esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research Methodology
### Model Performance Evaluation
#### 3.4.1 Confusion Matrix  
A Confusion Matrix is a well-known way of examining how a good classification model achieves the observed classes. The Confusion Matrix is presented in Table 1 as followsThe four boxes in the classification table are all assigned a name: _FN_, _FP_, _TP_, and _TN_.
* _TN_ stands for true negative. Here, the customers are observed as not being churners, and the model has also classified the customers as non-churners.
* _FP_ stands for false positive. Here, customers are observed as being non-churners, but the model has classified the customers as churners.
* _FN_ stands for false negative. Customers are observed as being churners, but the model has classified the customers as non-churners.
* _TP_ stands for true positive. Here, customers are both observed and classified as churners.  
The Confusion Matrix is not only a visually advantageous way of measuring the model's ability to classify correctly. Several calculations can also be made based on the four values. These calculations are all targets for more specific evaluations of the model and can, therefore, be used to identify the model's strengths and weaknesses.
# An Analysis of Commercial Noni Juice: LC-MS/MS Phenolic Profiles and Cytotoxic Activity
## 3 Results and Discussion
### Assessment of Antioxidant Activity of the Major Phenolic Compounds  
Previous preliminary work has isolated the most antioxidant-active fractions of noni juice and found that these included phenol and flavonoid compounds [35]. However, the exact identity of the most antioxidant-active compounds has not been described to date. Consequently, in order to identify the major phenolic compounds contributing to the high total phenolic content and antioxidant activity of the noni juice, the TPC and FRAP assays were performed on pure standards of the most abundant phenolic compounds identified in this study--rutin, gentisic acid and 4-hydroxybenzoic acid. These were chosen due to their high concentrations (i.e., >3 mg L\({}^{-1}\)) across one or more noni juice brands.  
These results (Table 7) indicated that gentisic acid showed the highest total phenolic content and antioxidant activity out of the three major phenolic acids. This compound showed a total phenolic content approximately 10% higher than gallic acid and an antioxidant activity over 7 times higher than Trolox, a synthetic derivative of vitamin E. This agreed with previous research, which found that gentisic acid had the second-highest antioxidant activity out of a number of phenolic compounds tested using the DPPH assay [36].  
Interestingly, 4-hydroxybenzoic acid showed no antioxidant activity using the FRAP assay. This concurs with theoretical modelling that suggests that 4-hydroxybenzoic acid would have the lowest antioxidant activity out of a number of 4-hydroxybenzoic acid derivatives [37].  
Although the possibility of interactions or matrix effects was not considered in this study, gentisic acid showed the highest antioxidant activity, followed by rutin. Interestingly, the LHN sample contained only slightly less gentisic acid than rutin; this sample also showed the highest antioxidant capacity (Table 4). In contrast, the rutin content of the DHN brand of noni juice was over 60 times higher than the gentisic acid concentration in this sample; it contained the second-lowest antioxidant capacity. Further investigations would be required to determine any possible matrix effects on the antioxidant activity.
# Energy Performance of LR-FHSS: Analysis and Evaluation
## 2 Related Work  
This section provides an overview of the literature related to LR-FHSS, with a particular focus on energy consumption. As aforementioned, energy consumption is a critical feature in IoT. For LoRaWAN, some detailed energy consumption models have been published [19; 20; 21]. However, there is only one contribution that models LR-FHSS energy consumption, and it only focuses on uplink transmission (i.e., it does not model the complete LoRaWAN transmission procedure) [22].  
The body of work on LR-FHSS has recently increased [7; 8; 9; 10; 11; 12; 23; 24; 23]. Most of the studies on LR-FHSS predominantly investigate its known strengths--namely, coverage and scalability--when applied to its main use case, DTS-IoT [8; 9; 10; 11]. Only three published works give some attention to the energy consumption performance of LR-FHSS [12; 13; 25].  
