esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Goals  
The primary goal of this project was to determine if spectral response characteristics could be identified that might allow development of commercial systems for detecting fecal contamination in produce fields using fluorescence imaging and low intensity UV or violet illumination. Goals did not include development and testing of detection algorithms. The ultimate goal was to reduce the potential cost of a commercial system to survey produce fields for fecal contamination. As fluorescence responses of feces to UV excitation are easily masked by solar illumination, test conditions included making measurements in low or no solar light conditions. Growers commonly conduct operations in fields at night, so a requirement that field surveys be conducted at night is not particularly onerous.  
Most leafy greens are grown around Yuma, AZ or Salinas, CA, and tests were to be conducted in Beltsville, MD. To facilitate interpretation of data relative to growing areas, graphs of insolation by time of day were constructed for Beltsville for the time data were to be collected and for growing areas during growing seasons. The general form of all graphs was similar with a broad peak during the middle portion of the solar day and sharp slopes near sunrise and sunset (Figure 2). Insolation values during the broad central peaks were generally similar across the tested situations; the major differences in graphs were the duration of the broad peaks, which is simply a function of seasonally dependent day length. The three selected testing times were meant to capture the impact of high insolation values  
Figure 1: Shroud with camera. Orange backpack is tethered to the camera and is used to house electronics when the camera system is used in a portable mode.  
that might occur during the broad peak times, the effects of low level ambient illumination that occurs near sunrise and sunset, and responses when solar illumination was largely absent.  
The light shroud used for testing consisted of a platform and drapes. For a commercial survey system, a platform will be necessary to mount a camera or cameras, and a light source. The drapes were added to see if it might be possible to conduct field surveys during the day by mitigating light exposure. However, in commercial use an overlapping split curtain would likely be used instead of the drapes. The 100 cm test condition was meant to mimic the light blocking effectiveness that might result from using a split curtain with actual plants. The 250 cm test condition was meant to mimic a worst case scenario while using a split curtain. The zero cm test condition was meant to examine whether daylight use of a survey system might be possible using more aggressive light mitigation procedures and to better contrast the impact of raising the drape height.
## 3 Proposed Method
### Concept of Data Embedding for Blind Detection and Frame Synchronization  
The LP scheme presupposes that the time sequence signal is a stationary process. Within a short duration, speech signals are stationary, so they can be accurately predicted using the LP scheme. Consequently, the host signals of adjacent frames have equal statistical properties during frame processing, and the host signals are highly correlated across adjacent frames. The LP residues obtained from the host signal are also expected to be highly correlated across adjacent frames.  
LP residue \(r_{k}(n)\) in the (\(k\))th frame is assumed to be equal to LP residue in adjacent frames in the (\(k-1\))th frame or (\(k+1\))th frame. On the basis of this assumption, the LP residue in the adjacent frame in the (\(k-1\))th frame or (\(k+1\))th frame can be used to embeda security code in the host signal \(x_{k}(n)\) in the (\(k\))th frame. The security code can also be detected by using the LP residue in adjacent frames in the (\(k-1\))th frame or (\(k+1\))th frame. Furthermore, if we consider the embedding of security code in the forms of front and back frames, when the LP residue \(r_{k}(n)\) in the (\(k\))th frame for embedding and the LP residue \(r^{\prime}_{k}(n)\) in the (\(k\))th frame for detection are the same (\(r_{k}(n)=r^{\prime}_{k}(n)\)), the expectation is calculated as \(E\{r^{\prime}_{k}(n)\}=1\).  
On the other hand, when the different LP residue (\(r_{k}(n)\
eq r^{\prime}_{k}(n)\)) in the (\(k\))th frame is used for embedding and detecting secure code into a speech signal, the expected value is \(E\{r_{k}(n)r^{\prime}_{k}(n)\}<<1\). The expected value \(E\{r_{k}(n)r^{\prime}_{k}(n)\}\) in the case of the frame being correctly synchronized is thus higher than in the case of the frame not being correctly synchronized.  
