esa-sceva/satcom-chunk-collection huggingface dataset:
# Challenges and Future Trends in the Treatment of Psoriasis [PERSON] and [PERSON]
## 4 Future Aspects in the Treatment of Psoriasis
### Advancements in Topical Treatments
#### 4.2.1 Microneedles  
Over the past decades, microneedles (MNs) have gained popularity for transdermal drug delivery [66]. They consist of a baseplate with an array of microsized needles, capable of piercing the skin and creating microchannels. Different types of MNs exist, including solid microneedles (SMNs), coated microneedles (CMNs), dissolving microneedles (DMNs), and hollow microneedles (HMMs) [67].  
In recent years, MNs have shown promise in the treatment of psoriasis. Clinical trials and animal studies have demonstrated their effectiveness in enhancing drug permeation and improving therapeutic outcomes. One study conducted a small clinical trial on psoriatic patients resistant to topical calcipotriol/betamethasone ointment therapy. They used an SMN patch based on hyaluronic acid (HA), which was placed over the lesions after ointment application, resulting in improved therapeutic effects with the assistance of MNs [68]. In another study using an imiquimod-induced psoriasisform model in mice, HA-based DMNs with methotrexate encapsulated in the needles were applied. These DMNs successfully penetrated the skin and released the drug, leading to amelioration of psoriatic lesions with lower drug doses and fewer systemic side effects compared to oral methotrexate administration [69]. More recently, researchers developed an HA-based DMN patch to mitigate inflammatory skin diseases, including psoriasis. The DMNs were used for transdermal delivery of NLRP3-targeted Cas9 nanocomplexes and nanoparticles loaded with dexamethasone (Dex), which specifically inhibited NLRP3 inflammasome via CRISPR-Cas9-induced genome editing, further enhancing psoriasis therapy [70]. These studies demonstrate the potential of microneedles as a novel and effective approach for psoriasis treatment, offering targeted drug delivery and reduced side effects.
# Agglomerative Clustering and Residual-VLAD Encoding for Human Action Recognition
## 3 Experimental Results and Discussion
### Comparison with State-of-the-Art  
A comparison with relevant state-of-the-art is given in Table 2. The results are computed by calculating average accuracy of three splits of UCF101 and HMDB51 datasets. First, the proposed scheme is compared with two-stream CNNs. Most of these methods combine the encoding schemes with improved dense trajectories. The proposed scheme performs better compared to these methods except in the case of iDT + VLMPF [34]. These results are computed by fusion of five different features, namely, SCN, TCN, C3D, HMG and iDT. However, the computational complexity involved in extracting these deep features and dense trajectories makes this method ineffective. Subsequently, the results for 3D ConvNets are compared. The results suggest that 3D CNNs trained on large video datasets outperform the ones trained on large image datasets. The proposed scheme improves the performance of existing 3D ConvNets as opposed to the addition of more expensive dense trajectories. Furthermore, the proposed approach can be extended to two-stream 3D Resnets architectures given that computational complexity is not a matter of concern. These two streams consist of RGB and flow data, which proves to be effective in terms of higher accuracies. As the results suggest, the results can be improved with a traditional BoW pipeline, but at the cost of additional computational overheads.