esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cluster-Based Channel Assignment Technique in IEEE 802.11 Networks
## 2 Network Model
### Signal to Interference Ratio and Utility of the Solutions  
To evaluate the performance of a specific channel assignment we use a two-step procedure. First, we compute the signal to interference ratio (\(SIR_{i}\)) for device \(i\), and later on we compute, from the \(SIR_{i}\), the utility for a certain wireless device \(i\), denoted by \(U_{i}\), which represents its normalized performance. In the following, we explain how we perform the the computation of both parameters, \(SIR_{i}\) and \(U_{i}\).  
To compute the \(SIR\) for a certain wireless device \(i\) we make use of the power levels of the interferences defined in the edge weights of the interference layer and also the power of the desired signal, using the same loss model as in Equation (1). Denoting by \(S_{V_{j}\to V_{i}}\) the power of the desired signal from vertex \(j\) to vertex \(i\), we can compute \(SIR_{i}\) by:  
\[SIR_{i}=\frac{S_{V_{j}\to V_{i}}}{\sum_{k}I_{V_{k}\to V_{i}}}. \tag{2}\]  
It is important to highlight that a STA will have only a \(SIR\) value but APs will have an \(SIR\) value for each of its attached \(STAs\). For that reason, we have chosen the worst case as the \(SIR\) value for a certain AP; i.e., we have chosen the minimum \(SIR\) from all of its STAs to be the final \(SIR\) for that AP.  
Although \(SIR\) is a well-known parameter that could represent the performance for a certain node, the user experience of such performance is upper and lower-bounded [32]. If \(SIR\) is above a certain maximum value, the user experience is limited by the performance of the technology, as the throughput is limited by the technology under use. On the other hand, if the \(SIR\) is below a minimum threshold, the devices cannot keep their connection and the user experience is unacceptable. For that reason, we have mapped the \(SIR\) for a device \(i\) into a new performance parameter called utility, denoted by \(U_{i}\), and that is a linear mapping of the \(SIR_{i}\) from a minimum value of 0 when \(SIR_{i}\) is below \(SIR_{min}\) up to a maximum value of 1 when \(SIR_{i}\) is above \(SIR_{max}\). Using this new parameter, the performance of every wireless device is in the range of \([0,1]\), so it can be considered as normalized throughput, as higher values of SIR will let higher values of throughput and vice versa.  
From the utility obtained by each device in the network, we can define the utility of the whole system for a certain channel assignment as the sum of utilities of all of its devices:  
\[U=\sum_{\forall i}U_{i}. \tag{3}\]
# Improving Short Term Clock Prediction for BDS-2 Real-Time Precise Point Positioning
## 3 Realization of the Improved Model  
### Preprocessing for Satellite Clock Offset  
The estimated clocks of ultra-rapid products of BDS-2 which are made available by several GNSS analysis centers, for example GFZ, are used as input in data processing. However, the raw clock time series may conceal abnormal data, such as gross errors and blunders, which should be detected before performing clock prediction. The epoch-differenced clocks are calculated in which outliers can be shown more apparently and then the outliers are detected using the median absolute deviation (MAD), which is a robust but simple method [30]. In addition, the principle not to delete outliers more than 5% of total data is followed here, which avoids losing too much useful information [25]. Now, this preprocessing method is applied for each BDS-2 satellite using ultra-rapid products of GFZ with 5 min sample interval. Considering satellite clock stability is inversely proportional to its operation time, the newest launched satellites C02, C13, and C14, belong to GEO (Geostationary Earth Orbit),  
Figure 2: Data processing diagram for the improved model.  
IGSO (Geosynchronous Satellite Orbit), and MEO (Medium Earth Orbit), respectively, are selected as examples to show the effectiveness of the MAD method in Figure 3.
