esa-sceva/satcom-chunk-collection huggingface dataset:
# Cross Domain Adaptation of Crowd Counting with Model-Agnostic Meta-Learning
## 4 Experiments  
This work studies two different model generalization approaches in crowd-counting tasks and proposes an adaptive crowd-counting framework for cross-domain scenarios. The crowd-counting algorithm, based on density estimation, mainly consists of three parts: feature extraction, density estimation, and count mapping. In cross-domain scenarios, the testing accuracy of the model trained in the source domain will drop considerably in the target domain without adaption. This issue is caused by domain shift. We took the  
Figure 4: The comparison of feature extraction between the source and target domains. (**a**) shows the input images, (**b**) shows the feature extraction results without adaptation, and (**c**) shows the feature extraction results with adaptation. When using real-world images for testing, we compared the results in (**b,c**). Generated feature maps with adaptation will have less noise than those without adaptation.  
Figure 5: The mapping flow of the crowd-counting mapper from a coarse density map to a refined density map.  
synthetic dataset as the source domain, as a synthetic dataset like GCC will generate data annotation points simultaneously when generating images, thus saving a lot of annotating work. Therefore, we propose several methods for adaptive crowd counting and finally conduct an ablation study analysis. This section will discuss the following aspects in detail:  
* Verify whether our proposed density-map estimator, based on dynamic-\(\beta\) MAML, can accelerate convergence and improve crowd-counting performance in few-shot learning scenarios over the baseline and FSCC performances.
* Verify and evaluate the effectiveness of our proposed domain-invariant feature representation in cross-domain scenarios.
* Perform additional ablation studies on the efficacy of our proposed method, to verify the effectiveness of two key phases: feature extraction and density estimation.  
We developed the crowd-counting algorithm based on the open-source crowd-counting project C3-Framework. The hardware environment we used was the Intel Core i7-6500k CPU 3.4 GHz with two TITAN RTX GPUs and 24 gb of memory. We conducted the cross-domain adaption experiments from the GCC dataset to various real-world datasets, such as ShanghaiTech, UCF, NWPU-Crowd, and WorldExpo. Furthermore, in this paper, two metrics are used to evaluate accuracy: mean absolute error (MAE) and mean square error (MSE). They are defined as follows:  
\[MAE=\frac{1}{N}\sum_{i}^{N}|z_{i}-\hat{z}_{i}| \tag{9}\]  
\[MSE=\sqrt{\frac{1}{N}\sum_{i}^{N}(z_{i}-\hat{z}_{i})^{2}} \tag{10}\]
## 2 Engineering Geological Conditions of the Landslide
### Lithology  
The geological conditions of landslide areas are complicated. Sections of Zones I and II were drawn according to borehole maps, as shown in Figure 2. The inclinometer used in this paper was a mobile inclinometer. The monitoring displacement direction of the mobile inclinometer was consistent with the direction of the profile, enabling it to truly reflect the main sliding direction of the landslide. The main strata in the area were as follows:  
1. Quaternary colluvial deposit (Q\({}_{4}\)col+dl): mainly gravelly clay with a particle size of 2-13 cm and poor roundness. Soil is red-brown and brown-yellow, with no shaking reaction and high dry strength. The thickness is approximately 5-12 m, distributed mainly on the surface of the mountain (Figure 3a).
2. Devonian system Pojiao group sandy mudstone (D\({}_{1}\)p): Gray-black thin-layered mudstone with partial gray, brown-yellow thin-layered silty mudstone (Figure 3a).
3. Devonian system Cuifengshan group argillaceous shale (D\({}_{1}\)c): The gray-black medium-thick argillaceous shale is approximately 10-20 m thick, with a strong weathering layer. The lower inclination angle is nearly vertical and the upper part is inverted, with an occurrence of 243\({}^{\circ}\)/18\({}^{\circ}\) (Figure 3b). The drill core showed that the bottom layer of D1b rock stratum is inverted, and the surface is characterized by bedding landslide. The dip angle in the sliding zone area is nearly vertical.  
