esa-sceva/satcom-chunk-collection huggingface dataset:
# Weed Detection and Classification with Computer Vision Using a Limited Image Dataset
## 3 Results
### GLCM Features  
The analysis of GLCM features demonstrated that with 10 test images, a 65% accuracy was achieved, while with 160 training images, it reached 82%, as illustrated in Table 10. This outcome surpasses the results obtained with HOG features.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Training Image Number** & & **5** & **10** & **20** & **40** & **80** & **160** \\ \hline
**Test Image Number** & & **495** & **490** & **480** & **460** & **420** & **340** \\ \hline
**SVM** & precision & 36.48 & 40.31 & 58.27 & 69.62 & 77.37 & 82.06 \\  & recall & 33.60 & 40.07 & 57.71 & 69.28 & 76.55 & 81.62 \\ \hline
**RF** & precision & 54.72 & 65.41 & 66.53 & 72.71 & 75.95 & 77.56 \\  & recall & 55.22 & 61.60 & 63.82 & 72.21 & 75.28 & 77.35 \\ \hline \hline \end{tabular}
\end{table}
Table 10: GLCM features’ results.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Training Image Number** & & **5** & **10** & **20** & **40** & **80** & **160** \\ \hline
**Test Image Number** & & **495** & **490** & **480** & **460** & **420** & **340** \\ \hline
**SVM** & precision & 42.05 & 45.46 & 48.73 & 52.60 & 55.42 & 60.42 \\  & recall & 40.27 & 44.69 & 47.36 & 51.99 & 54.68 & 60.15 \\ \hline
**RF** & precision & 41.25 & 46.23 & 50.78 & 56.12 & 59.24 & 64.13 \\  & recall & 40.10 & 45.51 & 50.45 & 56.20 & 58.93 & 64.17 \\ \hline
**KNN** & precision & 38.01 & 41.29 & 44.64 & 49.06 & 51.93 & 56.14 \\  & recall & 35.82 & 38.20 & 40.83 & 45.11 & 48.29 & 51.47 \\ \hline
**ANN** & precision & 33.28 & 36.76 & 40.35 & 43.85 & 47.25 & 57.98 \\  & recall & 33.91 & 36.16 & 40.07 & 44.38 & 47.46 & 58.09 \\ \hline
**NB** & precision & \(\begin{array}{c}\text{nan}\\ \text{recall}\\ \end{array}\) & 32.33 & 34.47 & 37.22 & 43.05 & 49.71 \\  & recall & 17.34 & 19.05 & 27.36 & 35.69 & 44.56 & 49.85 \\ \hline
**GBM** & precision & 28.47 & 31.65 & 41.41 & 50.80 & 53.66 & 62.54 \\  & recall & 28.69 & 31.46 & 41.18 & 48.80 & 52.14 & 61.76 \\ \hline \hline \end{tabular}
\end{table}
Table 9: HOG features’ results.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Training Image Number** & & **5** & **10** & **20** & **40** & **80** & **160** \\ \hline
**Test Image Number** & & **495** & **490** & **480** & **460** & **420** & **340** \\ \hline
**SVM** & precision & 36.48 & 40.31 & 58.27 & 69.62 & 77.37 & 82.06 \\  & recall & 33.60 & 40.07 & 57.71 & 69.28 & 76.55 & 81.62 \\ \hline
**RF** & precision & 54.72 & 65.41 & 66.53 & 72.71 & 75.95 & 77.56 \\  & recall & 55.22 & 61.60 & 63.82 & 72.21 & 75.28 & 77.35 \\ \hline \hline \end{tabular}
\end{table}
Table 10: GLCM features’ results.