## 1 Introduction
Out of nine total samples which were reanalyzed, the initial results from five samples exceeded the MCL for \({}^{226}\)Ra + \({}^{228}\)Ra based on \({}^{228}\)Ra alone, but were reported as below the MCL after reanalysis. The contract laboratory was unable to provide an explanation as to why the reanalysis produced significantly different results from those initially reported. Therefore, an ILC of \({}^{226}\)Ra and \({}^{228}\)Ra in selected groundwater wells and the adjacent Impoundment was conducted to evaluate the potential role of random variability and/or systematic error in producing the high variability in \({}^{226}\)Ra + \({}^{228}\)Ra concentrations and \({}^{228}\)Ra/\({}^{226}\)Ra ratios observed during recent years. Four separate laboratories were evaluated which are designated as Lab A (Site contract laboratory), Lab B, Lab C, and Lab D. The specific objectives were to (1) evaluate potential differences between \({}^{226}\)Ra results when using a screening method (M903.0) and compare those results to a \({}^{226}\)Ra-specific method  
Figure 1: \({}^{226}\)Ra + \({}^{228}\)Ra concentrations in Site monitoring wells and the Impoundment. (M903.1) and (2) evaluate potential differences between \({}^{228}\)Ra between laboratories using M904.0 and compare those results to \({}^{228}\)Ra when using Method Ra-05. Additional questions include the following: (1) Are there detectable levels of \({}^{232}\)Th in groundwater which could be migrating and influencing ratios of \({}^{228}\)Ra/\({}^{226}\)Ra? (2) Are there differences between total (unfiltered) and dissolved (filtered, 0.45 \(\upmu\)m) \({}^{226}\)Ra or \({}^{228}\)Ra, and if so, how do those fractions vary across laboratories? And (3) does M903.0 for \({}^{226}\)Ra (which measures all alpha-emitting isotopes) overestimate \({}^{226}\)Ra concentrations compared to M903.1 which is specific to \({}^{226}\)Ra? If the presence of additional alpha emitters were to produce higher apparent \({}^{226}\)Ra concentrations, it may be recommended to use M903.1, rather than M903.0, for future compliance monitoring at the Site.
## 2 Finite Element Modelling
### Model Description
#### 2.1.3 Dynamic Traffic Loading  
In the literature, the dynamic traffic load was simulated as sinusoidal cyclic load [28; 29; 31] and calculated as follows:  
\[P(t)=P_{0}+P_{\text{d}}\sin(wt) \tag{3}\]  
\[P_{\text{d}}=M_{0}\mu_{\
icefrac{{m}}{{t}}}(y)w^{2} \tag{4}\]  
\[w=\
icefrac{{2\pi v}}{{L}} \tag{5}\]  
where \(P_{0}\) is static vehicle wheel load and assumed to a quarter of total vehicle load. \(M_{0}\) is the unsprung weight, with a value of 250 N \(\cdot\) s\({}^{2}\)/m. \(\mu_{\
icefrac{{m}}{{t}}}(y)\) is the road roughness function, with a value of 2 mm according to the roughness of international expressway. \(v\) is the velocity of vehicles. \(L\) is the geometric curve wavelength of pavement, whose value is 6 m. \(t\) is the time of dynamic traffic load period. The static vehicle wheel load, unsprung weight,  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Materials** & \(\mathbf{h}\) **(m)** & \(\mathbf{\gamma}\) **(kN/m3)** & \(\mathbf{c^{\prime}}\)**(kPa)** & \(\mathbf{\varphi}\)**(c)** & \(\mathbf{E}\) **(MPa)** & \(\mathbf{\mu}\) & \(\mathbf{\lambda}\) & \(\mathbf{\kappa}\) & \(\mathbf{M}\) \\ \hline AC layer & 0.15 & 21.0 & / & / & 4000 & 0.25 & / & / & / \\ Base & 0.20 & 20.0 & / & / & 1000 & 0.25 & / & / & / \\ Subbase & 0.25 & 18.0 & / & / & 500 & 0.25 & / & / & / \\ Gravel & 5.9 & 17.0 & 1 & 30 & 25 & 0.20 & / & / & / \\ embankment & & & & & & & & & \\ Splidified soil & 5.9 & 17.0 & 75 & 39 & 100 & 0.20 & / & / & / \\ embankment & & & & & & & & & \\ Pile & 10.0 & 17.0 & / & / & 20,000 & 0.20 & / & / & / \\ Subsoil & 10.0 & 17.0 & / & 26 & / & 0.30 & 0.3 & 0.1 & 1.03 \\ \hline Geogrid & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of material parameters.  
