esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Antimicrobial Activity against Gram (+) and Gram (\(-\)) Strains  
Table 2 summarize the antimicrobial activity of pure AA and liposomal formulations with different concentrations of AA. Figure 4: Potentiometric titration with NaOH of azelaic acid and the corresponding first derivative. Based on the results, the first notable observation is expressed as the maximum antibacterial effect of the liposomal formulation AALipo 20% against _S. aureus_, the effect being even more intense compared to the antibiotic (Vancomycin) or compared to pure AA solutions. The second observation refers to the very good antimicrobial activity of both liposomal formulations and AA solutions (regardless of the concentration) against _E. faecalis_, while the antibiotic (Ampicillin) shows no effect. On the other hand, only modest results were obtained against the gram (\(-\)) strains (_E. coli_ and _P. aeruginosa_), as neither the AA solutions nor the liposomal formulation presented a superior effect compared to antibiotics. The antimicrobial assay results indicated that the optimum liposomal formulation is AALipo 20%. Increasing the AA concentration in the liposomes from 20% to 25% does not have an increased antimicrobial effect. However, for all the tested germs, the liposomal formulations exhibited a better effect compared to the free AA solution, except for _P. aeruginosa_. According to literature, the top ten bacteria present in sebaceous sites are Pseudomonas acnes, S. epidermidis, Corynebacterium tuberculosis of Staphylococcus capitis, Coryneb bacterium simulans, Staphylococcus mitis, Staphylococcus hominis, Coryneb bacterium aurimucosum, Coryneb bacterium kroppenstedtii, and Coryneb bacterium amyloc-latum [40]. Altering the skin microbiome with a topical antibiotic treatment can have significant effects on the cutaneous host defense, and some skin bacteria (such as Micrococcus luteus) have been found to enhance S. aureus pathogenesis [41; 42]. In this context, it is important to know the relative effects of antimicrobial agents on human microbiota in order to understand their potential to foster resistance and alter the microbiome composition. The first observation that AA may exert a bacteriostatic effect on aerobic and anaerobic bacteria (including Propionibacterium) appeared in 1983 as a comment in a clinical report [43]. Three years later, a clinical trial noted a 224-fold reduction in the population of Micrococcaceae and 30-fold decrease in the density of Propionibacterium sp. on the skin following the application of a 20% azelaic acid cream (compared to no effect from tetracycline) [44]. Azelaic acid inhibits the synthesis of cellular protein in aerobic and anaerobic microorganisms, such as _P. acnes_, and does not induce bacterial resistance [45]. In the microbiome of healthy skin, _S. epidermidis_ may limit the overcolonization of _P. acnes_ strains and reduce _P. acnes_-induced IL-6 and TNF-\(\alpha\) production by keratinocytes. On the other hand, _P. acnes_ may limit the proliferation of _S. aureus_ and _S. pyogenes_ by promoting triglyceride hydrolysis and propionic acid secretion. As a result, an acidic pH
## 2 Case Presentation  
A 61-year-old patient consulted the Oral Surgery and Implantology Unit of Geneva University Hospitals for a painful swelling of the anterior maxillary vestibule that was present for about two weeks and prevented him from wearing his complete upper denture. The patient smoked (30 pack-year history) and had type 2 diabetes, hypertension and hypercholesterolemia. The patient was taking metformin, atorvastatin and amlodipine.  
The extra-oral clinical examination was unremarkable. The intraoral examination showed a completely edentulous maxilla and mandible, and vestibular and palatal swelling around the edentulous sites of teeth #11, 12 and 13. On palpation, the affected area was soft, elastic and painful.  
An OPG X-ray revealed a radiotransparent lesion located in the area of the incisors and canines (Figure 1).  
Despite the lesion's well-defined borders, the analysis of the images was impeded by projection artifacts. A supplementary cone beam computed tomography (CBCT) exam was performed, which showed an expansile lytic lesion with well-delimited contours in the right paramedian region of the maxilla (Figure 2).  