# Chemical-Sensory Traits of Fresh Cheese Made by Enzymatic Coagulation of Donkey Milk
###### Abstract  
Making cheese from donkey milk is considered unfeasible, due to difficulties in coagulation and curd forming. Two recent studies have reported the protocols for making fresh cheese by using camel chymosin or calf rennet, but the chemical and sensory characteristics of the products were not thoroughly investigated. The present paper aims to give a further contribution to the field, by investigating cheesemaking with microbial rennet and evaluating the chemical composition, total fatty acid, volatile organic compounds (VOCs) and sensory profile of the resultant product. Six trials were undertaken at laboratory scale on donkey milk from a Martina Franca ass, by applying the technological scheme as reported for calf rennet, with some modifications. Bulk cow milk was used as a control. Donkey milk coagulated rapidly, but the curd remained soft, and was only suitable for making fresh cheese; differently, cow milk coagulated almost instantaneously under these strong technological conditions, giving rise to a semi-hard curd in very short time. The moisture level of donkey cheese was almost the same as reported in the literature, whereas the yield was higher, probably due to the high protein content of the milk used. The total fatty acid composition of cheese presented some differences with respect to milk, mostly consisting in a higher presence of saturated compounds. A connection with a better retention of the large sized fat globules into the curd was hypothesised and discussed. The VOC analyses, performed by solid-phase micro extraction gas chromatography-mass spectrometry, allowed the identification of 11 compounds in milk and 18 in cheese. The sensory characteristics of donkey cheese were strongly different with respect to the control, and revealed unique and pleasant flavours.  
cheesemaking; donkey milk; fatty acids; sensory analysis; VOC 2020, 9, 16; doi:10.3390/foods9010016
## 5 Results and Discussion
### Investment Costs  
For this paper, the civil engineering and power transmission line costs are the same for the two scenarios under study. The indirect costs can also be considered the same, as these types of costs due to scenario 2 will increase the costs of the control equipment. This is not the case for the electro-mechanical equipment costs in the two scenarios. The costs of the turbine, generator, gates, power substation, and mechanical auxiliary equipment are the same. In contrast, the costs of the control system and electrical auxiliary equipment increase in value in scenario 2.  
With regard to the costs of electro-mechanical equipment, as each installation is different, turbine manufacturers do not provide generic information on these costs which can be extrapolated to other installations. In contrast, the costs of the generator, the control system, the power substation, and the electrical auxiliary equipment are easier to determine.  
All costs used have come from ref. [34] for the Rebolluelo micro-hydropower plant. Table 3 shows the costs of the Rebolluelo micro-hydropower plant with fixed-speed technology (costs updated as of 8 November 2023). The micro-hydropower plant cost is EUR 493,802.15. Equation (7) shows a large difference in comparison to the actual costs.  
As can be seen in Table 3, the electro-mechanical equipment costs account for a very high percentage of the total cost of the micro-hydropower plant. This trend is consistent with several studies [46; 48].  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Scenario** & **Flow Rate (L/s)** & **Cavitation Factor** & **Critical Cavitation Factor** \\ \hline
1 & 1495 & 0.8 & 0.32 \\
1 & 598 & 0.27 & 0.31 \\
2 & 1495 & 0.8 & 0.32 \\
2 & 598 & 0.32 & 0.31 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results of the cavitation number for various flow rates.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Cost Type** & **Cost (t)** & (\%) & **Reference** \\ \hline Civil costs & 112,430.64 & 22.77 & [34] \\ Electro-mechanical equipment costs & 319,686.62 & 64.74 & [34] \\ Power transmission line costs & 47,302.30 & 9.58 & [34] \\ Indirect costs & 14,382.59 & 2.91 & [34] \\ \hline \hline \end{tabular}
\end{table}
Table 3: The cost of the Rebolluelo micro-hydropower plant with fixed-speed technology.  
The control system of the Rebolluelo micro-hydropower plant with variable speed technology has a cost of EUR 59,078.75 (costs updated as of 8 November 2023) [34]. Table 4 shows the costs of the Rebolluelo micro-hydropower plant with variable-speed technology (costs updated as of 8 November 2023). The micro-hydropower plant cost is EUR 552,880.9.  
