esa-sceva/satcom-chunk-collection huggingface dataset:
# ACE: A Routing Algorithm Based on Autonomous Channel Scheduling for Bluetooth Mesh Network
## 9 Conclusions  
In this paper, we have analyzed some drawbacks of the standard Bluetooth Mesh network caused by its managed flooding mechanism. Its disadvantages mainly include the following main aspects, inefficient spectrum resource utilization, potential collision issue, and prone to be interference by BLE devices. In order to address these issues, we design and implement ACE, which creates and maintains routes without additional traffic overhead by reusing existing mesh packets. Moreover, an autonomous channel scheduling mechanism and a beacon-aware route recovery scheme are proposed. To the best of our knowledge, this paper presents the first work to design and implement a routing algorithm for the standard Bluetooth Mesh network. Our study proves that ACE has the potential to support a variety of applications, not only for static sensor network applications with low-rate traffic but also a dynamic network or applications with heavy traffic. In contrast, the standard Bluetooth Mesh is more suitable for a sparse and small-scale network due to its potential broadcast storm problem. Our experimental evaluation verifies that ACE outperforms Bluetooth Mesh in several aspects: latency, reliability, spectrum utilization, and scalability.  
Conceptualization, M.W.; methodology, M.W., J.L.; software, M.W., Y.L.; validation, M.W., Y.L.; formal analysis, M.W., W.D., Y.G.; investigation, M.W., J.L.; resources, C.Q., B.L.; data curation, M.W., Y.L.; writing--original draft preparation, M.W.; writing--review and editing, Y.L., W.D., Y.G.; visualization, M.W.; supervision, W.D., Y.G.; project administration, M.W.; funding acquisition, W.D., Y.G., C.Q., B.L. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Key R&D Program of China under Grant No. 2019 YFB1600700, the National Science Foundation of China under Grant No. 62072396, and Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars under No. LR19F020001.  
The authors declare no conflict of interest.
## 3 Results
### Photothermal and Photodynamic Therapy
#### 3.4.1 Photothermal Therapy  
The use of light-to-heat energy conversion for tumor treatment has gained significant attention among researchers. Metal nanoparticles are particularly valuable in this area, playing a key role in treating tumors. Their strong absorption of near-infrared light is fundamental to their effectiveness in photothermal therapy. Unlike traditional treatments, metal nanoparticles offer high selectivity and efficiency with minimal invasiveness. Among these, gold-based nanomaterials are most commonly used due to their tunable surface plasmon resonance and excellent photothermal conversion efficiency [76].  
In this context, recent studies have investigated gold nanoparticles functionalized with albumin (Alb-AuNPs), designed to specifically bind to gp60 receptors, which are over-expressed on hepatocellular carcinoma cells. This approach enables precise thermal energy delivery upon exposure to near-infrared light. Following internalization by HepG2 cells, the nanoparticles generate localized heat, disrupting the Golgi apparatus and endoplasmic reticulum (ER), leading to caspase-3 activation and the initiation of apoptosis. This selective interaction triggered strong pro-apoptotic effects in tumor cells, with minimal impact on normal cells. Experimental evidence suggests the effectiveness of this strategy in reducing tumor mass; however, in vivo validation remains limited, and efficient nanoparticle accumulation may be influenced by vascular heterogeneity, the tumor microenvironment, and variability in target receptor expression [79].  
Figure 4: Schematic representation of the synergistic application of photothermal therapy and photodynamic therapy in hepatocellular carcinoma. The process illustrates how nanoparticle-loaded photosensizers, upon light irradiation, generate heat and reactive oxygen species, leading to tumor cell death through both thermal ablation and oxidative stress. Reproduced with permission from [PERSON] et al. [78].  
In parallel, combined strategies have been proposed, integrating PTT with the controlled release of chemotherapeutic agents such as sorafenib, using copper selenide nanoparticles. These facilitate thermally activated drug release at the tumor site, increasing local drug concentration and antitumor efficacy. Both in vitro and in vivo experiments have demonstrated superior apoptotic induction compared to treatments applied separately. In animal models, this therapy significantly inhibited tumor growth while showing reduced toxicity to surrounding healthy tissues. However, the use of subcutaneous tumor models limits their translational relevance, and the absence of long-term pharmacokinetic data and systemic toxicity studies calls for caution when extrapolating these findings [80].  
Despite these limitations, PTT remains a promising and minimally invasive therapeutic option, especially when integrated into targeted drug delivery platforms, with real clinical application potential for hepatocellular carcinoma treatment.
