esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Score Matching and DDPMs  
Given the domain-specific data (we represent the set of the data as \(\mathcal{D}\) and one of the data as \(\mathbf{x}\)), the purpose of the generative models is to estimate the probabilistic distribution \(\log p(\mathbf{x})\) of those data. However, the direct modeling of \(\log p(\mathbf{x})\) is very difficult. Therefore, we focus on _score_\(\
abla_{\mathbf{x}}\log p(\mathbf{x})\). However, the direct modeling of \(\
abla_{\mathbf{x}}\log p(\mathbf{x})\) is still difficult. Therefore, we estimate the score with implicit score matching [21]. However, the scale up of implicit score matching to high-dimensional data is difficult and overfitting is significantly problematic. Therefore, we divide the problem. We add the perturbation noise of different signal-to-noise ratios (SNRs) to data and prepare datasets characterized with different SNRs. The implicit score matching now learns _denoising_ from noisy data.  
The denoising diffusion probabilistic models (DDPMs) [3] are another expression of score-based diffusion models and formulated to maximize the evidence lower bound (ELBO) of the Kullback-Leibler divergence. Maximizing the ELBO is equivalent to learning noise prediction from noisy data. Specifically, we train a model, which is represented as a vector function conditioned on the time step \(f_{t,\mathbf{\theta}}\), so that the model can predict the noise component at a time step \(t\), which is linearly combined with the normalized image (\(\mathbf{x}_{t}\in[-1,1]\)):  
\[\mathbf{x}_{t}=\sqrt{\bar{\pi}_{t}}\mathbf{x}_{0}+\sqrt{\bar{\beta}_{t}}\mathbf{\epsilon}, \tag{1}\]  
and  
\[\mathbf{\hat{x}}_{t-1}=f_{t,\mathbf{\theta}}(\mathbf{x}_{t}), \tag{2}\]  
where the hat represents the predicted quantity throughout this paper, and  
\[\mathbf{\epsilon}\sim\mathcal{N}(\mathbf{0},\mathbf{I}), \tag{3}\] \[\mathbf{\alpha}_{t}:=1-\beta_{t},\] (4) \[\bar{\alpha}_{t}:=\prod_{s=1}^{t}\alpha_{s},\] (5) \[\bar{\beta}_{t}:=1-\bar{\alpha}_{t}, \tag{6}\]  
where \(0<\beta_{1}<\beta_{2}<\cdots<\beta_{T}<1\) are the control parameters for the magnitude of deviations. In this study, we adopt the Sigmoid scheduling [22] for the betas.  
Figure 1: We extend the denoising diffusion probabilistic model (DDPM) to handle high-resolution volumetric images, and furthermore, propose and validate a novel method to remove noise induced by local differential privacy (LDP) using DDPM.
## 4 Simulation Model and Results
### Model Description of the HMLI  
The HMLI was designed in the MATLAB/Simulink. The various parameter values are listed in Table 3. The first module is supplied by a DC source, while a capacitor is used for the other. For getting a nine-level output of the HMLI, the capacitor voltage needs to be 33% of the input DC voltage.
# Leveraging Expert Knowledge for Label Noise Mitigation in Machine Learning
## 2 Materials & Methods
### Rule-Remove Algorithm  
Algorithm 2 shows the pseudo code of the Rule-Remove algorithm. In general, it is similar to the Rule-Weight algorithm, except that the violating data are removed instead of weighted. In other words, the Rule-Remove algorithm is the Rule-Weight algorithm with the violating data samples being weighted zeros. In detail, after detecting the label noise set \(D_{t}\), a mini-batch data \(WeightedMB\) is formed by removing \(D_{t}\) from \(X_{t}\). In other words, the coefficient of the violating data samples in \(D_{t}\) are set to zero in \(X_{t}\). After that, \(WeightedMB\) is used to train \(Model_{t}\). Unlike the traditional approach, if the \(roughModel\) is not well trained, a lot of data samples are removed, leading to bad performance of \(FinalModel\). However, the proposed algorithm is able to avoid this problem because a data sample is considered to be label noise not only when its predicted output \(Y_{t}\) differs from the label \(Label_{t}\) but also when \(Y_{t}\) violates the given rules. Thanks to the removal of the violating data samples, the running time of the Rule-Remove algorithm is faster than the Rule-Weight algorithm. Both algorithms are evaluated in Section 3.  
