esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Satellite Selection Strategy for Receiver Autonomous Integrity Monitoring Applications
## 2 Problem Statement
### Accuracy and Integrity Models in Weighted Slope-Based RAIM Algorithm
#### 2.1.2 Integrity Model  
If the weighted least-squares RAIM process is employed under the single fault hypothesis scenario, the integrity performance of the navigation solution can be evaluated by the protection level which represents the upper bound of the position error under a given reliability assumption if no alert. The horizontal protection level (HPL) is the protection level in the horizontal direction and can be determined by projecting the minimum detectable error from the measurement domain to the position domain [37]:  
\[HPL=Slope_{max}P_{bias} \tag{8}\]  
The \(P_{bias}\) in Equation (8) is the minimum detectable error under a given missed detection rate assumption in the measurement domain. It is not relevant to the satellite geometry and can be calculated according to the given integrity requirement and satellite number in use [19]. \(Slope_{max}\) is the max slope of the satellites which will be used in the positioning process. The slope here means the sensitivity of the horizontal position error to the bias in each pseudo-range measurement and is defined as:  
\[Slope_{i}=\sqrt{\left(K_{1i}^{2}+K_{2i}^{2}\right)\sigma_{i}^{2}/S_{ii}},\;i= 1,2,\cdots,n \tag{9}\]  
where:  
\[K=\left(G^{T}WG\right)^{-1}G^{T}W,\;S=I-G\left(G^{T}WG\right)^{-1}G^{T}W \tag{10}\]  
As we can see, the HPL is highly correlated with the observation matrix \(G\). If a proper satellite geometry is constructed to minimize the max slope of the satellite subset, a lower HPL value will be obtained which means the positioning error can be constrained in a small region with the given reliability. By comparing the current HPL value with the horizontal alert limit (HAL) announced by the flight phase as in Table 1, the RAIM processor can determine whether the navigation system is suitable for the route segment.
## 2 Materials and Methods
### Functional Properties Evaluation
#### 2.6.2. Filtration Capacity  
The filtration tests were performed in a cylindrical acrylic chamber (\(\mathcal{O}\) = 194 mm \(\times\) length= 400 mm) developed for the controlled release of particles and mounted inside a 1 m\({}^{3}\) negative pressure flexible isolator (SolocontainmentTM,Cheshire, UK), to avoid the hazardous exposition of technicians to aerosolized particles during tests. Polystyrene beads (\(\mathcal{O}\) = 1 \(\upmu\)m, COUNT-CALTM Count Precision Size Standards Thermo Fisher) were aerosolized using a medical nebulizer (OMRON C28P) with a flow rate of 0.5 mL/min to obtain a final amount of particles between 1 \(\times\) 10\({}^{6}\) and 4 \(\times\) 10\({}^{6}\) inside the aerosolization compartment of the acrylic chamber. The number of particles were measured in real time using a laser particle counter (APEX R4 airborne particle counter, LighthouseTM, Fremont, CA, USA) installed above the aerosolization compartment of the chamber. The testing membranes were mounted in a leak tight sample port positioned above the aerosolization compartment and inside the counting compartment of the acrylic chamber, where the inlet of the laser particle counter was installed. Polystyrene particles released inside the aerosolization compartment were aspirated through the sample port at a rate of 28.2 L/min and measured three times during 20 s for a total of 60 s. The acrylic chamber was cleaned between tests using a HEPA vacuum cleaner (KARCHERTM A2204, Winnenden, Germany) and the cutoff value to consider the chamber clean for tests was below 4000 particles. A blank measurement between cleaning procedures and sample loading was performed, to count the residual amount of particles inside the acrylic chamber. The acrylic chamber used to perform these tests is shown in Figure 2.
# Effect of DEM Used for Terrain Correction on Forest Windthrow Detection Using COSMO SkyMed Data
## 1 Introduction
According to the CEOS recommendations [15], in order to topographically correct SAR images, the DEM should have a spatial resolution as good or better than the spatial resolution of the image to correct. This means that, optimally, to correct VHR SAR images, it is necessary to have a VHR DEM. Thus, the aim of this study is to evaluate the effect of DEM on terrain correction and layover/shadows mapping of VHR SAR images. These are fundamental steps in SAR data processing, and thus, a correct understanding of the most suitable DEM to use is very important for many applications. We considered VHR images acquired with the COSMO SkyMed constellation over an area located in the north of Italy. SAR images could be used in many types of applications; to evaluate the effect of the DEM also on a real application in this study, we decided to focus on change detection, in particular on windthrow detection. SAR images have been used successfully for this application previously in many studies [16; 17] and using VHR SAR images [18]. While windthrow detection is but one potential application of SAR data for change detection, its importance is expected to grow in response to forecasts of increased windthrow events due to global warming [19]. SAR data, with its ability to penetrate cloud cover, could emerge as a valuable tool for mapping damages resulting from such events.