Evidently, the implementation of variable-speed control meant an increase in the total cost of the Rebolluelo micro-hydropower plant. Specifically, the \(MHPCR\) is 11.96%.
## 2 Background Description
### Correlation Coefficient Calculation  
There are different delay characteristics between the sampling data of different characteristics of CFB boilers. Such delay characteristics have an impact on the similarity analysis between variables. The traditional Pearson correlation coefficient does not consider the influence of delay characteristics between variables on the correlation coefficient. In this paper, the time correspondence between the two sequences is corrected by sliding calculation. Moreover, the Pearson correlation coefficient of the corresponding time correction value is calculated. The maximum value of all correlation coefficients is used as the final correlation coefficient. The algorithmic process is as follows:  
(1) Randomly generated \(t\subseteq N_{+}\), \(d_{max}+l-1\leq t\leq L_{train}-k\). \(L_{train}\) is the size of the training set, and \(l\) is the length of the data sequence. \(d\) is the time correction value, and \(d_{\max}\) is the maximum of \(d\).  
(2) Data sequence \(X\{x_{t-d-l+1},x_{t-d-l+2},\ldots x_{t-d}\}\) and \(Y\{y_{t-l+k+1},y_{t-l+k+2},\ldots y_{t+k}\}\) are sampled in the input and output of the training set.  
(3) \(d\) changes from 0 to \(d_{max}-1\). The Pearson correlation coefficients of sequence \(X\{x_{t-d-l+1},x_{t-d-l+2},\ldots x_{t-d}\}\) and \(Y\{y_{t-l+k+1},y_{t-l+k+2},\ldots y_{t+k}\}\) are calculated, respectively. A set of correlation coefficients with the length of \(d_{max}\) is obtained. The Pearson correlation coefficient formula is shown in Equation (11).  
\[\rho_{d,X,Y}=\frac{\sum\limits_{i=t-d-l+1}^{t-d}(x_{i}-\overline{x})\times(y_{ i+k+d}-\overline{y})}{\sqrt{\sum\limits_{i=t-d-l+1}^{t-d}(x_{i}-\overline{x})^{2} \times\sum\limits_{i=t-d-l+1}^{t-d}(y_{i+k+d}-\overline{y})^{2}}} \tag{11}\]  
where \(\overline{x}\) and \(\overline{y}\) represent the average of the sampling sequences \(X\), \(Y\), respectively; \(x_{i}\) and \(y_{i}\) represent the \(i\)-th \(x\) in the \(X\), and the \(i\)-th \(y\) in the \(Y\), respectively.  
(4) Repeat step 1 to step 3 for \(M_{re}\) times. The average value \(\overline{\rho}_{d,X,Y}\) of \(M_{re}\) results is calculated.  
(5) The maximum value in \(\overline{\rho}_{d,X,Y}\) is used as the similarity \(\overline{\rho}_{X,Y}\) between variable \(X\) and \(Y\).  
