esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Characterization of Thermal Conductivity with a New Compact Hot-Box Prototype
###### Abstract  
In this study, a new compact \"hot-box\" prototype with a volume of 0.602 m\({}^{3}\) has been designed, instrumented, and implemented to experimentally characterize the thermal conductivity of specimens measuring 25 cm \(\times\) 25 cm, with the thickness of the specimen varying up to a maximum of 10 cm. The prototype features a novel design aimed at enhancing flexibility and speed in changing specimens, thereby reducing downtime when testing different materials. It requires minimal space and incurs low development and maintenance costs. To validate the prototype's functionality for measuring thermal conductivity, an oak wood specimen with a thickness of 3.81 cm was experimentally tested. The results indicate that the control system maintains key parameters under steady-state conditions for a significant duration. The thermal conductivity obtained for the oak wood specimen is 0.1695 W/m\(\cdot\)K, with an expanded uncertainty of 0.0183 W/m\(\cdot\)K for a 95% confidence interval.  
hot-box method; thermal conductivity; heat flux; surface temperature; steady state; oak specimen Article
## 4 Design of the PV Voltage Dynamics  
The design of the PV voltage dynamics is important for two main reasons:  
* Perturbations on the solar irradiance must be mitigated to avoid a large charge on the PV voltage.
* Design equations for the P&O algorithm are provided in terms of the PV voltage [27; 28].  
The complete control structure of the PV system is summarized in Figure 10, where a voltage controller is introduced between the SMC and the P&O algorithm to define the PV voltage dynamics. Such a voltage controller is based on a PI structure, which imposes the reference of the SMC to ensure the tracking of the voltage reference \(v_{r}\) defined by the P&O.  
The stable operation of the SMC, discussed in (53), defines the control signal \(u\); thus, the averaged model (14)-(17) can be rewritten considering the SMC closed-loop dynamics:  
\[\frac{dv_{cb}}{dt}=\frac{i_{1}\cdot(1-d)-i_{2}\cdot u}{C_{cb}}=0 \quad\Rightarrow\quad\text{Stable $v_{cb}$ voltage} \tag{55}\] \[\frac{dv_{pp}}{dt}=\frac{i_{pv}-(i_{1}+i_{2})}{C_{pv}}=\frac{-i_ {r}}{C_{pv}} \tag{56}\]  
Then, applying the Laplace transformation to the closed-loop relation in (56) leads to the following transfer function:  
\[\frac{v_{pp}}{i_{r}}=\frac{-1}{C_{pv}\cdot s} \tag{57}\]  
From the control structure of Figure 10, the Laplace relation between the voltage reference \(v_{r}\), the PV voltage \(v_{pv}\), and the current reference \(i_{r}\) is observed. Then, the closed-loop transfer function between the PV voltage and reference value, considering the effect of both the voltage controller and SMC, is the following one:  
\[\frac{v_{pv}}{v_{r}}=\frac{k_{p}\cdot s+k_{i}}{C_{pv}\cdot s^{2}+k_{p}\cdot s +k_{i}} \tag{58}\]
## 4 Discussion
### Limits and Future Perspectives  
The primary objective of this study is to develop cementitious materials using four types of ISWs for aeolian sand stabilization. Although the prepared materials achieved compressive strength comparable to cement-stabilized sand reported in the literature, limited research outcomes are available for direct comparison. This scarcity arises from the historical predominance of transportation infrastructure (e.g., roads, railways) in desert construction projects, with existing cement stabilization studies predominantly focusing on subgrade filler performance. Notably, fundamental disparities exist between subgrade and building foundation requirements regarding cement dosage, service environments, and loading patterns, rendering direct strength comparisons between these distinct applications scientifically invalid. Consequently, while preliminary strength comparisons were made between ISW-based materials and cement-stabilized sand, comprehensive evaluations of stabilization efficacy, including compressive, shear, and tensile strengths, should be further investigated.  
Among the ISWs employed, GGBFS demonstrated substantial pozzolanic reactivity, accounting for its dominant influence on _UCS_ variations in stabilized sand. A widely accepted paradigm suggests that alkaline activation can enhance the reactivity of most ISWs, with various alkali excitation techniques having been extensively applied in soft soil and sludge stabilization. However, the inherent low moisture content of aeolian sand in desert environments raises critical questions regarding the efficacy of alkali activation for ISW-based stabilization systems, necessitating dedicated investigation.
