esa-sceva/satcom-chunk-collection huggingface dataset:
# Challenges and Future Trends in the Treatment of Psoriasis [PERSON] and [PERSON]
## 2 Current Treatment Guidelines
### Treatments for Moderate-to-Severe Poriasis
#### 2.2.1 Phototherapy  
Phototherapy, including narrowband UV-B, broadband UV-B, and PUFA, has been used to treat moderate-to-severe psoriasis. Narrowband UV-B is preferred over the broadband form due to its higher effectiveness and better safety profile. UV-B phototherapy reduces DNA synthesis, leading to apoptosis of keratinocytes and decreased production of pro-inflammatory cytokines. Adverse effects may include erythema, pruritus, blistering, photoaging, and photocarcinogenesis. Narrowband UV-B is more commonly used due to its greater efficacy, longer remission duration, lower potential for skin cancer, and reduced erythema compared to broadband UV-B. Combining narrowband UV-B with systemic retinoids may enhance efficacy and reduce the potential for skin cancer [24]. PUFA therapy involves the use of psoralens, such as methoxalen, to suppress DNA synthesis followed by UV-A irradiation. Although oral PUFA is more effective than UV-B, it is no longer preferred due to the increased risk of skin cancer with long-term use. Adverse effects may include gastrointestinal upset, burning, pruritus, hypertrichosis, and photoaging. Topical PUFA therapy is commonly used for palmoplantar psoriasis, involving soaking hands and feet in water with psoralen followed by UV-A irradiation. The main challenge with phototherapy is the need for patients to travel to undergo office-based sessions. Home phototherapy is a convenient option but may be limited by insurance and space constraints.
## 3 Results
### Correlations and Regression Models  
Correlations between maturity offset, age and kinanthropometric variables and fitness variables are shown in Table 7. Both maturity offset and age showed positive low to moderate correlations with the long jump, medicine ball throw, CMJ and CMJ power (r = 0.221-0.629; \(p<0.001\)-0.006). The sit-and-reach test showed positive moderate or low correlations with body mass, sitting height, arm span, Cormic index, relative arm span, biacromial breadth and corrected calf girth (r = 0.161-0.360; \(p<0.001\)-0.026) and a low negative correlation with BMI (r = \(-0.189\); \(p=0.020\)). The back scratch test showed moderate to low positive correlations with arm span, relative arm span and upper limb length (r = 0.241-0.416; \(p<0.001\)-0.003) and low negative correlations with corrected arm girth, \(\sum\)8 skinfolds, BMI and muscle-bone index (r = \(-0.166\); \(-0.222\), \(p=0.006\)-0.040). The long jump test showed low positive correlations with arm span and liiosipnalle height (r = 0.172-0.200; \(p=0.013\)-0.034) and low negative correlations with \(\sum\)8 skinfolds and fat mass (kg) (r = \(-0.174\); \(-0.292\), \(p<0.001\)-0.032). The medicine ball throw test showed significant positive low or moderate correlations with all variables (r = 0.234-0.560; \(p<0.001\)-0.004), with the exception of the Cormic index and \(\sum\)8 skinfolds. The CMJ showed negative correlations with biliocristal breadth, \(\sum\)8 skinfolds, fat mass (kg) and BMI (r = \(-0.249\); \(-0.396\), \(p<0.001\)-0.002). All kinanthropometric variables showed significant positive moderate and high correlations (r = 0.411-0.850; \(p<0.001\)) with power in the CMJ, with the exception of relative arm span and Cormic index. The 20 m sprint time and agility test time showed low and moderate positive correlations with body mass, biliocristal breadth, \(\sum\)8 skinfolds, fat mass (kg) and BMI (r = 0.180-0.379; \(p<0.001\)-0.027).  
