esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results  
We evaluated the performance of the proposed local deep-features-based traffic sign detection method by conducting various experiments on 20 sections of multiple pairs of airborne color images and noisy LiDAR data collected from I-15 highway located in Utah, United States (e.g., I-15 North mileposts 284 to 307 and I-15 South mileposts 241 to 260). Airborne LiDAR data were collected by the Remote Sensing Service Laboratory (RSSL) at Utah State University (USU). The USU airborne LiDAR system was mounted in a single engine Cessna TP206 aircraft. The system consisted of a LiDAR scanner, inertial measurement unit (IMU), and flight navigation unit. This LiDAR instrument was composed of a Riegl Q560 transceiver and Novatel SPAN LN-200 GPS/IMU positioning and orientations system. Depending on the flight height, the LiDAR scanner could collect data at a pulse rate of 250,000 shots/s. The beam divergence was less than 0.5 mrad, which allows the LiDAR scanner to have a footprint of about 0.5 m at flight height of 1000 m above ground level (agl). Each section of the roads of interests was divided into multiple subsections, with each covered by a single flight line. The data were acquired at an average flight height of approximately 500 m agl or lower. The LiDAR scan rate was about 125 Hz, the pulse rate was 200,000 shots/s, and the average flight speed was about 180 km/h. In these settings, the point density of the LiDAR data could be up to 6.2 points/m\({}^{2}\)[1].  
Figure 2: The overview of the proposed deep-features-based sparse classifier optimization model.  
The UDOT provided the locations of traffic signs for each dataset. We used these locations to find the corresponding regions in the color images and cropped them as ground-truth for traffic signs. The quantitative evaluation of the proposed method was based on the traffic sign locations provided by the UDOT. If the detection result and the ground truth overlap, we considered it as true positive. It should be also noted that the data in the collected 20 sections contained different traffic flows since it was collected along the I-15 highway for 40 miles. It would be nice to collect data under multiple conditions including different traffic flows, different days and times, and different weather conditions to thoroughly test the performance of the proposed method. However, collecting data under these circumstances takes time and effort.  
In Section 3.1, we provide road extraction results on 20 sections of the road on color images. In Section 3.2, we provide traffic sign candidate extraction and classification results on the same 20 sections of the road. Due to the lack of space, we qualitatively show eight representative sections of the road and present the quantitative results of all 20 sections for evaluation. In Section 3.3, we compare the performance of the proposed traffic sign detection method with several state-of-the-art methods.
# Toward Comprehensive Chronic Kidney Disease Prediction Based on Ensemble Deep Learning Models
## 4 Experiments Results
### Medical Side
The results showed that lower albumin levels were strongly associated with kidney function decline (\(-\)0.12 mL/min/1.77 m\({}^{2}\) per year, with a standard deviation of \(-\)0.01, \(-\)0.020) when the results are divided into quarters. Serum albumin levels < 3.80 g/dL areassociated with kidney function decline (ratio 1.59; 1.22-2.27). This increased the risk of CKD incidence (ratio 1.29; 1.03-1.62). Urine albumin and creatinine levels are highly associated with kidney function decline (\(-\)0.08 mL/mil/1.72 per year for urine (ACR \(>\) 30 mg/g; \(-\)0.088 to \(-\)0.12) [70]. * In [71], authors explored the relationship between sodium, potassium, and kidney decline functions and concluded with a significant correlation between positum and urea (\(p\) = 0.005, r = 0.441). In terms of the correlation between creatine and sodium, it did not show a significant relation (\(p\) = 0.890, r = 0.023). * Blood pressure hypertension considers one of the main causes of kidney diseases as it leads to increased salt sensitivity, sympathetic tone, and upregulation of the aldosterone system [72]. Blood pressure also contributes to decreasing the slow progression of CKD as well as the risk of cardiovascular (CV) diseases [72]. Unless the certain relation between high blood pressure and CKD progression, a considerable debate still exists about optimal blood pressure [73]. * Blood urea considers the main source of nitrogen in a patient's body, which is filtered by the kidney to pass out through urine [74; 75]. The main function of the kidney is to get rid of metabolic waste and maintain water PH. A high amount of urea in the blood significantly affects kidney function and may lead to kidney failure [76]. * In terms of blood edema (PE), it has a relation with kidney failure (AKF). Multiple organ dysfunction syndrome (MODS) is commonly associated with AKF, but edema occurs in septic patients with severe inflammatory response syndrome even without ARF (SIRS) [6].
