esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Sequence Movie Recommendation System Using Deep Learning and KMeans
## 3 Methodology of Movie Recommendation Systems
### Model Architecture  
This section introduces the model architecture of the entire recommendation system as detailed in Figure 1. There are two main processes in our system. The first process involves applying deep learning and key transformer features to predict the next movie based on the user's sequence and information. The transformer model was implemented with a multi-head attention layer and positional embedding, which are adept at understanding the complex viewing patterns of users. The multi-head attention mechanism allowed the model to focus on different parts of the user's movie history to provide a comprehensive understanding of their preferences. The positional embeddings provide the model with an understanding of the order in which movies were watched, which was crucial for predicting future interests. Subsequently, we fed an embedding layer of the user behavior sequence, which was implemented using a transformer architecture. This was then combined with user demographics following multi-layer perceptron (MLP) to predict the rating. In the second step, after training the transformer model, we integrated the output predicted rating into K-mean clustering to generate a Top-N recommendation system for the target user. K-means clustering is a popular method in cluster analysis, which is designed to partition a set of objects into K clusters in such a way that the sum of the squared distances between the objects and their assigned cluster mean is minimized. Our objective was not only to develop recommendation algorithms that personalized the results based on historical preferences but also to dynamically adjust diversity in the recommendations list according to the user's interest. To achieve this, we utilized the K-means approach to personalized diversity, which automatically segments movies into distinct groups based on user preferences (target next movie) according to certain predefined categories (movie embeddings associated with the movie's genre). When creating the recommended list, we investigated these clusters to ensure a diverse suggestion of Top-N movies when a user interacts with our system. Similarly, for new movies with few or no ratings, clustering helps us identify and recommend unseen movies to target users based on their similarity score with other movies. The K-means clusters aid in diversifying the selected top N by ensuring that movies from different clusters are included in the recommendations. Therefore, our approach helps in providing a variety of recommendations to the user, ensuring that these recommendations are not limited to a specific genre or type of movie [45; 46].
## 3 Results
### Linearity and Sensitivity of the Glucose Sensor  
In order to evaluate and compare the performance of different sensors (the NO-P sensor, the SGL-P sensor and the SHPM-OS sensor), we performed chronoamperometry. Our results showed that the curve of the NO-P sensor was at the lowest position when the glucose concentration increased from 0 to 20 mM; the response current changed from 140 nA to 859 nA with the sensitivity of 35.95 nA/mM (Figure 3a), which is consistent with the sensors proposed before [10]. As the pore distribution on the PU surface expands, the limiting intensity of glucose transfer decreases. The current values of the SGL-P sensor increased from 200 nA to 1106.45 nA at the same glucose range and the corresponding sensitivity was 45.3 nA /mM, which was greater than the NO-P sensor (Figure 3a). However, when the glucose concentration was higher than 10 mM, the sensitivity of SGL-P rapidly decreased, and the R\({}^{2}\) coefficient value decreased to 0.9610, which was lower than NO-P the (0.9987, Figure 3b). Compared to the NO-P and SGL-P sensors, the response value of the SHPM-OS sensor increased from 309 nA to 2278.32 nA with a higher sensitivity and linearity (98.45 nA/mM and 0.9989, respectively, Figure 3a). Although the current intensity was slightly smaller than the SGL-P sensor under low glucose concentration (0 to 10 mM), the sensitivity remained stable while the current values still increased gradually under high glucose concentration due to the constant replenishment of the oxygen.  
Figure 2: SEM images of hierarchically porous PU membranes covered on the SHPM-OS glucose sensor. (**a**) SEM image of porous inner PU with the large-sized pores (6.01 \(\pm\) 0.925) \(\upmu\)m. (**b**) SEM image of porous outer PU with the small-sized pores (2.51 \(\pm\) 0.638) \(\upmu\)m.
# Deployment Protection for Interference of 5G Base Stations with Aeronautical Radio Altimeters
## 4 Results and Discussion
### Prohibited Zone with Beamforming Antenna
#### 4.2.1 Horizontal Prohibited Zone  
Figure 14 illustrates the horizontal prohibited zones for the RMa base station using the 16 \(\times\) 16 beamforming antenna at \(\theta=0^{\circ}\).  
* The largest prohibited zone occurs at \(AGL=2000\) ft. The prohibited zone at \(AGL=1000\) ft is smaller than that at \(AGL=200\) ft. This is because at 1000 ft, the interference is mainly generated by the side lobes of the antenna.
