esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
**SBERT-LGBMTC** demonstrated exceptional prowess by outshining both conventional estimation methodologies and four cutting-edge models. Notably, in 17 distinct projects, the model surpassed all compared models. Furthermore, a substantial enhancement with a significant effect size was observed in three projects, showcasing the model's robustness and ability to markedly improve estimation accuracy. This comprehensive analysis confirms the **SBERT-LGBMTC** model's quantitative superiority in estimation performance, demonstrated by significant improvements in MAE (2.15) and MdAE (1.85) metrics, resulting in a 12% improvement over the baseline model. The SA was 93%, setting a new benchmark in SP estimation and providing more reliable project management and planning. The structure of this paper unfolds as follows: Section 2 offers an in-depth review of studies conducted on effort estimation. Section 3 provides a concise overview of the methodologies employed in developing the proposed model. The intricacies of the proposed model are elucidated in Section 4. The empirical study design is delineated in Section 5, while Section 6 presents the results of the proposed model. An exhaustive experimental evaluation is expounded in Section 7. Potential threats to validity are meticulously addressed in Section 8, and concluding remarks and paving the way for future work are encapsulated in Section 9.
# Application of Sorbent-Based Extraction Techniques in Food Analysis
## 3 SPE in Food Analysis--Main Fields of Applications
### Flavor Analysis
As an appropriate method for the extraction of VOCs from honey, SPE with a divinyl benzene-N-vinyl pirrolydone cartridge can be successfully used [81]. Beer is another example of a complex mixture of over 100 organic chemical compounds that belong to different classes but the most important from the sensory point of view are aldehydes, esters, higher alcohols, diacetyl and thiols. Apart from a few exceptions, aldehydes have pleasant aromatic green leaf, fruity and grassy notes. Thiols, on the other hand, are introduced together with the raw material and can be transformed at the stage of fermentation. Additionally, they act as antioxidants and retain the aroma of the beer. Therefore, beer can be considered a demanding matrix for analysis due to its complexity and also because of the reactivity of these organic compounds. SPE with C8-H\({}_{2}\)PO\({}_{3}\) (thiols) and silica-COOH (aldehydes) is useful to enrich the sample and obtain more signals of target compounds [82]. Phenolic compounds are commonly found in foods of plant origin (e.g., beer, cereals, cider, cocoa, fruits, legumes, nuts, tea, vegetables, and wine) [83]. Phenols have a great influence on nutritional values and, above all, on the sensory quality of food. The phenolic content in food products is associated with astringency and bitter taste. Determination of phenolics in wines, spirits, and vinegar requires the use of sample preparation and purification stages. A suitable method for the determination of over 50 phenolic compounds (belonging to groups hydroxybenzoic acids, hydroxycinnamic acids, hydroxyphenylacetic acids, hydroxybenzaldehyde, hydroxycinnamaldehyde, simple phenols, alkylphenols, methoxy and alkylphenols, dimethoxyphenol and alkyldimethoxyphenols, alkylphenyl and alkylphenylmethoxy alcohols, hydroxyphenylpropenes, hydroxybenzoketones and derivatives, flavanols, and hydroxycumarins) is the SPE technique, which can be used in the online mode. The use of online SPE with a polystyrene DVB porous sorbent with urea functional groups enables convenient analysis and elimination of matrix interferences during chromatographic determinations. Only cresols, ellagic acid, phenol, and tryptophan were challenging in analysis and characterized by a high LOQ [84].
# Data Privacy and Ethical Considerations in Database Management
## 3 Best Practices
### Data Hashing  
Hashing is a fundamental cybersecurity and data management technique that transforms data into a hash value, a fixed-length sequence of characters. This procedure ensures that the data is represented by a distinct and different identifier called a hash. Hashing is widely used because it allows for the verification of data integrity and the protection of confidential information without exposing the original data.  
Many programs can transform the text into a hash string. Common hashing algorithms include the following:  
* MD5: A hash function compresses any length of data processes or messages into a 128 bit hash value [46], where the same input will always produce the same output and, as it is unidirectional, the process cannot be reversed. Figure 3, from [47], demonstrates how the MD5 algorithm is used. Even though it can be widely used for certificate authentication, it has vulnerabilities regarding hash collision [48]. Due to this, it is no longer acceptable within secure hash functions where collision resistance is required, such as in the digital signatures stated by [49]. This function can be used or is preferred due to its lower computational requirements than recent Secure Hash Strings.  
