esa-sceva/satcom-chunk-collection huggingface dataset:
# Combined Access Barring Scheme for IoT Devices Using Bayesian Estimation
## 3 The Proposed Technique: Combined Access Barring
### Analysis  
If the class of an IoT device is unbarred then it generates a random number and transmits if it is less than \(p_{t}\). From here on, we will omit \(t\) from subscript of \(p_{t}\) for notational simplicity. Let us denote the number of backlogged IoT devices within the system by \(u\) and the number of devices, that have generated random number less than the transmission probability, by \(v\), then we can write,  
\[P\left(v|u\right)=\binom{u}{v}p^{v}\left(1-p\right)^{u-v} \tag{1}\]  
where the probability \(P(v|u)\) denote the probability that \(v\) IoT devices among \(u\) have qualified for transmission. A success happens on a certain preamble if it is chosen by only one of the \(v\) IoT devices. We can write the probability of success as follows:  
\[P\left(S_{m}\right)=\sum_{u=0}^{\infty}\sum_{v=0}^{u}P\left(S_{m}|v\right)P \left(v|u\right) \tag{2}\]  
where \(P(S_{m}|v)\) denotes the probability that there is a success on preamble \(m\), when \(v\) devices are allowed to transmit, and can be written as:  
\[P\left(S_{m}|v\right)=\binom{v}{1}\frac{1}{R}\left(1-\frac{1}{R}\right)^{v-1} \tag{3}\]  
where \(R\) denotes the number of random access preambles. Now putting (1) and (3) in (2) and then solving, we get:  
\[P\left(S_{m}\right)=\frac{up}{R}\left(1-\frac{p}{R}\right)^{u-1} \tag{4}\]  
It is important to mention that on a single random access preamble, only one IoT device can get success, so the expected number of devices that get success on preamble \(m\) can be written as:  
\[E[S_{m}]=1\cdot P(S_{m})+0\cdot\left(1-P(S_{m})\right)=\frac{up}{R}\left(1- \frac{p}{R}\right)^{u-1} \tag{5}\]  
Then the average number of IoT devices over all \(R\) RA preambles can be written as:  
\[E[S]=up\left(1-\frac{p}{R}\right)^{u-1} \tag{6}\]  
To calculate the optimal transmission probability \(p\) that maximizes the success probability and \(E[S]\), we need to solve the following optimization problem:  
\[\max_{p_{opt}}\left[up\left(1-\frac{p}{R}\right)^{u-1}\right] \tag{7}\]where \(p\) is the optimal transmission probability that maximizes (7) which can be solved by taking derivative and equating it to zero. Then we can have the following relation for optimal transmission probability:  
\[p=\min\left(1,\frac{R}{u}\right) \tag{8}\]  
The whole procedure is shown in Algorithm 1 where it is assumed that the number of backlogged devices is known.  
```
1: Extract bitmap from SIB-14, \(p=1\)
2:if the class is unbarred then
3: Generate random number, \(G\in(0,1)\)
4:if\(G<p\)then
5: Choose preamble among \(R\) and transmit
6:endif
7:endif
8:\(p=\min\left(1,\frac{R}{u}\right)\) % To be used for the next time slot
9: Repeat the process from Step 2 in every time slot
```  
**Algorithm 1** Combined Access Barring (CAB) Scheme with Backlog Known
# Near Real-Time Freeze Detection over Agricultural Plots Using Sentinel-1 Data
## 2 Study Sites and Datasets
### Datasets
#### 2.2.2 Land Cover Map  
The behavior of SAR backscatter profiles after a freeze event can vary depending on the soil properties and vegetation types [44; 45]. Therefore, it can be beneficial for the mapping of frozen/unfrozen areas to study the C-band backscatters dependently of the main vegetation types. The graphical parcel registry (_Registre Parcellaire Graphique_; RPG) is a graphical declaration system used by farmers in order to provide a fine and annual geo-localized representation of the occupation of agricultural lands in France. The RPG includes around 6 million small parcels (corresponding to approximately 26 million hectares). For this study, we used the 2018 version of the RPG, henceforth named RPG-18, which contains 28 classes of land cover. The classes corresponding to agricultural activities (summer and winter crops, vines, fruit trees and meadows) were used in this study and grouped into three higher-level classes named: cereal grains, orchards and vineyards, and meadows.
