esa-sceva/satcom-chunk-collection huggingface dataset:
# Aromatic Profile of Hydroponically and Conventionally Grown Tomatoes
## 4 Conclusions  
Sensory analysis of hydroponically and conventionally grown tomatoes was carried out using e-nose and sensory panel. In both cases, there were demonstrable differences between the two groups and the study proved that there is the difference between tomatoes grown with nutrient solution and those grown conventionally in soil. Based on the obtained results, we can conclude that we can potentially use the electronic nose to detect the difference between the samples. E-nose device was able to divide the analyzed samples into two groups, namely hydroponically and conventionally grown tomatoes. This fact shows us that we could potentially use e-nose for detecting food adulteration, namely, conventionally grown tomatoes declared as hydroponically grown tomatoes and vice versa. For future research, to obtain more accurate results, we recommend testing a larger number of samples and purchasing samples from one location or country.  
In our research, we were also able to identify three main odor traces characteristic for hydroponically grown tomatoes: 2-methylpropanol, 2,3-pentanedione, (Z)-3-hexen-1-ol or 1-hexanol (substances registered as 15.31.1-A, 26.07.1-A and, marginally, 43.88.1-A). It is very likely that these substances characterize the difference between tested samples. Sensory panel was also able to detect the difference between hydroponically and conventionally grown tomatoes, which tells us that even consumers themselves are able to recognize the differences between the two groups. For the prediction of hydroponically and convectively grown tomatoes, we used a machine learning technique (ML), with the linear LDA algorithm performing best. There was no relationship between the sensory evaluation data and the artificial perception evaluation.  
Conceptualization, N.V., V.V. and J.S.; methodology, N.V., V.V., P.M. and J.S.; software, V.V. and A.M.; validation, V.V. and M.K.; formal analysis, N.V., J.S. and P.M.; investigation, M.K., V.V. and N.V.; resources, M.K., V.V., N.V. and A.M.; data curation, J.S., P.M. and V.V.; writing--original draft preparation, M.K., V.V. and A.M.; writing--review and editing, M.K., A.M. and V.V.; visualization, V.V., A.M., J.S. and N.V.; supervision, J.S. and V.V. All authors have read and agreed to the published version of the manuscript.  
Figure 9: Canonical correlation analysis X matrix (electronic nose data, R = 0.34), Y matrix (sensory analysis data, R = 0.22), bottom XY (cross-correlation matrix of XY data, R = 0.07).  
**Funding:** The research was also funded by the research grant APVV-17-0564 \"The Use of Consumer Neuroscience and Innovative Research Solutions in Aromachology and its Application in Production, Business and Services\".  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Decentralization of VANETs
### Enhancing Robustness and Privacy-Preserving Data Sharing with DLT
#### 5.5.1 Role in Enhancing VANET Security  
VANETs have vulnerabilities such as risks to data integrity, privacy violations, and vulnerability to different cyberattacks. This is why security is considered of utmost importance in VANETs.  
In several research works, the nature of DLT can improve the security of VANETs. The resilience and security of VANETs can be enhanced by the integration of blockchain technology because of certain features, such as decentralized structure and cryptographic security measures [79; 80; 81]. Blockchain technology makes sure that the information sent between vehicles is authentic and has integrity, and the blockchain provides a reliable way to check the accuracy of data, lowering the risks of data manipulation and cyberattacks [82].
# Depth-Oriented Gray Image for Unseen Pig Detection in Real Time
###### Abstract  
With the increasing demand for pork, improving pig health and welfare management productivity has become a priority. However, it is impractical for humans to manually monitor all pigsties in commercial-scale pig farms, highlighting the need for automated health monitoring systems. In such systems, object detection is essential. However, challenges such as insufficient training data, low computational performance, and generalization issues in diverse environments make achieving high accuracy in unseen environments difficult. Conventional RGB-based object detection models face performance limitations due to brightness similarity between objects and backgrounds, new facility installations, and varying lighting conditions. To address these challenges, this study proposes a DOG (Depth-Oriented Gray) image generation method using various foundation models (SAM, LaMa, Depth Anything). Without additional sensors or retraining, the proposed method utilizes depth information from the testing environment to distinguish between foreground and background, generating depth background images and establishing an approach to define the Region of Interest (RoI) and Region of Uninterest (RoU). By converting RGB input images into the HSV color space and combining HSV-Value, inverted HSV-Saturation, and the generated depth background images, DOG images are created to enhance foreground object features while effectively suppressing background information. Experimental results using low-cost CPU and GPU systems demonstrated that DOG images improved detection accuracy (AP50) by up to 6.4% compared to conventional gray images. Moreover, DOG image generation achieved real-time processing speeds, taking 3.6 ms on a CPU, approximately 53.8 times faster than the GPU-based depth image generation time of Depth Anything, which requires 193.7 ms.  
