esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Performance Forecasting of Residential Buildings Using Fuzzy Approaches
###### Abstract  
The energy consumption used for domestic purposes in Europe is, to a considerable extent, due to heating and cooling. This energy is produced mostly by burning fossil fuels, which has a high negative environmental impact. The characteristics of a building are an important factor to determine the necessities of heating and cooling loads. Therefore, the study of the relevant characteristics of the buildings, regarding the heating and cooling needed to maintain comfortable indoor air conditions, could be very useful in order to design and construct energy-efficient buildings. In previous studies, different machine-learning approaches have been used to predict heating and cooling loads from the set of variables: relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area and glazing area distribution. However, none of these methods are based on fuzzy logic. In this research, we study two fuzzy logic approaches, i.e., fuzzy inductive reasoning (FIR) and adaptive neuro fuzzy inference system (ANFIS), to deal with the same problem. Fuzzy approaches obtain very good results, outperforming all the methods described in previous studies except one. In this work, we also study the feature selection process of FIR methodology as a pre-processing tool to select the more relevant variables before the use of any predictive modelling methodology. It is proven that FIR feature selection provides interesting insights into the main building variables causally related to heating and cooling loads. This allows better decision making and design strategies, since accurate cooling and heating load estimations and correct identification of parameters that affect building energy demands are of high importance to optimize building designs and equipment specifications.  
energy performance; heating and cooling load; Fuzzy Inductive Reasoning (FIR); Adaptive Neuro-Fuzzy Inference System (ANFIS) 2020 10 720 10.3390/app10020720
## 4 Experiments
### Visualization  
Ubiquitous intra-class occlusion [55] in crowded scenes poses a challenge for pedestrian detectors, as proposals belonging to occluded pedestrians share a high similarity with duplicate proposals, leading to inaccurate detection results. In order to provide a clear illustration, we visualize the detection results of our proposed detector on the CrowdHuman and CityPersons datasets. Figure 6 shows the visualization of our method on CrowdHuman. It clearly shows that crowded pedestrians captured from multiple angles are accurately detected by our proposed detector, even those who are occluded by others. This is owing to the adaptive suppression in MA-NMS, which effectively preserves occluded pedestrians while significantly suppressing duplicate proposals. Figure 7 presents additional visualization examples on CityPersons, where our detector achieves accurate pedestrian detection in slightly crowded scenes. The visualized results collectively demonstrate the superiority of our MA-NMS in improving the performance of generic pedestrian detectors in both crowded and slightly crowded scenes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Backbone** & **R** & **H** & **P** & **B** \\ \hline RepLoss [39] & ResNet-50 & 13.2 & 56.9 & 16.8 & 7.6 \\ TLL [53] & ResNet-50 & 15.5 & 53.6 & 17.2 & 10.0 \\ TLL + MRF [53] & ResNet-50 & 14.4 & 52.0 & 15.9 & 9.2 \\ ALFNet [54] & ResNet-50 & 12.0 & 51.9 & 11.4 & 8.4 \\ PBM + R2 NMS [48] & VGG16 & 11.1 & 53.3 & - & - \\ NOH NMS [40] & ResNet-50 & 10.8 & 53.0 & 11.2 & 6.6 \\ AutoPedestrian [50] & ResNet-50 & 11.5 & 56.7 & - & - \\ CSP [13] & ResNet-50 & 11.0 & 49.4 & 10.4 & 7.3 \\ \hline MA-NMS (_w/o_) & ResNet-50 & 10.6 & 49.5 & 9.9 & 6.5 \\ MA-NMS (_w_) & ResNet-50 & **10.0** & **48.9** & **9.0** & **6.3** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison with the state-of-the-art methods on CityPersons validation set. _w (w/o)_ represents with (without) suppression factors.  
Figure 6: Visualization of our method on CrowdHuman.