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Encoding Schemes** & \multicolumn{2}{c}{**UCF101**} & \multicolumn{2}{c}{**HMDB51**} \\ \hline  & **ST-ResNet** & **ResNeXt-101** & **ST-ResNet** & **ResNeXt-101** \\ \hline VQ & 90.3 & 92.5 & 59.6 & 65.2 \\ SA [32] & 90.2 & 91.3 & 62.4 & 67.4 \\ VLAD [23] & 92.9 & 93.8 & 64.5 & 68.1 \\ Fisher Vector [33] & 91.0 & 90.1 & 64.2 & 64.3 \\ Proposed R-VLAD & 93.7 & 94.0 & 65.0 & 69.8 \\ Proposed HFV & 94.3 & 96.2 & 67.2 & 72.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The accuracy comparison of encoding schemes.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Features** & **Method** & **UCF101** & **HMDB51** \\ \hline  & iDT + ST-VLAD [35] & 91.5 & 67.6 \\  & LTC [11] & 92.7 & 67.2 \\  & iDT + ActionVLAD [36] & 93.6 & 69.8 \\ Two-Stream CNN & iDT + VLMPF [34] & 94.3 & **73.1** \\  & Two-Stream [10] & 88.0 & 59.4 \\  & ST-ResNet [24] & 93.4 & 66.4 \\  & **Proposed HFV-ST-ResNet** & **94.3** & 67.2 \\ \hline  & C3D [15] & 82.3 & 51.6 \\  & Res3D [37] & 85.8 & 54.9 \\  & F\({}_{ST}CN(SCIfusion)\)[38] & 88.1 & 59.1 \\ Single-Stream 3D CNN & P3D [39] & 88.6 & - \\  & iDT + C3D AdaScan [40] & 93.2 & 66.9 \\  & TSN [41] & 94.2 & 69.4 \\  & ResNeXt-101 [25] & 94.5 & 70.2 \\  & **Proposed HFV-ResNeXt-101** & **96.2** & **72.6** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with state-of-the-art results.
## 3 Unified White-Box ML Watermarking Framework
### Static White-Box Watermarking Schemes
#### 3.2.2 [PERSON] et al. Scheme (RIGA) [57]  
RIGA (Robust whlte-box GAn watermarking) [57] is a static scheme proposed to overcome the weaknesses of the approach by [PERSON] et al. [45] against WKA, PIA, and overwriting attacks. The main innovation of RIGA is the use of a DNN \(\text{Proj}_{\theta}^{\text{DNN}}\) with parameters \(\theta\) as the projection function, instead of the perceptron of [PERSON] et al. \(\text{Proj}_{\theta}\), and associating it with another DNN \(F_{\text{det}}\) to preserve the distance between watermarked and non-watermarked weight distributions for WKA robustness.  
Feature extraction: The feature extraction function of RIGA is similar to that of [PERSON] et al. [45]:  
* Calculate mean values \(\overline{W}_{ikh}=\frac{1}{n}\sum_{j=1}^{n}W_{ijkh}\), obtaining \(\overline{\mathbf{W}}\in\mathbb{R}^{d\times s\times s}\) and flattening it to vector \(\mathbf{w}\in\mathbb{R}^{p}\);
* From \(\mathbf{w}\), RIGA secretly extracts a subset of elements: \(\mathbf{w}_{s}=\text{Ext}(M,K_{\text{ext}})=\mathbf{w}K_{\text{ext}}\), where \(K_{\text{ext}}\) is a selection matrix.  
Training strategy: RIGA trains \(\text{Proj}_{\theta}^{\text{DNN}}\) with the dataset \(((\mathbf{w}_{s}^{\text{wat}},b),(\mathbf{w}_{s},b_{r}))\), where \(\mathbf{w}_{s}^{\text{wat}}\) and \(\mathbf{w}_{s}\) are features from watermarked and original models, respectively, and \(b\) and \(b_{r}\) are the owner's and random watermarks, respectively. This prevents the trivial mapping problem, where the projection function always outputs \(b\) regardless of the input.  
Loss function: RIGA watermarks a target model \(M\) through fine-tuning under the global loss:  
\[E=E_{0}(X_{\text{Train}},Y_{\text{Train}})+\lambda_{1}E_{\text{ wat}}^{\prime} +\lambda_{2}E_{\text{det}}\\ =E_{0}(X_{\text{Train}},Y_{\text{Train}})+\lambda_{1}E_{\text{ wat}}^{\prime}-\lambda_{2}\log(F_{\text{det}}(\mathbf{w}^{\text{wat}})) \tag{12}\]  
where \(\lambda_{1}\) and \(\lambda_{2}\) are adjustment parameters, and \(F_{\text{det}}\) is a binary classifier discriminating between watermarked and non-watermarked weights.  