* Within the prohibited zone (the largest red circle), there are still multiple smaller red circles. This is most evident in Figure 14c. This occurs because there are angles with minimal gain between different lobes of the beamforming antenna. At these angles, the interference is minimal. These smaller circles should be disregarded when determining the size of the prohibited zone.
* The prohibited zone of RMa exhibits irregular characteristics when \(AGL=200\) ft. The statistical features of LOS and NLOS can manifest at the edges of the prohibited zone. However, these features are not evident when \(AGL=1000\) ft at 2000 ft.  
Figure 15 depicts the prohibited zone when the vertical scan angle of the beamforming antenna is set to \(10^{\circ}\) (\(\theta=10^{\circ}\)). At 200 ft, \(\theta\) has a negligible impact on the prohibited zone. At 1000 ft, the receiving antenna (RA) captures the radiated energy of the main beam, resulting in a larger prohibited zone. At 2000 ft, the prohibited zone is the largest, with a radius of 6.7 km.  
Figure 14: Horizontal prohibited zones of RMa at \(AGL=200\) ft, 1000 ft, 2000 ft (beamforming antenna (\(\theta=0^{\circ}\))). (**a**) \(AGL=200\) ft. (**b**) \(AGL=1000\) ft. (**c**) \(AGL=2000\) ft.
# Secure On-Off Transmission in UAV Relay-Assisted mmWave Networks
## 3 Secrecy Evaluation
### Performance Analysis with Perfect Beam Alignment
#### 3.2.2 Non-On-Off Transmission Scheme  
For comparison, we also give the TP and SOP for conventional non-on-off scheme, in which the selected UAV relay \(R_{0}\) always transmits the signals after receiving the messages from \(S\). It is obvious that the TP in non-on-off transmission scheme is \(p_{tx}^{non}=1\). And the SOP is derived as  
\[\begin{split} p_{sop}^{non}&=1-\Pr\left(\gamma_{R_{0} D}>2^{C_{s}}(1+\gamma_{E})-1\right)\\ &=1-\sum\limits_{j\in\{L,N\}}A_{j}\sum\limits_{i}^{T}\bar{w}_{i}^ {(r)}g_{\tau_{j}}\left(r_{i}\right)\int_{0}^{\infty}\int_{\bar{\beta}(x)}^{ \infty}F_{\gamma_{R_{0}D}j}\left(y\right)f_{\gamma_{E}}\left(x\right)dzdy\\ &\overset{\varepsilon}{=}1-\sum\limits_{j\in\{L,N\}}A_{j}\sum \limits_{i}^{T}\bar{w}_{i}^{(r)}g_{\tau_{j}}\left(r_{i}\right)\int_{0}^{\infty }F_{\gamma_{E}}\left(x\right)f_{\gamma_{R_{0}D}j}\left(\beta\left(x\right) \right)\bar{\beta}^{\prime}\left(x\right)dy\\ &\overset{\delta}{\approx}1-\sum\limits_{j\in\{L,N\}}A_{j}\sum \limits_{i\
eq k}^{T}\bar{w}_{i}^{(r)}\bar{w}_{k}^{(x)}g_{\tau_{j}}\left(r_{i} \right)F_{\gamma_{E}}\left(x_{k}\right)f_{\gamma_{R_{0}D}j}\left(\bar{\beta} \left(x_{k}\right)\right)\bar{\beta}^{\prime}\left(x_{k}\right),\end{split} \tag{15}\]  
where \(F_{\gamma_{E}}\left(\cdot\right)\) and \(f_{\gamma_{R_{0}D}j}\left(\cdot\right)\) are given as (12) and (14), step (\(e\)) is obtained by using integration by parts, step (\(f\)) is due to the maximum \(\gamma_{R_{0}D}^{\max}\) is approximated as \(\gamma_{R_{0}D}^{\max}=\frac{5P_{i}N_{i}G_{R_{0}}^{M}G_{D}^{ML}(H)}{\sigma^{2}}\), \(\bar{w}_{k}^{(x)}=\frac{\pi}{T}\sqrt{\bar{x}_{k}\left(\gamma_{R_{0}D}^{\max}- \bar{x}_{k}\right)}\), \(\bar{x}_{k}=\frac{\gamma_{R_{0}D}^{\max}}{2}\left(1+\cos\frac{(2k-1)\pi}{2T}\right)\), and \(\bar{\beta}\left(y\right)=2^{C_{s}}\left(1+y\right)-1\).