Using a custom-made simulator, the authors of [12] concluded that LR-FHSS can improve the deployment scalability by a factor of 75x at the expense of 30% higher device power consumption compared to the legacy LoRa modulation. However, this comparison is only based on the transmission time-on-air of the different physical layer approaches considered, which misses several significant contributions to energy consumption (see Section 4). In another work [13], the impact of the frequency hopping sequence (FHS) in LR-FHSS was studied using a LoRaWAN Class B end device based on commercial transceivers and an SDR-based gateway. For a very specific setup and two FHS proposals, energy efficiency results were provided, with an increase of 5.20 times for the proposed dynamic frequency hopping (DFH) scheme compared to a tailor-made transmit power control method. However, it is not clear how the authors determined the energy consumption to derive those results. Finally, a technical report released by the LR-FHSS chip manufacturer presents the characteristics of the technology and provides several performance figures, including the power consumption and battery lifetime of an LR-FHSS end device [25]. However, the results were obtained by using a limited set of states based on current consumption values of unknown origin, and the model used to produce the results is not given.  
The authors of [22] provided a study on energy consumption of LR-FHSS. However, they only modeled LR-FHSS in the uplink and did not account for a complete LoRaWAN-based transmission procedure; they also included acknowledged and unacknowledged procedures. Furthermore, in the present paper, we also study the energy efficiency of LoRaWAN with LR-FHSS and compare its performance with that of LoRaWAN using the legacy LoRa PHY.  
Therefore, to the best of our knowledge, the present paper is the first to provide a detailed energy consumption model that allows for the prediction of the current consumption, the battery lifetime, and the energy efficiency of an LR-FHSS end device.
## 3 Satellite Sensor Instrument Technologies  
This historical overview provides the foundation for a detailed examination of satellite sensor technologies, which have continuously advanced atmospheric observation capabilities. These technologies are discussed in Section 3.  
The Automatic Picture Transmission (APT) TV Camera System for Meteorological Satellites [15] was installed on NIMBUS-1, a meteorological LEO satellite [16] launched on 28 August 1964. It was specifically designed to provide global coverage of Earth's cloud formations and to support various aspects of atmospheric research. The satellite (see Figure 1) carried a three-camera system that operated during daylight hours, complemented by an infrared scanner for nighttime observations. The collected data, including cloud formation imagery, was stored on magnetic tape and transmitted continuously in near-real time via the onboard communication system. The cloud formation imaging process operates as follows.  
Optical instruments onboard the satellite were utilized to focus on a specific cloud formation area directly beneath the satellite, projecting the image onto the face of a storage vidicon. The captured optical image was then converted into an electrical signal through an electron beam readout process (see Figure 2). This electrical signal was subsequently modulated onto a subcarrier, which in turn modulated the Very High Frequency (VHF)  
Each type operates based on distinct physical principles and serves specific applications in environmental monitoring, meteorology, and Earth science [17].  
Radiometers are passive microwave sensors designed to measure the thermal emission of microwave signals originating from the sun and interacting with the Earth's surface. These signals are partially backscattered by the surface, with their characteristics influenced by the physical temperature, electrical properties, and composition of the target area or volume. Upon reception, this information is used to generate an emission profile of the target. Due to their reliance on solar illumination as the source of microwave energy, these instruments are often referred to as daylight sensors. Radiometers typically operate over a wide bandwidth and employ real-aperture antennas, which have large physical dimensions and are opaque to radiation at specific wavelengths [18]. Sounders, a derivative instrument of the radiometers sub-class, are specialized instruments designed for extracting vertical profiles of atmospheric parameters.  
Active microwave sensors, such as radars, do not depend on daylight because they actively transmit microwave signals, eliminating reliance on the sun as a source. A meteorological radar is a typical example that uses real-aperture antennas to transmit modulated pulses. Through Doppler and range processing, it constructs backscatter images to detect rain, wind, snow, hail, clouds, ocean surface wave height, frequency and direction, temperature, wind speed, atmospheric moisture content, and vertical precipitation profiles [19]. This data is used for event warnings and short-range weather forecasts.