The results of linear regression in blocks are shown in Table 8. In the prediction models for the different physical tests, between 4 and 79% of the variance in the physical fitness test performance could be predicted by age, body mass, bone-related variables (biacromial and bileocrestal breadths, relative arm spam and Cormic index), fat-related variables (\(\sum\)8 skinfolds, fat mass) and muscle-related variables (corrected arm girth, muscle mass) (\(p<0.001\)-0.024).
## 2 Analysis of the State of the Art
### Device Models
#### 2.3.2 UAProf  
User Agent Profile [65] (UAProf) is concerned with collecting wireless devices capabilities and preferences. This information is provided to content servers to easy the content format selection process.  
UAProf is directly related to the W3C CC/PP specification and it is also based on RDF. This way, the document schema is extensible [66, 67]. These files, usually served as application/xml minetype, describe several mobile devices capabilities, e.g., vendor, model, screen size, multimedia capabilities, _etc_. Most recent versions have also information about MMS, video streaming and more multimedia features. UAProf profiles are voluntarily built by the vendors, e.g., Nokia, Samsung, LG,... for GSM devices, or by several telecommunications company for CDMA/BREW devices.  
The system works as follows:  
1. The device sends a header containing a URL and its UAProf within an HTTP request.
2. The server side analyses the received UAProf to adapt the content to the device's display size.
3. Finally, the server takes the decision and serves the corresponding items to the device.  
However, this approach has several drawbacks:  
* Not every device have a UAProf.
* Not every UAProf are available.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Advantages** & **Drawbacks** \\ \hline A good infrastructure for modeling devices & Device dependent \\ Content negotiation flexibility & It requires a more mature user \\  & preferences definition \\ Using CC/PP, Web based device developers & \\ and user agents can define accurate profiles & \\ for their products. Web servers and server & \\ proxies can use these profiles to perform the & \\ adaptation & \\ Open to new protocol proposals for profile & \\ exchanging & \\ \hline \hline \end{tabular}
\end{table}
Table 3: CC/PP: several advantages and drawbacks.  
* UAProf data can contain schema or data errors.
* There is no industry-wide data quality standard for the data within each field in an UAProf.
# Modeling and Profiling of Aggregated Industrial Network Traffic
## 3 Modelling Aggregated Traffic Break-Down
### Objective 2: State Modelling and Profiling  
The second objective is to identify the state space the communication in the network can obtain, i.e., identifying different states in which the multi-modal communication is functioning. That is, we want to partition the time into \(k\) consecutive and non-overlapping segments \(\{t_{1:k},s_{i}\}\) where \(t_{k}\) represents \(k^{th}\) segment of time with state \(s_{i}\), \(i=1,\ldots,m\), that ends at time \(t_{k}\). The data sequences in each segment should show similar dynamics and characteristics while dissimilar enough from the other segments to distinguish the states.  
The only available ground truth and previous knowledge about the network are the evident work-cycles in traffic patterns, obtained from the first objective and supported in the literature [9; 10; 14]. Hence, the number, duration, or characteristics of each of the states are unknown, i.e., there exists no knowledge about the true model. The parameters that can describe the underlying communication pattern need to be learned from the limited available data for modeling the network traffic.  
Furthermore, the available network data is unlabelled and lacks any additional information about the possible or existing operational states for deterministic network modeling.  
Figure 3: The work-cycles recognition is realised through: deploying communication intensity parameters to find work-cycles boundaries (**a**); the non-overlapping segments resemble similar repeated patterns (**b**); the Spearman’s correlation shows high degree of similarity between the identified work-cycles (**c**).  
In the absence of the ground truth for the operational modes evident in the network traffic patterns, the modes need to be approximated from the data. In other words, the data sequences with similar characteristics that can describe the dynamic characteristics of each state need to be identified.  
