esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Farming Monitoring System Concepts and Methods  
The main purpose of the designed system is to enable drones to fly optimally over large farms and collect data from different sensors deployed on the farm and convey them to the cloud. Figure 1 depicts the conceptual design of the FMS. The FMS development consists of four main phases, development of the water inspection system, LoRaWAN(r)-based network planning, integrating the LoRaWAN(r) gateway into the drone, and drone path planning optimization. The following subsections describe the methods used and works conducted to achieve the objectives of this study in detail.
# Enhancing Disease Diagnosis: Biomedical Applications of Surface-Enhanced Raman Scattering
## 5 Applications of SERS in Disease Diagnostics
### Cancer
This illustrates the potential of PCR-SERS to guide important clinical decisions regarding tumour biology with respect to heredity, diagnostics and treatment.
# Recovery of Post-Traumatic Temporomandibular Joint after
###### Abstract  
Considered as one of the most common traumatic injuries of the maxillofacial region, mandibular fractures remain among the complex causes of temporomandibular joint disorders (TMDs). Due to the complexity of the temporomandibular joint, the management of TMDs represents a challenge in real-life practice; although many treatment modalities have already been proposed, ranging from conservative options to open surgical procedures, a consensus is still lacking in many aspects. Furthermore, despite continuous improvement of the management of mandible fractures, the duration of immobilization and temporary disability is not reduced, and the incidence of complications remains high. The aim of the present study is to (i) review anatomophysiological components of temporomandibular joint; (ii) review concepts of temporomandibular joint fractures; and (iii) describe methods of the recovery of the temporomandibular joint after mandibular fracture immobilization.  
mandibular fracture; temporomandibular joint disorders; management; physical therapy +
Footnote †: journal: Medical  
path of the condylar movements and the rotation degree of the disc over the condyle. It has a convex shape in relation to the sagittal plane and a slightly concave shape in relation to the transverse plane [5,6].  
TMJ capsule, a fibrous membrane, originates from the margins of the mandibular fossa, encloses the articular tubercle of the temporal bone, and inserts at the mandible neck.  
Synovial fluid has the role of lubricant and shock absorber and facilitates the smooth movements of jaws due to the friction decrease and cushioning of the articular cartilage in the TMJ. Three ligaments are associated with the TMJ: the temporomandibular ligament (major ligament; it prevents the mandible excessive retraction), the stylomandibular, and phenomenadibular ligaments (accessory ligaments; they are not directly attached to TMJ) [1,2,4,5].  
The synovial membrane lines the fibrous capsule that encloses the TMJ. The synovial membrane is highly vascularized and is in contact with the capsule connective tissue. Each joint compartment is lined with its own synovial membrane [1].  
Temporomandibular disorders (TMDs) comprise a broad group of musculoskeletal and neuromuscular conditions involving the TMJ, muscular, and osseous components. Anatomical, histological, and functional anomalies of the muscular and/or articular components of the system are accompanied by a large range of clinical signs and symptoms. TMDs symptoms are as follows: reduced mandibular range of motion, pain in the TMJ areas, pain in the masticatory muscles, generalized myofasical pain, and limitation or deviation of the jaw opening [7, 8, 9].  
The TMDs etiology is multifactorial and includes biologic, environmental, social, psychological, and physical causes. Mandibular fractures are one of the etiological factors of TMJ disorders [10, 11, 12, 13] and can produce mechanical changes within the TMJ such as: limitation or deviation of jaw opening, hemorrhosis, dislocation, fibrous adhesion, and ankylosis [14, 15, 16].
# ErrorExplainer: Automated Extraction of Error Contexts from Smart Contracts
## 7 Evaluation
### RQ3: Matching Fitness  
Matching fitness quantifies how precisely ErrorExplainer links user-supplied evidence to the correct error context. For a contract component \(C\), we consider two granularities:  
1. **Function-level matching.** Let \(\mathbb{S}_{C}\subseteq\mathbb{E}_{C}\) be the subset in which every pair of contexts differs both in the enclosing function and in the encoded error output: \[i\
eq j\ \Rightarrow\ (i.f\
eq j.f)\ \wedge\ (i.\varepsilon\
eq j. \varepsilon).\]
2. **Trace-level matching.** Let \(\mathbb{S}^{\prime}_{C}\subseteq\mathbb{E}_{C}\) comprise contexts whose traces have disjoint _entry points_ but still have distinct error outputs: \[i\
eq j\ \Rightarrow\ (i.\bar{f}_{e}\cap j.\bar{f}_{e}=\emptyset)\ \wedge\ (i. \varepsilon\
eq j.\varepsilon).\] We define the two matching fitness scores as \[\mathcal{M}_{F}=\frac{|\mathbb{S}_{C}|}{|\mathbb{E}_{C}|},\qquad\mathcal{M}_{ T}=\frac{|\mathbb{S}^{\prime}_{C}|}{|\mathbb{E}_{C}|}.\] Across the entire dataset, ErrorExplainer achieves an _average_\(\mathcal{M}_{F}=0.954\) and \(\mathcal{M}_{T}=0.604\). In other words, once the target function is correctly identified, ErrorExplainer returns a unique match 95.4% of the time, while with only an entry function, that probability remains 60.4%. Function-level discrimination relies only on distinct error outputs within the same function, which is an easier requirement than discriminating across entire traces because many functions may share identical error outputs or selectors. Hence, \(\mathcal{M}_{F}\geq\mathcal{M}_{T}\) for every contract.  