Figure 1: Geological map of the K39 landslide.  
4. The structure along the way in this area is extremely complex, and it is extremely important to understand the rock stratum information of complex landslide masses. [PERSON], [PERSON] [28] proposed a method for classification of rocks in complex areas. [PERSON] [29] studied the impact of different permeability soils on landslide deformation. [PERSON] [30] pointed out that specific measures should be taken in the engineering design for special soils, and [PERSON], [PERSON] [31] indicated that landslide sliding often has multiple sliding surfaces. The K39 landslide has large differences in permeability of the rock and soil masses, and has a double-layer sliding surface, so the landslide can be categorized as complex rock masses.  
Figure 2: Cross-sectional views: **(a)** Cross-section (I-I); **(b)** Cross-section (II-II).
## 3 Results  
### Electrical Characterisation  
Pentacene OTFTs were fabricated with a bottom-gate top-contact configuration as shown in Figure 2. A highly doped n type Si wafer was used as the substrate and gate electrode, a 300 nm SiO\({}_{2}\) as a gate dielectric, and approximately 45 nm of polystyrene as a polymer electret. The thickness of Polystyrene was estimated from the capacitance value and was in good correlation with thickness measured on profilometer (48 nm). The capacitance (\(\mathrm{C_{tot}}\)) of the SiO\({}_{2}\)/PS dielectric was 3.26 nF at 1 kHz; the capacitance of the Polystyrene layer was 45.45 nF, as determined from the capacitance expression (1):  
\[\frac{1}{\mathrm{C_{tot}}}=\frac{1}{\mathrm{C_{SiO_{2}}}}+\frac{1}{\mathrm{C_{ PS}}} \tag{1}\]  
Figure 3 shows the output and transfer characteristics of the Pentacene transistors. Output characteristics were scanned at \(\mathrm{V_{d}}=\) from 0 to \(-15\) V at \(\mathrm{V_{gs}}=\) 0 V, \(-5\) V, \(-10\) V, and \(-15\) V. Transfer characteristics were scanned at \(\mathrm{V_{g}}=\) from 0 to \(-15\) V, at a constant \(\mathrm{V_{d}}=-15\) V. On/Off ratios of transistors were typically \(10^{3}\), and a threshold voltage of \(\mathrm{V_{th}}=-8.4\) V was extrapolated from \(\sqrt{\mathrm{I_{d}}}\) vs. \(\mathrm{V_{g}}\) curves at \(\mathrm{V_{d}}=-15\) V. The charge carrier mobility \(\mathrm{\mu_{sat}}=0.6\) cm\({}^{2}\)V\({}^{-1}\)s\({}^{-1}\) was derived from \(\mathrm{I_{d}}\) in the saturation regime at \(\mathrm{V_{d}}=-15\) V using Equation (2):  
\[\mathrm{\mu_{sat}}=\frac{2L}{\mathrm{WC}}\left(\frac{d\sqrt{I_{\mathrm{dsat}} }}{\mathrm{dV_{g}}}\right)^{2} \tag{2}\]  
Figure 1: A schematic representation of the setup used to irradiate the OTFT detector with (**a**) a 6 MV photon beam and (**b**) a 100 kVp photon beam. Measurements were performed in Solid Water to mimic a clinical setup and allow us to relate the response of the detector to a known dose in water. An ion chamber was used to calibrate both treatment machines. The dashed blue lines represent the projected shape of the radiation field. The black triangle in (**b**) represents the cone that is physically attached to the orthovoltage X-ray unit and helps collimate the field.  
