esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Field Test
However, the moderating capacity was generally stronger in summer than in winter [29]. Figure 9: Scatter plot of temperature (\({}^{\circ}\)C) and relative humidity (%) inside and outside a vineyard canopy in August and October. Data refer to values recorded every hour during the whole recording month. Values over the 1:1 indicate that the variable has the same values in the different conditions, while values above or below the line highlight that the temperature or relative humidity are higher or lower outside the canopy, respectively. Our results prove that AgriLogger, in the field, was able to collect and store, for long periods estimated for more than 10 years, and consistently, agrometeorological parameters data in a successful way that would be of great profit for agriculture. Such data provides real-time information about the weather, which is one of the key components that controls agricultural production, because it affects crops at all stages of their growth cycle. In some cases, more than 80% of the agricultural production variability is due to the changeability and undredictibility of weather conditions [30; 31; 32]. In addition, the success of mostly every operation within each farming system depends on the immediate former weather, the prevailing weather as well as the weather of the next few hours or days [33; 34; 35; 36]. Nowadays, modern farming has already acknowledged that timely availability and appropriate use of agrometeorological information are vital to successful farming operations. Air temperature, as an example, is considered as the main weather variable that regulates the rate of vegetative and reproductive development [37]. Therefore, getting access to such a variable, helps in applying the required treatments in real-time and thus in profitably regulating farm production, and in disaster risk reduction processes as well [38].
###### Abstract  
In this manuscript, we have numerically investigated and experimentally verified the six-element split ring resonator and circular patch-shaped multiple input, multiple output antenna operating in the 1-25 GHz band. MIMO antennas are analyzed in terms of several physical parameters, such as reflectance, gain, directivity, VSWR, and electric field distribution. The parameters of the MIMO antenna, for instance, the envelope correlation coefficient (ECC), channel capacity loss (CCL), the total active reflection coefficient (TARC), directivity gain (DG), and mean effective gain (MEG), are also investigated for identification of a suitable range of these parameters for multichannel transmission capacity. Ultrawideband operation at 10.83 GHz is possible for the theoretically designed and practically executed antenna with the return loss and gain values of \(-\)19 dB and \(-\)28 dBi, respectively. Overall, the antenna offers minimum return loss values of \(-\)32.74 dB for the operating band of 1.92 to 9.81 GHz with a bandwidth of 6.89 GHz. The antennas are also investigated in terms of a continuous ground patch and a scattered rectangular patch. The proposed results are highly applicable for the ultrawideband operating MIMO antenna application in satellite communication with C/X/Ku/K bands.  
MIMO; split-ring resonator; gain; directivity; gigahertz; TARC; ECC; DG; CCL
## 2 IDC Prototype Details and Fast Thermal Reconstruction Method
### Reward Details
#### 3.2.1 Reward of PUE Reduction  
PUE, as one of the most critical indicators for evaluating the energy-saving performance of an IDC system [36], is defined as the ratio of total facility energy to IT equipment energy. To simplify the energy structure, another facility energy is assumed to solely originate from air conditioning (AC) systems in this work. Therefore, PUE can be further reformulated as:  
\[\text{PUE}=1+P_{AC}\ /\ P_{IT} \tag{2}\]  
where, \(P_{IT}\) represents the time-varying IT equipment energy consumption, emulating daily workload fluctuations. Hereinto, the AC energy consumption \(P_{AC}\) is governed by:  
\[P_{\text{AC}}=\begin{cases}r_{AC}\cdot(T_{\text{env}}-T_{\text{set}})+P_{AC}^ {\text{basic}},\ \text{if}\ T_{\text{env}}>T_{\text{set}}\\ 0,\ \text{otherwise}\end{cases} \tag{3}\]  
where, \(T_{env}\) and \(T_{set}\) denote the environmental temperature and CRAC set temperature value, respectively; while \(r_{AC}\) and \(P_{AC}^{\text{basic}}\) are predefined coefficients. The PUE reward \(R_{PUE}\) incentivizes energy efficiency by penalizing deviations from the ideal PUE value of 1:  
\[R_{PUE}=-r_{PUE}\cdot(1-PUE) \tag{4}\]  
where, \(r_{PUE}\) is a tunable reward coefficient. This formulation guides reinforcement learning agents to optimize \(T_{set}\) dynamically, balancing cooling costs against IT workload variations.
