esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Total Cable Resistance vs. Per-Unti-Length Resistance  
Before comparing the total AC resistance of different twisted configurations, we distinguish among three quantities: R\({}_{\text{AC}}\), which is the total AC resistance for a single twisted section of length \(\lambda\); R\({}_{\text{AC-2D}}\), the effective AC resistance per meter calculated by \(R_{\text{AC-2D}}=R_{\text{AC}}/l_{\text{twist}}\); and R\({}_{\text{AC-2D-cable}}\), the per-meter resistance obtained by simply dividing R\({}_{\text{AC}}\) by \(\lambda\). Table 6 summarises these values for various lay lengths.  
While the effective AC resistance per meter \(R_{\text{AC-2D}}\) which is normalized by \(l_{\text{twist}}\), decreases for shorter lay lengths (\(\lambda\)), the actual AC resistance per meter of the cable, denoted \(R_{\text{AC-2D-cable}}\), can still be larger. The key distinction is that smaller \(\lambda\) values create a more tightly wound helix, increasing the actual conductor path length within the same longitudinal distance. This tighter twist adds more conductor material to the total circuit, so charges must traverse a physically longer route--therefore incurring a higher total resistance. In other words, even if the per-unit-length resistance is reduced by improved current distribution (skin/proximity mitigation), the added conductor length can outweigh that benefit, leading to a greater overall ohmic loss for the entire longitudinal length of the cable. This relation is clearly visible in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\lambda\) & \(l_{\text{twist}}\) & \(R_{\text{AC}}\) & \(R_{\text{AC-2D}}\) & \(R_{\text{AC-2D-cable}}\) \\ (**m**) & **(m)** & **(\(\mu\Omega\))** & **(\(\mu\Omega\)/m)** & **(\(\mu\Omega\)/m)** \\ \hline
0.1 & 0.2688 & 2.263 & 8.42 & 22.63 \\
0.2 & 0.2975 & 2.898 & 9.74 & 14.49 \\
0.4 & 0.4594 & 4.846 & 10.55 & 12.12 \\
0.6 & 0.6490 & 6.974 & 10.75 & 11.62 \\
1.4 & 1.4513 & 15.798 & 10.89 & 11.28 \\
2.0 & 2.0625 & 22.495 & 10.91 & 11.25 \\
2.2 & 2.2668 & 24.732 & 10.91 & 11.24 \\
2.6 & 2.6758 & 29.206 & 10.91 & 11.24 \\
3.0 & 3.0852 & 33.685 & 10.92 & 11.23 \\
3.4 & 3.4948 & 38.165 & 10.92 & 11.22 \\
3.6 & 3.6997 & 40.405 & 10.92 & 11.22 \\
3.8 & 3.9046 & 42.646 & 10.92 & 11.22 \\
4.0 & 4.1096 & 44.887 & 10.92 & 11.22 \\
5.0 & 5.1346 & 56.095 & 10.92 & 11.22 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Presentation of resistance values for models with varying lay length \(\lambda\) at 50 Hz.
## 4 Materials and Methods
### Selection Pressure Analysis  
To understand the selection pressure applied during the evolution of genes, we examined the classification of dN/dS in six species of _Phylot alca_. Using the Geneious software v2025.0 (Biomatters, Auckland, New Zealand) [85], we separately aligned the cp genomes of six species: _P. acinosa, P. insularis, P. polyandra, P. japonica, P. lattenia,_ and P. americana,_ with _A. clematidea_ as the reference genome. Next, we extracted the CDSs from each aligned genome and calculated the dN/dS values for each extracted sequence using PAML v4.9 [94]. The tree file required the use of the CODEML model in PAML, which was generated using the Kimura 2-parameter (K2P) model in MEGA11 [86] ([PERSON] et al., 2021), which considers the differences in transition and transversion rates when calculating genetic distances. To further explore the selection pressures acting on the genes, we applied both the M0 (one-ratio) and M2a (positive selection) models to PAML. The M0 model assumes a single dN/dS ratio (\(\omega\)) across all sites in the coding sequences, providing a baseline for the selective pressure analysis by averaging the effects across the entire gene. In contrast, the M2a model allows for more complex selective scenarios by incorporating three site categories: (1) those under purifying selection (\(\omega<1\)), (2) sites evolving neutrally (\(\omega=1\)), and (3) sites under positive selection (\(\omega>1\)). This model helps to identify the codons that may be under positive selection, suggesting functional adaptation. We performed a likelihood ratio (LR) test to compare these models and detect evidence of positive selection. The resulting LRT statistic was compared to a Chi-squared distribution with degrees of freedom equal to the difference in the number of parameters between the models, allowing us to determine whether the M2a model, which includes positive selection, significantly improves the fit compared with the simpler M0 model. The dN/dS values for the genes across the six _Phylot alca_ species were visualized using the R package ggplot2 [95].
