esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimentation and Results
### Performance of the Recommendation System
#### 4.10.1 Customer Engagement  
The combined system of YOLOv5 and DeepSORT has contributed to significant improvements in customer engagement:  
* Enhanced Tracking and Insights: The advanced tracking capabilities of YOLOv5 and DeepSORT have enabled retailers to accurately monitor customer interactions with products. These real-time data allow retailers to identify popular items and optimize product placement, influencing purchasing decisions.
* Real-Time Adjustments: With instant access to customer behavior data, retailers can rapidly adjust their strategies. Figure 18 illustrates the upward trend in conversion rates over the study period, showing a slight (about 3 percentage points) improvement after implementing the YOLOv5 + DeepSORT system.  
Figure 18 shows two curves representing monthly conversion rate data for two consecutive years. The blue curve depicts the baseline year before implementing the YOLOv5 + DeepSORT system. It shows the monthly changes in conversion rates that occurred naturally in the absence of the system's improvements. The YOLOv5 + DeepSORT system was actively employed the following year, which is represented by the green curve. Personalized promotions, engagement strategies based on consumer behavior patterns, and optimal product placements are just a few examples of the focused modifications made possible by insights from real-time monitoring data this deployment year. By comparing these two curves, we demonstrate how the YOLOv5 + DeepSORT system contributed to consistent monthly increases in conversion rates, exceeding the usual monthly or seasonal patterns observed in the baseline year. It is essential to consider the cumulative effect over the entire implementation year even though the increase from December to January may seem modest (about three percentage points). The YOLOv5 + DeepSORT method made it possible to make deliberate, continuous changes that increased month after month, leading to a noticeable annual increase in conversion rates. This cumulative trend indicates that the system's real-time data insights improved customer engagement and conversion outcomes, surpassing natural monthly growth. A clear picture of how real-time data insights impacted  
Figure 18: Conversion rates before and after implementation of YOLOv5 + DeepSORT.  
the retailer's operational strategy is provided by this year-over-year comparison, which successfully illustrates the system's contribution to improved conversion outcomes.
# Weighted Cluster-Range Loss and Criticality-Enhancement Loss for Speaker Recognition
## 4 Experiments and Results
### Results
#### 4.4.3 Evaluating Criticality-Enhancement Loss  
Given the current utterance as the anchor, the CEL tries to find the most easily optimized hard and semi-hard triplet. By combing the CRL and CEL, the hardest samples and the most easily optimized hard, semi-hard samples are considered concurrently. Considering a single batch in the CRL + CEL update, each step of the CEL update will push away the negatives mixed in the positive cluster, and the CRL pushes the negatives further away from the positive cluster whilst shrinking the cluster range. Features learned with CRL and CRL+CEL are presented in (a) and (b) of Figure 9, and it turns out that the cluster ranges of the two are similar. The ACC curves of CRL and CRL+CEL (both using the Res-SA architecture) are presented in Figure 7b. Compared with only using CRL, jointly employing CRL and CEL increases the ultimate top-1 accuracy by 1.7% on VoxCeleb1 with SA and by also 1.7% on CN-Celeb with DA. The EER curves and Detection Error Tradeoff (DET) curves are illustrated in Figure 8. Obviously, at most of the points, both the EER curve and the DET curve of CEL+CRL lie lower than that of other configurations.  
Compared with WCRL, the CEL alleviates the need for the selection of the weighting factor, and it indeed improves the performance compared with the baseline. Interested readers could add CEL in their loss function provided that they also use triplet-like loss, and we have every reason to believe that this will have a positive effect on their work.
# Study on Experimental and Constitutive Model of Granite Gneiss under Hydro-Mechanical Properties
## 4 Numerical Simulation
### Model Validation  
Parameters \(A\) and \(C_{0}\) in the plastic yield surface are obtained from different confining pressure conditions triaxial tests in order to obtain the trajectories of each rock specimen peak strength, where \(A_{0}\) = 950 and \(C_{0}\) = 18 MPa. The parameter = 0.021 in the plastic hardening function is a parameter describing the initial yield surface position. The parameter \(B\) = 0.0005 of another plastic hardening function is obtained by plotting the function \(\alpha_{p}\) and the plastic shear strain \(\gamma_{p}\) curve. \(\eta\)= \(-\)0.0025, which is the slope of the rock specimen compression and expansion area boundary line and is also the value corresponding to the plastic volume compression and expansion critical point. It could be specifically determined by the volume expansion point of the granite gneiss triaxial mechanical property test. The critical values of damage variables, and for calculating and simulating the conveniently, \(d_{c}=0.9\) and \(B_{d}=125\) are taken. The coupling variable coefficients \(\alpha\), \(\alpha_{1}\), and \(\alpha_{2}\) are all 1. The basic parameters in the coupling variables are all test parameters. Based on the research on the relationship between permeability and volume strain, the values are shown in Table 3.  
