esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study** & **Objectives** & **Method** & **Remarks** & **Data** & **Scale** \\ \hline \multirow{4}{*}{**[PERSON] et al., 2008 [25]**} & Measure the variation of building density by using BCR and FAR indices & \multirow{4}{*}{Statistics, Quantitative} & \multirow{4}{*}{Inaccurate for complex buildings} & \multirow{4}{*}{SI} & \multirow{4}{*}{DS, BS} \\ \cline{3-3} \cline{5-6}  & & & & & \\ \hline \multirow{4}{*}{**[PERSON] \& [PERSON], 2013 [26]**} & Build an algorithm to estimate urban density from Polar images & \multirow{4}{*}{POA Algorithm} & The urban density of multi-images generated is not comparable & \multirow{4}{*}{SAR} & \multirow{4}{*}{RS} \\ \cline{3-3} \cline{5-6}  & Develop an automated approach to assess building geometric information & LiDAR-derived building models & \multirow{4}{*}{DSM} & \multirow{4}{*}{BS, PS} \\ \cline{3-3} \cline{5-6}  & Describe methods to analyze building compactness & & & & \\ \cline{3-3} \cline{5-6}  & Propose an algorithm for estimating urban density from SAR image & Extended POA Algorithm & \multirow{4}{*}{SAR} & \multirow{4}{*}{RS} \\ \cline{3-3} \cline{5-6}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of Previous Studies. There has recently been a shift of the BCR extracting method from low accessibility data sources to more accessible ones, partly due to easy access to new technologies, such as drones. Drone imagery has proved its quality and accessibility for remote sensing applications compared to satellite imagery [31; 32]. The BCR extraction methods based on drone imagery can overcome the problems that plague satellite data sources, namely acquisition cost and accessibility. Besides, simplifying the pre-processing and calculation process would encourage the wider application of BCR calculating methods for real-world urban density analyses. Since previous studies required considerable human effort to be devoted to digitalizing mapping or pre-processing data, this was likely to result in lengthy analyses with high error rates when assessing large and complex data sets. Most previous statistical models are not well adapted to deal with the rapid growth of today's cities, failing to take advantage of the advent of urban Big Data [33]. The need for a robust and reliable BCR measuring tool to overcome the challenge of rapid urbanization is clear. Thanks to the advancements in ML and DL algorithms, robust processing tools that can reliably and effectively extract the critical information contained in remote sensing data have been proposed. Leveraging the advantages of these new tools, several studies on urban analysis based on ML and DL achieved considerable results [22; 34; 35]. However, the research on utilizing DL in the BCR measuring is limited. The use of DL in BCR calculating has yet to be nurtured in previous studies. Therefore, this study, which presents and evaluates the use of DL for the BCR extraction from UAV imagery, is designed to take advantage of the new tools and resources that are becoming available to researchers.
## 3 Results  
The proposed antenna was simulated and well-optimized according to the required design specifications. This work was simulated and studied through the full-wave simulation software HFSS. Based on the simulated results, the prototype of a dual-band shared-aperture phased array antenna was developed. Then, the fabricated antenna array prototype was tested, and the measured results of the antenna array prototype were compared with the simulated results.
# ASHM-YOLOv9: A Detection Model for Strawberry in Greenhouses at Multiple Stages
## 5 Discussion
### Limitations and Future Work  
Although this study has achieved remarkable outcomes in the multistage detection and recognition of strawberries, there are still areas for improvement and enhancement that need to be addressed. First, in terms of the model's accuracy, it is not yet at the cutting-edge level that can be achieved. With ongoing updates to the YOLO series and the introduction of more advanced functional modules, the model's accuracy can be further optimized and improved. Second, the operational load of the YOLOv9 model is not particularly low, raising questions about whether real-time monitoring results can be obtained during agricultural research in the future. Finding a reasonable way to reduce the model's operational load, especially in cases with a particularly large amount of data, is also an issue worth addressing. Our dataset has two potential biases: (1) collection was performed exclusively in January under consistent noon lighting, which may not represent seasonal variations in natural sunlight conditions; (2) all the samples came from homogeneous greenhouse environments with controlled humidity, potentially limiting their applicability to open-field agriculture with variable weather patterns. These factors could affect color-based maturity assessment accuracy. Deploying models prone to misclassification in field environments risks severe ecological and economic consequences. For instance, erroneous identification of strawberry ripeness can trigger premature harvesting, compromising fruit quality and shortening shelf life. Similarly, initiating irrigation during the flowering stage (misclassified as the growth stage) results in water resource wastage--a critical concern for greenhouses in arid regions. Therefore, addressing this issue is particularly important.  