## Results and Discussion  
### Physicochemical Indexes  
Nitrogen source supplementation is a critical process for fruits with low nitrogen source level or nitrogen deficit before wine fermentation process [9; 24]. Our preliminary study showed that a stuck fermentation (more than 30 days) was found in the bog bilberry syrup that was not supplemented with DAP, and its alcohol content was only about 3% (\(v/v\)). Meanwhile, the bog bilberry syrup fermentation under a 30 mg/L DAP supplementation appeared to be sluggish, and the fermented wine only had an about 7% (\(v/v\)) alcohol content (Supplementary Materials Table S1). Such low alcohol contents in these wines reduced their stability and these wines were expected to exhibit a poor sensory feature. A completed fermentation of the bog bilberry syrup with 60 mg/L DAP supplement was achieved in 19 days, whereas 90 mg/L DAP addition caused the bog bilberry syrup wine fermentation to be completed in 17 days. More importantly, higher amounts of the DAP supplement (120 and 150 mg/L) in the bog bilberry syrup significantly shortened the fermentation period to 14 days (Table 1). The density ratio variation of bog bilberry syrup wine fermentation under different DAP amount additions also indicated that higher amounts of DAP (120 and 150 mg/L) resulted in a rapid decrease in the wine density compared with that in the wine treated with 60 mg/L DAP. Meanwhile, the final density of the wine treated by 120 and 150 mg/L DAP were significantly lower than that in the wine with 90 mg/L DAP. These results indicated that DAP exerted a positive effect on the yeast development, which induced the differences of the wine density (Supplementary Materials Figure S1) Our results were similar to a study on grape wine fermentation supplemented with different amounts of DAP [24]. Additionally, a significant increase in the alcohol content was also observed in the bog bilberry syrup wine with the increase in the DAP supplement amount. For example, the alcohol content in the wine with 60 mg/L DAP was 9.1%, whereas the wine with 150 mg/L DAP exhibited the alcohol content at 11.0%. A significant enhancement on the consumption of the total and reducing sugar were also found in the wine with the increase of the DAP amount supplemented in the bog bilberry syrup. Wine pH and acidity affect the wine mouthfeel, and these physicochemical indexes determine the stability of wine [6]. In the present study, the wines supplemented with different amounts of DAP showed similar levels of pH and total acidity level, indicating that the application of the DAP supplement did not significantly impact the mouthfeel caused by wine pH level and acidity.
# Urban PM2.5 Concentration Prediction via Attention-Based CNN-LSTM
## 3 Data and Method
### Correlation between Meteorological Features and PM\({}_{2.5}\)  
PM\({}_{2.5}\) concentrations are correlated with meteorological features, as shown in the Figure 4. In Figure 4, the black dots in the box represent the average PM\({}_{2.5}\) concentration, and the horizontal line in the middle of the box represents the median. When the air temperature near the ground is high, the atmospheric convection is strengthened, and the concentration of pollutants can be reduced. When the air temperature near the ground is low, the atmosphere tends to form an inversion layer, which is not conducive to the diffusion of pollutants. It can be seen from Figure 4a that the concentration of PM\({}_{2.5}\) decreases with the increase in temperature, indicating that the increase in temperature is conducive to the diffusion and dilution of PM\({}_{2.5}\).  
A higher relative humidity results in a weaker diffusion ability of PM\({}_{2.5}\). Such an environment will cause an increase in the hygroscopicity of pollutants and accelerate the chemical transformation of pollutants, thus aggravating the degree of air pollution. As can be seen from Figure 4b, there is a correlation between PM\({}_{2.5}\) and relative humidity, that is, with the increase in relative humidity, PM\({}_{2.5}\) concentration will also increase.  