## 2 Materials and Methods
### Region Elimination Method Based on GLD Algorithm
Evaluate the minimal number \(N\) of iterations required to provide the sufficient (predefined) value of the \(\epsilon\): \[|x^{*}-\hat{x}^{*}|\leq\epsilon(x^{(0^{+})}-x^{(0^{-})}),\] (7) where \(|x^{*}-\hat{x}^{*}|\) is the absolute value of the difference between the true (unknown minimum \(x^{*}\)) and iterative solution \(\hat{x}^{*}\) (which is assumed to be in the center of \(\mathcal{D}^{(N)}\)). Step 2. For iteration \(k=1,\,\ldots,\,N\),
1. select a pair of intermediate points \(\hat{x}^{(k^{-})}\) and \(\hat{x}^{(k^{+})}\) (\(\hat{x}^{(k^{-})}<\hat{x}^{(k^{+})}\), \(\left\{\hat{x}^{(k^{-})},\,\hat{x}^{(k^{+})}\right\}\in\mathcal{D}^{(k-1)}\)),
2. reduce the range to \(\mathcal{D}^{(k)}\) based on REM fundamental rule: 1. \(x^{(k+1)}\in\mathcal{D}^{(k)}=\left[x^{(k-1^{-})},\,\hat{x}^{(k^{+})}\right]\) for \(f(\hat{x}^{(k^{-})})<f(\hat{x}^{(k^{+})})\), 2. \(x^{(k+1)}\in\mathcal{D}^{(k)}=\left[\hat{x}^{(k^{-})},\,x^{(k-1^{+})}\right]\) for \(f(\hat{x}^{(k^{-})})\geq f(\hat{x}^{(k^{+})})\), 3. start next iteration \(k:=k+1\). Step 3. Stop the algorithm; put \(\hat{x}^{*}=\frac{1}{2}\left(x^{(N^{+})}+x^{(N^{-})}\right)\). ```  
**Algorithm 1** Golden-search algorithm. For the given value of \(\epsilon\), the minimum number \(N\) of iteration in the GLD algorithm can be calculated according to:  
\[(1-\rho)^{N}\leq\epsilon, \tag{8}\]  
and for \(k=1,\,\ldots,\,N\) one may find the pair of intermediate points using  
\[\hat{x}^{(k^{-})} = x^{(k-1^{-})}+\rho(x^{(k-1^{+})}-x^{(k-1^{-})})\,, \tag{9}\] \[\hat{x}^{(k^{+})} = x^{(k-1^{-})}+(1-\rho)(x^{(k-1^{+})}-x^{(k-1^{-})})\,. \tag{10}\]
# Deep Learning for Predicting Traffic in V2X Networks
## 7 Conclusions  
In this paper, the unidirectional LSTM with the DL model was proposed for V2X traffic forecasting. LSTM allows time series prediction models to predict future values as a function of past values. This allows for more accurate predictions, leading to better decision making. It also has the advantage of remembering values over a long time, producing a more accurate prediction of future values. According to the simulation results, the highest prediction accuracy was achieved with a number of transmitted packets of 4 packets/s,  
Figure 8: The predicted output at a transmission rate of 14 packets/s.  
Figure 7: The predicted output at a transmission rate of 12 packets/s.  
which outperformed the competitors and represented outstanding performance. On the other hand, the models with a prediction of 14 packets/s have the lowest prediction accuracy compared to the others. The model with a prediction of 12 packets/s has the fastest processing time, but the models with a prediction of 14 packets/s have the slowest processing time compared to their competitors.  
Conceptualization, A.R.A. and A.K.; methodology, A.R.A.; software, A.R.A. validation, A.R.A., M.H.E., A.M., A.K. and A.R.A.; formal analysis A.R.A.; investigation, A.M. and M.H.E.; resources, A.K.; data curation, A.R.A. and A.K.; writing--original draft preparation, A.R.A.; writing--review and editing, A.R.A., A.K., M.H.E. and A.M.; visualization, A.R.A., A.M., M.H.E. and A.K.; supervision, A.K.; project administration, A.M. and A.K.; funding acquisition, A.M., A.K. All authors have read and agreed to the published version of the manuscript.  
