esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling the Accuracy of Estimating a Neighbor's Evolving Position in VANET
###### Abstract  
Accurate estimation of a neighbor's evolving position is essential to enhancing safety in intelligent transport systems. A vehicle can estimate a neighbor's evolving position via periodic beaconing wherein each vehicle periodically broadcasts a beacon including its own kinematic data (e.g., position, speed, and acceleration). Many researchers have proposed analytic models to describe periodic beaconing in vehicular ad-hoc networks (VANETs). However, those models have focused only on network performance, e.g., packet delivery ratio (PDR), or a delay, which fail to evaluate the accuracy of estimating a neighbor's evolving position. In this paper, we present a new analytic model capable of providing an estimation error of a neighbor's evolving position in VANET to assess the accuracy of the estimation. This model relies on a vehicle system using periodic beaconing and a constant speed and position estimator (CSPE) to estimate a neighbor's evolving position. To derive an estimation error, we first calculate the estimation error using a simple equation, which is associated with a probability of successful reception. Then, we derive the probability of successful reception that is applied onto the error model. To our knowledge, this is the first paper to establish a mathematical model to assess the accuracy of estimating a neighbor's evolving position. To validate the proposed model, we compared the numerical results of the model with those of the NS-2 simulation. We observed that numerical results of the proposed model were located within the 95% confidential intervals of simulations results.  
analytic model; estimation error; neighbor's evolving position; mobility; vehicular network 25 August 2020; Accepted: 24 September 2020; Published: 28 September 2020
## 7 Applications in SDN-based VANETs
### Left/Right Turn Assistant  
The left/right turn assistant is utilized to assist the driver with making the choice to turn left/right if there should be an occurrence of safe circumstance. It assembles the data related the left half of the roads by forwarding a solicitation information to the SDN-based RSUs; the RSUs will send back the information when it is safe to go across the roads.
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 1 Introduction
### Our Contributions  
In this study, we investigate domain adaptation for satellite-borne machine learning, specifically for the task of multispectral cloud detection. Our main contributions are the following:  
* We propose novel task definitions for domain adaptation, which we named _offline adaptation_ and _online adaptation_, that are framed in the setting of an EO mission that conducts onboard machine learning inference.
* For offline adaptation, we propose a bandwidth-efficient _supervised domain adaptation_ (SDA) technique that allows a satellite-borne CNN to be remotely updated while consuming only a tiny fraction of the uplink bandwidth.
* For online adaptation, we demonstrate _test-time adaptation_ (TTA) on a satellite-borne CNN hardware accelerator, specifically, the Ubotica CogniSAT-XE1 [13]. This shows that CNNs can be updated on realistic space hardware to account for the multispectral domain gap.  
Our study greatly improves the viability of satellite-borne machine learning, including dealing with the inevitable problem of the domain gap in multi/hyperspectral EO applications.
## 3 Methodology
### Entropy Method  
As is well known, the World Bank data have applied an equal weighting coefficient for LPI. However, since it is not realistic for all criteria to be equal, the entropy weighting coefficient has been suggested as an alternative in this study.  
Entropy is an advanced technique that objectively determines criteria weights with precision. The methodology involves the following steps [53; 54]:  
Step 1: A decision matrix is constructed.  
Assume there are m alternatives Ai (i = 1,2,\(\ldots\), m) and n selection criteria Cj (j = 1,2,\(\ldots\), n). The decision matrix can be formulated as follows:  
\[X=\left[x_{ij}\right]_{n\times m}=\left[\begin{array}{ccc}x_{11}&\cdots&x_{1n }\\ x_{21}&\cdots&x_{2n}\\ \vdots&x_{ij}&\vdots\\ x_{m1}&\cdots&x_{mn}\\ \end{array}\right]\]Step 2: The decision matrix is normalized:  
\[P_{ij}=\frac{x_{ij}}{\sum_{i=1}^{m}x_{ij}} \tag{1}\]  
where \(\left|P_{ij}\right|\) represents the projection value. xij denotes the crisp value of alternative Ai with respect to Cj.  