Advantages and limitations: RIGA demonstrates resistance to fine-tuning, pruning, and overwriting attacks, as well as robustness to watermark detection attacks (PIA and WKA) due to \(F_{\text{det}}\). However, its main limitations include the following: (i) Computational complexity from training three models simultaneously, (ii) contradictory objectives between watermark embedding and distribution preservation, leading to slow convergence, and (iii) requirement for 500+ non-watermarked models to train \(F_{\text{det}}\), making it extremely costly for large-scale applications.
# Intelligent Environment Monitoring System for University Laboratories
## 2 System Design and Implementation
### Each Module of Proposed System  
#### 2.3.1 Control and Communication Module  
The system uses a single chip microcomputer STC12C5A60S2 as the control core. It also contains a GPRS module (SIM900A, SIMCom, Shanghai, China) to complete the communication with the mobile terminal realizing real-time monitoring.  
The GPRS module is used to initialize a circuit and create a channel into the listening state when the serial port is connected to MCU. After collected data is written in the buffer, this module sends the data to the designated mobile terminals. This system chooses SIM900A module embedded TCP/IP protocol. For users, expanding TCP/IP AT commands is convenient to transmit data. Thus, we use AT command to control GPRS module. After querying the manual of the corresponding command, we know AT command consists of data format, initialization and waiting command.  
Based on STC12C5A60S2 and GPRS network, the remote communication framework of the system builds a communication mode. As shown in Figure 2, a user sends query request, in the form of short message using mobile phone, to the center control unit. Then, MCU detects the user's request from the GRPS network access point.
## 5 Results and Discussion
### Introduction to Methods
#### 5.1.2 MAJI for TOC Prediction  
Muti-attribute joint inversion (MAJI), a data-driven approach for TOC estimation, integrates multivariate geophysical attributes (e.g., acoustic impedance, Gamma Ray, density, and resistivity) through statistical or machine learning models to establish nonlinear relationships with TOC. This method employs least-squares optimization or Bayesian frameworks to minimize residuals between predicted and measured TOC values, typically expressed as  
\[\textit{TOC}=f(A)+\epsilon,\ A=[GR,DEN,RT]^{T} \tag{9}\]  
where \(f(\cdot)\) represents regression functions ranging from linear combinations to neural networks.  
Validated in the Haynesville and Eagle Ford shales, it achieves moderate accuracy when calibrated with >50 training samples. Its advantages include enhanced vertical resolution (up to 0.5 m) compared to \(\Delta\)logR and capacity to handle collinear attributes via principal component analysis [29; 30]. However, solution non-uniqueness persists due to overlapping petrophysical responses in mixed lithologies (e.g., quartz-clay-carbonate ternary systems), requiring Markov chain Monte Carlo (MCMC) uncertainty quantification [31].
# Examining the Link between Isokinetic Strength Metrics and Ball Speed in Women's Soccer
## 2 Materials and Methods
### Data Analysis  
Statistical analyses were performed using SPSS (version 25.0, Chicago, IL, USA). The results are reported as mean values \(\pm\) standard deviation (X \(\pm\) SD) and minimum-maximum (Range). Data normality and homogeneity of variances were assessed using the Shapiro-Wilk test, Q-Q plots, and Levene's test, respectively. The relationships between players' ball speed and isokinetic strength parameters were analyzed using Spearman correlation analysis. In correlation analysis, the correlations were categorized as weak (>0.30), moderate (0.3-0.5), high (0.6-0.8), and excellent (0.8-1.0) [44]. The significance level was set at \(p\leq\) 0.05.
