esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Computational Results
### Lower Bounds for Datasets B and X
#### 6.5.2 Dual Bounds Decomposing Scenario by Scenario
This is the case for most difficult instances B7, B8, and B9 as highlighted in Table 5. This situation seems paradoxical, and we know that \(LB(5)\leqslant LB\) with Proposition 1. Figure 2 illustrates these situations. For the easier instance B6, \(LB\) lower bounds are always better than \(LB(5)\) lower bounds. This situation will continue until the curves converge to the optimal values of \(LB\) and \(LB(5)\). For the more difficult instance B7, the \(LB(5)\) lower bounds are always better than \(LB\) lower bounds, which seems paradoxical as in the convergence state we have \(LB(5)\leqslant LB\) with Proposition 1. Considering reasonable computational times for our application, it is more efficient to use \(LB(5)\) formulation to compute partial lower bounds or the most difficult instances. For many instances, \(LB(5)\) dual heuristics are better than \(LB\) with the LP+cuts stopping criterion, as shown in Table 10. Having smaller MILPs with \(LB(5)\), more efficient cuts are generated, and it is decisive for the dual bound quality with LP+cuts computations. Good primal solutions generally plan four or five outages for each T2 unit. Hence, a 6 th outage induces more costs as T2 production is cheaper than T1 production. It explains that \(LB(5)\) dual bounds are good approximations, and the major approximation is due to the approximation of the penalization costs fore the remaining fuel. The possibility to have a 6 th outage induces more binaries and continuous variables, which is highly penalizing for the cutting planes generation and MILP solving capacities of \(LB\) dual bounds. For instances B10, X12, and X14, the LP+cuts dual bounds are better with \(LB(5)\) and allowing branching allows for having better dual bounds with \(LB\) relaxation. This illustrates the concept of dual heuristics: relaxations are parametrized to lead to few degradation quality of the dual bounds, but improving significantly the solving capacities. Our first motivation was to have dual bounds for larger sizes of instances than using only exact formulations, and to have a better scalability in computation times with the parametric surrogate relaxations. The notable point here is that dual heuristics \(LB(5)\) may also improve the dual bounds \(LB\), with approximation helping the MILP solvers to focus on a specific crucial part of the problem and relaxing a part of constraints that has little impact on the value of the objective function. This approach is similar to primal heuristics when some constraints have little influence on design solutions of good quality. In such applications, the expert knowledge of the problem and numerical properties are crucial to identify the most important constraints, variables, and difficulties of the problem. Lower bounds LB and LB(5) for an MILP sub-problem of instance B6  
Lower bounds LB and LB(5) for an MILP sub-problem of instance B7
## 2 Gas rarefaction Models  
In previous studies, several mathematical models describing rarefied gas flow in HDDs or MEMS applications were established. The first-order slip model of [PERSON], the second-order slip model of [PERSON], the FK model of [PERSON] and [PERSON] and the Boltzmann model are widely used in ultra-thin gas film lubrication. For small clearance spacing of the gas film, the Poiseuille flow rate correctors can be mainly expressed as follows:  
The first-order velocity slip model [15]:  
\[Q_{1}=1+6K_{n} \tag{1}\]  
The second-order velocity slip model [16]:  
\[Q_{2}=1+6K_{n}+6{K_{n}}^{2} \tag{2}\]  
The modified Boltzmann model [31]:  
\[Q_{B}=1+0.10842K_{n}+9.3593/{K_{n}}^{-1.17468} \tag{3}\]  
The Fukui-Kaneko (FK) model [18]:  
\[\left\{\begin{array}{ll}Q_{p}\ =\ \frac{D}{6}+1.0162+\frac{1.0653}{D}-\frac{2.13 54}{D^{2}}(5\leq D)\\ Q_{p}\ =\ 0.13852D+1.25087+\frac{0.15653}{D}-\frac{0.00969}{D^{2}}(0.15\leq D<5)\\ Q_{p}\ =\ -2.22919D+2.10673+\frac{0.01653}{D}-\frac{0.0000694}{D^{2}}(0.01\leq D<0.15 )\\ Q\ =\ \frac{Q_{p}}{Q_{con}}\\ Q_{con}\ =\ \frac{D}{6}\end{array}\right. \tag{4}\]  
where \(D\ =\ \frac{\sqrt{n}}{2K_{n}}\) is the inverse Knudsen number, \(K_{n}=\lambda_{0}/L\) is the Knudsen number, and \(\lambda_{0}\) is the mean free path of gas molecules, which takes the value of 65 nm. \(L\) is the characteristic length scale which is often replaced by gas-film thickness in the microbearing. The Poiseuille flow rate ratio \(Q\), which is defined as the ratio of rarefied gas flow Poiseuille flow rate to continuum flow Poiseuille flow. For the above modified models that are used to predict the Poiseuille flow rate, the Boltzmann model and FK model, which are derived from the relationship between distribution function of gas molecules and the change rate of spatial location and time, are regarded as the accurate model for simulating ultra-thin film gas lubrication problems in engineering practice.