Step 3: The entropy value for each criterion is determined:  
\[e_{j}=-k\sum_{i=1}^{n}P_{ij}\ln P_{ij_{j_{j}}} \tag{2}\]  
\[k=\frac{1}{\ln(m)}\]  
where m is the total number of evaluated alternatives, \(\ln\left(\mathrm{v_{ij}}\right)\) is logarithm based on e and ej is [0,1].  
Step 4: The level of differentiation among the criteria is evaluated:  
\[d_{j}=1-e_{j},\forall_{j} \tag{3}\]  
where dj is the contrast density in the j structure.  
Step 5: The weights of the criteria are calculated:  
\[W_{j}=\frac{d_{j}}{\sum_{k=1}^{n}d_{k}} \tag{4}\]  
\[\sum\mathrm{w_{j}}=1,\,0\leq\mathrm{w_{j}}\leq 1\]
# Efficient Intersection Management Based on an Adaptive Fuzzy-Logic Traffic Signal
## 5 Evaluation and Discussion
### Comparison against Other Approaches
Note that for stream \(C\), at a flow rate from 215 veh/h/ln, the FL Phase-Adjustment controller achieves waiting times above 882.88 s, which are higher than the achieved by the proposed controller  
Figure 8: Waiting time as a function of the averaged system’s arrival flow rate: (**a**) stream A, (**b**) stream B, and (**c**) stream C.  
in the worst case, which is 791.54 s with a flow rate of 249 veh/h/ln. Phase imbalance is aggravated by the green-extension approach, producing waiting times above the triple of the obtained with the Proposed Adaptive FL. This poor performance is explained by the fact that strategies based on phase extension lead to long signal cycles and consequently longer waiting times. Such an effect is worsened when traffic signal reconfiguration is executed every cycle, since overestimation/underestimation is promoted by sporadic traffic conditions such as the well-known phantom jams [80]. Furthermore, in the case of the FL Phase-Adjustment controller, performance results were more affected by the use of a fixed value for saturation flow, which is an essential adaptive parameter. Unlike the latter approaches, the proposed adaptive FL controller reaches an outstanding performance while preserving a fair balance among phases. This equilibrium is reached since the proposed controller adaptively computes the cycle length, which is proportionally split into different phases, instead of estimating phase extensions. Since cycle length is computed through a fuzzy inference, it does not depend on fixed/pre-established values such as saturation flow that may lessen adaptability to the controller. Moreover, the proposed adaptive FL controller avoids underestimations/overestimations by creating a virtual buffer of at least three signal cycles to contain sporadic effects such as phantom jams.
# Simulation-Based Analysis on Operational Control of Batch Processors in Wafer Fabrication
## 4 Simulation Experiments and Results
### Results from Simulation Experiments
#### 4.2.1 Effect of BP Utilization on System Performance  
Simulation experiments have been performed under the CONTIME lot release condition to see the effect of batch processors on lead time. The utilization for each station remains the same as seen in Table 1, except for the BP station that has three different utilizations. To have different utilizations, the MPT for station 1 is adjusted by using Equation (1). For example, to have a utilization of 70% at station 1, \(\text{MPT}=\left(0.70-\frac{2}{38+2}\right)\left(\frac{2\times 6}{1\times 5} \right)=1.56\) h. The reason why we change the MPT instead of changing the arrival rate to have different BP utilization levels is that we want to have the utilization of the other stations remain the same. Figure 6 shows the waiting time at each station. It is seen that station 1, the BP station, has the longest waiting time even with a low BP utilization, such as 60%. The waiting time at the BP station is even higher than the waiting time at the bottleneck station, station 3 with 95% utilization. The result indicates that to reduce production lead time, operation managers should pay close attention to the control of batch processors.  