Figure 1: Symmetric encryption.  
Figure 2: Asymmetric encryption.  
* SHA-256: The hash function successor of SHA-1 is one of the most robust hash functions available [50] and is a highly secure one-way hash function that makes it impossible to recover the original message from the hash value [51]. The SHA256 architecture describes the steps of message processing, including message input, padding, expansion, scheduling, and compression, to produce a 256-bit hash value. Figure 4 demonstrates the different stages, including Message Padding for message length adjustment, Message Expansion to determine the original message length before padding, Message Scheduler to schedule message block processing, and Message Compression to generate a 256-bit message digest after 64 rounds [51].  
In addition to MD5 and SHA-256, other hashing algorithms such as SHA-3, Blake2, and Whirlpool are also employed in various applications, offering a range of security and performance features to suit different cybersecurity and data management needs. These hashing algorithms ensure that multiple options are available, each with specific requirements for data integrity verification and secure information protection, enhancing the overall security of various systems.
# Schumann Resonances and the Human Body: Questions About Interactions, Problems and Prospects
## 1 Introduction
This is why, given the relevance and prospects of studying this scientific area for fundamental science, this review presents the results of a theoretical study on the conceptual integration of existing interdisciplinary and transdisciplinary knowledge in the fields of geomagnetism, magnetobiology, the physiology of the human body and medicine. The aim of the study is to create a working theoretical basis for the interaction of the human body with the Earth's magnetic field in the Schumann resonance frequency spectrum.
## 4 Results
### Real Data Study  
The purpose of the application study was to evaluate the efficiency, validity, and reliability of CAFT. CAFT was applied to real assessment system for the introduction to statistics course of an online university. Overall, 1000 learners were simulated for the simulation study and 120 learners were used in the real application study.
# Influence of Stress Anisotropy on Petrophysical Parameters of Deep and Ultradeep Tight Sandstone
## 4 Results and Discussion
### Influence of Burial Depth and Confining Pressure on Sonic and Resistivity Parameters  
(1) Rock physics experiments under the same stress ratio and different confining pressures  
Four groups of rock physical parameters were tested for rock sample No. 1-2-2, and the results show that (1) the P-wave velocity increased logarithmically with increasing confining pressure (Figure 10a) and (2) the S-wave velocity, similar to the P-wave velocity, had a positive logarithmic correlation with confining pressure. The S-wave velocity changed with the confining pressure in a slightly larger amplitude than the P-wave velocity (Figure 10b). (3) The P-S wave velocity ratio was negatively correlated with the confining pressure and decreased logarithmically with increasing confining pressure (Figure 11a). (4) There was a positive correlation between resistivity and confining pressure (Figure 11b).  
(2) Rock physics experiments under different confining pressures  
Four groups of rock physical parameters were tested for rock sample No. 3-2-14-1, and the results show that the P-S wave velocity ratio decreased with increasing confining pressure (Figure 12a). The resistivity increased with increasing confining pressure (Figure 12b).  
Figure 11: Curves of P-S wave ratio, resistivity and confining pressure.  
Figure 10: Curves of P-wave velocity, S-wave velocity and confining pressure.