## 3 Results
### Doping of HA Nanoparticles
#### 3.3.9 Hydroxapatite Doped with Other Metals  
Doping hydroxyapatite (HA) with metals introduces enhanced biological, mechanical, and antimicrobial properties, making it highly relevant for biomedical applications. Several metal-doped variants of HA have shown potential in promoting bone regeneration, improving material bioactivity, and preventing microbial infections [76; 77; 78; 79; 80; 81; 82; 83; 84; 85].  
Iron-Doped Hydroxyapatite (Fe-HA)  
Iron (Fe\({}^{3+}\))-doped HA has been extensively studied for its unique properties, particularly its application in magnetic scaffolds for bone regeneration. Iron imparts redox-active and magnetic properties, transitioning HA from diamagnetic to superparamagnetic as Fe\({}^{3+}\) concentration increases. This also affects the material's crystallinity, morphology, and solubility, with higher iron content leading to reduced crystallite size, rod-like particle morphology, and increased bioactivity and dissolution rates [69]. In vitro studies by [PERSON] et al. demonstrated that Fe-HA generates an electric field that enhances HA nucleation and crystallization, further increasing apatite layer thickness on surfaces. These findings underscore the potential of Fe-HA in supporting accelerated bone formation [70].  
Cerium-Doped Hydroxyapatite (Ce-HA)  
Cerium (Ce), known for mimicking calcium ions, plays a role in stimulating metabolic activity and promoting bone regeneration. Ce-doped HA combines biocompatibility, osteoconductivity, and antibacterial properties, making it a promising material for biomedical applications [71]. Ce-HA coatings effectively inhibited hospital-associated pathogens such as _Escherichia coli_ and _Staphylococcus aureus_, while in vitro studies confirmed its incompatibility and its ability to promote osteoblast proliferation and mineralization [72]. Ce-HA's neuroprotective effects and ability to inhibit oxidative stress add further value to its use in regenerative medicine.  
Fluoride (F), essential for bone and dental health, enhances mineralization by stimulating osteoprogenitor cell proliferation and differentiation. Fluoride-doped hydroxyapatite (F-HA) exhibits greater density and mechanical strength compared to pure HA, primarily due to the incorporation of fluoride ions into its structure. This improves ion adsorption, bioactivity, and cytocompatibility under controlled sintering conditions [73]. F-HA delivers therapeutic fluoride ions, making it particularly effective in treating osteoporosis and supporting bone mass growth. Its antibacterial properties against oral bacteria reduce the risk of cariogenic species development, offering additional benefits in dental applications [74]. The unique ability of F-HA to slowly release fluoride ions in the oral environment is also utilized in tooth decay prophylaxis [75].
## 3 Results and Discussion
### Adsorption Properties
#### 3.2.2 Effect of Initial Concentration and Time  
We studied the effect of time on Cu (II) adsorption at a pH of 5 by using solutions with different initial concentrations of Cu(II). The result is shown in Figure 6.  
The increase in the adsorption capacity was rapid in the first 30 min, slowed during the next 8 h, and then became constant. The maximum adsorption capacity increased from 4.50 to 20.66 mg/g when the initial concentration of the Cu(II) increased from 50 to 400 mg/L. A higher value of the initial concentration gradient provided a significant driving force in the adsorption process and thus increased the adsorption capacity.
# An Adaptive Ensemble Approach to Ambient Intelligence Assisted People Search
## 3 Adaptive Ensemble of Classifiers
### Estimation of the Mixture Matrix  
To combine the multiple decision functions, we need to properly estimate the mixture matrix in Equation (13). According to the Bayesian model, we can combine classifiers by their posterior beliefs, as in Equation (15):  
\[p(y|x_{n},x_{n}^{\prime})=\sum_{m=1}^{M}p(w_{nm}|x_{n}^{\prime})p\big{(}y|x_{n },w_{nm},x_{n}^{\prime}\big{)} \tag{15}\]  
where \(x_{n}\) and \(x_{n}^{\prime}\) are one atom from feature vectors \(X\) and \(X^{\prime}\), respectively. \(w_{nm}\) is one of the weight parameters of the classifier \(F_{m}(X)\), which measures the distribution of \(x_{n}\).  