The studies at St. Petersburg State University of Telecommunications. [PERSON] were supported by the Ministry of Science and High Education of the Russian Federation by the grant 075-15-2022-1137.  
Not applicable.  
The article contains the data, which are also available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
# Automatic Segmentation of Mandible from Conventional Methods to Deep Learning--A Review
## 3 Results
### Methodology
#### 3.4.5 Deep Learning-Based Methods
[32] presented a novel network U\({}_{a}\)-Net which contains detection and segmentation stages. The approximate location of the OARs can be identified in the detection stage, and then, fine segmentation can be further performed utilizing the results from the detection stage as a guide in the fine stage. [PERSON] et al. [101; 102] adopted a 3D Faster R-CNN to locate the H&N OARs in MRI and CT images and then applied an attention UNet to segment the OARs based on the location results. [PERSON] et al. [103] developed an automatic detection-segmentation network for OAR segmentation in H&N CT scans, in which the authors applied a Faster R-CNN [120] to locate the OARs and a fully convolutional network (FCN) [11] to further predict OAR masks based on the organ bounding boxes from the detection stage. [PERSON] et al. [104] applied a deep learning-based method from Mirada for OAR segmentation in H&N, in which the method was applied in the AAPM Challenge 2017 [122]. The method first used a CNN network to predict all OARs at a coarse resolution and then applied a series of organ-specific 10-layer networks for each organ segmentation using the predicted coarse results with full resolution images as input. [PERSON] et al. [105] developed a cascade CNN for the OAR delineation for radiotherapy in head-and-neck squamous cell carcinoma (HNSCC) of TCIA [33]. The cascaded CNN contains two stages (i.e., a coarse detection stage and a fine segmentation stage). The authors first used a shallow network as coarse detection to find the ROIs. In the fine segmentation stage, they used the coarse prediction as input for fine segmentation. [PERSON] et al. [106] utilized a VGG network to classify the slices where the mandible appears and then employed an FCN network to delineate the mandible. Table 11 shows the summary of using two-stage strategies for mandible segmentation. A new automatic segmentation algorithm was presented by [PERSON] et al. [80] that employs a novel hybrid neural-like P system to alleviate the challenges in tasks of segmentation of H&N scans. The new P system has the common advantages of cell-like and neural-like P systems so that it can solve more practical problems in parallel. In the new P system, the effective integrated CNNs are implemented at the same time through different initializations to perform the pixel-level segmentation of OARs in order to obtain more effective features and take advantage of the strength of ensemble learning.
# Carbon Storage Potential and Carbon Dioxide Emissions from Mineral-Fertilized and Manured Soil
###### Abstract  
Two important goals of sustainable agriculture are food production and preserving and improving soil health. The soil organic carbon content is considered an indicator of soil health. The evaluation of the methods to increase the soil organic carbon content in long-term experiments is usually carried out without considering its environmental effects, (e.g., CO\({}_{2}\)-C soil emission). This study hypothesized that sandy soils have a low carbon storage potential, and that the carbon accumulation in the soil is accompanied by increased CO\({}_{2}\)-C emissions into the atmosphere. The study was carried out as a long-term fertilization experiment in Central Poland using a rye monoculture. The changes in the soil organic carbon content (SOC), CO\({}_{2}\)-C emissions from soil, and plant yields were examined for two soil treatments: one treated only with mineral fertilizers (CaNPK) and one annually fertilized with manure (Ca + M). Over the 91 years of the experiment, the SOC content of the manure-fertilized treatment increased almost two-fold, reaching 10.625 g C kg\({}^{-1}\) in the topsoil, while the content of the SOC in the soil fertilized with CaNPK did not change (5.685 g C kg\({}^{-1}\) in the topsoil). Unlike mineral fertilization, soil manuring reduced the plant yields by approximately 15.5-28.3% and increased the CO\({}_{2}\)-C emissions from arable land. The CO\({}_{2}\)-C emissions of the manured soil (5365.0 and 5159.2 kg CO\({}_{2}\)-C ha\({}^{-1}\) in the first and second year of the study, respectively) were significantly higher (by 1431.9-2174.2 kg CO\({}_{2}\)-C ha\({}^{-1}\)) than those in the soils that only received mineral fertilizers (3933.1 and 2975.0 kg CO\({}_{2}\)-C ha\({}^{-1}\) in the first and second year of the study, respectively). The results from this experiment suggest that only long-term fertilization with manure might increase the carbon storage in the sandy soil, but it is also associated with higher CO\({}_{2}\)-C emissions into the atmosphere. The replacement of mineral fertilizers with manure, predicted as a result of rising mineral fertilizer prices, will make it challenging to achieve the ambitious European goal of carbon neutrality in agriculture. The increase in CO\({}_{2}\)-C emissions due to manure fertilization of loamy sand soil in Central Poland also suggests the need to research the emissivity of organic farming.  
