esa-sceva/satcom-chunk-collection huggingface dataset:
# From Recruitment to Retention: AI Tools for Human Resource Decision-Making
## 4 AI in Performance Management
### AI for Continuous Feedback and Performance Reviews  
Studies reveal that the kind and origin of feedback greatly impact its efficacy. A study with thirty-six participants showed that performance and motivation were positively impacted more by person-mediated feedback--that is, input given through direct interaction--than by computer-mediated feedback [69].  
When computer programs unfairly favor some groups over others, this is called AI bias. Unrepresentative training data, incorrect algorithms, and societal inequities are all potential causes of this bias. According to research, biased AI systems might worsen inequalities in areas such as healthcare and employment by leading to negative results, especially for marginalized populations [70].  
Making sure datasets are balanced is one way to reduce bias. By manipulating the interrelationships of variables throughout training, a mitigated causal model was presented in a study that produces datasets devoid of discrimination. To improve AI model fairness, our approach enables the simultaneous mitigation of numerous biased variables [71]. In adversarial debiasing, one neural network is trained to make predictions and the other to detect and fix biases in the training data. The goal of this dual-network strategy is to promote equity by balancing predictions across demographic groups [71].  
An innovative approach known as TWIX was created to offer explanations for AI assessments that resemble human speech. This strategy is especially useful in evaluating surgical skills. Improving overall performance across varied surgeon cohorts, TWIX significantly avoided underskilling and overskilling biases by integrating visual explanations alongside predictions [70; 72]. To build confidence in AI systems, openness is key. Researchers have shown that stakeholders need AI models to be explainable so they can comprehend the decision-making process. By explaining the reasoning behind AI evaluations, the TWIX method increases openness and fairness, for example [72]. In addition, accountability and openness in AI applications, especially in healthcare, are being required by new regulatory frameworks [70]. AI in performance management has introduced innovative tools for real-time feedback, objective evaluations, and predictive analytics, enabling organizations to enhance individual and team performance. These systems eliminate biases, provide data-driven insights, and streamline continuous feedback mechanisms. However, challenges such as data privacy, security, and AI bias must be addressed to ensure fairness and trust. By combining AI's capabilities with human judgment, organizations can create more dynamic and equitable performance management systems.
# Review of Indoor Positioning: Radio Wave Technology
## 4 Positioning Algorithms
### Machine Learning Localization Approach
In indoor localization, K-means, fuzzy C-Mans, neural network, and SVM-C have been used for the implementation of indoor positioning methods. Machine learning of RPs clustering and recognition algorithms can provide the determination of positioning accuracy. The traditional RPs clustering method is needed to pre-define the more accurate positions, since the uncertain number of clusters give rise to poor accuracy [227]. In [228], K-means FP clustering is applied to separate multi-floor levels for a smart building system. In [229], K-means-based approach was used to improve the performance of a distance estimation KNN which determines the close distance values of a mobile user's nearest location. Moreover, the fuzzy C-means clustering method is used to develop KNN performance [230; 231]. * Matching algorithm: a matching algorithm aims to find the best match resulting in the correct predicted location between the current FPs' location as measured by the client mobile device in the online phase fingerprinting [232; 233]. Although the fingerprinting-based localization algorithm finds the user location, this needs to obtain the exact location of the user inside the indoor environment. Moreover, the FP matching algorithm of WLAN-based positioning could have the enhanced ability for more accurate positioning performance. In [233] is described the superior FPs WLAN system which has a 26% better precision than the conventional fingerprinting localization method. The distance computing of KNN is commonly useful for a matching algorithm as the location determination method. A criticism of KNN is that the software computational time is high in the framework [234; 235]. To overcome this problem, the segmentation-based KNN method describing the improvement of the positioning accuracy is 9.24% in the magnetic field indoor location [235]. The magnetometer is one of the IMU sensors that measures the strength of the Earth's magnetic field. In [234] is shown the improvement in indoor positioning accuracy of 91.7% by using a matching KNN algorithm for positioning technology using a geomagnetic field, countering the issues of radio technologies effected from environments, such as multipath noise, human motion and impact obstacles. In [236], the path matching algorithm of indoor positioning for a magnetic field is proposed by solving the time-variant positioning system without influencing radio wave technology. It is certainly true that radio technology benefits wireless sensing networks of a practical indoor location, but there could be environmental influences. The updated indoor positioning system of the Wi-Fi-based RSSI can improve the positioning performance from digital map matching information which makes use of PDR [236; 237; 238; 239; 240; 241]. Indoor map matching methods could make use of the information by utilizing smartphones, already making three aspects accessible: mapping path data, user movement activities and position.