\[\overline{\rho}_{X,Y}=\underset{d}{max}\Big{(}\overline{\rho}_{d,X,Y}\Big{)} \tag{12}\]
## 2 Materials and Methods
### Analysis of Phenolic Composition
#### 2.5.3 HPLC-DAD  
The quantification of phenolic compounds was carried out using high-performance liquid chromatography (HPLC) (Vanquish system, Thermo Fisher Scientific, Waltham, MA,USA) equipped with a pump, auto-sampler, and coupled a diode array (DAD) detector [11]. An RP-18 pre-column was also used and thermostated at 35 \({}^{\circ}\)C, and the chromatographic separation was performed on a Luna C18 reversed phase (Luna 5 \(\upmu\)m C18 (2) 100 A, 250 \(\times\) 4 mm; Phenomenex) column. DAD detector was programmed to scan wavelength absorption from 200 to 798 nm at a speed of 1 Hz with a bandwidth of 5 nm. The detection of compounds was monitored using the individual channels (280, 320, and 360 nm) at a speed of 10 Hz with a bandwidth of 11 nm. The autosampler's temperature was set at 7 \({}^{\circ}\)C, and the injection volume was set at 20 \(\upmu\)L. The eluents used were eluent A (water-formic acid (99.5%:0.5%)) and eluent B (acetonitrile-formic acid (99.5%:0.5%)) at a flow rate of 0.30 mL/min. The solvents used in chromatography conditions were filtered through a 0.22 \(\upmu\)m PVDF membrane (Millipore, Billerica, MA, USA) prior to analysis. The following gradient program was used: 0-10 min from 99 to 95% A; 10-30 min from 95 to 82% A; 30-44 min from 82 to 64% A; 44-64 min at 64% A; 64-90 min from 64 to 10% A; 90-100 min at 10% A; 100-101 min from 10 to 95% A; 101-120 min at 95% A; and finally returning to the initial conditions. Calibration curves (range from 0.78-100 ppm) for chlorogenic acid, gallic acid, and quercetin-3-hexoside were used for the quantification of the phenolic compounds. The DAD data acquisition was performed using the software Chromeleon version 7.0 (Waltham, MA, USA).
## 3 Results
### Scaling and Reliability
#### 3.4.2 Stress Testing  
Comprehensive stress tests were conducted to assess JAVIS's robustness and scalability. Concurrent user interactions were simulated by dispatching simultaneous queries from increasing numbers of users, i.e., 10, 20, 30, 40, and 50, to evaluate system performance under scaling load conditions. Additional scenarios involving 75, 100, 125, and 150 concurrent users were analyzed and are presented in Appendix A (Table A1).  
Preliminary results, summarized in (Table 1), demonstrate that JAVIS maintains efficient response times as user load increases. Notably, multiple-instance (Ray) models consistently outperform single-instance models across all tested concurrency levels, underscoring the effectiveness of scaling mode containers in managing distributed workloads. This disparity emphasizes the substantial advantages of using multiple replicas to distribute computational demand and maintain low-latency performance.
# Energy Management Integrated Circuit for Multi-Source Energy Harvesters in WBAN Applications
## 4 Circuit Block Designs
### Design of the Digital Control Logic  
The digital control logic is designed with simple logic blocks as shown in Figure 8. A comparator enabling signal is generated in a 2n-bit Johnson counter as shown in Figure 8a. The clock signal generated from internal oscillator is divided by a frequency divider, and the Johnson counter uses the divided clock as a digital clock. To generate control signal of low-power path switch and high-power path switch for n energy harvesters, a 2n-bit counter is required. After enabling the comparator, the path switch is triggered with D flip-flop. The check period, \(\Delta\)t, is defined as twice as the period of the rising edge in the output signal.  
Control signals for low-power path switches can be generated by the block in Figure 8b. With n energy harvesters, n low-power path switches are required. The state of the ith input energy is defined with LSi and HSi state signals. When input energy is low, LSi turns to '0', and HSi turns to '1'. When input energy is high, LSi turns to '1', and HSi turns to '0'. In a low-power mode, the LSW_out, signal which connects the input capacitor, Ci, to a low power load is generated with Low_sw_trigger, from the trigger signal generator. ARi prevents the other harvesters from connecting to a low power load. As well, when the the i + 1 th harvester satisfies the condition to be connected, the ith harvester is disconnected by the ARi + 1 signal.  
Figure 8c shows the block diagram for the high-power switch control. As well as the low-power switch control, n energy harvesters need n low-power path switches. In a high-power mode, the HSW_out signal which connects the input capacitor, Ci, to a high power load is generated with High_sw_trigger, from the trigger signal generator. ARHi prevents the other harvesters to connect to a high power load. As well, when the the i + 1 th harvester satisfies the condition to be connected, the ith harvester is disconnected by the ARHi + 1 signal.  