In line with the establishment of the constitutive model and parameters determination, the mechanical characteristic test curve numerical simulation under the hydro-mechanical properties was carried out. The comparison between the simulation results and experimental data is shown in Figure 5.  
It is obtained from Figure 5 that the constitutive model would better simulate the granite gneiss triaxial test curve. Verification shows the results in the numerical and experimental match well. All peak strengths of rock samples R1 to R4 obtained by simulation are 237.56 MPa, 214.48 MPa, 248.91 MPa, and 235.82 MPa, respectively. Compared with the experimental data in Table 2, the differences are 0.18%, 0.59%, 0.06%, and 0.36%, respectively, none of which exceed 1%. And the simulated axial peak strains of rock samples R1 to R4 are 6.38, 6.05, 6.55, and 6.93, respectively. Compared with the experimental data in Table 2, the differences are 4.08%, 4.85%, 0.92%, and 4.84%, respectively, all of which do not exceed 5%. The established hydro-mechanical properties constitutive model can not only describe the plastic deformation, damage evolution, pressure dependence, compression to dilatancy transition, pre-peak plastic hardening, and post-peak strain softening of granite gneiss but also describe the changes in deformation and strength characteristics under different seepage pressure conditions. This model would better interpret the granite gneiss hydro-mechanical properties under seepage water pressure.
# Effect of Temperature, pH, and Reaction Duration on Microbially Induced Calcite Precipitation
## 3 Test Results
saprophyticus_ when the reaction duration was changed from 2 to 3 days. An increase of 14% was seen when optimal pH (7) and temperature (30 \({}^{\circ}\)C) were used. However, precipitation significantly increased for _S. pasteurii_, with an average increase of 37% and 22% for the optimal condition, indicating that reaction occurred more slowly for _S. pasteurii_ than for _S. saprophyticus_. Although _S. pasteurii_ reaches exponential phase of growth within 12 hours, the increase in the calcite precipitation was measured at a reaction duration of 2 days because cell viability is not solely related with urease activity and calcite precipitation. In fact, urease and its activity might not be associated with an active microbial population (cell viability) since there was no significant correlation between urease activity and respiration in soil bacteria [43]. This is because urease is a cytoplasmic protein [30,44]. For example, cell fractionation  
Figure 3: Effect of temperature on precipitation. studies generally demonstrate that urease partitions with the cytoplasmic fraction [44]. Thus, the urease enzyme activity could increase after cell lysis, which might result in an increase in calcite precipitation even after 2 days. Previous research along with this study reveal that the optimum pH and temperature for urease activity, calcite precipitation, and bacterial growth are the values as shown in Figure 5[19; 20; 21; 22; 27; 29; 31; 35; 45; 46; 47; 48; 49; 50]. The optimum pH of _S. pasteui_ for urease activity ranges widely from 7 to 9, with a negligible difference reported by [PERSON] et al. [45]. On the other hand, the pH required for optimum bacterial growth was nearly 9. Calcite precipitation is a complex process resulting from various factors, with the optimum pH not coinciding with either urease activity or bacterial growth. For the two microbes used in this study, the optimum pH for calcite precipitation was 7 (neutral), which is same for _B. megaterium_ and lower than _B. sphaericus_. Urease activity and bacterial growth tend to favor a highly alkaline environment. Since CaCO\({}_{3}\) solubility decreases with increasing pH, calcite precipitation is likely to increase with increasing pH. However, the pH was measured to be optimum at 7 because increasing the pH makes the medium alkaline during ureolysis. The optimum temperature for urease activity varies from 30 to 70 \({}^{\circ}\)C for _S. pasteui_. Previous research has shown that the optimum temperature for calcite precipitation is 50 \({}^{\circ}\)C [22], which is higher than 30 \({}^{\circ}\)C obtained from this study. However, it should be noted that the previous research adopted only two temperatures (22 and 50 \({}^{\circ}\)C), so the optimum temperature was not thoroughly investigated. Figure 4: Relative calcite precipitation of the two microbial species at a reaction duration of 3 days: (**a**,**b**) the precipitation relative to that at a temperature of 30 \({}^{\circ}\)C; (**c**,**d**) the precipitation relative to that at a pH of 7.
