esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Multi-Objective Optimization with Equivalent Static Loads for Parallel Mechanism
### Desirability Approach  
In application, designers usually optimize more than one aspect of the PM. For the machining process, designers pursue higher stiffness and lower vibration amplitude. For a fast pick-and-place process, a lower mass, lower energy cost, and higher accuracy are considered. Therefore, more than one objective function needs to be considered simultaneously. The linear static response optimization step in ESLs should be a multi-objective optimization process. The desirability approach is added to the ESLs to realize it. Since it is easy to implement and has a low computational cost, the desirability approach is widely used in multi-objective optimization design. The desirability approach transfers multiple objective functions into a single function by assigning different weight factors. In the first step, each objective function is transformed into a dimensionless variable. The objective function to be minimized is denoted as \(f_{i}\), and the objective function to be maximized is denoted as \(f_{j}\). Each one has the lower bounds \(L_{i},L_{j}\) and upper bounds \(H_{i},\ H_{j}\). Then, according to Equations (30) and (31), \(f_{i}\) and \(f_{j}\) are transformed into dimensionless individual desirability functions \(D_{i}\) and \(D_{j}\), ranging from 0 to 1. \(w_{i}\) and \(w_{j}\) are the weight coefficients on each objective function [34].  
\[D_{i}=\begin{cases}1&f_{i}<L_{i}\\ \left(\frac{H_{i}-f_{i}}{H_{i}-L_{i}}\right)^{w_{i}}&L_{i}<f_{i}<H_{i}\\ 0&f_{i}>H_{i}\end{cases} \tag{30}\]  
\[D_{j}=\begin{cases}0&f_{j}<L_{j}\\ \left(\frac{f_{j}-L_{j}}{H_{j}-L_{j}}\right)^{w_{j}}&L_{j}<f_{j}<H_{j}\\ 1&f_{j}>H_{j}\end{cases} \tag{31}\]  
Then, the overall desirability function \(D\) is obtained as the geometric mean of all individual desirability functions \(D_{k}\) (\(k=1,2,\cdots,n\)).  
\[D=\left(\prod_{k=1}^{n}D_{k}\right)^{\frac{1}{n}} \tag{32}\]
## 3 Methods  
### The Bus Traffic Flow Prediction Problem  
In this section, we first give some notations and then define the bus traffic flow problem.  
**Definition 1**.: _(Alighting/boarding flow): We divide the city into \(M\times N\) grids based on the latitude and longitude, and each grid represents a region. For each region, there are two kinds of bus traffic flow, which are alighting flow and boarding flow. They are defined respectively as_  
\[f^{\text{alight}}_{t\text{m},n}=\sum_{Rt\in S}|\left\{i\geq 1|r_{i}\in(m,n) \wedge r_{i+1}\
otin(m,n)\right\}| \tag{1}\]  
\[f^{\text{board}}_{t\text{m},n}=\sum_{Rt\in S}|\left\{i>1|r_{i-1}\
otin(m,n) \wedge r_{i}\in(m,n)\right\}| \tag{2}\]  
_where \(R_{t}:r_{1}\to r_{2}\rightarrow\cdots\to r_{i}\) is a trajectory in a set of trajectories \(S\), and \(r_{i}\)is the geospatial coordinate; \(r_{i}\in(m,n)\) means the point \(r_{i}\) lies in region\((m,n)\)._  
Therefore, we can get a bus traffic flow matrix at each time interval using the above definition, as is shows in Figure 1. The matrix of alighting flow and boarding flow can stack to a two-channel image-like tensor.  
**Problem 1**.: _(Bus traffic flow prediction): The bus traffic flow prediction problem is that given the historical alighting flow \(y_{a;i}\) and boarding flow \(y_{bj}\) for \(i=0,1,\cdots,t-1\), to predict alighting flow \(y_{a;t}\) and boarding flow \(y_{b;t}\) at future time interval \(t\), respectively._  
\[y_{a;t},y_{b;t}=F(y_{a;i},y_{b;i})\quad i=0,1,\cdots,t-1 \tag{3}\]  
_where \(F\) is prediction function._  
Figure 1: Bus traffic flow matrix.