Figures 7 and 8 break fitness down by Solidity version and lines of code, respectively. \(\mathcal{M}_{F}\) stays in the range between 0.9 and 1.0 for recent versions. At Solidity 0.4.24, the dip resembles the earlier completeness drop and reflects blank error messages that provide no single matches distinguishable by error outputs. For mid-size smart contracts (1800 lines of code), multiple business logics are aggregated but reuse identical error messages, which leads to a drop in \(\mathcal{M}_{T}\).  
Figure 6: Information completeness vs. lines of code. Contracts are binned by LOCs. Uniformly high values across all versions indicate ErrorExplainer’s static patterns are robust to increasing contract complexity.
# Enhancing Real-Time Kinematic Relative Positioning for Unmanned Aerial Vehicles
## 4 Proposed Dual Kalman Filter Structure for RTK and Its Rationales
### Dual Kalman Filter Structure
\(\mathbf{L}\) denotes a unit lower triangular matrix and \(\mathbf{D}\) is a diagonal matrix with conditional variances of \(d_{i}\). The LAMBDA search process sequentially determines integer candidates from those with the smallest to those with the largest conditional variances, i.e., a move-down step from \(z_{n}\) to \(z_{1}\). Once a complete first set of integer vector solution with a tentative minimum cost function has been obtained, the second solution is set identically to the first solution except that the vector element with the largest conditional covariance is added by \(\pm 1\). The search for the second solution is performed by adding \(\pm 1\) integers in a search space from \(z_{2}\) to \(z_{n}\), a move-up step. If a lower cost function is found during the move-up steps, the search direction then changes to the move-down steps. The search process is terminated when no further integer candidates are found in the search space. Figure 10: Comparison of the Kalman filter gain before and after the inclusion of an AIA state. Now, when an AIA state is introduced into the KF, there are \(n+1\) states, and \(z_{AIA}\) is assigned to the newly added state. Then, the cost function becomes:  
\[ \begin{split} F(\mathbf{z}_{AIA})=&\frac{(\tilde{z}_{AIA}- z_{AIA})^{2}}{d_{AIA}}\\ &+\frac{(\tilde{z}_{1}-z_{1})^{2}}{d_{1}}+\cdots+\frac{(\tilde{z} _{n}-z_{n})^{2}}{d_{n}}\end{split}  \tag{28}\]  
Here, because of the large covariance of the AIA state, \(d_{AIA}\) is much larger than \(d_{1}\) through \(d_{n}\). Assuming that \([\tilde{z}_{1},\cdots,\tilde{z}_{n}]\) and \([d_{1},\cdots,d_{n}]\) in Equations (25) and (28) are very close, then the minimized cost functions in Equations (25) and (28) would also be very close because \(\frac{(\tilde{z}_{AIA}-z_{AIA})^{2}}{d_{AIA}}\) is negligible in Equation (28). The almost same values of Equations (25) and (28) indicate that the fixed integers of \(\tilde{z}_{1}\) through \(\tilde{z}_{n}\) are essentially identical. Again, \(\tilde{z}_{AIA}\) would be incorrect and differed by 1 in the best and the second-best integer solution vectors while \(\tilde{z}_{1}\) through \(\tilde{z}_{n}\) are the same in both solutions.