The main reason for the loss of visibility is air pollution, with particulate matter having the greatest impact on visibility. Low visibility means heavy air pollution, while high visibility means light air  
Figure 4: Box charts of meteorological features and fine particulate matter (PM\({}_{2.5}\)) concentration: (**a**) air temperature; (**b**) humidity; (**c**) visibility.  
pollution. Figure 4c shows that a higher visibility denotes a lower concentration of PM\({}_{2.5}\). There is a significant correlation between PM\({}_{2.5}\) concentration and visibility.  
Weather is an important factor affecting air quality. Rain and snow wash particulate matter and other pollutants from the air, effectively purifying the air. However, fog, sandstorms, and haze will increase the pollution level and reduce air quality. In addition, air pressure affects the flow of air, which affects the migration of PM\({}_{2.5}\).  
Wind direction determines the direction of migration and horizontal diffusion of atmospheric pollutants. The relationship between wind direction, wind speed, and PM\({}_{2.5}\) is shown in Figure 5. Taiyuan is located in north-central China, with northerly and northwesterly winds prevailing. In the north and west of Taiyuan, there are a large number of factories, such as steel mills and power plants. When northerly wind prevails in Taiyuan, a large number of pollutants are transported from north to south, aggravating the urban pollution. According to Figure 5, when the wind direction is westerly or northerly, PM\({}_{2.5}\) concentration is relatively high. A higher wind level facilitates PM\({}_{2.5}\) migration, which leads to more pollution downwind.
# Maximally Deficiency: Treatments and Applications for Adolescents
## 3 Orthognathic Surgery Approaches
### Skeletally Mature Adolescents
#### 3.1.1 Conventional Surgically Assisted Rapid Maximly Expansion (SARME)  
Originally designed to correct maxillary transverse deficiency, the conventional SARME, first introduced in 1938 by [PERSON] et al., involves the segmental splitting of the maxilla or palatal bone [112]. It has since been renovated to improve efficiency and outcomes by reducing resistance to lateral expansion in the midface (Figure 2).  
By splitting the maxilla into two halves at the midline between the central incisors, the conventional SARME has proven to be a reliable method for skeletally mature, non-growing adolescents, providing transverse expansion of the dental arch, adequate space for tongue positioning, and future dental arch harmonization [113].  
Conventional SARME is a straightforward and easy-to-follow procedure that requires minimal time; thus, it is a well-established and reliable surgical technique. While conventional SARME effectively addresses occlusal concerns, it compromises actual bone expansion at the palatal level. Moreover, patients often experience temporary aesthetic issues, such as midline diastema [92]. Retrospective studies have shown that the conventional two-segment SARME has minimal impact on periodontal tissues, with no significant changes in attachment levels or gingival recession observed under clinical and CBCT assessments. However, a reduction in the buccal cortex of the alveolar bone has been noted. Potential complications following the two-segment SARME include effects on the central incisors, such as tooth mobility, root resorption, discoloration, and even necrosis, due to the osteotomy being performed between the central incisors [92].  
Collectively, SARME is typically chosen when the maxillary transverse discrepancy is too severe for MARPE to handle, especially in patients with significant skeletal hypoplasia or those in need of orthognathic surgery. It is also recommended for adult patients or older adolescents, as the mid-palatal suture becomes less responsive to expansion in these age groups. One of its main benefits is its ability to provide more stable and robust long-term results compared to less invasive methods like MARPE, especially in severe cases. SARME is also suitable for various age groups, particularly older patients whose mid-palatal suture has completed its growth. In addition, it can be used as an adjunctive treatment in orthognathic surgery, addressing both dental and skeletal issues simultaneously. Another  
Figure 2: Bone-borne SARME. (**a**) Surgical image showing palatal segmental splitting with Le Fort I osteotomy; (**b**) intraoral image of the bone-borne expander.  
advantage is its effects on facial soft tissues, as it can improve facial aesthetics by correcting skeletal discrepancies. However, SARPE requires longer treatment time, greater trauma, and a longer recovery period, making it more suitable for cases that cannot be adequately addressed by less invasive methods.
# Context-Aware Trust and Reputation Routing Protocol for Opportunistic IoT Networks
## 3 Proposed CATR Routing Protocol for OppIoT Networks
### Indirect Trust Computation  
The Indirect Trust (IT) value of a node is computed by the Trustor \(m\) by seeking recommendations from the neighbors (referred to as Trustee nodes; an example of such node is denoted by \(n\)). These recommendations are obtained in the form of a Reputation Index (RI), as indicated earlier. The Indirect Trust \(IT_{mn}\) is then computed by taking the average of all these reputations, i.e.,  
\[IT_{mn}=\frac{\sum\limits_{k=1}^{K}RI_{mk}}{K} \tag{15}\]  
where \(K\) is the total number of neighbors of \(n\), and \(RI_{mk}\) is the Reputation Index computed using the trust value of \(m\) as per the recommendation of the \(k\)th neighbor of \(n\). The procedure to calculate \(IT_{mn}\) is given in Algorithm 6.