## 4 Discussion
In the present research, a linear regression analysis showed that the implant macro-design, the length, and the insertion torque were the most influential ISQ predictors at the moment of implant placement. The location, implant macro-design, and age of the patients were the most significant predictors for the ISQ after three months of healing. Moreover, this study showed that although some implant macro-designs are specifically designed to achieve high primary stability values on the day of the surgery, after 3 months of osseointegration, some implant designs achieved greater secondary stability (Table 2). Clinicians should know that the implant design, site, and diameter will modulate the primary and secondary stability of implants placed in healed sites. The secondary stability also depends on other surface conditions of the implants, such as their surface topography or roughness [47; 48; 49]. A limitation of the present study was the sample size. The calculated minimum number of implants per group to achieve adequate statistical power (31 implants) was obtained for comparing the insertion torque and the implant stability quotient at implant placement and after three months for the cylindrical and the conical implants. However, lower frequencies were observed when comparing the different characteristics of the different microgeometries of the implants, such as the influence of the location in the arch or the implant diameter and length in the stability in the implant macro design groups. Even though only two subgroups of comparison were performed, anterior (incisors and canines) and posterior (bicuspids and molars) and standard and wide/long made the sample size reduction lower. A relatively small sample of wide-diameter implants (\(n=11\)) was obtained, specifically for the cylindrical group, with only one implant. A 4.5 mm diameter was the limit for considering a standard or wide-diameter implant. The cylindrical implants used in this study had a standard width of 4.3 mm, so most of the implants placed were considered standard in this study, but they were used in many cases as wide-diameter implants clinically. Future research should conduct clinical trials, maybe multicentric, on the main predictor variables affecting primary and secondary stability to obtain more conclusive statements with larger samples, a wider range of implant dimensions (diameter and length), and a longer follow-up period.
## 5 Numerical and Simulation Results
### Baseline Schemes  
#### 5.2.1 Baseline 1 Scheme (RIS)  
The objective of this scheme is to provide a conventional UAV-assisted RIS network with a NOMA system model and prove that using STAR-RIS can enhance performance. Therefore, the STAR-RIS is replaced by the RIS in the conventional system model. We assume that the power consumption of this system is the same as that of the proposed system. Apart from this replacement, the other elements will remain consistent.
## 3 Results and Discussion
### Physicochemical Properties
#### 3.2.2 Color  
Color is a key sensory indicator of the quality of fermented meat products and directly influences consumer acceptance. Figure 3A shows the appearance of the sausage matrix before and after fermentation, while Figure 3B, Figure 3C, and Figure 3D illustrate the specific changes in L*, a*, and b* values, respectively.  
As shown in Figure 3B, fermentation led to a decrease in L* in the control group, while the addition of corn starch in the FCC group effectively slowed this decline. Corn starch, with its water absorption and expansion properties, reduced transparency and formed a uniform structure with proteins, enhancing light scattering and reflection, thereby maintaining a higher L* value [32]. The soy protein isolate in the FNP group, with its inherent yellowish color, may undergo browning during fermentation, leading to lower L* values before and after fermentation. Figure 3C shows that the a* value in the FNP group was significantly higher than in the other groups (\(p<0.05\)). The relatively stable  
Figure 2: pH (**A**), Aw (**B**), and water-holding capacity (**C**) of the sausage medium before and after fermentation. CK: control; CS: sucrose; CC: corn starch; NL: leucine; NP: soy protein isolates. FCK, FCS, FCC, FNL, and FNP represent the samples after fermentation. Different letters indicate significant differences between groups (\(p<0.05\)).  
pH observed in the FNP group may help explain the preserved color. Previous studies have suggested that increased acidity may accelerate myoglobin oxidation and cause discoloration [33]. Figure 3D illustrates that fermentation caused a decrease in b* values for all groups. During fermentation, the increased NaCl concentration decreased oxygen solubility in the system, promoting the formation of high-iron myoglobin, which led to a decrease in b* [34]. The yellow color inherent in soy protein isolates resulted in higher b* values in the FNP group both before and after fermentation.  