# Active Noise Control over Space: A Subspace Method for Performance Analysis
###### Abstract  
In this paper, we investigate the maximum active noise control performance over a three-dimensional (3-D) spatial space, for a given set of secondary sources in a particular environment. We first formulate the spatial active noise control (ANC) problem in a 3-D room. Then we discuss a wave-domain least squares method by matching the secondary noise field to the primary noise field in the wave domain. Furthermore, we extract the subspace from wave-domain coefficients of the secondary paths and propose a subspace method by matching the secondary noise field to the projection of primary noise field in the subspace. Simulation results demonstrate the effectiveness of the proposed algorithms by comparison between the wave-domain least squares method and the subspace method, more specifically the energy of the loudspeaker driving signals, noise reduction inside the region, and residual noise field outside the region. We also investigate the ANC performance under different loudspeaker configurations and noise source positions.  
active noise control (ANC); performance analysis; wave domain; spatial noise; reverberant room
## 4 Solution Method
### Population Selection Strategy  
Calculate the fitness values of all individuals in the initial population and two crossover populations \(P_{2}\) and \(P_{3}\), that is, the cumulative number of reserve points participating in the allocation and the cumulative allocation risk. The concepts of Pareto non-dominated sorting and individual crowding distance in non-determined sorting genetic algorithm (NSGA) are introduced to cut the population hierarchically [39], and the \(PS\) optimal individuals are retained as the initial population \(P_{0}^{1}\) of the next iteration according to the elite retention mechanism. The steps are as follows:  
Step 1: Calculate the double-objective fitness value of all individuals, determine the dominance relationship between each individual according to the non-dominance ranking, and retain the non-dominated individuals in it as the first Pareto frontier \(\Gamma_{1}\).  
Step 2: Individuals in \(\Gamma_{1}\) are removed from the three populations above, the dominance relationships among the remaining individuals are determined again, and the non-dominant individuals among them are retained as the second Pareto frontier \(\Gamma_{2}\). The process is repeated until the hierarchy of all individuals is divided.  
Step 3: The optimal individuals are retained as the initial population for the next iteration according to the hierarchical relationship. The more advanced the hierarchy, the better the quality of the individual solution. If the number of individuals exceeds \(PS\) in the hierarchical retention, the last layer to be retained is cut according to the individual crowding distance, and the individuals with smaller crowding distance are removed to meet the population size \(PS\). The population selection strategy is shown in Figure 5.  
Crowding distance is generally used to describe the size of the density around individuals and the retention of individuals with larger crowding distance makes the preserved individuals less similar and maintains the diversity of the population. The crowding distance is calculated by the following equation.  
\[\sigma_{x}=\frac{g_{x+1}-g_{x-1}}{g_{\max}-g_{\min}}+\frac{f_{x+1}-f_{x-1}}{f_ {\max}-f_{\min}} \tag{23}\]  
Figure 5: Population selection strategy based on non-dominated sorting.  
where \(g_{x+1}\), \(g_{x-1}\), \(f_{x+1}\) and \(f_{x-1}\) are the corresponding values taken by the individuals before and after in the Pareto frontier of the individual when sorted by the number of cumulative reserve points participating in the allocation and cumulative allocation risk, respectively; \(g_{\text{max}}\), \(g_{\text{min}}\), \(f_{\text{max}}\) and \(f_{\text{min}}\) are the maximum and minimum values of the number of cumulative reserve points participating in the allocation and cumulative allocation risk on the Pareto frontier of the individual, respectively.
