esa-sceva/satcom-chunk-collection huggingface dataset:
# A Brief Review of Mueller Matrix Calculations Associated with Oceanic Particles
## 2 Fundamental Concepts for Mueller Matrix Calculations
### Amplitude Scattering Matrix and Mueller Matrix
If the incident light is unpolarized, the scattering cross-section can be given in terms of the element \(\mathbf{P}_{11}\) by  
\[C_{\text{sca}}\ =\frac{1}{k^{2}}\!\!\int\limits_{4\pi}d\Omega\mathrm{P}_{11}( \theta,\varphi). \tag{14}\]  
The phase function is defined as:  
\[p=\frac{4\pi}{k^{2}C_{\text{sca}}}\mathrm{P}_{11}, \tag{15}\]  
and the scattering phase matrix can be defined as:  
\[\mathbf{F}=\frac{4\pi}{k^{2}C_{\text{sca}}}\mathbf{P}. \tag{16}\]  
The symmetry relations of the phase matrix have been extensively discussed in general and also for forward and backward scattering [20; 21; 22]. For an arbitrary particle without mirror symmetry in the scattering plane, the scattering phase matrix of a particle in random orientation is in the form  
\[\mathbf{F}=\left( \begin{array}{cccc}a_{1}&b_{1}&b_{3}&b_{5}\\ b_{1}&a_{2}&b_{4}&b_{6}\\ -b_{3}&-b_{4}&a_{3}&b_{2}\\ b_{5}&b_{6}&-b_{2}&a_{4}\end{array} \right), \tag{17}\]  
where there are only 10 independent parameters. For a particle with mirror symmetry in the scattering plane, the scattering phase matrix of a particle in random orientation is reduced to a block-diagonal matrix, as follows:  
\[\mathbf{F}=\left( \begin{array}{cccc}a_{1}&b_{1}&0&0\\ b_{1}&a_{2}&0&0\\ 0&0&a_{3}&b_{2}\\ 0&0&-b_{2}&a_{4}\end{array} \right), \tag{18}\]  
where there are only six independent parameters. Equations (17) and (18) represent the scattering phase matrix of a particle in random orientation. For a collection of particles with a size distribution, the collective scattering phase matrix can also be defined in terms of the distribution and the reader is referred to the book by [PERSON] [23]. All of the scattering quantities have been presented and we are now faced with the problem of obtaining the near fields or directly the far fields satisfying [PERSON]'s equations.
## 3 Results  
Forty-four patients (31 females and 13 males, aged between 22-80) were recruited and were equally divided among the treatment groups (22 subjects in each group). The enrolment period was from January 2018 to May 2019. As shown in the CONSORT Flow Diagram (as illustrated in Figure 2), all participants completed the proposed treatment and were included in the final analysis, as nobody was lost to follow-up or violated the protocol.  
The baseline characteristics of the two groups are shown in Table 1; there were no statistically significant differences at the baseline between EG and CG both for general parameters (age, sex, body mass index, years since the onset of symptoms) and for the scores of all the analyzed evaluation scales.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **CG** & **EG** \\ \hline
**Sex** & & \\ \hline _Female_ & 16 (72.7\%) & 15 (68.2\%) \\ \hline _Male_ & 6 (27.3\%) & 7 (31.8\%) \\ \hline
**Age (years)** & & \\ \hline _Mean \(\pm\) SD_ & 58.9 \(\pm\) 15.2 & 56.6 \(\pm\) 15.8 \\ \hline _Median (min–max)_ & 57.0 (22.0–80.0) & 59.5 (26.0–80.0) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Baseline characteristics of two groups.