The charge carrier mobility \(\mu_{\rm{lin}}=0.79\) cm\({}^{2}\)V\({}^{-1}\)s\({}^{-1}\) was derived from I\({}_{\rm{d}}\) in the linear regime at V\({}_{\rm{d}}=-1\) V using Equation (3):  
\[\mu_{\rm{lin}}=\frac{\rm{L}}{\rm{WCV_{d}}}\frac{\rm{dI_{d}}}{\rm{dV_{g}}} \tag{3}\]
# Recent Advances in the Targeting of Head and Neck Cancer Stem Cells
## 6 Therapeutic Targeting of CSCs
### Targeting Metabolism  
CSC metabolism is often reprogrammed to meet their needs. CSCs preferentially utilize glycolysis for survival, but studies have shown that CSCs may also rely on OXPHOS. Previous research demonstrated that lipid metabolism plays an important role in maintaining the stemness of CSCs and their energy needs [168]. Therefore, targeting either of these pathways should be a good strategy in CSCs therapy. Metformin has been studied frequently as a potential drug for CSC therapy because it may impede glycolysis. There are numbers of studies on the metformin anticancer effect in oral cancer (for a detailed and recent review see [169]), and some of them also show an inhibitory effect in CSCs [170]. In addition, therapy by HIF inhibitors might also be a promising approach for CSC elimination considering the importance of reactive oxygen species (ROS) in CSCs preservation [171].
# Reversible Hydrogen Storage Using Nanocomposites
## 2 Nanocomposite Materials for Hydrogen Storage
### Nanocatalyst Doping in Complex Borohydrides  
For an on-board hydrogen vehicular application, a proton exchange membrane (PEM) based fuel cell that can operates with available hydrogen that is obtained from the reversible hydrogen storage materials needs to be explored. One such reversible hydrogen storage system is the complex metal hydrides that are weightless due to light metal/carbon/organics and store a large amount of pure hydrogen [12, 61, 62]. A series of complex hydrides have been explored in the past decade, such as sodium aluminum hydrides [63, 64], lithium amides [65, 66], and magnesium or lithium borohydrides [67, 68, 69], and their combinations [70, 71]. Two important limitations such as bulk thermodynamic effects and surface assisted kinetic constraints [7, 71] are to be solved from the first principles in order to greatly implement these complex hydrides for practical applications. The low temperature dissociation of hydrogen and its reversible absorption behavior with a capacity \(>\)5.0 wt.% can be achieved by doping these complex materials with few mole concentrations of mono- or bi- metallic nanoparticles as catalysts. The short reaction pathways, greater gas-solid interactions, rapid diffusion kinetics, efficient transport of atomic hydrogen between the grains of the complex matrix, superb surface, and interfacial interactions are some of the features offered by the nanostructured catalytic particles.  
Figure 12 demonstrates the thermal analysis of the novel complex hydride, Zn(BH4)2 systems undoped and doped with nanocatalyst (Nano-Ni). The Zn(BH4)2, is prepared from the mechano-chemical treatment of (Na(Li)BH4 + \(\frac{1}{2}\) ZnCl2), exhibits an endothermic DSC peak due to melting at 80 \({}^{\circ}\)C and a weight loss occurring at 120 \({}^{\circ}\)C because of hydrogen decomposition at this temperature.  
Preliminary experiments were conducted by adding different nanocatalyst (nano-Ni) to the complex hydride in systematic mole fractions from 1 to 4 mol%. It is clearly discernible from Figure 12 that nanocatalyst doping enables the reduction in the melting transition and decomposition temperature. A concentration of the 3 mol% nanocatalyst was found to be optimum for the gravimetric weight loss due to thermal decomposition at low temperature. At these low temperatures, the nanocatalyzed Zn(BH4)2 exhibited a reduction in the amount of borane (BxHy) gases released by a factor of 20 as compared to the undoped sample (Figure 13). From the series of catalysts investigated, we found that nano-Ni shows excellent H2 decomposition behavior (kinetics and overall hydrogen storage capacity) due to their unique size distribution, high specific surface area, and microstructural characteristics.  
Figure 11: Pressure–composition isotherms of LiBH4 + \(\frac{1}{2}\)nanoMgH2 + 3 mol% nanoNi.