While the ASHM-YOLOv9 architecture shows promise for multistage crop detection, direct application to other species faces challenges: (1) crops with less distinct color transitions during ripening (e.g., cucumbers) may require additional spectral data; (2) plants with complex canopy structures (e.g., tomatoes) could increase occlusion-related errors; and (3) scaling to larger fields would necessitate drone-based image acquisition and corresponding algorithm adaptations. Future work should validate transfer learning approaches using minimal target crop samples [47]. For practical agricultural deployment, further validation across diverse strawberry cultivars and planting densities is essential. Variations in fruit morphology (e.g., size, color distribution) among cultivars and high-density planting systems may impact detection accuracy, requiring cultivar-specific model fine-tuning before large-scale implementation.
# Multi-Objective Service Placement Scheme Based on Fuzzy-AHP System for Distributed Cloud Computing
## 2 Related Work
### Service Placement Metric
#### 2.3.2 Energy Efficiency  
Recently, high EC has led to an increase in CDCs' operating costs and a decrease in service performance. As a result, concerns about energy efficiency are also growing.  
Therefore, the SP approaches for preserving server energy are important for CSP and service users.  
Table 4 shows the metrics that have been used to evaluate the energy efficiency of SP in previous studies. Recent studies that have evaluated SP energy efficiency typically applied one or more of the following four metrics: RU, EO, NM, and EC.  
Previous approaches to reduce EC have included RW minimization, and RU maximization for resource management [9; 10; 17; 19; 20]. Table 4 presents a list of the metrics adopted in previous research and their optimization behavior.
## 3 Development of Scott Transformer with Electromagnetic Transients Program
### Electromagnetic Transients Modeling
#### 3.1.1 Matrix Representation for EMT Programs  
The generalized equivalent circuit of a single-phase transformer is represented in Figure 6. Based on the circuit above, the terminal voltages can be expressed as follows:  
\[\left[\begin{array}{c}v_{1}(t)\\ v_{2}(t)\end{array}\right]=\left[\begin{array}{cc}\mathrm{R}_{1}&0\\ 0&\mathrm{R}_{2}\end{array}\right]\left[\begin{array}{c}i_{1}(t)\\ i_{2}(t)\end{array}\right]+\left[\begin{array}{cc}\mathrm{L}_{1}+\mathrm{L}_{ \mathrm{m}}&\mathrm{L}_{\mathrm{m}}\\ \mathrm{L}_{\mathrm{m}}&\mathrm{L}_{2}+\mathrm{L}_{\mathrm{m}}\end{array} \right]\frac{d}{dt}\left[\begin{array}{c}i_{1}(t)\\ i_{2}(t)\end{array}\right] \tag{4}\]  
To explain the n-winding transformer, it can be reorganized as follows, including the resistance matrix \(\mathbf{R}\) and the inductance matrix \(\mathbf{L}\)[14].  