## 2 Materials and Methods
### Scanning of Patients and Moulds  
Sets of 3D scans were obtained for each patient's body and plaster-cast moulds following their appointment at the clinic for a TLSO. Two different investigators performed the scans in a randomised order on the same day, walking around the patient maintaining a distance between 30-60 cm using floor markers as a reference, and ensuring the scanner was perpendicular to the patient's surface. The scans started all at the same point, in front of the patient and moving in the clockwise direction from the patient's point of view. The lighting of the environment was kept uniform to avoid any inaccuracies caused by changes in brightness. The patients were asked to stand with their arms raised and flexed anteriorly away from the body during the scanning process.  
Patient Geometry, Pre-Rectification, and Post-Rectification Mould Scans  
White spherical markers were placed over anatomical landmarks deemed important by the orthobtists involved in the study for the design of a scoliosis brace and based on previous surface topography studies that have used markers to evaluate AIS [29; 30; 31; 32]. The location of the markers used in this study are shown in Figure 1. The markers placed at the left and right angulus inferior scapulae are L-AI and R-AI, respectively. The CLAV marker is located at the manubrium, and the STRN marker is placed at the xiphisternal joint. On the anterior side of the pelvis markers, R-ASIS and L-ASIS correspond to the right and left anterior superior iliac spine, respectively, while R-IC1 and L-IC1 are located at the tubercle of the iliac crest on the right and left side. On the posterior side of the pelvis, R-PSIS and L-PSIS are located at the right and left posterior superior iliac spine. Lastly, R-TRO and L-TRO mark the right and left greater trochanter. Placement of the markers was performed by an experienced orthotist. Manual measurements of anatomical distances were obtained using a vernier calliper with a resolution of 0.02 mm.  
The patients' moulds created using the plaster-casting method were scanned before and after the rectification process. Following the markers placed on the patient's torso,  
Figure 1: (**a**) Anterior and (**b**) posterior view of an AIS patient with spherical markers placed at anatomical landmarks. Magenta markers (triangle) are only placed on the patient’s body, and blue markers (circle) represent markers placed on the patient’s body, as well as the pre- and post-rectification moulds.  
seven spherical markers (blue markers (circle) in Figure 1) were placed at the main anatomical landmarks on the pelvis and sternum on the anterior side (R-ASIS, L-ASIS, and STRN) (Figure 1a) and at the scapulae and pelvis on the posterior side (L-AI, R-AI, L-PSIS, and R-PSIS) (Figure 1b). These seven anatomical landmarks are used as references by the ortho-tist during the plaster rectification process and have also been found in a previous study to be the most common locations for evaluating AIS in surface topography studies [33].
## 1 Introduction
### Contribution and Outline  
The primary contributions of this paper are as follows:  
1. Introduction of Dense-Timed Deontic Interpreted Systems (DTDISs). We extend existing interpreted system models by incorporating both deontic reasoning (norm compliance) and real-time continuous behaviors. This allows verification under both timing and deontic constraints, addressing a major gap in classical models.
2. Translation from EMTLKD to \(\text{ELTLKD}_{\text{q}}\). We introduce a new translation from EMTLKD to \(\text{ELTLKD}_{\text{q}}\) that enables efficient verification of EMTLKD properties using SMT-based BMC. Unlike previous methods that focus separately on epistemic or temporal logic, our approach integrates epistemic, deontic, and real-time reasoning into a unified framework.
3. A specialized SMT-based BMC approach for real-time multi-agent system verification. Our approach extends standard SMT-based BMC techniques to support dense-time semantics and deontic constraints, which enables the verification of norm violations and compliance tracking in real-time multi-agent environments.  
Application to a faulty train controller system. We illustrate our methodology with a faulty timed train controller system (FTTCS), demonstrating how our framework detects safety violations under epistemic, deontic, and timing constraints.  
The remainder of this paper is structured as follows. Section 2 defines dense-timed deontic interpreted systems (DTDISs). Section 3 introduces the EMTLKD logic and its semantics, enabling reasoning about real-time, deontic, and epistemic behaviors. Section 4 presents the translation from EMTLKD to the existential deontic linear temporal logic, \(\mathrm{ELTLKD_{q}}\). Section 5 describes our SMT-based BMC approach and demonstrates its application to the faulty train controller system.