## 1 Introduction
They also compared the radiation shielding parameters for the undoped glass with the glasses doped with TiO\({}_{2}\), Bi\({}_{2}\)O\({}_{3}\), and BaO to understand the effect of these oxides on the shielding features of the borosilicate glasses. [PERSON] and [PERSON] [15] fabricated five glass samples containing a fixed content of TeO\({}_{2}\) (5 mol%) and different amounts of BaO (5 to 25 mol%), and studied the physical and attenuation characteristics of these glasses using several techniques. They concluded that the glass with 25 mol% of BaO has a thinner half-value layer than the other samples. All of the aforementioned studies indicated that the inclusion of heavy metal oxides such as PbO, BaO, Bi\({}_{2}\)O\({}_{3}\) and Sb\({}_{2}\)O\({}_{3}\) may help to enhance the shielding capability of the glass system. In fact, adding Sb\({}_{2}\)O\({}_{3}\) to the glasses leads to the enhancement of the physical properties such as the density, refractive index, thermal and mechanical durability etc. [16]. Additionally, glasses with Sb\({}_{2}\)O\({}_{3}\) exhibit novel optical properties with high transparency to infrared radiation making them potential candidate materials for non-linear optical devices. Moreover, the addition of Sb\({}_{2}\)O\({}_{3}\) to the glasses may cause glasses to be more resistant to the formation of high energy radiation-induced defects [5]. Furthermore, the addition of Sb\({}_{2}\)O\({}_{3}\) has an important role to enhance the attenuation performance of the glasses against ionizing radiation [5]. In the current work, the effects of antimony on the radiation shielding and mechanical features of soda-lime-silica glasses were investigated. The mass/linear attenuation parameters, transmission factor, dose rate, and the half-value layer of all compounds were evaluated to evaluate the glasses' radiation shielding efficiency. These properties reveal the interactions between \(\gamma\)-rays and these glasses for uses in medical and industrial applications. All of the previously reported studies evaluating some systems' shielding features have been carried out either experimentally or theoretically based on XCOM software. However, there are a limited number of studies in the literature that have used the MCNP-5 code to investigate the shielding characteristics of glass samples [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19]. This comprehensive study may stimulate scientists working in synchrotron-based facilities to perform experimental studies on these compounds to compare these results with their future experimental data on these samples. This theoretical study may also help people working in industrial and medical applications to design new related compounds for tunable radiation resistance devices. In this work, the mechanical and shielding features for soda-time-silica glasses were reported. The effects of Sb\({}_{2}\)O\({}_{3}\) on the mechanical and shielding parameters of the selected soda-time-silica glasses were also investigated.
# Quality Assessment of DSMs Produced from UAV Flights Georeferenced with On-Board RTK Positioning
## Materials and Methods
### BBA Settings and Block Control Configurations  
Three experiments were performed varying the block control configuration. In the first experiment, which is referred to as **12 GCP** in the following, only the coordinates of 12 well-distributed GCPs in the survey area were used (see Figure 2): no a priori information on camera stations was exploited. In the second experiment, which is referred to as **RTK** hereafter, only the camera positions from the navigation data encoded in the images were used: no a priori information on GCPs was exploited. In the last experiment (**RTK+1 GCP**), besides the camera positions as in **RTK**, the coordinates of one GCP (in the middle of the block) were also used. This last configuration was tested, as it is well known [48] that unless GCPs are used, camera self-calibration might be inaccurate in radial blocks, with bias possibly affecting the ground coordinates. To distinguish between corrections being sent from a local master station and by a NRTK, the letter **m** (master) and **n** (network) was respectively added to **RTK** if necessary. Overall, 24 different BBA (three for each of the eight flights) were executed.  
In all of the BBA, a standard deviation of one pixel was assigned to the tie points' image coordinates, according to the mean reprojection error. The empirical precision found from repeated measurements of GCPs is in the order of 1 cm. However, a much lower standard deviation of 1 mm was assigned to the coordinates of the GCPs, as experience with PhotoScan processing shows that residuals on GCP coordinates would otherwise be much larger than expected. Finally, camera position standard deviations between 1-2 cm in horizontal coordinates, and between 3-5 cm in elevation were used, following the findings in [PERSON] et al. [39]. In all of the cases, a self-calibrating BBA was executed. Table 1 in Appendix A reports the list of estimated parameters included in the camera model.  
In all of the experiments, the remaining 14 known points were used as checkpoints to evaluate the RMSE of the ground coordinates.