Equation (15) is comprised of two parts:  
1. \(p(w_{nm}|x_{n}^{\prime})\) is an unknown value. We decompose it further as Equation (16), where \(p(w_{nm})\) is the prior belief of each classifier. \(p(x_{n}^{\prime}|w_{nm})\) is the marginal likelihood for \(x_{n}^{\prime}\) under the distribution of \(w_{nm}\). \[p\big{(}w_{nm}|x_{n}^{\prime}\big{)}=p(w_{nm})p\big{(}x_{n}^{\prime}\big{|}w_{ nm}\big{)}/p\big{(}x_{n}^{\prime}\big{)}\] (16)
2. \(p(y|x_{n},w_{nm},x_{n}^{\prime})\) is a conditional probability. In our decision function (Equation (10)), each feature \(z_{n}=\big{(}x_{n}-x_{n}^{\prime}\big{)}^{2}\) from vector \(X\) gives its vote on the final decision independently. We simply estimate it by: \[p\big{(}y|x_{n},w_{nm},x_{n}^{\prime}\big{)}=w_{nm}\big{(}x_{n}-x_{n}^{\prime} \big{)}^{2}\] (17) By substituting Equation (17) into Equation (14), it becomes: \[F_{c}(Z)=\sum_{m=1}^{M}\sum_{\forall n}a_{nm}w_{nm}z_{n}+\sum_{m=1}^{M}b_{m}d_ {m}\] \[F_{c}\big{(}\big{(}x_{n}-x_{n}^{\prime}\big{)}^{2}\big{)}=\sum_{m=1}^{M} \sum_{\forall n}a_{nm}w_{nm}\big{(}x_{n}-x_{n}^{\prime}\big{)}^{2}+\sum_{m=1} ^{M}b_{m}d_{m}\] \[F_{c}\big{(}\big{(}x_{n}-x_{n}^{\prime}\big{)}^{2}\big{)}=\sum_{m=1}^{M} \sum_{\forall n}a_{nm}p\big{(}y|x_{n},w_{nm},x_{n}^{\prime}\big{)}+\sum_{m=1} ^{M}b_{m}d_{m}\] (18)  
Comparing the above equation with Equation (15), it is easy to see that there exists a one-to-one relation between \(a_{nm}\) and \(p(w_{nm}|x_{n}^{\prime})\). This can be formally written as Equation (19):  
\[\left[\begin{array}{ccc}A_{1}&\cdots&A_{m}\\ \end{array}\right]=\left[\begin{array}{ccc}p\big{(}w_{11}|x_{1}^{\prime} \big{)}&\cdots&p\big{(}w_{1m}|x_{1}^{\prime}\big{)}\\ \vdots&\ddots&\vdots\\ p(w_{n1}|x_{n}^{\prime})&\cdots&p(w_{nm}|x_{n}^{\prime})\\ \end{array}\right] \tag{19}\]  
Each element in the mixture matrix A can be calculated through Equation (16). Here, the prior belief \(p(w_{nm})\) of each classifier is a known value, and it can be obtained by verification on a set of test samples. \(p(x_{n}^{\prime})\) is a constant value for all \(F_{m}(Z)\), but the marginal likelihood \(p(x_{n}^{\prime}|w_{nm})\) is hard to estimate because \(X^{\prime}\) is a single sample.
# Sliding Mode Control of Electro-Hydraulic Position Servo System Based on Adaptive Reaching Law
## 2 Problem Formulation and Dynamic Models
Linearizing Equation (7), we obtain  
\[q_{L}=\frac{\partial q_{L}}{\partial x_{v}}x_{v}+\frac{\partial q_{L}}{ \partial p_{L}}p_{L}=K_{v}x_{v}-K_{c}p_{L} \tag{12}\]  
where \(K_{v}\) is the flow gain of the valve and \(K_{c}\) is the flow-pressure coefficient of the valve. From Equations (1), (2), (5) and (6), the load flow is obtained as  
\[ \begin{array}{ll}q_{L}=q_{1}&=A_{1}\frac{dx_{p}}{dt}+C_{ip}(p_{1}-p_{2})+C_{ cp}p_{1}+\frac{V_{1}}{\beta_{e}}\frac{dp_{1}}{dt}\\ &=A_{1}\dot{V}_{1}+C_{ic}p_{L}+C_{tc}P_{s}+\frac{\dot{p}_{L}V_{e}}{4\beta_{e}} \end{array}  \tag{13}\]  
where \(C_{ic}=\frac{(1+n^{2})C_{ip}+C_{cp}}{1+n^{3}}\) is the equivalent leakage coefficient of the cylinder; \(C_{tc}=\frac{n^{2}[n(C_{ip}+C_{cp})-C_{ip}]}{1+n^{3}}\) is the incidental leakage coefficient of the cylinder; and \(V_{e}=\frac{4V_{1}}{1+n^{3}}\) is the equivalent volume of the cylinder. The resulting force equation between the hydraulic cylinder and the load is  
\[A_{1}p_{1}-A_{2}p_{2}=A_{1}P_{L}=m\ddot{x}_{p}+B\dot{x}_{p}+Kx_{p}+F \tag{14}\]  
