esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Optimization of Downward Mining Schemes of Sanshandao Gold Mine
### Stress Analysis After All Stopes Are Mined and Filled  
The vertical stress distribution contour map of the stope after complete mining and filling is shown in Figure 10(a). Figure 10(a) demonstrates that the surrounding rock in the edge area of the model is predominantly subjected to compressive stress. In contrast, within the ore body model area, the presence of the mined and filled ore body leads to the generation of both compressive and tensile stresses at different locations within the backfill. Overall, the backfill body experiences a combination of compressive and tensile stresses. The tensile stress value in the backfill of Scheme 2 is the lowest, and the maximum principal stress value in the excavation area and its vicinity is less than that in the surrounding rock at the model's edge. After the stope is fully mined and filled, the minimum principal stress distribution map, as illustrated in Figure 10(b), reveals that the surrounding rock on both sides of the filling area is subjected to significant compressive stress. Additionally, the interval distribution of high and low stress areas within the filling area exhibits distinctive characteristics. The vertical stress distribution contour map of the stope after the completion of mining and filling operations is shown in Figure 11(a). Figure 11(a) indicates that the vertical stress in Schemes 1 and 3 is predominantly compressive, while Scheme 2 exhibits a tensile stress area, albeit with a relatively low tensile stress value of 0.027 MPa. The distribution of high and low stress intervals is evident in the filling areas of all three schemes. It is observed that regions subjected to greater compressive stress correspond to low stress areas. Additionally, a significant concentration of vertical stress is evident in the surrounding rock on both sides of the filling area, with local stress in some mining areas exceeding 50 MPa. Figure 11(b) presents the horizontal stress distribution contour map following the full excavation and backfilling of the stope. Analysis of the Figure 11(b) also reveals that the stress orientation in the majority of the backfill regions aligns with the Z-axis, with stress magnitudes ranging approximately from 2.5 to 2.6 MPa.  
Figure 19: The principal stress in excavation steps 16–18 of Scheme 3.
## 3 Results
### Reliability  
The repeatability of each strength test was evaluated using the two-way random-effects model with absolute agreement and a single rater ICC (ICC 2,1). The results demonstrated high reliability across all tests (Table 3), with ICC values of 0.969 for the three-finger drag test (95% CI: 0.953-0.981), 0.962 for the half-crimp test (95% CI: 0.942-0.976), and 0.936 for the handgrip (95% CI: 0.895-0.961). The CV was calculated to assess the within-subject variability for each set of measurements. The CV values were 5.35% for the three-finger  
Figure 3: (**A**) Muscle quality in three-finger drag comparing males and females; (**B**) muscle quality in half-crimp comparing males and females; (**C**) muscle quality in handgrip comparing males and females. Blue bars represent non-climbers, and purple bars represent climbers. In the gender-based comparisons, light blue with a dotted pattern represents non-climbing males, solid blue represents non-climbing females, solid purple represents climbing males, and purple with a dotted pattern represents climbing females. The boxplots display the distribution of muscle quality across these groups for different grip types. * differences between groups (\(p\) < 0.05); ES: effect size (Cohen’s d).  
Figure 2: Muscle quality between climbers and non-climbers in three-finger drag, half-crimp, and handgrip. Blue bars represent non-climbers, and purple bars represent climbers. The boxplots display the distribution of muscle quality across these groups for different grip types. * differences between groups (\(p\) < 0.05); ES: effect size (Cohen’s d).  
drag, 5.81% for the half-crimp, and 5.67% for the handgrip test, reflecting a consistent level of variation across tests.  
Absolute reliability was further evaluated through the SEM and the MDC95, reported both in absolute terms and as percentages. The SEM values were 17.52 for the three-finger drag (5.93% of the mean), 22.65 for the half-crimp (6.79% of the mean), and 30.23 for the handgrip (7.44% of the mean). The MDC95 values were 48.55 (16.42% of the mean) for the three-finger drag, 62.79 (18.82% of the mean) for the half-crimp, and 83.79 (20.63% of the mean) for the handgrip.