long-term experiment; soil organic carbon; climate change +
Footnote †: journal: Applied Sciences
# Online Sparse DOA Estimation Based on Sub-Aperture Recursive LASSO for TDM-MIMO Radar
## 2 Materials and Methods
### Proposed Method
#### 2.2.1 Cyclic Minimization  
By the minimization of Equation (6), only one component \(s_{i}\) in \(\mathbf{s}\) is considered at a time. Let \(\mathbf{\tilde{y}}_{i}\triangleq\mathbf{y}-\sum_{k\
eq i}\mathbf{a}_{k}s_{k}\). Then, the cost function can be written as [28]  
\[J(s_{i})=\|\mathbf{\tilde{y}}_{i}-\mathbf{a}_{i}\mathbf{\tilde{s}}_{i}\|_{2}^ {2}+\lambda|s_{i}| \tag{8}\]  
To solve the scalar minimization problem, the \(i\)-th variable to be solved is transformed into the polar form \(s_{i}=r_{i}e^{i\theta_{i}}\), where \(r_{i}\geqslant 0\) and \(\theta_{i}\in[-\pi,\pi)\). Then, the quadratic term in Equation (6) can be rewritten as  
\[\|\mathbf{\tilde{y}}_{i}-\mathbf{a}_{i}s_{i}\|_{2}^{2} =\left\|\mathbf{\tilde{y}}_{i}-\mathbf{a}_{i}r_{i}e^{i\theta_{i} }\right\|_{2}^{2}\] \[=\|\mathbf{\tilde{y}}_{i}\|_{2}^{2}+\left\|\mathbf{a}_{i}r_{i}e^ {i\theta_{i}}\right\|_{2}^{2}-2\operatorname{Re}\Bigl{  
where \(*\) denotes the conjugate transpose of a matrix. Hence, Equation (10) can be rewritten as  
\[J\left(r_{i},\hat{\theta}_{i}\right)=\alpha_{i}+\beta_{i}r_{i}^{2}-2\gamma_{i}r_ {i}+\lambda r_{i} \tag{13}\]  
The first-order and second-order derivatives of Equation (13) are  
\[\frac{dJ}{dr}=2\beta r-2\gamma+\lambda \tag{14}\]  
\[\frac{d^{2}J}{dr^{2}}=2\beta=2\|a_{i}\|^{2}\geqslant 0 \tag{15}\]  
Therefore, (13) is convex, and the minimization of \(r\) should be achieved at \(dJ/dr=0\) and denoted as  
\[\hat{r}_{i}=\max\biggl{(}\frac{2\gamma_{i}-\lambda}{2\beta_{i}},0\biggr{)} \tag{16}\]  
Ultimately, the element-level minimization can be expressed as [28]  
\[\hat{s}_{i}=\left\{\begin{array}{cc}\hat{r}_{i}e^{i\hat{\theta}_{i}},&\text{ if }\gamma_{i}>\lambda/2\\ 0,&else\end{array}\right. \tag{17}\]  
According to Equations (16) and (11), when updating each element \(\hat{s}_{i}\), the convex optimization cost function (8) decreases monotonically. Therefore, the result \(\$\) of the target source distribution can be achieved by traversing \(i=1,2,\cdots,K\) for Equation (17).
