esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Dab-Deformable Model for Runway Foreign Object Debris Detection in Airport Optical Images
## 3 Methodology
### Cel  
In Deformable DETR, a composite loss function is employed, consisting of classification loss, bounding box regression loss, and cardinality loss. This comprehensive design enables the model to achieve optimization from multiple perspectives:  
\[\mathrm{Loss}_{\mathrm{total}}=\mathrm{L}_{\mathrm{cls}}+\mathrm{L}_{\mathrm{ bbox}}+\mathrm{L}_{\mathrm{cardinality}} \tag{23}\]  
For the detection of FOD, which fundamentally involves small object detection, the overlap area between predicted boxes and ground truth boxes is relatively small. As a result, the bounding box regression loss based solely on GIoU lacks sensitivity to localization errors for small objects. To enable more stable learning and training of the model, we introduce a Conditional Enhancement Loss:  
\[\mathrm{Loss}_{\mathrm{total}}=\mathrm{L}_{\mathrm{cls}}+\mathrm{L}_{\mathrm{ bbox}}+\mathrm{L}_{\mathrm{cardinality}}+\mathrm{L}_{\mathrm{ce}} \tag{24}\]  
\[L_{ce}=\begin{cases}L_{giou}+\rho\big{(}giou_{threshold}-\frac{1}{N}\sum_{i=1 }^{N}L_{giou}\big{)}&if(\frac{1}{N}\sum_{i=1}^{N}L_{giou}<giou_{threshold})\\ L_{giou}&otherwise\end{cases} \tag{25}\]A threshold is set to determine whether the model effectively captures the position and shape of the target. By introducing an additional loss term, the model is encouraged to improve its poor predictions, prompting it to focus more on optimizing prediction results in subsequent iterations. The inclusion of the CEL contributes to smoother changes in the overall loss function of the model, leading to a more stable training process.
## 4 Practical Application
### Measurement Result and Improvement Method  
Using the experimental apparatus, the upper parallelogram joint was driven five times repeatedly. In order to soften the joint, all the fasteners of the hinge joints constituting the parallelogram joint were released sufficiently. To confirm the balancing effect of the springs on the joint, the measurement was repeated by changing the number of the connected springs. As shown in Figure 12, the curves showed significant hysteresis between the upward and downward movements, although all the joint bolts were released. This is because any rolling bearing is not adopted to the joints of the parallelogram structure. To maintain current posture, the joint required the greatest external torque to be supported with both springs removed, and the direction of required torque always appeared in the opposite direction to gravity. When both springs were combined, the direction of the required torque changed depending on the joint angle. Tightening the joint fasteners widened the gap between the upper upward movement curve and the lower downward movement curve. If the \(x\)-axis is located between the two curves within a certain interval, the joint will always be able to maintain all the postures within that interval. As shown in the bottom curve of the graph, when the joint was bent below \(-15^{\circ}\), it tended to spread upward by the force of the balancing springs.  
When springs are used for balancing, they apply a large load to the joints. As a result, the friction applied to the joints becomes larger and the gap between the upper and lower torque curves becomes wider. The balanced-arm lamp used in the measurement does not need to change its posture over and over again, so the torque curve is not a problem even with large hysteresis. However, considering an ease of use, it is obvious that a product that can easily bend and stretch its joints is convenient. In an ideal case, both curves of upward and downward movements will appear horizontally, and the interval between them will become narrower. However, it is impossible to achieve this effect with a simple point-to-point connection of the springs. An easy way to improve the operating performance of the product in the present situation is to move the bottom torque curve loop upwards by 0.1 N\(\cdot\)m. This will significantly reduce the tendency of the joint to stretch out even when the joint is bent to about \(-30^{\circ}\). Increasing the number of turns of the applied springs or utilizing thinner wires for manufacturing will reduce the spring constant and will achieve this weakening effect.  
Figure 11: Experimental setup. An additional shaft is installed for external torque transmission.