## 3 Results and Discussions
### Analysis of Monitoring Results of Near Foundation Pit  
During the tunneling process, the drilling and blasting method will inevitably affect the surrounding structure. The blasting vibration wave contacts the surrounding rock first, making that different surrounding rock conditions have different propagation and attenuation laws of the blasting vibration. The line tunnel's influence on the excavation distance from the adjacent foundation pit and the propagation law of blasting vibration along different surrounding rock conditions were studied. Table 5 shows the surrounding rock conditions of the J-Station line near the foundation pit.  
The sensors are sequentially arranged in different lithologies, and the blasting monitoring is performed five times. The monitoring experiment whose test number is from T1 to T5 is carried out to study the difference in blasting vibration speed of different lithologies. As illustrated in Figure 9, different types of surrounding rocks have different responses to blasting vibrations. From J1 to J6, the surrounding stones' nature ranges from soil to slightly weathered granite. The lithology transitions from weak stratum to hard rock. The blasting vibration speed gradually decreases from the hard rock area to the soil layer. At the interface between the soft rock and the hard rock, the blasting vibration speed decreases slowly but decreases sharply at other strata. The amplitude is not much different. It is shown that the blasting vibration attenuation law is different under different lithological conditions, especially at the boundary between soft and hard rocks. Blasting vibration speed changes significantly, mainly because that the blasting vibration wave attenuates quickly in the hard rock area. In contrast, the rock area's attenuation speed becomes slower, causing the blasting vibration speed in soft rock to decrease rapidly. However, the elastic modulus and the blasting vibration speed gradually decreases from the hard rock to the soft rock, indicating that the rock vibration speed affected by the blasting vibration is proportional to the elastic modulus of rock.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor Number** & **Origin of the Age** & **Geotechnical Name** & **Layer Thickness/m** \\ \hline J1 & Q4\({}^{ml}\) & Plain fill & 1.85 \\ J2 & Q4\({}^{ml}\) & Miscellaneous fill & 2.11 \\ J3 & \(\gamma\)5\({}^{3}\) & Strongly weathered & 5.44 \\  & & granite & 3.69 \\ J4 & \(\gamma\)5\({}^{3}\) & Slightly weathered & 7.15 \\ J5 & \(\gamma\)\(\pi\)5\({}^{3}\) &
\begin{tabular}{c} Slightly weathered \\ granite porphyry \\ \end{tabular} & 4.76/excavated \\ \hline \hline \end{tabular}
\end{table}
Table 5: Lithology distribution of surrounding rock of foundation pit in J-Station.  
The prediction of Peak Velocity-Z is consistent with the variation trend of measured data, with an overall error of \(\sim\)20%. Model-Stratum can effectively reflect the mutation of real data in the transition section of hard and soft rock, indicating that the established model and its monitoring system can effectively predict the vibration propagation law.