Information_**2015**, \(6\), 399-410; doi:10.3390/info6030399  
_information_  
**ISSN 2078-2489**  
www.mdpi.com/journal/information  
_Article_  
**Multi-Criteria Vertical Handover Comparison between Wimax and Wifi**  
**[PERSON] \({}^{1,*}\) and [PERSON] \({}^{1,2}\)**  
\({}^{1}\) GSCM-LRIT Associated Unit to CNRST - URAC n'29, Faculty of Science,  
Mohammed V University, Rabat 10 000, Morocco; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) High School of Technology, Mohammed V University, Sale 11 000, Morocco  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +212-65-3254865.  
Academic Editor: [PERSON]  
_Received: 8 June 2015 / Accepted: 14 July 2015 / Published: 21 July 2015_  
**Abstract:** In next generation wireless networks, the most tempting feature is the ability of the user to move smoothly over different access networks regardless of the network access technology. In this paper we study the benefit of Multiple Attribute Decision Making (MADM) strategies for network selection. We compare three of these methods naming Simple Additive Weighting (SAW), Multiplicative Exponential Weighting (MEW) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) in a realtime ns-3 simulation. Analytic Hierarchy Process (AHP) provides the weights of attributes which allow the comparison in different types of applications. Therefore, we propose a performance evaluation model with a reconfiguration of AHP parameters used in the literature. Simulation results show that the proposed parameters provide an improvement of Delay and offer allowable Packet loss in different types of applications.  
**Keywords:** 4G; Multiple Attribute Decision Making (MADM); Analytic Hierarchy Process (AHP); heterogeneous network; network selection; vertical handover; WIMAX; WLAN; NS-3
# Differential Ultra-Wideband Microwave Imaging: Principle Application Challenges
## 3 Device Requirements for Differential Imaging
### Random Effects
(**B**) maximum input signal; sample taken from flat signal part. (**C**) maximum input signal, sample taken from steepest signal part. Hence, the probability that a voltage sample captured at time position \(i\Delta t\) hits the threshold is as follows:  
\[P(i\Delta t)=\int\limits_{V_{TH}}^{\infty}p_{b(i\Delta t)}(V)\,\mathrm{d}V\,. \tag{43}\]  
Further, the probability that the threshold is passed the first time at sample position \(k\Delta t\) is (probability of first success) as follows:  
\[P_{H}(k\Delta t)=P(k\Delta t)\prod_{i=0}^{k-1}(1-P(i\Delta t));k>i\,. \tag{44}\]  
Such a probability distribution is illustrated in Figure 16, indicating that the most probable time point for threshold crossing is located before the correct value. In order to minimize this bias error, the noise at the signal edge should be weak and the density of the sampling points should not be much larger than required by the Nyquist sampling criteria. However, the determination of the correct position of the threshold crossing then needs a suitable interpolation between the voltage samples laying on either side of the threshold.