# Robust Additive Value-Based Efficiency Analysis
## 5 Case Study concerning Evaluation of Healthcare System in Poland
### The Category of Financial Management
Notably, the necessary ranking (see Figure 14) is a good starting point to find the improvement paths for provinces. OPO has multiple paths to achieve efficiency. For example, it can take PDL as the first benchmark, follow WLKP, and finally refer to POM or LBU. An alternative improvement path runs through KP and POM. Figure 13: Extreme and expected ranks for Polish voivodeships when considering financial management level. The analysis of _PEO1s_ (see Table 11) is helpful for pairs related by mutual possible preference. For some, one province attains greater efficiency more often (e.g., \(PEOI^{F}(SW,ZPM)=91.2\%\)). For other pairs, indicating a better voivodeship is more challenging, as the shares of weights confirming the advantage of either unit are similar. An example of such a pair is LDZ and KP with \(PEOI^{F}(LDZ,KP)=48.3\%\) and \(PEOI^{F}(KP,LDZ)=51.7\%\). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline
**ERAI** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** & **14** & **15** & **16** \\ \hline ZPM & 0 & 0 & 0 & 0 & 0 & 0.011 & 0.046 & 0.182 & 0.062 & 0.624 & 0.068 & 0.007 & 0 & 0 & 0 & 0 \\ POM & 0.377 & 0.623 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ WM & 0 & 0 & 0 & 0.048 & 0.318 & 0.207 & 0.089 & 0.143 & 0.195 & 0 & 0 & 0 & 0 & 0 & 0 \\ PDL & 0 & 0 & 0 & 0 & 0.001 & 0.206 & 0.200 & 0.205 & 0.133 & 0.164 & 0.091 & 0 & 0 & 0 & 0 \\ LBU & 0.623 & 0.377 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ WLKP & 0 & 0 & 0.900 & 0.093 & 0.007 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ KP & 0 & 0 & 0.100 & 0.088 & 0.248 & 0.169 & 0.066 & 0.077 & 0.074 & 0.061 & 0.117 & 0 & 0 & 0 & 0 \\ MAZ & 0 & 0 & 0 & 0 & 0 & 0 & 0.003 & 0.151 & 0.054 & 0.563 & 0.187 & 0.042 & 0 & 0 & 0 \\ LBL & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.012 & 0.046 & 0.379 & 0.424 & 0.139 & 0 & 0 \\ DSL & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.382 & 0.618 & 0 \\ OPO & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.005 & 0.059 & 0.114 & 0.300 & 0.339 & 0.092 & 0.091 & 0 \\ LDZ & 0 & 0 & 0 & 0.100 & 0.126 & 0.365 & 0.332 & 0.077 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ SL & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 \\ SW & 0 & 0 & 0 & 0 & 0 & 0.013 & 0.267 & 0.313 & 0.380 & 0.026 & 0.001 & 0 & 0 & 0 & 0 \\ MLP & 0 & 0 & 0 & 0.671 & 0.300 & 0.029 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ PKR & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.127 & 0.195 & 0.387 & 0.291 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Distribution of ranks for Polish voivodeships when considering financial management level. Figure 14: The Hasse diagram of the necessary efficiency preference relation when considering financial management level.
# Rare Earth Elements Uptake by Synthetic Polymeric and Cellulose-Based Materials: A Review
## 6 Factors Affecting REE Uptake
### Polymeric Material Structure  
Most, if not all, materials used for REE uptake are porous. The porosity of a material affects the kinetics and the effective surface of the material: more pores in a material could lead to better REE uptake due to a higher sorption surface and faster REE sorption [28]. In terms of pore size, smaller pores could increase the surface of the material [17; 28]. Likewise, particle size affects the effective sorption surface of the material, and smaller particles have a higher effective surface for REE uptake, although the use of nanomaterials for REE uptake should be carefully addressed due to the tendency of nanomaterials to aggregate [17; 28]. Taking advantage of the described effects of the material structure should be considered in the design of new materials for REE uptake. Mesoporous materials (pore sizes between 3-10 nm) are described for REE uptake, showing high surface areas (between 350-650 m\({}^{2}\)/g) and acceptable REE uptake capacities [28; 29]. Nevertheless, using more porous materials and smaller particle sizes does not guarantee higher REE uptake because the nature of the interactions between the surface of a material and REEs impacts the most the REE uptake capacity, as could be observed in the reviewed works next to this section. In general terms, if the same type of material with different porosity and/or a different particle size is evaluated, the statement about a more porous and smaller particle size means a more effective sorption surface and a higher REE uptake could be applied. The surface area of the material is commonly determined by the application of nitrogen at 77 K adsorption/desorption isotherms, in which the BET model is applied [17; 27].