To examine the impact of the BP utilization and DP utilization on system performance, we have performed experiments for the systems with varying utilization levels for station 1 (BP) and station 7 (DP). Here, to see the throughput change over different utilization levels, the closed-loop lot release rule, CONWIP, is used. The experimental results are given in Figure 7. When we examine the performance over varying BP utilization levels, the throughput decreases as the BP utilization increases. The decrease rate is larger in higher BP utilization levels than in lower BP utilization levels. When BP  
Figure 6: Waiting time at each station with different BP utilization levels.  
utilization is 80%, the system produces 6492 wafer lots, which is 94.9% of the capacity. (Note that the system capacity for nine months is 6840 wafer lots.) The lead time also increases slowly with the lower BP utilization, but increases rapidly with the higher BP utilization. When we compare the impact of BDs and DPs, we can find that the change of the throughput and lead time under different utilization levels are smaller in the DP case than in the BP case. The BP station is more sensitive than the DP station in terms of the effect of the utilization levels on the system performance. For example, the system with 60% BP utilization and 80% DP utilization produces 6656 wafer lots, while the system with 80% BP utilization and 60% DP utilization produces 6537 wafer lots (\(-\)119 lots). The results indicate that the batch processor should have more planned excessive capacity than the discrete processors. This is quite an important result for practitioners.
# Novel Reliable and Energy-Efficient Routing Protocols for Wireless Sensor Networks
## 4 Maximizing the Minimal Residual Energy
### Two-Hop Routing  
Let us assume that packets are forwarded to the BS in a two-hop path, and the sender node is denoted by \(s\), while the intermediate node relaying this packet to the BS is denoted by \(l\). Then, there are two components that change in vector \(c(k+1)\) compared to \(c(k)\),  
\[c_{s}(k+1)=c_{s}(k)-\left(-d_{sJ}\frac{\theta\sigma_{Z}^{2}}{\ln(P_{sl})}\right) \tag{2}\]  
and  
\[c_{l}(k+1)=c_{l}(k)-\left(-d_{l,BS}\frac{\theta\sigma_{Z}^{2}}{\ln\left(P_{l, BS}\right)}\right) \tag{3}\]  
Furthermore, to ensure that the packet sent by node \(s\) reaches the BS with a given probability \((1-\varepsilon)\), then the reliability constraint can be expressed \(P_{s,l}*P_{l,BS}=1-\varepsilon\). Thus, in the case of two-hop routing, one can determine the relay node \(l\), by solving the following constrained optimization problem:  
\[l_{opt}:\underset{l}{\max}\left\{c_{s}(k)-(-d_{sl}\frac{\theta\sigma_{Z}^{2}} {\ln(P_{sl})}),c_{l}(k)-(-d_{l,BS}\frac{\theta\sigma_{Z}^{2}}{\ln\left(P_{l, BS}\right)})\right\} \tag{4}\]  
subject to the constraint \(ln\left(P_{s,l}\right)+ln\left(P_{l,BS}\right)=ln(1-\varepsilon)\).  