## 3 Results and Discussions
The periodic modulations of the concentration between the HCL and DI liquids were adopted to test measurement repeatability, as shown in Figure 10a. The mean phase variation versus concentration is shown in Figure 10b. It shows a good linear relation between them. The summarized table of the mean values and RMSEs is shown in the inset. The phase difference is 223 (degrees) in the concentration change range of 2.5 wt. %. Therefore, the sensitivity is around 9 \(\times\) 10\({}^{3}\) (deg./wt. %). According to Figure 10c, the liquid switching time is around 9 s to achieve a stable phase change. By considering the stable phase period as shown in Figure 10d, the best phase stability of 0.8 (degree) is achievable in the fluidic HCL solution. Finally, the  
Figure 8: Phase response curve for the small flow cell. measurement resolution of the concentration is 0.0088 wt. %. The chemical stability of the KTP material has been mentioned in [27]. Typically, the chemical solutions used for domain inversion observations of the KTP plate are the mixture of KNO\({}_{3}\) and KOH [28]. The difference of the etching rate between the \(+c\) and \(-c\) surface of the KTP plate can be used to observe a contrast of the surface morphology. It means that the etching rate is higher on the \(-c\) surface (electrical poling) in comparison with the \(+c\) surface (without poling). By the way, the chemical etching methods have been widely used to check the defect density after crystal growth. The crystals were put for 60 min into a boiling 37 wt. % HCL solution in order to obtain suitable etch pits [29]. According to the reported etch results [28,29], only the poling regions and defects are easily etched due to the structure changes. Therefore, the idea surface of the KTP plate could be used in harsh environments. Figure 10: \(Cont.\)  
Figure 2:0 Phase response for different concentrations of SW solution: (**a**) step-increased concentrations of SW; (**b**) phase versus concentration; (**c**) liquid change time; and (**d**) phase stability.
## 2 Experimental Analysis
### Scanning Electron Microscopy Test  
To further evaluate the forming quality of ME parts, scanning electron microscope (SEM) tests were performed with the field-emission SEM (Model: Zeiss ULTRA 55) to microscopically identify the parts' defects, mainly inclusion and pores. This can help understand the built parts' reliability and failure mechanism. For each specimen, multiple random positions were measured to ensure the representativeness and repeatability of the results. The testing steps are mainly (1) preparing samples (cutting each sample flat on one side, cleaning it, and then performing spray gold and vacuum treatment); (2) placing samples properly; (3) adjusting the observation focal length; and (4) observing and saving the data.
## 4 Simulation and Results
### Scenario 1: Pairwise Case  
For the pairwise case, the initial position and the destination are shown in Table 2. All the results are illustrated in Figure 6.  
We set the number of random searches in one iteration \(SN\) as 10, and the number of iterations \(IN\) as 5000. The expected time to detect collisions is 2 \(\triangle t\). Therein, \(\triangle t\) denotes the time it takes for a UAV to fly across a cubic cell. That is, the experiment can detect collisions two steps ahead. The initial collision-free trajectory and the 5000 th collision-free trajectory are shown in Figure 6a,b. The total distance is 894.77 and 759.56, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**UAV Number** & **Initial Position** & **Destination Position** \\ \hline
1 & 0, 0, 0 & 200, 200, 200 \\
2 & 200, 200, 0 & 0, 0, 200 \\ \hline \end{tabular}
\end{table}
Table 2: The initial and destination position of UAVs in Scenario 1.  
Figure 6: The situation and results in Scenario 1. (**a**) The initial collision-free trajectory and total distance in Scenario 1; (**b**) The 5000 th collision-free trajectory and total distance in Scenario 1; (**c**) The distance changes with iteration number increasing in Scenario 1; (**d**) The relative distance of the best trajectory in Scenario 1.  
Figure 6c demonstrates the change of total distance with the iteration number. We find that the proposed algorithm can get effectively rid of the local optimization point. The total distance tends to be stable after 3000 iterations.  