Figure 8: _Cont_.
## 4 Experimental Result and Analysis
### Displacement Response  
The displacement response of the model was obtained through quadratic integration of the data collected by the acceleration sensor. The maximum lateral displacement curve of the model relative to the table is shown in Figure 11. The result showed that the lateral displacement of the structure gradually increased with the enhancement of seismic action. The lateral displacement curve of the structure was relatively smooth in the shear wall structure, indicating that the lateral stiffness of the shear wall structure was evenly distributed along the height. The structure has been transformed into the steel truss structure since the 26 th floor and there has been a significant change in lateral stiffness, resulting in a significant turning point in the curve on this floor. The X-direction floor lateral displacement increased after the seismic wave delay and amplification under the action of the Coalinga wave and the Mongan Hill wave. Meanwhile, the Coalinga wave floor lateral displacement increased by 41.40% at most, appearing on the 26 th floor. The lateral displacement of the Mongan Hill wave floor increased by 77.10% at most, appearing on the 23 rd floor. Under the action of artificial waves, the lateral displacement of seismic waves in the X direction decreased after amplification, with a maximum reduction of 26.54%, appearing on the 16 th floor. For the Y-direction floor side shift, the difference between the data obtained by the three seismic waves before and after the delay and amplification was tiny, with a maximum difference of 9.57%. Since the amplification coefficients of seismic waves in the X and Y directions were basically the same, there were significant differences in seismic wave delay. Therefore, the difference between the X-direction and Y-direction  
Figure 10: Model acceleration response: (**a**) Coalinga wave; (**b**) Morgan Hill wave; and (**c**) artificial wave.  
floor lateral displacement changes was caused by the delay effect of seismic waves. In other words, seismic wave delay was dominant in influencing floor lateral displacement. In addition, due to the differences in seismic wave spectrum characteristics across regions, the lateral displacement of the X-direction floor increased and decreased, but the changing trend was consistent.  
Figure 11: Model displacement response: (**a**) Coalinga wave; (**b**) Morgan Hill wave; and (**c**) artificial wave.
## 5 Experimental Outcomes, Comparative Analysis, and Discussion
### Statistical Evaluation  
To further examine the simulation results and determine their statistical significance, we gathered and examined the highest scores attained from 30 independent runs of each metaheuristic approach considered. These data were evaluated as a series of data points. The first step involved deciding the appropriate type of statistical test, more precisely if parametric or non-parametric tests should be employed. To start, we evaluated the suitability of parametric tests by observing the independence, normality, and homoscedasticity of the variance in the data [77]. The independence condition was met, since each individual execution of the metaheuristics commenced by producing a random set of solutions.  
The normality condition was evaluated by conducting a Shapiro-Wilk test, with individual problem analysis [78] for all three experiments carried out in this research: marine vessel classification (with error and Cohen's kappa as objectives) and marine trajectory prediction. A Shapiro-Wilk test was executed independently for each approach considered in all three scenarios, resulting in p-values for each test. In all cases, the obtained p-values were below 0.05, indicating that it was possible to reject the null hypothesis (H0). Consequently, we can conclude that the obtained results in all three simulations did not follow a normal distribution. The Shapiro-Wilk test outcomes for the three scenarios considered are presented in Table 12.  
As the normality assumption was not met, it was not appropriate to employ parametric tests. Therefore, the next step involved utilizing a non-parametric Wilcoxon signed-rank test [79], which was applied to the same data series, consisting of the best values achieved in each run for each metaheuristic.  