## 3 Design of MCSS  
To support the cluster-tree topology and various types of traffic in WPT-enabled WSN, the MCSS defines multiple TSCH concurrent slotframes, allocates power cells and data cells, and determines the transmission priorities. In the following subsections, we describe the operations of MCSS in detail.
## 3 Leach  
LEACH is a hierarchical routing protocol based on clusters in WSNs and selects a CH among sensor nodes. Then, the CH collects the data from the neighboring sensor node and transmits it to the BS. LEACH has a set-up phase and a steady-state phase. Figure 4 shows the process of the LEACH protocol.  
The two phases constitute one round. The rounds have a constant time interval. After a set time, a new round begins. The BS sends the start message for a new round, and the sensor node executes the set-up phase and the steady-state phase [37; 38].  
Step 1. Set-up phase  
In the set-up phase, CHs are selected among the sensor nodes, and the CHs configure the cluster. The CH is selected according to the probability of the sensor nodes. Each sensor node generates an arbitrary number. Then, an arbitrary number is compared with \(T(n)\) in Equation (4).  
\[T(n)=\begin{cases}\frac{P}{1-\left(r\mod\frac{P}{P}\right)}&\text{if }n\in G\\ 0&\text{otherwise}\end{cases} \tag{4}\]  
Here, \(G\) is a set of sensor nodes that are not selected as the CH. \(P\) is the probability that the sensor node will be a CH, and \(r\) is the current round. If any number of sensor nodes n is smaller than the value of \(T(n)\) and included in \(G\), the sensor node n becomes the CH. The set \(G\) initializes at regular intervals. The CH transmits an advertisement message (ADV) to the surrounding sensor nodes indicating that it is a CH. The surrounding sensor node receiving the ADV compares the signal strength of the ADV. The sensor node sends a join request message (Join-REQ) to the CH with the highest ADV signal strength. The CH receives the Join-REQ from surrounding sensor nodes and assigns the time slot to the sensor nodes that have sent Join-REQ. The CH next sends TDMA schedules to the surrounding nodes. The sensor nodes become member nodes of the CH, and the CH then completes the cluster configuration.  
Step 2. Steady-state phase  
The steady-state phase is the data transmission stage. Figure 5 shows the steady-state phase. All member nodes in the cluster transmit data to the CH according to the TDMA schedule. The CH merges and compresses the sensor node data and transmits them to the BS.  
Figure 4: LEACH protocol process.  
Multi-hop LEACH (MH-LEACH) is a routing protocol that uses multi-hop transmission [18; 37]. Figure 6 shows the steady-state phase of MH-LEACH.  
MH-LEACH increases the transmission energy efficiency of the CH. In LEACH, if the CH is located far from the BS, the CH uses a large mount of transmission energy. MH-LEACH constructs an optimal path by selecting multi-hop between CH and BS. The MH-LEACH designates an upper and lower CH according to the distance from the BS position. The lower CH transmits the data to the upper CH, and the uppermost CH transmits the data to the BS. MH-LEACH can configure wide-area networks by enabling transmission of data between CHs. In this study, we found a method that can reduce unnecessary energy consumption caused by reverse transmission by improving the LEACH protocol.
