consumption. Access to the population for the purpose of this study was achieved through a company that installs self-consumption facilities, and semi-structured interviews were conducted with Spanish PV self-consumption owners. A total of 16 out of 35 owners whose installations were commissioned from 2016 to April 2019 by the company agreed to participate in the interview.  
The main goal of the interview questionnaire developed by the authors was to understand the owners' decision-making process when adopting solar energy, as well as the experience in selecting and installing a PV self-consumption system. PV adoption is a multicriteria decision process where the different economic, social, and environmental criteria have to be considered. Thus, the interviews, which were carried out in Spain from May to June 2019, included specific questions closely related to these three criteria.  
Prosumers were asked to participate in a semi-structured interview composed of four parts. Part A concerned the general information of the prosumer for stratification. In the case of a company, questions were related to the identification of the firm (business name, address, ID number), as well as the number of employees (size), location, and sector. In the case of persons, the focus was on household characteristics. Part B determined the technical and financial aspects of the installations. Parts C (pre installation) and D (post installation) included closed and open questions related to the decision-making process and to the subsequent experience of the prosumer with respect to the installation. The methodological technique adopted in this study involved a nonprobabilistic sample, given that the number of complete interviews was 16.  
The profile of the respondents encompasses 37.5% (6 out of 16) of the companies and 62.5% (10 out of 16) of the individual users. The size of the PV systems installed by the companies ranged from 1.56 to 41.6 kW, while the size of the PV systems installed by the individual users ranged from 0.52 to 9.9 kW.
## 3 Results
### The TS Comparison of the Measurement and Model  
For the same frequency (199 kHz) used at the ex situ measurements, the TS as a function of body length and tilt angle were calculated using the KRM model (Figure 5). Figure 5a shows the TS model as a function of fish length (50\(-\)100 cm) when tilt angle is 90\({}^{\circ}\). The TS model fluctuated within the range of \(-\)38.2 and \(-\)25.2 dB. The six blue dots with error bars showed the ex situ TS of the six specimens. Overall, the calculated and measured TSs had a good agreement with less than 1 dB difference (Table 4). Figure 5b shows the TS model of the No. 6 specimen (Table 4, BL = 74.0 cm) as a function of tilt angle. The TS varied: its range was from \(-\)44.0 to \(-\)26.1 dB when tilt angle was between 80\({}^{\circ}\) and 100\({}^{\circ}\). Roughly speaking, the TS range at the tilt angle (80\({}^{\circ}\) and 100\({}^{\circ}\)) is close to that of the ex situ measurements (\(-\)38.1 to \(-\)23.8 dB, i.e. 5.9 and 2.3 dB difference, respectively). In addition, when the tilt angle was 90\({}^{\circ}\), which means that a fish swims horizontal, the calculated TS was \(-\)31.6 dB (Figure 5b), which was very close to the measured TS (\(-\)31.4 \(\pm\) 2.9 dB), only about 0.2 dB difference (Table 4).
## 4 Discussion
### CDF External Load  
In this study, CDFs covered the least total distance compared to other playing positions, with significant differences appearing only in comparison to the CMFs. In a recent study [16] conducted on a similar sample, the running profile of playing positions was compared in the formations 1-4-4-2, 1-4-3-3, and 1-3-5-2. The results of the present study are in agreement with those of previous studies, where, regardless of the number of defenders, the central defenders (CDFs) were shown to cover the shortest total distance [16; 25]. The tactical role of the position, as well as the strict defensive discipline required in the area in front of the goal, is likely the reason why these players cover the shortest distance.  
In a study examining the effects of the 1-4-2-3-1 formation on physical and technical performance in the Chinese First Division League, it was observed that CDFs covered the least TD, while CMFs and WMFs covered the most [11]. The findings of the present study regarding CDFs align with those of other studies, regardless of formation differences. This suggests that the tactical role of CDFs strictly determines their movement on the pitch.  
In the present study, CDFs also covered the shortest distance in high-speed running, and their performance was significantly different compared to the CMFs and WDFs. Regarding sprints, CDFs again covered the shortest distance among all positions. Additionally, along with FWDs, the CDFs recorded the fewest accelerations and decelerations. Similar findings regarding the distance covered at a high speed have also been reported in previous studies [11; 24; 25], regardless of the formation the team used. This may indicate the significant influence of match tactics. More specifically, the tactical role of certain playing positions does not change significantly with a change in formation, resulting in consistent running demands. Regarding accelerations and decelerations, previous studies have also reported similar results, with CDFs performing the fewest in both adult and youth players [20; 33; 34; 35; 36].