# An Ultra-Stable Polysaccharide Gel Plugging Agent for Water Shutoff in Mature Oil Reservoirs
## 3 Results
### Effect of pH Value on the Gelation Behavior  
CTS requires acidic conditions to dissolve, and changes in the amount of acid added will directly affect the pH of the solution. Therefore, it is necessary to clarify the effect of pH on the gelation behavior of the CTS gel system. The CTS gel base fluid was prepared using 30,000 mg/L sodium chloride brine, with the CTS concentration fixed at 2.0 wt% and the MBA concentration fixed at 0.6 wt%. The pH values were set to 3.2, 3.9, 4.2, 4.9, and 5.6, respectively. All gelation experiments were conducted at 120 \({}^{\circ}\)C, and the results are shown in Figure 4. The gel strength increased with increasing pH values and then remained relatively stable, while the gelation time progressively decreased. The pH had a significant impact on the gelation behavior of the CTS gel system, which is similar to other biopolymer gel systems [42]. [PERSON] et al. found that the gelation time of the xanthan/Cr\({}^{3+}\) gel system was delayed as pH decreased, and hexa-aqua ions Cr(H\({}_{2}\)O)\({}_{6}\)\({}^{3+}\) were transformed into the more reactive hydroxides Cr(OH)(H\({}_{2}\)O)\({}_{5}\)\({}^{2+}\) and Cr(OH)\({}_{2}\)(H\({}_{2}\)O)\({}_{4}\)\({}^{2+}\) as pH increased, shortening the gelation time at higher pH.  
However, this explanation for the effect of pH on gelation does not apply to the CTS gel system. A possible explanation for the experimental observations is that as the concentration of acetic acid increases, the degree of protonation of CTS also increases. This means that as pH decreases, more NH\({}_{2}\) groups are converted into NH\({}_{3}\)\({}^{+}\), reducing the available reactive sites for MBA. As a result, the cross-linking reaction slows down and the gel strength decreases at lower pH values.  
Considering the gelation behavior across different pH values, the optimal pH range for the CTS gel system is between 4.2 and 5.6.
## 4 Experimental Design
### Research Results Analysis
#### 4.3.1 Fixation Regions  
Five primary fixation regions were generated based on early-stage gaze data to serve as the initial AOIs for subsequent structural modeling. These regions are labeled sequentially as follows: Region 1 presents the boatman and riverside scenery; Region 2 includes the vast river surface with scattered boats; Region 3 represents the foreground riverside; Region 4 covers distant settlements and tree clusters; and Region 0 showcases layered mountain landscapes.The resulting fixation cluster distribution is illustrated in Figure 13a. In terms of fixation point distribution, Region 1 (boatman and riverside scenery) accumulated the highest number of 658 fixation points, followed by Region 4 (distant settlements and tree  
Figure 12: Data collection equipment and environment. (**a**) Participant wearing eye-tracking glasses viewing materials in laboratory setting. (**b**) ARGUS SCIENCE ETVision wearable 180 Hz eye-tracking device.  
clusters) with 529 fixation points, and Region 2 (distant mountains) with 472 fixation points. These three primary areas of focus are all centrally located in the composition. Notably, although Regions 4 and 1 contain more complex visual elements, the viewer's attention is not strictly limited to specific objects. Specifically, Region 2, despite being predominantly composed of blank space with only a few painted elements, still received a significant amount of viewing attention. In parallel with the gaze-based analysis, twenty think-aloud verbal utterances were collected during the viewing process. Each utterance was mapped to a specific time segment within the eye-tracking dataset, allowing for integrated multimodal alignment. Using the semantic classification framework established in [PERSON] et al. [37], these utterances were categorized into four types: evaluative judgment, spatial context, image-text interaction, and object recognition. However, for the purposes of detailed structural modeling, only eight utterance-gaze segments were selected, based on the dual condition of semantic relevance and observable gaze dynamics within the corresponding time window. This dual-filtering approach ensured that the analytical focus was placed on integrated verbal-visual moments of cognitive significance, providing a robust foundation for the subsequent time-series analysis. Notably, some analytic segments were formed by merging adjacent utterances when they collectively marked a dynamic gaze transition, ensuring that the segmentation reflected multimodal coherence rather than strictly adhering to individual sentence boundaries.
## 4 Classification Process
### K-Nearest-Neighbor (KNN)  
The data from November 1 st to 10 th were used to build the model of this study's classification method (KNN, Lib-svm). The data from 11-30 November were adopted as the validation data.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **PP (Ku/C)** \\ \hline Ice & \(<\)3 \\ Water & \(>\) = 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Threshold segmentation.  