## 3 Results and Discussion
### Sound Absorption Performance of the Composite Sound Absorbing Structure  
#### 3.2.1 Optimal Structural Parameters  
Optimal structural parameters of the composite sound-absorbing structures were obtained and summarized in Table 1, and comparisons of the theoretical sound absorption coefficients without and those with the optimization are shown in Table 2. It could be found that the sound absorption performance of the polyurethane foam was obviously improved by the prepositive microperforated polymethyl methacrylate panel, especially for the conditions of small thickness of the sample and low frequency of the sound. Meanwhile, when the frequency range was 100-2000 Hz, the absolute value and relative percentage of improvement of the theoretical average sound absorption coefficient fell from 0.3690 and 247.76% to 0.1050 and 15.44% respectively with thickness of the polyurethane foam increased from 10 mm to 40 mm. The major reason for this phenomenon was that sound absorption performance of the polyurethane foam in the entire frequency range was raised when its thickness rose, which was consistent with normal absorption property of the porous material [15; 16; 17; 31; 33; 35].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Investigated** & \multicolumn{3}{c}{**Polyurethane Foam**} & \multicolumn{3}{c}{**Microperforated Polymethyl**} & \multirow{2}{*}{
\begin{tabular}{c} **Optimal Average** \\ **Sound Absorption** \\ **Coefficient** \\ \end{tabular} } \\
**Range (Hz)** & \(d_{pf}\) **(mm)** & \(\sigma\) **(P\(\ast\)s/m\({}^{2}\))** & \(\phi\) & **\# (mm)** & \(d\) **(mm)** & \(b\) **(mm)** & **Coefficient** \\ \hline
100–1000 & 10 & 13078 & 0.956 & 0.3 & 0.79 & 12.79 & 0.3969 \\
100–2000 & 10 & 13078 & 0.956 & 0.3 & 0.38 & 4.11 & 0.5179 \\ \hline
100–1000 & 20 & 13078 & 0.956 & 0.3 & 0.48 & 6.51 & 0.5409 \\
100–2000 & 20 & 13078 & 0.956 & 0.3 & 0.25 & 2.02 & 0.6611 \\ \hline
100–1000 & 30 & 13078 & 0.956 & 0.3 & 0.39 & 4.59 & 0.6283 \\
100–2000 & 30 & 13078 & 0.956 & 0.3 & 0.21 & 1.31 & 0.7374 \\ \hline
100–1000 & 40 & 13078 & 0.956 & 0.3 & 0.36 & 3.71 & 0.6884 \\
100–2000 & 40 & 13078 & 0.956 & 0.3 & 0.17 & 0.86 & 0.7849 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Theoretical optimal structural parameters of the composite sound-absorbing structures.  
Figure 8: Further verification of theoretical and actual sound absorption coefficients of polyurethane foam. (**a**) With the thickness of 10 mm; (**b**) With the thickness of 60 mm.
# Phenolic Profiling of Five Different Australian Grown Apples
## 3 Results and Discussion
### Correlation between Phenolic Compounds and Antioxidant Activities  
The correlation between the polyphenols and antioxidant activities was performed with a Pearson's correlation test (Table 2). TPC shows a strong positive correlation with TFC with \(r^{2}\) = 0.975, \(p\leq 0.01\), this indicates that TFC contributes largely to the total phenolic content. Additionally, TPC was strongly correlated with TAC with \(r^{2}\) value of 0.920 (\(p\leq 0.05\)). A previous study by [PERSON] and [PERSON] [45] reported a similar correlation between TPC and TAC.  
TFC had a significantly strong correlation with DPPH and TAC with \(r^{2}\) value of 0.903 (\(p\leq 0.01\)) and 0.952 (\(p\leq 0.05\)) respectively indicating that flavonoids were one of the significant contributors for the antioxidant activities. The results confirm with the previous studies of [PERSON] et al. [46] and [PERSON] et al. [47], on phenolic compounds contributing towards antioxidant potential. A non-significant correlation were observed between TTC and antioxidant assays indicating the contribution of tannins to antioxidant activity is limited, which confirms with [PERSON] et al. [48] study.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variables** & **TPC** & **TFC** & **TTC** & **DPPH** & **ABTS** & **FRAP** \\ \hline
**TFC** & 0.975 ** & & & & & \\
**TTC** & \(-\)0.736 & \(-\)0.702 & & & & \\
**DPPH** & 0.832 & 0.903 * & \(-\)0.685 & & & \\
**ABTS** & 0.754 & 0.815 & \(-\)0.830 & 0.952 ** & & \\
**FRAP** & 0.681 & 0.756 & \(-\)0.614 & 0.961 ** & 0.938 ** & \\
**TAC** & 0.920 * & 0.952 ** & \(-\)0.751 & 0.980 ** & 0.931 * & 0.912 * \\ \hline \hline \end{tabular}  
* Significant correlation with \(p\leq 0.01\);
* Significant correlation with \(p\leq 0.05\).  
\end{table}
Table 2: Correlation coefficients (\(r^{2}\)) between phenolic contents and antioxidant assays.  
