esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Multi-Objectives Optimization of the Frame Based on Interval Analysis Method
### Interval Multi-Objectives Optimization Method  
The uncertain parameters could be characterized as interval value, and the interval multi-objectives optimization function could be described as follows [25]:  
\[\left\{\begin{array}{c}\min f_{i}(X,U)\\ s.t.\ :\ g_{i}(X,U)\leq b_{i}^{t}=\left[b_{i}^{L},b_{i}^{R}\right]\\ i=1,2,...,l;X\in\Omega^{t}\\ U\in U^{l}=\left[U^{L},U^{R}\right],U_{i}\in U_{i}^{l}=\left[U_{i}^{L},U_{i} ^{R}\right]\\ i=1,2,...,q\end{array}\right. \tag{11}\]  
where \(f_{i}(X,U)\) is objective function, \(g_{i}(X,U)\) represents constraint function. \(X\) describes n-dimensional design variable within the range of \(\Omega^{n}\), and \(U\)is q-dimensional uncertain variable within the range of \(U^{l}\). \(L\) and \(R\) were used to mark upper and lower bound of the interval parameter, respectively. In order to ensure optimization efficiency and convergence, the uncertain objective could be transformed to the certain one and expressed by [25]:  
\[\left\{\begin{array}{c}\min(f^{c}(X),f^{w}(X))\\ f^{c}(X)=\frac{\min(f(X,U))+\max(f(X,U))}{2}\\ f^{w}(X)=\frac{\max(f(X,U))-\min(f(X,U))}{2}\end{array}\right. \tag{12}\]  
where \(f^{c}(X)\) is the midpoint value of objective function, \(f^{w}(X)\) is the radius value of objective function. Optimizing the midpoint value \(f^{c}(X)\) could improve mean value of objective function, and optimizing the radius value \(f^{w}(X)\) could reduce the sensitivity on the uncertainties and enhance the stability and robustness for the objective function. The solution of multi-objectives optimization function could be transformed to search for the optimal result in the single-objective optimization problem, which was evaluated by [25]:  
\[\min f(X)=\beta f^{c}(X)+(1-\beta)f^{w}(X) \tag{13}\]  
where \(\beta\) is weight coefficient. Its range was within \(0\leq\beta\leq 1\). In this paper, it was taken as 0.5 [25]. In this paper, the interval analysis method was utilized to optimize the frame structure for improving mechanical performances. The optimization process is shown in Figure 12.  
Figure 12: Interval multi-objective optimization process.
Sensors_**2010**, _10_, 9590-9608; doi:10.3390/s101109590  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**An Adaptive Fault-Tolerant Communication Scheme for Body Sensor Networks**  
**[PERSON], [PERSON], [PERSON] * and [PERSON]**  
School of Software, Dalian University of Technology, Dalian 116620, China;  
E-Mails: [EMAIL_ADDRESS] (G.W.); [EMAIL_ADDRESS] (J.R.); [EMAIL_ADDRESS] ([PERSON].)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-411-8757-1582.  
_Received: 12 September 2010; in revised form: 15 October 2010 / Accepted: 18 October 2010 / Published: 28 October 2010_  
**Abstract:** A high degree of reliability for critical data transmission is required in body sensor networks (BSNs). However, BSNs are usually vulnerable to channel impairments due to body fading effect and RF interference, which may potentially cause data transmission to be unreliable. In this paper, an adaptive and flexible fault-tolerant communication scheme for BSNs, namely AFTCS, is proposed. AFTCS adopts a channel bandwidth reservation strategy to provide reliable data transmission when channel impairments occur. In order to fulfill the reliability requirements of critical sensors, fault-tolerant priority and queue are employed to adaptively adjust the channel bandwidth allocation. Simulation results show that AFTCS can alleviate the effect of channel impairments, while yielding lower packet loss rate and latency for critical sensors at runtime.  