## 4 Control-Law Design
### Design of Active Disturbance Rejection Controller
#### 4.2.3 Fourth-Order LADRC-SMC Comparison Group with or without SP Loop
Considering the time delay in practical systems, a LADRC-SMC-SP scheme was constructed (refer to the control-law design in Sections 4.2.1-4.2.3 and the simulation analysis in Figures 12-16 in Section 5.3). Through the design of the controllers and relevant theoretical derivations, we completed the theoretical part of the design and proof. In the subsequent analysis, a series of numerical simulations were conducted to systematically compare and validate the efficacy of the designed LADRC-SMC-SP in mitigating external disturbances and alleviating the impact of time delays on the system. Additionally, a simulation analysis further validates and enhances the theoretical design outcomes of the three distinct schemes elucidated in Section 4.
# Protecting Hybrid ITS Networks: A Comprehensive Security Approach
## 4 Experimental Evaluation and Results Analysis
### Network Latency
#### 4.2.1 Latency Measurements Analysis: Cellular Network  
The latency values involving the cellular network are shown in Table 9.  
The M_RSU \(\rightarrow\) Smartphone X flow shows better results than the flows in which smartphones are the source. This may happen because, as shown in the testing environment (Figure 11), the M_RSU is in a privileged position as it is connected via Ethernet to the ISEL network, as is the MQTT broker. It is also observed that there are higher latencies in communications between smartphones, which is justified by the fact that both are on the cellular network, which contributes to higher latencies.  
Upon individual analysis of each communication flow, the omission of a security protocol results in the most favourable latency measurements, which is expected due to message payload overhead. Among the results of each protocol, DLAPP, with four less bytes of overhead than MFSPV, exhibits slightly better performance on two occasions when compared to MFSPV. These results indicate that the difference of 4 bytes does not significantly influence the use of one protocol over the other. On average, when compared to scenarios where no security is used, DLAPP increases the cellular network latency by 6.8%, while MFSPV increases it by 7.5%.
# ODIN112-AI-Assisted Emergency Services in Romania
## 2 Related Work
### Speech Emotion Recognition
#### 2.2.2 Models  
Almost all emotional speech classification algorithms use four classes of features: prosodic, spectral, voice quality features, and Teager energy operator [25]. Currently, most emotional speech classifiers are based on deep neural networks or a combination of traditional classifiers and deep neural networks, such as in [26; 27; 28; 29; 30; 31]. Results vary between 50% to more than 92% in overall accuracy [25]. A big factor in the overall performance is the corpus used for training and testing.
## 2 Methodology
### Procedure for Loading the Monitoring Data into the Database
In this case, a single daily CSV file is generated, with all the records of each day, with the particularity that, inside the files, there is no reference to the day to which the data correspond; the date only appearing in the name of the file (Figure 3). Therefore, the script programmed to migrate the data from these files to the databases must be different from the one developed for the XML files. Figure 3: Directory containing CSV files with the monitoring of a set of nine inverters corresponding to a sector of a PV plant. Figure 2: Fragment of the information contained in an XML file with the information record of a group of inverters of a PV plant. In both formats, the migration scripts were coded in such a way that each piece of data must be assigned to the parameter to which it corresponds, the inverter or sensor that records it, as well as the day and time to which the data corresponds. As already indicated, how this process was carried out will depend on the internal structure of the file (Figures 2 and 4). In general, to process the data, a series of steps will be required in the migration script in order to have access to read the directories where the data are located, decompress the files if necessary, read the files where the data are located (for example, in the case of XML files, only those beginning with Mean), extracting information from the headers or first lines of the file, which are usually standardised, to know which parameters are being measured and to which inverters or sensors they correspond, in the case of CSV files, or extracting this information through the structure of the XML format files. It is also essential, for each piece of data, to extract the date and time to which it corresponds, which, as has been seen, can be both in the name of the file and in the information stored in its internal structure, depending on the file format. It should be noted that one of the fields that are stored in the common database referring to the characteristics of each PV installation is the coordinates corresponding to the geographical location of the plant. This allows the date and time of the data to be associated with the local time of that geographical location; and when the measured data are stored in the individual databases of each installation, they are obviously stored with the local time. Once all this information is located, all the parameters of each inverter and sensor are distributed and entered in the corresponding databases, and specifically, in the fields of the tables assigned to them. Since each PV plant to be studied has its own particularities, it is important to adapt the migration script to each one so that it corresponds to the file format they have and adapts to the characteristics of each so that there are no errors in the data migration. For example, whether or not they have weather data, and if not, from where to read these values. This avoids mixing configurations and minimises errors.