## 2 Principle  
### The Quaternary Grating Design Principle  
The traditional binary gratings in PMP are shown in Figure 1a,b. The duty cycle \(\omega_{0}/T\) generally maintains 1/2 but can be 1/3 or else.  
Figure 1: The binary gratings: (**a**) binary grating with 1/2 duty cycle; and (**b**) binary grating with 1/3 duty cycle.  
The color binary fringe projection (CBF) proposed by [PERSON] et al. [24]. Three monochromatic binary fringes occupy 1/3 period, respectively, and are arranged periodically. When the binary fringes are encoded in red (R), green (G), and blue (B) channels as shown in Figure 2, by separating the captured color fringe pattern into R, G, and B components, respectively, a real-time measurement can be achieved. However, to capture the fringe images, a color CCD must be adopted, and generally the frame capturing rate may be limited.  
The schematic diagram of a real-time 3D measuring method based on single-shot quaternary grating projection is shown in Figure 3. The quaternary grating is encoded and saved into the flash memory of the (Digital Light Processing) DLP. The DLP projects the quaternary grating with the period width of 9 pixels onto the reference plane and the object, and the project angle \(\alpha\) is 27 degrees. At the same time, a high frame rate monochrome Charge-coupled Device (CCD) camera is capturing the corresponding fringe pattern synchronously on the same plane.  
The projected matrix of the quaternary grating is shown in Figure 4a. The three non-zero gray levels of 1, 2, and 3 are arranged and encoded in equal width in sequence. Figure 4b shows the cutaway view of the single-shot quaternary grating. When the quaternary grating is projected onto the reference plane and object, respectively, with DLP4500, by selecting the 2-bit coding mode, the correspondingly captured fringe pattern on the reference and the deformed pattern on the object are shown in Figure 4c,d. The shadow in the deformed pattern corresponds to the 0 grayscale in the quaternary grating projection as shown in Figure 4d, which is why the 0 grayscale is not encoded into the quaternary grating. Because the captured deformed pattern is of 256 grayscale, it is firstly segmented into a quaternary deformed pattern, and then decomposed into three binary deformed patterns, therefore the 3D shape of the object can be reconstructed by the binary grating projection PMP. Because the projected quaternary grating is encoded with only 2-bit depth with DLP-4500, the refresh rate can reach up to 1428 Hz. If the higher frame capturing rate a monochrome camera is used, it will have a broad application prospect in real-time and fast online measurement.  
Figure 3: Online 3D measurement system based on quaternary grating projection.  
Figure 2: The features of the composite CBF and the segmentation.  
\[I=\begin{bmatrix}1&1&1&2&2&2&3&3&3\\ 1&1&1&2&2&2&3&3&3\\ 1&1&1&2&2&2&3&3&3\\ 1&1&1&2&2&2&3&3&3\\ 1&1&1&2&2&2&3&3&3\\ 1&1&1&2&2&2&3&3&3\\ 1&1&1&2&2&2&3&3&3\\ 1&1&1&2&2&2&3&3&3\\ 1&1&1&2&2&2&3&3&3\end{bmatrix}\]
###### Abstract  
With the arrival of the big data era, it has become possible to apply deep learning to the health monitoring of mine production. In this paper, a convolutional neural network (CNN)-based method is proposed to monitor the health condition of the balancing tail ropes (BTRs) of the hoisting system, in which the feature of the BTR image is adaptively extracted using a CNN. This method can automatically detect various BTR faults in real-time, including disproportional spacing, twisted rope, broken strand and broken rope faults. Firstly, a CNN structure is proposed, and regularization technology is adopted to prevent overfitting. Then, a method of image dataset description and establishment that can cover the entire feature space of overhanging BTRs is put forward. Finally, the CNN and two traditional data mining algorithms, namely, k-nearest neighbor (KNN) and an artificial neural network with back propagation (ANN-BP), are adopted to train and test the established dataset, and the influence of hyperparameters on the network diagnostic accuracy is investigated experimentally. The experimental results showed that the CNN could effectively avoid complex steps such as manual feature extraction, that the learning rate and batch-size strongly affected the accuracy and training efficiency, and that the fault diagnosis accuracy of CNN was 100%, which was higher than that of KNN and ANN-BP. Therefore, the proposed CNN with high accuracy, real-time functioning and generalization performance is suitable for application in the health monitoring of hoisting system BTRs.  