## 1 Introduction
Device states are described by feature vectors (at points in time) or feature matrices (time-varying feature vectors) in edge computing and include all relevant features (states, parameters) of both normal operation and impending wear and tear, failure, and attack, for example. GenAI also enables anomaly detection by learning the normal behavior of the system and generating deviations that signal potential failures before they become critical failures. GenAI-powered DTs can run multiple failure simulations to predict the impact of different faults, allowing industries to proactively optimize maintenance strategies. One of the major issues they address is the challenge of incomplete or noisy data, as GenAI can fill in missing information and remove signal noise to increase diagnostic accuracy. The integration also mitigates the high costs and risks associated with physical fault testing by creating realistic virtual failure scenarios for analysis. Another problem it addresses is the inability of traditional AI models to generalize across machines and environments, as generative models can adapt to changes in operating conditions. By continuously updating DTs with new synthetic and live data, the system remains dynamic and responsive to evolving industrial processes. This approach enhances predictive maintenance by reducing false positives and false negatives, ensuring that maintenance actions are taken only when necessary. In this way, generative AI empowers DTs, transforming them from static models into self-learning, adaptive systems that provide more accurate and timely fault diagnosis in Industry 4.0/5.0 [33]. This paper presents the current state of the art and prospects for using generative AI-based DTs to diagnose faults for predictive maintenance in Industry 4.0/5.0.
## 3 Methodology
### Generation of Synthetic Vp Log  
Figure 5 describes the procedure to generate synthetic Vp in this study. The first step is data pre-processing. Well-logging data are acquired from a target field, to determine and analyze the logging interval; subsequently, the well-logging data were normalized using a robust scaler (Figure 5a). The second step is selecting the input logging variables (Figure 5b). Input selection is followed by the third step--designing the structure of the deep-learning-based predictive model (e.g., the number of hidden layers, activation function, and dropout rate) (Figure 5c). We then performed a hyperparameter sensitivity analysis to refine the predictive model and confirm the generality (Figure 5d). The process from Figure 5b to 5d was repeated for all available combinations of the input variables to search for the most efficient model. The efficiency is assessed in terms of two performance indicators: \(R^{2}\) and root mean square error (RMSE) (Figure 5e):  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_{i}- \overline{y})^{2}}\, \tag{9}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\ \sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}, \tag{10}\]  
where \(n\) is the number of data points, \(y_{i}\) is the reference value, \(\hat{y}_{i}\) is the predicted value, and \(\overline{y}\) is the mean value. Finally, k-fold cross-validation was conducted to verify model consistency (Figure 5f).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Wellbore Name**} & \multicolumn{5}{c}{**Input Variable**} \\ \cline{2-6}  & **GR** & **RT** & **RHOB** & **TNPH** & **PEF** \\ \hline UBGH2-1\_1 & 0.00004 & 0.00023 & 0.27144 & 0.21068 & 0.14138 \\ UBGH2-1\_2 & 0.15054 & 0.33640 & 0.01392 & 0.02958 & 0.10049 \\ UBGH2-2\_1 & 0.47748 & 0.02856 & 0.25503 & 0.52418 & 0.00063 \\ UBGH2-2\_2 & 0.11022 & 0.07453 & 0.44090 & 0.47472 & 0.06503 \\ UBGH2-3 & 0.25000 & 0.36120 & 0.32149 & 0.29594 & 0.26214 \\ UBGH2-4 & 0.02789 & 0.01000 & 0.37577 & 0.32604 & 0.05856 \\ UBGH2-5 & 0.00518 & 0.41474 & 0.66912 & 0.64160 & 0.27040 \\ UBGH2-6 & 0.17140 & 0.08352 & 0.31584 & 0.29052 & 0.01103 \\ UBGH2-7 & 0.24404 & 0.33989 & 0.30250 & 0.27458 & 0.22090 \\ UBGH2-8 & 0.00012 & 0.19536 & 0.55801 & 0.41732 & 0.31923 \\ UBGH2-9 & 0.02690 & 0.16241 & 0.18490 & 0.22468 & 0.01210 \\ UBGH2-10 & 0.06812 & 0.10758 & 0.06350 & 0.12110 & 0.00130 \\ UBGH2-11 & 0.05063 & 0.22753 & 0.15524 & 0.12180 & 0.04040 \\ \hline Average & 0.12173 & 0.18015 & 0.30213 & 0.30406 & 0.11566 \\ Minimum & 0.00004 & 0.00023 & 0.01392 & 0.02958 & 0.00063 \\ Maximum & 0.47748 & 0.41474 & 0.66912 & 0.64160 & 0.31923 \\ Standard Deviation & 0.13854 & 0.14451 & 0.18346 & 0.17392 & 0.11485 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Coefficient of determination between the input variable and Vp in each wellbore.