```
0:\(TrainingSet,trainAlgo(),model,T\)
0:\(FinalModel\gets trainAlgo(model,TrainingSet)\)
0:\(Model\gets model\) for\(t=0\) to T do \(X_{t}\), \(Label_{t}\Leftarrow SampleMiniBatch(TrainingSet)\) \(Y_{t}\Leftarrow roughModel(X_{t})\) \(D_{t}\Leftarrow RuleCheck(X_{t},Y_{t},Label_{t})\) \(WeightedMB_{t}\Leftarrow Weight\{X_{t},Label_{t},D_{t},\epsilon_{t+1}=0\}\) \(Model_{t+1}\Leftarrow trainAlgo(Model_{t},WeightedMB_{t})\) endfor \(FinalModel\Leftarrow Model_{T}\)
```  
**Algorithm 2** Rule-Remove.
## 5 Experimentation, Results, and Analysis
### Performance Metrics
#### 5.2.4 Composite Evaluation Score (CES)  
In addition to the standard evaluation metrics like BLEU and METEOR, we introduce a custom metric, Contextual Accuracy (CA), designed to measure how well the generated captions capture the contextual details in the image. The metrics use in CES evaluation has been presented in Table 5. To provide a more comprehensive evaluation of the DCAT model's performance, we propose a Composite Evaluation Score (CES):  
\[CES=\delta\times\text{BLEU}+e\times\text{METEOR}+\zeta\times\text{CA} \tag{27}\]Weighting Factors (\(\delta\), \(\epsilon\), \(\zeta\))  
The weighting factors \(\delta\), \(\epsilon\), and \(\zeta\) control the contribution of each metric to the CES. These were determined through cross-validation to be \(\delta=0.5\), \(\epsilon=0.3\), and \(\zeta=0.2\), respectively.  
Rationale  
The choice of \(\delta\) being the highest reflects the importance of linguistic accuracy, as measured by BLEU. \(\epsilon\) accounts for more advanced linguistic features like synonyms, stemming, and paraphrasing, captured by METEOR. \(\zeta\) is given the lowest weight but is crucial for ensuring that the captions are not just linguistically accurate but also contextually relevant, which is the core focus of the DCAT model.
# Self-Learning Power Control in Wireless Sensor Networks
###### Abstract  
Current trends in interconnecting myriad smart objects to monetize on Internet of Things applications have led to high-density communications in wireless sensor networks. This aggravates the already over-congested unlicensed radio bands, calling for new mechanisms to improve spectrum management and energy efficiency, such as transmission power control. Existing protocols are based on simplistic heuristics that often approach interference problems (i.e., packet loss, delay and energy waste) by increasing power, leading to detrimental results. The scope of this work is to investigate how machine learning may be used to bring wireless nodes to the lowest possible transmission power level and, in turn, to respect the quality requirements of the overall network. Lowering transmission power has benefits in terms of both energy consumption and interference. We propose a protocol of transmission power control through a reinforcement learning process that we have set in a multi-agent system. The agents are independent learners using the same exploration strategy and reward structure, leading to an overall cooperative network. The simulation results show that the system converges to an equilibrium where each node transmits at the minimum power while respecting high packet reception ratio constraints. Consequently, the system benefits from low energy consumption and packet delay.  
wireless sensor network; transmission power control; Q-learning; reinforcement learning; game theory; multi-agent; energy efficiency; quality of service +
Footnote †: journal: Sensors
# Improved Dab-Deformable Model for Runway Foreign Object Debris Detection in Airport Optical Images
## 4 Results
### Generalization Capacity Evaluation of Improved Dab-Deformable  
To comprehensively evaluate the effectiveness of the improved Dab-Deformable model, a category analysis was conducted between the insect and the screw. The corresponding results are presented in Table 4.  
In addition, Figure 11 shows the trend of the error ratio of the improved Dab-Deformable model in the training and test sets, and it can be seen from the figure that the loss value finally converges to a stable value, and this also indicates that the model eventually has a relatively good performance.  
To present the accuracy improvement in Dab-Deformable more clearly and intuitively, the experimental data is visualized in Figure 12.  