health monitoring; hoisting system; balancing tail ropes; convolutional neural network; image processing; ANN-BP +
Footnote †: journal: Applied Soft Soft 2018, 8, 1346; doi:10.3390/app8081346
# Recent Advances in Tunable External Cavity Diode Lasers
## 3 Research Progress of ECDL
### External Cavity Quantum Dot Laser
conducted a detailed study on external cavity InAs/InP QD lasers with lasing wavelength around 1.5 \(\upmu\)m [57]. The results showed that reasonable selection of coatings could effectively increase the range of the laser and reduce the threshold current. The tuning spectra of QD EC laser with gain chip with no facet coating, anti-reflection (AR) coating, and anti-reflection/high-reflection (HR) coatings were measured. Blue shift can be seen when comparing the EC laser with AR coating to that with no facet coating. In the short wavelength region, there was a larger density of states, and it was easier for carriers to fill high energy levels. The tunable range of the long wavelength region can be increased by coating the QD laser with high reflective coating. The threshold current of the EC lasers was compared with different facet coatings. HR/AR coating could effectively reduce cavity surface loss and obtain the minimum threshold current. These results demonstrated that implementing AR/HR facet coatings can significantly enhance the performance of QD EC lasers by optimizing their tunable range and minimizing threshold currents. In 2021, [PERSON] et al. also studied the effects of facet coatings on the EC laser [58]. They fabricated EC semiconductor lasers incorporating InAs/InP quantum dots and reached conclusions consistent with previous research. Their results demonstrated that without facet coatings, the EC laser exhibited a limited tuning bandwidth of only 45 nm. However, by applying AR/HR facet coatings, the tuning range was significantly expanded to 92 nm, highlighting the critical role of facet coatings in enhancing the tunability and overall performance of EC lasers. External cavity quantum dot lasers exhibit outstanding performances. Table 1 summarizes the research advancements in external cavity quantum dot lasers over recent years, highlighting significant progress in this field.
## 3 Problem Definition and Formulations
### Mathematical Formulation
#### 3.3.2 Decision Variables
Specifically, each truck is allowed to depart from a certain depot and is required to return to the same or another depot after departure. Constraints (9)-(22) secure the network flow restrictions between vertices, while forbidding trucks from moving between specific vertices. Constraints (9) and (10), respectively, regulate that no network flow is allowed between depots or between identical vertices. Constraints (11)-(14), (15)-(18), (19)-(20), and (21), (22), respectively, maintain network flow conservation at request vertices \(N_{DEC}\), \(N_{PEC}\), \(N_{DFC}\), and \(N_{PFC}\). For instance, constraint (11) indicates that trucks are restricted to moving from \(N_{DEC}\) to request vertices \(N_{PEC}\), \(N_{PFC}\), and \(N_{DFC}\) or to the return vertex \(N_{d}\), while the total network flow is constrained to 1. Constraint (12) only permits the trucks traveling from \(N_{PEC}\) or \(N_{d}\) to the ED request vertices \(N_{DEC}\cap N_{c}\). Similarly, Constraint (13) makes sure that the trucks can only move from ER request vertices \(N_{PEC}\cap N_{c}\) or depots \(N_{d}\) to the OE request vertices \(N_{DEC}\cap N_{s}\). Constraints (14) and (17) forbid the movement of empty containers from IE request vertices \(N_{PEC}\cap N_{s}\) to OE request vertices \(N_{DEC}\cap N_{s}\), i.e., the arc from IE request vertex direct to OE request vertex is a forbidden arc. Constraint (23) calculates the departure time of a truck from a depot before visiting an empty container delivery vertex \(N_{DEC}\), whereas constraint (24) determines the exact time at which a truck leaves an empty container pickup vertex \(N_{PEC}\) and later returns to the depot. Both times are employed to represent the constraints on the number of empty containers in the depot, as outlined in constraint (25). This constraint stipulates that a depot must retain at least zero empty containers after an empty container is picked up from it by a truck. Constraint (26) defines the continuity of arrival times at request vertices. Constraint (27) ensures adherence to request service priority requirements. Constraints (28) and (29) impose limits on the time window for each request vertex. Constraints (30) and (31) specify the departure and return times for a truck at a depot. Constraint (32) calculates the duration for which a truck exceeds the maximum working hours. Constraint (33) requires that the return time to the depot of a truck must be equal to or later than its departure time. Constraint (34) states that the truck's total working time should be no less than its transfer time. Constraints (35)-(40) define the scope of time-related decision variables.