```
0: Trustor m, Trustee n, Reputation Index (RI)
0:\(IT_{mn}\)
1:for each neighbour, \(k\) of node \(n\)do
2:\(RI_{nk}=(s_{n}^{k}+1)/(s_{n}^{k}+u_{n}^{k}+2)\)
3:endfor
4:// Compute Indirect Trust; \(K\) being the total neighbors
5:\(IT_{in}=\frac{\sum\limits_{k=1}^{K}RI_{mk}}{K}\)
```
**Algorithm 6** Calculation of Indirect Trust (\(I_{mn}\)).
# Refining Zero-Shot Text-to-SQL Benchmarks via Prompt Strategies with Large Language Models
## 4 Methods
### Prompt Generation
#### 4.3.2 Extra Guidelines in Prompt  
Based on common errors observed during initial testing iterations, we introduced additional guidelines into the prompt to mitigate these issues and better isolate core reasoning challenges in zero-shot prompting. These guidelines, detailed in Figures 9-11, address specific problems like handling ties in ranking, data type precision, NULL values, and avoiding unfounded assumptions. They were designed based on common errors and paraphrased for clarity. It is important to note that while these guidelines offer general advice, they do not provide task-specific input-output examples related to the benchmark questions. For instance, advising on NULL or MAX/MIN handling is general SQL knowledge, not specific to any particular question in our dataset. Therefore, we maintain that this setup constitutes a zero-shot scenario, as the model does not learn from task-specific demonstrations but rather operates with enhanced general instructions and context, akin to how a developer might provide documentation or standard practices. Experiments were conducted both with and without these extra guidelines to assess their impact.  
Figure 8: Fixed format rules for LLM-generated SQL answers in the refined BIRD benchmark. These rules ensure automated extraction and execution: (1) SQLite grammar matches the ’_sqlite’ database type, (2) backticks handle spaced column names (e.g., ’xxx xxx’), and (3) a structured output with evidence and a semicolon-terminated SQL command standardizes responses for correctness validation.  
Figure 9: The first set of prompt guidelines (Part 1 of 3) for zero-shot Text-to-SQL in the refined BIRD benchmark. These rules tackle column selection ambiguities and bring down the occurrence of common errors, e.g., missing DISTINCT and misuse of OR and AND.
## 3 Research Methodology
### Problem Formulation and Research Questions  
As the decoy state protocol is often employed in high performance QKD systems, and particularly the most impressive technology demonstrations to date (in terms of delivered key rate [29] and network size [30]), there is a need to understand the protocol's ability to detect PNS attacks more fully. Moreover, it is important for system developers and users to understand how the protocol can be optimized to maximize both quantum throughput for secret key generation and detect PNS attacks with high confidence. Therefore, it is desirable to address the following research questions:  
1. How do the signal and decoy state MPN values affect the system's ability to detect PNS attacks?
2. How does the difference between the signal and decoy state MPN values affect the system's ability to detect PNS attacks?
3. How do the signal, decoy, and vacuum state occurrence percentages affect the system's ability to detect PNS attacks?
4. How does variation in the generation and detection of signal and decoy states affect the system's ability to detect PNS attacks?
5. How does propagation distance (i.e., loss) affect the system's ability to differentiate between normal behavior and physical disturbances indicative of PNS attacks?
# Effects of Driving Frequency and Voltage on the Performances of Vibrating Mesh Nebulizers
## 4 Discussion  
The driving frequency of the PZT used in the present study was 127-143 kHz, that is, 6% removed from its nominal frequency of 135 kHz. Measured resonance frequencies of before and after impedance matching differed by \(-\)7.3-13.3% and 4.4-10.4%, respectively. Driving voltage and frequency significantly influenced output rates, power consumptions, and particle sizes. As the driving voltage increased, particle size decreased significantly, presumably because higher driving voltages deformed the nebulizing mesh [14,16]. For a difference of only 1% in resonance frequency, output rates decreased by 11.0-30.1%, particle sizes increased by 1.6-7.7%, and power consumption increased by 6.6-13.6%.  