Figure 2: Schematic diagram of loading process.  
road roughness function, speed of the vehicles, etc., were taken into consideration in the calculation of the dynamic traffic load.  
The vehicle wheel load was assumed to uniformly distribute in a rectangular zone according to Zhuang and Wang [29] and Huang [37]. It was applied to the top of the pavement, shown in Figure 1d. Different vehicle wheel loads were analyzed and shown in Table 3.  
\[\sigma_{\text{m}}=\frac{P(t)}{S} \tag{6}\]  
where \(\sigma_{\text{m}}\) is stress of dynamic vehicle wheel load, kPa. \(S\) is the area of the rectangular zone 0.30 m \(\times\) 0.24 m.
## 4 IoT Applications
### Smart Health Care  
In the first instance, wearable IoT technology allows hospitals to track the safety of their patients at home and thereby minimize medical visits while also delivering knowledge that can save lives in real time. For hospitals, intelligent beds can keep workers updated about the bed supply for more efficient use of rooms. Placing IoT captors on vital hardware ensures fewer faults and greater durability, which can make a life-or-death difference. Reactive medical networks will become adaptive, wellness-based devices with IoT implementations. Important real-world knowledge is scarce as a tool of modern medical science. It utilizes much of the available info, managed environments, and medical test volunteers. Through observing, utilizing real-time data, and evaluating, IoT opens a door to a world of usable data. IoT also increases the strength, efficiency, and usability of installed equipment. IoT is not only about hardware but also about building structures [80]. Some smart healthcare features are shown in Figure 13.
# Mobility-Multihoming Duality
## 7 Discussion
### Combutting OSification at the Network Layer and Transport Layer  
Over time, the increasing use of the Internet has led to a resistance to change. Change causes disruption, and disruption is unwelcome for those who rely on the services provided by the Internet.  
At least part of this resistance to change is because of a reliance on in-network functions implemented in middleboxes and proxy systems. These various middleboxes and proxy systems assume protocols operated in a fixed way, which in turn has lead to concerns over the ossification of protocol design and behaviour [70]. This is true for both the network layer and the transport layer [71]. Indeed, this is part of the motivation for the development of QUIC over UDP. There are also related concerns over ossification being linked to the centralisation of service provision and network functions, as well as its overall impact on society [72].  
However, ILNP takes a decentralised, end-to-end approach, and it strives to be in keeping with the spirit of the original design philosophy of the Internet [73]. Note that in our testbed, we uncovered the following:  
* We observed that the commercial routers used were not ILNP-aware but treated ILNP packets as if they were IPv6 packets.
* The commercial routers only provided unicast routing and forwarding, with no special routing or addressing provision needed for mobility or multihoming to work.
* As only normal unicast routing and addressing were used, the provision and deployment of ILNP, including mobility and multihoming, could use provider-aggregated addressing, so this will have no detrimental impact on upstream or core routing state.
* As ILNP is a network layer architecture, which is backwards compatible with the IPv6, it will work for any transport protocol, including existing and new transport protocols. It should be possible for it to work in complement to QUIC (which works over UDP), and it could be possible even to adapt MP-TCP to operate over ILNP.
* As ILNP is deployable via host updates, it is incrementally deployable and does not require major infrastructure changes.  
Overall, we believe that the ILNP approach as demonstrated could yield at least some reprive from the concerns within the Internet community around the ossification and centralisation of the Internet infrastructure.
# An Enhanced Multi-Constraint Optimization Algorithm for Efficient Network Topology Generation
## 4 Algorithm Design
### Link Weight Calculation Using Multi-Attribute Decision Making
#### 4.2.2 Calculating Attribute Weights  
In this paper, the method of information entropy is adopted to determine the weights of various attributes. The selection of attribute weights has a direct impact on the comprehensive weight of the links. The attribute weights should be directly proportional to the significance of the attribute in the entire decision-making process.  