# Microstructure and Properties of AA6061/SiCp Composites Sintered under Ultra High-Pressure
## 3 Results and Discussion
### Evolution Law of Density of Composite Materials  
Figure 11 shows the density tendency of AA6061/N-SiCp composite. Figure 11a shows a variation trend of the composite density with volume fraction at 4.5 GPa and 550 \({}^{\circ}\)C. It can be seen that the density gradually decreases as the volume fraction of SiCp increases. The density of pure AA6061 reaches 99.38%. For AA6061/1.0 vol.% N-SiCp, the density decreases to about 98%. At this time, the content of N-SiCp is relatively small, and there is no obvious agglomeration. For the small amount of N-SiCp around the Al matrix, the UHSP causes the Al particles to become irregular, and the contact area between particles increases, thereby promoting the diffusion of the elements and leading to the  
Figure 10: XRD patterns of AA6061/5 vol.% N-SiCp sintered at different temperatures: 450, 550, 600, and 650 \({}^{\circ}\)C.  
Figure 9: SEM micrographs of AA6061/5 vol.% N-SiC composites sintered at 450 (a), 550 (b), 600 (c) and 650 (d) \({}^{\circ}\)C.  
large density of the material. When the volume fraction is increased to 15 vol.%, the density drops to about 95%. In this condition, more agglomeration of the N-SiC could be discovered in the composites. Due to the small self-diffusion coefficient of SiC, and poor ability of sintering, the sintered material seldom achieves full densification. The sintering temperature of the SiC ceramic at normal pressure is as high as 2000 \({}^{\circ}\)C, and the density is only 96%. After being sintered at 4.5 GPa and 1300 \({}^{\circ}\)C by UHSP, the density of the SiC reported by [PERSON] [26] can just reach 93%. Although UHSP can improve the densification of SiC, to some extent, the volume shrinkage between SiCp is small, and the density cannot reach that of pure Al. Therefore, the larger the volume fraction of N-SiCp is, the smaller the volume shrinkage is during sintering. The small pores between the particles reduce the density of the material.  
Figure 11b draws the density variation of AA6061/5 vol.% N-SiCp composites sintered at different temperatures and 4.5 GPa. As the temperature increases, the density of the composite rises gradually. The density is 98% at 650 \({}^{\circ}\)C. Ultrahigh pressure could promote the plastic flow of the Al matrix. The higher temperature usually represents the better plastic flow, more volumetric shrinkage, and higher density of the material. Combined with the analysis of the microstructure of the material in Section 3.1, due to the high-temperature effect, the internal atoms of the Al matrix migrate to the large pores formed at the initial stage of sintering, and the filled pores could further increase the density.
# A Combined Dynamic-Kinematic Extended Kalman Filter for Estimating Vehicle Sideslip Angle
## 3 Experimental Setup
### Signal Pre-Processing  
The equations of motion of both dynamic and kinematic approaches, (1), (2) and (10), (11), respectively, are referred as to the vehicle center of gravity (COG). Therefore, it is important to process both input and measurement signals to eliminate undesired effects, moving the signals from the actual measurement location to the COG [23]. Yaw rate is independent from the position of the gyro, while lateral and longitudinal accelerations should be pre-processed according to the location of the vehicle inertial measurement unit (IMU). Assuming planar motion, the acceleration of the center of gravity \(\mathbf{a}_{COG}\) is computed from the acceleration of any generic point \(\mathbf{a}_{P}\) as  
\[\mathbf{a}_{COG}=\mathbf{a}_{P}+\dot{r}\mathbf{k}\times\mathbf{PG}-r^{2} \mathbf{PG} \tag{20}\]  
where \(\mathbf{PG}\) is the vector distance of COG from generic point P [24]. Longitudinal and lateral accelerations of the COG are then obtained  
\[a_{x} =a_{P,x}-\dot{r}\,\mathrm{PG}_{y}-r^{2}\mathrm{PG}_{x} \tag{21}\] \[a_{y} =a_{P,y}+\dot{r}\,\mathrm{PG}_{x}-r^{2}\mathrm{PG}_{y} \tag{22}\]  
where \(\mathbf{a}_{P}=a_{P,x}\,\mathbf{i}+a_{P,y}\,\mathbf{j}\) and \(\mathbf{PG}=\mathrm{PG}_{x}\,\mathbf{i}+\mathrm{PG}_{y}\,\mathbf{j}\). In the considered case, P corresponds to the location of the vehicle IMU, namely \(\mathbf{PG}=(0.625\,\mathbf{i}+0\,\mathbf{j})\,m\).  