The correlation among the antioxidant assays had strong correlation with each other. Significant positive correlation was observed between DPPH with ABTS, FRAP and TAC (\(r^{2}=0.952\), \(r^{2}=0.961\), and \(r^{2}=0.980\), \(p\leq 0.01\)). The correlation displayed in our study was similar to [PERSON] et al. [49], where a high correlation was observed between the four assays. Similarly, ABTS was observed to have high significant correlation with FRAP and TAC with \(r^{2}=0.938\), \(p\leq 0.01\) and \(r^{2}=0.931\) (\(p\leq 0.05\)), respectively. On the other hand, FRAP was correlated with TAC with \(r^{2}=0.912\) (\(p\leq 0.05\)).  
Overall, phenolic compounds were highly correlated with antioxidant assays, which indicated that both classes of phenolic compounds including phenolic acids and flavonoids have strong antioxidant potential. The four antioxidants' assays were strongly correlated with each other.
# An Algorithm for Rescheduling of Trains under Planned Track Closures
## 4 Joint Problem of Train Rescheduling and Track Closure Planning  
The formalization of the considered joint problem is discussed in this section. The graph-matrix representations of a railway network, a train timetable, and track closures are given in Sections 4.2-4.4, respectively. It follows the summarization of the most crucial notation in Section 4.1. The mathematical model of the joint problem presented in Section 4.5 is preceded by the introduction of corresponding models for sub-problems of the track closure planning and the train scheduling.
# Insights into Native Fermentation Process of Apples (_Malus domestica_) in Low Sodium Conditions
###### Abstract  
Although it is one of the most important methods of fruit and vegetable preservation, pickling provides multiple interesting vistas for study, from the variety of the raw vegetal material and the composition of pickling media to the diversity of the microorganisms involved in the process or the quality of the final product. The purpose of this study is to investigate the effects of sodium chloride substitution with potassium or magnesium chloride on the pickling process of apples. Physical (mass, color, texture), chemical (dry matter, acidity, salinity, reducing sugars) and phytochemical parameters of the apples were analyzed during 35 days of fermentation, with a frequency of 7 days. The results show a decrease in dry matter from 14.94 \(\pm\) 0.25% for all the samples and a continuous increase of lactic acid concentration to a maximum of 0.248 \(\pm\) 0.032 g lactic acid/100 g product for the magnesium samples. At the same time, the phytochemical profile is enhanced, while the texture becomes softer (a decrease in firmness from 2.53 \(\pm\) 0.08 N to 0.72 \(\pm\) 0.02 N was registered for potassium samples). The main conclusion of the study is that sodium chloride could be successfully replaced by potassium or magnesium chloride in the fermentation process of apples.  
apple preservation; low sodium; bioactive compounds; texture; color Article  
Faculty of Food Science and Engineering, Duniarea de Jose University of Galati, 111 Domneascal Street, 80020 Galati, Romania; [EMAIL_ADDRESS] (D.C.) [EMAIL_ADDRESS] (D.-G.A.); [EMAIL_ADDRESS] (O.V.N.); [EMAIL_ADDRESS] (D.I.M.); [EMAIL_ADDRESS] (I.-A.S.); [EMAIL_ADDRESS] (E.B.)
## 1 Introduction
Creating, sedimentation, flocculation, coalescence, phase inversion, and Ostwald ripening are examples of mechanisms that initiate physical instability. Oxidation and hydrolysis are common examples of creating chemical instability [2]. A concentrated emulsion is a class of emulsions, which is characterized by a minimum internal phase volume fraction of 0.74 [7; 8]. It is generally applied for food, agrochemical, and pharmaceutical products. In this work, the concentrated water-in-oil (W/O) emulsion is developed for crop protection purposes. The emulsion that was formulated contains a certain amount of polysaccharides in the internal aqueous phase which was dispersed into the vegetable oil continuous phase. In its application, polysaccharides serve a thickening adjuvant of the water-based spray solution which reduces the drift of the spray [9]. For commercial reasons, the emulsion designed has to present long stability and destabilize immediately into the water dilution system during spraying application. In our previous works [10; 11], we have shown that it was possible to get such W/O emulsion containing the high internal aqueous phase (75% v/v). The combination of polyglycerol polyricinoleate (PGPR) as surfactant and glycerol co-surfactant in the vegetable oil continuous phase was beneficial to retain the emulsion without any phase inversion. During the water dilution process, the destabilization was intentionally created to release the entrapped polysaccharides from the primary W/O emulsion by introducing a certain amount of chemical agents [12]. In understanding the basics of the emulsion developed from the droplets formation point of view, the objective of this study is to track the evolution of the droplets during preparation (formation), storage (stability), and destabilization (deformation) over time. The way the droplets evolve with time was observed by differential scanning calorimetry (DSC) along with laser diffraction granulometry and optical microscopy as the control of the observations. Many techniques have been used to characterize the emulsion which are generally based on the analysis of the droplets size distribution. However, the majority of these measurements require a dilution of the sample. It may create perturbation on the surface properties of the droplets and on interactions between the droplets, inducing flocculation or even coalescence phenomena [13]. For the emulsion system, DSC detects the freezing and melting phenomena of the dispersed phase and the continuous phase during a heating or cooling program. It monitors the energies involved including the heat exchanges between the sample and a reference either versus time at a constant temperature or versus temperature. These give information on the droplets dispersed state without any dilution of the system [14]. Hence, this work shows that calorimetry provides a simple, fast and precise technique to characterize the droplet size distribution of a concentrated (W/O) emulsion.