Keywords:foundation model; image processing; pig detection; unseen environment; real-time application +
Footnote †: journal: Applied sciences
## 2 Materials and Methods
### Methods
#### 2.3.1 Data Preparation  
Each data source required different data preparation, including logical control, determination and investigation of extreme values, cloud removal from satellite data, etc. The power system data preparation included logical control and completeness level determination to determine whether it could be used. Meteorological and air-quality measurements are non-controlled data, so they also require basic logical control to determine completeness and investigate extremes. Extreme values were compared with values recorded at neighboring measurement stations and the synoptical situations in the moment of suspicious data records.  
Except for the logical control and extreme values investigation, satellite data required handling of cloud cover. Also, Sentinel 3 and Sentinel 5p data required investigating the sensing time from day to day and the location of recorded values. After determining that data are recorded at a similar time each day, further preparation and analysis were conducted.  
The extremes of Sentinel 2 and Sentinel 3 LST data were investigated by comparing them with the synoptical situation and the landscape at the period of interest. The extreme FRP values were compared with the landscape cover, and the newspapers were checked for extreme fire event reports. The Sentinel 5p data were compared with the measured PM\({}_{10}\) particle concentration and with the synoptical situation.  
Cloud cover signifies the challenge in the data analysis. From the Sentinel 2 dataset, all products with a cloud cover higher than 10% were eliminated from further analysis. In the case of a lower level of cloud coverage, the cloud mask was applied, and only pixels where clouds were located were removed. The used cloud mask is obtained as part of the Sentinel 2 MSI product.  
In the case of Sentinel 3 LST data, cloud coverage was removed by comparing LST and surface air temperature. In that way, suspicious pixels from the dataset were removed, but not all those that contained cloud coverage. Situations when a pretty-low air temperature was recorded were exceptionally challenging because it was not easy to distinguish between low water clouds' (e.g., stratus) temperature and the temperature of the land surface.  
EUMETSAT Meteosat Second Generation (MSG) data were compared with other MSG cloud cover products. Images from the most appropriate product were chosen for further analysis.
## 6 Conclusions  
At present, the research and application of aviation magnetic compensation methods are almost all based on the T-L model, which can only compensate the magnetic interference caused by the ferromagnetic and conductive materials of the aircraft platform under maneuvering action. It is ineffective regarding the magnetic interference caused to the UAV platform, even leading to the deterioration of compensation results.  
Aiming at the problem of UAV magnetic interference, this paper analyzed the causes of magnetic interference in a UAV and actually measured the changes in magnetic field and current in different operating states. Using experiments, it was found that the magnetic interference of UAV airborne electronic equipment is mainly generated by the power supply's current. The magnetic interference vector of UAV electronic equipment should be similar to the characteristics of permanent magnet materials, regardless of the magnetic field's magnetization. The filtered waveform of the magnetic interference caused by the sudden change in current when the engine starts and stops is similar to the magnetic target signals. This causes serious interference to target recognition. In this paper, through monitoring the changes in power supply current related to magnetic interference, a linear compensation model based on current interference sources was established using inspiration from the T-L modeling method of the constant magnetic field. The modeling magnetic interference was subtracted from the OPM data to complete compensation. In fact, the compensation method proposed in this paper can also compensate for magnetic interference, similar to the characteristics of permanent magnetic materials on the other carrier platform, by monitoring the related electrophysical quantities.  
The experimental data show that the compensation method proposed in this paper greatly reduces the magnetic interference caused by UAV airborne electronic equipment and significantly improves the quality of aeromagnetic data. It also can be combined with the traditional T-L model to improve the signal-to-noise ratio of magnetic anomaly signals and is beneficial to the development of subsequent magnetic anomaly detection algorithms.  