**Keywords:** body sensor networks; fault tolerance; quality of service; priority; resource reservation; health monitoring
## 9 Model Validation  
### Model Validation Methods  
The accuracy of predictive models is critical as it determines the quality of the resultant predictions. The accuracy is often assessed based on model validation methods that may include the following:  
1. Hold-out validation;
2. K-fold cross-validation;
3. Leave-one-out cross-validation;
4. Leave-q-out cross-validation;
5. Bootstrapping cross-validation;
6. Using any new samples that are not used for model training.  
In environmental sciences, the most commonly used validation methods are hold-out and leave-one-out [18]. However, of these validation methods, five- or 10-fold cross-validation was recommended [7; 116].
# Effect of LNR-g-MMA on the Mechanical Properties and Lifetime Estimation of PLA/PP Blends
###### Abstract  
Polylactide (PLA) polymer, polypropylene (PP) polymer, and a PLA/PP (70:30 wt%) blend, with liquid natural rubber\(-\)graft\(-\)methyl methacrylate (LNR\(-\)g\(-\)MMA) of 0.0, 2.5, 5.0, and 10.0 phr as compatibilizers, were prepared by internal mixing and compression molding. The effect of LNR-g-MMA content on the morphology, mechanical properties, water absorption, thermal degradation, and a lifetime of blends based on PLA and PP was investigated. Scanning electron microscopy (SEM) revealed that the PLA/PP blend underwent phase separation, and the presence of LNR\(-\)g\(-\)MMA in the PLA/PP blend showed a more homogenized and refined blend morphology. Hence, the addition of LNR\(-\)g\(-\)MMA was used as a compatibilizer to induce miscibility in the PLA/PP blend. The values of tensile strength, elongation at break, and impact strength of the polymer blends increased, whereas water absorption values decreased with increased LNR\(-\)g\(-\)MMA content. Thermal degradation kinetics was studied over a temperature range of 50-800 \({}^{\circ}\)C with multiple heating rates. The results demonstrated that the thermal stability of blends without LNR-g-MMA was greater than that of blends with LNR\(-\)g\(-\)MMA and that the thermal stability decreased with increasing LNR\(-\)g\(-\)MMA content. The activation energy (E\({}_{\text{a}}\)) was calculated by using the Kissinger-Akahira-Sunose method. The E\({}_{\text{a}}\) value of PLA was much lower than that of PP, and incorporating PP in the PLA matrix increased the E\({}_{\text{a}}\). The addition of LNR\(-\)g\(-\)MMA to the PLA/PP blend decreased the E\({}_{\text{a}}\). The lifetime of PLA/PP blends was reduced with the addition of LNR\(-\)g\(-\)MMA.  
blends; polylactide; polypropylene; compatibilizer; activation energy Article
# Cost-Benefit Assessment of 5G Rollout: Insights from Brazil
## 3 5G International Practices
### Europe  
The study by [14] performs a techno-economic evaluation of 5G infrastructure sharing models in rural Europe, focusing on different approaches such as the single-host network (SHN), the multiple-host network (MHN), and the NHN. The goal is to assess the costs, returns, and feasibility of these models by analyzing capital expenditures, operational expenditures, and key financial metrics such as the net present value (NPV) and return on investment (ROI). The study highlights the importance of population density in determining the success of these models.  
The analysis groups European countries into five categories based on the density of the rural population and settlement patterns. It examines two types of areas: rural settlement areas, which require substantial data capacity, and non-residential rural areas, where the priority is providing mobile coverage. The NHN model, where a single infrastructure provider manages the network, incurs higher total costs due to the extensive coverage and higher density management. However, this model also benefits from shorter payback periods and a higher NPV in densely populated areas thanks to the revenue generated from wholesale agreements through network slicing.  
In contrast, less collaborative models, such as no sharing (NS) and passive site sharing (PSS), have longer payback periods, especially in sparsely populated areas, making them less economically viable in rural regions. The study suggests that the NHN is the mostcost-efficient model for rural deployments due to its ability to spread costs over multiple service providers, resulting in greater economic benefits and a higher ROI, particularly in densely populated areas.  