## 4 The 5G Use Cases and Spectrum Requirements
### ITU-Defined Generic 5G Use Cases
#### 4.1.1 eMBB  
This use case scenario is an enhanced version of the existing LTE broadband, and it allows the integration of new application scenarios and requirements to guarantee an improved user experience. Mobile broadband has been a renowned primary application of the 5G NR technology, and it is currently being embraced by mobile operators to launch the 5G network across countries. The eMBB affords users higher data rates with improved reliability, eMBB will equally address ubiquitous access to wide area coverage and multimedia services, such as 3D video and Ultra-High-Definition (UHD) Screens [2]. The first phase of 5G New Radio Access Technology (RAT) implementation is enhanced mobile broadband [57]. Generally, the mobile broadband UC of 5G will, unlike the 4G, provide improved performance by allowing seamless access to applications requiring high bandwidth for data-driven and human-centric applications, thus increasing the technology lifestyle of end users through offering needed digital services, such as access to cloud-based applications, virtual reality (VR), fixed wireless internet access at homes, and outdoor broadcasting.
# Toward Sustainable Mars Exploration: A Perspective on Collaborative Intelligent Systems
## 1 Introduction  
Mars, alongside the Moon, represents one of the most compelling frontiers in space exploration. The quest to explore Mars is driven by our innate curiosity to uncover the mysteries of the cosmos, seek signs of ancient life, and investigate the possibility of human habitability. Currently, there are two autonomous vehicles from the National Aeronautics and Space Administration (NASA) actively exploring Mars: _Curiosity_ and _Perseverance_. _Curiosity_ investigates the Martian climate and geology, searching for signs of planetary habitability [1]. The more recently launched _Perseverance_ rover carries several scientific instruments, cameras, sensors, and the first Mars helicopter _Ingenuity_[2]. _Perseverance_ collects rock and soil samples with the goal of seeking evidence of former microbial life and preparing for human exploration missions [3]. As listed in Table 1, many other Mars exploration missions are planned worldwide. For example, within the _ExoMars_ program of the European Space Agency (ESA), the _Rosalind Franklin_ rover will search for the existence of past life on Mars by analyzing undergroundsamples [4]. The mission was scheduled to launch in 2022 but due to geopolitical tensions, it will not start before 2028. Another noteworthy mission is the _Mars Sample Return_ mission, in which _Perseverance_ will collect samples using a robotic arm developed by ESA. The mission aims to collect the samples and return them to Martian orbit through the first rocket (_Mars Ascent Vehicle_) launched from Mars. This will in turn be captured by the _Earth Return Orbiter_, which finally returns to Earth [5]. Robustness, flexibility, and scalability represent the key bio-inspired attributes that are desirable in swarm robotic systems [6]. Robustness shows the capacity of the system to withstand variations in different environmental conditions despite failures in the individuals. Flexibility ensures the system's capacity to provide solutions for diverse tasks through various coordination strategies. Finally, scalability implies the practicality of adjusting the size of a swarm system. Applying swarm systems on Mars is even more challenging. For swarms to operate on Mars, the system must be highly autonomous, as Mars-to-Earth communications may take as long as 22 min. As a result, for a swarm mission on Mars, not only must robustness, flexibility, and scalability be optimized, but fault detection and diagnosis (FDD) need to be managed by the system itself. Limitations in such a setup imply that the FDD must take place locally, be computationally lightweight, and remain quick and accurate [7]. The goal of this paper is neither to provide a comprehensive literature review on Mars exploration missions like [8; 9] nor to present experimental results. Instead, our goal for this concept paper is to provide a futuristic vision of how to explore Mars in a sustainable manner. In the future, we envision that multiple rovers, drones, and satellites will be exploring Mars collaboratively. This raises the idea of collaborative vehicles, with the aim
# Wireless Energy Harvesting for Internet-of-Things Devices Using Directional Antennas
## 5 Simulation Results
### Comparative Performance Evaluation
#### 5.3.4 Different Topologies  
In the following experiments, we use four different topologies with 30 nodes to evaluate the performance. The topologies are shown in Figure 16, where each topology is a region of 5m\(\times\)5m. These topologies are described below.  