No visible signs of calcification were found within the lesion or periosteal reaction. The lesion had extended to the vestibular side, and, to a lesser degree, to the palatal side, and involved the anterior part of the right nasal cavity floor. In this area, the cortices were extremely expanded, thin, resorbed in places, and could no longer be distinguished.  
Figure 1: Panoramic X-ray at first examination.  
The lesion extended from sites #13 to 21, and measured about 29 mm \(\times\) 27 mm \(\times\) 23 mm. The cystic lesion occupied the region of the incisive canal, which was visible only in its cranial part, opposite to the nasal cavity floor, and was no longer visible elsewhere.  
Radiological semiology led to a differential diagnosis of a residual odontogenic cyst, an NPDC or an unicyclic ameloblastoma. The lesion was totally enucleated (Figure 3).  
Histopathological examination revealed a fibrous wall lined in part by a thin stratified non-keratinized squamous epithelium and in part by a cuboidal or respiratory-type epithelium with ciliated cells containing a scanty lymphoplasmocytic inflammatory infiltrate. These features allowed for a diagnosis of an NPDC (Figure 4a,b).  
Figure 3: Intraoperative view (**a**) before and (**b**) after the cystectomy.  
Figure 2: Cone beam computed tomography (CBCT): (**a**) frontal view; (**b**) sagittal view; (**c**) axial view; (**d**) 3D reconstruction.
## 6 Video Technology in M-Health Systems
### Video and Physiological Data Platforms  
The literature includes a variety of platforms for m-Health support systems. In one, the standard H.264/AVC has been applied to telemedicine support situations of ambulance transferring emergency patients, where Ubiquitous Health (U-Health) is currently utilized without breaking medical law. Remote Medical Support System (RMSS) used in ambulances enables a doctor at a remote place to identify the patient status through video, and supply remote support to the emergency [53]. Another application is a mobile tele-care system installed in moving vehicles to support emergency cases, which provides videoconference and vital sign transmission services in real time [54]. The integration of the videoconference is an important support tool for a specialist located in the hospital to maintain visual inspection of the patient at a remote place.  
The transmission of medical data traffic and video over the network, includes an adaptive rate scheme for multimedia medical data (video, ECG, medical scans) multiplexing in order to reduce the required resources from the telemedicine application [55]. The quality of service (QoS) through a step-by-step improvement of HSPA for m-Health services (video, ECG, vital signs, file transfers) to manage and meet the requirements for different scenarios is presented in [56]. It can be seen that the transmission of data and video is a common implementation in the architecture of a medical care system in an emergency scenario. Therefore, the inclusion of audio/video technology in a WBSN is needed to ensure the successful care of the patient with a mobile health system.
# The Significance of IL-36 Hyperactivation and IL-36R Targeting in Soriasis
## 3 IL36R-Dependent Responses in Seriatric Resident Skin Cells
### Dermal Endothelial Cells and Fibroblasts  
In addition to human keratinocytes, dermal endothelial cells also express the IL-36R complex, and its activation by IL-36\(\gamma\) induces proliferation, as well as tube formation and branching [21; 23] (Table 1). Consistently, we recently observed that supernatants from IL-17A-treated keratinocytes strongly promotes proliferation of dermal endothelial cell cultures, and this effect was specifically reduced by IL-36 Ra treatment (unpublished data). IL-36\(\gamma\), alone or in cooperation with TNF-\(\alpha\), also has pro-inflammatory effects on human endothelial cells, by inducing the release of IL-6, CXCL, CCL20, CXCL1, and also expression of the adhesion molecules, such as the intracellular adhesion molecule (ICAM)-1 and vascular cell adhesion molecule (V-CAM), thus determining an increased migration of T cells [13; 21; 23]. These findings support a role for IL-36\(\gamma\) in enhancing psoriatic skin inflammation by activating angiogenesis and promoting leucocyte recruitment. In addition, IL-36\(\gamma\) induces proliferation and migration of dermal endothelial cells indirectly through the release of the pro-angiogenic molecule VEGF-A from dermal fibroblasts, being these last able to constitutively express high levels of IL-36R [23] (Table 1).  