## 4 Results
### Whole-Rock Chemical Composition  
#### 4.3.1 Major Elements Composition  
The LG samples have SiO\({}_{2}\) contents ranging from 75.1 to 76.3 wt%, Al\({}_{2}\)O\({}_{3}\) contents ranging from 12.7 to 13.4 wt%, K\({}_{2}\)O contents ranging from 4.6 to 5.9 wt%, Na\({}_{2}\)O contents ranging from 2.5 to 3.5 wt%, CaO contents ranging from 0.59 to 0.75 wt%, Fe\({}_{2}\)O\({}_{3}\)T contents ranging from 1.2 to 1.6 wt%, MgO contents ranging from 0.07 to 0.15 wt%, TiO\({}_{2}\) contents ranging from 0.01 to 0.05 wt%, and P\({}_{2}\)O\({}_{5}\) contents ranging from 0.007 to 0.013 wt%. Overall, the LG petrochemical composition shows obvious characteristics of high silicon and alkali contents, higher K\({}_{2}\)O contents than Na\({}_{2}\)O contents, and low CaO and P\({}_{2}\)O\({}_{5}\) contents.  
According to the TAS diagram (Figure 6a) and the Q-ANOR normative diagram (Figure 6b), the LG belongs to syenogranite. The aluminum saturation index (ASI) range of LG is from 1.06 to 1.23, which belongs to weak-strong peraluminous granite (Figure 6c), and the fine-grained granite generally shows stronger peraluminous characteristics. LG has K\({}_{2}\)O/Na\({}_{2}\)O values ranging from 1.34 to 2.02, all of which fall into the high-K calc-alkaline series on the K\({}_{2}\)O-SiO\({}_{2}\) diagram (Figure 6d).  
Figure 5: LA-ICP-MS zircon U–Pb Concordia age and weighted mean age of the LG.
## 2 Content Sharing Graphs
### Detailed Content Sharing Graph (DCSG)  
Some applications need more detailed, chunk-level content sharing information for a set of deduplicated files. An example of such applications is the size computation for a subset of files in a deduplicated storage system (described in Section 3.3). For this reason, we introduce the _DCSG_ graph where there are two types of vertices: _file_ vertices and _sub-file_ vertices. _File_ vertices represent files sharing content with each other (as in _SCSG_ graphs). A _sub-file_ vertex represents a sharing unit among files, it can be a chunk or a range of adjacent chunks. In the following discussions, if not otherwise  
Figure 3: _SCSG_ graph with CHAIN connectivity for the 19 files described in Table 1.  
Figure 2: _SCSG_ graph with STAR connectivity for the small folder with 19 files described in Table 1.  
specified, we take individual chunks as default _sub-file_ vertices for its simplicity, and we name these vertices _chunk_ vertices. All edges in a _DCSG_ are between _file_ vertices and _sub-file_ vertices. For this reason, _DCSG_ graphs are bi-partite graphs.  
To build up a _DCSG_ graph one first needs to assign identifiers for unique chunks, where the identifer can be the hash value of the chunk or part of the hash. Next, one can get all edges associated with each _file_ vertex or _sub-file_ vertex by scanning the \"deduplication metadata\" that for each file has its attributes and the sequence of chunk hashes. Note that the edges do not have any associated properties (e.g., weights). All file-level attributes (e.g., file size, file modification time) are stored in the _file_ vertices, and all chunk-level attributes (e.g., chunk size) are stored in the _chunk_ vertices.  
Figure 4 shows the _DCSG_ graph created from the small example, whose data is shown in Table 1. The rectangle vertices represent files, and ellipse vertices represent chunks. File identifers (\(f01-f19\)) are marked in the graph, but the _chunk_ vertices are not marked. Each _chunk_ vertex represents a chunk in the storage system, and its degree reflects how many times it has been referred to in the storage system. For example, _chunk_ vertices with degree 1 belong exclusively to 1 file, while _chunk_ vertices with degree more than one are shared by some files. The degree of a _file_ vertex represents how many chunks belong to the particular file. Note that multiple edges between the same pair of _file_ vertices and _chunk_ vertices reflect the intra-file duplicates.  
Because _DCSG_ graphs model the detailed sharing between files, they have a wide range of applications. While deduplication is about a set of files, individual files do not have a complete view regarding the deduplication information. When the set of files needs to be decomposed into smaller pieces or needs to be extended, individual files in the new set need to re-compute the deduplication information based on their chunks. For this reason, _DCSG_ graphs are important for these applications.