## Appendix A Experimental Procedure  
Making reference to the notation in Figure 2, the determination of the transmission and reflection coefficients defined in Equation (1) requires the three following measurements:* Excitation in duct 1, with anechoic terminations in ducts 2 and 3, so that \(A_{1}\
eq 0\) and \(A_{2}=A_{3}=0\), and thus, \[R_{1}=B_{1}/A_{1};\ T_{12}=B_{2}/A_{1};\ T_{13}=B_{3}/A_{1}\;.\] (11)
* Excitation in duct 2, with anechoic terminations in ducts 1 and 3, so that \(A_{2}\
eq 0\) and \(A_{1}=A_{3}=0\); then, \[R_{2}=B_{2}/A_{2};\ T_{21}=B_{1}/A_{2};\ T_{23}=B_{3}/A_{2}\;.\] (12)
* Excitation in duct 3, with anechoic terminations in ducts 1 and 2, so that \(A_{3}\
eq 0\) and \(A_{1}=A_{2}=0\), so that, \[R_{3}=B_{3}/A_{1};\ T_{31}=B_{1}/A_{3};\ T_{32}=B_{2}/A_{3}\;.\] (13)  
In order to perform the above-indicated tests, the modified version of the impulse method described in [28] was used, since pressure components, which all the previous developments are based upon, are directly obtained in the time domain with a simple procedure, and the consideration of three-port elements is straightforward. In Figure 11 both the experimental setup used and the relevant pressure waves recorded are illustrated.  
The incident pulse is generated by means of a high speed electrovalve that controls the discharge from a high-pressure tank. A proper choice of the opening time ensures that the spectrum associated with the incident pulse is essentially flat. The length of the ducts placed between the valve and transducer 0, transducer 0 and the junction, and the junction and the open ends is chosen so that no windowing is necessary in order to isolate the incident, the reflected, and the transmitted pulses, as indicated in the figure. Transducer 0 was located 15 m away from both the valve and the junction, and transducers 2 and 3 were placed 0.15 m downstream of the junction, and 15 m away from their corresponding open end.  
At the position indicated for transducer 1 in Figure 2, it is clear that this transducer records the addition of the incident and the reflected pulses, as illustrated in the figure. In order to surpass this difficulty, the solution adopted is to estimate the pulse incident on the junction at section 1 (whose Fourier transform will give the complex amplitude of the \(A_{1}\) component) from an additional test performed without any element, using the pressure recorded by transducer 0 only to check the comparability of the excitations used in both types of tests (with and without junction).
# Significance of Camera Pixel Error in the Calibration Process of a Robotic Vision System
###### Abstract
The image sensor mounted on flange of multi-axis robot is navigated from position \(i\) to \(j\). Similarly, let \(Q\) be a transformation matrix that defines the transformation of coordinates from the object coordinates to the camera coordinates. The matrix \(Q\) is again of dimension \(4\times 4\), which is defined below. \[Q= \begin{bmatrix}R_{Q}&T_{Q}\\ 0&1,\end{bmatrix}  \tag{3}\]  
where \(R_{Q}\) is a \(3\times 3\) rotation matrix, and \(T_{Q}\) is a \(3\times 1\) translation vector. Let the matrix \(Q\) be known for positions \(i\) and \(j\) by capturing images at both positions and performing extrinsic camera calibration, then matrix \(B\) can be estimated using:  
\[B=Q_{j}Q_{i}^{-1}. \tag{4}\]  
Although various robotic vision systems have been proposed for implementation of in-process 3D measurement with the advantages of flexibility and reconfigurability, the level of measurement uncertainties caused by system calibration errors has not been fully investigated. It is worthy to note that developing a robotic vision system for 3D assessment is a challenging task because all the three calibration processes involved in the system are prone to inaccuracies in evaluation of transformation matrices. It is well expected that inaccuracy in estimating matrices \(A\) and \(B\) can significantly impact the hand-eye calibration process. In this paper, the analyses focuses on propagation of errors from matrices \(A\) and \(B\) to matrix \(X\). The theoretical and experimental investigations have revealed that due to difference in evaluation of transformation matrices \(A\) and \(B\), errors in matrix \(B\) predominately impacts the overall calibration process. Remainder of the paper is arranged as follows: Section 2 gives an overview of the experimental setup and possible sources of measurement uncertainties for hand-eye calibration based on the kinematic model of a six-axis KUKA robot manipulator. Section 3 presents some results on impact of measurement uncertainties on the calibration process. Section 4 demonstrates a case study for measuring the dimension of 3D parts. Finally, the paper is concluded in Section 5.