## 4 Dynamic Parameter Adaptation  
In this section, we explain in detail the structure of the fuzzy system used for each of the HS and DE algorithms. The fuzzy system used for both algorithms has one input and one output. In the case of the HS algorithm, the input parameter is the iterations and for the output, the HMR parameter is used, and for the DE algorithm, the input parameter is the generations and the output parameter is F (mutation). Equation (21) is used to calculate the input of the fuzzy system according to the method:  
\[Experiment=\ \frac{Current\ Experiment}{Maximum\ of\ experiments}. \tag{21}\]  
In Equation (21), the experiment refers to iterations for the FHS method and generations for the FDE method. The current experiment represents the current iterations or generations and the maximum of experiments represents the maximum number of iterations and generations.  
The parameters of the outputs mentioned above are converted into fuzzy parameters based on the following Equations (22) and (23):  
\[HMR=\ \frac{\sum_{i=1}^{r_{lnm}}\mu_{i}^{hmr}\ (hmr_{1i})}{\sum_{i=1}^{r_{lnm}} \mu_{i}^{hmr}} \tag{22}\]  
where \(HMR\) is the memory consideration; \(r_{hmr}\) is the number of rules of the Shadowed Type-2 Fuzzy System corresponding to \(hmr\); \(hmr_{1i}\) is the output result for rule \(i\) corresponding to \(hmr\); \(\mu_{i}^{hmr}\) is the membership function of rule \(i\) corresponding to \(hmr\).  
\[F=\ \frac{\sum_{i=1}^{r_{F}}\mu_{i}^{F}\ (F_{1i})}{\sum_{i=1}^{r_{F}}\mu_{i}^{F}} \tag{23}\]  
where \(F\) is the mutation; \(r_{hmr}\) is the number of rules of the Shadowed Type-2 Fuzzy System corresponding to \(F\); \(F_{1i}\) is the output result for rule \(i\) corresponding to \(F\); \(\mu_{i}^{F}\) is the membership function of rule \(i\) corresponding to \(F\).  
Both fuzzy systems in the input and outputs are granulated into three membership functions, as shown in Figures 3 and 4 respectively. They are granulated into _low_, _medium_, and _high_, and the rules are described in Tables 1 and 2.  
The rules are based on previous experimentation as presented in [16; 35], and for the ST2 FDE method are used in a decreasing fashion and for the ST2 FHS method are used in an increasing fashion, respectively.
# Study on the Effect of Insulation Materials on the Temperature Field of Piles in Ice-Rich Areas
## 3 Modeling of Pile-Soil Heat Transfer
### Hydration Heat Generation Rate of Concrete  
The heat from the concrete's hydration zone is the main heat source affecting the distribution of the permafrost temperature field. By performing a literature analysis, we adopted the exponential heat of hydration formula and implemented the concrete hydration heat release process using finite element software by defining the heat of hydration heat generation rate (HGEN) [18]. The formula is as follows:  
\[Q(t)=Q_{0}\big{(}1-e^{-mt}\big{)} \tag{7}\]  
\[HGEN=mQ_{0}e^{-mt}(W+kF) \tag{8}\]  
\(Q(t)\)--1 kg cumulative heat of hydration of cement in kJ/kg;  
\(t\)--Age;  
\(Q_{0}\)--Heat dissipation per l kg of cement, in kJ/kg;  
\(m\)--Heat of hydration coefficient, i.e., the coefficient of cement species related to the rate of the heat of hydration. See Table 3 for details;  
\(HGEN\)--Hydration heat generation rate of concrete in kJ/(m\({}^{3}\):h);  
\(W\)--Amount of cementing material per m\({}^{3}\) concrete, kg/m\({}^{3}\);  
\(F\)--Amount of concrete mix per unit volume, kg/m\({}^{3}\);  
\(k\)--The discount factor for fly ash is taken as 0.25.  