To confirm the generalizability of the proposed improved-Dab, the experiments were set up on the test set, and Table 5 shows the test results.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Category** & **mAP\({}_{\text{50-95}}\)** & **mAP\({}_{\text{50}}\)** & **Recall** & **Score** & **F1** &
\begin{tabular}{c} **Params** \\ **(M)** \\ \end{tabular} \\ \hline Insect & 0.298 & 0.759 & 0.406 & 0.650 & 0.824 & 47.05 \\ Screw & 0.408 & 0.966 & 0.560 & 0.920 & 0.981 & 47.06 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of different categories.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Category** & **LDSFF** & **MDDCA** & **FBFFN** & **TS** & **mAP\({}_{\text{50-95}}\)** & **mAP\({}_{\text{50}}\)** & **Recall** & **Score** & **F1** &
\begin{tabular}{c} **Params** \\ **(M)** \\ \end{tabular} \\ \hline
1 & & & & & 0.302 & 0.825 & 0.506 & 0.658 & 0.865 & 47.05 \\
2 & ✓ & & & & 0.314 & 0.840 & 0.512 & 0.703 & 0.900 & 47.58 \\
3 & & ✓ & & & 0.326 & 0.854 & 0.523 & **0.746** & 0.899 & 47.28 \\
4 & & & ✓ & & 0.321 & 0.843 & 0.511 & 0.740 & **0.906** & 47.06 \\
5 & & & & ✓ & 0.326 & 0.832 & **0.536** & 0.688 & 0.897 & 47.05 \\
6 & ✓ & ✓ & & & 0.319 & 0.860 & 0.479 & 0.690 & 0.889 & 47.75 \\
7 & ✓ & ✓ & ✓ & & 0.329 & 0.856 & 0.464 & 0.723 & 0.886 & 47.75 \\
8 & ✓ & ✓ & & ✓ & 0.330 & 0.857 & 0.491 & 0.713 & 0.888 & 47.75 \\
9 & ✓ & ✓ & ✓ & ✓ & **0.338** & **0.867** & 0.529 & 0.739 & 0.903 & 47.75 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Scalability on Dab-Deformable architecture.  
After incorporating our method, the positional information of objects in shallow feature maps is more prominent, and contour details are clearer, which enables the model to locate and discriminate targets more accurately. As shown in Figure 15, even when there are other unknown FODs in the environment, our model can still accurately infer the correct target location and label. This strongly indicates that incorporating our method effectively enhances the model's accuracy and robustness.  
Figure 14: These pictures show the results of our improved model on the test sets which contained other undefined FOD.  
Figure 15: These images depict the detected targets from the experimental results in Figure 14.
## 3 Results
### Validation of the Derived Wind
#### 3.2.2 Different Weather Conditions
On clear nights, the traditional \(CNR\) threshold method may exclude some high-quality data points, leading to a decrease in data availability and lower R\({}^{2}\). In contrast, the optimized method not only retains more original data, with an increase of 9.5%, but also significantly improves the inversion accuracy. Figure 11: Relationship between the apparent emittance of the sky (\(\in_{A}\)) and the ratio of water vapor pressure to temperature (\(\frac{\epsilon}{\gamma}\)) at S3. The black curve represents the fitting Equation (17) used to determine the coefficient k. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Condition**} & \multicolumn{1}{c}{**Number**} & \multicolumn{1}{c}{**R\({}^{2}\)**} & \multicolumn{1}{c}{**RMSE (m/s)**} & \multicolumn{1}{c}{**MAE (m/s)**} & \multicolumn{1}{c}{**Significance**} \\ \hline \multirow{3}{*}{Cloudy period} & Daytime & **168** & **0.925** & **1.734** & **1.358** & \multirow{3}{*}{**Significance**} \\  & & 197 & 0.813 & 2.646 & 1.699 & \\  & Nighttime & **118** & **0.932** & **1.275** & **0.978** & \\  & & 134 & 0.905 & 1.585 & 1.130 & \\ \hline \multirow{3}{*}{Clear-sky period} & Daytime & **381** & **0.927** & **1.294** & **0.987** & \\  & & 411 & 0.845 & 2.031 & 1.222 & \\ \cline{1-1}  & Nighttime & **433** & **0.918** & **0.909** & **0.699** & \\ \cline{1-1}  & & 388 & 0.889 & 1.096 & 0.765 & \\ \hline \hline \end{tabular} \({}^{*}\)The difference passed the Kolmogorov–Smirnov test with a confidence level of \(\alpha=0.05\).  
\end{table} 
Table 3: Comparison of day and night wind speed from Lidar and tower observations under cloudy and clear-sky conditions at S3. Bold and plain numbers are for the optimized and the traditional methods, respectively.