\[\left[\begin{array}{c}v_{1}(t)\\ v_{2}(t)\\ \vdots\\ v_{n}(t)\end{array}\right]=\underbrace{\left[\begin{array}{cccc}\mathrm{R} _{11}&0&\cdots&0\\ 0&\mathrm{R}_{22}&\cdots&0\\ \vdots&\vdots&\vdots&\vdots\\ 0&0&\cdots&\mathrm{R}_{\mathrm{m}}\end{array}\right]}_{\mathbf{R}}\left[ \begin{array}{c}i_{1}(t)\\ i_{2}(t)\\ \vdots\\ i_{n}(t)\end{array}\right]+\underbrace{\left[\begin{array}{cccc}\mathrm{L} _{11}&\mathrm{L}_{12}&\cdots&\mathrm{L}_{\mathrm{m}}\\ \mathrm{L}_{21}&\mathrm{L}_{22}&\cdots&\mathrm{L}_{2n}\\ \vdots&\vdots&\vdots&\vdots\\ \mathrm{L}_{\mathrm{m}1}&\mathrm{L}_{\mathrm{m}2}&\cdots&\mathrm{L}_{\mathrm{m }}\end{array}\right]}_{\mathbf{I}}\left[\begin{array}{c}i_{1}(t)\\ i_{2}(t)\\ \vdots\\ i_{n}(t)\end{array}\right] \tag{5}\]  
Consequently, the matrix representation for the n-winding transformer is presented as (5). Then, the voltage and current values are calculated and updated in each solution time step for the EMT program of the corresponding network.
# A Compact Monopole Wideband Antenna Based on DGS
###### Abstract  
This paper presents a compact monopole wideband antenna based on DGS. The ultimate geometry of the designed antenna is obtained after many design modifications and optimizations. A commercially available Taconic TLY substrate with a dielectric constant (\(\epsilon_{r}\)) = 2.2, loss tangent (tan \(\delta\)) = 0.0009, and thickness (h) of 1.524 mm is used. The dimension of the substrate is 34 mm \(\times\) 28 mm. A 50\(\Omega\) microstrip transmission line of size 12 mm \(\times\) 3 mm is used to feed the antenna. Simulation results demonstrate a bandwidth from 4.08 to 18.92 GHz, a percentage bandwidth of 129% for S11 \(<-\)10 dB, and a peak gain of 7.4 dB. The DGS slots are embedded into the ground plane to enhance the antenna's bandwidth, impedance matching, gain, and efficiency. For verification, the proposed antenna is fabricated and measured. Good agreement between measured and simulated results is observed. Thus, this antenna is appropriate for various modern wireless communication systems.  
defected ground structure (DGS); compact antenna; monopole antenna; optimization 2025142311  
A Compact Monopole Wideband Antenna Based on DGS  
1
## 3 Methodology
### Preprocessing Raw Data
#### 3.1.6 Divided Brain Hemispheres  
The EEG channel on the left and right brain hemispheres were divided into left (red) electrodes (Fp1, F3, C3, P3, O1, F7, T3, T5, Fz, and Pz) and right (blue) electrodes (Fp2, F4, C4, P4, O2, F8, T4, T6, and Cz), as shown in Figure 1, following the 10-20 electrode placement system. For a given signal \(x_{BPF}^{\text{band}}[n]\), the signals were further separated into the hemisphere-specific signal \(x_{band,hemisphere}[n]\).
## 4 UbiPriSEQ: The Proposed Framework
### Device Module (DevM)  
The main functions of Device Module (DevM) are to offload computations to the fog or cloud, or run locally, while managing privacy, priority, energy-efficiency, and QoS. The algorithm for Device Module is given in Algorithm 1. The explanation of Device Module and its algorithm follows in Section 4.3.1 to Section 4.3.5.  