Solution  
Let us assume that we have chosen a relay node denoted by index \(l\), then the condition \(P_{sl}P_{lBS}=1-\varepsilon\) can be paraphrased as:  
\[\ln\left(P_{s,l}\right)+\ln\left(P_{l,BS}\right)=ln(1-\varepsilon). \tag{5}\]  
From Equation (1):  
\[-\frac{d_{sJ}^{a}}{g_{sJ}}\theta\sigma_{Z}^{2}-\frac{d_{l,BS}^{a}}{g_{l,BS}} \theta\sigma_{Z}^{2}=ln(1-\varepsilon). \tag{6}\]  
From Equation (6), we can define \(\varphi(g_{sl})\) which expresses the relationship between \(g_{l,BS}\) and \(g_{sJ}\) due to the constraint as:  
\[\varphi(g_{sl})=g_{l,BS}=\frac{-g_{sl}d_{l,BS}^{a}\theta\sigma_{Z}^{2}}{g_{sl} ln(1-\varepsilon)+d_{l,BS}^{a}\theta\sigma_{Z}^{2}}. \tag{7}\]  
The minimum residual energy (if node \(l\) is selected) will be maximum if the residual energy at the source node and the relay node are equal to each other, i.e., \(c_{s}(k+1)=c_{l}(k+1)\). So, from Equations (2) and (3): \(c_{s}(k)-g_{sJ}=c_{l}(k)-g_{l,BS}\) and by using Equations (6) and (7), we have:  
\[c_{s}(k)-g_{sJ}=c_{l}(k)-\varphi(g_{sl}). \tag{8}\]  
We can use Equation (8) to determine \(g_{ls,opt}:c_{s}(k)-g_{sJ}=c_{l}(k)-\varphi(g_{sl})\). Since the source node \(s\) is given, we can calculate \(g_{ls,opt}\) for all possible relay nodes \(l=1,\ldots,N\). Then \(l_{opt}\) can be selected by solving the problem:  
\[l_{opt}:\underset{l\in 1,\ldots,N,l\
eq s}{\max}c_{s}(k)-g_{sl,opt}. \tag{9}\]  
This requires the solution of (8) \(N-1\) times and then \(N-1\) comparison of the value \(c_{s}(k)-g_{ls,opt}\forall l=1,\ldots,N,l\
eq s\).
## 2 Materials and Methods
### Measurements and Calculations  
The samples extracted at different times were tested after they were filtered through 0.45 \(\upmu\)m pore diameter syringe filters. The phosphate concentration was measured using the ammonium molybdate spectrophotometric method (photoLab(r) 7600 UV, WTW, Munich, Germany) [30]. The pH was tested using a pH meter (pH 7110, WTW, Munich, Germany). The voltage across the resistor was monitored at 1 min intervals using a data acquisition system (PISO-813, ICPDAS Co., Ltd, Taiwan, China). The current density (I) was calculated based on [PERSON]'s law (I = U/(RA)), and the power density (P) was calculated as P = IU/A, where U is the voltage (mV), R is the external resistor (\(\Omega\)), and A is the surface area of the cathode. Polarization curves and electrode potential curves were obtained by recording the voltages under various external resistances (5-1000 \(\Omega\)). A saturated calomel electrode (SCE, 0.242 V vs. a standard hydrogen electrode) was inserted into the reactor chamber as the reference electrode to test the electrode potential. The theoretical dissolved aluminum ion  
Figure 1: Schematic diagram (**a**) and photo (**b**) of the aluminum–air fuel cell for electrocoagulation.  
concentration was calculated, and the number of aluminum ions was calculated according to [PERSON]'s law. The equation for [PERSON]'s law is as follows:  
\[\text{m}=\frac{\text{I}\times\text{A}\times\text{t}\times\text{M}}{\text{Z} \times\text{F}} \tag{1}\]  
Here, m is the theoretical dissolved mass of aluminum ions (g), I is the current density (A/cm\({}^{2}\)), t is the operation time (s), A is the surface area of the cathode (40 cm\({}^{2}\)), M is the molecular weight of the aluminum material (M = 26.98 g/mol), Z is the number of electrons released from the aluminum anode (the Z of aluminum is 3 eq/mol), and F is [PERSON]'s constant (96,485 C/mol). The volume of the electrolyte solution used for the aluminum ion concentration calculation was V = 80 cm\({}^{3}\). The P-precipitants obtained from the 10 mmol/L NaCl solution were collected and vacuum-dried at 40 \({}^{\circ}\)C for chemical phase analysis. The precipitates were tested by X-ray diffraction (D8 ADVANCE, Bruker, Karlsruhe, Germany) under operating conditions of 100 mA and 40 kV in the range of 20 = 10-90\({}^{\circ}\). The aluminum anode and precipitates were analyzed using scanning electron microscopy (Carl Zeiss AG, Sigma500, Oberkochen, Germany) and energy-dispersive spectroscopy.