## 1 Introduction
SnIn\({}_{4}\)S\({}_{8}\) possesses a narrow band gap (Eg \(\sim\) 2.3 eV), leading to an obvious absorption of visible light and generation of the electron-hole pairs upon excitation in this region. These features reflect the prospects of SnIn\({}_{4}\)S\({}_{8}\) for visible-light driven photocatalytic applications [28]. For instance, [PERSON] et al. reported the synthesis of SnIn\({}_{4}\)S\({}_{8}\) microspheres using a solvothermal synthesis and its catalytic activity in the visible-light region, reaching photodegradation efficiency of 95% of azo-dynes in 3.5 h [24]. [PERSON] et al. described the synthesis of flower-like SnIn\({}_{4}\)S\({}_{8}\) particles and studied their photocatalytic activity for reduction of Cr(VI) in aqueous solutions, the reduction rate was 97% under irradiation for 60 min [26]. Although removal of carbamate pollutants is an intensive research area, to the best of our knowledge, no investigations of the photocatalytic degradation of ethiofencarb have been reported yet. Considering other carbamates, for example thiobencarb, several efficient photocatalytic processes were previously reported such as using TiO\({}_{2}\) with an efficiency of 99.8%, MoS\({}_{2}\) achieving 95% removal efficiency, and BiVO\({}_{4}\) with 97% degradation rate [5; 13; 29]. This motivated us to present a systematic study of visible-light driven photocatalytic degradation of ethiofencarb using an efficient ternary chalcogenide, SnIn\({}_{4}\)S\({}_{8}\), and the influence of various parameters on this process. Secondly, this work providesa better understanding of the contribution of major reactive species in this photodegradation process vis scavenger and ESR experiments. Thirdly, the reaction intermediates of ethiofencarb degradation are investigated, providing plausible mechanistic details of the degradation process with the SnIn\({}_{4}\)S\({}_{8}\)/visible light catalytic system. This report lastly assesses the stability and recyclability of the SnIn\({}_{4}\)S\({}_{8}\) photocatalyst and its potential uses for practical treatment of environmental water samples.
# Towards Automatic Image Enhancement with Genetic Programming and Machine Learning
## 3 The Proposed Approach
### Machine Learning Model  
As reviewed in Section 2, GANs are, most of the time, the \"go-to\" architecture for current state-of-the-art image processing due to their ability on delivering automatically relevant results for most problems. Therefore, the possibility of applying a similar approach to our problem was explored. Briefly defined, GANs are a generative Machine Learning model proposed by [PERSON] et al., in 2014 [32], taking advantage of the concept of adversarial learning to create generative models capable of creating samples from the same distribution of the training data. This work focused on experimenting with an iteration of this architecture proposed by [PERSON] et al., in Reference [21], referred to as _Pix2 Pix GAN_, and is introduced as a _\"general-purpose solution to image-to-image translation problems\"_. One main change that is provided over the typical GAN is that, instead of learning to map noise input to an output that goes accordingly to the training dataset, it learns to map an input image to a variance of that input image based on the training dataset. Furthermore, _Pix2 Pix_ suggests the training of the generator not only by adversarial loss but also by direct comparison with the target image using _L1_ loss. Both loss metrics in conjunction encourage the generator to produce images in the domain of the target set that are plausible translations for the original input image. Because of this, _Pix2 Pix_ GAN is deemed as a good solution to be applied to the IE field. This hypothesis was explored by implementing and testing a _Pix2 Pix GAN_ according to the original paper specifications. _Pix2 pix_ GAN is a supervised Machine Learning model, and, as such, it requires labeled datasets during training. As we will see  
Figure 4: ELAINE’s filter tester website.  
in the experiments, to include the Pix2 Pix GAN, it was necessary to train on a set of images suitable to our problem, labeled with enhanced images and original ones.
## 4 Experiments and Analysis of Results
### Comparison Experiments  
#### 4.5.1 Comparison of Different Receptive Field Enhancement Modules  
To evaluate the improved receptive field enhancement module, the unimproved RFE module was fused with the C3k2 module to form the C3k2-RFE module, whose performance was compared with that of the C3k2-PRFE module through an experiment. The results are shown in Table 3.  