## 3 Results
### Logistic Regressions  
#### 3.2.1 Results on the Entire Sample of Memories  
The Logistic Regression Model returned a significant level (Chi-Square = 209.10; \(p\) < 0.001), attesting that the factors considered (PVF, GSM, EpF, REp) significantly predicted the Single Episodic Event (SEpE) when taken together. The variables considered also showed significant effects when analyzed individually (see Table 1). Looking at the Table 1, it is interesting to note that the presence of these incomplete memories makes it less likely that the memory is structured as an episodic memory. However, the decrease in the probability of SEpE changes according to the type of memory, and the greatest decrease is associated with the presence of Perceptual Visual Fragment (the probability of SepE is reduced by 7.4 times).  
When analyzing how frequently SEpE and other types of memory were present, it could be observed that SEpE was present in 11.8% of cases in which PVF details were present, in 9.3% of cases with GSM, in 4.1% with EpF and in 15.3% of cases which contained Rep.  
To evaluate the relationship between the number of Non-Single Episode Event (NSEpE) memories and the presence of SEpE, a second Logistic Regression analysis was carried out using NSEpE (continuous variable, ranging from 0 to 4) as the independent variable and SEpE as the dependent variable. The results showed that with the increase in incomplete memories, the likelihood of episodic memory significantly decreases (B = \(-\)3.67 (SE = 0.43), Wald's Chi\({}^{2}\) = 73.10 (df = 1); \(p\) < 0.001, Exp(B) = 0.026).  
\begin{table}
\begin{tabular}{l l l l l} \hline \multicolumn{4}{c}{_Single Episodic Event (SEpE)_} \\ \hline  & **B (SE)** & **Wald’s Chi\({}^{2}\) (df)** & \(p\) & **Exp(B)** \\ \hline _Perceptual-Visual Fragment (PVF)_ & \(-\)2.60 (0.74) & 12.44 (1) & \(<\)0.001 & 0.074 \\ _General Semantic Memory (GSM)_ & \(-\)3.80 (0.54) & 49.91 (1) & \(<\)0.001 & 0.022 \\ _Episodic Fragment (EpF)_ & \(-\)5.24 (0.71) & 54.20 (1) & \(<\)0.001 & 0.005 \\ _Repeated Episode (REp)_ & \(-\)2.97 (0.51) & 34.05 (1) & \(<\)0.001 & 0.052 \\ \hline \end{tabular}
\end{table}
Table 1: Predictive effect of the perceptual-visual fragment, general semantic memory, episodic fragment, and repeated episode on Single Episodic Event memory (n = 277).  
Figure 1: Percentages of reported memories related to the two epochs.
# Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives
## 3 Satellite Sensors for SUHI Studies  
The satellite era of UHI studies begins in 1972 [62]. Table 1 shows the proportion of the reviewed studies using different sensors/satellites imagery. Those sensors possess a common specification. The sensors are passive and, thus, receive and measure both reflected shortwave radiation (non-thermal spectral bands) and emitted longwave radiation (thermal bands) by the Earth's surface and atmosphere [39]. This is critical for SUHI studies as the understanding of drivers of SUHI is widely grounded in decoding the relationship between two key factors including the LST/SUHI intensity of a region and the associated underlying land cover/use composition and configuration. Remote sensing imagery is, thus, unique data for researchers to derive these factors for an entire city through processing a single data set (satellite imagery) [36]. In this subsection, we provide an overview of advantages and disadvantages of three satellite/sensors as the main data providers for SUHI studies (Landsat, MODIS, and ASTER). Less frequently used sensors such as AVHRR, SEVIRI, and GOES are not reviewed in this paper.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sensor** & **Landsat Series** & **MODIS** & **ASTER** & **Multiple Sensors** & **AVHRR** & **Others 1** \\ \hline Proportion & 53\% & 25\% & 7\% & 6\% & 4\% & 5\% \\ \hline \hline \end{tabular} \({}^{1}\) SEVIRI, GOES, HCMM, HJ-1B, AATSR, ITOS-1, COMS, FY-2E, AMSR-E, AMSR2.  