Interestingly, due to endothelial activation and increased monocyte adherence, this altered interplay between immune and resident skin cells evoked by IL-36R activation could explain the association with cardiovascular comorbidities occurring in psoriatic patients. As a consequence of IL-36R expression in endothelial cells, recombinant IL-38 has anti-inflammatory and anti-angiogenic effects on dermal endothelial cells activated by IL-36\(\gamma\)[13], whereas IL-38 action on dermal fibroblasts remains to be investigated. In these cells, IL1 RAPL receptor, an aforementioned target of IL-38 cytokine, is constitutively expressed and strongly induced by psoriasis-related cytokines, including IL-17A, TNF-\(\alpha\), IFN-\(\gamma\) and IL-22 (our unpublished observation).  
_Vice versa_, IL1 RAPL1 expression is totally absent in human dermal endothelial cells and present, at low levels, in human keratinocytes. Further studies will be needed to delineate the function of this poorly characterized receptor in fibroblasts and the potential impact of IL-38 on its biological activity.
# Application and Performance Evaluation of Resource Pool Architecture in Satellite Edge Computing
## 3 Proposed Architecture
### Pool Capacity Planning
The author of Ref. [21] gives the empirical value tables of \(\varepsilon\) and \(\alpha\). When \(\alpha\) is 0.2 and \(\varepsilon\approx 1\). This means that when the average response time is less than 20%, the minimum number of servers that can be solved is \(k_{a}^{*}\approx R+\sqrt{R}\). After determining the number of satellite nodes, the problem remains that the satellite is different from the ground computer room. As the satellite is in space orbit, as shown in Figure 3, the motion of the satellite is not considered in the queuing theory model. Therefore, one of the difficulties of creating a satellite resource pool is to select the suitable satellites from all satellites. Satellite resource sharing in the resource pool will be further described below, and a satellite node selection method will be proposed.
## 3 Experimental Results
### Model Verification Experiment
#### 3.2.3 Model Comparative Experiment
The precision comparison results of each classification method are shown in Figure 17. The Gabor filter works on the principle of mutual modulation between the Gaussian kernel and sine wave, specializing in dealing with multi-scale and multi-directional texture features. We imported the features extracted by the Gabor filter into SVM and KNN classifiers, respectively to classify six kinds of defects. The traditional LBP (local binary pattern) algorithm has the advantages of rotation invariance and gray invariance and is better at describing local texture information. On the other hand, HOG (histogram of oriented gradient) has no rotation and scale invariance. The amount of calculations can be greatly reduced. From the classification results, it can be seen that the four kinds of algorithms have their own advantages in dealing with the classification effect of a single defect, among which, Gabor filter+SVM and Gabor filter+KNN methods achieve 100% classification accuracy for Sc, Ps and Cr, because the fine-grained texture features of Cr and Ps defects with different scales are obvious. However, in the overall classification accuracy of the six types of defects, the classification effect of SARSN101 and RKD-SARSN101 proposed in this paper is better. Figure 17: Comparison of the results of each classification algorithm.
## 3 Numerical Algorithm of the Proposed Damage Model
### Elastic Prediction  
According to the plastic increment theory, total strain can be decomposed into the increment of elastic strain and that of plastic strain for any time step (see Formula (29)):  
\[\Delta\varepsilon_{ij}=\Delta\varepsilon_{ij}^{\mathrm{e}}+\Delta\varepsilon_ {ij}^{\mathrm{p}}\quad(i\ =\ 1,\ 2,\ 3) \tag{29}\]  
where \(\Delta\varepsilon_{ij}\) is strain increment; \(\Delta\varepsilon_{ij}^{\mathrm{e}}\) is increment of elastic strain; and \(\Delta\varepsilon_{ij}^{\mathrm{P}}\) is increment of plastic strain.  