On the contrary, random simultaneous phases and couplings in the two-dimensional polarization space cannot be autocompensated. A similar situation is found for spatial mode subspaces by using two codirectional or collinear modes of MCFs or FMFs, respectively, and by assuming that there is no cross-polarization. Moreover, we find that such phase drift autocompensation is not simple, and not very favorable in terms of the trade-off between perturbation removal and maximal achievable distance. Therefore, as an alternative, we propose a Bell-state-exchange-parity-based protocol (BSEP, in short), which allows for easy, passive full compensation in both the polarization subspace and spatial subspaces (collinear and codirectional modes), assuming that polarization is maintained under spatial encoding. Moreover, the BSEP protocol has measurement-device-independent (MDI) characteristics [19; 20], in line with previous work in the field [21; 22; 23]. Dealing with the issue of particular attacks in the BSEP protocol, we will show a simple attack that can be launched against the protocol and how to tackle it. Regarding other cases, for instance, a phase-remapping attack [24], which affects bidirectional protocols that encode the information via phase modulation, we shall assume that appropriate ad hoc countermeasures can be applied. The outline of the paper is as follows: In Section 2, we recall the BBM92 protocol and describe the problems (errors) associated with optical fiber perturbations. We then continue with the phase drift autocompensation technique. In Section 3, we proceed in a similar fashion but with a novel QKD protocol based on Bell state exchange parity. We describe the protocol's built-in autocompensation technique (general SU(2) perturbations for either polarization or spatial modes), which turns out to be much simpler than for the BBM92 case. In Section 4, we compare both protocols in various scenarios, assessing their performances through a key rate analysis. Finally, the conclusions are presented in Section 5.
## 4 Numerical Simulations
### Simulation II: Doubly Spread Channel Estimation
The high mutual coherence of the measurement matrix also makes the optimization landscape of non-differentiable \(\ell_{1}-\ell_{1}\) objective function more challenging, impacting both the convergence rate and the accuracy of the optimization algorithm. As shown in Figures 6 and 7, the DD functions estimated by the mADMM algorithm appear dispersive even after extensive iterations, although it retains a similar performance in both AWGN and impulsive noise settings. This is also evident in Figure 10, which illustrates the iteration curves of objective functions for both the mADMM and nmADMM algorithms. One can see that the accelerated gradient scheme and non-monotone line search strategy of the nmADMM algorithm facilitate faster convergence and greater precision. The proposed nmADMM algorithm can identify both the locations and amplitudes of the DD channel paths successfully, achieving the lowest BER with minimal iterations across both AWGN and impulsive noise environments. Table 4 presents the comparison results of the BER performance of various algorithms at INR of 15 and 25 dB across different probabilities (\(q\)) of impulsive noise occurrence. The results demonstrate that the nmADMM algorithm achieves the lowest BER, closely approximating the BER derived from the GT channel information across all scenarios.
# Direct-Write Spray Coating of a Full-Duplex Antenna for E-Textile Applications
## 3 Results and Discussion
### Radiation Pattern and Far-Field Results for the Printed Textile-Based Antenna  
The radiation pattern of the antenna was measured in an anechoic chamber (Figure 7a). The measurement chamber system was controlled by a computer and equipped with a closed-circuit television camera to visualize the measurements. According to previous work [23], a diagonally directed surface current can be predicted as a combination of both the surface current of the conductive patch and the orthogonally polarized current of the load strips. This yields diagonally polarized far-field radiation, which differs from a conventional patch antenna. The broadside gain profile is provided in Figure 7b. Clearly, a 9.12 dB maximum gain was measured at the on-resonance frequency, around 2.3 GHz. This result is a significant improvement over the gain (5.5 dB) of typical textile patch antennas that have been previously reported [46]. The E-plane radiation pattern at 2.3 GHz for the proposed textile antenna with the amalgamated structure (two layers of nonwoven fabric and six layers of knit fabric spray coated with BT dielectric ink) is provided in Figure 7c, while the corresponding 3D radiation pattern is shown in Figure 7d. These results indicate that the antenna exhibits a uni-directional radiation performance.  