# Network Attack Path Selection and Evaluation Based on Q-Learning
## 2 FPN-Q Learning Algorithm to Determine the Best Attack Path  
### Attack Model  
Cyber attacks have complex and random characteristics. FPN has the capability of describing concurrent events and graphical representation of Petri nets. Moreover, FPN can express this transition process concisely and clearly, avoiding the problem of state space explosion. In addition, FPN also has the fuzzy inference ability of fuzzy systems. Its place credibility and transition credibility can well represent the process of network attacks and the ambiguity of the attacks. Since the above characteristics of FNP meet the modeling needs, this study uses FPN to establish a network attack model.  
This paper is based on the network attack model established by FPN, which is a four-tuple:  
\[M=\{H,T,\alpha,\mu\}\]  
1. \(H=\{h1,h2,h3,\ldots,hm\}\) is a finite set of places \(h\), which represents the host of the information system in the model;
2. \(T=\{t1,t2,t3,\ldots,tm\}\) is a finite set of transitions \(t\), which represents the exploitable vulnerabilities of the system host in the model;
3. \(\alpha\) represents the risk value caused by the system host represented by the place after being invaded, that is, the threat index; and
4. \(\mu\): \(T\times H\rightarrow\{\)1,10\(\}\) represents the confidence of the transition rule, that is, the probability that a certain transition is triggered. In the network attack model, it represents the complexity of the attack process. The higher the attack complexity, the lower the possibility of being attacked. Attack complexity is affected by many factors such as attack tools and attacker experience. Its value is given according to the Common Vulnerability Scoring System (CVSS) [19].  
This method uses the FPN place to represent the information system host, and uses transitions to represent the exploitable vulnerabilities of the system. This method makes the complex network attack processes more concise and intuitive while reasonably considering the actual network attack. In addition, the concept of the FPN place's credibility and transition confidence is also used, which can well represent the ambiguity and uncertainty of the network attack process and its impact. Moreover, it can make subsequent analysis more reasonable and effective.
# Development of Digital Device Using ZigBee for Environmental Monitoring in Underground Mines
## 5 Discussion
The XBee module operates at a voltage of 3.3V, but the required power increases when a microcontroller, such as an Arduino or other sensors, is connected. Therefore, we must determine whether the battery system supplies sufficient power for communication. In the preliminary experiment, the XBee S2C product was used, but in this study, it was replaced with the XBee 3 Pro product to improve communication performance. 6. Although electromagnetic waves can be partially transmitted over obstacles, owing to diffraction and reflection, they are emitted along a straight path. Therefore, it is important to secure LOS between devices for smooth communication. Because the underground mine is surrounded by solid rock walls, the LOS must be secured such that there are no obstacles between the modules. However, it was difficult to fully consider the communication range and LOS of the module because the installation work was conducted depending on the paper map and intuition of the installer in a dark and inconvenient environment. Therefore, if 3D modeling and LOS analysis of underground tunnels are performed in advance, a more effective network can be built.
## 7 Conclusions  
Aiming at the trajectory generation and optimization of mobile robots in an uneven environment, a hybrid scheme using mutual learning and adaptive ant colony optimization (MuL-ACO) was proposed in this paper. The initial trajectory was generated by an improved adaptive ant colony algorithm, and then a mutual learning-based trajectory optimization algorithm completed the trajectory optimization. The comprehensive heuristic function and the adaptive method based on the improved temperature reduction function greatly improved the performance of the ant colony algorithm. Another advantage is that the proposed scheme had a clear division of labor to stably provide high-quality feasible solutions.  
Experiments conducted in uneven environments of different scenes and sizes modeled by 2D-H maps showed that the trajectory planned by MuL-ACO was superior to the other five algorithms in terms of smoothness, height difference, length, and algorithm convergence. Especially in more complex and larger maps, the MuL-ACO scheme was more adaptable and could stably plan a trajectory with a higher comprehensive quality. In the future, the proposed hybrid method may be further optimized and could be applied to more complex scenarios, such as multi-agent scenarios with multiple objects and social interaction scenarios.  
