esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimating Interception Density in the BB84 Protocol: A Study with a Noisy Quantum Simulator
## 2 Related Works  
A lot of work has been proposed to analyze the QKD in general and BB84 in particular.  
The general unconditional security of the BB84 protocol has been demonstrated under ideal noise-free implementations [16; 17]. Only specific studies have considered particular device imperfections [18; 19], but they have typically employed an information theory-like approach to allow the extraction of a secure key. Conversely, the goal of this work is not to evaluate the security of the BB84 protocol but to develop an intrusion detection method for the partial intercept-and-resend attack on this protocol.  
The simulation of the BB84 protocol has also been performed in several studies, such as [20; 21; 22; 23]. They demonstrate its working principles, along with its limitations and the effects of noise in a quantum system.  
There has been significant research on eavesdropper detection in the context of intercept-and-resend attacks within QKD protocols. [PERSON] and [PERSON] initially assumed that a communication could be considered free from eavesdropping activity if the measured QBER were zero [11]. [PERSON] et al. calculated that, in the four-state BB84 QKD protocol, an eavesdropper would go undetected with a probability of \((3/4)^{K}\), where \(K\) represents the number of qubits used to compute QBER [24]. [PERSON] and [PERSON] extended this analysis to the limit case of infinite-state BB84 and quantum Diffie-Hellman protocols, determining that the probability of an eavesdropper remaining undetected is, at minimum, \((1/2)^{K}\)[25]. [PERSON] and [PERSON] proposed a two-way QKD protocol and calculated the probability of undetected eavesdropping in relation to both \(K\) and the number of key exchanges [26]. However, a common assumption across these studies is the consideration of an ideal quantum system, wherein any QBER greater than zero is solely attributed to eavesdropping. This idealized perspective does not account for quantum system noise, which can also contribute to QBER.  
A few works have investigated the possibility of developing an intrusion detection system for the BB84 scheme. Among these, the most similar to the approach presented in this paper are [12; 27]. They present satisfactory results, often with high detection accuracy in their considered conditions. However, they do not examine some details, either the noisy simulation of a real quantum computer or the possibility for the eavesdropper to intercept only a fraction of the qubits being sent.
## 5 Proof of Concept
### Flow Rules to Optimize Response Times
#### 5.1.3 Dynamic Flow Modification  
A server is selected at the beginning of each flow modification interval (\(ft\) seconds) based on the chosen algorithm by a separate thread. The value of \(ft\) is chosen to be greater than the values of the intervals \(dt\) and \(mt\). In addition to Q-Learning and the SLA algorithms, results obtained with Round-Robin (\(RR\)) have been included as reference performance. The agent performs the following steps to modify the flow entries of the switch at the beginning of each flow modification interval:  
1. [leftmargin=*]
2. Selects a server based on a policy. * SLA--the agent selects a server based on the performance of the server from the previous flow modification interval as in Algorithm 3. * Q-Learning--the agent selects a server that has the minimum Q-value as in Algorithm 4. * RR--the destination server is selected in sequential order. In the former two methods (i.e., learning methods), the forward flow rule of the switch is not modified if the selected server in an interval \(ft\) is the same in the previous interval \(ft-1\).
3. Looks up the flow table for a match with the VIP as the destination IP address and the client IP address as the source IP address. If it finds a matching entry, it modifies the flow rule using the OFPFC_MODIFY_STRICT command with actions to set the destination IP address and destination MAC address fields to that of the selected server, and the out_port to the port, the server is connected to the switch.
4. It adds a new flow rule to handle the reverse traffic from the newly selected server if there is no matching rule exists in the flow table.