## 3 Results  
In this section, the influences of the sampling interval and the a priori dynamic models on the accuracies of integrating the STPs of satellite will be analyzed according to the accuracy requirement of 0.01 m for the STPs, which is sufficient for the POD of LEOs, such as GOCE [37]. Moreover, the RD_STP method will be applied to process the real GPS observations of GOCE, and the POD results will be discussed in detail.
## 4 Experimental Results and Discussion
### Model Testing
#### 4.4.1 Comparison of the mAPs of YOLOv7 and Mask R-CNN in Each Model
This shows that the appropriate addition of noise and interference in the training set as well as the use of Canny edge detection increased not only the number of images in the training set but also their variety and completeness, thereby increasing the mAP. Furthermore, a detailed comparison of the mAPs of YOLOv7 and Mask R-CNN in M3 and M4 revealed that  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & mAP of YOLOv7 in M3: 79.30\% & mAP of YOLOv7 in M4: 85.80\% \\ \hline
**Category** & & \\
**Class B** & & \\
**Class A** & & \\
**Class B** & & \\  
based on the mAP of YOLOv7, M4 outperformed M3 by six-and-a-half percentage points, and based on the mAP of Mask R-CNN, M4 outperformed M3 by two percentage points. This implies that the addition of Canny edge detection to YOLOv7 resulted in greater benefits than Mask R-CNN. As YOLOv7 extracts objects through framing, and Mask R-CNN extracts objects by outlining their contours, the addition of Canny edge detection for contour feature enhancement complemented YOLOv7's functions more effectively. Therefore, the increase in mAP using YOLOv7 was greater (even though the new mAP of YOLOv7 was not higher than that of Mask R-CNN). Interestingly, when comparing the APs of M3 and M4 with respect to Class A and Class B, M4 outperformed M3 by five percentage points on the basis of Class A and by eight percentage points on the basis of Class B. Consequently, the addition of Canny edge detection was significantly beneficial to Class B in comparison to Class A. Because rockfalls, potholes, and car crashes may occur at more random locations (unlike speed limit signs, which are typically located on both sides of the road or above it), they are more likely to be partially obscured by other objects or to become poorly illuminated compared to speed limit signs. Therefore, the original contour features of rockfalls, potholes, and car crashes may be less prominent or incomplete than those of speed limit signs. It is important to note that because Canny edge detection emphasizes the enhancement of object contours, in terms of the increase in AP, the addition of Canny edge detection is relatively more conducive to the detection of rockfalls, potholes, and car crashes than the detection of speed limit signs (even though the new AP of rockfalls, potholes, and car crashes was not higher than that of speed limit signs).
# Transmission Lines Insulator State Detection Method Based on Deep Learning
## 2 Materials and Methods
### BRA-CBS: Fused Attention Convolution Module
Therefore, we initially gather the key and value tensors as shown in Equation (4):  
\[K^{g}=\text{gather}(K,~{}I^{r}),~{}V^{g}=\text{gather}(V,~{}I^{r}) \tag{4}\]  
where \(K^{g},~{}V^{g}\in\mathbb{R}^{S^{2}\times\frac{HW}{S^{2}}\times C}\) are the gathered key and value tensors. Subsequently, we can employ attention mechanisms on the gathered key-value pairs as shown in Equation (5):  
\[O=\text{Attention}(Q,~{}K^{g},~{}V^{g}) \tag{5}\]  
Compared to the original CBS module, the BRA-CBS module relies on its bi-level routing attention mechanism to retain more detailed features of small targets. It focuses more on target areas during feature fusion, reducing the overall method's false detection rate.