Sensitivity analysis shows that factors such as average revenue Per user (ARPU) and projected demand levels significantly affect investment viability. The NHN model, in particular, benefits from the ability to charge for wholesale access, which boosts its profitability. However, it also carries the risk of creating monopolies in rural areas, which would require regulatory interventions.  
In general, the study concludes that deeper infrastructure sharing models such as the NHN and MORAN provide the best financial results for rural 5G deployment, while less collaborative models are less attractive due to higher costs and lower returns.
###### Abstract  
In this paper, we test three prototypes with different characteristics for controlling the quantity of organic fertiliser in the agricultural irrigation system. We use 0.4 mm of copper diameter, distributing in different layers, maintaining the relation of 40 spires for powered coil and 80 for the induced coil. Moreover, we develop sensors with 8, 4, and 2 layers of copper. The coils are powered by a sine wave of 3.3 V peak to peak, and the other part is induced. To verify the functioning of this sensor, we perform several simulations with COMSOL Multiphysics to verify the magnetic field created around the powered coil, as well as the electric field, followed by a series of tests, using six samples between the 0 g/L and 20 g/L of organic fertiliser, and measure their conductivity. First, we find the working frequency doing a sweep for each prototype and four configurations. In this case, for all samples, making a sweep between 10 kHz and 300 kHz. We obtained that in prototype 1 (P1) (coil with 8 layers) the working frequency is around 100 kHz, in P2 (coil with 4 layers) around 110 kHz, and for P3 (coil with 2 layers) around 140 kHz. Then, we calibrate the prototypes measuring the six samples at four different configurations for each sensor to evaluate the possible variances. Likewise, the measures were taken in triplicate to reduce the possible errors. The obtained results show that the maximum difference of induced voltage between the lowest and the highest concentration is for the P2/configuration 4 with 1.84 V. Likewise, we have obtained an optimum correlation of 0.997. Then, we use the other three samples to verify the optimum functioning of the obtained calibrates. Moreover, the _ANOVA simple_ procedure is applied to the data of all prototypes, in the working frequency of each configuration, to verify the significant difference between the values. The obtained results indicate that there is a significate difference between the average of concentration (g/L) and the induced voltage, and another with a level of 5% of significance. Finally, we compare all of the tested prototypes and configurations, and have determined that prototype three with configuration 1 is the best device to be used as a fertiliser sensor in water.  
Keywords:inductive coils; precision agriculture; organic fertiliser; inductive sensors; agricultural monitoring; irrigation control +
Footnote †: journal: Applied Sciences
## 3 Experiment and Analysis
### Analysis of AMG Combined Cycle Slip Detection Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**PRN**} & \multirow{2}{*}{**Position**} & \multirow{2}{*}{**Cycle Slips**} & **(Traditional Methods)** & \multirow{2}{*}{**Cycle Slips Repair**} & **(AR Assisted Model)** & **Cycle Slips Repair** \\  & & & **Calculated Value of** & & **Calculated Value of** & **Value/Cycle** \\  & & & **Cycle Slips/Cycle** & & **Cycle Slips/Cycle** & \\ \hline C38 & 200 & \((1,1)\) & \((0.789,2.238)\) & \((1,2)\) & \((0.947,1.061)\) & \((1,1)\) \\ C38 & 400 & \((2,4)\) & \((3.114,3.076)\) & \((3,3)\) & \((2.010,4.018)\) & \((2,4)\) \\ C38 & 800 & \((8,6)\) & \((9.482,4.879)\) & \((9,5)\) & \((7.905,6.018)\) & \((8,6)\) \\ C38 & 1200 & \((1,-2)\) & \((-0.788,-1.825)\) & \((-1,-2)\) & \((1.053,-2.026)\) & \((1,-2)\) \\ C38 & 1500 & \((-1,1)\) & \((0.747,2.945)\) & \((1,-3)\) & \((-1.095,0.939)\) & \((-1,1)\) \\ C38 & 1800 & \((5,3)\) & \((3.959,3.489)\) & \((4,3)\) & \((4.958,3.057)\) & \((5,3)\) \\ \hline C40 & 200 & \((1,1)\) & \((3.245,0.868)\) & \((3,1)\) & \((1.037,1.065)\) & \((1,1)\) \\ C40 & 400 & \((2,4)\) & \((2.736,3.568)\) & \((3,4)\) & \((1.895,3.961)\) & \((2,4)\) \\ C40 & 800 & \((8,6)\) & \((6.255,7.120)\) & \((6,7)\) & \((8.042,5.982)\) & \((8,6)\) \\ C40 & 1200 & \((1,-2)\) & \((1.047,1.822)\) & \((1,2)\) & \((0.8897,-2  
repaired in severe multipath environments. As shown in Figure 12, the cycle slips' repair success rate of the MW + GF detection model in the multipath environment is 63.4%, because when the pseudo range multipath effect is serious, the false detection rate rises linearly, and it is easy to mistakenly detect the pseudo range multipath effect as the cycle slips. Especially for the combined wavelengths of the dual-frequency signals, the false detection is more serious. For the simulated large and small cycle slips, the AMG combined detection model can detect them effectively. Compared with the traditional TurboEdit methods, the AMG method can effectively detect cycle slips under severe multipath conditions, whose cycle slips' estimation is not affected by the multipath residual, and the success rate of the cycle slips' recovery is even higher. As shown in Table 3, due to the serious influence of the multipath effect on the accuracy of the pseudorange, the cycle slips' repair with the traditional method failed (such as: 700, 900, 1300 epoch, etc.). The results show that the accuracy of the pseudorange seriously affects the success rate of the cycle slips' repair. When the AMG method was used to repair the cycle slips, we used AR to predict and restore the pseudorange in the severe multipath environment, which improved the success rate of the cycle slips' repair (such as: 700, 900, 1300, epoch, etc. ), and the success rate of the rounded repair reached 99.9%.
## 3 Experimental Results and Discussions
### Energy Dissipation  
The energy dissipation (ED) capacity of the tested bent specimens is comparatively presented, in terms of ED per loop and cumulative ED, as shown in Figure 11. The ED per loop of the bent B2-CIP and bent B2-PGS demonstrates an approximate equivalence. Before the lateral displacement hits 50 mm, bent B2-PGS exhibits superior single-loop energy dissipation capacity. This scenario reverses beyond this displacement threshold, with the B2-CIP outperforming the B2-PGS. As the loading displacement of the two bents nears their ultimate lateral displacement, a decrease in the ED per loop is observed in both specimens. Comparatively, the precast bent B2-PGT with the GSS-GCT connection has less single-loop energy dissipation capacity than the former two. The reason is that the pinching effect of bent B2-PGT is more pronounced, which leads to its single-loop hysteretic loops being not as full as those of the other two bents.  
Figure 11: ED per loop and cumulative ED of the three bents.  
Conversely, the cumulative ED of bent B2-CIP is basically the same as that of bent B2-PGS. Bent B2-PGT has a greater cumulative energy consumption capacity than the former two, partly due to its more significant ultimate lateral displacement. Figure 11c compares the cumulative ED from the beginning of the loading to the yield point of the three specimens, representing the cumulative ED when the columns remain elastic. The cumulative ED at the yield point of both precast segmental bents is greater than that of the cast-in-place bent B2-CIP. Figure 11d compares the cumulative ED from the beginning of the loading to the ultimate point, representing the total energy the specimen dissipates during both the elastic and plastic stages. The results indicate that the total energy dissipation of bent B2-PGS at the ultimate point is essentially the same as that of bent B2-CIP. Comparatively, bent B2-PGT demonstrates a more substantial energy dissipation capacity due to its higher load-bearing capacity and ultimate displacement, showing an increase in cumulative ED of 22% compared to bent B2-CIP.