###### Abstract  
Land cover classification is able to reflect the potential natural and social process in urban development, providing vital information to stakeholders. Recent solutions on land cover classification are generally addressed by remotely sensed imagery and supervised classification methods. However, a high-performance classifier is desirable but challenging due to the existence of model hyperparameters. Conventional approaches generally rely on manual tuning, which is time-consuming and far from satisfying. Therefore, this work aims to propose a systematic method to automatically tune the hyperparameters by Bayesian parameter optimization for the random forest classifier. The recently launched Sentinel-2A/B satellites are drawn to provide the remote sensing imageries for land cover classification case study in Beijing, China, which have the best spectral/spatial resolutions among the freely available satellites. The improved random forest with Bayesian parameter optimization is compared against the support vector machine (SVM) and random forest (RF) with default hyperparameters by discriminating five land cover classes including building, tree, road, water, and crop field. Comparative experimental results show that the optimized RF classifier outperforms the conventional SVM and the RF with default hyperparameters in terms of accuracy, precision, and recall. The effects of band/feature number and the band usefulness are also assessed. It is envisaged that the improved classifier for Sentinel-2 satellite image processing can find a wide range of applications where high-resolution satellite imagery classification is applicable.  
Sentinel-2 satellite; random forest; bayesian optimization; hyperparameter tuning; urban management; land cover classification  
10 December 20205 January 2021  
5 January 2021  
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## 4 Propagation Characteristics and Phenomena of LMS Communication
### Ionospheric Scintillation  
Ionospheric scintillation is produced by irregularities in the electron density in the ionized layers of the ionosphere. It represents a cloud of electrons with a density that is highly different from the ionosphere layers. These non-homogeneities create reflections and diffusions of the radio waves, but the contributions of this diffusion rapidly decrease as the frequency increases, so their contribution can be ignored. Non-homogenous ionized layers cause scattered reflection of radio waves in the L-band, resulting in fluctuations in the amplitude and phase of the received signal. The scattered signals decrease rapidly as the frequency increases. The use of high frequencies is also motivated by the need to use small-sized antennas with very high gain that compensate for the considerable attenuation due to the large distances involved, taking into account the limited powers that are available on the satellites [24].
# Sink-Type-Dependent Data-Gathering Frameworks in Wireless Sensor Networks: A Comparative Study
## 2 Data-Gathering Frameworks Depending on Sink Types
### Static Sink-Based Frameworks
#### 2.2.2 Power-Efficient Data Gathering in Sensor Information System (PEGASIS)  
[PERSON] et al., proposed PEGASIS [57], which is an energy-efficient data-gathering mechanism for static sink-based WSNs. PEGASIS is an extension of the well-known LEACH clustering algorithm. In LEACH, the aggregated data in the CH are sent to the sink in a direction transmission manner. PEGASIS adopted a multi-hop approach to deliver data to the sink. The primary purpose is to reduce the transmission distance for every sensor node, resulting in energy-efficient data transmission. The node deployment is assumed to be random in this case. The chain formation among the sensors is performed either by the sensors themselves or by the sink node. In the case of chain formation by the sink nodes, the sink node computes the chain information and broadcasts it to the node. The primary assumption of this algorithm is that the sink node is far away from the ROI. The chain formation algorithm is a greedy algorithm. Another major assumption of PEGASIS is that the data fusion capability belongs to every node in the network. When a node receives data from any other node, it fuses the data with its own and then transmits the data to the next node. The node that transmits the data to the sink is chosen randomly from the WSN. This is done to ensure that the nodes are dying from different positions of the chain. Figure 2 shows the working mechanism of the PEGASIS protocol. As shown in the figure, the original PEGASIS algorithm forms one chain and selects one leader/CH node for each round of transmission.  
Advantages: The mechanism adopted in PEGASIS is heavily dependent on the aggregation percentage. However, assuming that the nodes have low homogeneity, PEGASIS will form a load-balancing mechanism in terms of energy consumption.  
Limitations: Despite having a better load-balancing mechanism, the leader node in a chain still needs a higher transmission power to deliver the data to the sink. The assumption about global knowledge is costly in terms of memory and beaconing. The strict one-packet forwarding mechanism assumption is not realistic; the nodes may need more data packets to transmit the fused data.