# Side-Channel Vulnerabilities of Unified Point Addition on Binary Huff Curve and Its Countermeasure
## 5 Countermeasures
### Countermeasures
To be secure against Type 2 vulnerabilities, it is necessary to reconstruct the calculation process of unified point addition. For this reason, we propose a new unified point addition formula for the binary Huff curve as follows:  
\(m_{1}=X_{1}X_{2},\quad m_{2}=Y_{1}Y_{2},\quad m_{3}=Z_{1}Z_{2},\)  
\(m_{4}=(X_{1}+Z_{1})(X_{2}+Z_{2}),\quad m_{5}=(Y_{1}+Z_{1})(Y_{2}+Z_{2}),\)  
\(m_{6}=m_{1}m_{3},\quad m_{7}=m_{2}m_{3},\quad m_{8}=m_{1}m_{2}+m_{3}^{2},\)  
\(m_{9}=m_{6}(m_{2}+m_{3})^{2},\)\(m_{10}=m_{7}(m_{1}+m_{3})^{2},\quad m_{11}=m_{8}(m_{2}+m_{3}),\)  
\(m_{12}=m_{8}(m_{1}+m_{3}),\)  
\(Z_{3}=m_{11}(m_{1}+m_{3}),\)  
\(X_{3}=(m_{4}+m_{11})m_{11}+m_{11}^{2}+\alpha m_{9}+Z_{3},\)  
\(Y_{3}=(m_{5}+m_{12})m_{12}+m_{12}^{2}+\beta m_{10}+Z_{3}.\)The proposed unified point addition operation is based on masking by \(m_{4}\) and \(m_{5}\). To use the advantage of almost no computational cost for squaring in a binary field, we configured the calculation of masking \(m_{4}\) and \(m_{5}\) by squaring. Thus, the proposed method needs 17 field multiplications, which is exactly the same as in [9]. Furthermore, we explain Type 1 and Type 2 vulnerabilities of several unified point addition formulae and propose countermeasures in the Appendix A.
## 2 Materials and Methods
### Methods
As a last step of our analysis the time\(-\)frequency spectrum has been calculated using Continuous Wavelet Transform. A _CWT_ is used for time series stationarity analysis. A comprehensive description of the application of the wavelet transform can be found in [47]. The Morlet wavelet was adopted as the mother wavelet. This kind of wavelet is commonly used in the time\(-\)frequency analysis and provides results with equal variance in time and frequency domains [48]. _CWT_ allows for the examined signal to be presented in the form of a scalogram--a time\(-\)frequency spectrum, which permit for the identification of both frequencies present in the signal and their location in time. _CWT_ is defined as a convolution of the analysed signal _s(t)_ with scaled and shifted wavelet function \(\psi(t)\)[49]:  
Figure 3: Stages of data processing. (**a**) Original clock correction data (phase data). (**b**) Clock correction rate (frequency data). (**c**) Clock correction rate after elimination of outliers larger than \(10^{-10}\) s. (**d**) Clock correction rate values after removal of the detected jump. (**e**) Clock correction rate after removal of the trend and outliers with the MAD method. (**f**) Lomb-Scargle periodogram. Figure 3.0  
where,  
\(a\) represents the scale  
\(b\) denotes the translation  
_\(\psi\)_ represents the mother wavelet, and * denotes the complex conjugate  
The normalization factor \(1/\sqrt{a}\) is used to preserve the direct comparability of the transform for all of the scales. The Morlet wavelet used here to calculate the time - frequency spectrum is expressed as follows:  
Figure 3.1  
where \(f\) denotes the center frequency and _f_\({}_{b}\) represents the bandwidth. The continuous wavelet transform method does not optimize the time and frequency resolution simultaneously. In the _CWT_ analysis, a trade-off must be found between these quantities [41]. In the present study, the center frequency was taken as \(f\) = 1.5 Hz and the bandwidth parameter was _f_\({}_{b}\) = 2.0 for all pf the clock data time series analyzed. The parameter values were selected empirically to ensure a satisfactory balance between the time and frequency resolution for the analyzed data.
# A Machine Learning Approach for Mapping Chlorophyll Fluorescence at Inland Wetlands
## 2 Materials and Methods
### Field Measurements  
During 2022, five field campaigns were conducted at different times between April and October to collect in situ data, coinciding with Sentinel-2 satellite overpasses around noon local time. Ten sites were selected across various wetland habitats and both sides of the Biebraz River (as shown in Figure 2), and their geographic coordinates were determined using a GNSS receiver. The vegetation's growth stage was also recorded during each field campaign. The OSSp+ Pulse Modulated Chlorophyll Fluorometer was used to measure ChF in this study [7]. The instrument registers the maximum quantum yield (Fv/Fm) through a dark-adapted test, which is a ratio that represents the maximum potential quantum efficiency of photosystem II (PSII) if all capable reaction centers were open. It estimates the maximum portion of absorbed quanta used in PSII reaction centers [33]. For best results, a dark adaptation time of 20-30 min is recommended [34], and the system provides a series of ten dark-adaption white clips to measure this. After dark adaptation, the end of the fiber optic bundle is placed in the cylindrical opening, and the dark slide of the clip is opened to expose the sample to the fiber optic bundle. Optimal values of Fv/Fm for most land plant species vary within the range of 0.79 to 0.83, while lower values indicate plant stress, particularly photoinhibition [2; 8].