The hidden Markov model (HMM) is an effective unsupervised method with strong support in Bayesian inference that has been proven effective applied on sequential data where the correct model, the order of the HMM, is unknown. HMM can represent the probability distributions over a sequence of observations and model the observations as a probabilistic function of the latent states. In compact notation an HMM can be defined as \(\lambda=(A,B,\pi)\), where \(\pi=\{\pi_{j}\}\) is the initial state distribution, \(A=\{a_{ji}\}\) is the state transition probability, and \(B=\{b_{i}\}\), \(1\leq j,i\leq m\) is the probability of the observation in the current hidden state.  
To deploy HMM for modeling the network traffic, one important parameter to estimate is the order of HMM, i.e., the number of states that best describes the data.
## 5 Conclusions  
This paper evaluated the possibility of using water potabilization sludges (WPSs) as an alternative supplementary cementitious material in calcium sulfoaluminate (CSA)-blended cements. WPSs were thermally treated (TT) at 700 \({}^{\circ}\)C with the aim of dehydroxylating the silico-aluminate crystalline phases to an amorphous state; TTWPSs are very interesting as their use as a secondary cementitious component can permit the saving of raw materials and the avoidance of their landfilling; moreover, its use allows for the dilution of the CSA cement and thus determines the following environmental benefits: (i) decreased CO\({}_{2}\) emissions; (ii) energy saving per unit mass of cement; (iii) withdrawal reduction in natural resources.  
From DT-TG and QXRD analyses, it was established that TTWPSs did not affect the formation of the main hydration products of CSA cement, namely, ettringite, aluminum hydroxide and calcium silicate hydrates; the latter derived from both the hydration of di-calcium silicate present in the cement and the pozzolanic reaction between SiO\({}_{2}\) (belonging to TTWPSs) and Ca(OH)\({}_{2}\) (formed as secondary phase from C\({}_{2}\)S hydration).  
Binders containing 20% by mass of TTWPSs (CSA20) displayed the same compressive strength as the neat CSA cement; furthermore, when 35% by mass of TTWPSs was added, only a slightly negative effect (\(-18\%\)) on the technical behavior of CSA cement was detected  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Kilin Thermal Requirement**} & \multicolumn{1}{c}{**CO\({}_{2}\) Emissions**} \\ \hline OPC CEM I & 3280 & 0.82 \\ \hline CSAR & 2295 & 0.51 \\ \hline CSA20 & 2278 & 0.45 \\ \hline CSA35 & 2265 & 0.40 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Calculated kiln thermal requirement (MJ/t\({}_{\text{binder}}\)) and total CO\({}_{2}\) generated (t\({}_{\text{CO2}}\)/t\({}_{\text{binder}}\)).  
at the longest curing period. Moreover, the utilization of TTWPSs, up to 35% by mass, did not significantly influence both the porosimetric features and the dimensional stability of blended cement pastes.  
Finally, the experimental findings, coupled with the calculations of both kiln thermal requirement and CO\({}_{2}\) emissions, open up the possibilities for producing binders (e.g., CSA20) characterized by reliable technical properties and a reduced impact on the environment.  
A.T.: conceptualization; methodology; formal analysis; investigation; data curation; writing--original draft preparation; writing--review and editing. N.I.: formal analysis; investigation. M.M.: conceptualization; methodology; formal analysis; investigation; data curation; writing--original draft preparation; writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Lightweight NFC Protocol for Privacy Protection in Mobile IoT
## 4 Security and Performance Analysis
### Performance Analysis and Simulation  
In Table 4, we compare the computational cost and memory cost, as well as cost functions of the protocols.\"_Rot_\" and \"_MixBits_\" represent two different displacement operations, and \"_PRNG_\" represents the random number generator. \"_Hash_\" is the relative high-cost operation. As shown in Table 4, only [PERSON]'s protocol and ours avoid using the \"_Hash_\" function. Compared to [PERSON]'s protocol, our protocol uses fewer computation operations and less computation time. Our protocol costs relatively less computational resources. When it comes to the memory cost, our devices need to store two keys and an ID, which will bring a higher storage overhead compared to the protocols with hash operations. However, this problem can be overcome because the storage overhead is related to the length of the keys and the ID. Our protocol's memory cost is consistent with [PERSON]'s. Overall, our protocol has a smaller resource cost.  