## 2 Study Area and Materials  
### Study Area  
The western part of the continental U.S. (CONUS) in the range of latitude and longitude between 32 \({}^{\circ}\)N-49 \({}^{\circ}\)N and 125 \({}^{\circ}\)W-102 \({}^{\circ}\)W was selected as the study area in this research (Figure 1), since nearly all the PBO H2O sites used for NMRI monitoring are distributed in this region. In addition, to the best of our knowledge, the PBO H2O network in the west of the CONUS is the only operational network based on the GNSS-IR principle to produce archived and publicly available vegetation information products. Meanwhile, serious drought events occurred in this region during 2012-2014, and VWC is recognized as a key indicator for drought monitoring and prediction. This can provide a validation method for us to evaluate the fusion results through the drought events.  
Large areas of land cover in the western part of the CONUS consist of low vegetation types, such as shrubland, grassland, and cropland, except the regions in the western of Washington, Oregon along the Pacific Ocean, and central and eastern part of Idaho, where the main topography is mountain dominated by tree cover (Figure 1b). The land cover map is from the European Space Agency Climate Change Initiative (ESACCI) project ([[http://maps.elie.ucl.ac.be/CCI/viewer/index.php](http://maps.elie.ucl.ac.be/CCI/viewer/index.php)]([http://maps.elie.ucl.ac.be/CCI/viewer/index.php](http://maps.elie.ucl.ac.be/CCI/viewer/index.php))) [37; 38]. The climate of the study area is known to be arid to semi-arid with three typical climate types: the temperate oceanic climate, Mediterranean climate, and plateau mountain climate [39]. The oceanic climate along the Pacific coast is warm in winter and cool in summer, with abundant rainfall. The dry climate of the western plateau is an inland climate, and the annual temperature difference of the plateau area is large. The Mediterranean climate is characterized as mild and wet in winter and warm and dry in summer [40].
## 3 Results and Discussion
### Phase 2: Study Low-Cost Elements for the Prototype  
In this research, high-precision and high-cost instruments were used. However, other research with CO2 measuring devices allowing CO2 concentrations to be monitored less expensive were also analysed. The cheapest cost of these clonic devices, according to the latest research by [PERSON] et al. [77], was EUR 75. The clonic device proposed has an open programming language, which is available in Appendix C. In addition, its price is approximately EUR 60, which is 20% cheaper. Therefore, users and construction companies can include it as an element for the transformation of buildings into smart buildings.  
Regarding the parametric considerations, the error of the prototype compared to equipment calibrated was in the range between 0 and 7.49%. This error was much lower for Scenario 1 and comparable to commercial equipment in Scenario 4, according to research by [PERSON] et al. [77]. The connection between prototype and cloud allows the collection of data and its further management, which is not possible with static devices that only provide a point measurement of the CO2 concentration.
# Performance Evaluation of Directional Antennas in ZigBee Networks under NLOS Propagation Conditions
## 2 Materials and Methods  
### Measurement Locations  
The experiments with antennas considered three locations to evaluate different situations of NLOS propagation. The measurements were made on a university campus where several technologies transmit signals in the same frequency band of the ZigBee network.  
The first location for channel measurements was the terrace of the University of Madeira, represented in Figure 1a. Figure 1b shows the site plan. The transmitter was placed inside a room on the floor below the terrace, 20 cm away from a wall. The environment was strongly affected by the Wi-Fi network and another ZigBee network used for monitoring purposes. We used six positions to measure the received power, marked in the figure by numbered circles.  
The second location was the environment around the university building, as shown in Figure 2a. This figure also shows some measurement positions. A street surrounds the building, and a wall is on the opposite side. Figure 2b represents the site plan with the  
Figure 1: Location 1—terrace of the University of Madeira: (**a**) image of the location; (**b**) plan with the transmitter and receiver positions.  
receiver positions used for the measurements. For position 1, we oriented the receiver toward the transmitter. In this case, the transmission occurred in the line-of-sight. In the other cases, the building blocked the direct signal, and the received power was mainly due to reflections and diffraction of the transmitted signal. The distance between the transmitter and the last receiver position was 95 m.  
The third location was a zone with dense vegetation on the university campus, as shown in Figure 3a. Figure 3b represents the site plan. We performed measurements between 20 m and 110 m from the transmitter. The Wi-Fi network also affected the transmissions. For some measurement positions, the building was visible from the receiver.
# A Web-Based Application for Smart City Data Analysis and Visualization
## 5 Results
### Discussion
As illustrated in Figure 15, the output retains enough detail to detect parking occupancy while effectively protecting the privacy of pedestrians and vehicle owners. Despite the limited resolution, the system could still accurately detect parking spot occupancy, demonstrating that privacy-preserving sensing can coexist with effective urban monitoring. Securing that no personally identifiable information is collected or inferred is a foundational principle. Additionally, transparency in data governance remains crucial: stakeholders must communicate what data are being collected, for what purpose, and who can access it. Particularly when sensor data influences urban planning or regulatory outcomes.