# FuGeF: A Resource Bound Secure Forwarding Protocol for Wireless Sensor Networks
## 5 Performance Evaluation
### Attack-Based Network
#### 5.1.3. Single Attacker  
* _Attacker A1:_ It can be observed from Figures 6 and 7 that this optimally positioned attacker poses a serious threat to all protocols. DWSIGF-P has zero chance of bypassing such a treat due to its predictable forwarding strategy. DWSIGF- R with 7% slightly outperforms FuGeF with 6% packet delivery. This is because the FLS consensus bearly notices changes in initial value parameters yielded among all nodes. These poor performances were observed in all protocols due to the high correlation the maliciously placed node has to the first hop.
* _Attacker A2:_ The modal positioned attacker A2, unlike A1, has a small impact on the forwarding node selection. This is because the position is easily bypassed when the first hop chooses the highest distance towards the destination. DWSIGF-P, DWSIGF-R, and FuGeF were able to achieve 82%, 83% and 88% packet delivery, respectively, as shown in Figure 6. FuGeF, with 2% possibility of choosing the attacker (Figure 7), outperforms both DWSIGF-P and DWSIG-R that have 7% and 5% respectively.
* _Attacker A3:_ This modal attacker shows a significant impact on the network performance. A3 has a higher chance of being selected than A2 during the routing process. 77%, 74% and 43% possibility of choosing the attacker was observed, leading to 20%, 23% and 53% packet delivery in DWSIGF-P, DWSIGF-R, and FuGeF, respectively, as shown in Figures 6 and 7.
* _Attacker A4:_ The attacker A4 is presumed to be the last hop node before the final destination. Due to network density, the impact of this optimal attacker was minimal such that DWSIGF-P, DWSIGF-R, and FuGeF achieved 80%, 84%, and 88% packet delivery (Figure 6) with 10%, 5% and 4.5% possibility of choosing the attacker (Figure 7), respectively.
###### Abstract  
Several strategies to deal with the trajectory tracking problem of Unmanned Underwater Vehicles are encountered, from traditional controllers such as Proportional Integral Derivative (PID) or Lyapunov-based, to backstepping, sliding mode, and neural network approaches. However, most of them are model-based controllers where it is imperative to have an accurate knowledge of the vehicle hydrodynamic parameters. Despite some sliding mode and neural network-based controllers are reported as model-free, just a few of them consider a solution with finite-time convergence, which brings strong robustness and fast convergence compared with asymptotic or exponential solutions and it can also help to reduce the power consumption of the vehicle thrusters. This work aims to implement a model-free high-order sliding-mode controller and synthesize it with a time-base generator to achieve finite-time convergence. The time-base was included by parametrizing the control gain at the sliding surface. Numerical simulations validated the finite-time convergence of the controller for different time-bases even in the presence of high ocean currents. The performance of the obtained solution was also evaluated by the Root Mean Square (RMS) value of the control coefficients computed for the thrusters, as a parameter to measure the power consumption of the vehicle when following a trajectory. Computational results showed a reduction of up to 50% in the power consumption from the thrusters when compared with other solutions.  