## 3 Results
### Concentrations of Pollution in sewage at the Subsequent Treatment Stages
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(Sk\) & \(Kurt\) & \(CV\) & \(STD\) & \(Max\) & \(Avg\) & \(Min\) & **Polluting** \\ \hline  & (\(-\)) & \multicolumn{6}{c}{**(mg/L)**} & \multicolumn{2}{c}{**Substance**} \\ \hline  & \multicolumn{6}{c}{Raw sewage} \\ \hline
0.45 & 3.68 & 0.09 & 15.33 & 229.00 & 169.94 & 127.00 & Ether extract \\
0.76 & 1.06 & 0.38 & 0.009 & 0.051 & 0.022 & 0.009 & Phenols \\
3.68 & 15.63 & 0.40 & 60.50 & 473.00 & 150.76 & 89.00 & Chlorides \\ \(-\)0.04 & \(-\)0.76 & 0.21 & 20.23 & 141.25 & 96.02 & 53.50 & Sulfates \\ \hline  & \multicolumn{6}{c}{Mechanically treated sewage} \\ \hline \(-\)0.17 & \(-\)0.40 & 0.10 & 12.78 & 149.50 & 122.21 & 89.50 & Ether extract \\
1.09 & 2.60 & 0.47 & 0.008 & 0.049 & 0.018 & 0.001 & Phenols \\
3.68 & 17.23 & 0.39 & 55.25 & 447.00 & 143.44 & 51.00 & Chlorides \\
1.05 & 4.41 & 0.25 & 24.10 & 203.25 & 96.81 & 38.25 & Sulfates \\ \hline  & \multicolumn{6}{c}{Totally treated sewage} \\ \hline \(-\)1.15 & 1.72 & 0.06 & 0.87 & 16.30 & 14.69 & 11.80 & Ether extract \\
2.85 & 10.69 & 0.60 & 0.001 & 0.007 & 0.002 & 0.001 & Phenols \\
2.25 & 6.91 & 0.29 & 40.90 & 310.00 & 138.68 & 78.00 & Chlorides \\
1.76 & 8.77 & 0.29 & 22.63 & 194.50 & 79.37 & 33.50 & Sulfates \\ \hline  & \multicolumn{6}{c}{Where \(Min\) is minimum, \(Avg\) is average, \(Max\) is maximum, \(STD\) is standard deviation, \(CV\) is coefficient of variation, \(Kurt\) is kurtosis, \(Sk\) is skewness.} \\ \hline  & \multicolumn{6}{c}{Where \(Min\) is minimum, \(Avg\) is average, \(Max\) is maximum, \(STD\) is standard deviation, \(CV\) is coefficient of variation, \(Kurt\) is kurtosis, \(Sk\) is skewness.} \\ \end{tabular}
\end{table} 
Table 2: Basic descriptive statistics for pollution in sewage at the various treatment stages. Figure 2: Minimum, maximum and average pollution concentrations at the various treatment stages: (**a**) ether extract; (**b**) phenols; (**c**) chlorides; (**d**) sulfates. As can be seen from Figure 3, in totally treated sewage, concentrations of the analysed pollutants were lower than the maximum permissible levels in sewage as determined by the Polish Regulation [26]. The average concentration of the ether extract was lower than the permissible concentration of 50 mg/L by 70%, phenols (permissible 0.10 mg/L) by 98%, chlorides (permissible 1000 mg/L) and sulfates (permissible 500 mg/dm\({}^{3}\)) by about 85%. Only for the ether extract were concentrations in raw sewage and in mechanically treated sewage clearly higher than the permissible value of 50 mg/L (Figure 3a).