## 5 Results and Analytical Discussion
### Evaluating Feature Selection  
#### 5.3.1 Performance Improvements  
The analysis of the base results compared to the enhanced results reveals significant improvements achieved through feature selection and data pre-processing approaches. Table 5 presents a comparison of the base versus the enhanced accuracy across all algorithms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Training Time (s)** & \begin{tabular}{c} **Detection** \\ **Time (s)** \\ \end{tabular} & \
\begin{tabular}{c} **Training Rank** \\ \end{tabular} &
\begin{tabular}{c} **Detection Rank** \\ \end{tabular} \\ \hline KNN (K = 7) & 0.162 & 37.119 & 1 & 5 \\ KNN (K = 3) & 0.164 & 38.923 & 2 & 6 \\ KNN (K = 5) & 0.175 & 36.457 & 3 & 4 \\ RFC & 16.455 & 0.584 & 4 & 1 \\ SVM & 5.322 & 93.092 & 5 & 7 \\ CNN & 1618.678 & 4.121 & 6 & 2 \\ LSTM & 4986.316 & 13.580 & 7 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Computational efficiency analysis of evaluated models: training and detection times with ranks.  
Figure 3: Training time comparison across all models.  
The most notable improvements were observed in CNN, with a 25.22% increase in accuracy, and SVM with a 24.20% increase. All of the KNN variants experienced substantial improvements of approximately 18%. RFC exhibited a 6.79% improvement, while LSTM showed the smallest enhancement at 0.42%.
# A Comprehensive Review of Stingless Bee Products:
## 3 Stingless Bee Propolis  
Generally, propolis contains resins gathered by honeybees from their botanical sources mixed with saliva and beeswax inside the hive (Figure 3). Some of the roles of propolis are to seal cracks or openings, protect from threats, and most importantly, for breeding and food storage [77]. In addition, stingless bees also produce propolis called geopropolis, but with the addition of soil materials, which results in a less malleable resin compared to European honeybee propolisis [78]. Propolis contains more than 150 compounds, which are a mixture of polyphenols, flavonoids, aglycones, phenolics, and ketones. Additionally, flavonoids and phenolic compounds have been reported as having antioxidant properties [79]. Currently, the needs to discover sources of antioxidant agents are very important, as humans commonly produce free radicals that could lead to cell damage and mutation [80].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Stingless Bee** & **Study** & **Findings** & **Reference** \\ \hline _Heterotrigona itama_ & Oral squamous cell carcinoma & The study showed that _Heterotrigona itama_ honey could inhibit cancerous cells. The stingless bee honey needed to inhibit 50\% of cell growth was only less than 1\% of the dose. & [70] \\ \multirow{6}{*}{_Trigona_ sp.} & \multirow{3}{*}{Breast} & \multirow{3}{*}{The study demonstrated the potential use of stingless bee honey in treating breast cancer. The author compared three different samples of stingless bee honey that were collected across Malaysia, and the results showed that the ideal honey sample was that which had the greatest cytotoxic activity towards ER- and PR-positive cells compared to triple-negative breast cancer cells. & [70] \\ \multirow{6}{*}{_Heterotrigona itama_} & \multirow{3}{*}{Malignant glioma} & \multirow{3}{*}{The study displayed high anticancer activities of stingless bee honey, which can inhibit cell proliferation and prevent malignant glioma in cell lines.} & [68] \\ \cline{3-4}  & & & The study reported potential chemopretive properties of stingless bee honey against colon cancer cells. & [76] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Anticancer potential of stingless bee honey.
# Analysis of Factors Influencing the Prices of Tourist Offers
## 3 Selecting Optimal Price Predictors
### Preliminary Analysis of Method Parameters
#### 3.3.2 Performance of Selected Classifiers  
In the case of some feature selection methods, their effectiveness in the classification task is evaluated using a classifier. Before selecting features, the classifiers were trained on the basis of a full set of features in order to verify whether the classifiers chosen as evaluation  
Figure 3: Mutual information values obtained for time varying features for different values of series lengths.  