Figure 6: Simulated and measured S-parameters of the proposed full-duplex textile antenna with the combination structure (two layers of nonwoven fabric and six layers of knit fabric spray coated with BT dielectric ink). Experimental data are represented by solid curves, while simulation data are represented by dotted curves.
## 3 Permeability Test Results and Discussion
### Effect of \(Fer\)  
Figure 6a presents the conditioned sand permeability results for the ZD30 foam under various FERs. With the increase in FER, the permeability of the sand first decreased and then increased, and the permeability was the lowest when FER was 15. In 60 min, the permeability coefficient is less than \(10^{-4}\) cm/s at FERs between 5 and 20. When FER \(>\) 20, the rate of change in permeability increased rapidly. In the experiments, it was also found that FER had a very significant effect on the fluidity  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Standard Sand** & & & **Foam** \\ \hline
**Weight (g)** & **Density (g/cm\({}^{3}\))** & **Volume (mL)** & **FIR (\%)** & **FER** & **Weight (g)** \\ \hline  & & & 25 & 10 & 10.5 \\ \cline{3-5}
750 & 1.78 & 420 & 50 & 10 & 21 \\ \cline{3-5}  & & & 75 & 10 & 31.5 \\ \cline{3-5}  & & & 100 & 10 & 42 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Weight at different foam injection ratios.  
Figure 5: Effect of \(T_{\rm{hl}}\) on conditioned sand permeability. (**a**) Change in conditioned sand permeability coefficient with time under different foam products, and (**b**) the bubble image of ZD10 and ZD30 foam sizes within 5 min.  
of the foam. When FER < 10, the solution did not sufficiently foam to form a wet foam with good fluidity (Figure 6b). As the FER increased to 20, the solution was fully foamed to form dry foam that had a lower liquid content. However, the foam liquid film became thinner (the liquid content of foam decreased) as the FER increased, until the sand particles could not be completely wetted by the liquid. After mixing the foam and sand, most foams began to dehydrate and defoam (Figure 6c), which eventually led to an increase in permeability. Therefore, effect of FER on sand permeability was related to the water content. Given that the sand particles were moist by the water of the foam, the conditioned sand permeability decreased as the FER increased. However, as the FER continues to increase, the decrease in foam volume led to a rapid increase in sand permeability.
## 2 Research Status of Key Technology for Environmental Perception of Intelligent Excavators
### Research Status of Segmentation and Recognition Technology of 3D Operation Scenes
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Literature** & **Keywords** & **Major Contribution** & **Time** \\ \hline \multirow{3}{*}{[77]} & \multirow{3}{*}{Multi-scale edge detection} & A multi-scale edge detection algorithm is proposed by constructing & \multirow{3}{*}{2004} \\  & & quadratic B-spline wavelet, and based on this algorithm, the edge features of & \\  & & intelligent excavator loaded vehicles are detected and recognized. & \\ \hline \multirow{3}{*}{[78]} & Point cloud clustering & In this paper, a method based on point cloud clustering feature histogram is & \multirow{3}{*}{2017} \\  & feature histogram & proposed, which can identify multiple targets such as intelligent excavator & \\  & & loading vehicle and material pile at the same time and has good stability. & \\ \hline \multirow{3}{*}{[79]} & \multirow{3}{*}{Maximum evidence} & A aiming at the intelligent mining excavator under the influence of dust, a & \multirow{3}{*}{2018} \\  & & method of target segmentation recognition based on maximum evidence is & \\  & & proposed, which realizes the segmentation and recognition of operation & \\  & & targets such as buckets and piles. & \\ \hline \multirow{3}{*}{[80]} & \multirow{3}{*}{Buckets and mining cards} & A aiming at the problem of anti-collision between the mine excavator bucket & \multirow{3}{*}{2016} \\  & & and the mine card, the paper divides and identifies the bucket and mine card. & \\ \hline \multirow{3}{*}{[85]} & Random forest classifier, & By using a random forest classifier and conditional random field, & \multirow{3}{*}{2014} \\  & conditional random field & three-dimensional point cloud segmentation is realized by using reflection & \\  & & intensity and curvature. & \\ \hline \multirow{3}{*}{[86]} & Graph cutting method, & The point cloud scene is segmented by graph cutting method, then the & \multirow{3}{*}{2009} \\  & support vector machine & features of target location and distance are extracted, and the target is & \\ \cline{1-1}  & classified and recognized by support vector machine. & \\ \hline \multirow{3}{*}{[87]} & AdaBoost classifier & The spectral and spatial features of point cloud are extracted, and the target & \multirow{3}{*}{2013} \\  & & is identified by AdaBoost classifier. & \\ \hline \multirow{3}{*}{[88]} & \multirow{3}{*}{Scan line algorithm} & The three-dimensional point cloud is segmented by scanning line algorithm, & \multirow{3}{*}{2010} \\  & and the features such as height and normal vector variance are extracted, & \\ \cline{1-1}  & and then the target is classified and recognized by support vector machine. & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Research status of segmentation and recognition technology for 3D scenes.