Conceptualization: F.Q.; methodology: F.Q. and W.Y.; software: F.Q., W.Y., W.L. and K.X.; validation: W.Y. and C.L.; formal analysis, F.Q. and W.L.; investigation, W.L. and F.Q.; resources, F.Q. and W.Y.; data curation, F.Q. and W.Y.; writing--original draft, F.Q.; writing--review and editing, F.Q. and W.Y.; visualization, F.Q.; supervision, F.Q. and C.L.; project administration, F.Q. and W.Y.; funding acquisition, F.Q. and W.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation (grant nos. 61602529 and 61672539). This work was also supported by the Hunan Key Laboratory of Intelligent Logistics Technology (2019 TP1015) and Scientific Research Project of Hunan Education Department (No. 17C1650).  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Map NO.** & **Length** & **Turns** \\ \hline \multirow{6}{*}{(un)MuL-ACO} & a & 41.6 & 7 \\  & b & 42.0 & 9 \\  & c & 40.8 & 11 \\  & d & 40.2 & 8 \\  & e & 40.8 & 7 \\  & f & 42.6 & 6 \\ \hline \multirow{6}{*}{Mul-ACO} & a & 39.2 & 2 \\  & b & 39.1 & 3 \\ \cline{1-1}  & c & 38.6 & 4 \\ \cline{1-1}  & d & 38.3 & 3 \\ \cline{1-1}  & e & 39.5 & 2 \\ \cline{1-1}  & f & 39.3 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Simulation results of (un)MH-ACO and MuL-ACO.
# Effect of Cation Influx on the Viability of Freeze-Dried _Lactobacillus brevis_ WiKim0069
## Materials and Methods
### Fluorescence Polarization Measurements  
_L. brevis_ WiKim0069 cells from a 24-h culture were harvested by centrifugation at 5000\(\times\)\(g\) for 10 min, washed twice with 50 mM morpholineethanesulfonic acid (MES) at pH 6.2, and resuspended in the same buffer. Then, 5 uL of 1,6-diphenyl-1,3,5-hexatriene (DHP) solution (in 6 mM tetrahydrofuran) was added to 2 mL of the cell suspension to obtain a final DHP concentration of 30 uM [25]. The cell suspension was vigorously stirred for 1 min and centrifuged, and the pellet was resuspended in 2 mL of MES buffer and used immediately for fluorescence polarization (P) measurements at 30 \({}^{\circ}\)C using a microplate reader (SpectraMax i3X, Molecular Devices, Sunnyvale, CA, USA) with excitation and emission at 360 and 430 nm, respectively. P (which is unitless) was calculated using the following equation:  
\[\mathrm{P}=I_{\parallel}-I_{\perp}/I_{\parallel}+I_{\perp}, \tag{1}\]  
where \(I_{\parallel}\) and \(I_{\perp}\) are the fluorescence intensities of the emitted light with the polarization plane parallel and perpendicular to the excitation beam, respectively [26; 27].
## 4 The Proposed Finger Photo Color Net (FPCN)
### Finger Photo Enhancement and Generation of Patches  
A fingerprint enhancement algorithm is applied to improve the clarity of ridge and valley structures [22]. A bank of Gabor filters is used as band-pass filters to remove noise while preserving the ridge/valley structures. These filters are tuned to the orientation and frequency of the ridge configurations. The general Gabor filter function is reported in Equation (8), where \(\theta\) and \(f\) are the orientation and frequency of the filter, and \(\delta_{X}\) and \(\delta_{y}\) are the space constants of the Gaussian envelope along the x and y axes. For the enhancement, the segmented finger photos are converted into grey-scale images and then normalized to estimate the orientation and frequency parameters.  
\[G(x,y:\theta,f)=\exp\left\{-\frac{1}{2}\left[\frac{x_{\theta}^{2}}{\delta_{x}^ {2}}+\frac{y_{\theta}^{2}}{\delta_{y}^{2}}\right]\cos(2\pi fx_{\theta})\right\} \tag{8}\]  
The enhanced images are binarized and further processed via ridge thinning to convert them into skeletonized images [23]. The ridge ending and bifurcation minutiae points are extracted by scanning the local neighborhood of each pixel in the ridge-thinned image using a 3 \(\times\) 3 window; then, the crossing number (CN) is computed [23]. CN is half of the sum of the differences between pairs of neighboring pixels \(p_{i}\) and \(p_{i+1}\); see Equation (9). The coordinates of the minutia points extracted from the ROI of the finger photos are simultaneously mapped into the various color spaces used in this study. For each color space, a patch image is cropped over the coordinates of minutia points; see Figure 5.  
\[CN_{(x,y)}=\frac{1}{2}\sum_{i=1}^{8}|p_{i}-p_{i+1}|,p_{1}=p_{9} \tag{9}\]  
Figure 4: Examples of predicted output masks by using the U-Net framework.  
Figure 5: Architecture illustrating image segmentation, enhancement and minutia points extraction.