where \(m\) is the equivalent load mass; \(B\) is the equivalent load damping; \(K\) is the equivalent load stiffness; and \(F\) is the equivalent load force. Equations (5), (12) and (14) are the three basic equations and the displacement equation of the valve-controlled asymmetric hydraulic cylinder, Laplace transformed, can be obtained as  
\[X_{p}=\frac{Q_{L0}(s)-\frac{K_{ce}}{A_{1}{}^{2}}(1+\frac{V_{e}}{4\beta_{e}K_{ cc}}s)F-\frac{C_{tc}P_{s}}{A_{1}}}{4\beta_{e}A_{1}{}^{2}}s^{3}+(\frac{mK_{ce}}{A_{1 }{}^{2}}+\frac{BV_{e}}{4\beta_{e}A_{1}{}^{2}})s^{2}+(1+\frac{BK_{ce}}{A_{1}{}^ {2}}+\frac{KV_{e}}{4\beta_{e}A_{1}{}^{2}})s+\frac{KK_{ce}}{A_{1}{}^{2}}} \tag{15}\]  
where \(K_{ce}=K_{c}+C_{ic}\) is the total flow-pressure coefficient; \(Q_{L0}(s)\) is obtained by the Laplace transform of \(q_{L0}\); \(q_{L0}=K_{v}x_{v}\) is the change in load flow caused only by the displacement of the spool; and \(P_{s}\) is the Laplace transform of the supply pressure \(p_{s}\). Supposing the spring stiffness is \(K=0\), the damping coefficient \(\frac{{A_{1}}^{2}}{{K_{ox}}}\) is usually much larger than \(B\), so that \(\frac{{BK_{ox}}}{{A_{1}}^{2}}<<1\). Under Equation (15), it reduces to  
\[ \begin{array}{ll}{X_{p}}&=\frac{{\frac{{Q_{L0}(s)}}{{A_{1}}}- ({K_{cr}}+\frac{{V_{e}}}{{4\beta_{e}}})\frac{{F}}{{A_{1}}^{2}}-\frac{{C_{tc}}{ P_{s}}}{{A_{1}}}}}{{\frac{{mV_{e}}}{{4\beta_{e}}{A_{1}}^{2}}s^{3}+\frac{{mK_{cr}}}{{A_{ 1}}^{2}}s^{2}+s}}\\ &=\frac{{\frac{{Q_{L0}(s)}}{{A_{1}}}-({K_{cr}}+\frac{{V_{e}}}{{4 \beta_{e}}}s)\frac{{F}}{{A_{1}}^{2}}-\frac{{C_{tc}}{P_{s}}}{{A_{1}}}}}{{s( \frac{{s}^{2}}{{\omega_{h}}^{2}}+\frac{{2\xi_{h}}s}{{\omega_{h}}}+1)}}\end{array}  \tag{16}\]  
where \(\omega_{h}=\sqrt{\frac{{4\beta_{e}{A_{1}}^{2}}}{{mV_{e}}}}\) is the hydraulic natural frequency and \(\xi_{h}=\frac{{K_{cc}}}{{A_{1}}}\sqrt{\frac{{\beta_{e}}{m}}{{V_{e}}}}\) is the damping ratio. When \(x_{v}<0\), the same formula as Formula (16) can be deduced in the same way.
# Retrieval and Comparison of Multi-Satellite Polar Ozone Data from the EMI Series Instruments
## 4 Results and Discussions
### Ozone TOCs diurnal Variations Analysis  
Because of the different overpass times of the EMI series, we investigated the differences in the datasets to characterize the diurnal variations in the ozone TOCs. Figure 6 shows the overpass TOC values of the EMI-DQ01, EMI-GF5 (01A), and EMI-GF5(02) datasets at four ground stations from 20 November 2023 to 24 December 2023. The figure indicates that the EMI-DQ01 and EMI-GF5 (01A) datasets were consistent, with an average deviation of 0.58%. The results also showed that morning and afternoon variations in the ozone TOC values varied in different latitude regions. In high-latitude regions, daylight hours were shorter and ozone generation activity was relatively low. The morning and afternoon changes in the ozone concentrations were relatively stable. The average difference between the EMI-DQ01, EMI-GF5(01A), and EMI-GF5(02) datasets at Dumont station was 0.8%. In mid-latitude regions, the morning and afternoon variations in the ozone concentration were slightly large, with the average difference between the morning and afternoon datasets being 1.4%. At the low-latitude Reunion station, sunlight was strong during the day and ozone generation was more active. The concentration of ozone changed significantly in the morning and afternoon, with the average difference between the morning and afternoon datasets being 2.49%, since daylight conditions were more favorable for ozone generation.  