Figure 8: Vessel trajectory best predictions (denormalized)—LSTM-BPSO vs. LSTM-PSO. (**a**) LSTM-BPSO; (**b**) LSTM-PSO.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Problem** & **BPSO** & **PSO** & **GA** & **ABC** & **BA** & **WOA** & **HHO** & **ChOA** \\ \hline Vessel error & 0.032 & 0.041 & 0.046 & 0.018 & 0.021 & 0.033 & 0.035 & 0.040 \\ Vessel kappa & 0.043 & 0.015 & 0.044 & 0.031 & 0.032 & 0.038 & 0.021 & 0.035 \\ Trajectory & 0.011 & 0.003 & 0.015 & 0.009 & 0.019 & 0.006 & 0.010 & 0.013 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Shapiro-Wilk normality tests.  
The provided BPSO was employed as the control algorithm, and a Wilcoxon signed-rank test was executed on the mentioned data series. In all three observed scenarios, the determined \(p\)-values were below 0.05. These results indicated that the suggested BPSO algorithm exhibited statistically significant superiority compared to the competing methods, considering a significance threshold of \(\alpha=0.1\). In the case of \(\alpha=0.05\), the introduced BPSO was significantly better than the other contenders, except BA in the third scenario (trajectory experiment), and that result is marked bold. The overall results of the Wilcoxon signed-rank test are presented in Table 13.
# A Novel Intelligent IoT System for Improving the Safety and Planning of Air Cargo Operations
## 2 System Description
### Hardware
The power switching and monitoring unit incorporate several safety features, such as battery overheat protection, so that it can ensure that its battery packs operate within their proper operating limits at all times. Therefore, the power switching and monitoring unit is an integrated unit that can provide power redundancy to the system and inform the aircraft's crew about the charge level of the battery packs. Each battery pack, when fully charged can provide uninterrupted power to the system for 16 to 18 h, depending on the time interval between two consecutive reports and the number of ULDs that are being loaded and unloaded on the aircraft. Therefore, two fully charged battery packs can provide power to the system for 32 to 36 h, being able to fully support long flights.
# Adaptive Allocation Algorithm for Multi-Radio Multi-Channel Wireless Mesh Networks
## 4 Adaptive Channel Allocation Algorithm
### The Initial Channel Allocation  
Through this section, an initial allocation of the channels is built, considering the case where the network is fully loaded. This phase consists of two stages, the NIC deploying stage and the fixed channel allocation stage to solve the channel allocation problem in the design mode for multi-radio multi-channel WMN. This configuration is considered as FCA, in which the output is a static channel allocation. In wireless mesh networks, the APs topology is fixed; however, APs must collect information regarding the hosts to deploy the initial stage, such information collected by the _hello_ protocol as in [26] or other suitable protocols as this is essential to implement the following method.
## 2 Materials and Methods
### Satellite Imagery Selection and Processing
#### 2.5.3 Intercalibration of Sentinel-2 Scenes  
The images from different dates of the Sentinel-2 time series may present discrepancies in their reflectance values because of different acquiring conditions among the days (see [69]). Likewise, the reflectance values of the two adjacent Sentinel-2 scenes that encompass the entire area of interest were not directly comparable, mainly because of the inherent differences in the reflection of light captured by the twin satellites, despite the fact that the acquisition times were virtually the same (Figure S1). Hence, we undertook an inter-date and inter-scene image calibration. Firstly, for the inter-date calibration, we intercalibrated the images from different dates, as described in [PERSON] et al., (2021), using the _Imodel2_ package [70] in R to calculate a band-wise major axis regression between one reference image (any image can be selected) and all the other images. For the inter-scene calibration, we then used one reference scene (either of the two scenes can be selected as a reference) of each date of the time series and merged it with the adjacent scene. As the two adjacent Sentinel-2 scenes had an overlapping area (Figure 1), we extracted that region from both scenes and performed another major axis regression to rescale the reflectance values of the second scene to match the range of reflectance values of the reference scene (Figure S1). Thus, we obtained a final time series of larger Sentinel-2 images covering the whole study area by merging the adjacent intercalibrated scenes. The merging was performed by keeping the entire extension of the T28 PCT scene and cropping out the overlapping area from the T28 PDT.