1. Uniform (UN): The nodes are evenly distributed in the region.
2. Power Law (PL): There are more nodes in the center area.
3. BA: There are fewer nodes in the center area.
4. ER: Nodes are uniformly distributed at the region while any two nodes are not close to each other.  
Figures 17 and 18 show the charging time and overflow energy of different algorithms for different topologies for the 30 devices, where the transmission angle is 90 and the offset is 10. The charging time of the DE algorithm is about 1% higher than that of the greedy algorithm for all topologies. Because the nodes in the topologies of BA and PL are close to each other, the greedy algorithm can thus achieve better performance. The Fix method  
Figure 16: The topology types, where the orange node in each topology is the AP and the blues nodes are rechargeable devices.  
Figure 15: The overflow energy of different offset.  
has a better performance for these uneven topologies. All algorithms consume a longer charging time for both UN and ER topologies because the nodes in these topologies are uniformly distributed.  
For the overflow energy, the DE algorithm still has the best performance among all schemes for all topologies. The improvement is relatively small for ER and UN topologies because of the even distribution of nodes. The Fix method suffers from more overflow energy for the PL topology because most nodes are located in the center area. When the nearby nodes are fully charged quickly, the distant nodes are still charged, resulting in high overflow energy. In contrast, the overflow energy of all algorithms decreases for the BA topology because there are only a few nodes in the center area.
# SARS-CoV-2 Treatment: Current Therapeutic Options and the Pursuit of Tailored Therapy
## 11 Baricitinib  
Baricitinib modulates cytokine signaling through a selective and reversible inhibition of JAK1 and JAK2 enzymatic activity (Table 2), resulting in reduced phosphorylation and the activation of signal transducers and activators of transcription (STATs). It also shows a dose-dependent inhibition of IL-6-induced STAT3 phosphorylation. Furthermore, it has been postulated to have an antiviral effect involving SARS-CoV-2 endocytosis, reducing, as a consequence, the virus' ability to infect lung cells [96] (Tables 3-5).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Selection of Drugs** & **Mechanism of Interaction** & **Effect of the Combination** & **Clinical Comment** \\ \hline Hydroxyl chloroquine & CYP2D6 inhibition & Potential \(\uparrow\)of ritonavir, hydroxychloroquine, rendesivir & Risk for adverse events. CQ and HCQ seem to interfere with rendesivir \\ \hline Tacrolimus, everolimus, & Potential \(\uparrow\)of remdesivir, & \\ \(\mathrm{ciclosporin}\), & CYP3A4 inhibition & \(\mathrm{lopinavir/ritonavir, hydroxychloroquine}\) & Risk for adverse events \\ sirolimus [83; 87; 88; 89] & & \(\mathrm{hyrreccetin, corticosteroids}\) & \\ \hline Ciclosporin [90] & P-gp inhibition & Potential \(\uparrow\)of remdesivir, & Risk for adverse events \\ \hline \hline \end{tabular}  
If not different specified, data reported can be found in SmPC. * other anticancer drugs inhibit or induce UGTs, or certain drug transporters and could change the pharmacokinetics of favipiravir, lopinavir/ritonavir, hydroxychloroquine, or remdesivir [65]. HCQ, hydroxychloroquine; CQ, chloroquine; CYP, cytochromes P450; OAT, organic anion-transporting polypeptide; P-gp, P-glycoprotein; \(\uparrow\), increased levels; \(\downarrow\), decreased levels.  
\end{table}
Table 5: _Cont._In a double-blind, randomized, placebo-controlled trial, baricitinib (4 mg daily for 14 days, os) combined with remdesivir (200-mg loading dose administered intravenously on day one, followed by a 100-mg maintenance daily dose for up to 10 days) reduced recovery time and accelerated clinical improvements in hospitalized patients with moderate to severe COVID-19, more than remdesivir alone [17]. In subgroup analyses, the greatest benefits have been reported in patients who needed high-flow oxygen or noninvasive ventilation [17].  
In order to clarify the effects of baricitinib in COVID-19, some phase 2 (NCT04321993) and phase 3 clinical trials have been performed (NCT04640168, NCT04421027). Baricitinib should be avoided in patients with ANC < 1 \(\times\) 10\({}^{9}\) cells/L, and it is not recommended when creatinine clearance is <30 mL/min and in severe hepatic impairment [16].
# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
## 5 Advancements in Optical Wireless Communications
### Vehicular Visible Light Communication (V\({}^{2}\)Lc)
Currently, vehicles can exchange limited information with and among pieces of traffic network infrastructure. This knowledge gives information about traffic patterns, accidents, and point of interest. In addition to digital maps, real-time traffic information is available to drivers in different urban locations. The coverage area of such accessibility and exchange of information is, however, small. The current ad-hoc-based short-range communication network IEEE 802.11 V\({}^{2}\)IC and V2C do not provide delay guarantees and QoS support for broadband applications. When vehicles push towards higher levels of automation and cope with increasingly complex road conditions, diverse data may need to be exchanged. As a result, there is a growing need for a complementary communication technology to share cooperative information between V\({}^{2}\)IC and V\({}^{2}\)C, with higher bandwidth, lower cost, and improved reliability. The potential specifications of vehicle networks well outweigh the latency capacities of current wireless and 5G/6G cellular networks' accessibility and reliability. Ubiquitous network connectivity is critical for the widespread use of ITS resources. Network coverage should still be available along roads, even in low-vehicle-density circumstances. Direct V\({}^{2}\)C or device-to-device communication (D\({}^{2}\)C) should be facilitated effectively in the absence of infrastructure. 5G/6G is a new communication technology offering a unification forum for various heterogeneous networks and ensures the critical support needed by emerging vehicle networks for ultra-reliable and low latency applications. Thus, there is tremendous growth in automation in vehicles and vehicular networks. The ever-increasing population and traffic are leading to priority being given to the safety of life, and efficient and energy-saving techniques. Bluetooth and ZigBee, due to their short-range communicationsand mm wave technology, and due to blockage, shadowing, atmospheric absorption, interference, etc., are not feasible options for long-range coverage; thus, a 5G/6G network with V\({}^{2}\)LC is a suitable solution for both short- and long-range V\({}^{2}\)LC applications. 5G/6G networks can use the technique of cell densification to maximize channel efficiency and reuse the spectra. Because of 5G/6G's reliance on LoS for data transmission, V\({}^{2}\)LC agrees with the configuration of small atto-cell deployment, allowing the reuse of spectra with minimal interference between two adjacent V\({}^{2}\)LC atto-cells [313; 320]. This interference may only be absolutely nullified if APs communicate in adjacent cells using different colors (wavelength). Considering these advantages and the possibility for RF-V\({}^{2}\)LC hybrid technology, car manufacturing industries may incorporate V\({}^{2}\)LC systems into their fully automated vehicles.
## 4 Results and Discussions
### The S-Parameter  
Figure 10 presents the S-parameter of the UWB MIMO antenna at a separation distance of 44 mm. The S\({}_{11}\) (Figure 10a) and the S\({}_{12}\) (Figure 10b) parameters were presented with and without MBEG structure. The overall average isolation without decoupling is \(-13\) dB. However, the MC effect was improved by the addition of the decoupling structure. With introduction of the MBEG structure, the MC between the arrays is improved to about \(-23\) dB in the large portion of the UWB band. This is around a 10 dB improvement over the reference antenna (antenna without the decoupling structure).  
Figure 8: Antenna total gain with and without MBEG decoupling structure.  
Figure 9: Antenna VSWR with and without MBEG decoupling structure.
# Stability of Paracetamol Amorphized by Co-Melting with Various Cellulose Derivatives
## 3 Results  
### DSC Analysic  
#### 3.1.1 Paracetamol Amorphization  
Figure 1 shows the DSC curves of paracetamol and physical mixtures analyzed initially in the heating-cooling-heating cycle. In this figure, vertical lines mark the melting point of the first and second crystal forms of paracetamol. DSC measurement values, including melting enthalpy, peak onset (T\({}_{\text{on}}\)) and peak maximum (T\({}_{\text{p}}\)) for single paracetamol and paracetamol in mixtures are summarized in Table 1. The values are presented based on the results obtained in the first cycle (1 st and 2 nd heating; average of three measurements) and heating after 2 and 4 months.  