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **CG** & **EG** \\ \hline
**Body Mass Index (BMI)** & & \\ \hline _Mean \(\pm\) SD_ & 26.4 \(\pm\) 3.4 & 25.3 \(\pm\) 3.7 \\ \hline _Median (min–max)_ & 27.3 (18.7–29.8) & 24.9 (18.1–29.8) \\ \hline
**Years since the onset of symptoms** & & \\ \hline _Mean \(\pm\) SD_ & 15.2 \(\pm\) 12.7 & 14.4 \(\pm\) 15.3 \\ \hline _Median (min–max)_ & 14.0 (1.0–40.0) & 8.5 (0.5–60.0) \\ \hline
**VAS at T0** & & \\ \hline _Mean \(\pm\) SD_ & 52.5 \(\pm\) 27.7 & 50.0 \(\pm\) 21.9 \\ \hline _Median (min–max)_ & 54.5 (3.0–98.0) & 52.0 (12.0–98.0) \\ \hline McGill Pain Questionnaire at T0 & & \\ \hline _Mean \(\pm\) SD_ & 25.4 \(\pm\) 14.9 & 24.7 \(\pm\) 12.2 \\ \hline _Median (min–max)_ & 22.5 (3.0–54.0) & 22.5 (7.0–51.0) \\ \hline
**Oswesty Disability Index at T0** & & \\ \hline _Mean \(\pm\) SD_ & 28.4 \(\pm\) 15.0 & 28.6 \(\pm\) 13.9 \\ \hline _Median (min–max)_ & 24.0 (4.0–52.0) & 26.0 (10.0–66.0) \\ \hline
**EuroQol-5D at T0** & & \\ \hline _Mean \(\pm\) SD_ & 0.5 \(\pm\) 0.3 & 0.5 \(\pm\) 0.3 \\ \hline _Median (min–max)_ & 0.6 (\(-\)0.2 to 0.8) & 0.7 (\(-\)0.1 to 0.8) \\ \hline \hline \end{tabular} Notes: CG = control group; EG = experimental group; SD = standard deviation; min-max = minimum–maximum; T0 = baseline.  
\end{table}
Table 1: _Cont._  
Figure 2: CONSORT Flow Diagram showing processes of enrollment, allocation, follow-up, and analysis.  
In Table 2 the scores obtained at the different time points for each outcome measure are reported as mean (standard deviation) and median (minimum-maximum) for both the control and the experimental group.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c} \hline
**Eurocode 8 [16]** & **Type 1 Spectrum: “Most Contributing”** & **Type 2 Spectrum: “Most Contributing”** \\
**Ground Types** & **Earthquakes with MS \textgreater{} 5.5** & **Earthquakes with MS \textless{} 5.5** \\ \hline A, & \(a_{\text{g}}\times 2.5\) & \(a_{\text{g}}\times 2.5\) \\ \(V_{5,30}>800\) m/s & \(0.15\leq T\leq 0.40\) s & \(0.05\leq T\leq 0.25\) s \\ Rock, at the surface up to 5 m of weaker material. & & \\ \hline Ground type B, & & \\ \(V_{5,30}=360\)–800 m/s & \(a_{\text{g}}\times 1.2\times 2.5\) & \(a_{\text{g}}\times 1.35\times 2.5\) \\ At least several tens of meters thick deposits. Very dense & \(0.15\leq T\leq 0.50\) s & \(0.05\leq T\leq 0.25\) s \\ sand, gravel, or very stiff clay. & & \\ \hline Ground type C, & & \\ \(V_{5,30}=180\)–360 m/s & \(a_{\text{g}}\times 1.15\times 2.5\) & \(a_{\text{g}}\times 1.5\times 2.5\) \\ Deep deposits, several tens of meters up to hundreds of & \(0.20\leq T\leq 0.60\) s & \(0.10\leq T\leq 0.25\) s \\ meters thick. Dense or stiff clay. & & \\ \hline Ground type D, & & \\ \(V_{5,30}<180\) m/s & \(a_{\text{g}}\times 1.35\times 2.5\) & \(a_{\text{g}}\times 1.8\times 2.5\) \\ Deposits. Loose-to-medium cohesionless soil or & \(0.20\leq T\leq 0.80\) s & \(0.10\leq T\leq 0.30\) s \\ predominantly soft-to-firm cohesive soil. & & \\ \hline Ground type E & & \\ Alluvium layer at the surface, between 5 and 20 m thick, & \(a_{\text{g}}\times 1.4\times 2.5\) & \(a_{\text{g}}\times 1.6\times 2.5\) \\ above stiffer material. \(V_{5,30}<360\) m/s. & \(0.15\leq T\leq 0.50\) s & \(0.05\leq T\leq 0.25\) s \\ \hline \end{tabular}
\end{table} 
Table 1: Maximum amplitudes of horizontal pseudo-absolute acceleration spectra _PSA_ and corresponding vibration periods, \(T\), for different ground types according to Eurocode 8 [16]; \(a_{\text{g}}\) represents the horizontal PGA value obtained from the accompanying seismic hazard map. The primary goal of this study is to assess the amplification (or de-amplification) effects of deep soil atop deep geological sediments on various spectral amplitudes and the incorporation of these effects into regional GMPEs. Local soil is classified according to [PERSON] et al. [23; 24], taking into account depths of more than 30 m. Hence, the term \"deep soil\" will be used to describe sites where the thickness of the soil layer overlying the layer with V\({}_{5,30}>800\) m/s is larger than 100 m.  