In order to get the computational consumption of the tags in our protocol, we simulated the tag's communication on the field programmable gate array (FPGA) simulation platform (Software Version:vivado 2017.3(64 bit); Virtual Board Version: Kintex-7:xc7k70 tfbv676-1; Xilinx, Silicon Valley, California, US, 2017) shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Protocol** & **Cost Function** & **Tag’s Computational Cost** & **Tag’s Memory Cost** \\ \hline [PERSON] protocol & \(\oplus\), +, _Rot_2, \(||\) & 4\(\oplus\), 18+, 10 Rot, 3_MixBits_ & 3 \\ \hline Xie protocol & \(\oplus\), \(||\), _Hash_ & 1\(\oplus\), 4 Hash, 1 PRNG & 3 \\ \hline [PERSON] protocol & \(\oplus\), \(||\), _Hash_ & 2\(\oplus\), 5 Hash, 1 PRNG & 2 \\ \hline Wang Protocol & \(\oplus\), \(||\), _Hash_ & 1\(\oplus\), 3 Hash, 1 PRNG & 1 \\ \hline Wei Protocol & \(\oplus\), \(||\), _Hash_ & 5\(\oplus\), 3 Hash, 1 PRNG & 2 \\ \hline Baek protocol & \(\oplus\), \(||\), _Hash_ & 2\(\oplus\), 2 Hash, 1 PRNG & 2 \\ \hline New protocol & \(\oplus\), +, \(||\) & 9\(\oplus\), 4+, 2 PRNG & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The cost comparison.  
Figure 6: The proposed authentication protocol.  
Table 5 shows the synthesis report (resource utilization) of the proposed design on Kintex-7 with 32-bit input data, where the \"Available\" column denotes the available resources provided by the FPGA system whereas the \"Used\" column shows the computational costs of the tag.  
As indicated in Table 5, the implementation of the Kintex-7 xc7k70 ftbv676-1 device for 32-bit architecture occupied 149 LUTs and 262 slice registers. The utilization of the LUTs and registers in our scheme is less than 0.5%. The result shows that fewer resources are required for the tags.  
Therefore, our authentication scheme is lightweight. Such a result is also shown in the NFC authentication.
# Instantaneous Networking Service Availability for Disaster Recovery
## 6 Numerical Examination
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(\mathbf{Y}\) **in A** & \(\mathbf{Y}\) **in B** & \(\mathbf{Y}\) **in C** &
\begin{tabular}{c} **Y Is Outside of** \\ **Disaster Area** \\ \end{tabular} \\ \hline X in A & 0.2 & 0.025 & 0.025 & 0.08 \\ \hline X in B & 0.025 & 0.2 & 0.025 & 0.09 \\ \hline X in C & 0.025 & 0.025 & 0.2 & 0.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Location distribution setup in area A, B, and C.  