Similarly, the velocity measured by the Kistler Correvit S-motion should be corrected to obtain the velocity, and hence the sideslip angle, of the COG, in order to have a proper comparison with estimated quantities.  
\[\mathbf{v}_{COG}=\mathbf{v}_{S}+r\mathbf{k}\times\mathbf{SG} \tag{23}\]  
Figure 5: Geometry of the double lane change.  
Figure 6: Geometry of the slalom test.  
Namely  
\[v_{x} =v_{S,x}-r\,\text{SG}_{y} \tag{24}\] \[v_{y} =v_{S,y}+r\,\text{SG}_{x} \tag{25}\]  
where \(\mathbf{v}_{S}=v_{S,x}\,\mathbf{i}+v_{S,y}\,\mathbf{j}\) is the velocity measured at the S-Motion location and \(\mathbf{SG}=\text{SG}_{x}\,\mathbf{i}+\text{SG}_{y}\,\mathbf{j}\) is the vector distance from the COG, namely \(\mathbf{SG}=\left(2.25\,\mathbf{i}-0.46\,\mathbf{j}\right)m\). Figure 7 shows a schematic of the test vehicle with the relevant distances depicted.
# Multi-Parametric Analysis of Reliability and Energy Consumption in IoT: A Deep Learning Approach
## 4 Results and Discussion
### Energy Consumption
#### 4.2.1 Dense Data  
The values for \(EC_{\text{dense}}\) range between 0.13 and 1.15 with more than 98% of the values less than 0.5, thus offering a range of 0.37. Prediction results are presented in Figure 7. True values and predicted values are plotted which lie in a close proximity to the ideal diagonal line. Most of the data lie between 0.13 and 0.5, and the prediction results do not diverge a lot from the expected diagonal. To have a closer look, predicted values for three portions of selected data are plotted against actual values in Figure 8 from three different ranges (0.170 and 0.181, 0.244 and 0.256, 0.331 and 0.342). These ranges indicate an even spread of data. It is visible from the plots in Figure 8 that the predicted values are highly aligned with the actual values, in general. More detailed prediction results are discussed in Figure 9. According to Figure 9a, \(PS\) and \(TP\) are two of the best features resulting in a lesser \(RMSE\) than others. The rest of the features, individually, achieve almost the same prediction accuracy. Overall \(RMSE\) reaches its minimum (0.006) with six features. Figure 9b consists of \(OF\), \(AT\), \(TP\), \(PS\), \(DT\), and \(RD\), yielding an accuracy of more than 98% even if a close range of max (0.5) \(-\) min (0.13) is considered. However, the improvement after three features (\(TP\), \(PS\) and \(DT\)) is only 1.1%. Therefore, it can be concluded  
Figure 6: Prediction results for \(PDR\).  
that \(TP\), \(PS\) and \(DT\) are the most important and contributing features for capturing the variations in predictions for \(EC_{\text{dense}}\) data. It makes a lot of sense, as \(TP\) directly defines the amount of power used for transmissions, and \(PS\) plays a decisive role because varying sizes change the proportion of control data that directly influence the amount of energy used per data bit. The third most important feature, \(DT\), in combination with \(TP\), influences the radio success probability, which is also very important in defining the \(EC\)/bit. The \(x\)-axis label codes used in Figure 8(b) are explained in Figure 8(d). The prediction error is characterized in Figure 8(d), where 90% of the predictions have an error of 5.4% or less of the actual \(EC_{\text{dense}}\) values.  
Figure 8: Actual values vs. predictions for \(EC_{\text{dense}}\) (selected data).  
Figure 7: Actual values vs. predictions for \(EC_{\text{dense}}\).