Figure 2 shows the block diagram of the proposed method, where (a,b) show the embedding and detection processes for the security code, respectively. The difference between the non-blind LP-DSS method and the blind LP-DSS method is that the host signal is classified into even-numbered and odd-numbered frames, and the LP residue from the host signal of the even-numbered or odd-numbered frame is used to embed the security code in the host signal of other frames. This process enables the LP residue to be processed from the watermarked signal of the unprocessed frame and the security code to be detected from the watermarked signal of the other frame. In addition, even if the frame synchronized position is shifted during the security code detection, the proposed method can correctly detect the security code by synchronizing the frames. The data embedding, the frame synchronization, and the blind detection processes are explained in Sections 3.2-3.4, respectively.
# Relief Aircraft Dispatch Strategies Based on Different Levels of Information Sharing Systems
## 6 Numerical Experiments
### Result of Numerical Experiment  
In this section, we performed simulations using different Gini coefficients and D/C ratios. To make our experiment more representative, we designed 20 cases, including different demand imbalance degrees and different demand/capacity ratios, as shown in Table 2. For each case, we simulated our four transport methods (three different information sharing levels) and analyzed the result using three indicators (total flight time, relief activities finishing time, and waiting time per injury). In this simulation, the injuries in each case are listed in Appendix B. For each case, the input was unchanged. From the beginning of the simulation, the increase in injuries for each shelter was input at 60-min intervals until the simulation had run for 5 h. At this point, the injuries stopped updating, but the simulation did not stop until all the injured persons were transported. The parameters used in the simulation are listed in Table 3. The results of the simulation are summarized in Table 4.  
To simplify the interpretation of the results, we converted the data from a tabular form into the charts shown in Figure 9. The left column indicates the total time (in minutes) of the different methods using different D/Cs and Gini coefficients, the middle column shows the finishing time (in minutes), and the right column shows the waiting time (in minutes). Each row represents a different D/C ratio. In each figure, there are four categories representing different Gini coefficients. In each Gini histogram, different shades of green bars represent  
\begin{table}
\begin{tabular}{c c c c c} \hline
**20 Cases** & **Gini = 0.1** & **Gini = 0.3** & **Gini = 0.5** & **Gini = 0.7** \\ \hline D/C ratio 0.6 & Case 1 & Case 2 & Case 3 & Case 4 \\ D/C ratio 0.8 & Case 5 & Case 6 & Case 7 & Case 8 \\ D/C ratio 1.0 & Case 9 & Case 10 & Case 11 & Case 12 \\ D/C ratio 1.2 & Case 13 & Case 14 & Case 15 & Case 16 \\ D/C ratio 1.4 & Case 17 & Case 18 & Case 19 & Case 20 \\ \hline \end{tabular}
\end{table}
Table 2: Different simulation scenarios. As the Gini coefficient increases, the injury distribution becomes more unbalanced. As the D/C ratio increases, the demand increases, and the transport capacity decreases. Four transport methods were simulated for each case.  
Figure 8: Gini coefficient of all shelters.  
different transport methods. Based on Table 4 and Figure 9, alongside previous analysis, we can now answer the questions posed in Section 1.
## 4 Discussion  
IR thermography has been used as a tool to non-invasively assess the tear film, both statically and dynamically. [PERSON] et al. [21] emphasized the potential of this technology for tear film assessment and, later on, [PERSON] and [PERSON] demonstrated the existing connection between the tear film stability and OST [24]. The present study contributes to those developments by providing information about the analysis of OST dynamic variations of DED eyes and controls under more realistic and natural non-controlled blinking conditions. The outcomes of the current study revealed no significant differences between DED and control groups in any of the thermal parameter analyzed. Besides age being considered as a key factor affecting both DED and OST [21; 32; 33], the OST between groups was not significantly different despite the DED group being significantly older. Additionally, the number of blinks during the 40 s sequence was similar between both groups. DED patients blinked \(13\pm 6\) times and control patients blinked \(11\pm 4\) times, which translates into approximately 20 and 17 blinks per minute, respectively in agreement with the normal blinking rate previously reported [34; 35; 36]. It is known that when the lids are opened, the ocular surface loses heat by convection, radiation and evaporation [22]. However, there is some controversy regarding OST changes in DED eyes. In the current study, no differences in thermal measurements were observed between DED and control subjects for the recorded sequence. These results are in disagreement with some studies [32; 37], which observed that DED subjects presented cooler OST and faster cooling rates compared to controls. [PERSON] et al. found that DED patients have a significantly lower temperature in the geometric center of the cornea than controls [22]. [PERSON] et al. found lower temperatures in DED subjects after 5 and 10 s of eye-opening (geometric center of the cornea, mean OST, minimum and maximum temperature, and different areas of the eye) [38]. [PERSON] et al. also observed a greater decrease in OST in DED eyes at 10 s after eye-opening and, in addition, they found that eyes with a shorter FBUT are more likely to have a decrease in the OST, but the mean OST in DED eyes did not differ from controls [23].