Another goal of this research is to see if the spectral acceleration factor of 2.5 (i.e., the ratio between maximum spectral amplitudes and PGA values), as indicated by Eurocode 8, offers reasonable estimates for maximum spectral amplitudes. This paper begins by outlining regional seismicity and site conditions in Osijek. It then goes on to present new regional empirical formulae for horizontal _PSA_ prediction. Finally, uniform hazard spectra for a site in Osijek are created and compared to Eurocode 8 spectra. In addition, seismic hazard maps for several vibration periods and probabilities of exceedance are constructed.
# Traffic Aware Scheduler for Time-Slotted Channel-Hopping-Based IPv6 Wireless Sensor Networks
## 3 Related Work
### Static Approaches
#### 3.1.2. Escalator  
Escalator [8] uses two slotframes to deal with the traffic. One slotframe is for RPL control traffic and downward traffic (the baseline slotframe) and the other is for TSCH traffic and upward traffic (the convergecast slotframe). On the convergecast slotframe, Escalator schedules one transmit cell for every node, based on the node's parameters. This cell is used to transmit data originating from the node to its parent. For all the nodes in the subgraph of a node, two additional cells are allocated: one for receiving packets and one for transmitting them to the parent. In this case, the timeslot offset is computed based on the parameters of the nodes in the subgraph. This implementation reduces the end-to-end delay and increases PDR. Compared with Orchestra, Escalator performs better in dense networks with high traffic loads. The drawback is an increase in the average duty cycle, even for low traffic.  
The discussed schedulers tackle several drawbacks of Orchestra, but they do not adapt to traffic bursts. Our scheduler, described in Section 4, adjusts the schedule in case of traffic bursts. During high traffic loads the most congested nodes are the children of the root. Congestion is measured at the level of these nodes by counting the remaining packets in their queues.
# Hardware Countermeasures Benchmarking against Fault Attacks
## 3 Dfa on AES  
Differential fault analysis is one of the most widely used techniques in the field of cryptanalysis [3]. This technique consists of assuming the fault injection into a cryptographic circuit, using any of the previously described FIAs, and comparing its faulty output with the one obtained when it works correctly. The comparison between the correct and faulty results obtained during the encryption or decryption process makes it possible to recover the secret information from the device. This analysis, performed on different block functions or cipher rounds, makes it possible to recover the secret key of the attacked cipher. In order to accurately analyze AES DFAs, the AES cipher and the types of faults that can occur during its operation are briefly described below.
# Ext-LOUDS: A Space Efficient Extended LOUDS Index for Superset Query
## 2 Problem Definition and Necessary Preliminaries
### Louds  
LOUDS encodes each trie node using a unary code, which has a compression ratio close to the theoretical lower bound of information theory. It is a compressed trie with the highest known compression ratio [17]. LOUDS has a simple encoding rule which encodes nodes hierarchically starting from the root node. When encoding a node, k bit 1s are set in a bit vector \(B\) for its k children and then another 0 is filled between this node and the next node. Elements are stored in vector Elems hierarchically. Given an example dataset in Figure 1a, the corresponding trie and LOUDS are shown in Figure 1b,c. The number to the right of each node in Figure 1b is the element of this node, and the number in each circle is its corresponding level ordered node ID (NodeID in short). The alternative background colors in LOUDS of Figure 1c denote the boundaries of each node.  