Figure 5: Average classification accuracy.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(n\) & Metric & Down & No Change & Up & Avg \\ \hline \multirow{3}{*}{7} & Precision [\%] & 90.05 & 76.98 & 80.13 & 82.39 \\  & Recall [\%] & 74.14 & 87.24 & 80.15 & 80.51 \\  & F1 score[\%] & 80.9 & 81.34 & 79.35 & 80.53 \\ \hline \multirow{3}{*}{14} & Precision [\%] & 83.51 & 69.04 & 61.61 & 71.39 \\  & Recall [\%] & 70.97 & 66.07 & 68.72 & 68.59 \\  & F1 score [\%] & 76.31 & 65.32 & 63.67 & 68.43 \\ \hline \multirow{3}{*}{21} & Precision [\%] & 74.29 & 59.02 & 52.05 & 61.79 \\  & Recall [\%] & 59.47 & 51.17 & 63.89 & 58.18 \\  & F1 score [\%] & 65.15 & 52.59 & 55.63 & 57.79 \\ \hline \multirow{3}{*}{28} & Precision [\%] & 70.9 & 54.38 & 56.38 & 60.55 \\  & Recall [\%] & 56.74 & 59.16 & 58.52 & 58.14 \\ \cline{1-1}  & F1 score [\%] & 62.53 & 55.3 & 54.15 & 57.33 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification results for data from both tour operators and series length 3.  
The presented results demonstrated that the smaller \(n\), the higher classification accuracy, which can be observed especially for data from tour operator T1 and for the combined set of feature vectors. It is not equally clear for tour operator T2, but it should be noted that the number of data from T2 is lower than from T1. It can be also said that some splits, i.e., split 4 and 5, show much lower accuracies. In the case of data from tour operator T2, there were very little samples for split 5 and \(n=28\), which resulted in low accuracy, and can be observed in Figure 4; for split 6, there were not enough data samples from T2 to train the models at all. The highest scores obtained for \(n=7\) exceed 80% for each metric. The lowest results obtained for \(n=28\) are slightly below 60%, which is still much higher than a random guess in the case of three classes. Such results demonstrate that the classifiers might be chosen as evaluation models during the feature selection process.  
The other important observation is that there are no significant differences between the results for different series lengths \(k\), even if some relation between \(k\) and mutual information was discovered for inflation and unemployment rates, as it was shown in Section 3.3.1. This observation led us to choose \(k=3\) for further experiments on feature selection. Lower value of \(k\) let us to achieve larger data sets, as it has already been mentioned.
## 2 Results
### Subcellular Localization of AfSakA in A. flavus  
In order to identify subcellular location of the target AfSakA, mCherry tag was assembled at the C-terminal of AfSakA (_AfSakA-mCherry_) under the control of the native promoter (Figure 8A). The constructed _AfSakA-mCherry_ vector was transferred into the protoplast of _A. flavus_ CA14 strain. The _AfSakA-mCherry_ showed the same phenotype as the WT (data not shown), implying that _mCherry_ fusion did not affect the phenotype of _A. flavus_. The _AfSakA-mCherry_ strain was grown overnight in YES liquid medium and osmotic stress agents were added to this medium while the control was left untreated. We found that, during the vegetative growth, the control showed a frail and diffused fluorescence signal in the cytoplasm of hyphae, whereas a strong fluorescence was observed in the hyphae's nucleus on a medium with 1.2 mol/L NaCl after 15 and 30 min of incubation. (Figure 8B). These findings suggest that AfSakA is located in the cytoplasm in the absence of stress, while it translocated into the nucleus under osmotic stress.  
Figure 7: Effect of _AfSakA_ deletion on fungal pathogenicity on peanut seeds and maize kernel. (**A**) Maize kernels were treated with \(\Delta\)_AfSakA::AfSakA_, \(\Delta\)_AfSakA_, WT strains and water (mock) and grown for 6 days at 29 \({}^{\circ}\)C in the dark. (**B**) Peanut seeds were treated with \(\Delta\)_AfSakA::AfSakA_, \(\Delta\)_AfSakA_, WT strains and water (mock) and grown in the dark for 6 days at 29 \({}^{\circ}\)C. (**C**) Quantitative evaluation of the amount of conidia produced on the infected crop seeds in every strain from (**A**,**B**). The abbreviation ND stands for “no detection.” (**D**) TLC analysis of Aflatoxin B\({}_{1}\) (AFB\({}_{1}\)) produced by both the infected maize kernels and peanut seeds. (**E**) Relative amount of AFB\({}_{1}\) produced in (**D**). The abbreviation Std corresponds to the AFB\({}_{1}\) standard. The line bar in every column indicates standard errors of three replicates and the asterisks show significant difference level between WT and other strains (\({}^{**}p\leq 0.01\), \({}^{***}p\leq 0.001\)).