# Computing a Clique Tree with the Algorithm Maximal Label Search
## 5 Extended Results  
### Clique Tree of a Minimal Triangulation  
The algorithm MLSM computes an MEO and the associated minimal triangulation of the input graph \(G\). It computes an MMO of \(G\) if the order on labels is total [12]. It can be modified into the algorithm moplex-MLSM computing an MMO of \(G\) whether the order on labels is total or not, which can be extended to the algorithms MLSM-CliqueTree and DCL-MLSM-CliqueTree computing an MMO, the associated minimal triangulation \(H\) of \(G\), a clique tree and the minimal separators of \(H\). Below are the algorithms moplex-MLSM and DCL-MLSM-CliqueTree (Algorithms 10 and 11). **Theorem 8**. : _The algorithm moplex-MLSM computes an MMO and the associated minimal triangulation of the input graph._  
To prove Theorem 8, we will use the following lemmas. **Lemma 5**. : _[_8_]_ _A moplex of a minimal triangulation of \(G\) is a moplex of \(G\)._  
**Lemma 6**. : _If \(\alpha\) is an MEO of \(G\) and a PMO of \(G_{\alpha}^{+}\), then it is an MMO of \(G\)._  
Proof. : Let \(H=G_{\alpha}^{+}\). We prove this by induction on the size \(k\) of the perfect moplex partition \((X_{1},\ldots,X_{k})\) associated with \(\alpha\) in \(H\). If \(k=1\), then \(H\) is a clique, so \(G\) is a clique, as well, since \(H\) is a minimal triangulation of \(G\), and we are done. We assume that the property holds for a perfect moplex partition of size \(k\geq 1\). Let \((X_{1},\ldots,X_{k+1})\) be the perfect moplex partition associated with \(\alpha\) in \(H\). As \(X_{1}\) is a moplex of \(H\), by Lemma 5, it is a moplex of \(G\). Let \(G_{1}\) be the graph obtained from \(G\) by saturating \(N_{G}(X_{1})\) and removing \(X_{1}\), and let \(\alpha_{1}\) be the restriction of \(\alpha\) to \(V\setminus X_{1}\). As \(\alpha\) is an MEO of \(G\), \(\alpha_{1}\) is an MEO of \(G_{1}\) and as \((G_{1})_{\alpha_{1}}^{+}=H(V\setminus X_{1})\), \(\alpha_{1}\) is a PMO of \((G_{1})_{\alpha_{1}}^{+}\). Hence, by the induction hypothesis, \(\alpha_{1}\) is an MMO of \(G_{1}\), and therefore, \(\alpha\) is an MMO of \(G\). Note that the fact that \(\alpha\) is a PMO of \(G_{\alpha}^{+}\) does not imply that it is an MMO of \(G\). For instance, if \(G\) is a non-clique graph with a universal vertex, then any ordering \(\alpha\) of \(G\) such that \(\alpha(1)\) is universal is a PMO of \(G_{\alpha}^{+}\) (since \(G_{\alpha}^{+}\) is a clique), but not an MMO of \(G\) (since it is not an MEO of \(G\)). Proof. : (of Theorem 8) Let \(\alpha\) be the ordering and \(H\) be the graph computed by an execution of moplex-MLSM on input graph \(G\). As this execution is also an execution of MLSM, \(\alpha\) is an MEO of \(G\) and \(H=G_{\alpha}^{+}\). As moreover at each iteration, the labels are increased exactly in the same way as in an execution of moplex-MLS on \(H\), \(\alpha\) is a PMO of \(H\) and, therefore, an MMO of \(G\) by Lemma 6.
# Preparation Techniques of TiO\({}_{2}\) Nanofluids and Challenges: A Review
## 2 Preparation Methods
### Hydrothermal Synthesis
#### 2.2.2 Preparation of Nanofluids  
Preparation of nanofluids involves the suspension and dispersion of nanoparticles into the base fluid prepared by using the previously described techniques. It is very difficult to suspend nanoparticles uniformly and stably into the base fluid due to the presence of strong attractive van der Waals forces between them that lead to colliding and aggregation effects. So, different techniques have been used for proper dispersion of nanoparticles into the base fluid. Ultrasonication, magnetic stirring, surfactant addition and pH adjustment are the techniques used most commonly for two-step methods, with the last two techniques mostly used in combination with the first two techniques for better dispersion and stability of nanofluids.