Given a normalized standard decision matrix, \(R=\left(x_{ij}\right)_{n\times m}\), the entropy value for the \(j\)th attribute \(S_{j}\) can be calculated using the information entropy Formulas (17) and (18), as shown below:  
\[p_{ij}=\frac{x_{ij}}{\sum_{i=1}^{n}x_{ij}},\forall i,j \tag{17}\]  
\[S_{j}=-k\sum_{i=1}^{n}p_{ij}\ln p_{ij}. \tag{18}\]  
Here, \(k\) is a constant, and the inclusion of its value ensures that the range of entropy values lies within the interval \([0,1]\). The information entropy difference, \(h_{j}\), can be defined as:  
\[h_{j}=1-s_{j}. \tag{19}\]  
Substituting Formula (19), we can obtain the attribute weights:  
\[q_{j}=\frac{h_{j}}{\sum_{j=1}^{m}\beta_{j}h_{j}},\forall j. \tag{20}\]  
This formula ensures that \(0\leq q_{j}\leq 1,\quad\sum_{j=1}^{m}q_{j}=1\). Moreover, the introduction of the variable \(\beta\) in the formula allows for the adjustment of attribute weights in the decision-making process, permitting varying degrees of importance to be assigned to different attributes. In cases without preference, the value of \(\beta\) is set to 1.
## 2 Research Methods
### Search and Selection Strategy  
The research strategy consisted of the following terms and their synonyms, used in a single query and combined with the Boolean operators (AND, OR): fractal dimension, fractal analysis, oral oncology, oral carcinoma, oral squamous cell carcinoma, oral cancer, oral potentially malignant disorders, lip cancer, actinic cheilitis, leukoplakia, erythroplakia, oral lichen planus, oral ulcers, oral submuc fibrosis, reverse smokers, lupus erythematosusosus, dyskeratosis congenita, epidemolysis bullosa, chronic hyperplastic candidiasis, oral lichenoid lesions, exophytic verrucous hyperplasia, oral lesions of graft vs. host disease.  
The search was conducted in all databases without any restrictions on year of publication, type of publication, language, or species studied. The research and export phase ended on 31 July 2023, and a re-check for novel published articles was performed on 20 September.  
The PICO strategy was considered to focus the question [19]:  
* The study population (P) consists of humans with oral cancer and oral potentially malignant disorders;
* The intervention (I) was fractal analysis for diagnostic purposes;
* The comparison (C) was with the conventional gold standard diagnosis;
* The outcome (O) was the evaluation of the capability of fractal analysis to diagnose oral cancer and distinguish among oral cancer, oral potentially malignant disorders, and non-neoplastic oral lesions and diseases;
* The study designs (S) included cross-sectional studies, retrospective cohort studies, prospective comparative studies, case-control studies, case series, and case reports.  
The following articles were eligible: original studies on humans that underwent fractal analysis (from different kinds of sources, later reported), available full text, and published in English. The exclusion criteria were studies on animal or cellular models. All the reviews, letters, proceedings, meeting abstracts, and editorials were excluded from qualitative analysis but were read for searching the eventually cross-referenced eligible articles. Further exclusion criteria included languages different from English and lack of full-text availability. Two authors performed the quality assessment of the articles (M.C. and F.F.) while, in case of disagreement, a third reviewer was involved (F.D.S.). Data from the eligible papers were organized in tables summarizing, for any study, the reference, the aims, the methods, the sample source that had undergone fractal analysis (clinical macroscopic photographs of the lesions, histological samples at microscopic resolution, vessel architecture, and so on \(\ldots\)), the main findings, and the conclusions.