# Design of a Tri-Band Wearable Antenna for Millimeter-Wave 5G Applications
## 2 Antenna Design Methodology
### Parameteric Analysis  
A parametric analysis of the important parameters is carried out to get the optimum results with a broad bandwidth and to enhance impedance matching. Tri-band operation may be achieved at the ideal height of \(H=0.25\) mm, with 28 GHz, 38 GHz, and 60 GHz all  
Figure 2: Parametric Analysis of the optimized parameters; (**a**) variation in \(W_{6}\), (**b**) variation in \(W_{7}\), (**c**) variation in \(W_{1}\), (**d**) variation in \(L_{g}\), and (**e**) variation in \(W_{g}\).  
supported. It is critical to consider the slot's length and width while designing an antenna that will provide the desired resonance frequencies, broad impedance, and high impedance matching. For the truncated edges of the slot's width (\(W_{6}\)), parametric analysis is shown in Figure 2. Impedance mismatches are seen in the 28 GHz range when W\({}_{6}\) is raised to 0.68 mm; the other two bands remain the same. The bandwidth and impedance mismatch are both reduced when \(W_{6}\) is set to 0 mm, however. Because of this, it is proposed that the best value for \(W_{6}\) in terms of bandwidth and triband functioning at mm-wave frequencies is 0.68 mm. The parametric analysis of the upper C-shaped slot is varied from 0.8 mm to 1.2, as shown in Figure 2b. It can be seen that by varying the values of the C-shaped slot, the lowest band (28 GHz) and the upper band (60 GHz) remained unchanged, while the middle band (38 GHz) changed. Similarly, the V-band frequency ranges from 56 to 61 GHz when the U-shaped slot's width is adjusted to 1 mm. As indicated in Figure 2c, the other two bands show signs of mismatching and shifting to undesirable frequencies. Decreased impedance matching at resonance bands and a wider V-band are both found when W\({}_{7}\) is reduced to 1.4 mm. This has a substantial impact on antenna performance. Again, impedance mismatching, and band narrowing occur when W\({}_{7}\) is further lowered to 1 mm. In other words, the optimal U-slot width displays the optimum performance in terms of bandwidth and reflection coefficient when it is 1 mm long. The parametric study for the length and width of the ground planes '\(L_{6}\)' and the '\(ug\)' is shown in Figure 2d,e. With the help of parametric analysis, it is analyzed that each slot in the radiator is independent to the other, which means that by varying the length and width of one slot, we can easily shift one frequency by setting the rest of the frequencies.
# Design of CubeSat-Based Multi-Regional Positioning Navigation and Timing System in Low Earth Orbit
## 2 Materials and Methods
### Mission Statement
This would enhance applications like drone mobility, autonomous driving, precision agriculture, and emergency response.
## 2 Datasets
### MODIS Data  
#### 2.2.1 MCD43 BRDF/Albedo Product  
The MODIS Albedo product (MCD43A3 V06) provides daily albedo data on the Earth's surface for each pixel at a grid resolution of 500 m. A 16-day period of cloud-free surface reflectance from both the Terra and the Aqua is used to derive the daily data, with weight as a function of quality, the observation coverage, and the temporal distance from the day of interest. The newly broadcasted albedo product has been utilized for regional applications (e.g., forest, agriculture, and disturbance monitoring), and is now downloadable from the Land Processes Distributed Active Archive Center (LP DAAC). The 16-day composed daily albedo product has been validated over typical agricultural landscapes [10; 41], grasslands, and agriculture and forest surfaces [39], but has not been extensively validated for other land cover types using massive amounts of data.