Figure 16: Daily classification accuracy (November): Figure 16A,B used the data in the Arctic regions; Figure 16C,D used the data in the Antarctic regions; The data of Band Ku were used in Figure 16A,C; The data of Band C were used in Figure 16B,D.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Satellite** & **Band** & **Sea Ice** & **S seawater** \\ \hline \multirow{4}{*}{Arctic} & \multirow{2}{*}{HY2A} & C & 89.86\% & 96.22\% \\  & & Ku & 88.28\% & 96.32\% \\  & & C & 92.33\% & 97.85\% \\  & HY2B & Ku & 88.89\% & 98.37\% \\ \hline \multirow{4}{*}{Antarctic} & \multirow{2}{*}{HY2A} & C & 86.55\% & 96.27\% \\  & & Ku & 88.66\% & 96.49\% \\ \cline{1-1}  & & C & 87.93\% & 96.09\% \\ \cline{1-1}  & & Ku & 88.42\% & 95.85\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average classification accuracy.  
This study chose four parameters for the classifications: AGC and PP values from the C Band and Ku Band, respectively.  
The principle of the KNN involved the statistical accuracies of the adjacent areas. Therefore, the K (number of selected points in the adjacent areas) had impacted the results. In this study, K was set in the range of 10 to 200.  
In Figure 17, all the accuracy rates are greater than 80%. The overall accuracy of the two different bands from the two different satellites had remained within a stable change range, with the exception of the Ku Band in the Arctic regions. In the Antarctic regions, the classification accuracy rate of the seawater was observed to be relatively lower than the others.
## 3 Classification of Optimization Objectives
### Multi-Objective Optimization Focussed on Operation Related Problems in WSNs
#### 3.2.1 Energy Conservation  
Wireless sensor nodes need to increase their transmission power in order to increase signal to noise ratio and to decrease the bit error rate. On the other hand, increase in transmission power will compromise the energy conservation, minimization of interference and the life time of the network. Therefore, multi-objective optimization algorithms are used to obtain trade offs involving energy efficiency and other conflicting objectives. For example in [39], the authors investigated the effect of various parameters of energy consumption in nanosensor networks and proposed a multi-objective optimization formulation to achieve a balance between the energy consumption, delay and bit error rate.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Ref.** & **Objectives** & **Equation** & **Details** \\ \hline
[141] & Maximize the total throughput, minimize the total transmission power & \(\min\limits_{\varepsilon,\mu_{i},\rho_{s}}\,F=w_{1}(1-F_{1})+F_{2}w_{2}\) & Normalize the first objective between 0 and 1 and second objective is to reduce the carbon footprint. \(C_{k}\) represents the channel capacity of the \(k\)th user for shared band; \(C^{m}ax_{k}\) represents upper bound of the sum-rate capacity \(C_{k}\); \(E^{CO_{2}}\) is the \(CO_{2}\) emission; \(w_{1}\) and \(w_{2}\) are used to create a joint minimization (or maximization) objective. \\ \hline
[142] & Minimum interrogation cycle, maximum reader utilization, and energy efficiency & Three stage optimization to single stage optimization problem. \(\xi_{1}\) and \(\xi_{2}\) are constants which depend on the number of readers and the maximum output power; \(W\) represents the RFID reader utilization; \(E\) represents the RFID power consumption; \(S\) denotes the interrogation circle. \\ \hline
[143] & To balance network communication ability and energy efficiency & \(Q^{*}=\max\limits_{a_{t}}Q_{i,t+1}(s_{t},p_{t})\) & Where \(Q^{*}\) mean Q optimal value. \\ \hline
[144] & Operation & & Where \(i\)=1,2,3...,\(N\); \(b_{i}\) is the number of bits; \(p_{i}\) is allocated power per subcarrier. \\ \hline \end{tabular}
\end{table}
Table 3: _Cont._In [80], a multi-objective optimization algorithm has been proposed to maximize energy conservation and lifetime of the network by using a data aggregation route algorithm. A trade-off has been obtained between energy efficiency and end-to-end delay by using a multi-objective routing algorithm in [81]. A multi-objective optimization scheme has been used in [82] to simultaneously optimize the conflicting objectives namely, energy conservation, lifetime and coverage. The authors have used a probabilistic scheduling strategy to achieve a balance between the two conflicting objectives. A cross-layer mutli-objective approach has been used in [83] to obtain a trade-off between energy efficiency and packet loss.