```
1:system state \(\mathbf{s_{t}}\)
2:action \(\mathbf{a_{t}}=[a_{t}^{l},a_{t}^{l},a_{t}^{b},f]\)
3:Set \(Q(s_{t},a_{t})\) with random weights \(\theta_{0}\)
4:while -converged do
5: observe \(d_{t},b_{t},\text{ and }l_{t}\)
6: compute \(\zeta_{t}\)
7: estimate \(h_{t}\)
8:\(s_{t}\leftarrow\{d_{t},b_{t},l_{t},h_{t},\zeta_{t},sinr_{t}\}\)
9:if\(t\leq K\)then
10: Select \(\mathbf{a_{t}}\in\mathbf{A}\) at random
11:else
12: Form experience sequence
13:\(\Phi_{t}=((\mathbf{s_{t-K}},\mathbf{a_{t-K}}),\cdots,(\mathbf{s_{t-1}},\mathbf{a_{t-1}}),\mathbf{s _{t}})\)
14: Input \(\Phi_{t}\) to the DNN
15:\([Q(\Phi_{t},\mathbf{a_{t}})]\leftarrow\) get \(Q\)-value for \(\mathbf{A}\) using DNN
16:\(\epsilon\leftarrow\) random(0,1)
17: take action \(\mathbf{a_{t}}\) with probability \(\epsilon^{t}\)
18:\(a_{t}(a_{t}^{l},a_{t}^{l},a_{t}^{b},f)\leftarrow\mathbf{a_{t}}\)
19:endif
20: locally compute \(a_{t}^{l}\); keep \(a_{t}^{b}\) in buffer; and migrate \(a_{t}^{l}\) tasks to fog node \(f\)
21:\(\mathit{Pri}_{d}(\mathbf{s_{t}},\mathbf{a_{t}})\leftarrow|g_{t}-t_{t}|+w_{l}\)
22: obtain the latency \(t_{t}\)
23:\(\mathit{uf}_{n}\leftarrow\mathit{Pri}_{n}(\mathbf{s_{t}},\mathbf{a_{t}})-\mathit{\mu} \cdot\zeta_{t}\cdot t_{t}-\rho+\eta\cdot sinr_{t}\)
24:\(\mathcal{P}\leftarrow\mathcal{P}\cup(\Phi_{t},\mathbf{a_{t}},\mathit{ud}_{d}^{(f },\Phi_{t+1}))\)
25:for\(x\in\) DNN mini-batch do
26: Select \(\mathbf{\Phi_{t}}\in\mathcal{P}\) at random
27: Calculate DNN loss as per Equation (6)
28:endfor
29: Update DNN weights \(\theta_{t}\) using gradient descent
30:\(t\gets t+1\)
31:endwhile
```  
**Algorithm 1** Device Module (DevM)
## 3 Results
### Metabolic Rate  
All data passed [PERSON]'s test and the Kolmogorov-Smirnov test (\(p<0.05\)). In summer 2019, the RBDs and FTDs showed significant increases in AS downstream from the MWWTP (Figure 3a). There were no significant changes in FMR, as the increase in AS observed was from an increase in MMR. Values are presented as means \(\pm\) SEM. In the RBDs, there was a significant 2.2-fold increase of AS (mg O\({}_{2}\)/kg/hr) at DS2 compared to US1 (\(p=0.0287\), \(n=14\), 10, 10, 14 for US1, US2, DS1, and DS2, respectively). There were no other significant differences, but a trend of increased AS at DS1 (2.1-fold; n.s.). The RBDs' AS at US2 was 0.9-fold lower than at US1. There were no significant differences in AS for the GSDs (\(n=13,12,13,14\)). The AS of the GSDs was 1.2-fold higher at US2, 1.0-fold at DS1, and 1.6-fold at DS2. The FTDs (\(n=11\), 11, 11, 13) at DS1 had a significant 2.7-fold increase in AS compared to US1 (\(p=0.013\)). The FTDs' AS increased 1.8-fold at US2 and 1.5 at DS2, but were not statistically significant. In fall 2019 (Figure 3b), there were no statistically significant differences in FMR, MMR, or AS.  
Figure 3: (**a**) Field metabolic rate (FMR) and maximum metabolic rate (MMR) of the RBDs, GSDs, and FTDs in summer 2019. (**b**) FMR and MMR of the RBDs, GSDs, and FTDs in fall 2019. Data are represented as means \(\pm\) SEM (oxygen consumption—mg O\({}_{2}\)/kg/hr). AS is represented as the smooth area (MMR (entire bar)—FMR (checkered bar)). The Waterloo MWWTP is between US2 and DS1. An asterisk indicates a significant difference (\(p<0.05\)) between US1 (reference site) and that location using [PERSON]’s post hoc test. RBD, rainbow darter; FTD, fantail darter; GSD, greenside darter; US1, upstream 1; US2, upstream 2; DS1, downstream 1; DS2, downstream 2.