In summary, supplementation with exogenous carbon and nitrogen sources had varying effects on the growth characteristics, enzyme activities, and physicochemical properties of _D. hansenii_ in the fermented sausage model. Among the different treatments, the complex carbon source FCC group (corn starch) and the complex nitrogen source FNP group (soy protein isolates) exhibited the most significant improvements. Both groups significantly increased the colony count and protease activity of _D. hansenii_, while maintaining high lipase activity. Additionally, improvements in the color attributes and water-holding capacity of the fermented sausage were likely due to their macromolecular structures. In contrast, the simple carbon source FCS group (sucrose) and the simple nitrogen source FNL group (leucine) showed fewer improvements. The subsequent section of this manuscript focuses on the effects of corn starch and soy protein isolates on the accumulation of amino acids, fatty acids, and volatile flavor compounds during _D. hansenii_ fermentation.
## 3 Materials and Methods
### Electromagnetic and Magnetic Field Response Modeling
#### 3.1.2 Magnetic Forward Modeling  
The magnetic method similarly employs a dipole model to capture the static magnetic field generated by the target. In this case, the target is treated as a magnetic dipole with an effective magnetic moment that aligns with the target's principal magnetic response axes, which are characterized by the same magnetic polarizability tensor \(M\) used in TEM  
Figure 3: Schematic of dipole model.  
modeling. This approach ensures that the EM and magnetic forward models maintain a unified physical description of the target's magnetization properties.  
The static magnetic field \(\vec{B}_{M}\) observed at an observation point \(\vec{r}\) relative to the target can be represented as follows:  
\[\vec{B}_{M}=\frac{\mu_{0}}{4\pi}\left(\frac{3(\vec{m}_{\text{eff}}\cdot\vec{r}) \vec{r}}{|\vec{r}|^{5}}-\frac{\vec{m}_{\text{eff}}}{|\vec{r}|^{3}}\right) \tag{5}\]  
where the effective magnetic moment \(\vec{m}_{\text{eff}}\) is defined as  
\[\vec{m}_{\text{eff}}=\frac{2\pi}{\mu_{0}}M\cdot\vec{B}_{earth} \tag{6}\]  
By utilizing the same magnetic polarizability tensor \(M\) across both EM and magnetic models, the static magnetic response aligns with the transient response in the TEM model, thereby achieving a consistent description of the target's physical properties.  
In this study, we assume that the magnetic response is dominated by induced magnetization rather than remanent magnetization. This assumption is based on prior research in UXO detection, which has shown that most UXO undergo significant demagnetization due to mechanical shocks and high-temperature exposure during manufacturing or detonation [33; 34; 35]. Consequently, remanent magnetization is often negligible compared to induced magnetization in practical detection scenarios.
## 4 Results
### Comparison among Different Theoretical Methods Used to Build the Dispersion Curves  
The comparison presented above between the experimental and the numerical results produced using the SAFE [28] allowed to verified the consistency of this method to build the dispersion curves. On the other hand, the numerical results presented by SAFE [28], described and shown in Figure 5a, and the results using the method proposed by [PERSON] [25], described in Figure 7a, are practically concurrent. The decomposition method in harmonics [28] is easier to implement; however, the methodology presented in [25] displays the modes in 3D aiding the propagation modes interpretation.  
The comparison between the dispersion curves based on the beam theory proposed in [9] and its comparison with the SAFE method computed using [22] or [25] are presented in Figure 13. In this figure, it can be noticed that the results are visually consistent up to around 10,000 Hz.  