## 5 Validation
### Results
In Figure 12a,b, configuration 1 shows worse results. In the case of Figure 12a, having the messaging history turned off probably had a detrimental effect, while in Figure 12b, due to all experiments being run in the same session, some basic assumptions were interpreted incorrectly, which influenced the results of all experiments. Configurations 2 and 8 employ one common fine-tuned LLM (fLLM) that is used by the Parser, the Recognizer, and the Generator. The Evaluator was a vLLM, as our dataset does not influence the evaluation process. For this configuration, we observed an improvement in both the validity and correctness of the generated constraint models. Configurations 3-4 and 9-10 use four distinct LLMs; two are vLLMs used by the Parser and the Evaluator, and two are fLLMs used by the Recognizer and the Generator. Each fLLM is fine-tuned with different datasets, as described in more detail in the ablation study (Section 5.3). The differences between configurations 3 and 4 and 9 and 10 are the datasets used. In configurations 3 and 9, and 4 and 10, a specialized (a subset of the dataset tailored to the generation of constraint models) and complete dataset were used for fine-tuning the LLM used by the Generator, respectively. It seems that using a specialized dataset yielded better results in relation to the percentage of valid and correct constraint models generated, even though both had the same data. Furthermore, configurations 3 and 9 indicate that two fLLMs seem to be working better than one or no fLLM. Configurations 5-6 and 11-12 again use four distinct LLMs, but only one is a vLLM, that used by the Evaluator. The three fLLMs are used by the Parser, the Recognizer, and the Generator. The differences between configurations 5 and 6 and 11 and 12 are the datasets used. In configurations 5 and 11 and 6 and 12, a specialized (a subset of the dataset tailored to the generation of constraint models) and complete dataset were used for fine-tuning the LLM used by the Generator, respectively. These configurations confirm that specialized datasets yield better results in terms of the percentage of valid and correct constraint models generated. These configurations further reaffirmed that more fLLMs yielded better results, as seen in configurations 5 and 11. All aforementioned behaviors and conclusions are valid for all experiments in Figure 12a-d. It was interesting to notice that in the case of experiments run on different versions of LLMs, the experiments in Figure 12a, which were new original tests created by us [51], were run on GPT-3.5-turbo, while the experiments in Figure 12b were the tests were run on GPT-4o; the results were almost identical. This was not the case for the experiments from Mixed CP [17] in Figure 12c, which were run on GPT-3.5-turbo, and in Figure 12d, which were run on GPT-4o. We observed clear improvements in the results in Figure 12d. We suspect this was caused by data leakage, as the Mixed-CP dataset is based on an introductory university course on constraint programming.