# Clock Ensemble Algorithm Test in the Establishment of Space-Based Time Reference
## 3 Experimental Analysis
### Observed Data  
Observed data used in this paper are GFZ GNSS clock corrections (deviations of the satellite clock with respect to the GPS time, download at [[ftp://ftp.gfz-potsdam.de/GNSS/products/mgex/](ftp://ftp.gfz-potsdam.de/GNSS/products/mgex/)]([ftp://ftp.gfz-potsdam.de/GNSS/products/mgex/](ftp://ftp.gfz-potsdam.de/GNSS/products/mgex/)), accessed on 10 June 2021), the start and end time are from 10 June 2021 to 10 October 2021, and the sampling interval is 300 s.  
For measured data, the reading of a single clock cannot be known, and all that can be known is the time difference between two clocks. Therefore, we need to choose one clock as the reference and compare it with other clocks. TA is represented by the time deviation with the reference clock. In view of this, the specific time deviation of TA is still unknown. Therefore, this paper divides these data into four groups to generate four TAs and subtract them to eliminate the offsets caused by the unpredictability of a single clock.  
In this experiment, 16 onboard clocks of BDS, GPS, and Galileo systems are included, which are grouped as follows:  
Group A: C25-C28 satellite onboard clocks of Beidou-3, all of which are hydrogen clocks, take C28 as the reference clock and make the difference with other clocks to obtain three groups of time deviation, denoted as A1, A2, and A3;  
Group B: C29, C30, C34, and C35 satellite onboard clocks of Beidou-3, all of which are hydrogen clocks, take C29 as the reference clock, and make the difference with other clocks to obtain three groups of time deviation, denoted as B1, B2, and B3;  
Group C: G08, G15, G19, and G22 satellite onboard clocks of GPS, in which G08 is a cesium clock, and the others are rubidium clocks, take G19 as the reference clock and make the difference with other clocks to obtain three groups of time deviation, denoted as C1, C2, and C3;  
Group D: E02, E05, E13, and E19 satellite onboard clocks of Galileo, in which E19 is a rubidium clock, and the others are hydrogen clocks, take E02 as the reference clock and make the difference with other clocks to obtain three groups of time deviation, denoted as D1, D2, and D3.
# Advancing TinyML in IoT: A Holistic System-Level Perspective for Resource-Constrained AI
## 5 Opportunities and Cross-Cutting Themes
### Ethical and Sustainability Considerations  
While technical progress in embedded AI is crucial, ethical and sustainability dimensions cannot be overlooked. Data bias and representativeness pose major concerns, particularly when training or refining models on localized datasets that may not capture the diversity of real-world conditions [79]. This risk is aggravated if multiple edge devices gather skewed or incomplete data, inadvertently reinforcing biases in collaborative systems. Careful curation, federated data sharing among diverse demographics, and ongoing validation of model outputs can mitigate this problem, though each step increases complexity [50].  
Privacy also emerges as both a motivator and a challenge. On-device analysis does lower the risk of mass data collection, but it does not inherently solve transparency issues around what is being inferred and how decisions are made. Regulators and consumers alike demand clarity on how AI-based devices handle personal or environmental data. Furthermore, the surge in IoT device manufacturing raises environmental concerns, as large-scale hardware production consumes materials and energy [22]. Proponents of edge AI argue that locally processed data can significantly lower global data center energy usage; however, the net ecological impact depends on device lifecycles, e-waste handling, and responsible recycling [80]. Thus, a holistic perspective--integrating the entire product chain--is key to ensuring that AI in constrained devices yields net benefits to society and the planet [56].  
Understanding the interdependencies between resource limitations, technical solutions, application requirements, and broader societal concerns is crucial for advancing the field. Figure 1 provides a system-level perspective, illustrating how these various elements interact and contribute to the successful deployment of AI on resource-constrained devices, synthesizing the key challenges, solutions, and considerations discussed in the preceding sections.  
Figure 1: System-level perspective for TinyML deployment: an architectural flowchart outlining key challenges, solutions, and cross-cutting themes.