The DSC 1 st heating curve of paracetamol (Figure 1A, a) shows a sharp melting peak beginning at 170.1 \({}^{\circ}\)C. The DSC curves of all physical mixtures during 1 st heating (Figure 1A, b-m) show the melting peak of form I shifted to lower temperatures (166.3-169.3 \({}^{\circ}\)C). This shift is usually greater the lower the paracetamol content in the mixture. If there are no interactions between the components of the mixture, the enthalpy of melting is proportional to the amount of the crystalline form of the substance. Based on the melting enthalpy of paracetamol during 1 st heating, the amount of crystal form I of paracetamol in the mixture was calculated. These values are listed in Table 1.  
No crystallization peak was observed in the DSC cooling (Figure 1B) curves of paracetamol and mixtures. The DSC curve of 2 nd heating of paracetamol (Figure 1C, a) shows a small signal at approximately 23 \({}^{\circ}\)C, characteristic of a glass transition. An exothermic crystallization peak starting at approximately 76 \({}^{\circ}\)C was then observed and subsequently, an endothermic, sharp melting peak at approximately 158 \({}^{\circ}\)C.  
Figure 1: DSC 1 st heating (**A**), cooling (**B**) and 2 nd heating (**C**) curves of paracetamol (a), and its mixtures containing 75%, 50% and 25% of paracetamol (respectively) with HPMC 86 (b–d), HPMC 90 (e–g), HPMC 120 (h–j) and MC (k–m).  
In the DSC curves of mixtures with 2 nd heating (Figure 1C, b-m), the signal of glass transition was difficult to interpret reliably. For this reason, in order to prevent an error in determining the temperature of glass transition in the mixtures, the determination of these values was not undertaken. In the DSC curves of mixtures containing 75% paracetamol with various HPMCs (Figure 1C, b,e,h), signals above 130 \({}^{\circ}\)C were observed. Then, endothermic crystallization, a broad peak and subsequently a melting point of approximately 154 \({}^{\circ}\)C was observed. The DSC curve of the 2 nd heating of the mixture containing 75% paracetamol and MC (Figure 1C, k) showed an endothermic peak below 170 \({}^{\circ}\)C.  
In the case of mixtures containing 50% and 25% paracetamol (Figure 1C, c,d,f,g,i,j,l,m) no peaks during re-heating were observed.
# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 1 Introduction
**: **Drift Characteristics**: How do concept versus data drift and sudden, gradual, incremental, or recurring drift affect the design of explainability methods? **SQ3. **: **Datasets and Benchmarks**: Which datasets are most often used for evaluation? **SQ4. **: **Models**: What model families (e.g., tree-based, neural, GNNs) are paired with explainability in drift scenarios? **SQ5. **: **Metrics**: Which quantitative metrics capture gains in interpretability, user trust, decision quality, or regulatory compliance? **SQ6. **: **Industrial Adoption**: In which real-world sectors have explainable drift detection methods been deployed, and what evidence demonstrates benefits such as reliability, maintenance efficiency, or compliance? **SQ7. **: **Stakeholder Perception**: What types of users, domain experts, data scientists, or end-users benefit most from these techniques, and how do they derive opportunities from them? * **SQ8. ****Online Adaptation of XAI:** How are traditional XAI methods--designed for static models--adapted to online learning environments to maintain interpretability under concept/data drift? * **SQ9. ****Local Drift Granularity:** To what extent do XAI methods enable fine-grained analysis of local drift characteristics (instance-level or subgroup shifts) compared to global drift detection? * **SQ10. ****Challenges and Limitations**: What major obstacles, side effects, or scalability issues arise when integrating drift detection with explainability? * **SQ11. ****Future Directions**: Which research avenues and interdisciplinary approaches are proposed to overcome current limitations and foster wider adoption? The responses to the GQs and SQs are integrated into the following discussion to offer a detailed overview of the current state of the field. The structure of the paper is delineated as follows: After the Introduction, Section 2 introduces essential definitions and concepts foundational to drift phenomena and their explainability. Section 3 elaborates on the methodology employed for collecting the papers. Then, Section 4 aims to provide an empirical account of the rank inclusion and exclusion process, while Section 5 presents a comprehensive overview of the papers selected for review. Section 6, which constitutes the core of this review, examines the various technological and methodological approaches utilized for the explainability and interpretability of drift, and presents our taxonomy. A thorough discussion of the findings and responses to the previously mentioned research questions is contained in Section 7. Finally, in Section 8, conclusions are drawn.