The difference between the two proposals is justified as follows: when the wavelengths decrease, the relationship between the cross-section waveguide dimensions and the modal wavelength under analysis no longer corresponds to the condition for the Euler-Bernoulli hypothesis. This hypothesis said that the beam cross-section remains flat and perpendicular to the neutral line after the deformation [11]. Rigorously speaking, this beam theory lost consistency for the ratio (\(\lambda\)/2)/h \(<\) 10, where h = 5 mm for the flexion mode around Axis 3 and h = 15 mm for the flexion mode around Axis 2. Only the longitudinal mode maintains coherence with the SAFE curves for wave number out this limit.  
Another analytical solution for the prismatic waveguide is considering the superposition of the dispersion curve of two plates with thicknesses as being equal to the dimensions of the rod section. In doing so, it was possible to represent the axial and flexural mode around the Axis 2. The bending mode around Axis 3 presented a distortion concerning the reference curve but maintained the same derivative. As aforementioned, this methodology cannot capture the torsional mode because this mode is not present in the plate analytical solution. Figure 14 shows the comparison between the analytical curves obtained with the plate superposition solution and the SAFE solution.  
Figure 13: Comparison made between the analytical beam model [11] (color lines) and SAFE [28] solution (grey lines). (I: longitudinal, t: torsional, f2: flexion around Axis 2, f3: flexion around Axis 3. Notice that in this graph the wave number is defined as k = 2\(\pi\lambda\) [m\({}^{-1}\)].
# Transparent Task Delegation in Multi-Agent Systems Using the QuAD-V Framework
## 3 Delegation Model
### Evaluation Metrics
**Competence** (associated with arguments from class \(A_{4*}\)): This dimension evaluates whether a partner is capable of meeting the delegator's expectations regarding task execution, considering the partner's performance estimations. A delegator votes for or against arguments related to competence based on the following measures: * **Competence score** (\(COMP(\beta,\tau)\in[0,1]\)): This measure provides an up-to-date value of the partner's ability to fulfill its performance estimations. The competence score is computed as the simple average of the scores associated with the most recent impression produced by a delegator \(\alpha\) regarding a delegatee \(\beta\) during the task execution phase of the delegation process. * **Social image** (\(SI(\alpha,\beta,\tau)\in[0,1]\)): A social measure calculated by the aggregation of the set of impressions produced by a delegator \(\alpha\) regarding the competencies of a partner \(\beta\) as a delegatee of a task \(\tau\). (To aggregate a set of impressions, a delegator \(\alpha\) first averages such impressions for each task criterion, forming a single aggregated impression. Then, the final value is obtained by averaging the scores across all criteria [13; 35].) The social image can be seen as \(\alpha\)'s personal opinion about \(\beta\)'s competencies, taking into account a task \(\tau\)[10]. * **Reputation** (\(REP(\beta,\tau)\in[0,1]\)): A delegator \(\alpha\) calculates \(\beta\)'s reputation regarding \(\tau\) by aggregating impressions obtained from third parties (other delegators). Reputation, therefore, represents a collective evaluation shared within a group, where most members agree without necessarily verifying the truth or sources of the impressions [10; 35]. In our case, the group consists of delegators who assess a common delegatee based on the execution of a specific task. * **Know-how** (\(KW(\beta,\tau)\in[0,1]\)): It is a specialized form of reputation where delegators share impressions with their delegateees [8]. As \(\beta\) interacts with delegators while executing \(\tau\), it accumulates impressions on its competencies. These impressions, similar to job references, can be sent to a delegator \(\alpha\) during partner selection [39; 40]. By aggregating them, \(\alpha\) computes a social measure of \(\beta\)'s know-how. It is important to note that the QuAD-V structure adopted in our delegation is robust enough to handle inconsistent information during the processing of argument strengths. An inconsistency occurs when an argument is simultaneously supported and attacked by different sources. This issue is naturally resolved by the argumentation-based voting mechanism, which balances supporting and opposing arguments during the recursive computation of argument strength. By considering both supporting and opposing contributions, this process ensures that conflicting viewpoints influence the final acceptance score in a structured manner.