###### Abstract  
Electric load forecasting for buildings is important as it assists building managers or system operators to plan energy usage and strategize accordingly. Recent increases in the adoption of advanced metering infrastructure (AMI) have made building electrical consumption data available, and this has increased the feasibility of data-driven load forecasting. Self-organizing map (SOM) has been successfully utilized to cluster a dataset into subsets containing similar data points. These subsets are then used to train the forecasting models to improve forecasting accuracy. However, some buildings may have insufficient data since newly installed monitoring devices such as AMI have no choice but to collect a limited amount of data. Using a clustering technique on small datasets could lead to overfitting when using forecasting models following an SOM network to be trained with clusters. This results in a relatively high generalization error. In this study, we propose to address this problem by employing the stacking ensemble learning method (SELM) that is well-known for its generalization ability. An experimental study was conducted using the electricity consumption data of an actual institutional building and meteorological data. Our proposed model outperformed other baseline models, which means it successfully mitigates the effect of overfitting.  
building electric load forecasting; self-organizing map; stacking ensemble; small-size dataset; overfitting +
Footnote †: journal: Applied Sciences
# Multi-Patch Time Series Transformer for Robust Bearing Fault Detection with Varying Noise
## 1 Introduction  
Bearings are a critical component in mechanical systems, significantly influencing the performance and reliability of machines. Consequently, anomaly detection and condition classification based on sensor data have become key areas of research. However, industrial environments present various forms of noise that adversely affect data analysis and model performance, posing a significant challenge to the effectiveness of current methodologies.  
Previous studies have primarily employed Gaussian noise [1] and white noise techniques to evaluate model robustness to noise. While useful in controlled scenarios, these techniques fail to capture the complexity and variability of real-world industrial noise. Industrial environments introduce a diverse range of noise sources, including electrical interference, machine wear, mechanical vibration, harmonic disturbances, and environmental factors. These noise types are difficult to represent accurately using simple noise models, highlighting the need for new approaches that better simulate realistic noise conditions.  
To address this gap, this study proposes novel noise techniques that overcome the limitations of conventional Gaussian and white noise methods. These techniques aim to reflect the complex noise patterns encountered in industrial environments, enabling the generation of more realistic data. Furthermore, we introduce a new deep learning model specifically designed to classify bearing-sensor anomalies effectively, even when subjected to these noise conditions.  
The significance of this study lies in addressing the challenges posed by complex industrial noise, which has been insufficiently explored in previous research. The proposednoise techniques and deep learning model contribute to the development of robust systems capable of maintaining a stable performance across various noisy environments. In addition, we analyze the impact of different noise types on sensor data and evaluate the robustness of the proposed model under these conditions.  
The main objectives of this study are twofold:  
* To develop novel noise techniques that accurately reflect the diverse and realistic noise characteristics of industrial environments, generating synthetic data that closely mirrors real-world conditions.
* To propose and evaluate a new deep learning model capable of effectively classifying bearing-sensor anomalies in noisy environments, demonstrating the utility and robustness of the proposed techniques and model.
## 2 Materials and Methods
### MRI Acquisitions  
MR images were acquired at the IRCCS E. Medea (Bosisio Parini) using a 3T Philips Achieva dStream scanner (Philips Medical Systems NV, Best, The Netherlands) using a 16-channel body coil coupled with the 12-channel receiver coil embedded in the bed.  