unmanned underwater vehicles; trajectory tracking; Sliding Mode Control; finite-time convergence 2021  
16 February 2021  
11, 1836. [[https://doi.org/10.3390/app11041836](https://doi.org/10.3390/app11041836)]([https://doi.org/10.3390/app11041836](https://doi.org/10.3390/app11041836))
# A Survey of Blockchain Applications for Management in Agriculture and Livestock Internet of Things
## 4 Applications of Blockchain in Agricultural and Animal Husbandry IoT
### Efficient Resource Allocation
* Tailored protocols for low-power solutions: Developing specialized blockchain architectures for constrained devices ([105; 106]) curbs overhead without sacrificing security. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Targeted Challenges** & **Papers** & **Short Introductions** & **Blockchain-Enabled Opportunities** \\ \hline \multirow{8}{*}{Resource Allocations} & \multirow{8}{*}{[107]} & The paper describes a system rooted in & \multirow{8}{*}{Merging blockchain with IoT devices in} \\  & & blockchain technology designed to & \\  & support low-power, resource-limited & & this framework creates avenues for \\  & & IoT devices in directly engaging with a & transparent, economical, and secure \\  & & public blockchain to support & \\  & & sustainable water management & \\  & & practices in agriculture. & \\ \hline \multirow{8}{*}{Resource Allocations} & \multirow{8}{*}{[108]} & The approach integrates IoT and & \multirow{8}{*}{Blockchain technology offers} \\  & & blockchain technologies to optimize & \\  & resource management in agriculture, & & distribution and precise monitoring in \\  & focusing on water resources and seed & & agriculture and addresses security and \\  & quality monitoring, enhancing security, & transparency, and agricultural output. & transparency challenges in smart \\  & & livestock farming and supply & \\  & & chain management. & \\ \hline \multirow{8}{*}{[108]} & This system utilizes IoT and blockchain & \multirow{2}{*}{Blockchain technology improves} \\  & technologies to boost resource & & resource management in livestock IoT, \\  & management efficiency in smart & & including the management of animal \\  & livestock farming through enhanced & & feed, fertilizers, and greenhouse gas \\  & data transparency and security. & & emissions control. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._* Holistic agriculture management: Integrating sensor data and smart contracts helps streamline activities such as irrigation scheduling and livestock feeding, boosting productivity and environmental stewardship. In summary, these works demonstrate blockchain's promise to improve agricultural resource management by increasing transparency, security, and sustainability. However, practical challenges such as technical complexity, scalability, and energy use must be addressed to fully realize these benefits in diverse farming contexts.
## 4 Analysis of Experimental Results
### Plastic Hinge Length  
The plastic hinge length was calculated using the model proposed in past research, as shown in Table 3, to evaluate the lateral confinement performance of specimens [13; 16; 23; 24]. Since the curvature of the plastic hinge zone serves as a basis for measuring the ductility of RC members, this study shows the plastic hinge zone-curvature relationship, as shown in Figure 10.  
Figure 10: _Cont._  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Researcher Reference** & **Plastic Hinge Length Model** \\ \hline [PERSON] and [PERSON] (1992) & \(0.08z+0.022d_{b}f_{y}\) (for RC columns) \\ \hline [PERSON] and [PERSON] (1993) & \(1.0h\) (for columns under high axial loads) \\ \hline [PERSON] and [PERSON] (2001) & \(0.18z+0.021d_{b}f_{y}\) (for RC beams and columns) \\ \hline [PERSON] and [PERSON] (2008) & \(l_{p}/h=\left[0.3(p/p_{0})+3\big{(}A_{s}/A_{g}\big{)}-0.1\right](z/h)+0.25\geq 0.25\) (for columns) \\ \hline \end{tabular} \(A_{g}\): Gross area of concrete section; \(A_{s}\): Area of tension longitudinal reinforcement; \(d_{b}\): Diameter of longitudinal reinforcement; \(f_{y}\): Yield stress of longitudinal reinforcement; \(h\): Overall height of member; \(P\): Applied axial force; \(P_{g}\): Nominal axial load capacity as per ACI318-19; \(z\): Distance from critical section to point of contraflexure.  
\end{table}
Table 3: Previous studies of plastic hinge length for RC columns.  