Conceptualization, B.C. and L.H.; methodology, B.C.; validation, B.C.; investigation, J.H.; data curation, B.C.; writing--original draft preparation, B.C. and K.Z.; writing--review and editing, L.H.; project administration, W.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 2021 YFB3900202.  
Not applicable.  
The authors would like to thank the editors and reviewers for their efforts to assist in the publication of this work.  
The authors declare no conflict of interest.
# A Comparative Analysis of Feature Detectors and Descriptors for Image Stitching
## 3 Materials and Methods
### Method: Rank Computation  
A rank is assigned to each combination of detector and descriptor based on the inlier ratio, stitching image quality and execution time. For comparison, six metrics are available: inlier ratio, execution time, PSNR, SSIM, FSIM and VSI. The rank computation algorithm has been adopted from [67; 76]. The methodology for rank computation is outlined below:  
1. For method \(m\) (detector-descriptor pair) with dataset \(d\) and metric \(t\), find the positional rank \(P_{m,d,t}\) by sorting the metric values (a low value denotes better positional rank for execution time, while a high value denotes better positional rank for all other metrics).
2. Compute the average rank of \(m\) for dataset \(d\), over all metrics: \(R_{m,d}=\frac{1}{N_{t}}\sum_{t}P_{m,d,t}\). Here, \(N_{t}\) denotes the number of metrics.
3. Compute the positional rank of each method \(m\) for each dataset \(d\) based on the value of \(R_{m,d}\), by sorting \(R_{m,d}\) in ascending order (low \(R_{m,d}\) indicates better positional rank). If two successive rankings have extremely close values within a given threshold T, then the same positional rank is assigned. As a result, the positional ranks start with 1 and increment when two successive ranks differ by a value greater than the threshold T. The positional rank is denoted by \(P_{m,d}\). For the rank computation, we have selected T = 0.05 \(\times\) SD, where SD is standard deviation.
4. Find the average rank across all datasets: \(R_{m}=\frac{1}{N_{d}}\sum_{d}P_{m,\,d}\). Here, \(N_{d}\) represents the number of datasets.
5. Finally, determine \(P_{m}\), the positional rank across datasets by sorting \(R_{m}\) in ascending order.  
Using the abovementioned algorithm, we calculated the rank of each method \(m\) (detector and descriptor combination). We computed the rank of each method \(m\) for datasets 1, 2 and 3 separately.
###### Abstract  
Rail welds are widely used in high-speed railways and short-wave irregularities usually appear due to limitations in welding technology. These irregularities can excite a high wheel/rail force and are regarded as the main cause of deterioration in track structures. To measure this fierce force (or deterioration of the rail weld), axle box acceleration is treated as an effective and economic measure, though an exact quantitative relation between these two quantities remains elusive. This paper aims to develop such a relation in order to provide a new theoretical basis and an analysis method for monitoring and controlling weld geometry irregularity. To better understand the characteristics of axle box acceleration, the paper consists of two parts: an observation and a numerical simulation of axle box acceleration by rail welds. Based on measured data from field tests, axle box acceleration at rail welds was found to have high-frequency vibrations in two frequency bands (i.e., 350-500 Hz and 1000-1200 Hz). Upon analyzing the vibration characteristics in time-frequency domains, the exact location of the rail weld irregularity could be identified. Subsequently, a 3D high-speed wheel/rail rolling contact finite element model was employed to investigate the effect of rail weld geometry on axle box acceleration, and led to the discovery that the weld length and depth determine the vibration frequency and amplitude of the axle box acceleration, respectively. A quantitative relation between axle box acceleration and wheel/rail force has also been determined. Finally, we propose an approach for real-time health detection of rail welds and discuss the influence of other defects and rail welds on the acceleration signal of the axle box.  