According to the test, the heat dissipation \(Q_{0}\) of the cement was 315.8 kJ/kg. According to the concrete's inlet temperature, \(m=0.318\). From Table 4, we can see that the amount of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & **Dry Density** & \multicolumn{2}{c}{**Specific Heat Capacity (J/kg-K)**} & \multicolumn{2}{c}{**Thermal Conductivity (W/m-k)**} & \multirow{2}{*}{**Porosity (\%)**} \\ \cline{3-3} \cline{5-6}  & **(kg/m\({}^{3}\))** & & **Frozen** & **Melt** & **Frozen** & **Melt** \\ \hline
**Peaty soils** & 1170 & 1338.0 & 1259.8 & 3.41 & 1.45 & 80 \\
**Ice layer** & 470 & 1799.5 & 1799.5 & 1.88 & 1.88 & 91 \\
**Powdery clay** & 640 & 710.1 & 715.0 & 2.02 & 1.48 & 63 \\
**Round grains** & 1830 & 466.8 & 485.6 & 2.07 & 1.55 & 41 \\
**Strongly weathered tuffs** & 2050 & 643.4 & 639.3 & 3.66 & 1.57 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Permafrost thermal property parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Density (kg/m\({}^{3}\))** & **Thermal Conductivity (W/m-K)** & **Specific Heat Capacity (J/kg-K)** \\ \hline Concrete & 2500 & 1.24 & 970 \\ Polyurethane foam & 38 & 0.02 & 2000 \\ Steel sleeve & 7500 & 43 & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material thermal property parameters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Inlet Temperature (\({}^{\circ}\)C)** & **5** & **10** & **15** & **20** & **25** & **30** \\ \hline m & 0.295 & 0.318 & 0.340 & 0.362 & 0.384 & 0.406 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Reference value of m depending on the inlet temperature.  
cement used in each m\({}^{3}\) of concrete in the project is 322 kg and 57 kg of fly ash. According to Equation (8), we obtain:  
\[\begin{split} HGEN&=mQ_{0}e^{-mt}(W+kF)\\ &=33,768\cdot e^{-0.318\text{kJ}}/\left(\text{m}^{3}\text{:h} \right)\end{split}\]
## 3 Results and Discussion
### Question 4 Distribution of Respondents  
Question 4 solicited respondents' general opinion about the topic prior to them considering the other questions that might frame this opinion. Most respondents did not read the entire survey before beginning to answer; however, a few did. A few also opted to change answers to earlier questions as they were answering later questions or at the end. In both cases, this was observed to be a very isolated phenomenon. The responses were divided on this question with 19% of respondents indicating that they agreed, 16% indicating no preference, 23% indicating that they disagreed and 9% indicating that they strongly disagreed. The agree-no preference and disagree-no preference categories had 10% and 13% of respondents in them, respectively.  
In total, 50% of respondents indicated some form of disagreement (between disagree-no preference and strongly disagree), 33% indicated some form of agreement (between agree-no preference and strongly agree) and 16% indicated no preference. Note that the percentages do not add up to 100% due to rounding and one survey where this question was not responded to. This data is presented in Table 3 and Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Strongly Agree** & \multicolumn{2}{c}{**Agree**} & \multicolumn{2}{c}{**No Preference**} & \multicolumn{2}{c}{**Disagree**} & \multicolumn{2}{c}{**Strongly Disagree**} & \multicolumn{2}{c}{**No Response**} \\ \hline
4 & 5 & 44 & 24 & 37 & 29 & 55 & 10 & 21 & 1 \\ \hline \end{tabular}
\end{table}
Table 3: Responses to the statement “I would trust computer analysis (without human involvement) of planetary science data”.  
Figure 3: Graph of years in field distribution of respondents.