###### Abstract
This system consists of (1) Field Operator System (FOS), (2) Communication Server, and (3) Office Manager System (OMS). FOS is a system for the field operator who inspects the construction site while walking around the site and is composed of an HMD and a mobile application. The Communication Server serves as a connector between the FOS and OMS. Finally, the OMS is a system for a manager at a remote office.
## 4 Results
### Results for the 2D Problem  
#### 4.2.1 Sound Absorption and ILR  
The absorption properties of the structure shown in Figure 4 are characterized experimentally in an anechoic chamber and are compared to numerical simulations solving a 2D model using FEM. In both cases, the method employed to measure the sound absorption coefficient consists of a modified version of the TFM, as explained in Section 2.4. Figure 6 illustrates both the sound absorption coefficient and the ILR, evaluated numerically and experimentally. The absorption coefficient from numerical simulations, as shown in Figure 6a, follows a very similar trend as in the 1D system (see Figure 5a): Almost perfect absorption for the optimized frequency range, \(f=[400,560]\) Hz, including 5 resonant peaks corresponding to each of the building blocks with different geometries, with absorption values \(\alpha>0.95\). Experimental results agree quite well with simulations, with a maximum absorption of \(\alpha=0.996\) at \(f=525\) Hz and \(\alpha>0.95\) for the whole frequency range except a small reduction around \(f=435 Hz\), where the absorption reaches its minimum, \(\alpha=0.93\). Despite these small differences and due to the similarity of the absorption values obtained in the 1D and 2D systems, we can validate the hypothesis that the use of 1D modeling to design a 2D array of Helmholtz resonators is accurate enough for the purpose of this work. The corresponding ILR values in dB, shown in Figure 6b emphasize the aforementioned differences between theory and experiments. However, experimental ILR results are above 12 dB at all frequencies of interest, reaching experimentally peaks up to 25 dB.
# Generating Job Recommendations Based on User Personality and Gallup Tests
## 3 Proposed Approach
### Similarity/Distance Metrics
#### 3.1.3 Cosine Similarity  
Cosine similarity measures the cosine of the angle between two vectors, which gives an indication of how similar they are in direction. The formula is defined as:  
\[\text{Cosine Similarity}=\frac{\mathbf{A}\cdot\mathbf{B}}{\|\mathbf{A}\| \|\mathbf{B}\|} \tag{6}\]  
where \(\mathbf{A}\) and \(\mathbf{B}\) are the weight vectors of the two trait lists, and \(\mathbf{A}\cdot\mathbf{B}\) is the dot product of the vectors. The magnitude of each vector is represented by \(\|\mathbf{A}\|\) and \(\|\mathbf{B}\|\).  
This metric gives a value between \(-1\) (completely opposite directions) and \(1\) (completely similar directions), with \(0\) indicating orthogonality (no similarity).  
These metrics provide diverse ways to analyze the relationship between the two lists, whether focusing on vector similarity, or distance of rankings. Depending on the  
Figure 4: The function used to weigh the importance of differences in each personality trait in the distance metrics.  
application, one metric may be more suitable than another based on the specific nature of the data and the desired outcome.  
The three distance/similarity metrics each have distinct characteristics that make them suitable for different aspects of trait-based matching:  
* Manhattan distance (L1 norm) provides a linear, robust measure of absolute differences between corresponding trait weights. Its weighted variant (Equation (2)) can emphasize specific trait mismatches through customizable weighting functions (Figure 4), making it particularly suitable for rank-sensitive comparisons, where the magnitude of individual trait discrepancies matters.
* Euclidean distance (L2 norm) measures the straight-line distance between trait vectors in the multidimensional space. While sensitive to large individual differences (due to its quadratic nature), it provides a comprehensive view of overall vector dissimilarity that accounts for both the direction and magnitude of trait weight variations.
* Cosine similarity evaluates the angular alignment between trait vectors, focusing purely on their directional similarity regardless of magnitude. This makes it effective for identifying patterns in relative trait importance while being invariant to absolute weight scaling differences.  
Each metric offers unique advantages: Manhattan distance's robustness to outliers, Euclidean distance's comprehensive dissimilarity measurement, and cosine similarity's focus on directional alignment. The choice between them depends on whether the matching process prioritizes individual trait discrepancies (Manhattan), overall dissimilarity (Euclidean), or proportional similarity (cosine).  
The experimental evaluation that follows demonstrates the differences between these metrics.