Based on the retrieval results for the EMI series, the diurnal variations in the TOCs for different regions were analyzed; this process improved the temporal resolution of the EMI observations and can provide a reference for more refined detection of diurnal variations in ozone holes.  
Figure 6: Analysis of the changes in the TOCs at several ground-based stations in the morning and afternoon from 20 November 2023 to 24 December 2023.
# Influence of Ingestion of Lactulose on \(\gamma\)-Lactones Enanating from Human Skin Surface
###### Abstract  
Lactulose is known to grow health-promoting bacteria, with an increase in the production of beneficial metabolites, such as lactic acid and short-chain fatty acids (SCFAs) in the colon. Ingestion of lactulose powder at a food dosage level is known to reduce the dermal emanation of ammonia, a typical human skin gas which potentially affects body odour. However, no study has reported the effect of lactose on human skin gases other than ammonia. In this study, the influence of lactose ingestion on the dermal emissions of \(\gamma\)-lactones, volatile cyclic esters with sweet smells, was investigated in healthy subjects. Healthy participants ingested the lactulose powder with a food dosage of 4 g d\({}^{-1}\) once a day for 2 weeks. \(\gamma\)-lactones emanating from the skin surface were collected from each participant's forearm by using a passive flux sampler, and six kinds of \(\gamma\)-lactones, namely, \(\gamma\)-hexalactone (C6), \(\gamma\)-heptalactone (C7), \(\gamma\)-octalactone (C8), \(\gamma\)-nonalactone (C9), \(\gamma\)-decalactone (C10), and \(\gamma\)-undecalactone (C11), were determined by gas chromatography-mass spectrometry. Quantification of bifidobacteria in faeces collected before and after ingestion of lactulose for 2 weeks was carried out by using real-time PCR. The results showed a significant increase in the dermal emission fluxes of sweet-smelling C10 and C11 lactones as the number of bifidobacteria increased in the faeces, presumably mediated by SCFAs produced in the colon.  
human skin gas; \(\gamma\)-lactones; prebiotic; lactulose; SCFAs +
Footnote †: journal: Applied Sciences
# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 6 Taxonomy
### Distance/Divergence Geometry
#### 6.1.3 Multidimensional Class Evaluation  
As visible from Table 14, this class predominantly addresses concept drift, with 11 papers covering various forms--sudden, gradual, incremental, recurrent, or general. Only two papers explicitly deal with data drift, suggesting a stronger orientation toward conceptual shifts rather than purely data-driven anomalies. The application contexts are notably diverse, spanning from manufacturing and healthcare to energy, cybersecurity, IoT, and environmental sensors. This diversity underscores the versatility and broad applicability of distance/divergence geometry methods across different domains. Healthcare and cybersecurity emerge as significant application contexts, indicating that these fields particularly benefit from the geometric interpretability offered by this mathematical approach. Regarding the scope, this group is balanced between local (instance-level) explanations and global (model-level) insights, although there is a slight emphasis on local interpretations (nine papers local vs. four global). This suggests that distance-based methods naturally lend themselves to pinpointing precise causes of drift at a granular level, which can be especially valuable for targeted diagnostic or corrective actions. In terms of audience, the primary target users of these methods are clearly data scientists, although a smaller subset is directed toward end-users, indicating that most explanations require technical literacy to fully interpret and leverage. Interestingly, only one paper explicitly specifies quantitative metrics through user-study methodologies. The absence of defined interpretability metrics across the rest of the papers might highlight a potential gap: this technique appears less suited for direct quantitative evaluation of interpretability gains, relying instead on visual or qualitative assessments. Finally, concerning the learning mode, there is a mixed approach with the notable usage of hybrid methods, online learning, and fewer offline methods. This indicates adaptability in different operational contexts and aligns well with real-world scenarios where data streams continuously evolve.