## 2 Material and Methods
### Investigation of Anti-Inflammatory In Vitro Activity by Inhibition of Protein Denaturation  
Albumin denaturation inhibition activity of different extracts was investigated to explore the anti-inflammatory action [27; 28]. A strong anti-inflammatory non-steroidal drug, i.e., ibuprofen, was used as a reference drug in this test. The tubes containing bovine serum albumin (BSA) (500 uL, 1% aqueous solution) and 100-600 uL of different dilutions of _P. emblica_ or _A. indica_ extract or 200 ug/mL of ibuprofen, were incubated at normal temperature for 20 min. Denaturation of BSA was carried out for 30 min by wet heating the samples at 70 \({}^{\circ}\)C. The absorbance of different samples was recorded at 660 nm just after cooling the samples. In this test, blank contained simple distil water. The experiment was run in triplicates, and the average absorbance was taken. The inhibition of protein denaturation percentage was calculated as per the following equation.  
\[\text{Inhibition of albumin denaturation (\%)}=[(\text{Oc - Os/Oc}]\times 100\]  
where Oc = denotes the absorbance of the control, and Os denotes the absorbance in the presence of extract/ibuprofen.
## 2 Diffusion Characteristics and Proportional Coefficients of Space States
### Diffusion Analysis of Transient Electromagnetic Field in Three Space States
The induced current was generated in the surrounding rock with low resistivity above and below, and the induced current spread to the horizontal direction in a symmetrical form. The maximum value of the induced current was distributed in the center of the two circles. As illustrated in Figure 4, the magnetic field presented an asymmetric distribution in the 3/4 space state. The maximum value of the magnetic field was distributed in the center of the three-dimensional model. The equipotential line on the left side of the magnetic field was far from the upper air medium with high resistivity and tended to move downward. The equipotential line on the right side of the magnetic field was close to the upper surrounding rock medium with low resistivity and tended to move upward. In addition, the induced current gradually propagated to the lower right in the form of a ring. The angle between the ring plane of the induced current and the horizontal plane was approximately 15 degrees. The symmetrical line of the induced current on the left and right sides deviated from the symmetrical line of the surrounding rock in the 3/4 space state, and the amplitude of the induced current on the left side was smaller than that on the right side. Meanwhile, the Z-direction magnetic field and induced voltage at the center of the model showed linear changes in the double logarithmic coordinates at the three space state conditions, as presented in Figure 5. With an increase in turn-off delay time, the Z-direction magnetic field and induced voltage at the center of the model gradually decayed. Comparing the measurement results under three different space conditions, it could be found that the amplitude of the magnetic field and induced voltage in the full space state was greater than that in the 3/4 space state. The amplitude of the magnetic field and induced voltage in the 3/4 space state was greater than that in the half space state. On the whole, the fluctuation range of the induced voltage amplitude was more extensive than that of the magnetic field amplitude, and the variation curve of the induced voltage was steeper than the variation curve of the magnetic field. Therefore, the variation curve of the induced voltage was more sensitive to the instantaneous turn-off process of the transmitting current, which also showed that the induced voltage was more suitable for the measurement and interpretation of the transient electromagnetic response signal.
## 4 Effect of Burial Depth of Utility Tunnels
### Effects of Burial Depth on the Structural Seismic Response
When the burial depth of the structure changed from 2 m to 2.5 m, the change in the uplift value is not obvious; in contrast, when the burial depth increased from 3 m to 3.5 m, the vertical displacement decreased from 6.2 cm to 4.0 cm, and the uplift decreased to a greater extent; when the covering depth is 4 m, the uplift of the soil above the structure drops to 2.0 cm. This is partly due to the increase in overburden pressure; on the other hand, it is because the liquefaction ratio of the soil beneath the structure decreases with increasing depth, and the uplift behavior induced by excess pore water pressure is reduced. Uplift of underground structures in liquefied soils is a serious form of damage, especially for constructions such as utility tunnels where the longitudinal dimension is much larger than the cross-sectional dimension. In practice, the stratum in the extension direction of the utility tunnel is usually not uniform, and the structure in the liquefiable and non-liquefiable areas will have large vertical displacement differences, which in turn will generate large shear forces in the direction perpendicular to the axis and induce damage to the structure. Increasing the burial depth of the underground structure increases the internal forces of the structure while significantly reducing the uplift, and usually, the underground structure has a large design strength, so appropriately increasing the burial depth will increase the seismic stability.