The PZT resonance frequencies were sensitive to medium temperature, pressure, and viscosity. In addition, changes in resonance frequency due to IM should be considered. Because it is not practical to determine the actual resonance frequencies of every PZT and adjust driving frequencies accordingly, we propose using ARFT. In combination, ARFT and IM provided better glycerol nebulization, higher output rates, and smaller particle sizes, but increased power consumptions. Driving with '30 V\({}_{\text{PP}}\) + ARFT + IM' decreased power consumption by 31% and increased output rate by 45% compared with 50 V\({}_{\text{PP}}\) at a nominal resonance frequency. Commercial nebulizers have an output rate of about 0.25 mL/min and power consumption of 1.4-2 W. '30 V\({}_{\text{PP}}\) + ARFT + IM' produced an output rate of 0.29 mL/min at 0.69 W for salbutamol solution, which means 'ARFT + IM' enabled the use of lower voltages at power consumptions but maintained performance and extended battery lifetime. [PERSON] et al. showed that higher concentrations of glycerol reduced output rates [5], and in the present study, glycerol output rates were lower than salbutamol output rates, and the use of 'ARFT + IM' increased output rates.  
Figure 7: Temperature profile during nebulization.  
The limitations of this study are that only one type of PZT was tested, particle sizes were evaluated by MMD instead of MMAD, and the same impedance matching transformer was used for salbutamol and glycerol nebulization. Although the driving voltages used were up to 50 V\({}_{\text{PP}}\) (Figure 6) in glycerol solution, higher output rates are expected by higher driving voltage.
# Estimation of Stride Length, Foot Clearance, and Foot Progression Angle Using UWB Sensors
## 3 Results  
### Sensor Offset Compensation  
Examples of the motion capture data acquired from the pre-processing experiment to compensate for the UWB sensor data are shown in Figure 5. In Figure 5, the blue line represents the motion capture data used as the gold standard, the black dotted line represents the UWB sensor data, and the red line represents the UWB data that underwent offset compensation using SVR. As shown in Figure 5, compensating for the UWB data using SVR reduces the error. This value was similar to that of the MCS after compensation.  
The offset compensation method applied in this research was used to compensate for the offset of the data acquired from six anchors, namely, the \(Anchor_{hel}\), \(Anchor_{utter}\), and \(Anchor_{toe}\) of the right and left foot. These showed mean square errors (MSE) of 10.27, 24.77, 9.27, 8.32, 10.62, and 9.91 mm, respectively. Thus, the average MSE was 12.19 mm.  
Figure 4: Schematic of foot progression angle definition.  
Figure 5: Comparison of the UWB data with motion capture system data. Black-dotted line: UWB raw data with the Savitzky–Golay filter, blue-solid line: data obtained using motion capture system, red-solid line: UWB data offset compensated using SVR.
# The Influence of Fermentation Vessels on Yeast Microbiota and Main Parameters of Sauerkraut
###### Abstract  
The aim of this study was to compare the yeast microbiota occurring during cabbage fermentation, along with selected parameters important for the quality of sauerkraut, depending on the vessel used. For this purpose, glass jars were used, in which anaerobic conditions are quickly created, and stoneware vessels, whose appearance and characteristics are similar to tanks used on an industrial scale. WL nutrient agar with chloramphenicol and 10% NaCl was used to enumerate yeast. Isolates were differentiated using RAPD-PCR and identified by sequencing of the 5.8S-ITS rRNA gene region and PCR-RFLP. Selected organic acids, sugars, and polyols were analyzed using UHPLC-UV-Vis and -RI. Sauerkraut obtained in stoneware vessels was characterized by the presence of a larger amount of yeast, including those considered spoilage, such as _Rladotorula_ and _Wikderhamomyces_. It also contained 50% less lactic acid and a few times more acetic acids than that obtained using glass jars. A pH around 3.8 and 1.5% lactic acid, which are parameters indicating the end of fermentation of sauerkraut, were obtained in glass jars on the tenth day of fermentation. The yeast _Wikkerhamomyces anomalies_ may be an indicator of the presence of oxygen during sauerkraut fermentation, while _Clawpora lustiniae_ may indicate anaerobic conditions.  
Wikkerhamomyces anomalies; Clawispora lustiniae; stoneware vessels; organic acids Article