It can be seen in the data in Table 3 that YOLOv11n with the C3k2-RFE module shows a 0.3% precision improvement, along with a 1.5% recall reduction, a 0.5% decrease in mAP50, and a 0.1% decline in mAP50-95. In contrast, the model with the C3k2-PRFE module demonstrates consistent improvements: a 0.7% increase in precision, a 1.2% recall increase, a 0.9% gain in mAP50, and a 2.8% enhancement in mAP50-95. The results confirm the effectiveness of the improved receptive field enhancement module.
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 3 Machine Learning in Physical Layer of IVLC
### Optimal Decision
#### 3.3.3 GMM  
The Gaussian mixture model (GMM) refers to decomposing a complex probability density function into several Gaussian probability density functions. In brief, GMM can make use of the linear combination of several single Gaussian probability density functions, so that the model can fit a more complex probability distribution that cannot be described by a single Gaussian function. Theoretically, if GMM contains enough Gaussian probability density functions and the weight is set reasonably, the model can fit samples with an arbitrary distribution. In a low-order modulation VLC system, the clustering algorithm deals with nonlinear problems in the constellation decision of the received signal. However, in a high-order modulation VLC system, the nonlinear effect is more obvious. When it comes to strong nonlinearity, the constellation points on the outer ring may not be a regular circular distribution. In [67], GMM is used to cluster the observation vectors formed by continuous symbols to obtain the distribution relationship between continuous symbols. The traditional soft-decision or hard-decision algorithm will directly remove the correlation between symbols, leading to linear and nonlinear damage, which will result in the lack of information leading to system performance degradation. When the correlation between adjacent symbols is taken into account, the GMM system achieves 1 to 1.5 dB sensitivity improvement. The more continuous symbols are considered, the more obviously performance improves.
# Acoustic Voice Analysis as a Tool for Assessing Nasal Obstruction: A Systematic Review
## 2 Materials and Methods
### Data Extraction  
Using a standard template, data were independently extracted by each author from the selected studies. Extracted data included study design, sample size, and demographics (Tables 1 and 2), as well as outcome measures assessing the correlation between voice and nasal obstruction. (Table 3). Outcome measures of voice and nasal obstruction used in the different studies are listed in the Results section.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Study Design** & **Number of Subjects** & **Study/Control** & **Gender (m:f)** \\ \hline
[18] & Prospective experimental study & 16 & 16/0 & 0:16 \\
[19] & Prospective observational study & 46 & 46/0 & 29:17 \\
[20] & Prospective observational study & 30 & 30/0 & 20:10 \\
[21] & Non randomized prospective study & 60 & 20/40 & 60:0 \\
[22] & Prospective observational study & 42 & 42 (62 initially)/0 & 25:17 \\
[23] & Prospective observational study & 69 & 69/0 & 54:15 \\
[24] & Case-control observational study & 41 & 20/21 & 21:20 \\
[25] & Prospective observational study & 43 & 43/0 & 33:10 \\
[26] & Prospective observational study & 43 & 43/0 & 31:12 \\
[27] & Prospective controlled trial & 63 & 33 (50 initially)/30 & 19:14 (study), 18:12 (control) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Publication details, research, and participant characteristics of the included studies.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Intervention** & **Control** & **Acoustic Analysis Instrument** & **Speech Material** & **Acoustic Parameters** \\ \hline
[18] & Patient simulation & no & Praat [28] & Oral and nasal sentences & spectrogram and LTAS \\  & & & Nasometer II: Model 6450, & Oral, nasal and or-nasal sentences, vowel sounds & jitter, NHR, F0, Shimmer \\
[19] & FESS & no & Computerized Speech Lab & [a], [u], [i], [a], [e], [o] vowels & jitter, shimmer, HNR, F0 \\
[20] & FESS & no & Computerized Speech Lab & [a], [u], [i], [a], [e], [o] vowels & \\
[21] & septoplasty & yes & MDVP (Multi Dimensional Voice & Oral, nasal and or-nasal sentences, a, e, i vowels, F0, jitter, shimmer, HNR, F1, F2, F3, F4 \\
[22] & septoplasty & no & Praat [28] & [a] vowel, /ma/ syllable & F0, jitter, shimmer, NHR, F1, F2, F3, F4 \\
[23] & septoplasty & no & Praat [28] & [a] vowel, / mana/ word & F0, jitter, shimmer, NHR, F1, F2, F3, F4 \\
[24] & no intervention & yes & Multi Speech Program & [a], [ta], [ka] syllables & Voice Onset Time \\
[25] & septoplasty & no & Voice Analysis Software & [a] vowel & F0, jitter, shimmer \\
[26] & FESS & no & Multi-dimensional voice program & [a] vowel & F0, jitter, shimmer, NHR \\
[27] & septoplasty & yes & Multi-dimensional Voice Program & [i] in [mini] word and [a] & F0, jitter, shimmer, NHR, F1, F2, F3, F4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experiment details of the included studies.