# Structural Damage Features Extracted by Convolutional Neural Networks from Mode Shapes
###### Abstract  
This paper aims to locate damaged rods in a three-dimensional (3D) steel truss and reveals some internal working mechanisms of the convolutional neural network (CNN), which is based on the first-order modal parameters and CNN. The CNN training samples (including a large number of damage scenarios) are created by ABAQUS and PYTHON scripts. The mode shapes and mode curvature differences are taken as the inputs of the CNN training samples, respectively, and the damage locating accuracy of the CNN is investigated. Finally, the features extracted from each convolutional layer of the CNN are checked to reveal some internal working mechanisms of the CNN and explain the specific meanings of some features. The results show that the CNN-based damage detection method using mode shapes as the inputs has a higher locating accuracy for all damage degrees, while the method using mode curvature differences as the inputs has a lower accuracy for the targets with a low damage degree; however, with the increase of the target damage degree, it gradually achieves the same good locating accuracy as mode shapes. The features extracted from each convolutional layer show that the CNN can obtain the difference between the sample to be classified and the average of training samples in shallow layers, and then amplify the difference in the subsequent convolutional layer, which is similar to a power function, finally it produces a distinguishable peak signal at the damage location. Then a damage locating method is derived from the feature extraction of the CNN. All of these results indicate that the CNN using first-order modal parameters not only has a powerful damage location ability, but also opens up a new way to extract damage features from the measurement data.  
structural state detection; convolutional neural networks; mode shapes; mode curvature differences; feature extraction +
Footnote †: journal: Applied Sciences
# Peat Thickness Changes at the \"Wolosate\" Raised Bog in the Western Bieszczady Mountains
###### Abstract  
The study aims to present and analyze the rate of changes that occur in the area of Wolosate bog dome as a result of human pressure, which has been especially intense the period between 1967-2016. Additionally, the work presents current stratigraphic cross-section of the peat deposit, and illustrates and interprets the course of the peat accumulation process, that depends on hydrological conditions. The described study had been conducted on the area of \"Wolosate\" raised bog (nature reserve) in Western Bieszczady Mountains. From the analysis of data obtained from precise satellite surveys, it can be concluded that over 49 years, the average thickness of peat in the characteristic levelling-stratigraphic cross-section decreased by 27.2%. The most significant changes (34.3%) took place up until 1988, as during that period, the stream valley of Wolosate river had been drained. The results of the discussed study indicate one more circumstance. In later period (1988-2016), following the restoration measures, a 35 cm acrotelm layer of _Sphagnum_ peat (_Eusplagnet_) with a living layer of peat moss which happened to form itself on the western side of the peatland (along a section of approx. 50 m).  
peatland disappearance; stratigraphic profile; peat species; valley drainage; restoration; topographic surveys +
Footnote †: journal: Water  
11 September 20221  
the Orawa and Nowy Targ Valley [10; 11], and some raised bogs in the Western Bieszczady Mountains [12; 13; 14].  
Due to the impact of human pressure, peatlands' area and thickness decrease over the years and, therefore, they are being a subject to the process of decline. With a view to this, research into magnitude and rate of peatland decline is highly purposeful and advisable, as its results motivate to restore and protect these wetlands of significant natural value. The disappearance of peatlands due to drainage (decession) is mainly caused by three factors: settlement (the so-called compression), peat shrinkage, and the organic matter oxidation [15; 16; 17]. Findings and the literature on the subject matter have been extensively presented in numerous publications by e.g., [PERSON] [18; 19]; [PERSON] and [PERSON] [20]; [PERSON] [21; 22]; [PERSON] et al. [23; 24; 25]; [PERSON] [26]; [PERSON] [27]; [PERSON] [28]; [PERSON] et al. [29], [PERSON] [30], [PERSON] [31] and [PERSON] at al. [32]. All of these publications unanimously indicated that the peatland disappearance was mainly due to their drainage.  
The main aim of the study was to analyze the changes caused by human pressure during the period 1967-2016 in the dome cross-section of the \"Wolosate\" raised bog located in Western Bieszczady Mountains. Moreover, it was to present the peat deposit stratigraphy on the background of the hydrological conditions at the time when individual peat species were being formed.