# Transmission Line Fault Classification of Multi-Dataset Using CatBoost Classifier
## 3 Methodology
### Training of Datasets Using CatBoost Algorithm  
About 93,340 datasets of four types of faults, including single-line, double-line to ground, three-phase to ground fault, and no-fault were generated from the Matlab/Simulink fault detection model. The data were divided into training and test datasets of 70% and 30%, respectively. The CatBoost classifier was used as a machine-language tool to train the dataset. The choice of classifier was based on performance and ease of usage. It also had to handle categorical features automatically (without any explicit pre-processing to convert the categories of fault data into numbers), and reduce hyperparameter tuning and the chances of data overfitting. The machine language trainer was simulated with the following parameters:  
The input data for the classifier were the fault current and voltage of the transmission line model in Figure 2, and the parameters in Table 4 were used to train the data. The main reason that the CatBoost classifier was preferred is that it is easy to use, efficient, works well with categorical variables, and doesn't require data pre-processing. It also completed the training in limited time. An effective fault management system requires fast detection and fault classification to protect the power system. This technique is superior to that of other methods, which have longer training times. The parameters were carefully selected through tuning and training to obtain better results and ensure the data was fitted.
## 2 Data
### Drought Indices  
Drought indices have been used as proxies for meteorological drought conditions in studies of drought using remote sensing [6; 10; 13; 31]. We used two of the most widely used drought indices, SPI and PDSI. The SPI was calculated by fitting historical precipitation to a Gamma probability distribution function and transforming the Gamma distribution to a normal distribution, with a mean of zero and standard deviation of one [8], which allowed precipitation to be determined over different historical periods. The relationships between various different time scales of SPI and SIF (VIs) indicate the vegetation response to water stress over different historical periods. To compare the sensitivity of SIF and VIs to short-term drought conditions, this study used SPIs based on one month (SPI-1), two months (SPI-2), and three months (SPI-3). The PDSI is based on a simple water balance model that uses an algorithm to compare monthly precipitation to a value required to sustain a climatically appropriate water balance for that month, considering the long-term record for precipitation and temperature. The PDSI can synthetically evaluate the drought condition induced from both water and heat stress.  
The relationship between the PDSI and SIF indicates a change in photosynthesis under temperature and water stress. Monthly SPI and PDSI for the CDs studied from 2008 to 2013 were obtained from the NCDC website: [[http://gis.ncdc.noaa.gov/](http://gis.ncdc.noaa.gov/)]([http://gis.ncdc.noaa.gov/](http://gis.ncdc.noaa.gov/)).
# Machine Learning-Based Solutions for Handover Decisions in Non-Terrestrial Networks
## 3 Proposed Method
### Proposed Classification Algorithm
Here, the distance is the distance between two points on a sphere given their longitudes and latitudes; having the centroid of each cluster or cell, we calculated the distance between all the UE and their cell center. The handover ready feature is a binary feature (1 or 0); we assumed that UE close to the cell center (C3\({}_{UE}\)_and_C2\({}_{UE}\)) is not ready for handover (we assigned zero as a value to them) those who are close to the edge of the cell \(C1_{UE}\) are ready for handover (we assigned one as value to them) as shown in \[\theta=\frac{d}{r}, \tag{2}\]0 As a result, we have a binary classification problem, and then we applied a machine learning algorithm to predict if a specific UE is ready for handover or not in Non-Terrestrial Networks.
## 4 Improvement to CAD  
Having developed the distance function for medical images described in Section 3, we used the results to help improve the computer-aided detection of breast cancer. Computer-aided detection (CAD) of mammograms could be used to avoid missed diagnoses, and has been shown to increase the number of cancers detected by more than nineteen percent [4]. Improving the effectiveness of CAD could improve the detection of breast cancer, and could improve the survival rate by detecting the cancer earlier.  
The typical CAD system takes in a mammogram set and displays it for the radiologist. The system also provides markers on potential cancerous sites as found by the system. The determination of these markers and the evaluation of their effectiveness in helping radiologists are the main thrust of CAD research. The hope for CAD is that the cancers missed by the radiologist are marked by the computer and brought to the attention of the radiologist.  
Most computer-aided detection (CAD) systems are tested on images which contain cancer on the assumption that images without cancer would produce the same number of false positives. However, a pre-screening system is designed to remove the normal cases from consideration, and so the inclusion of a pre-screening system into CAD dramatically reduces the number of false positives reported by the CAD system. We define three methods for the inclusion of pre-screening into CAD.