# Improving Symbolic System-Level Synthesis by Solver Coordination and Domain-Specific Heuristics
## 2 Coordinated SMT-Based Synthesis Approach: Motivation & Detailed Overview
### Coordinated SMT-Based System Synthesis  
In the work at hand, we realize the coordination between logic solver and background theory solver by introducing a _scheduling-aware binding and routing refinement_ (highlighted green in Figure 2) in the logic solver. With the scheduling-aware binding and routing refinement, the logic solver is assigned an additional fixed amount of time to solely perform an lexicographical optimization of an initially derived binding and routing solution. Here, the optimization includes load balancing and a minimization of both the number of sent messages by computational tasks and the overall number of hops of messages in the routing. In contrast to the initial solution, that can be compared to the binding and routing solutions computed in the _classical_ approach, the \(\mathcal{T}\)-solver is expected to decide feasibility and perform a conflict analysis on the refined solutions within a reasonable amount of time, as shown in the timeline in the middle of Figure 3 (orange and red boxes). This comes with the cost that now more time is spent in the logic solver during synthesis. On the one hand, this is due to the additional time assigned for the refinement of an initial binding and routing solution (green boxes in Figure 3). On the other hand, additional rules are necessary to realize the refinement's objectives which results in larger instances for the logic solver. For the hardest system specifications we consider in this paper (see Section 8), this results in an increase of up to 40% in the number of variables and up to 35% in the number of constraints in the logic solver. Thus we observe a larger blue box for the _coordinated_  
Figure 3: Illustration of the disadvantages of the _classical_ synthesis approach along with the advantages of the _coordinated_ and _coordinated_+_DSH_ approach. The _classical_ approach spends a considerable amount of time within the background theory until reaching a timeout. The _coordinated_ synthesis approach significantly increases the applicability of symbolic system synthesis by introducing an additional scheduling-aware binding and routing refinement. The _coordinated_+_DSH_ approach, which includes domain-specific heuristics (DSH) in the logic solver, furthermore increases the scalability of the _coordinated_ approach and reduces the average synthesis time, too.  
approach in Figure 3. Nevertheless, despite the additional effort for the logic solver (blue and green box in Figure 3), the _coordinated_ SMT-based synthesis significantly increases the applicability of SMT-based system synthesis as we will show in the experimental section.
# TACRP: Traffic-Aware Clustering-Based Routing Protocol for Vehicular Ad-Hoc Networks
## 4 Traffic-Aware Clustering-Based Routing Protocol (TACRP)
### Updating the CH  
During the process of updating CH, each CM vehicle measures and transmits its weight value to the corresponding CH. If any of the CM nodes maintain a weight value which is higher than the present CH then, automatically, the CM with the maximum weight value is chosen as the new CH. The calculation of cluster updating weight value is measured using parameters such as buffer size of the CM, speed, and node degree [32]. The mathematical expression for the calculation of this weight value is shown below:  
\[CH_{weight}=\alpha*\left[\frac{B_{fs}}{B_{initial}}\right]+\beta*\left[\frac{D }{D_{max}}\right]+\gamma*\left[\frac{N}{N_{max}}\right] \tag{6}\]  
where, \(B_{fs}\) indicates the vehicle buffer in free space value, \(B_{initial}\) indicates the vehicle buffer initial value, D and \(D_{max}\) are the distance of the vehicles and maximum distance the cluster can cover, \(N\) and \(N_{max}\) indicates the vehicle neighbor node and maximum vehicles present in the coverage area, \(\alpha,\ \beta\) and \(\gamma\) are the weight factors where \(\alpha+\beta+\gamma=1\). The mathematical expression used for the calculation of \(N_{max}\) is shown below:  
\[N_{max}=\frac{2T_{range}}{V_{l}}*n_{l} \tag{7}\]  
where, \(T_{range}\) indicates the vehicle transmission range, \(V_{l}\) indicates the vehicle length and \(n_{l}\) indicates the number of lanes.