Figure 3: Two types of launching time scenarios. Figure 4 shows the evaluation results of utilizing LI-Clouds with uniform distribution of launching time and parallel deployment. Compared with the conventional recovery scheme, using LI-Cloud increases networking service availability by 10.2 percent on average, and has a maximum of 42.1 percent increase in networking service availability. Figure 5 shows the evaluation results of utilizing LI-Clouds with Possion distribution of launching time and parallel deployment. The I-NSA of using LI-Cloud achieves a 19.9% increase on average in networking service availability, and a maximum of 60% increase in networking service availability. Figure 6 shows the evaluation results of utilizing LI-Clouds with uniform distribution launching time and sequential deployment. The I-NSA of using LI-Cloud achieves a 10.6% increase on average in networking service availability, and a maximum of 32.4% increase in I-NSA. Figure 7 shows the evaluation results of utilizing LI-Clouds with Poisson distribution of launching time and sequential deployment. Using LI-Cloud achieves an approximate 19% increase on average in networking service availability, and a maximum of 59.9% in I-NSA. Figure 4: I-NSA for users in the scenario of parallel deployment of networking services with (uniform distribution of restoration. Ta, Tb, and Tc are set to 200, 300, and 400, and \(T_{AG}\), \(T_{BG}\), and \(T_{CG}\) are set to 1200, 1300, and 1400, respectively). Figure 5: I-NSA for users in the scenario of parallel deployment of networking services with (Possion distribution of restoration. Ta, Tb, and Tc are set to 200, 300, and 400, and \(T_{AG}\), \(T_{BG}\), and \(T_{CG}\) are set to 1200, 1300, and 1400, respectively. Better performance achieved by utilizing LI-Clouds is attributed to the following two facts. (1) The LI-Cloud enables the local networking services even though there is no global networking service. (2) The fast launching of the LI-Cloud is enabled before the restoration of global networking services.
## 3 Results
### Comparison between Groups  
In Figure 3a, it can be observed that, in the coronal wall, the OPA + GIC subgroup showed a significantly lower interfacial gap than the rest of the subgroups. In the axial wall, the interfacial gap was significantly lower in subgroups in which dentin conditioning with PA or with OPA was used, and in the SDF/KI + GIC subgroup, than in the GIC subgroup (Figure 3b). In the cervical wall from the subgroup in which the dentin was conditioned with OPA, significantly less interfacial gap was found than in GIC subgroup (Figure 3c).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **Median** & **IQR** & **Mean** & **SD** & \(p\) \\ \hline \multirow{5}{*}{Coronal} & GIC & 25.30 & 37.95 & 32.26 & 22.44 & \multirow{5}{*}{0.72} \\ \cline{2-6}  & SDF/KI + GIC & 34.25 & 54.17 & 43.04 & 34.23 & \\ \cline{2-6}  & PA + GIC & 52.50 & 68.05 & 58.04 & 44.03 & \multirow{5}{*}{0.11} \\ \cline{2-6}  & PA + SDF/KI + GIC & 40.10 & 26.85 & 41.99 & 32.27 & \\ \cline{2-6}  & OPA + GIC & 00.00 & 24.58 & 8.95 & 14.05 & \\ \cline{2-6}  & OPA + SDF/KI + GIC & 63.50 & 103.2 & 57.59 & 54.91 & \\ \hline \multirow{5}{*}{Axial} & GIC & 29.10 & 14.70 & 34.17 & 17.09 & \multirow{5}{*}{0.04} \\ \cline{2-6}  & SDF/KI + GIC & 04.93 & 32.37 & 21.70 & 37.19 & \\ \cline{2-6}  & PA + GIC & 00.00 & 36.70 & 15.58 & 20.05 & \\ \cline{2-6}  & PA + SDF/KI + GIC & 12.50 & 50.30 & 37.54 & 57.77 & \\ \cline{2-6}  & OPA + GIC & 00.00 & 00.00 & 03.25 & 09.19 & \\ \cline{2-6}  & OPA + SDF/KI + GIC & 00.00 & 110.7 & 42.04 & 59.94 & \\ \hline \multirow{5}{*}{Cervical} & GIC & 19.30 & 15.71 & 16.19 & 08.56 & \multirow{5}{*}{0.63} \\ \cline{2-6}  & SDF/KI + GIC & 07.38 & 51.87 & 29.05 & 41.15 & \\ \cline{2-6}  & PA + GIC & 00.00 & 34.15 & 12.14 & 18.39 & \multirow{5}{*}{0.84} \\ \cline{2-6}  & PA + SDF/KI + GIC & 00.00 & 83.20 & 28.76 & 43.25 & \\ \cline{2-6}  & OPA + GIC & 00.00 & 19.22 & 8.20 & 11.61 & \\ \cline{2-6}  & OPA + SDF/KI + GIC & 00.00 & 99.50 & 40.34 & 58.05 & \\ \hline \multirow{5}{*}{Total} & GIC & 21.44 & 20.42 & 27.15 & 12.70 & \multirow{5}{*}{0.54} \\ \cline{2-6}  & SDF/KI + GIC & 18.47 & 43.02 & 31.26 & 30.37 & \\ \cline{2-6}  & PA + GIC & 37.40 & 31.00 & 28.59 & 17.70 & \\ \cline{2-6}  & PA + SDF/KI + GIC & 29.46 & 33.02 & 36.10 & 36.73 & \\ \cline{2-6}  & OPA + GIC & 04.70 & 13.82 & 06.80 & 07.61 & \\ \cline{2-6}  & OPA + SDF/KI + GIC & 46.00 & 80.18 & 46.66 & 42.15 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Interfacial gap values in \(\upmu\)m.  