According to [PERSON]'s law, the relationships among the deviations of elastic stress, hydrostatic stress and strain are denoted by Formula (30):  
\[\left\{\begin{array}{ll}\sigma_{x}-\sigma_{\mathrm{m}}=2G(\varepsilon_{x}- \varepsilon_{\mathrm{m}}),\ \tau_{xy}=G\gamma_{xy}\\ \sigma_{y}-\sigma_{\mathrm{m}}=2G(\varepsilon_{y}-\varepsilon_{\mathrm{m}}), \ \tau_{yz}=G\gamma_{yz}\\ \sigma_{z}-\sigma_{\mathrm{m}}=2G(\varepsilon_{z}-\varepsilon_{\mathrm{m}}), \ \tau_{zx}=G\gamma_{zx}\end{array}\right. \tag{30}\]  
where \(\tau_{xy}\), \(\tau_{yz}\) and \(\tau_{zx}\) are shear stress; and \(\gamma_{xy}\), \(\gamma_{yz}\) and \(\gamma_{zx}\) are shear strain.  
Assuming that all strains are elastic, Formula (30) is expressed as Formula (31) in incremental form, and Formula (21) is expressed as Formula (32) in incremental form:  
\[\left\{\begin{array}{ll}\sigma_{x}^{n+1}=\overline{\sigma}_{x}^{n}+\Delta \sigma_{\mathrm{m}}^{n}+2G(\Delta\varepsilon_{x}-\Delta\varepsilon_{\mathrm{m }}),\ \tau_{xy}^{n+1}=\overline{\tau}_{xy}+G\Delta\gamma_{  
(1) When \(\theta_{n}>\theta_{c}\), tensile damage of element occurs, and is obtained by Formulae (4) and (11):  
(2) Calculate the current equivalent tensile strain by Formula (34):  
\[\theta=\left\{\begin{array}{ll}(\theta_{n}+\theta_{n+1})/2,&\theta_{n+1}> \theta_{\rm c}\\ (\theta_{n}+\theta_{\rm c})/2,&\theta_{n+1}\leq\theta_{\rm c}\end{array}\right. \tag{34}\]  
where \(\theta_{n}\) is equivalent tensile strain in the \(n\)th load step; and \(\theta_{\rm c}\) is critical tensile strain.  
(3) Calculate crack density increment \(\Delta C_{\rm d}^{n+1}\) according to Formula (11) and update the history variable of crack density \(C_{\rm d}^{n+1}=C_{\rm d}^{n}+\Delta C_{\rm d}^{n+1}\).  
(4) Update damage variable \(D\) using Formula (4).  
(5) When \(\theta_{n}\leq\theta_{c}\), do not calculate tensile damage and directly calculate the Cowper-Symonds kinetic hardening model.
# Three Cube Packing for All Dimensions
###### Abstract  
Let \(V_{n}(d)\) denote the least number, such that every collection of \(n\)\(d\)-cubes with total volume \(1\) in \(d\)-dimensional (Euclidean) space can be packed parallelly into some \(d\)-box of volume \(V_{n}(d)\). We show that \(V_{3}(d)=\frac{r^{d}-4}{d}\) if \(d\geq 11\) and \(V_{3}(d)=\frac{1+1}{r^{d}+\left(\frac{1}{r}-r\right)^{d}+1}\) if \(2\leq d\leq 10\), where \(r\) is the only solution of the equation \(2(d-1)k^{d}+dk^{d-1}=1\) on \(\left(\frac{\sqrt{2}}{2},1\right)\) and \((k+1)^{d}(1-k)^{d-1}(dk^{2}+d+k-1)=k^{d}\Big{(}dk^{d+1}+dk^{d}+1\Big{)}\) on \(\left(\frac{\sqrt{2}}{2},1\right)\), respectively. The maximum volume is achieved by hypercubes with edges \(x\), \(y\), \(z\), such that \(x=\left(2r^{d}+1\right)^{-1/d}\), \(y=z=r\pi\) if \(d\geq 11\), and \(x=\left(r^{d}+\left(\frac{1}{r}-r\right)^{d}+1\right)^{-1/d}\), \(y=r\pi\), \(z=\left(\frac{1}{r}-r\right)x\) if \(2\leq d\leq 10\). We also proved that only for dimensions less than \(11\) are there two different maximum packings, and for all dimensions greater than \(10\), the maximum packing has the  
that \(V(2)=\frac{2+\sqrt{3}}{3}\). The estimate of \(V(2)\) was improved by [PERSON] [12]\(V(2)<1.53\). Later, this result was improved by [PERSON] [13]\(V(2)\leq\frac{2867}{2048}<1.4\) and [PERSON] [14]\(V(2)<1.37\).  