# A High Throughput Hardware Architecture for Parallel Recursive Systematic Convolutional Encoders
## 2 Materials and Methods
### RSC Encoders Introduction
\[Y[n]=(c_{0}[n]\,c_{1}[n])^{T}. \tag{6}\]  
Figure 1: Architecture of a recursive systematic convolutional (RSC) encoder with \(R=\frac{1}{2}\). For RSC encoders with a different value of \(R\), since \(Y[n]\) contains all the system outputs, \(Y[n]\) is \(N_{o}\) dimensional vector. To describe the timing evolution of the RSC states and outputs with modulo-2 operations, we can exploit the ABCD model considering the current input and state:  
\[ \begin{cases}S[n+1]=A\cdot S[n]+B\cdot u[n]\\ Y[n]=C\cdot S[n]+D\cdot u[n]\end{cases} , \tag{7}\]  
where \(A,B,C,D\) are matrices, \(u[n]\) is the input bit and \(S[n+1]\) is the state at instant \(n+1\). A is \(N\times N\) matrix; as it is shown in (7), A is function only of the linear feedback shift register (LFSR) structure, and it is made by the tap elements of \(g(x)\), with \(g(0)\) excluded, in the first row and a partial identical sub-matrix accountable of the shift operation in the second one. \[A= \begin{pmatrix}g_{1}&g_{2}&g_{3}&\cdots&g_{N}\\ &I_{(N-1)\times(N-1)}&&|&0\end{pmatrix} , \tag{8}\]  
where \(I_{(N-1)\times(N-1)}\) indicates the \((N-1)\times(N-1)\) identity matrix and \(g_{1}\ldots g_{N}\) are the coefficients of the polynomial \(g(x)\). The \(B\) vector describe the impact of the current input to the state evolution. In case of a base realization of RSC code, the \(B\) vector is equal to the N dimensional vector shown in (9). \[B= \begin{pmatrix}1&0&0&\ldots&0\end{pmatrix} ^{T}. \tag{9}\]  
C is a \(N_{o}\times N\) matrix representing the relation between the current state and the output coded bit. For RSC encoders that have code rate \(R=\frac{1}{2}\), only two rows are present. In particular, since \(c_{0}\) is the systematic output, which is only function of the input \(u\), the first row of \(C\) is filled with zeros. On the contrary, the second row is computed by the \(c_{1}\) parity output equation described by (10). \[c_{1}[n]=h_{0}\cdot u[n]+\sum_{i=1}^{N}(h_{0}\cdot g_{i}+h_{i})S[n], \tag{10}\]  
where \(h_{i}\) and \(g_{i}\) are coefficients of the polynomials \(h(x)\) and \(g(x)\). For RSC encoders having \(N_{o}>2\) outputs, the \((k+1)^{th}\) row of C is populated by using the coefficients of the \(h_{k}(x)\) polynomial according to Equation (10). Finally, D is \(N_{o}\)-dimensional vector describing the contribution of the inputs in the generation of the output code. By using the systematic code \(c_{0}[n]=u[n]\) and Equation (10), it is possible to retrieve the \(D\) vector values. For a \(R=\frac{1}{2}\), the \(D\) vector is shown in Equation (11):  
\[D= \begin{pmatrix}1\\ h_{0}\end{pmatrix} . \tag{11}\]  
Equations (5)-(10) permit us to redraw the RSC circuit as a finite state machine described by the ABCD matrices model, whose block diagram is shown in Figure 2. For each operation, the modulo-2 operation is performed.