Relative distance in the best result with fixed intervals is illustrated in Figure 6d. As expected, the shortest distance between two UAVs is larger than the radius of the protected sphere, which is 10 in the scenario.
###### Abstract  
_Background and Objectives_: CBCT images have been successfully used for CAD/CAM crown restorations; however, their use for ceramic inlay restorations remains unclear. This study aimed to evaluate the marginal and internal fit of CAD/CAM ceramic inlay restorations fabricated using intraoral scanner, model scanner, and CBCT data. _Materials and Methods_: Inlay preparations were performed on 11 mandibular molar typodont teeth. The teeth were scanned using an intraoral scanner, an extraoral scanner, and CBCT (0.075 mm voxel size). CBCT-generated DICOM data were converted to STL format with dedicated software. All scan data were transferred to CAD software, and a total of 33 restorations were designed. Feldspathic ceramic blocks were used for milling. Micro-CT was employed to measure marginal and internal gaps, with 60 measurement points taken from three cross-sections per sample. Data were analyzed using ANOVA and Bonferroni tests (_p_ < 0.05). _Results_: CBCT exhibited greater marginal and internal gap dimensions (mean: 169.27 \(\pm\) 38.64 \(\upmu\)m), which were approximately 60-70 \(\upmu\)m higher than those of the intraoral (97.00 \(\pm\) 10.1 \(\upmu\)m) and model scanner groups (109.67 \(\pm\) 9.72 \(\upmu\)m), exceeding clinically acceptable limits (\(\leq\)120 \(\upmu\)m) (_p_ < 0.05). Intraoral and model scanners showed similar, clinically acceptable results. _Conclusions_: CBCT was less accurate for inlay restorations, likely due to their complex geometry. Nevertheless, fabrication was possible, and further research may improve its clinical applicability.  
inlay; CBCT; intraoral scanner; model scanner; marginal fit +
Footnote †: journal: Applied sciences
# Efficient LDPC Encoder Design for IoT-Type Devices
## 5 LDPC Encoder Design for a Microcontroller Implementation
### Microcontroller Implementation Issues  
We implemented the encoder for LDPC codes with a broad range of code vector and information vector sizes \((N,K)\). The encoder operates according to the block diagram shown previously in Figure 2.  
The algorithm implemented in the microcontroller (the low-power STM32L476 mentioned above was used in the experimental design) was divided into four parts: input data preparation, two parts for calculating the \(\mathbf{p}_{1}\) and \(\mathbf{p}_{2}\) vectors, and the output assembly. The parity-check matrix \(\mathbf{H}\) was divided into \(\mathbf{A}\), \(\mathbf{B}\), \(\mathbf{T}\), \(\mathbf{C}\), \(\mathbf{D}\), and \(\mathbf{E}\) for the LDPC and QC-LDPC encoding, and this way, it was stored in the memory. For the experimental comparison, we implemented encoding for both LDPC codes with index representation and for QC-LDPC with cyclic shift representation. The \(\mathbf{H}\) matrix was a constant initialized variable and was stored in flash memory.  
In the first part of the algorithm, data were prepared to start the encoding procedure. The information vector \(\mathbf{u}\) is a parameter of the encoding function and stored in the stack; likewise, every buffer and variable during the encoding process is stored in stack memory in the RAM. The available RAM memory in the current microcontroller is 128 KB; thus, the operations needed to use memory efficiently. The buffer usage was minimized; in most cases, one main and one temporary buffer were used to execute the operations.