## 4 Conclusions  
The results of the experiment demonstrated the potential of O-MWCNT\({}_{\text{S}}\) for the absorptive removal of Pb(II)/Cd(II) from water environments. The major conclusions from this study are summarized as follows:  
Figure 7: XPS fine spectra of O-MWCNTs\({}_{\text{S}}\) before and after the adsorption of metal ions Pb(II) and Cd(II). (**a**–**c**) The C 1s spectra of O-MWCNT\({}_{\text{S}}\); O-MWCNTs’ adsorption of Pb(II) and Cd(II); (**d**–**f**) the O 1s spectra of the three scenarios; (**g**) the Pb 4f spectrum of O-MWCNTs’ adsorption of Pb(II); (**h**) the Cd 3d spectrum of O-MWCNTs’ adsorption of Cd(II).  
* Batch kinetic experiments indicated that O-MWCNT\({}_{\rm S}\) can effectively absorb metal ions Pb(II)/Cd(II) in water. The batch kinetic tests showed that a pH of 6 was most favorable for the absorption of Pb(II)/Cd(II). When the pH was increased to 6, the removal rate of Pb(II) and Cd(II) was increased to 90.15 and 31.47%, respectively. The removal rate of 76.34% decreased to 55.59% for Pb(II) and from 29.83% to 16.68% for Cd(II), when the starting concentration of Pb(II)/Cd(II) ranged from 5 to 15 mg\(\cdot\)g\({}^{-1}\). The removal rate in the competitive tests was about 60.46% with Pb(II) and 9.70% with Cd(II), and the tests proved that Pb(II) is more easily adsorbed on the surface of the O-MWCNT\({}_{\rm S}\). The Langmuir model was better at describing the absorptive data for both ions. And the Q\({}_{\rm m}\) of Pb(II) was 5.73 mg\(\cdot\)g\({}^{-1}\), while that of Cd(II) was 3.34 mg\(\cdot\)g\({}^{-1}\) in the single-ion system; the Q\({}_{\rm m}\) was 7.11 mg\(\cdot\)g\({}^{-1}\) with Pb(II) and 0.78 mg\(\cdot\)g\({}^{-1}\) with Cd(II) in the competitive system.
* Based on XPS analyses, it can be summarized that the absorbed lead/cadmium species on the surface of the O-MWCNT\({}_{\rm S}\) was (-COO)\({}_{2}\)Pb and (-COO)Pb(-O)/(-COO)\({}_{2}\)Cd and (-COO)Cd(-O).
* Thermodynamic tests indicated that the activating energy was 83.68 kJ\(\cdot\)mol\({}^{-1}\) for Pb(II) and 172.88 kJ\(\cdot\)mol\({}^{-1}\) for Cd(II). The adsorption of Pb(II) on the surface of O-MWCNT\({}_{\rm S}\) was more convenient than that of Cd(II).  
Conceptualization, X.L. and H.L.; methodology, X.L. and H.L.; validation, X.L., Y.C. and W.D.; formal analysis, X.L. and W.C.; investigation, H.L. and L.H.; resources, H.L.; writing--original draft preparation, X.L. and H.L.; writing--review and editing, X.L. and H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China: 41807130.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
# Object/Scene Recognition Based on a Directional Pixel Voting Descriptor
## 1 Introduction
### The Scene Recognition Problem  
Scene recognition is a key issue in several computer vision applications such as robotics and autonomous driving. In recent years, deep learning-based approaches have demonstrated high performance for scene recognition. In particular, convolutional Neural Networks (CNNs) demonstrated remarkable capabilities in learning representations of scenes directly from raw pixel data, enabling accurate scene categorization across diverse datasets and scenarios [15; 16; 17; 18].  
Unlike object recognition, which focuses on identifying individual objects within images, scene recognition involves understanding and categorizing entire scenes or environments depicted in images or video frames. This task presents its own set of challenges, primarily regarding the interaction on numerous objects, textures, structures, and spatial arrangements within a scene, see Figure 2.  