# Algorithmic Aspects of Some Variations of Clique Transversal and Clique Independent Sets on Graphs
## 1 Introduction
A function \(f\) is a signed dominating function (abbreviated as SDF) of \(G\) if the domain and range of \(f\) are \(V(G)\) and \(\{-1,1\}\), respectively, and \(f(N_{\mathbb{G}}[x])\geq 1\) for \(x\in V(G)\). If the domain and range of \(f\) are \(V(G)\) and \(\{-1,0,1\}\), respectively, and \(f(N_{\mathbb{G}}[x])\geq 1\) for \(x\in V(G)\), then \(f\) is a minus dominating function (abbreviated as MDF) of \(G\). The number \(\gamma_{\mathbb{s}}(G)=min\{f(V(G))\mid f\) is an SDF of \(G\}\) is the signed domination number of \(G\). The minus domination number of \(G\) is \(\gamma^{-}(G)=min\{f(V(G))\mid f\) is an MDF of \(G\}\). The signed domination problem (abbreviated as SDP) is to find a minimum-weight SDF for a graph. The minus domination problem (abbreviated as MDP) is to find a minimum-weight MDF for a graph. Our main contributions are as follows. 1. We prove in Section 2 that \(\gamma^{-}(G)=\tau_{\mathbb{C}}^{-}(G)\) and \(\gamma_{\mathbb{s}}(G)=\tau_{\mathbb{C}}^{\mathbb{s}}(G)\) for any sun \(G\). We also prove that \(\gamma_{\times k}(G)=\tau_{\mathbb{C}}^{k}(G)\) and \(\gamma_{\{k\}}(G)=\tau_{\mathbb{C}}^{\{k\}}(G)\) for any sun \(G\) if \(k>1\). 2. We prove in Section 3 that \(\tau_{\mathbb{C}}^{\{k\}}(G)=k\tau_{\mathbb{C}}(G)\) for any graph \(G\) with \(\tau_{\mathbb{C}}(G)=\alpha_{\mathbb{C}}(G)\). Then, \(\tau_{\mathbb{C}}^{\{k\}}(G)\) is polynomial-time solvable if \(\tau_{\mathbb{C}}(G)\) can be computed in polynomial time. We also prove that the SCTP is a special case of _the generalized clique transversal problem_[8]. Therefore, the SCTP for a graph \(H\) can be solved in polynomial time if the generalized transversal problem for \(H\) is polynomial-time solvable. 3. We show in Section 4 that\(\gamma_{\times k}(G)=\tau_{\mathbb{C}}^{k}(G)\) and \(\gamma_{\{k\}}(G)=\tau_{\mathbb{C}}^{\{k\}}(G)\) for any split graph \(G\). Furthermore, we introduce \(H_{1}\)-_split graphs_ and prove that \(\gamma^{-}(H)=\tau_{\mathbb{C}}^{-}(H)\) and \(\gamma_{\mathbb{s}}(H)=\tau_{\mathbb{C}}^{\mathbb{s}}(H)\) for any \(H_{1}\)-split graph \(H\). We prove the NP-completeness of SCTP for split graphs by showing that the SDP on \(H_{1}\)-split graphs is NP-complete. 4. We show in Section 5 that \(\tau_{\mathbb{C}}^{\{k\}}(G)\) for a _doubly chordal graph_\(G\) can be computed in linear time, but the \(k\)-FCTP is NP-complete for doubly chordal graphs as \(k>1\). Notice that the CTP is a special case of the \(k\)-FCTP and the \(\{k\}\)-CTP when \(k=1\), and thus \(\tau_{\mathbb{C}}(G)=\tau_{\mathbb{C}}^{1}(G)=\tau_{\mathbb{C}}^{\{1\}}(G)\) for any graph \(G\). 5. We present other NP-completeness results in Sections 6 and 7 for \(k\)-trees with unbounded \(k\) and subclasses of total graphs, line graphs, and planar graphs. These results can refine the \"borderline\" between P and NP for the considered problems and graphs classes or their subclasses.