Finally, when comparing the mean gap in all the walls, the OPA + GIC subgroup again showed lower gap values than the rest of the subgroups (Figure 3d).
# Sigmoidal Mathematical Models in the Planning and Control of Rigid Pavement Works
## 3 Materials and Methods
### Sigmoidal Models  
#### 3.3.1 Proposed Model  
The model is predicated on the logistic function, which describes the planning behavior of rigid pavement works. The implementation of this model enables managerial personnel to predict project progress, optimize resource allocation, and regulate cost over time. Furthermore, its adaptable structure enables the accommodation of diverse scenarios and conditions, thus establishing it as a pivotal instrument for the strategic planning and operational control of intricate projects. Equation (10) provides a complete description of the model, which guarantees \(f(0)=0\) an upper asymptote \(f(x)\to c\) according to \(x\rightarrow\infty\).  
\[f(x)=\frac{cb(e^{ax}-1)}{b(be^{ax}-1)+1} \tag{10}\]  
It describes an asymmetric growth. The lower limit, when \(\lim\limits_{x\rightarrow\infty}f(x)=0\) which represents the start of the project with no cumulative physical progress, and \(\lim\limits_{x\rightarrow\infty}f(x)=c/b\) which represents the maximum possible progress of the modeled process. These limits reflect a cumulative progression typical of construction projects, where one starts from an initial condition of zero and gradually advances to a final state.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Variable** & **Count** & **Mean** &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & **Minimum** & **25\%** & **50\%** & **75\%** & **Maximum** \\ \hline Time & & & & & & & & \\ (Month) & 718 & 2.87 & 2.13 & 0.00 & 1.00 & 3.00 & 4.00 & 8.00 \\ \hline Cost (USS) & 718 & 2,556,345.00 & 3,344,270.00 & 0.00 & 446,702.70 & 1,669,521.00 & 3,311,307.00 & 24,470,880.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics of the variables.  
Demonstrations of the limits of extremes of intervals 0 and 1.  
Verification of \(f(0)=0\)  
\[f(0)=\frac{cb(1-1)}{b(1-1)+1}\ =\frac{0}{1}\ =0 \tag{11}\]  
Limit when \(x\rightarrow\infty\):  
\[\lim_{x\rightarrow\infty}f(x)=\frac{cb(e^{ax}-1)}{b(be^{ax}-1)+1}=c \tag{12}\]  
This derivative is positive for \(a\ >\ 0\); therefore, the function is monotonically increasing. The maximum slope depends on \(a\), while \(b\) controls the curvature (asymmetry of growth).  
\(f(x)\) is the estimated cost of the project ($). \(a\) controls the growth rate of the model, i.e., the rate at which the volume of work performed increases over time. \(b\) is the form factor that adjusts the inflection point of the curve, determining the moment at which the maximum productivity of the project is reached. \(c\) represents the final value of the expected progress, corresponding to the total planned work at the end of the project, and \(x\) is the project execution time (Month) (See Figure 4).  
Figure 3: Representative database projects.