Scene recognition algorithms must consider a wide range of factors, including variations in scene composition, lighting conditions, viewpoints, occlusions, and clutter. In addition, scenes often exhibit semantic context and spatial relationships between objects, further complicating the recognition process. Overcoming these challenges involves the development of complex computational models capable of extracting robust visual features  
Figure 1: The object recognition problem: features (such as blobs and corners) are extracted and matched from a template to a query. Then, depending on the percentage of “successfully” matched features, the object can be recognized within the scene. By interpolating the matched features, it is possible to determine the corresponding bounding box.  
from scene representations and effectively capturing the contextual information suitable for accurate scene categorization.  
In the current literature, scene recognition approaches consist of techniques such as bag-of-visual-words, spatial pyramid matching, and deep learning architectures like Convolutional Neural Networks (CNNs). These methods aim to encode both local and global spatial information, enabling robust recognition across diverse scene categories. However, achieving reliable scene recognition performance is not possible, with opportunities for innovation in feature representation, model design, and training strategies [19; 20; 21; 22].
## 3 Results  
### Yield Components and Physicochemical Composition of Marastina Musts and Wines  
Yields components and basic physiochemical composition were determined for Marastina must and wine subjected to defoliation at preflowering (PF), after berry set (BS), or at veraison (V); cluster thinning (CT); and an untreated control (C) (Table 1). Significant differences in the number of clusters per vine were determined between PF and CT. Significant differences in yield were determined in untreated C (1.7 \(\pm\) 0.8) kg and BS (1.2 \(\pm\) 0.5) kg, with other treatments in-between. The highest cluster weight and number of berries per cluster was in CT, followed by untreated C and V defoliation. BS caused the greatest reduction in yield per vine and cluster architecture parameters. Defoliation significantly reduced the total soluble solids in must, with a maximum reduction in BS (18.7 \(\pm\) 0.1) \({}^{\circ}\)Brix compared to CT (19.6 \(\pm\) 0.2) \({}^{\circ}\)Brix or the untreated C (19.5 \(\pm\) 0.2) \({}^{\circ}\)Brix. BS significantly reduced total acidity (4.3 \(\pm\) 0.1) g L\({}^{-1}\), followed by treatments performed at veraison (CT and V). The highest pH (3.74 \(\pm\) 0.01) was in treatments conducted at BS and at veraison, which were significantly higher than in PF and C. As expected, defoliation significantly reduced the alcohol content of the wines, regardless of the timing of treatment. The greatest alcohol reduction, determined as alcoholic strength by volume, of 0.8 vol % was in PF wines, while postponing the treatment reduced differences between those and untreated C or CT wines. In wine, the lowest pH was in early defoliation treatments (PF and BS), followed by C and CT.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **PF** & **BS** & **V** & **CT** & **C** \\ \hline Yield components & 8.9 \(\pm\) 3.2 a & 7.5 \(\pm\) 2.9 ab & 7.5 \(\pm\) 2.4 ab & 5.9 \(\pm\) 1.7 b & 7.7 \(\pm\) 2.5 ab \\ Yield/vine (kg) & 1.3 \(\pm\) 0.8 ab & 1.2 \(\pm\) 0.5 b & 1.4 \(\pm\) 0.6 ab & 1.4 \(\pm\) 0.7 ab & 1.7 \(\pm\) 0.8 a \\ Cluster weight (g) & 165.6 \(\pm\) 99.2 b & 161.4 \(\pm\) 54.2 b & 183.0 \(\pm\) 72.0 ab & 229.1 \(\pm\) 84.2 a & 222.5 \(\pm\) 57.3 a \\ Berries per cluster & 225 \(\pm\) 50 a & 192 \(\pm\) 50 a & 224 \(\pm\) 53 a & 253 \(\pm\) 44 a & 227 \(\pm\) 79 a \\ \hline \hline \end{tabular}
\end{table}
Table 1: Yield components and basic physiochemical composition of Marastina musts and wines subjected to defoliation and cluster-thinning treatments.