## 4 Method
### Network Architecture  
As illustrated in Figure 2, our PoseTrack framework comprises three crucial components: (1) person detection in videos, (2) human pose estimation in videos, and (3) multi-person articulated tracking in videos. As mentioned in Section 3.1, person detection in videos plays a pivotal role in the overall pipeline, particularly in mitigating the effects of frame degeneration. The frame degeneration, attributed to factors such as motion blur artifacts and occlusions, significantly amplifies the inaccuracy of single-frame person detection. As a consequence, top-down approaches encounter difficulties in accurately delineating the true boundaries of overlapping bodies, while bottom-up methods face challenges in effectively associating key points amidst occlusion scenarios. Therefore, to enhance the performance of the entire framework, it is imperative to obtain more robust detection results.  
The objective of this paper is to design a module that can effectively exploit temporal information for pose estimation and tracking in unconstrained videos. Let us use I to represent the t-th frame in the video and consider the scenario described in Figure 2. Suppose the person in I is affected by motion blur or occlusions from other objects. Under such circumstances, it becomes challenging for an image-level detector to accurately detect the person. However, if a nearby frame I+s or I+s contains the same person instance, which is clearly visible and not occluded by other instances, it is natural to leverage the information from I+s or I+s to improve the accuracy of detection in frame I. Hence, the main challenge lies in sampling useful person-level information from the nearby frame I+s or I+s to enhance the current frame I. For ease of explanation, we refer to the supporting frames as the nearby frames used for sampling and the reference frame as the current frame that requires improvement. In practice, we employ 2S supporting frames to enhance person detection accuracy in the reference frame. The supporting frames are randomly selected from a large range, encompassing S frames before the reference frame and S frames after it, respectively. This technique involves four crucial steps, which are elucidated in Figure 3.  
We incorporate various sub-networks within the person detection stage, and the structure of the complete spatiotemporal sampling module is depicted in Figure 3.
## 4 The Use of Mycobacteriophages in Diagnostics and Therapy
### In Vitro Studies  
Bacteriophages demonstrate a strong potential for therapeutic use against Mycobacterium due to their specificity. [PERSON] et al. showed that phages could infect and lyse antibiotic-resistant bacteria and work synergistically with antibiotics. A cocktail of D29, TM4, and DS6A phages inhibited the growth of slow-growing _M. tuberculosis_ for several weeks [106]. Phage cocktails designed to target _M. tuberculosis_ typically consist of obligately lytic mycobacteria capable of infecting and lysing the bacterium through a classical  
Figure 4: Phage amplification assay. 1—inoculation: adding mycobacteria to the test sample; 2—incubation for 20–24 h to allow phages to infect any target bacteria present in the sample; 3—treatment of the mixture with virucidal agent to eliminate exogenous phages; 4—adding _M. smegmatis_ (indicator mycobacteria/sensor cells); and 5—pouring the mixture and 1.5% agar into a Petri dish (incubation for 48 h and imaging the bald spots forming on the bacterial turf/lawn). Based on Ref. [42].  
lytic cycle. This involves adsorption, genome injection, replication, and eventual lysis of the host cell, releasing progeny virions. The approach is particularly relevant for _M. tuberculosis_, which is known for its slow growth and ability to persist in a dormant state. While the lytic cycle remains the primary mechanism of action, some phages may also exert indirect effects, such as metabolic disruption or interference with biofilm integrity, which can sensitize otherwise recalcitrant cells. The effectiveness of such cocktails depends not only on phage-host compatibility, but also on the physiological state of the bacterial population--actively replicating cells are generally more susceptible to productive infection than dormant or nutrient-limited ones [107; 108].  
Certain phage proteins may enhance antibiotic effectiveness. [PERSON] et al. demonstrated that gp39 from phage SWU1 increases _M. smegmatis_ cell wall permeability, facilitating antibiotic penetration and enhancing treatment efficacy [109]. However, antibiotics like kanamycin, hygromycin, or streptomycin inhibit phage infection, while spectinomycin does not [110].