# Effects of Microbiota on the Soiling Process of PV Modules in Arid Zones of the Atacama Desert
## 3 Results and Discussion
### Mineralogical Characterization of Soiling
This can be explained by the fact that the inorganic elements that influence soil nutrition are different in the soil and in the soiling, as the soiling can be transported from the upper layers of the soil, while P and N are usually found in deeper layers at the root level of plants. The soil C:N:P stoichiometry ratio for studying nutrient limitation, as proposed by [PERSON] and [PERSON] [33], is 186:13:1 (a \"Redfield-like\" ratio), and the soil microbial biomass ratio is 60:7:1 at the global scale. On the other hand, [PERSON] et al. [34] suggested that the C:N:P stoichiometry of ecological enzymes is 1:1:1 on a global scale, which was supported and denied by studies [35; 36; 37]. Considering the stoichiometric C:N:P ratio of the present study (Figure 3), which reflects the environmental soiling inputs, there was a clear limitation of carbon followed by nitrogen, which undoubtedly hindered the conditions for rapid biofilm development on panel surfaces. However, the deposition of polymeric organic matter from TOC, such as polysaccharides and/or proteins, could act as a binder or seed for particles on the surface of photovoltaic panels. In the case of the UA, depending on the crystal size, CaCO\({}_{3}\) could adhere to the glass surface under environmental conditions and influence the adhesion process of particles smaller than 10 \(\upmu\)m [38]. Furthermore, the presence of halite and primarily kaolinite, which are salts with hygroscopic characteristics, can adsorb water molecules into their structure due to their ion exchange capacity and the presence of hydroxyl groups, which creates an environment conducive to the development of microorganisms [39]. Figure 4: Average concentrations of TOC and total NP determined from the soiling (UA and PSDA).
## 2 Materials and Methods  
### Site Descriptions  
The experimental site is a typical alpine steppe--_Korbrella_ meadow on the northern Tibetan Plateau (30.49783\({}^{\circ}\)N, 91.06636\({}^{\circ}\)E, elevation 4333 m)--located at the Damxung grassland station, a long-term positioning monitoring station of the Chinese Academy of Sciences (Figure 2). The dominant climate  
Figure 1: Conceptual graph for satellite-based estimation and partitioning of ecosystem respiration in an alpine meadow on the Tibetan Plateau. MOD17A2, MOD09A1, and MOD11A2 are the MODIS products. GPP is gross primary production. GM is short for ground measurements, including meteorological and vegetation measurements (Methods). MOD5, MOD and MODR are the MODIS version 5, version 6 GPP products from MOD17A2 and revised version 6 GPP products based on ground measurements. VPM, PCM, and AVM are different satellite-based GPP estimation models that are vegetation photosynthesis model, photosynthetic capacity model, and alpine vegetation model, respectively. EC is short for eddy covariance, which is the state-of-the-art method to measure CO\({}_{2}\) flux. Re, Rs, Rm and Ra are ecosystem respiration, soil respiration, heterotrophic respiration and autotrophic respiration, respectively. Re_MODM, Re_ECM, and Re_VPMM are the satellite-based Re estimations based on GPP_MOD, GPP_EC and GPP_VPM, respectively. Blue, green and orange boxes are the best satellite-based estimations of corresponding measurements based on multi-model comparison, Re estimations in growing season (or Ra_EC), and Re estimations in non-growing season (or Rm_EC), respectively.  
characteristics are strong radiation (annual mean sunlight and solar radiation are 2880.9 h and 7527.6 MJ m\({}^{-2}\), respectively), low air temperature, and short cool summers, which is generally categorized as a plateau monsoon climate. Fifty years of climatic records from 1963 to 2012 show that the average annual air temperature is 1.8 \({}^{\circ}\)C, with coldest monthly mean of \(-9.0\)\({}^{\circ}\)C in January and the warmest of 11.1 \({}^{\circ}\)C in July. Average annual precipitation is 476.6 mm, over half of which concentrated in July and August. Flat terrain (less than 2\({}^{\circ}\) slope) of this alpine meadow is dominated by three species, _Stipa capillacea_, _Carex momtis-everestii_ and _Kobresia pygmaea_, the total coverage is about 50-80%. The soil is classified as meadow soil with sandy loam [26] with a shallow soil layer depth of about 30 cm, and the deeper is gravel layer.