## 3 Proposed Method
### Decoder Structure  
By introducing a dual-branch parallel encoder and a cross-attention feature fusion module, we achieve multi-scale semantic feature representation. To maintain high spatial resolution, skip connections are used to combine detailed and comprehensive features, which are then passed to the decoder branch for precise weld seam recognition.  
The decoder adopts a pyramid structure with an upsampling mechanism, gradually mapping features from low to high resolution. First, the features fused from the ResNet50 branch and the Swin Transformer branch in the third stage are sent into a double convolution module for processing. Then, upsampling is performed to obtain the feature map \({F^{\prime\prime}}_{3}\in\mathbb{R}^{\frac{W}{8}\times\frac{W}{8}\times 4C^{ \prime\prime}}\). Next, the upsampled result is concatenated with the fused features from the second stage's ResNet50 and Swin Transformer branches and sent into the second double convolution module for further feature extraction. This process is repeated. After upsampling in the second stage, the result is concatenated with the fused features from the first stage's ResNet50 and Swin Transformer branches and sent into another double convolution module. Through this step-by-step concatenation, fusion, and upsampling process, the decoder progressively restores the spatial resolution of the feature map while integrating multi-scale encoded features from different stages. This pyramid structure, with its stepwise upsampling and multi-scale feature fusion, combines features at different resolutions. It preserves global contextual information while enhancing detailed edge and detail features. This significantly improves the recognition performance and boundary recognition accuracy for weld seam detection.  
In the decoder branch, the fused multi-scale features are further processed through convolutional layers (including \(3\times 3\) convolution layers, batch normalization layers, and ReLU activation layers) to extract high-level features and recover boundary information. Upsampling is carried out step by step through transpose convolution layers. This increases the spatial resolution of the feature map and accurately maps the low-dimensional information from the encoder to the original image size.
Sensors_**2010**, _10_, 5548-5568; doi:10.3390/s100605548  
_Article_  
**An Energy-efficient Rate Adaptive Media Access Protocol (RA-MAC) for Long-lived Sensor Networks**  
**[PERSON] \({}^{1,\star}\), [PERSON] \({}^{2}\), [PERSON] \({}^{1}\) and [PERSON] \({}^{1}\)**  
\({}^{1}\) ICT Centre, CSIRO, Brisbane 4069, Australia; E-Mails: [EMAIL_ADDRESS] (P.C.);  
[EMAIL_ADDRESS] (D.O.R.)  
\({}^{2}\) School of Computer Science and Engineering, The University of New South Wales, Sydney 2052, Australia; E-Mail: [EMAIL_ADDRESS]  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +(61)-7-3327-4043.  
_Received: 30 April 2010; in revised form: 18 May 2010 / Accepted: 18 May 2010 / Published: 2 June 2010_  
**Abstract:** We introduce an energy-efficient Rate Adaptive Media Access Control (RA-MAC) algorithm for long-lived Wireless Sensor Networks (WSNs). Previous research shows that the dynamic and lossy nature of wireless communications is one of the major challenges to reliable data delivery in WSNs. RA-MAC achieves high link reliability in such situations by dynamically trading off data rate for channel gain. The extra gain that can be achieved reduces the packet loss rate which contributes to reduced energy expenditure through a reduced numbers of retransmissions. We achieve this at the expense of raw bit rate which generally far exceeds the application's link requirement. To minimize communication energy consumption, RA-MAC selects the optimal data rate based on the estimated link quality at each data rate and an analytical model of the energy consumption. Our model shows how the selected data rate depends on different channel conditions in order to minimize energy consumption. We have implemented RA-MAC in TinyOS for an off-the-shelf sensor platform (the TinyNode) on top of a state-of-the-art WSN Media Access Control Protocol, SCP-MAC, and evaluated its performance by comparing our implementation with the original SCP-MAC using both simulation and experiment.  
keywords:wireless sensor networks; energy efficient; rate adaptive; performance evaluation
## 4 Simulation Results and Discussion  
The architecture of the considered power system, illustrated in Figure 2, was modeled in the MATLAB(r)/Simulink environment to explore the fault behavior of a single line-to-ground fault with an r-SFCL using different shunt resistors. The fault was located at a distance of 10 km from the substation, corresponding to a total transmission line resistance of 0.6 \(\Omega\) per phase.  
Several case studies were investigated to assess the impact of the shunt resistors, including 4, 8, and 16 \(\Omega\) shunt resistors, and one case study without a shunt resistor. The fault was applied at the time point of 2.0 s and cleared after 100 ms. In order to showcase and study both the behavior of the faults in the system as well as the system's response, including the r-SFCL, the protection system was suppressed.