\end{table}
Table 1: Proportion of reviewed SUHI studies using various satellite images.
# Mathematical Apparatus of Optimal Decision-Making Based on Vector Optimization
## 4 Research, Analysis, and Formulation of the Problem of Decision-Making with Uncertain Data
#### 4.1.1 Characteristics of Certainty and Uncertainty  
Building a mathematical model of an \"object or system for making an optimal decision\" (Equations (1)-(4)) is possible, under conditions of certainty and uncertainty, which happen often. Conditions of certainty are characterized by the fact that the functional dependence of each criterion \(f_{k}\), \(k=\overline{1,K}\) (1) and the constraints \(G\)(3) on the system parameters \(x_{j},j=\overline{1,N}\), is known [6; 12; 22].  
To build a mathematical model of a system under certainty, studies of the physical processes occurring in the system are conducted. At the creation of a mathematical model of such processes, fundamental laws of physics are used, for example, models of magnetic and temperature profiles, and laws of conservation of energy and movement. A complete list of all functional characteristics of technical systems and parameters on which these characteristics depend is formed. Their verbal description is given. The technical and information interrelationships of all components of a technical system is established, i.e., the structure is under construction. At this stage, the problem of the choice of the best structure of a technical system (a problem of structural optimization) is solved [4; 23].  
As a result of the conducted research, the functional interrelationship of a set of characteristics of \(F_{1}(X)\), \(F_{2}(X)\) and restrictions of \(G(X)\) from parameters \(X\) has to be constructed.  
Conditions of uncertainty are characterized by that there is no sufficient information on the functional dependence of each characteristic and the restrictions on the parameters [8; 9; 12; 20].  
At the same time, there are two problems associated with decision making.  
The first problem is characterized by the fact that only data for some of the indicators are known (such a task is presented in the following section, see Equation (37)). The second problem is that data on some set of parameters, as well as relevant data on some set of characteristics (criteria), of a problem are known (38).  
Both problems arise when carrying out pilot studies based on the principle of \" input-output\". On the basis of the conducted pilot studies, there is a problem with the adoption of the acceptable decision. We present the analysis of these problems and decision making on their basis in the following sections.  
Thus, under conditions of certainty the function \(f_{k}(X)\), \(k\in K\) is known, for the infinite set of parameters there is a corresponding infinite set of estimates of the function (criterion). Conversely, under conditions of uncertainty, only a finite set of parameters and the corresponding set of function (criterion) estimates are known, the smaller the set of parameters, the higher the uncertainty.
## 2 Materials and Methods  
To utilize local weather condition information, along with particle matter concentration measurements, for classifying and forecasting air quality via AQI predictions, the authors propose a new framework that takes, as input, a combination of past meteorological measurements and particle concentrations. Following a susceptible transformation, this data augmentation can be used as input into different types of deep learning models. Two types of models were examined, taking into account the time series depth, as part of the framework: (1) A composite-stranded NN model, and (2) a variable-length GRU Recurrent Neural Network. Both model categories were further classified into edge computing and cloud computing models based on the number of parameters they pertained to.  
Meteorological measurements used as normalized inputs by the framework's models include local measurements of the temperature, humidity, and wind speed direction vectors. For particle concentrations, the framework measures PM1, PM2.5, PM4, and PM10 particulate matter concentrations in \(\text{ug}/\text{m}^{3}\). PM4-10 measurements track coarse pollution from dust and industrial emissions, while PM1-2.5 measurements track more hazardous materials related to heart and lung diseases. Furthermore, temperature, humidity, and wind measurements are used to try to encode how these conditions affect the dispersion of particulate matter (PM) in the atmosphere or to predict the PM concentrations under specific localized meteorological conditions. In conclusion, the proposed model utilizes the vector data of combined and normalized meteorological and particulate matter measurements as inputs, aiming to either classify or forecast current or future AQI index values.