## 2 DEA Lens
### Liquid Encapsulation  
Inspired by the lens structure of a human eye, a lens is fabricated by encapsulating a small amount of liquid between two DEA films [26]. The side-view structure of the lens is shown in Figure 2. The liquid constrained by the DEA films presents a biconvex shape. The electrode in this area is removed. Since the liquid and boundary DEA films are symmetrical and transparent, the liquid exhibits a lensing behavior. In the relaxed state, the stretched boundaries cause the radius of the curvature of the liquid to be maximal, as shown in Figure 2a. Therefore, the focal length of the liquid lens is the longest. When a DC voltage is applied, the peripheral DEA films expand synchronously due to the actuated pressure. At the same time, the boundary of the liquid is relaxing, and the liquid is contracting, as Figure 2b shows. As a result, the radius of the curvature of the liquid is reduced, as is the focal length of the lens.  
Figure 1: Working principle of a DEA film: (**a**) voltage-off state and (**b**) voltage-on state.  
In the experiment, a lens with such a structure was fabricated and its imaging properties were characterized. As the diameter decreases from -7.65 mm to -7.13 mm, the radius of curvature decreases from R\({}_{0}\) = 19.55 mm (with discharged electrodes) to R = 14.38 mm (for an applied electric field E = 58 V/\(\upmu\)m), resulting in a focal length change from f\({}_{0}\) = 22.73 mm to f = 16.72 mm. The peak current is -40 \(\upmu\)A during charging at 4 kV, which corresponds to a peak power of \(\sim\)160 mW. The response time is in the second level or below. Adaptive lenses based on this device structure usually have the advantages of a large dynamic range, good optical performance, and good stability. Although the driving voltage is high, the power consumption is at the microwatt level or below. Since the actuator part is outside the optical path, a significant fraction of the lens' lateral area is occupied by the actuating mechanism. Therefore, the usable optical area is reduced.  
To solve this issue, we may replace the black electrode (carbon conductive grease) of the DEA film with a transparent electrode (single walled carbon nanotube mats) for the lens [27]. Because the actuator part covered by the electrode can be the boundaries of the liquid lens, the effective optical area in contrast to the total area of the device is largely increased. In a lens demo, the optical transmittance at \(\uplambda\) = 550 nm is over 88%, and the focal length is changed from \(\sim\)36 mm to -72 mm when the voltage increases from 0 to 5000 V. The response time is below 1 s. Because it is not necessary to extremely deform the shape of the lens, the operating voltage is safe without the concern of electrical field breakdown.  
An adaptive lens based on this configuration faces some challenges: (1) It is not easy to tightly encapsulate a liquid; (2) for a large-aperture lens, the gravitational effect which distorts the lens shape is not negligible; (3) it is difficult to fabricate a microlens; and (4) the driving voltage is extremely high because the electrodes are separated by two DEA films.
## 4 Comparative Study between Pile Group Foundation and the GPF  
To better illustrate the performance of the proposed GPF, the widely-used pile foundation in the practical bridge structures was adopted here for comparison. The pile foundation was designed based on the same concrete usage of the GPF as shown in Section 2, which has four piles with a diameter of 0.75 m, and the distance between the pile center is 3.5 m. Both foundations have a length of 30 m and have the same soil conditions. By using the numerical simulations, the stiffness of the pile group foundation and GPF in six degrees of freedom can be obtained, as presented in Table 4. It is shown in Table 4 that the translation stiffness of the pile group foundation in the x and y directions is 0.8494 \(\times\) 10\({}^{6}\) N/m, while the translation stiffness of GPF is 0.1831 \(\times\) 10\({}^{7}\) N/m. The translation stiffness of the GPF increased by 116% compared to that of the pile group foundation. It is also found in Table 4 that the rotational stiffness in the x and y directions of the GPF is 0.8296 \(\times\) 10\({}^{8}\) N/m, which is greater than that of the pile group foundation, resulting in a 25% increase in rotation stiffness. Additionally, the GPF showed a 45% increase in vertical translation stiffness and a 75% increase in vertical rotational stiffness compared to the pile group foundation. These results indicate that the GPF has greater lateral load-bearing capacity than the pile group foundation.