###### Abstract  
Considering the advantage of the ability of data-mining techniques (DMTs) to detect and classify patterns, this paper explores their applicability for the protection of voltage source converter-based high voltage direct current (VSC-HVDC) transmission systems. In spite of the location of fault occurring points such as external/internal, rectifier-substation/inverter-substation, and positive/negative pole of the DC line, the stated approach is capable of accurate fault detection, classification, and location. Initially, the local voltage and current measurements at one end of the HVDC system are used in this work to extract the feature vector. Once the feature vector is retrieved, the DMTs are trained and tested to identify the fault types (internal DC faults, external AC faults, and external DC faults) and fault location in the particular feeder. In the data-mining framework, several state-of-the-art machine learning (ML) models along with one advanced deep learning (DL) model are used for training and testing. The proposed VSC-HVDC relaying system is comprehensively tested on a symmetric-monopolar-multi-terminal VSC-HVDC system and presents hearting results in diverse operating conditions. The results show that the studied deep belief network (DBN) based DL model performs better compared with other ML models in both fault classification and location. The accuracy of fault classification of the DBN is found to be 98.9% in the noiseless condition and 91.8% in the 20 dB noisy condition. Similarly, the DBN-based DMT is found to be effective in fault locations in the HVDC system with a smaller percentage of errors as MSE: 2.116, RMSE: 1.4531, and MAPE: 2.7047. This approach can be used as an effective low-cost relaying support tool for the VSC-HVDC system, as it does not necessitate a communication channel.  
HVDC; fault detection; fault location; data-mining; machine learning; deep learning; voltage source converter +
Footnote †: journal:  
0000-0002-119-222-1881][PERSON]
## 4 Results and Discussion
### Predictive Capability of the Models
#### 4.3.4 Comparison of Prediction Results with Other Prediction Models  
To further verify the predictive capability of the PCA-LSTM-GA model established in this study, PCA-BP-GA, PCA-XGBOOST-GA, and PCA-RNN-GA models were constructed for comparative tests. Among the 182 pieces of data, the proportions of the training and test sets were the same as those in Section 4.3.2. The prediction curves of all the models are illustrated in Figure 12, and the prediction results of these deep-learning models are disclosed in Table 4.  
Figure 11: Comparison of errors among the three models. (Note: A\({}_{1}\), A\({}_{2}\), A\({}_{3}\) denote the prediction of temperature \(T_{fau}\) by the LSTM, LSTM-GA, and PCA-LSTM-GA, respectively; B\({}_{1}\), B\({}_{2}\), B\({}_{3}\) denote the prediction of relative humidity \(RH_{out}\) by the LSTM, LSTM-GA, and PCA-LSTM-GA, respectively).  
Figure 12: Comparison of prediction curves of different prediction models for: (**a**) temperature; (**b**) relative humidity.  
As can be seen from Table 4, the PCA-LSTM-GA model exhibits the finest prediction capability for both temperature and relative humidity. Moreover, in Figure 12, the prediction curves of the PCA-LSTM-GA model are the closest to the actual change curves, with its error closer to 0 than other models.
## 4 Experiments and Results
### Metrics  
We evaluated how well EMGSCC generates ADLs matched with the ground-truth data. This article first used the _RecallRate@k_ and _Precision@k_ evaluation metrics, which are widely used in the baseline and relevant approaches [4; 6; 14]. _RecallRate@k_ calculates whether one of the \(k\) dependency libraries with the highest probability of model generation is practicable. It is calculated as follows:  
\[RecallRate@k=\frac{1}{N}\sum_{i=1}^{N}isFound(S_{i}) \tag{8}\]  
and  
\[is\;Found(x)=\begin{cases}1&x\;\text{is in groud truth}\\ 0&\text{otherwise}\end{cases} \tag{9}\]  
where \(S_{i}\) is the \(i\)-th model result.  
_RecallRate@k_ merely measures whether the result list has practical dependency libraries while paying insufficient attention to the number of useful ones. Therefore, we also considered adopting more stringent metrics commonly used in recommendation systems; _Recall@k_ and _Precision@k_[23; 24] are prevalent metrics to evaluate the reasonable degree of the generated result, and they are calculated by:  
\[Recall@k=\frac{1}{N}\sum_{i=1}^{N}\frac{|RealLibs_{i}\cap PredictLibs_{i}@k|}{| RealLibs_{i}|} \tag{10}\]  
\[Precision@k=\frac{1}{N}\sum_{i=1}^{N}\frac{|RealLibs_{i}\cap PredictLibs_{i}@k|}{| PredictLibs_{i}|} \tag{11}\]