Regardless of the configuration of transverse reinforcement, the specimens developed a plastic hinge with a rapid increase in curvature after the yielding of longitudinal reinforcement at 250 mm (width) away from the 0 on the horizontal axis (stub end). This is similar to the plastic hinge proposed by [PERSON] and [PERSON] in 1993 [16]. H-series specimens with an axial force ratio of 20% saw an increase in curvature after the peak load, but those with an axial force ratio of 30% did not show any increase in curvature after the peak load. As such, H-series transverse reinforcement with ties were unable to provide effective lateral confinement under high axial force, and eventually experienced failure. On the other hand, the curvature of S-series specimens increased after peak load in the plastic hinge zone (0-250 mm), and specimens having transverse reinforcement with a yield strength of 1050 MPa had a large curvature even when the axial force ratio was 30%. Spiral transverse reinforcement with alternating squares and octagons provided more effective lateral confinement to the core concrete and longitudinal reinforcement within the plastic hinge zone even with 55% less transverse reinforcement; the use of high-strength transverse reinforcement in RC members under high axial force is expected to significantly improve lateral confinement performance.
# Total Ionizing Dose Effects on a Delay-Based Physical Unclonable Function Implemented in FPGAs
## 3 Experimental Setup
### TID Setup  
The TID tests have been performed at the RADLAB facility, the Gamma Radiation Laboratory installed in the Centro Nacional de Aceleradores (CNA), Spain. The RADLAB [12,13] is based on a Co-60 radioactive source, placed into a Gamma beam X200 irradiator. The average value for the photons energy is 1.25 MeV, which is usually established for testing purposes.  
The irradiator has a conical opening which contains a variable collimator, providing different square irradiation fields. During a first irradiation setup, the maximum irradiation field was used and no shielding was applied on the board, so all the components of the PCB were exposed to radiation, not only the DUT (FPGA). As a consequence, some issues were observed before detecting any effect in the DUT. For the subsequent campaigns, the irradiation field was reduced to focus the main gamma beam on the FPGA under study. Moreover, the setup was additionally improved with a custom partial shielding on the board, significantly decreasing the dose rate on the most sensitive components of the PCB (Figures 2 and 3 ).  
Since the DUT is located in one specific position of the PCB submitted to radiation, a dummy board was placed for each setup preparation in the same position as the SAMPLE in order to carry out the dosimetry (Figure 4), that is, to measure the dose rate on the DUT, before starting the irradiation test.  
The dosimetry system is composed by a Farmer ionization chamber connected to the UNIDOS Webline electrometer, both of them by PTW. First air kerma rate is obtained and the dose rate in silicon (Si) is calculated taking into account the conversion factors. The dose rate uniformity in the radiation field was better than 95% and the expanded uncertainty associated with the measurement was \(\pm\)4.2%.  
The dosimetry and the irradiation run were performed using a filter box, with the DUT inside, according to the TID standard from European Space Agency [12]. This container has 1.5 mm Pb (lead) with an inner lining of 2 mm Al (aluminium). The front cover is made of Al, except in the region close to the DUT, where the build-up material is polymethilmetacrilate (PMMA). In Figure 5 is depicted the final setup.  
The FPGA1 and FPGA2 were exposed to a total dose of 500 krad(Si), with the dose rates of 5.2 krad(Si)/h and 5.3 krad(Si)/h, respectively.  
Figure 4: PCB customized to carry out the dosimetry measurements with the reference point of the ionization chamber placed exactly in the position of the device under test (DUT).  
Figure 3: Profile view of the shielding between the PCB and the cover of the filter box.  
Figure 2: Shielding of the PCB attached to the cover of the filter box.