# Hypersentized Metamaterials Based on a Corona-Shaped Resonator for Efficient Detection of Glucose
## 2 Structure and Design of the Metamaterial-Based Sensor  
The proposed metamaterial-based sensor was designed and numerically investigated by Computer Simulation Technology (CST) Studio Suite 2018. The designed structure used in this work is composed of three main layers, as shown in Figure 1. The top layer consists of a corona-shaped resonator with a transmission line, where P1 and P2 represent the first and second port, respectively. The space between the closed circular ring (CCR) and the conductive inner star shape is utilized as the sensor layer to be filled with samples under investigation. The top layer is made of copper with a conductivity of 5.8 \(\times\) 107 S/m and thickness of 0.035 mm, while both ends of the transmission lines are excited by discrete port 1 and 2. The middle layer is a Rogers RT5870 substrate with a thickness of 1.6 mm and a dielectric constant and loss tangent of 2.33 and 0.0012, respectively. This substrate layer was chosen due to some advantages such as uniform electrical properties over a wide frequency range; lowest electrical loss; being able to be easily cut, shared and machined to the desired shape; being ideal for high-moisture environments; and being a well-established material. The bottom layer is fully covered with a copper metal of 0.035 mm thick, which behaves as a ground plate for the structure. The rest of the required dimensions are shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Dimensions** & **Size (mm)** \\ \hline Radius of resonator & 9 \\ Width of resonator & 1.5 \\ Length of transmission line & 7.5 \\ Width of transmission line & 1.5 \\ Radius of star shape & 7.3 \\ Length of substrate & 35 \\ With of substrate & 35 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dimensions of the proposed structure.  
Figure 1: (**a**) Front view of the proposed metamaterials based on a corona shape and (**b**) its prospective view.  
In the simulation software, there were some boundary conditions used depending on the desired applications, such as free space, periodic distribution, perfect electric conductor/perfect magnetic conductor (PEC/PMC) and perfect electric conductor (PEC). In this work, the boundary open (add space) was assigned at the X, Y and Z axes to be compatible with the experimental studies, as shown in Figure 2a.  
To better understand the sensing mechanism of the proposed structure, an equivalent circuit diagram was drawn, as shown in Figure 2b. The designed structure has two ports, namely port 1 and port 2.
## 2 Model
### IN Model  
Impulsive noise can be (i) generated from the electrical mains or by direct induction on the receiver; or (ii) captured by the receiver antenna. In the first category, the main noise sources are the ignition system of ovens, the control system of dishwasher machines, thermostats of heaters, and switches of fluorescent and incandescent lamps. In the second category, typical sources are lightning and the ignition system of cars.  
Figure 1: CR receiver diagram (adapted from [8]).  
Several models are available in the literature for characterizing IN [21, 22, 23]. Here we adopt the one discussed in [22], in which the IN waveform is generated by properly gating a white noise signal, as illustrated in Figure 2. The main parameters that govern the IN waveform are also shown in this figure. They are configured according to the noise source type, as described in detail in [22].  
To adhere the above parameters to the context of spectrum sensing, we translated them into five other parameters: \(K\) is the ratio between the time-series average IN power and the average thermal noise power; \(p_{\text{IN}}\) denotes the probability of occurrence of IN during a given sensing period, and \(p_{\text{CR}}\) represents the fraction of CRs hit by IN, when it occurs. As a result, the probability of the occurrence of IN is a Bernoulli random variable with probability of success \(p_{\text{IN}}\), and the number of CRs independently hit by IN, when it occurs, is a binomial random variable with parameters \(m\) and \(p_{\text{CR}}\). A configurable number \(N_{\text{b}}\) of IN bursts occurs during a sensing period, each burst having configurable length \(N_{\text{s}}\), _i.e._, each IN burst corrupts \(N_{\text{s}}\) consecutive samples collected by a given CR. The separation between consecutive bursts is uniformly distributed in the discrete-time interval \([0,n-N_{\text{b}}\times N_{\text{s}}]\).