high-speed railway; rail welds; axle box acceleration; wheel/rail force; explicit finite element method +
Footnote †: journal: Applied Sciences
# Hardware-in-the-Loop Test of a Prosthetic Foot
## 2 State-of-the-Art
### Basic Human Gait Models
The total GRFs result:  
\[GR\text{j} =\sqrt{F_{\text{sj}}^{2}+F_{\tau\text{j}}^{2}}=F_{\text{sj}} \sqrt{1+\tan^{2}\bigl{(}\gamma_{\text{VPPj}}\bigr{)}} \tag{2}\] \[GR\text{j}_{\text{jx}} =GR\text{j}\frac{x_{\text{h}}-x_{\text{FPj}}}{\text{l}_{\text{j}}}\] (3) \[GR\text{j}_{\text{jy}} =GR\text{j}\frac{y_{\text{h}}}{\text{l}_{\text{j}}}, \tag{4}\]where the indices \((\cdot)_{\rm x}\) and \((\cdot)_{\rm y}\) denote the projections of the forces into the \(x\) and \(y\) direction and the index \((\cdot)_{\rm h}\) denotes the hip joint. The GRFs are calculated when the leg is on the ground, i.e., between Touch Down (TD) and Take Off (TO). As the legs are massless, they do not contribute to the dynamics during the swing phase and their GRFs are zero. After TO, the VPP model assumes that the leg is instantaneously repositioned with a TD angle \(\alpha_{0}\). This TD angle is defined by the user. Using Equations (3) and (4), as well as the gravitational forces on the CoM \(m_{\rm VPP}\) and the moment of inertia \(J_{\rm VPP}\), the EoMs result:  
\[m_{\rm VPP}\dot{x} =GRF_{\rm L,x}+GRF_{\rm R,x} \tag{5}\] \[m_{\rm VPP}\dot{y} =GRF_{\rm L,y}+GRF_{\rm R,y}-m_{\rm VPP}g\] (6) \[-J_{\rm VPP}\dot{\phi}_{\rm VPP} =r_{\rm VPP}\big{(}\sin(\phi_{\rm VPP})(GRF_{\rm L,y}+GRF_{\rm R, y})-\cos(\phi_{\rm VPP})(GRF_{\rm L,x}+GRF_{\rm R,x})\big{)}. \tag{7}\]  
The principle of angular momentum can also be set up differently, as is described in Appendix B. The VPP model is able to replicate the CoM motion, the shape of the GRFs, as well as the shape of the hip torque observed in humans [18]. Due to the VPP controller (hip torques \(\tau_{\rm VPP}\dot{\rm J}\)), the model is able to stabilize the trunk and overcome some disturbances, such as small steps. Still, this is a simple conceptual model representing human gait dynamics.
###### Abstract  
The Chinese Altay is located in the western segment of the Central Asian Orogenic Belt (CAOB) and preserves critical records of the Paleo-Asian Ocean (PAO) Plate evolution during the Paleozoic era. This region also hosts significant mineral deposits, making it a focal point for geological research. In this paper, field investigation, petrology, mineralogy, and petrography studies were conducted on volcanic rocks in the Fuyun-Qinghe area, southern margin of the Chinese Altay, and the paper provided new zircon LA-ICP-MS dating data, Lu-Hf isotope data, and whole-rock geochemical data of the basaltic to andesitic volcanic rocks. Thus, the formation age, petrogenesis, and tectonic setting of these rocks were discussed, which was of great significance to reveal the nature of the PAO Plate. The findings showed that the basaltic andesitic volcanic breccia was formed at 382.9 \(\pm\) 3.4 Ma, the basalt was 401.7 \(\pm\) 4.7 Ma, and the andesites were 405.1 \(\pm\) 5.6 Ma and 404.8 \(\pm\) 6.7 Ma, which indicated that the above rocks were formed in the Early-Middle Devonian. The volcanic rock assemblages were hawaite, mugearite, potassic brachy basalt, basaltic andesite, andesite, benmoreite, etc., which contained labeled magmatic rocks such as adakite, subboninite, niobium-enriched arc basalt (NEAB), picrite, high-magnesium andesite (HMA), and magnesium andesite (MA). Comprehensive analysis indicated that magma probably mainly originated from three sources: (1) partial melting of the PAO slab, (2) partial melting of the overlying garnet-spinel hierarchite mantle peridotite metasomatized by subducting-related fluids (melts), and (3) a possible input of the asthenosphere. Comparative analysis with modern analogs (e.g., Chile Triple Junction) indicates that ridge subduction of the PAO had existed in the Fuyun-Qinghe area during the Early-Middle Devonian. Based on available evidence, we tentatively named the oceanic plates in this region the central Fuyun-Qinghe Ridge and the Junggar Ocean Plates, separated by the ridge on both sides. Although the ocean had a certain scale, it had entered the climax period of transition from ocean to continent.  