## 4 Techniques of In-Network Join Processing
### Techniques with Filtering of Non-Joinable Tuples
The hash partitioning of the tuples is executed in association with partitioning of the joining attribute value range into subranges. This amounts to the _logical_ (and _partial_) _ordering_ of the tuples where the tuples in a subrange are not sorted. _SRJA (Synopsis Refinement iceberg-Join Algorithm)._ In [12], [PERSON] et al. proposed _SRJA_ to answer a one-shot join query. It executes an _iceberg join_[38] which is represented as an inter-region equi-join \(R\bowtie^{\mathrm{i}}_{A=B}S\). An iceberg join in WSNs is to find out the prominent patterns of correlation among the sensor readings. For a joining attribute value \(v\), it contributes to the join result only if the number of joined tuples for \(v\) exceeds some given _iceberg threshold_\(\alpha\). Figure 11 shows an example when \(\alpha\) is 2. In _SRJA_, each of regions \(R\) and \(S\) generates a _synopsis_ of the joining attribute as follows. The value range of the joining attribute is divided into \(k\) subranges. For each subrange that covers, say, \(n\) values, the number of occurrences of each of those \(n\) values is counted. Among those \(n\) counts, the minimum and the maximum values are picked. Then, the synopsis of region \(R\) (\(S\)) is a series of \(k\) triples _(subrange, mincount, maxcount)_'s. The two region synopses are sent to the node \(c_{T}\), which is located at the midpoint between the two roots of the routing trees. In \(c_{T}\), for each matching subrange pair, joinability for the subrange is checked. For a subrange, if the product of the two corresponding maxcount's is less than \(\alpha\), then the subrange is flagged as _PRUNE_, because the tuples belong to that subrange do not contribute to the result. If the product of the two mincount's is greater than \(\alpha\), then the subrange is flagged as _JOIN_, because the tuples belong to that subrange might contribute to the result. In other cases, the subrange is flagged _DIVIDE_. The flagged synopsis of each region is sent back to the corresponding region where the tuples for the PRUNE-flagged subrange are filtered out. For the JOIN-flagged subrange, a variant of the semijoin is carried out between two regions to filter out non-joinable tuples, and the tuples that have survived are sent to the query sink for the final result. For the DIVIDE-flagged subrange, the corresponding value range is further divided, and the above process is repeated. Figure 11: (a) Relation \(R\); (b) Relation \(S\); (c) Result of natural join \(R\bowtie_{B=B}S\); (d) Result of iceberg join \(R\bowtie^{\mathrm{i}}_{B=B}S\) with iceberg threshold \(\alpha=2\).
## 4 Accurate Earthquake Locating
We noticed that the three clusters are not spatially continuous, especially since there is a seismic gap of about 20 km in length between C1 and C2 (Figure 7a dotted box), and the future Luding \(M_{\rm S}\) 6.8 earthquake sequence occurred near the boundary of this seismic gap. The entire profile does not show a significant low-\(b\)-value region in this period. Figure 7: Profiles of focal depth and \(b\)-value; the position of the profile is in Figure 3b: (**a**) 2009–2014, (**b**) 2015–31 May 2022, (**c**) the cross-section of the mainshock’s co-seismic slip and the earthquake sequence’s profile. The co-seismic slip results are provided by Dr. [PERSON]. The gray circles mark the earthquakes in the profile, dashed boxes indicate the seismic gap, and the red stars show the position of the \(M_{\rm S}\) 6.8 Luding earthquake. From 2015 to May 2022, the earthquake distribution showed significant changes compared to the previous period (Figure 7b): earthquakes in C1 extended into and broke the earthquake gap. At the same time, the scale of C2 and C3 expanded and connected. The \(b\)-value profile showed that, during this period, a 50 km low-\(b\)-value anomaly area (\(b<0.7\)) formed in the lower boundary area of C1 near the seismic gap. The future Luding \(M_{5}\) 6.8 earthquake is just located in this anomaly area. Combined with the co-seismic slip map (Figure 7c), we can see that the location, scale, and shape of the large co-seismic slip area are highly consistent with the low-\(b\)-value area, and the earthquake gap is filled with the Luding \(M_{5}\) 6.8 aftershock. The low-\(b\)-value anomaly area and the seismic gap constitute this earthquake sequence's rupture range.