###### Abstract  
Algae-based supplements are gaining attention for their potential metabolic, antioxidant, and anti-inflammatory properties in sports nutrition. **Methods:** A 30-day pilot randomized controlled trial was conducted in 70 healthy male athletes (mean age 25.4 \(\pm\) 4.9 years) from competitive soccer and handball teams. Participants were randomly assigned to a supplementation group (6 g/day of _Ulw_-derived algae powder) or a control group. Both groups followed identical training routines and adhered to standardized nutritional recommendations, including macronutrient distribution and permitted supplements (e.g., isotonic drinks, protein shakes). Biochemical markers analyzed at baseline and post-intervention included HbA1c, lipid profile, malondialdehydehyde (MDA), catalase, myeloperoxidase (MPO), erythrocyte sedimentation rate (ESR), and cortisol. Genetic polymorphisms related to metabolic traits were also assessed. **Results:** Significant group \(\times\) time interactions (_p_ < 0.001) were observed for HbA1c, LDL, triglycerides, MDA, MPO, ESR, and cortisol, all of which improved in the algae-supplemented group. Correlation analysis revealed associations between HbA1c and LDL/TG as well as between cortisol and MPO. No significant genetic modulation of responses was detected, although a trend was noted for cortisol variation and insulin resistance risk. **Conclusions:** Algae-based supplementation led to favorable metabolic, oxidative, and inflammatory changes. These findings suggest its potential utility as a nutritional strategy to support recovery in athletes during periods of high training load or competition.  
algoeb 2025, 15, 6381
# A New Partitioned Spatial-Temporal Graph Attention Convolution Network for Human Motion Recognition
## 5 Experimental Results  
### Data Description  
The NTU-RGB+D 60 dataset is a large dataset for human activity analysis. It contains 60 action categories with a total of 56,880 RGB+D video samples using Microsoft Kinect V2. Some sample data from the NTU RGB+D 60 dataset are shown in Figure 6. Every action video should not exceed two human skeleton sequences, and there are 25 key joints in each skeleton sequence.  
The NTU-RGB+D 60 dataset provides two subsets: (1) Cross-Subject (CS) is a dataset based on different behavioral subjects. It divided the daily action videos of 40 volunteers into two training groups and test groups. There were 40,320 videos for training samples and 16,560 videos for testing samples in the other group. (2) Cross-View (CV) is a dataset based on different perspectives. It uses three cameras to take three different views from the same location at the same time.  
The Kinetics-Skeleton dataset is based on the Kinetics (Kinetics human action dataset) large-scale motion recognition dataset. Kinetics contains about 300,000 video clips retrieved from YouTube, covering up to 400 human actions, and each clip in the video lasts about 10 s.  
The evaluation indicators are \(Top-1\)\(accuracy\) and \(Top-5\)\(accuracy\). \(Top-1\)\(accuracy\) means the possibility that the category ranking first is consistent with the actual results; \(Top-5\)\(accuracy\) is the accuracy of the top five categories containing the actual result. These equations are as follows:  
\[Top-1\)\(accuracy =\frac{N}{M} \tag{14}\] \[Top-5\)\(accuracy =\frac{Y}{M} \tag{15}\]  
Figure 6: NTU RGB+D 60 dataset partial sample data.  
where N is the sample size of all test images with the best probability of being correctly labeled; M is the total sample size; and Y is the sample size of all test images with the top five probabilities of being correctly labeled.
# Development of a Multi-Radio Device for Dry Container Monitoring and Tracking
## 4 Results
### Current Consumption Measurement  
For current consumption measurement, the Nordic PPK2 module was used. For monitoring the measurements, the Power Profiler Kit App with nRF Connect for Desktop software (v3.4.3) was used. The practical setup is sketched in Figure 16.  