Chinese Altay; Early-Middle Devonian; magmatic arc; ridge subduction; PAO licenses/by/4.0/. +
Footnote †: journal: Applied sciences
## 2 Literature Survey
### Fairness  
Past research has dealt with how to optimally allocate limited resources through maximizing utility under various constraints. Limited research, however, has considered fairness in a live communication network environment. In [11], \"fairness\" has three definitions: max-min fairness, proportional fairness and balanced fairness. These three criteria evaluate fairness based on the channel conditions of various subscribers. The max-min fairness concept proposed by [PERSON], [PERSON] and [PERSON] [12] incorporates the selection of routing paths, the allocation of bandwidth and the improvement of system utilities. In short, achieving max-min fairness means optimally allocating resources under the worst conditions until the system utilities are specified. From [13], Max-min fairness can be defined as: \"A rate \(r\) is said to be _max-min fair_ if it is feasible, and for each session \(p\in P\), the allocated rate for session \(p\), \(r_{p}\), cannot be increased while maintaining feasibility without decreasing \(r_{p}\cdot\) for some session \(p\)' for which \(r_{p}\leq r_{p}\)\". Figure 2 can illustrates the max-min fairness, which means the max-min fairness means maximizing the allocation for the most poorly treated of those other sessions, and so forth, until all allocations are specified. The summation of traffic Session 0, 1, and 2 will be approached to the link capacity and each session has the same QoS conditions.  
Max-min fairness is a way to maximize the total throughput considering the optimal fairness. Sometimes it can strike a balance between fairness and throughput by adopting the proportional fairness [14]. Proportional fairness is a compromise between fairness and throughput. It tries to maximize total throughput (but might not be the maximal throughput), while at the same time allowing all users at least the same level of service in the network [15].  
Figure 2: Max-min fairness.
# Improved Dragonfly Optimization Algorithm for Detecting IoT Outlier Sensors
## 4 Optimize Coverage with an Improved Dragonfly Optimization Algorithm
A composite covering vector of \(c_{i}\) and \(c_{j}\) can be obtained by the Boolean union between \(\pi_{i}\) and \(\pi_{j}\):  
\[\overline{w}\big{(}c_{i},c_{j}\big{)}=\pi_{i}\vee\pi_{j}=\big{[}LO_{i,1}\lor LO _{j,1},LO_{i,2}\lor LO_{j,2},\ldots,\ LO_{i,M}\lor LO_{j,M}\big{]} \tag{22}\]  
where, \(\overline{w}\) is a combined coverage vector that shows the coverage or non-coverage of all POIs by \(c_{i}\) and \(c_{j}\). The combined coverage vector for a particular individual is calculated by the following equation:  
\[\overline{w}(k)=(\ \ell_{k,1}\cdot\pi_{1})\vee(\ \ell_{k,2}\cdot\pi_{2})\vee \ldots\vee(\ \ell_{k,N}\cdot\pi_{N}) \tag{23}\]  
The process of checking the coverage has been simplified to binary operators. The coverage ratio for every one, \(k\), is:  
\[\varepsilon^{k}=\frac{\|\omega(k)\|^{2}}{M} \tag{24}\]  
where, \(\|\omega(k)\|^{2}\) indicates the active nodes quantity. The nodes utility ratio, \(U_{t}^{k}\), for individual \(k\) is evaluated as follows:  
\[U_{t}^{k}=\frac{\sum_{g=1}^{M}\ell_{k,g}}{N} \tag{25}\]  
Figure 4: Wireless sensor network coverage with 16 active nodes. The nominator of the relation signifies the nodes quantity selected for activation. The quality of individual \(k\) is defined by \(f_{c}^{k}\):  
\[f_{c}^{k}=\alpha_{1}\times\left(\varepsilon^{k}\right)^{\lambda_{1}}-\alpha_{2} \times\left(U_{t}^{k}\right)^{\lambda_{2}} \tag{26}\]  
Substituting \(\varepsilon^{k}\) and \(U_{t}^{k}\) from the above equations, we have:  
\[f_{c}^{k}=\alpha_{1}\times\left(\frac{\|\omega(k)\|^{2}}{M}\right)^{\lambda_{1 }}-\alpha_{2}\times\left(\frac{\sum_{g=1}^{M}\ell_{kg}}{N}\right)^{\lambda_{2}} \tag{27}\]  
where, \(\alpha_{1}\) and \(\alpha_{2}\) are the weighting constants, and \(\lambda_{1}\) and \(\lambda_{2}\) represent the power factors.