# Scene Text Detection Based on Multi-Headed Self-Attention Using Shifted Windows
## 3 Method
### Loss Function  
Because three prediction maps are generated in the training phase, which are the probability map, threshold map, and binary map, for the loss function, it is also necessary to combine these three prediction maps with their corresponding true labels to construct the 3-part loss function. As the overall process of the proposed method in this paper is similar to DBNet, the design of the loss function refers to the implementation code of DBNet. The formula of the total loss function is defined as shown in Equation (18):  
\[L=L_{b}+\alpha\times L_{p}+\beta\times L_{t}, \tag{18}\]  
where \(L\) is the total loss; \(L_{b}\) is the loss function of the binary map, which was used in this experiment as Dice Loss; \(L_{p}\) is the probability map loss, which was used in this experiment as the Binary Cross-Entropy Loss (BCE Loss) function, while a hard-negative sample sampling strategy was used in order to balance the positive and negative samples with a 3:1 setting for the background and foreground regions; \(L_{t}\) is the threshold map loss, which was used in this experiment as \(L_{1}\) Loss, which calculates the \(L_{1}\) distance between predicted values and labels. Only the inner region of the expanded polygon was calculated while the rest of the region was ignored; \(\alpha\) and \(\beta\) are the weight factors, which were set to 5 and 10, respectively, in this experiment.  
The formula of Dice Loss is shown in Equation (19):  
\[L_{b}=\frac{2|X\cap Y|}{|X|+|Y|}, \tag{19}\]where \(X\) is the labeled graph; \(Y\) is the predicted graph; \(\mid X\mid\) and \(\mid Y\mid\) denote the number of elements of \(X\) and \(Y\), respectively; \(\mid X\cap Y\mid\) denotes the number of elements of the intersection of \(X\) and \(Y\).  
The formula for BCE Loss is shown in Equation (20):  
\[L_{p}=\sum_{i\in S_{l}}y_{i}logx_{i}+(1-y_{i})log(1-x_{i}), \tag{20}\]  
where \(S_{l}\) is the sample set with a positive to negative sample ratio of 1:3; \(y_{i}\) is the label value of the probability map; \(x_{i}\) is the predicted value of the probability map.  
The formula for \(L_{1}\) Loss is shown in Equation (21):  
\[L_{t}=\sum_{i\in R_{d}}\lvert y_{i}^{*}-x_{i}^{*}\rvert, \tag{21}\]  
where \(R_{d}\) is the expanded polygon; \(y_{i}^{*}\) is the label value of the threshold map; \(x_{i}^{*}\) is the predicted value of the threshold map.
# Validation of Copernicus Sea Level Altimetry Products in the Baltic Sea and Estonian Lakes
## 2 Material and Methods
### Sentinel-3 Data Products
At the tide gauge, the geoid undulation (\(N\)) approximately coincidences with the vertical datum zero \(N\)\(\approx\) mean sea surface height above ellipsoid.