## 3 Identification and Quantification of Volatile and Semi-Volatile Compounds in Sparkling Wines
### Sample Preparation
#### 3.1.1 Liquid-Liquid Extraction (LLE)  
Liquid-liquid extraction (LLE) is a reference method for the isolation of volatile and semi-volatile compounds in wine due to its efficiency in partitioning analytes between two immiscible liquid phases. This technique allows the extraction of esters, terpen, alcohols, and organic acids, with solvent polarity playing a critical role in analyte recovery--non-polar solvents favor esters and terpen, whereas polar solvents enhance the extraction of alcohols and organic acids [63]. Despite its effectiveness, LLE presents significant drawbacks, including the use of environmentally harmful and costly solvents and a lack of automation, resulting in high labor demands [64].  
Even though there are not many works regarding sparkling wines and LLE, [PERSON], [PERSON] et al. [65] characterized the volatile compounds of several commercial sparkling wines from the Friuli Venezia Giulia region using three different extraction methods--LLE, solid-phase extraction (SPE), and solid-phase microextraction (SPME). LLE was used for the analysis of non-varietal aromas [65]. In addition, [PERSON], [PERSON] et al. [66] extracted volatile compounds using LLE in a study of white and rose base wines elaborated from different autochthonous grape varieties in Spain and their evolution to sparkling wines [66].  
To minimize solvent consumption, liquid-liquid microextraction (LIME) can be employed as an efficient alternative [67]. This technique involves the use of very small volumes of solvents and samples (typically in the microliter range), allowing for the effective extraction of target compounds with reduced reagent use and shorter processing times. This technique has been successfully applied to the determination of flavor compounds in wine matrices [68; 69].
# Nutritional Profile and Antioxidant Properties of Hemp (_Camanabis sativa_ L.) Seed from Romania
## 4 Discussion
### Total Polyphenols Content (TPC)  
Phenolic compounds in hemp seeds can mainly be categorized into three primary classes: flavonoids, stilbenes, and lignans [5; 9]. These compounds are crucial in combating diseases related to oxidative stress, due to their potent antioxidant properties. Among the specific phenolic compounds in hemp seeds are fertile acid, p-cumaric acid, and N-trans-caffeoyl tyramine [32].  
Studies have shown that the phenolic content can vary based on factors such as the geographical location where the hemp is grown and the specific hemp cultivar.  
The determined values for TPC aligned with those documented in previous research. [PERSON] et al. reported a TPC value of 767 mg GAE/kg for seeds in their comparative study of the chemical, biochemical, and characteristic properties of seeds, oil, and flour from the Fedora hemp cultivar [6]. [PERSON] and colleagues found a total phenolic content of 96 \(\pm\) 35 mg GAE/100 g for whole hemp seeds [44]. [PERSON] et al. reported a total polyphenol content of 0.92 \(\pm\) 0.04 mg GAE/g for polar extracts from Fedora hemp seeds, flour, and oil [45]. [PERSON] et al. documented a higher total polyphenol content of 2.33 \(\pm\) 0.07 mg GAE/g dw for hemp seeds and sprouts [46]. Similarly, [PERSON] et al. analyzed ten industrial hemp cultivars approved for cultivation in Canada, revealing TPC values from 1.37 to 5.16 mg GAE/g [7]. [PERSON] and colleagues documented an average TPC of 588.8 mg GAE/100 g in their study of seven hemp seed varieties grown in Greece, which was higher than previously reported values [47].  
Polyphenol levels exhibit quantitative variation due to biotic and abiotic factors affecting their biosynthesis pathways. Ultrasound-assisted extraction (UAE) yields plant extracts with higher concentrations of active compounds and improved biological activity. The advantages of this method over traditional techniques are well established.
# Strategic Development of Piezoelectric Nanogenerator and Biomedical Applications
## 2 Piezoelectric Materials  
Materials that can transform mechanical energy into electrical energy are referred to as piezoelectric materials. Piezoelectric materials lack a center of symmetry, which endows them with the unique property of converting mechanical deformations into electrical energy; furthermore, materials with highly piezoelectric properties can enhance the performance of devices. Piezoelectric materials can be divided into various categories, such as single crystals, perovskite materials, and polymers. This section focuses on the different piezoelectric materials used for the fabrication of PENGs and their properties (Figure 1).