Accessing the parent node and the child nodes in LOUDS can be realized by two operations: RANK and SELECT [22; 23]. Given a position \(p\) (starting from 0) in LOUDS bit vector, \(rank_{b}(p)\) returns the number of \(b\)s in range [0,\(p\)] and \(Select_{b}(p)\) returns the position of the \(p\)-th \(b\) in LOUDS bit vector, where \(b\in\{0,1\}\). In LOUDS, the position of the parent node and the first child node for a node starting from \(p\) can be computed by \(Select_{1}(B,rank_{0}(B,p))\) and \(Select_{0}(B,rank_{1}(B,p))+1\), respectively. The position of the \(i\)-th node can be obtained by \(Select_{0}(B,i)+1\).  
Figure 1: An example dataset and its corresponding trie and LOUDS: (**a**) An example dataset; (**b**) trie constructed based on (**a**); (**c**) LOUDS.
# Disturbance Observer-Based Anti-Shock Controller for Laser Beam Steering Systems
## 1 Introduction
Second, nonperiodic disturbances occur when external shocks are applied to the target platform under operating conditions. Typically, the closed-loop bandwidth of the control system should be increased to ensure that the FSM system can eliminate the errors caused by such disturbances. However, doing so may reduce the stability of the FSM system owing to its increased sensitivity to high-frequency disturbance components. Consequently, a controller design that considers closed-loop bandwidth alone is inadequate [19; 20]. Therefore, various studies have been conducted to design robust controls in the presence of external shocks, one of which is a robust control system that uses observer-based sliding mode control to manage shocks [21]. In addition, a direct measurement-based feedforward algorithm is proposed to suppress most of the theoretical external disturbances [22; 23]. However, while the feedforward method works well in specific environments with low measurable noise, its implementation under more complex conditions is limited owing to the additional cost of another sensor. To avoid the abovementioned problems, an emerging basic approach to suppressing disturbances can be used that estimates the influence of an external disturbance independently by using a disturbance observer (DOB) and then eliminating the perturbation by using the feedforward method. This feedforward method is called the DOB method [24; 25]. It has been applied to several mobile devices with High-precision control systems, such as robot motion control, swing arm actuators in hard disk drives, VCAs in optical disk drives, and permanent magnet synchronous motor control [26; 27; 28]. To improve the control performance of the original control system for use in FSM systems, high open-loop gain is needed in the dynamic disturbance frequency domain to sufficiently attenuate disturbances. FSM systems commonly use high-bandwidth proportional-integral (PI) controllers owing to their robustness to modeling errors and simple implementation [29; 30]. The addition of a DOB to the base controller significantly improves the disturbance attenuation performance because of its simplicity and suitability for real-time implementation [31; 32]. In this study, we propose a more reliable anti-shock control algorithm by using a DOB to enhance the disturbance-suppression performance of FSM systems at low frequencies. The rest of this paper is organized as follows: Section 2 introduces the basic FSM control system. In Section 3, the DOB-based anti-shock controller is presented, and its performance is analyzed. Section 4 describes the experiments conducted to verify the proposed approach. Finally, our concluding remarks are presented in Section 5.