## 5 Experimental Results and Analysis
### Comparison of Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **A*** (ms)** & **Hybrid A* (ms)** \\ \hline Goal 1 & \(332.9\pm 13.04\) & \(129.0\pm 10.75\) \\ \hline Goal 2 & \(336.9\pm 17.46\) & \(161.2\pm 13.28\) \\ \hline Goal 3 & \(465.2\pm 21.30\) & \(217.6\pm 18.59\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The mean \(\pm\) standard deviation for ten executions is used to compare the route planning timings of the two algorithms under various target point circumstances. Figure 34: Hybrid algorithm dynamic environment path planning. Compared with the traditional algorithm, the safe arrival rate is 50% higher than the traditional algorithm. The results of the research further support the fusion algorithm's superiority since it includes a dynamic obstacle avoidance capability. It can avoid new dynamic impediments in the route in a timely and reliable manner and has high application and security in actual and complicated dynamic settings. To verify the experimental results more accurately, this paper draws the simulation environment using the example of a real factory environment. The actual factory environment is shown in Figure 35, and the actual factory simulation environment drawn in the end is shown in the Figure 36b. The whole factory is surrounded by walls. Figure 36a is a top view of the factory environment. The simulation environment is a complex simulation of multiple components and devices, including rooms, oil storage tanks, vehicles, trees, grass, roads, and fences. As Figure 37 shows, the fusion A* algorithm can plan the global safe path, avoid obstacles in the path, and ensure that the inspection robot reaches the target point smoothly. Compared with the path planned by the A*, DWA, and DQN algorithms, the path length of the improved fusion algorithm is shorter, smoother, and the obstacle avoidance effect is better, which is more suitable for robot motion. Figure 35: Realistic factory diagram. Figure 36: Top view of the factory and the overall construction diagram. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of** & **A*** & **Hybrid A*** & **Variance (A*)** & **Variance (HA*)** \\ \hline Path planning time & 5.54S & 4.98S & 3.012 & 0.969 \\ \hline Path planning node & 8 & 5 & 2 & 1 \\ \hline Target reached without collision & 12 & 18 & 2 & 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Traditional and hybrid algorithms are compared. To ensure the accuracy of the experiment, this paper randomly published 20 target points through the host computer and statistically analyzed the relevant data. The final experimental results are shown in Table 9, which can effectively avoid deviation and error in the experiment, to ensure the reliability and effectiveness of the experimental results.
## 3 Results
### Case Studies of Fog Detection  
#### 3.2.1 High-Performing Cases  
Figure 7 presents time series of visibility, meteorological variables, and GK2A_FDA results at Suwon from 15:00 LST (UTC = LST-9 h) on 28 September to 15:00 on 29 September 2022, depicting fog development and the performance of the satellite detection algorithm. Here, LST refers to Local Standard Time, and UTC stands for Coordinated Universal Time. After sunset on the 28 th, favorable conditions for radiation fog formation developed, with clear skies, light winds, and increasing RH due to radiative cooling [4; 37]. The surface temperature dropped below the air temperature starting from 17:00 and remained lower until fog onset. GK2A_FDA detected the fog from 02:30 (using the nearest pixel method) and from 02:00 (using the 3 \(\times\) 3 adjacent pixel method) until 09:00 in both cases (Figure 7a). Visibility meter data indicated fog from 02:20 to 06:50.  
Figure 6: Spatial distribution of visibility meter stations classified by the top and bottom 25% of seasonal frequency for GK2A fog detection performance: (**a**) POD \(\geq\) 0.78 (top 25%), (**b**) POD \(\leq\) 0.43 (bottom 25%), (**c**) FAR \(\leq\) 0.83 (top 25%), (**d**) FAR \(\geq\) 0.97 (bottom 25%), (**e**) bias \(\leq\) 3.42 (top 25%), (**f**) bias \(\geq\) 15.46 (bottom 25%). Quartered circular markers are displayed only at locations where the corresponding POD, FAR, or Bias values meet the specified thresholds.  
At the same time, human visual observations reported fog from 02:15 to 07:10. After sunrise, visibility significantly improved. Both the lower cloud base height and the GK2A cloud-top height (CTH) increased (Figure 7b), indicating a transition from fog to low stratus. Nevertheless, the GK2A_FDA continued to detect fog for approximately two additional hours. Around 09:00 on the 29 th, the satellite detection status changed to 'unknown,' reflecting a condition where only some fog detection thresholds were satisfied.  
This case demonstrates the stable nighttime performance of the GK2A_FDA in detecting radiation fog while also highlighting a limitation during fog dissipation, in which  
Figure 7: A time series analysis of the formation, development, and dissipation processes of radiation fog at Suwon located in the south-central region of Gyeonggi Province, South Korea, using (**a**) GK2A fog detection imagery, (**b**) fog detection result and ground visibility data, (**c**) GK2A cloud top height and ceilometer observations, and (**d**) ASOS meteorological data (period: 15:00 LST on 28 September to 15:00 on 29 September 2022).  
residual low-level clouds are misclassified as fog due to lingering radiative conditions after sunrise.  
Fog detection performance corresponding to each image in the time series shown in Figure 7 is summarized in Table 8. The results indicate that the initially high accuracy during the fog development phase significantly declined during the dissipation phase, as reflected by a decrease in POD and increases in both FAR and bias. This pattern demonstrates the algorithm's tendency to over-detect fog when low-stratus clouds persist after fog dissipation.