## 4 Enterprise Network Functionality
### Embedded Security Architecture: Zero Trust and SASE
#### 4.3.1 Zero Trust Network Architecture  
ZTNA is an enterprise cybersecurity framework that is based on 'never trust, always verify' principles. It is designed to reduce internal lateral movement and prevent data breaches. ZTNA protects data and services and minimizes unauthorized access by performing checks on network requests, based on an established access policy [7; 8; 66]. The type of access requests could range from enterprise applications, infrastructure components, devices, and virtual and cloud components. Hence, the ZTNA concept can broadly be seen as cybersecurity strategy for an organization that incorporates operational policies and network infrastructure (both virtual and physical). Based on the lateral movement of threats and zero trust assumptions, the network security can further be improved by incorporating micro-segmentation policy [67]. As shown in Figure 5, access will only be granted to an enterprise facility through a policy enforcement point (PEP) and a policy decision point (PDP) [8]. Thus, ZTN applies two basic functions of authentication and authorization through policy decision/ enhancement point (PDP/PEP) to grant access to any resources.
## 4 Security and Performance Analyses
### Authentication Proof of the Proposed Scheme Using BAN Logic
#### 4.1.4. Assumptions  
* \(AS_{1}\): \(MCS\mid\equiv\#\,h(X_{P}\|P_{P}\|Cer_{P}\|R_{P})\)
* \(AS_{2}\): \(MCS\mid\equiv\#\,h(ID_{D}\|Cer_{P}\|PHI\|R_{D})\)
* \(AS_{3}\): \(Pat\mid\equiv Pat\stackrel{{ T_{PP}MCS}(x)}{{\leftrightarrow}}MCS\)
* \(AS_{4}\): \(Doc\mid\equiv Doc\stackrel{{ T_{PP}MCS}(x)}{{\leftrightarrow}}MCS\)
* \(AS_{5}\): \(MCS\mid\equiv MCS\stackrel{{ T_{PP}MCS}(x)}{{\leftrightarrow}}Pat\)
* \(AS_{5}\): \(MCS\mid\equiv MCS\stackrel{{ T_{PP}MCS}(x)}{{\leftrightarrow}} Doc\)
* \(AS_{7}\): \(MCS\mid\equiv Pat\stackrel{{ R_{P}}}{{\leftrightarrow}}R_{P}\)
* \(AS_{8}\): \(MCS\mid\equiv Doc\Longrightarrow R_{D}\)
* \(AS_{9}\): \(Doc\mid\equiv MCS\Longrightarrow h(ID_{D}\|R_{D})\)
* \(AS_{10}\): \(Pat\mid\equiv MCS\Longrightarrow h\big{(}NID_{P}^{new}\|ID_{P}\|SK_{MCSP}\|R_{ P}\|X_{P}^{new}\big{)}\)
* \(AS_{11}\): \(Doc\mid\equiv Doc\stackrel{{ T_{PP}(x)}}{{\leftrightarrow}}Pat\)
* \(AS_{12}\): \(Pat\mid\equiv Pat\stackrel{{ T_{PP}(x)}}{{\leftrightarrow}}Doc\)
* \(AS_{13}\): \(Doc\mid\equiv Pat\stackrel{{ C_{P}}}{{\leftrightarrow}}Cer_{PD}\)
* \(AS_{14}\): \(Pat\mid\equiv Doc\Longrightarrow ID_{P}\)
## 4 Measurement Processing
### Weighted Least Square  
For datasets with data that is not uniformly distributed over the range of possible input ranges, or heteroscedastic datasets, the weighted multiple linear regression is more suitable.  