## 3 Discussion
In summary, we have to stress that, to our knowledge, this is the first report showing sCLU expression stimulated by isoprenoids and water-soluble cholesterol; however, it was not the case for GGGH administration in empty-vector cells. Stating, inhibitors of FTase, GGTase I, and SQS, as well as cholesterol chelator reduced viability of neuronal PC-12 cells. As the MEV pathway is vital for life functions in eukaryotic cells we suggest that the defective mechanism of HMGCR induced by statins is overruled by GGGOH, but not cholesterol administration. In the past, similar observations were reported by [PERSON] et al. [89] from examination of THP-1 cells. Besides, if sCLU secretion is believed to be a good biomarker of inflammatory response, the lack of response to GGGOH in empty-vector cells confirms the assumption that sA\(\beta\)PP\(\alpha\) (W) or A\(\beta\) (S) are connected to inflammation in neuronal cells. Inflammatory response (not determined in our study) was previously reported to occur in neuronal cells with alteration of the MEV pathway, however, this reaction was repressed by isoprenoids but not cholesterol [90; 91]. In cells with a debilitated MEV pathway, the mechanism that triggers inflammation is the enhanced expression of _NLPR3_-inflammatory, which in turn leads to damaged mitochondria [92; 93]. In consequence, mitochondrion is to be the most likely target organelle by impaired cholesterol metabolism with the subsequent fall in cellular energy homeostasis, production of reactive oxygen/nitrogen species (ROS/NOS) in extreme cases leading to cell death by apoptosis. There is an urgent need to scrutinize the molecular mechanism of impaired mitochondrial function evoked by MEV pathway inhibitors as some results point to wrecked mitophagy in AD, which obviously plays a critical role in neuronal cell survival [94].
## 3 Results
### Multi-Metal Adsorption Capacity
For an adsorbent dose of 4 g/L, the maximum adsorption capacity for Sr\({}^{2+}\) was 24.3 mg/g, for Mn\({}^{2+}\) 24.9 mg/g, for Pb\({}^{2+}\) 24.9 mg/g and for As\({}^{5+}\) 24.5 mg/g. These values for the four metals were higher than those under mono-metal conditions. Thus, the ratio of the adsorption capacity of one metal ion in the multi-system to that in the mono-system \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**adsorbent Dose 4 g/L**} \\ \cline{2-7}  & \multicolumn{3}{c}{**Langmuir Model**} & \multicolumn{3}{c}{**Freundlich Model**} \\ \cline{2-7}  & **Q (mg/g)** & **b (L/mg)** & **R\({}^{2}\)** & **k (L/g)** & **1/n** & **R\({}^{2}\)** \\ \hline Sr\({}^{2+}\) & 65.359 & 0.198 & 0.829 & 7.723 & 0.888 & 0.984 \\ Mn\({}^{2+}\) & 31.949 & 7.825 & 0.984 & 51.463 & 0.663 & 0.975 \\ Pb\({}^{2+}\) & 27.322 & 20.333 & 0.986 & 41.985 & 0.491 & 0.711 \\ As\({}^{5+}\) & 7.728 & 0.342 & 0.497 & 5.182 & 1.508 & 0.857 \\ \hline  & \multicolumn{6}{c}{**adsorbent Dose 2 g/L**} \\ \cline{2-7} Sr\({}^{2+}\) & 78.740 & 0.219 & 0.284 & 13.674 & 0.680 & 0.851 \\ Mn\({}^{2+}\) & 25.773 & 77.6 & 0.969 & 53.015 & 0.502 & 0.779 \\ Pb\({}^{2+}\) & 54.645 & 20.333 & 0.986 & 83.985 & 0.491 & 0.712 \\ As\({}^{5+}\) & 81.301 & 0.583 & 0.508 & 27.327 & 0.698 & 0.850 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Isotherm model parameters of heavy metals for multi-metal adsorption. Figure 3: Adsorption isotherms of (**a**) Sr2+, (**b**) Mn2+ at adsorption dose 4 g/L, (**c**) Pb2+, (**d**) As5+ at adsorption dose 2 g/L, for multi-metal adsorption. was greater than 1 in all cases (1.97 for Sr, 1.05 for Mn, 1.01 for Pb and 1.97 for As), implying that metal ions promoted the adsorption of each other. On the other hand, for an adsorbent dose of 2 g/L the maximum adsorption capacity of almond shell biochar for Sr\({}^{2+}\), Mn\({}^{2+}\), Pb\({}^{2+}\) and As\({}^{5+}\) could be raised up to 47.6 mg/g, 49.9 mg/g, 25 mg/g and 48.7 mg/g, respectively. Unfortunately, such data of the current study cannot be compared with the literature, because the competitive adsorption of selected metals on biochars has not been previously reported, to the best of the authors' knowledge.