## 3 Results
### Omics Analysis Using Proteomics and Metabolomics Techniques
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Combination No.** & **Chemicals** & **LCTx a ** & **Dose b ** & **CTP** & **Mortality** & **SR c ** \\ \hline Adults and Nymphs & & & & & & & \\
1 & Control & - & 0 & 0 & \(3.3\pm 3.3\) & \\  & EF & LCT10 & 6 & 21.3 & \(14.7\pm 2.6\) & \\  & PH3 & LCT25 & 0.1 & 0.29 & \(32.9\pm 4.8\) & \\  & EF + PH3 & LCT10 + LCT25 & 6 + 0.1 & \(19.7+0.3\) & \(56.0\pm 7.8\) & 1.17 \\
2 & Control & - & 0 & 0 & 0.0 \(\pm\) 0.0 & \\  & EF & LCT25 & 10 & 27.70 & \(25.0\pm 5.0\) & \\  & PH3 & LCT25 & 0.1 & 0.29 & \(30.0\pm 7.6\) & \\  & EF + PH3 & LCT25 + LCT25 & 10 + 0.1 & \(28.3+0.3\) & \(63.3\pm 7.3\) & 1.15 \\
3 & Control & - & 0 & 0 & \(3.3\pm 1.7\) & \\  & EF & LCT50 & 17 & 44.1 & \(46.7\pm 6.0\) & \\  & PH3 & LCT25 & 0.1 & 0.28 & \(28.3\pm 4.4\) & \\  & EF + PH3 & LCT50 + LCT25 & \(17+0.1\) & \(41.6+0.3\) & \(85.0\pm 2.9\) & 1.13 \\ \hline Eggs & & & & & & \\
4 & Control & - & 0 & 0 & 0.0 \(\pm\) 0.0 & \\  & EF & LCT10 & 5 & 16.1 & \(13.2\pm 3.4\) & \\  & PH3 & 1 mg/L & 1 & 3.4 & \(42.9\pm 7.1\) & \\  & EF + PH3 & LCT10 + 1 mg/L & \(5+1\) & \(16.5+3.2\) & \(75.0\pm 5.8\) & 1.33 \\
5 & Control & - & 0 & 0 & \(0.0\pm 0.0\) & \\  & EF & LCT25 & 10 & 30.2 & \(26.4\pm 3.4\) & \\  & PH3 & 1 mg/L & 1 & 3.4 & \(41.7\pm 1.7\) & \\  & EF + PH3 & LCT25 + 1 mg/L & \(10+1\) & \(31.6+3.4\) & \(84.7\pm 3.4\) & 1.24 \\
6 & Control & - & 0 & 0 & \(1.7\pm 1.7\) & \\  & EF & LCT50 & 15 & 38.4 & \(45.0\pm 2.9\) & \\  & PH3 & 1 mg/L & 1 & 3.4 & \(46.7\pm 4.4\) & \\  & EF + PH3 & LCT50 + 1 mg/L & \(15+1\) & \(40.6+3.4\) & \(98.3\pm 1.7\) & 1.07 \\ \hline \hline \end{tabular}  
\end{table} 
Table 2: Evaluation of synergistic effects with different combinations of ethyl formate (EF) and phosphine (PH3) on _P. citri_ adults, nymphs, and eggs under 4 h exposure at 20 \({}^{\circ}\)C (\(n\) = 20, triplicate). reduced in EF+PH3-treated samples compared to the control. The highest reduction was a 100-fold decrease in the phospholipid PI (O-16:0). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multirow{2}{*}{**Identified Proteins**} & \multirow{2}{*}{**ANOVA**} & \multicolumn{4}{c}{**Average Quantitative Value 1** } & \multirow{2}{*}{**Biological Process**} \\ \cline{5-6}  & & & **Con** & **EF** & **PH3** & **EF + PH3** & \\ \hline
1 & Pyrruvate kinase & \textless{}0.0001 & 6.71  & 7.31  & 2.59  & 3.55  & Glycolysis \\
2 & Putative dihydroplioyl & \textless{}0.0001 & 1.35  & 0  & 0  & 0  & 0  & 0  & 0  & Glycolysis \\
3 & Putative 50S ribosomal protein L11 & 0.0006 & 1.33 [FOOTNOTE:1]Footnote 1: Data were analyzed using one-way ANOVA, followed by post hoc Tukey’s tests. Different lowercase letters indicate significant differences among groups; a \textgreater{} b \textgreater{} c.