Thereby, a weight matrix \(\mathbf{W}\) is added to the original least square Equation (4) leading to  
\[\hat{\mathbf{\beta}}=(\mathbf{X}^{T}\mathbf{W}\mathbf{X})^{-1}\mathbf{X}^{T}\mathbf{W}\mathbf{y}. \tag{5}\]  
The weight matrix can arbitrarily weight each data point individually or even eliminate their impact on the regression completely. Even though the weight matrix might be manually constructed, different approaches of how to generate these automatically and the impact of the weight functions on the regression were studied in [PERSON] and [PERSON] [22].
# Fast Pre-Diagnosis of Neoplastic Changes in Cytology Images Using Machine Learning
## 5 Conclusions  
This work presents a pilot study of the model for object detection in vet cytology. A new dataset composed of 1219 images with 28,149 objects was collected and annotated by a vet doctor for lymphoma, mastocytoma, and histiocytoma. The model using the proposed training protocol achieved a performance of 0.7412 mAP with a low cross-class variance of the result.  
An in-depth analysis of the model performance was provided. It was mainly focused on the discrepancies between ML-based and vet doctor performance. We think it may helpbring ML solutions closer to real-life practice by exposing the weaknesses and strong sides of the ML-based solutions using the provided prototype and the dataset as an example.  
Conceptualization, D.L. and M.W.; Data curation, J.C., D.L. and R.F.; Formal analysis, M.W.; Funding acquisition, K.W.; Investigation, J.C. and D.L.; Methodology, M.W.; Project administration, P.R. and K.W.; Resources, K.W.; Software, J.C., M.K. and R.F.; Supervision, M.W.; Validation, M.W.; Visualization, J.C. and M.W.; Writing--original draft, J.C., D.L. and M.W.; Writing--review and editing, M.W. and P.R. All authors have read and agreed to the published version of the manuscript.  
This research was financed by Polish Government throughout Narodowe Centrum Badan i Rozwoju NCBiR (The National Centre for Research and Development) grant number Panda 2 September 2016 and by AGH University of Science and Technology subsidy funds No. 1616-230-434 from the Polish Ministry of Education and Science. The APC was funded by AGH University of Science and Technology subsidy funds No. 1616-230-434 from the Polish Ministry of Education and Science, with discount vouchers provided by M.W. The computations were carried out using high-performance computing resources of ACC Cyfronet AGH.  
Not applicable.  
**Data Availability Statement**: Publicly available datasets were analyzed in this study. These data can be found here: [[https://git.plgrid.pl/projects/CYFROVET/repos/dataset-detection/browse](https://git.plgrid.pl/projects/CYFROVET/repos/dataset-detection/browse)]([https://git.plgrid.pl/projects/CYFROVET/repos/dataset-detection/browse](https://git.plgrid.pl/projects/CYFROVET/repos/dataset-detection/browse)), accessed on 10 July 2021.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
A1 Artificial intelligence  
AP Average precision  
ECM Extended confusion matrix  
IoU Intersection over union  
ML Machine learning  
VOC Visual object classes  
YOLO You only look once
## 4 Results and Discussion
### Temporal Variation of Glacial Lakes during the Year of 2018  
In the high mountainous areas, some glacial lakes develop rapidly, i.e., within some weeks or a few months [33; 34]; therefore, low-frequency monitoring is not sufficient to track changes and detect hazardous developments in a timely manner. In these conditions, high-frequency monitoring would be highly valuable for capturing the seasonal changing pattern of glacial lakes and for the early detection of related hazards. Here, we examine the applicability of the developed method for the continuous monitoring of glacial lakes in the study area. Figure 6 shows the multi-temporal glacial lake change detection from Sentinel-1A/1B SAR data using the proposed method. In the whole study region of the southeastern Tibetan Plateau, there were 847 glacial lakes in early May, covering a total area of 106.72 km\({}^{2}\). The lake number showed an increasing trend during this period, with an average increase of about two lakes for each revisit cycle of six days. Meanwhile, the total area of the glacial lakes also increased with time, increasing by 0.73 km\({}^{2}\) until October (107.45 km\({}^{2}\)), which was a +0.68% change relative to May. A linear fit to all the data shows a mean expansion rate of 0.031 km\({}^{2}\) per six days. Based on the analysis of temporal changes, a relatively high seasonal variability of glacial lakes was detected in this study, which revealed the formation and expansion of glacial lakes between May and October.