## 3 Results
### Preliminary Structural Features of CFPS  
#### 3.2.1 FT-IR Spectra  
FT-IR was used to pre-identify (footprint) functional groups of CFPS (Figure 1). CFPS presented a typical polysaccharide spectrum [39; 40] with a strong and broad absorption bond observed at 3273 cm\({}^{-1}\) that was assigned to O-H stretching vibrations of water and carbohydrates. The peak at 2361 cm\({}^{-1}\) was due to the lack of CO\({}_{2}\)/H\({}_{2}\)O normalization during the data treatment. The two peaks at 1596 cm\({}^{-1}\) and 1402 cm\({}^{-1}\) were attributed to carboxylate groups of uronic acid residues but could also correspond to the overlap of glucuronide/glucose or methyl-sugar structures. The absorption band at 1095 cm\({}^{-1}\) (from 1000 cm\({}^{-1}\) to 1200 cm\({}^{-1}\)) corresponded to the stretching vibration of polysaccharides. The bands around 885 cm\({}^{-1}\) could be due to \(\beta\)-linked polysaccharides. Note that some proteins (traces) could also be detected owing to the band close to 1321 cm\({}^{-1}\).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \begin{tabular}{c} **Extraction** \\ **Yield** \\ **(\%, \(w\)/\(w\))** \\ \end{tabular} & \begin{tabular}{c} **Total** \\ **Carbohydrates** \\ **(\%, \(w\)/\(w\))** \\ \end{tabular} & \begin{tabular}{c} **Uronic Acids** \\ **(\%, \(w\)/\(w\))** \\ \end{tabular} & \begin{tabular}{c} **Neutral** \\ **Sugars** \\ **(\%, \(w\)/\(w\))** \\ \end{tabular} & \begin{tabular}{c} **Proteins** \\ **(\%, \(w\)/\(w\))** \\ \end{tabular} & \begin{tabular}{c} **Phenolic** \\ **Compounds** \\ **(\%, \(w\)/\(w\))** \\ \end{tabular} & \
\begin{tabular}{c} **(NaCl eq.** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Conductivity** \\ **(\%)** \\ \end{tabular} \\ \hline \multirow{2}{*}{6.5} & 34.5 \({}^{a}\)\(\pm\) 0.35 & \multirow{2}{*}{17.81 \(\pm\) 0.25} & \multirow{2}{*}{16.11 \(\pm\) 0.12} & \multirow{2}{*}{2.5 \(\pm\) 0.45} & \multirow{2}{*}{1.6 \(\pm\) 0.12} & \multirow{2}{*}{8.26} & \multirow{2}{*}{110} \\  & 38.1 \({}^{b}\)\(\pm\) 1.1 & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Global composition of CFPS fraction extracted from _Clematis flammula_.
## 3 Simulation Environment and Approaches
### Software in the Loop
This transformation from subsystem to S function implies a conversion of the block into C code with its corresponding.mex file, where the mex file links all the C functions to distinct Simulink operations. Consequently, the model was greatly optimized for a streamlined approach within a local CPU allowing for a less demanding simulation and SIL testbed testing. Once the S functions were implemented in the Simulink simulation environment, Monte Carlo simulation was carried out, and sensitivity analysis was used to iterate withrandom values evaluating the variability of the pointing error under different external perturbations and initial conditions. Sensitivity analysis requires the use of global variables to facilitate variations in the initial parameters, which requires the implementation of inputs to Simulink blocks that use those parameters. As initial parameters, an analysis of \(K_{p}\) and \(K_{d}\) was carried out through 350 iterations of random values using a normal distribution. Consequently, (\(K_{p}=0.00013555\)) and (\(K_{d}=0.0743\)) were determined as optimal gains for the ADCS. Using these gains, an analysis of variations in the initial orbit, inertia matrix, Euler angles, orbit elements, and angular velocity was implemented. For the initial orbit condition, Euler angles, inertia matrix, and angular velocity, the generation of random values was through a uniform distribution using custom requirements to obtain the results of the Monte Carlo simulation. The said custom requirements in sensitivity analysis determined the standard deviation and mean of the pointing error starting from a 10,000 s mark to avoid initial noise measurements. Finally, to derive a proper model for SIL testbed evaluation, a Simulink model was constructed using the optimized S-function performance, C caller embedded code, and optimal Monte Carlo gains. Figure 7 shows the overall SIL framework and methodology for testbed evaluation and data derived towards PIL and HIL evaluations.