In 2021, [PERSON] [15] reduced the problem of \(V(2)\) to a problem of a finite set of squares, and he limited their number to about 26,000.  
The estimate of \(V(3)\) was also gradually improved. [PERSON] and [PERSON] [16] proved \(V(3)\leq 4\), and later [PERSON] [17] proved \(V(3)\leq 2.26\). The exact results are known for \(n=2\), \(3\), \(4\), \(5\): [PERSON] [18]\(V_{2}(3)=\frac{4}{3}\), \(V_{3}(3)=1.44009951\), [PERSON] [19]\(V_{4}(3)=1.5196303266\), and in [17], [PERSON] proved \(V_{5}(3)=V_{4}(3)\).  
Some results for higher dimensions are known too: \(V_{3}(4)=1.63369662\), by [PERSON] and [PERSON] in [20]; \(V_{3}(6)=1.94449161\), by [PERSON] and [PERSON] in [21]; \(V_{3}(8)=2.14930609\), by [PERSON] in [22]; \(V_{3}(5)\doteq 1.802803792\) and, without a proof, \(V_{3}(7)\doteq 2.05909680\), \(V_{3}(9)\doteq 2.21897778\), \(V_{3}(10)\doteq 2.27220126\), \(V_{3}(11)\doteq 2.31533581\), \(V_{3}(12)\doteq 2.35315527\), \(V_{3}(13)\doteq 2.38661963\), by [PERSON] and [PERSON] in [23].  
[PERSON] and [PERSON] proved \(\lim\limits_{d\rightarrow\infty}V_{n}(d)=n\) for \(n=5,6,7,\ldots\) in [24]. Their proof works for \(n\in 2,3,4\) as well.  
Packing squares into a rectangle is an over half-century-old problem, and even though there are multiple partial results, it remains unresolved. We investigated a modified problem: packing three \(d\)-cubes in \(d\)-dimensional space. Some results for smaller dimensions are known. We provide the solution for all dimensions \(d\geq 2\).
## 1 Introduction  
Over the past decade, there has been an on-going wave of interest and effort in developing optical metamaterials which exhibit exotic electromagnetic properties [1; 2; 3]. These metamaterials, constructed with subwavelength building blocks that are artificially arranged in 3D configurations, can be used to obtain novel optical phenomena that are unattainable from materials which exist in nature. Plasmonic colors, which emerge from resonant interactions between light and metallic nanostructures, are examples of such novel optical phenomena and can be used in color printing, filters, etc. [4]. Plasmonic resonances of metallic nanostructures have enabled high-resolution color printing beyond the optical diffraction limit at a resolution of -100,000 dots per inch [5; 6; 7; 8]. Plasmonic metasurfaces are easy to fabricate and have the ability of unprecedent control over the reflected or transmitted light while featuring relatively low losses even at optical wavelengths [9]. More importantly, today's optical techniques demand the compaction of the physical dimensions of optical systems and development of a smaller and low-energy system [10]. Hence, plasmonic metasurfaces, which can be considered as the two-dimensional analog of metal-based metamaterials, have attracted increasing attention over recent years [9]. However, metallic nanostructures have inherent plasmon damping and possess mainly electric-like resonances. Plasmonic colors suffer from poor color saturation with a gamut that occupies only a small subset of perceivable colors on the International Commission on Illumination (CIE) 1931 chromaticity diagram [5]. Thus, there is an urgent need for another degree of controlling of structural colors, for example, the magnetic resonance modes [11]. The all-dielectric nanostructures which generate structural colors of high saturation and high resolution then entered the view of researchers [12; 13; 14; 15; 16; 17]. Furthermore, finding materials that achieve broadband absorption and are eco-friendly is also a long-sought-after goal of researchers [18; 19]. Based on previous research [5], this paper uses silicon nanostructures, which are fabricated onto a silicon substrate divided with a Si\({}_{3}\)N\({}_{4}\) layer, to realize the display of different colors. Instead of metals, silicon nanostructures exhibit localizedmagnetic and electric dipole resonances and can achieve relatively high transmission efficiency with full phase range control, low costs and wide application prospects. It is well known that slight changes in the parameters of nanostructures will create major shifts in perceived colors [20]. Therefore, we further analyze the impact of nanostructure parameters, and find that the diameter of nanodisk decides the distribution of structural colors. The height of the nanodisk and thicknesses of the dielectric layer and substrate mainly have effects on the saturation of structural color. Finally, we use the nanostructures to realize the display of letter T with three different colors.