# A Visual Mining Approach to Improved Multiple-Instance Learning
## 3 Background and Related Concepts
### Instance Prototype Selection
#### 3.2.2 Medoids Instance Selection Strategy  
The Medoids Instance Selection [34; 35] strategy computes the medoid for each bag using the \(k\)-Medoids algorithm. Each medoid will represent a bag, and then the multiple-instance learning task is reduced to a traditional supervised learning task. The k-Medoids algorithm is adapted to cluster the multiple-instance data so as to partition the unlabeled training bags into \(k\) groups. After that, it re-represents each bag by a \(k\)-dimensional feature vector, where the value of the \(i\)th feature is the distance between the bag and the medoids of the \(i\)th group. In other words, the medoid of each bag has the minimum average distance to the other bags in the same group, with all bags represented as \(k\)-dimensional feature vectors in a regular supervised learning approach.  
```
Input:\(B\), SalNum Output: Salient instances (prototypes) \(T\). \(B^{-}=\{B_{rt}|B_{rt}\in B_{r}^{-},r=1,2,...,n^{-}\}\); \(maxDist=0\); // Rough Selection for\(i=0\) to \(n^{+}\)do  Compute \(Sal(B_{i1}^{+})\) for each instance in \(B_{i}^{+}\) ;// (see Equation (1))  Re-sort all instances in \(B_{i}^{+}\) in descending order of salience.;  Compute \(D(B_{i1}^{+},B^{-})\) and \(D(B_{im}^{+},B^{-})\); // (see Equation (3)) if\(D(B_{i1}^{+},B^{-})>D(B_{im}^{+},B^{-})\) and \(D(B_{i1}^{+},B^{-})>maxDist\); then \(maxDist=D(B_{i1}^{+},B^{-})\) ; \(optPosInst=B_{i1}^{-}\); else if\(D(B_{im}^{+},B^{-})>D(B_{i1}^{+},B^{-})\) and \(D(B_{im}^{+},B^{-})>maxDist\)then \(maxDist=D(B_{im}^{+},B^{-})\) ; \(optPosInst=B_{im}^{+}\);  end if  end for // Fine Selection \(optNegInst=\operatorname*{arg\,max}_{j\in B^{-}}d(j,optPosInst)\); for\(i=1\) to \(n^{+}\)do if\(d(B_{i1}^{+},optNegInst)>d(B_{im}^{+},optNegInst)\)then \(T=T\cup B_{i1}^{+},...,B_{ISalNum}^{+}\); // Set of salient instances else \(T=T\cup B_{i(m-SalNum+1)}^{+},...,B_{im}^{+}\) ;  end if  end for Return \(T\);
```  
**Algorithm 1**MILSIS: Salience Instance Selection.
## 6 Security Analysis
### Formal Security Analysis through AVISPA
#### 6.2.2 Results of Verification  
The verification results using models OFMC and CL-AtSe are shown in Figure 10. Two simulations are able to check whether the protocol withstands man-in-the-middle and replay attacks. The CL-AtSe verification shows that three states are analyzed and translated to 0.11 s. The results of OFMC shows that it visits 1040 nodes with a search time of 9.57 s and 9 plies depth. The summary part of CL-AtSe and OFMC indicates SAFE, so we can say that the proposed protocol resists replay and man-in-the-middle attacks.