The acquisition protocol consisted of:  
* A fast field echo (FFE) T1-weighted sequence, acquired with resolution \(1\times 1\times 6\) mm\({}^{3}\), 30 slices, echo time (TE) = 2 ms, repetition time (TR) = 620 ms, SENSE factor 2, number of averages (NA) = 2, acquisition time 2 min 35 s;
* A Dixon FFE sequence for fat fraction (FF) quantification, acquired with resolution \(1.6\times 1.6\times 6\) mm\({}^{3}\), 40 slices, 12 echoes, flip angle 3\({}^{\circ}\), echo time (TE) = 1.2 ms, inter-echo time 2.7 ms, repetition time (TR) = 16.17 ms, SENSE factor 2, acquisition time 1 min 37 s;
* A multi-echo spin-echo T2 sequence (MESE) with 15 echoes for T2 quantification, acquired with resolution \(2\times 2\times 6\) mm\({}^{3}\), 40 slices, TE = 9.3 ms, inter-echo time 12.5 ms, TR = 14 s, SENSE factor 2, NA = 1, acquisition time 15 min 30 s;
* A multi-shell dMRI echo-planar imaging (EPI) sequence including 14 volumes at b = 0 s/mm\({}^{2}\), 6 directions each at b = 2, 5, 10, 20, 50, 100, and 200 s/mm\({}^{2}\), 10 directions at b = 400 s/mm\({}^{2}\), 15 directions at b = 700 s/mm\({}^{2}\), 25 directions at b = 900 s/mm\({}^{2}\), and 30 directions at b = 1100 s/mm\({}^{2}\) Data were acquired with resolution \(2.5\times 2.5\times 5\) mm\({}^{3}\), 20 slices, TE = 58 ms, TR = 4.5 s, SENSE factor 2, NA = 1, acquisition time 11 min 33 s. Fat suppression was performed using Spectral Presaturation with Inversion Recovery (SPIR) and Spoiled Gradient Recalled (SPGR) approaches.  
The field of view was centered on the hip of the weakest thigh and its positioning standardized taking into account the femoral head. Muscles of the gluteal, lateral rotator, adductor, and liiospoas group were included in the exam.  
The total scan time amounted to about 30 min.
## 2 Methodology
### Sun Detection  
To build a system capable of estimating irradiance values based on sky images, as a first step, the correct detection of the Sun's position in the image throughout the day is necessary. To fulfill this purpose, an image processing algorithm capable of accurately calculating the position of the Sun, through the elimination of any element different from it in the image, has been implemented.  
With the proposed methodology, the system is able to perform the detection, regardless of the conditions of the day. For the above, a completely clear sky (sunny day), a partially sunny day (partial occlusions of the Sun) and a cloudy day (where in a period of time, a complete occlusion of the Sun is presented) have been analyzed. Furthermore, the system is smart enough to perform the detection without any extra information other than the image by combining several image processing algorithms. The followed methodology, as well as the implemented algorithms that compose the core of the system are detailed below.
# Maritime Cyber Security: A Global Challenge Tackled through Distinct Regional Approaches
## 2 Literature Review
### Flag States  
Flag states are national authorities representing the respective country responsible for establishing and overseeing the regulatory regime governing ship operations within that country's registry. Flag states operate their registries according to their domestic laws and regulations while complying with a set of international codes [15].  
The predominant regulatory entity issuing such codes is the IMO. The IMO is a specialized agency of the United Nations with the authority to regulate maritime affairs, including international shipping, port safety, and security. Most flag states transpose the IMO-generated international requirements in their national legislation.  
However, the most popular flag states are considered \"open registries\" or \"flags of convenience\" that do not require a meaningful economic or financial tie to the country of registry [16]. One of the attractions of open registries is their lower fee structure related to registering and complying with the flag state's requirements. They are also less stringent in their staffing requirements and allow for the hiring of cheaper labor. Ships registered under these schemes are, in general, considered \"lightly regulated\", although some of them are well managed and highly reputable.  
In the wake of the IMO's decision to incorporate cyber risk management under the International Safety Management (ISM) Code, a very small number of flag states have started addressing cyber risk management requirements. An apt example is the USA, where the US Coast Guard [17] issued the Navigation and Vessel Inspection Circular No. 01-20 \"Guidelines for addressing cyber risk at maritime transportation security act (MTSA) regulated facilities\", providing specific guidance for the incorporation of cyber risks in safety management systems. Because cyber risks represent a current, clear, and persistent threat to the maritime transport sector, overall, shipping companies are strongly encouraged to go above and beyond the compliance requirements and lay the foundations for comprehensive cyber security strategy and plans as early as possible.