## 3 Results  
A total of 26 studies were included in this study (Figure 1). The sample comprising the different papers included in the review amounted to a total of 3117 soccer players, being 1025 defenders (combining central defenders and lateral defenders), 1093 midfielders (combining defensive midfielders, central defenders, offensive midfielders, and wingers), 691 forwards, and 308 goalkeepers. Table 2 shows the qualitative characteristics of the included articles. Figure 2 shows the risk of bias summary: authors' judgments broken down for each risk of bias criterion across all included studies. In relation to the method of measurement, on 12 occasions, they were based on the use of anthropometry [23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34], six in BIA [35; 36; 37; 38; 39; 40], seven in DXA [41; 42; 43; 44; 45; 46; 47], and one combined anthropometry and BIA [48]. In relation to the anthropometric measuring instrument, the main one used was the Harpenden picometer (n = 7) [23; 24; 26; 27; 31; 32; 48]. For BIA, the most commonly used model was the Tanita BC 418 MA (n = 3) [37; 38; 48]. Lastly, the most common DXA machine model was Hologic QDR Series Discovery A (n = 2) [43; 44]. Of the 13 articles included where anthropometry was used as a method of evaluation, seven used the International Society for the Advancement of Kinnanthropometry protocol (ISAAK) [24; 25; 29; 30; 32; 33; 48], one utilized the Anthropometric Standardization Reference Manual (ASRM) protocol [28], one used the International Biological Program(IBP) protocol [26], one used Sistema de Accreditacion en Tecnicas Antropometricas (SATA) protocol [34], and the remaining three articles did not specify the protocol applied [23; 27; 31]. As for the sum of skinfolds, the most commonly used formulas were the sum of six skinfolds (triceps, subscapular, supraspinal, abdominal, mid-thigh, and calf) (n = 3) [25; 30; 33]. Regarding the percentage of FM, none of the seven equations predominated, being present in only one article each. Information on the anthropometric FM equations used in each of the studies is available in the Supplementary Tables S1-S3. Meta-analysis grouped according to the playing position (1 = goalkeeper; 2 = defender, 3 = midfielder; 4 = forward) is shown in Table 3 and Supplementary Figures S1-S14. The sample had a mean age of 24.4 years old, 180 cm in height, 76.8 kg in weight, 54.56 of the sum of skinfolds, 11.89% of FM, 11.14 kg of FM, 47.01% of MM, 38.59 kg of MM, 64.7 kg of FFM, 2.31 of endomorphy, 2.30 of ectomorphy, 5.06 of mesomorphy, 1.35 for bone mineral density (BMD), and 3.33 for bone mineral content (BMC). Figure 1: Flow diagram showing the process used to select the studies.
## 2 Proposed Software Architecture
### Software Architecture Design
#### 2.2.1 Conceptual View
Presentation layer: This layer provides a system interface for viewing information or performing tasks. On the other hand, the layers and components of the manager tier of our architecture are described below:  
Plugin layer: It obtains the sensorization of different data from the plugins. Plugins can be physical, logical, or virtual. Sensorization layer: It is responsible for managing the different plugins involved in the administration and control systems of the activities of the brigades. This layer contains a component whose function is as follows:  
* Pre-process layer: It is responsible for eliminating out-of-range values and converting the format for the upper layers. Storage layer: It is responsible for saving information such as the location of all people, emergency reports, and information on buildings and people. This layer is made up of four main components:  
* Location module: It is responsible for saving information on the location of people and brigades. * Emergency module: It oversees keeping the list of emergencies that people indicate. Buildings module: It saves all the information related to buildings, such as the name, departments, floor, support material, etc. * People module: This module is responsible for keeping a list of people inside the building during an emergency. Information gathering layer: It is responsible for extracting the information that the user needs. In this layer, the components perform the following functions:
* Indoor location detection module of people: This module is responsible for extracting the indoor location of the people currently in the building. * Module for detecting the location of the interiors of the brigades: This module is responsible for extracting the location indoors of the brigades that are currently in the building. * Exit route detection module: This module extracts the route to the nearest exit. * Emergency obtaining module: This module is responsible for obtaining the emergency that the person is indicating. Report layer: It is responsible for generating the reports that the brigades need to pass on to the CPIU coordinator or civil protection. Emergency actions layer: It controls functionalities such as sending notifications to the brigades, messages, etc. The modules that this layer contains are:
* Module for sending notifications to the brigades: It oversees notifying the brigades about a possible problem indicated by the person. * Message-sending module: This module is responsible for sending messages between a person and the brigade. Communication layer: This layer maintains communication and security with the mobile tier. The modules that this layer contains are:
* Security module: This module oversees the security of access to the system. * Connection module between tiers: This module maintains a connection between the manager and mobile tiers. Presentation layer: It provides a system interface for viewing information or performing tasks.