## 2 System Identification Technique by Parameter Estimator
### Least-Squares Method-Based System Identification Technique  
The proposed system identification technique is based on the least-squares method. The parameter estimator estimates the \(\Delta X\) that minimizes the error function between the \(\widetilde{\varnothing}_{\text{real}}\) of the real loading device and the mathematically modeled \(\widetilde{\varnothing}_{\text{model}}\) in Equation (4). The error function between the \(\widetilde{\varnothing}_{\text{real}}\) and the \(\widetilde{\varnothing}_{\text{model}}\) is Equation (5).  
\[rr=\sum_{i=1}^{k}\Bigl{\{}\widetilde{\varnothing}_{\text{real}}(i)-\widetilde {\varnothing}_{\text{estimated}}(i)\Bigr{\}}^{2}=\sum_{i=1}^{k}\Bigl{\{}\ \ddot{\varnothing}_{\text{real}}(i)-N(i)^{T}\ \bigl{(}\ X_{\text{empty}}+\Delta X(i)\bigr{)}\Bigr{\}}\ ^{2} \tag{5}\]  
To minimize the \(Err\), Equation (6) which is the derivative of the \(Err\) should be 0. The processes of calculating the \(\Delta X(k)\) when the \(Err\) is 0 are Equations (7) and (8). The system identification technique estimates the \(X\) in real-time by adding the \(X_{\text{empty}}\) and the \(\Delta X(k)\) in Equation (8).  
\[\frac{\partial E}{\partial\Delta X(k)}=\sum_{i=1}^{k}\{-2\ N(i)\ \ddot{\varnothing}_{\text{real}}(i)+2\ N(i)\ N(i)^{T}\ X_{\text{empty}}+2\ N(i)\ N(i)^{T}\ \Delta X(i)\}=0 \tag{6}\]  
\[N(i)\ N(i)^{T}\Delta X(k)+\sum_{i=1}^{k}\{-N(i)\ \ddot{\varnothing}_{\text{real} }(i)+N(i)\ N(i)^{T}\ X_{\text{empty}}\}+\sum_{i=1}^{k-1}\{N(i)\ N(i)^{T}\ \Delta X(i)\}=0 \tag{7}\]  
\[\Delta X(k)=[N(k)\ N(k)^{T}]^{+}\ [\ \sum_{i=1}^{k}\{N(i)\ \ddot{\varnothing}_{\text{real} }(i)-N(i)N(i)^{T}\ X_{\text{empty}}\}-\sum_{i=1}^{k-1}\{N(i)N(i)^{T}\Delta X(i) \}\ ] \tag{8}\]  
The identification technique can estimate the dynamic equation that moves in equal motion with the real system of the loading device. The system identification technique does not need experimental results and only needs an IMU sensor and encoder of the motor. However, since there are countless solutions in calculating \(\Delta X(k)\), the accuracy of the parameter estimator is sometimes poor. Although the dynamic equation that produces the equal motion with the real loading device is estimated, the large error of the parameter may cause an inappropriate controller. This is because the gain scheduler uses the unknown parameters as the inputs. As shown in \(\left[N(k)\ N(k)^{T}\right]^{+}\) of Equation (8), the system identification technique is dependent on the terms of the known parameter vector \(N\). Therefore, the estimation degree of each term of the \(X\) varies depending on the relative size of each multiplied term of the \(N\). This paper proposes an improvement in the accuracy of the parameter estimation by using a null-space solution.