## 3 Determination of the Energy-to-Mass Ratio
In articles [25; 26], the authors have presented and discussed two pieces of medical rehabilitation equipment, one designed for the recovery of the lower limb (Figure 8), and the other for the recovery of the wrist (Figure 9). Both devices are actuated by pneumatic muscles, a technical solution selected due to the favorable energy-to-mass ratio. For these devices, Table 8 features a comparison of the computational results obtained by the method proposed in this paper. The values of the energy-to-mass ratios of the utilized pneumatic muscles were compared to those of the single-acting cylinders that could have been used alternatively as actuators of the two devices. Table 8 includes the dimensions of the compared actuators. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Motor Type**} & \begin{tabular}{c} **Maximum** \\ **Exterior** \\ **Diameter** \\ \end{tabular} & \
\begin{tabular}{c} **Total** \\ **Length** \\ \end{tabular} & **Mass** & **Force** &
\begin{tabular}{c} **Energy-to-Mass** \\ **Ratio** \\ \end{tabular} \\ \hline Round cylinders ESNU & + & + & = & – & – \\ \hline Pneumatic muscles & \(-\) & \(-\) & = & + & + \\ DMSP & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Overview of the results obtained by the comparative study of the two types of pneumatic motors. Figure 7: Variation of the energy-to-mass ratios versus strokes of the pneumatic motors. Figure 8: Assistive rehabilitation device for the joints of the lower limb. Figure 9: Pneumatic muscle actuated wrist rehabilitation equipment. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Energy-to-Mass Ratio [Nm/kg]**} \\ \hline \hline \multicolumn{2}{c}{**Assistive rehabilitation device for the joints of the lower limb**} \\ \hline \hline \multicolumn{2}{c}{Pneumatic muscle} & 108.4113 \\ L = 750 mm; d = 20 mm; s = 150 mm & \\ \hline \multicolumn{2}{c}{Single-acting cylinder} & 15.4851 \\ D = 20 mm; s = 150 mm & \\ \hline \multicolumn{2}{c}{**Wrist rehabilitation equipment**} \\ \hline \hline \multicolumn{2}{c}{Pneumatic muscle} & 95.1454 \\ L = 300 mm; d = 10 mm; s = 60 mm & 21.4019 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of the energy-to-mass ratios computed for the two types of actuators. 7-times greater than that of the equivalent single-acting cylinder, while in the case of the wrist rehabilitation equipment, the proportion is of 4.45. These two examples confirm the superior behavior of pneumatic muscles from the energy point of view.
## 5 Experimental Verification and Analysis
### Case Study II: Fault Diagnosis of Gearbox
#### 5.2.3 Comparative Analysis of Classifiers  
To demonstrate the superiority of the used SOF over other traditional machine learning classifiers and explore the adaptability of RMSDE in various machine learning algorithms, this experiment additionally uses three classical machine learning classifiers to classify the extracted RMSDE features, including Random Forest (RF), Naive Bayesian (NB), and K-Nearest Neighbor (KNN). To minimize the impact of randomness and contingency, each algorithm is run ten times. Each training set and test set are randomly constructed. The average classification accuracy and its standard deviation are used to evaluate the performance of each method. The comparison results of the four machine learning classifiers are shown in Figure 19. It can be seen that when the RMSDE proposed in this paper is combined with any machine learning classifier, the average classification accuracy reaches more than 93%. When the method combination is RMSDE+KNN, the average classification accuracy reaches the highest of 95.83%, and its standard deviation also reaches the lowest of 1.13%, which reflects the superiority and robustness of the proposed method.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Method** & **RMSDE-SOF** & **MSE-SOF** & **MFE-SOF** & **MPE-SOF** \\ \hline Max accuracy (\%) & 98.33 & 91.67 & 83.33 & 70.00 \\ Min accuracy (\%) & 91.67 & 83.33 & 76.67 & 60.00 \\ Average accuracy (\%) & 95.58 & 87.17 & 80.58 & 63.77 \\ Run time (s) & 45.05 & 311.23 & 448.12 & 47.98 \\ \hline \end{tabular}
\end{table}
Table 7: Diagnostic accuracy of different diagnostic methods in case study II.  
Figure 16: The classification accuracies of four methods in case study II.  
Figure 17: Multiclass confusion matrix of four methods: (**a**) RMSDE-SOF, (**b**) MSE-SOF, (**c**) MFE-SOF, and (**d**) MPE-SOF.  
Figure 18: t-SNE scatter diagram of the extracted features.