## 4 Theoretical Analysis of DE-MPFSC Algorithm
### Validity Analysis of DE-MPFSC Algorithm
Markov Processing Model of DE-MPFSC Algorithm  
Assume the individuals in the population are expressed as \(X_{i}^{t}=(x_{i1}^{t},x_{i2}^{t},\cdots,x_{iD}^{t})\), \(i=1,2,\cdots,NP\)[20], where \(t\) is the number of iterations, \(NP\) is population size [20], the mutation operator, crossover operator, and selection operator are, respectively, \(F,T_{c},T_{s},T=F\cdot T_{c}\cdot T_{s}\). Then, the iterative equation of the DE-MPFSC algorithm is \(X(t+1)=T(X(t))=(F\cdot T_{c}\cdot T_{s})(X(t))\). We obtain the population sequence \(\{\hat{X}_{t};t\geq 0\}\) after initializing the population. **Theorem 1**. : _Let the population sequence of the DE-MPFSC algorithm be \(\{\hat{X}_{t};n\geq 0\}\); then, the sequence is a finite, homogeneous, irreducible and nonperiodic Markov chain._  
Proof. : Let \(X(t)\) be the population of the DE-MPFSC algorithm, the population individuals can be \(X_{i}^{t}=(x_{i1}^{t},x_{i2}^{t},\cdots,x_{iD}^{t}),i=1,2,\cdots,NP\). \(S=\{\hat{X}_{n};n\geq 0\}\) is the state space of the population sequence, which is the finite space. Because of \(X(t+1)=T(X(t))=(F\cdot T_{c}\cdot T_{s})(X(t))\),where there is no connection between operators \(F,T_{c},T_{s},T=F\cdot T_{c}\cdot T_{s}\) of the DE-MPFSC algorithm and the iterative times \(t\), and \(X(t+1)\) is only related to \(X(t)\). In other words, the evolved population individual is only related to the corresponding individual before the evolution, not the number of evolutions. Therefore, \(\{\hat{X}_{t};t\geq 0\}\) is a finite state Markov chain. Due to  
\[ \begin{split}& P\{T(\hat{X}_{t})_{k}=X_{k}(t+1)\}\\ &=\sum_{X_{i}^{\prime}(t+1)\in S}\sum_{(X_{1}^{\prime}(t),X_{2}^ {\prime}(t))\in S}P\{T_{s}(X(t))(X_{1}^{k}(t),X_{2}^{k}(t))\}\cdot P\{T_{c}(X_ {1}^{k}(t),X_{2}^{k}(t))X_{k}^{{}^{\prime}}(t+1)\}\cdot P\{F(X_{k}^{{}^{\prime }}(t+1))\\ &=X_{k}(t+1)\},\end{split}  \tag{21}\]  
where \(X_{k}^{{}^{\prime}}(t+1)\) is a population of individuals generated by a crossover operation, and \(\forall X(t)\in S\). Then, there is the case in which \((X_{1}^{k}(t),X_{2}^{k}(t))\) and \(X_{k}^{{}^{\prime}}(t+1)\) make  
\[P\{T_{s}(X(t))=(X_{1}^{k}(t),X_{2}^{k}(t))\}>0 \tag{22}\] \[P\{T_{c}(X_{1}^{k}(t),X_{2}^{k}(t))=X_{k}^{{}^{\prime}}(t+1)\}>0. \tag{23}\]  
Further, for \(\forall X_{k}^{{}^{\prime}}(t+1)\) and \(\forall X_{k}(t+1)\), there is  
\[P\{F(X_{k}^{{}^{\prime}}(t+1))=X_{k}(t+1)\}>0 \tag{24}\]  
Then,  
\[P\{T(\hat{X}_{t})_{k}=X_{k}(t+1)\}>0. \tag{25}\]  
Based on formula \((\ref{eq:22}),(\ref{eq:23}),(\ref{eq:24})\), we know that formula \((\ref{eq:25})\) is connected with \(n\), then  
\[P\{T(X(t))=X(t+1)\}=\prod_{i=1}^{NP}P\{T(\hat{X}_{t})_{k}=X_{k}(t+1)\}>0 \tag{26}\]  
has no connection with \(n\). Relying on these proving processes, we know that the population sequence \(\{\hat{X}_{t};n\geq 0\}\) generated by the DE-MPFSC algorithm is a homogeneous, irreducible and nonperiodic Markov chain. The conclusion is true.