## 4 Discussion
It is observed that the cooling down process is more gradual in the test pieces without addition and containing 1% of CNFs, particularly in the latter, which is an important piece of information concerning the behavior of the material with regard to the shrinkage that it could experience. The rapid temperature rise in the test pieces containing 2% of CNFs, encourages the appearance of cracks due to excessive expansion of the cementitious mass with the subsequent rapid drop in temperature in barely 50 min, this significant thermal fluctuation provokes enormous cracks in the material. The good thermal behavior of test pieces containing 1% of CNFs is surprising. This is the optimum addition amount, since it ensures that the cement mass absorbs less heat, this being absorbed by the CNFs, preventing the cement matrix from experiencing an excessive temperature increase. On the other hand, a gradual heat loss also results in less cracking of the material and consequently better mechanical behavior. Table 5 compares the strengths between mass concrete test pieces without addition and with the addition of CNFs in different percentages, before and after being exposed to fire testing and the severity of the fire, understood to be the area enclosed in the temperature-time curves. As can be observed, the concretes with the addition of 1% of CNFs withstand strengths which are 20% greater than those tolerated by the test pieces without additions, whilst they increase by 10% when the percentage of CNFs is 2%. In the case where the concrete is exposed to direct fire, the concrete containing 1% of CNFs is that which performs the best, increasing its strength by 13.5% compared to the concrete without additions that is exposed to fire. On the other hand, when the addition is 2% of CNFs, strength is lost compared to the concrete without additions and exposed to fire, due to the fact that the severity of the fire tolerated is greater. Figure 15: Evolution of the mean temperature reached in direct fire testing, in test pieces without additions and with 1% or 2% additions.
# Fast and Interactive Positioning of Proteins within Membranes
## 2 Materials and Methods
### Optimization and Extension of the Implementation
#### 2.3.1 Extension of IMPALA to Coarse-Grained Representations  
The original implementation of IMPALA was based on an all-atom representation. Nowadays, coarse-grained representations are commonly used to model membrane systems and allow the management of large and complex molecular assemblies. To limit the computational costs associated with an all-atom representation, we started to incorporate the coarse-grained representation of Martini version 3 [26] into our implementation. The BioSpring program has a general procedure for particle reduction. It is necessary to generate a set of instructions for this procedure so that it can represent the grouping of one or more atoms in Martini 3 grains. In particular, two input files are needed to perform a reduction procedure. The first contains the instructions for grouping the atoms into so-called large grain particles, and the second contains the parameters associated with each type of grain (electric charge, radius, Lennard-Jones energy parameters, mass and transfer energy for IMPALA). The input files are used to translate a PDB structure into CDL format. CDL stands for Common Data Language, a type of human-readable dataset from the netCDF library (or its binary equivalent), and is required by the BioSpring engine to set up a simulation. Most of the work required to integrate Martini 3 into BioSpring was to implement the code that formalizes the nomenclature of Martini 3 grains and their parameters to generate input files that can be used to reduce any molecular system into Martini 3 grains. In particular, in the context of membrane insertion, a new method was needed to calculate the contribution of the transfer energy used for IMPALA. The required information can be found in reference [26] (particularly in its Supplementary Tables S17-S19 on pages 15-17). The free energies of transfer were extracted from this work and divided by the calculated theoretical surface area of each grain type (normal, small, and tiny) to give an equivalent for the energy of transfer per surface unit of individual atoms in kJ mol\({}^{-1}\) A\({}^{-2}\), as shown in Table 1 of [18]. A future extension would be to specify additional distance constraints between CG particles, typically the internal distances between grains of the same amino acid residue formalized in Martini. The current implementation specifies the position of the CG particle on the center of mass of its particle group. At first glance, the implementation of such a geometric calculation is not trivial and will require some development time. Therefore, the current method provides an alternative for taking these spacing constraints into account by defining certain equilibrium distances between springs. To ensure that the inter-grain spacing meets the Martini specifications, a relaxation step is required prior to simulation. This implementation, of course, is only a temporary solution.