# Online Control for Biped Robot with Incremental Learning Mechanism
## 2 System Description and Some Preliminaries
### Kinematics and Dynamics  
Usually, biped robot can be simplified as connective link model as shown in Figure 1, in which each link is uniform mass distribution. The relationship between joint speed and end-effector velocity is defined as  
\[\dot{\mathbf{r}}=J(\mathbf{q})\dot{\mathbf{q}} \tag{1}\]  
where \(\dot{\mathbf{r}}\in R^{m}\) is task-space velocity, \(\mathbf{q}=\left[q_{1},...,q_{n}\right]\in R^{n}\) represents joint angles, \(n\) is the number of degrees of freedom, \(\dot{\mathbf{q}}=\left[q_{1},...,q_{n}\right]\) denotes joint angle velocities and \(J(\mathbf{q})\in R^{m\times n}\) is the Jacobian matrix from joint space to task space.  
From the Lagrangian approach, the dynamics of biped robot can be expressed as follows  
\[M(\mathbf{q})\ddot{\mathbf{q}}+C(\mathbf{q},\dot{\mathbf{q}})\dot{\mathbf{q}}+ G(\mathbf{q})+\mathbf{F}_{e}=\tau \tag{2}\]  
where \(M(\mathbf{q})\in R^{n\times n}\) is the positive definite inertial matrix, \(C(\mathbf{q},\dot{\mathbf{q}})\in R^{n\times n}\) is the Coriolis and centrifugal matrix, \(G(\mathbf{q})\in R^{n}\) is the gravitational force, \(\mathbf{F}_{e}\in R^{n}\) denotes the external disturbance, \(\tau\in R^{n}\) is the joint torques.
###### Abstract  
This paper proposes a novel approach for appearance-based loop closure detection using incremental Bag of Words (BoW) with gradient orientation histograms. The presented approach involves dividing and clustering image blocks into local region features and representing them using gradient orientation histograms. To improve the efficiency of the loop closure detection process, the vocabulary Clustering Feature (CF) tree is generated and updated in real time using the Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm, which is combined with an inverted index for the efficient selection of candidates and calculation of similarity. Moreover, temporally close and highly similar images are grouped to generate islands, which enhances the accuracy and efficiency of the loop closure detection process. The proposed approach is evaluated on publicly available datasets, and the results demonstrate high recall and precision.  
simultaneous localization and mapping (SLAM); loop closure detection; Bag of Words +
Footnote †: journal:  
which exhibit pose invariance while preserving the overall structure of the image region. Aiming at the problem that the traditional BoW model requires offline training and the effect of dictionary construction is easily affected by the training dataset, an incremental BoW model is proposed. To generate and update the vocabulary in real time, we use the BIRCH algorithm [15], which allows an incremental construction of the vocabulary CF tree and avoids the disadvantages of the offline method. The vocabulary CF tree in combination with an inverted index is used for the efficient selection of candidates and calculation of similarity. Finally, to avoid images taken from the same location competing among themselves as loop closure candidates, we group temporally close and highly similar images to generate islands.  
The rest of this paper is organized as follows: Section 2 provides a review of related work on appearance-based loop closure detection algorithms. Section 3 presents our new loop closure detection algorithm. In Section 4, we evaluate the performance of our algorithm on publicly available datasets. Finally, a brief summary is provided in Section 5.
# Minimal Collagen-Binding Epitope of Glycoprotein VI in Human and Mouse Platelets
## 1 Introduction  
Platelets are small anucleated cells, which play a central role in the maintenance of hemostasis, but in pathological conditions, abnormal platelet functions may trigger thrombosis or bleeding complications [1, 2]. During vessel injury, platelets adhere to the damaged endothelium and become activated. This process involves adhesion receptors, including integrins and platelet-specific glycoproteins, that link platelets to extracellular matrix (ECM) proteins, such as collagen, exposed on the surface of the damaged endothelium [1, 2, 3]. In pathological conditions, the exposure of collagen from the subendothelial matrix of the atherosclerotic vessel wall induces platelet activation and thrombus growth, thereby increasing the risk of thromboembolism, myocardial infarction, and stroke [4, 5, 6, 7]. Collagen fibers are composed of three polypeptide chains, characterized by a repeating Gly-X-Ysequence, where X is frequently proline (P) and Y is 4-R-hydroxyproline (O) [8]. The GPO triplets are essential for ligand binding, activating receptors on the platelet surface. Integrin \(\alpha\)2\(\beta\)1, glycoprotein (GP) V, and GPVI can directly bind type I collagen fibers [3; 9; 10; 11].