## 3 Discussion
This has been reported in many studies, for example, acetyl CoA- benzyl alcohol acetyltransferase (_CbBEAT_) from _Clarkia breweri_[27], benzoyl-CoA: benzyl alcohol benzoyl transferase (_PtBEBT_) from _Populus trichocarpa_[28], and alcohol acetyltransferasea 1 (_RhAAT1_) from rosa [29]. As several types of alcohol acyltransferases in different species of plants, _CbBEAT_ and _RhAAT1_ can catalyze a broad range of acyl-CoAs and alcohols to form the relative aromatic esters [12]. Further, the esterification of alcohols, either benzyl or cinnamyl alcohol, results in the synthesis of a number of important fragrance and flavor compounds [26]. This suggests that formation of benzyl acetate and cinnamyl acetate may be catalyzed by one enzyme in _P. mume_, utilizing benzyl alcohol and cinnamyl alcohol as substrates, respectively. Though enzymes, including _PmBEAT_[12] and coniferyl alcohol acetyltransferasea (_PmCEAT_) [30], with similar biochemical characteristics in _P. mume_ have been reported, the process by which enzymes catalyze cinnamyl alcohol to produce cinnamyl acetate needs further investigation. Comparative transcriptome analysis can be an essential and effective strategy for fully understanding the scent biosynthesis in _P. mume_.
## 4 Simulation Example
: _Consider the following MISO-OE system with time-delays,_  
\[y(t) =\sum_{i=1}^{4}z^{-d_{i}}\frac{B_{i}(z)}{A_{i}(z)}u_{i}(t)+v(t),\] \[A_{1}(z) =1-0.1z^{-1}+0.7z^{-2},\ \ B_{1}(z)=1.5z^{-1}+0.9z^{-2},\] \[A  
Taking \(m=130\), \(\sigma^{2}=0.10^{2}\), and \(\text{TH}=0.001\), employ the AM-BPDNI algorithm to identify the system in Equation (36). The estimated parameter vector is  
\[\hat{\mathbf{\theta}}_{2} =[-0.0874,0.6849,\mathbf{0}_{20},1.4682,0.9218,\mathbf{0}_{28},0.2880,0.471 1,\mathbf{0}_{10},0.1658,1.7708,\mathbf{0}_{38},-0.1842,\] \[-0.3968,\mathbf{0}_{30},-0.0759,1.9503,\mathbf{0}_{18},-0.4563,-0.0556,\bm {0}_{40},1.0429,0.7457,\mathbf{0}_{8}]^{T}.\]  
The time-delay estimates are  
\[\hat{d}_{1}=z_{1}=20,\;\hat{d}_{2}=z_{3}=10,\;\hat{d}_{3}=z_{5}=30,\;\hat{d}_{4 }=z_{7}=40,\]  
which are identical to the true time-delays. The parameter estimation errors of the systems in Equations (34) and (36) versus \(k\) are shown in Figure 1:0  
The running time of the proposed method for the systems is \(t_{1}=1.374637\) s and \(t_{2}=2.398602\) s. It can be concluded that the computational burden increases as the dimension of the parameter vector increases. The simulation results show that for the MISO-OE model, the proposed AM-BPDNI algorithm can obtain efficient estimation of parameters from few observations (\(m<n\)) with good robustness. Moreover, the AM-BPDNI algorithm can effectively estimate the time-delays according to the sparse characteristic of the estimated parameter vector. However, as the number of the input channels increases, the computational burden of the proposed algorithm increases.