## 1 Introduction
Given these mixed findings, our study aimed to assess and compare the impact of an original vibration therapy protocol and passive rest on the recovery of upper limb muscle strength and plasma lactate levels after intense anaerobic exercise in elite boxers and kickboxers. Compensation of upper limb muscle strength was assessed using isokinetic dynamometry, functional performance tests, and lactate measurement. The aim of this experiment was to assess and compare the impact of a specifically designed vibration therapy protocol, which we refer to as 'original vibration therapy', and passive rest on the compensation of upper limb muscle strength and plasma lactate levels after intense anaerobic exercise, predominantly anaerobic exercise. This original vibration therapy involves a unique combination of frequency, amplitude, and duration tailored for optimal muscle recovery. This study includes both boxers and kickboxers to capture the effects of recovery methods on athletes with varying muscle usage patterns. As boxers primarily utilize upper limb strength, while kickboxers engage both upper and lower limbs, this approach provides a comprehensive understanding of the recovery process across different combat sports disciplines.
## 1 Introduction  
Computer vision is one of the disciplines that have seen the greatest increase in the number of applications in recent years. The development of deep learning along with the increase in computing capabilities has made it possible to solve problems that were previously difficult using traditional techniques. A key aspect for the good performance of modern image classifiers is the number of training samples. While a human being is able to recognize an object after only seeing it once, computers require datasets with a lot of images per class to be trained and deliver acceptable results. On an industrial level, image processing that requires a single example to be implemented is a desire and a need that, until now, could rarely be fulfilled. These systems expand the possibilities for implementing new visual inspection systems in the industry.  
Siamese networks are one of the techniques that can deal with the problem of _one-shot learning_[1], where there is only one image per category. They are commonly implemented with two convolutional networks that share their weights. They receive two images as input and can detect whether they belong to the same category or not. Although different methods have been applied to the grocery products classification, there has not been so much research on the use of Siamese nets. In this paper, two existing techniques are integrated within the Siamese model: a convolutional net and a Local Maximal Occurrence (LOMO) descriptor. [PERSON] et al. [2] presented the LOMO descriptor as a method for the Re-ID problem (people re-identification), but our paper has shown its effectiveness for improving the values of a traditional Siamese with only a convolutional sister. Several classifiers have been first evaluated to decide on a convolutional model to be used with the Siamese. This model also improves the baseline results obtained in the baseline of Grocery Store Dataset [3]. The whole network is trained on categories and responds to new different classes, showing its strong capacity to deal with the problem of having only one image per category.  
The present paper is structured as follows: Section 2 explores the state-of-art technologies considered in this paper. Section 3 describes the two procedures carried out. The first procedure evaluates different classification models that are valid for the Siamese net and improves the results stated in Grocery Store Dataset [3]. The second procedure presents the Siamese net integrating a ResNeXt-101 [4] and a LOMO descriptor. Different regularization mechanisms are evaluated. In Section 4, the different experiments and results obtained with the system are reported. An overall discussion on the obtained results is set out. Finally, Section 5 notes the advantages and limitations of the presented system and suggests future developments.
# Mitigating Electrochemical Effects in Resistive Frost-Detection Sensors
## 2 Materials and Methods
### Acquisition Circuit  
The sensor readings were acquired simultaneously under the same environmental conditions. This was performed using a voltage divider circuit, as shown in Figure 3, which, as previously discussed, converts variations in the sensor's resistance into a measurable voltage signal. A fixed resistor, \(R_{f}\), of 2.7 k\(\Omega\) was selected despite the sensor's initial measured resistance of 16 k\(\Omega\). This choice was made because the conductivity of distilled water is expected to increase significantly over time due to electrochemical reactions occurring at the electrodes. While distilled water has very low ionic content and high resistance, the presence of dissolved ions in water significantly lowers resistance by increasing conductivity. As these reactions progress, ion concentrations in the water will rise, leading to a decrease in the sensor's resistance, making 2.7 k\(\Omega\) a more suitable fixed resistor value to maintain an optimal voltage range for measurement.  
To analyze potential electrochemical effects, a camera was positioned in front of the sensors, programmed to capture images at 5 min intervals. This visual data complemented the electrical measurements, providing additional insight into the formation of electrochemical byproducts and changes in electrode appearance over time. The collected data helped assess the effectiveness of pulsed DC excitation in mitigating electrochemical effects, guiding potential improvements for future sensor implementations.