# Radiation Damage of Polydimethylsiloxane and Polyimide by X-ray Free-Electron Laser
## 3 Results
### Radiation Damage of PDMS and PI by XFEL  
In the SFX experiment, the polymer film can be used as an enclosing film to prevent dehydration of the crystal suspension in a fixed-target sample holder [25; 27] or as a support for crystals in the BITS system [37]. In both sample delivery approaches, crystals on polymer films are raster scanned to the XFEL in both the vertical and horizontal directions, according to the motion of the translation stage. To better understand the phenomenon of XFEL-induced radiation damage during data collection, PDMS and PI films were raster scanned in both the vertical and horizontal directions during XFEL exposure. During raster scanning, the scanning interval was 50 \(\upmu\)m; therefore, the XFEL exposure position did not affect the next XFEL exposure position based on the XFEL beam size (calculated full size of XFEL: \(<\)8 \(\upmu\)m). In the real-time monitoring, in the case of the PDMS film, radiation damage was observed by a clearly visible brightness change in the part through which the XFEL was transmitted (Figure 2A and Supplementary Video S1). For the PI film, no notable phenomena that could be considered radiation damage were observed during raster scanning on a real-time monitor (Figure 2B and Supplementary Video S2); however, actual radiation damage occurred (see below).  
Figure 1: Analysis of synchrotron X-ray scattering of PDMS and PI. X-ray scattering images of (**A**) PDMS and (**B**) PI. (**C**) 2D profile of background scattering of PDMS and PI.  
Furthermore, the XFEL-exposed films were analyzed using high-resolution microscopy. In both films used in the experiment, holes due to radiation damage were commonly generated in the positions through which the XFEL transmitted, indicating that the XFEL fluences exceeded the damage threshold of both materials. Meanwhile, the physical phenomena for radiation-damaged holes on PDMS and PI films were different. For the PDMS film, the shape of the hole generated by the XFEL radiation damage varied, and the hole size was 15-40 \(\upmu\)m, which is 5-20 times larger than that of the XFEL beam (FWHM) used in the experiment (Figure 2C). For the PI film, a crater-like hole formed in the transmission area of the XFEL beam, and the size of the hole due to radiation damage was 3 and 2 \(\upmu\)m in the vertical and horizontal directions, respectively, similar to the FWHM size of the focused XFEL beam (Figure 2D). In addition, approximately 2-4 \(\upmu\)m of the film around the hole was depressed, and the total size of the depressed area was similar to the full size of the XFEL beam (Figure 2D). These depressions appeared to have been melted by XFEL exposure. The hole pattern on the PI film in this experiment is similar to the pattern shown in the PI film for enclosing the crystals in the sample holder in the previous fixed-target SFX experiment [25; 27].
# Type Hierarchy Enhanced Event Detection without Triggers
## 3 Methodology  
### Task Definition  
In this section, we briefly introduce the task definition to facilitate understanding the following subsections. Following the previous work [10; 19], we are given a training dataset, \(\{(x_{i},y_{i})\}_{i=1}^{N}\), wherein N denotes the number of sentence-event pairs. \(x_{i}\) is the \(i\)-th sentence. Let \(x_{i}=w_{1},w_{2},\cdots,w_{L}\) be a sentence of L words, and the \(y_{i}\) is the corresponding event type, where \(y_{i}\in Y_{s}\), which indicates an event label sequence \([y_{1},y_{2},\cdots,y_{N}]\). We treat \(Y_{s}\) simply as 34 distinct event types, including 33 event types based on the ACE 2005 corpus and an \"NA\" to denote that sentences do not contain any events. Our goal is to train a model to detect the corresponding event type, which may contain one or more events, as accurately as possible given the input data. It should be noted that the training set does not contain annotated triggers. Consider the following sentence, \"He claimed Iraqi troops had destroyed five tanks\", our eventual goal is to detect the \"Attack\" event.  
Our task can be divided into (1) how to capture the effective features in the input text, (2) how to learn more precise representations to embed semantic information between input sentences and event types and (3) how to detect each event in a sentence which may contain an arbitrary number of events.