There are two modes in the application, i.e., Source Meter and Ampere Meter. In Source Meter mode, the PPK2 device acts as a battery source to power up the development board. In the Ampere Meter mode, the PPK2 device acts as an ammeter to measure the current. Since the goal of the task was to measure the current consumption, the Ampere Meter mode was used. In the application, the sampling rate can also be changed. It should be noted that more samples imply more accurate data but more consumption of resources. Since spending the resources was not an issue, the maximum sampling rate has been selected, i.e., 100 kSPS (kilo-Samples Per Second). Sampling time can also be changed in the application. Since the scenario for the prototype is that the CTD will transmit data for 5 min, 4 times a day, the sampling time is kept at approximately 5 min. This will only capture the power consumption in the active mode of the device, and from that, the behavior of the device can be estimated over one year.  
Figure 12: Monitoring console of the devices [36].  
Figure 13: Provisioning of BLE Mesh server nodes by the client [36].  
Figure 14: Console output showing data produced by a BLE server mesh node [36].  
Figure 15: Data published to the ICON platform from a BLE server mesh node [36].  
Figure 17 shows as an example the current measurement over one minute, for the gateway node with all interfaces active. During this period, two transmissions with NB-IoT and BLE Mesh occur, lasting about 15 s each. Figure 18 shows the current consumption when the ESP32 processor is running in active mode with the BLE radio turned on, transmitting periodic beacons. Figure 19 shows the current consumption when using the BLE Mesh radio channel to transmit sensor data.
# Privacy-Preserved Visual Simultaneous Localization and Mapping Based on a Dual-Component Approach
## 3 Method
### Point Cloud Encryption
#### 3.3.2 Algorithm Details  
Based on the above theory, our encryption algorithm for point cloud proceeded as follows: For each point \(p_{i}\) in the point cloud \(\mathcal{P}=\{p_{1},p_{2},\ldots,p_{N}\}\), a line \(l_{i}\) was generated to replace it. Firstly, the direction vector of \(l_{i}\) was randomly generated with components randomly decided from a standard normal distribution (mean 0; standard deviation 1). Secondly, the direction vector was normalized to ensure that the length of each line was 1. Finally, \(p_{i}\) was replaced by \(l_{i}\) to generate the line cloud \(\mathcal{L}\). The advantage of the algorithm is that it introduces an additional degree of freedom for each point, as any point on the lifted line could be the original point, thus complicating the accurate restoration of the original 3D point cloud. Moreover, the generated line cloud is difficult for humans to understand, and no information about the original scene can be directly derived from it.
When the satellite load current changes according to a certain transmission-reception time slot due to mission modes (such as antenna transmission-reception switching), the array output current exhibits obvious short-term fluctuations, especially under asymmetric panel configurations (such as one-wing faults). In short-term scenarios, the dynamic response of the solar array is significantly affected by changes in load current. As a core characteristic factor reflecting external demand and system dynamic response, the potential value of load current has not been fully exploited. Although existing studies have shown that feature selection is crucial for model accuracy [23], there is still a lack of systematic research on the temporal characteristics and nonlinear correlations of load current in short-term fluctuation forecasting, requiring methodological innovation. To address the above challenges, this paper first elaborates on the dynamic compensation mechanism of shunt regulation in the design of the Main Error Amplifier (MEA) based on the topological structure of the satellite power supply system S3R (Sequential Switching Shunt Regulator). Through Pearson correlation coefficient analysis, feature factors are screened, excluding temperature and MEA parameters, and retaining load with clearer physical meaning as the key factor for short-term prediction of solar array current. This paper constructs a multi-algorithm comparison framework based on load current and time phase, aiming to reveal the internal correlation between them and the output fluctuations of solar arrays. The three constructed neural network models can control the root mean square error (RMSE) within 0.05 A. Ground operation and maintenance can design dynamic threshold alarm strategies based on the prediction results.