esa-sceva/satcom-chunk-collection huggingface dataset:
# Clustering-Based Channel Allocation Method for Mitigating Inter-WBAN Interference
## 3 System Model

Table 1: Comparison of the channel allocation methods (GC represents a graph coloring and DT represents decision time). data rates \(\gamma(t)\). However, this is an NP-complete combinatorial problem. Thus, to resolve the problem, we propose a heuristic algorithm by using both a graph partitioning method and a graph coloring method. If the number of available channels (i.e., \(|K|\)) is less than \(|N(t)|\), some WBANs in \(\mathcal{S}\) inevitably use the same channel. The amount of interference between the WBANs using the same channel increases as the distance between them decreases. Therefore, to mitigate the inter-WBAN interference, the WBANs using the same channel have to be separated as far away as possible. To design a channel allocation algorithm, we represent the interference relationship among the WBANs in \(\mathcal{S}\) at the \(t\)-th time slot as an undirected graph \(G(t)=(N(t),E(t),W(t))\), where \(E(t)\) is the set of edges, and \(W(t)\) is the set of edge weights. The path loss increases as the distance from a sender to a receiver increases [18]. Thus, the amount of interference that a WBAN \(j\) imposes on a WBAN \(i\) is inversely proportional to the distance between \(i\) and \(j\). Thus, we define an edge between two WBANs according to the severity of the interference between them. In other words, if there is an edge \(e_{i,j}(t)\in E(t)\) between a WBAN \(i\) and a WBAN \(j\), it means that \(i\) and \(j\) will interfere with each other considerably when they use the same channel during the \(t\)-th time slot. Specifically, we define \(e_{i,j}(t)\) as follows. \[e_{i,j}(t)= \begin{cases}1,&d_{i,j}(t)<\delta_{d}\\ 0,&\text{otherwise}\end{cases} , \tag{4}\]  
where \(d_{i,j}(t)\) is the distance between a WBAN \(i\) and \(j\), and \(\delta_{d}\) is the distance over which the amount of interference between two WBANs can be considered marginal. To reflect the influence of the distance on the amount of interference, we also impose an weight on an edge \(e_{i,j}(t)\) as follows. \[w_{i,j}(t)= \begin{cases}\frac{1}{d_{i,j}(t)},&e_{i,j}(t)=1.\\ 0,&\text{otherwise}.\end{cases}  \tag{5}\]  
The inter-WBAN interference among the WBANs in \(\mathcal{S}\) can be reduced if we group the WBANs based on the severity of the interference among them if they use the same channel and allocate different channels to the WBANs in the same cluster. Let us assume that the interference relationship graph \(G(t)\) is partitioned into a set of clusters \(C(t)=\{C_{1}(t),\cdots,C_{|C|}(t)\}\) and denote a WBAN \(j\) in a cluster \(C_{i}(t)\) as \(v_{i,j}(t)\). Then, the total interference imposed on \(v_{i,j}(t)\) by the WBANs in the same cluster \(C_{i}\) is given as. \[I_{i,j}(t)=\sum_{k\in N_{j}(t)\wedge C_{i}(t)}I_{j}(t) \tag{6}\]  
Thus, to mitigate the inter-WBAN interference among the WBANs in \(\mathcal{S}\), we need to reduce \(\sum_{C_{i}(t)\in C(t)}\sum_{j\in C_{i}(t)}I_{i,j}(t)\). To achieve the goal, we propose the WBAN clustering process and the channel assignment process, which will be detailed in the next section.
## 3 Security Issues And Challenges
### Security Issues
* _Virtualization:_ VMs that do not have a proper disconnection procedure may inadvertently permit a guest client to run codes on the hypervisor. The multi-tenancy property of the VM provides an opportunity for adversaries to breach the enforced security by exploiting the weaknesses in the VM to obtain unauthorized data from another VM on CC. * _Identity management (ID):_ ID management allows the system to recognize and manage clients, such as controlling their permission-based access. ID administration should be configurable to comply with the corporation rules [36]. * _PaaS-related issues:_ PaaS allows CSP to let CCCs create an application through the platform service. The owners of CSP are accountable if there is illicit data move between the applications. The hypervisor is the responsibility of CSP owners. PaaS CSP is more elastic and adaptable than SaaS CSP, but at the expense of security, exposing it to cyber-attacks. * _IaaS related issues:_ VM in IaaS service model may encounter security issues. According to the CC service model, the security duties and accountability of CSP and CCC vary enormously. * _Economics DoS attacks:_ One of the sundry characteristics of CC is taking care of the scalability issues. The CCC's service bill increases dramatically without reasonable grounds. Such a covert kind of attack that releases through DDoS attacks towards CC yet with an alternate malignant target is the EDoS attack. * _Denial of service:_ One of the most significant security threats confronting CC is DDoS attacks. DDoS attacks render services, such as web applications, unavailable to legitimate users. Adversaries are usually the instigator of such attacks by employing various readily available tools to make the targeted services inaccessible to users. One example of an application layer DDoS attack on CC is an HTTP-GET flood attack.
## 3 Results and Discussion
### Performance of BDS-3 SAIM Based on On-Orbit Data
#### 3.1.3 Satellite Clock Frequency and Phase Hopping Monitoring  
The phase and frequency step monitoring values of the C30 and C34 satellites are given in Figure 11. The upper subfigures display the time series of the frequency step monitoring data, and the bottom part of each figure shows the time series of the phase step monitoring data.  
It can be seen from the figure that the frequency and phase steps mainly show monitoring noise. The standard deviations of the frequency step monitoring data of the C30 and C34 satellites are both \(0.06\) mHz, respectively. The standard deviation of the phase step monitoring data of the C30 and C34 satellites are both \(0.01\) ns.  
Figures 12 and 13 are the QQ analysis charts of the C30 and C34 satellites' frequency and phase step monitoring data, respectively. The left-hand graph in each figure shows the frequency step monitoring results, and the right-hand graph shows the phase step monitoring results.  
Figure 11: SAIM monitoring data of the clock hopping (top) and phase hopping (bottom) of the (**a**) C30 and (**b**) C34 satellites.  
The distribution deviation of the frequency step monitoring data of the C30 satellite is 0.23 and the kurtosis is 2.35. The distribution deviation of the phase step monitoring data of the C30 satellite is 0.28 and the kurtosis is 2.34.  
The distribution deviation of the frequency step monitoring data of the C34 satellite is 0.16 and the kurtosis is 2.27. The distribution deviation of the phase step monitoring data of the C34 satellite is 0.20 and the kurtosis is 2.25.  
Figures 12 and 13 show that a separation on the left in the QQ Plot of the frequency and phase step monitoring data of the C30 satellite. This is because the original telemetry data of frequency hopping and phase hopping are taken as absolute values when output, so it shows a unilateral normal distribution characteristic.
## 3 The Proposed Method
### The Design of the Data Augmentation Model
The process is as follows:  
\[Y_{c}=\frac{1}{H\times W}\sum_{i=1,j=1}^{H}X_{c,i,j} \tag{10}\]  
where c is the number of channels and \(H\) and \(W\) are the height and width of the feature map, respectively. Next, the channel features \(Y_{c}\) are reshaped into a sequence format suitable for MHA calculations, serving as the input for subsequent dynamic modeling. To enable dynamic modeling of channel dependencies, MHA constructs the \(Q\), \(K\) and \(V\) through linear mappings, as follows:  
\[\text{head}_{i}=f_{\text{softmax}}\left(\frac{Q_{i}K_{i}^{\intercal}}{\sqrt{ d_{k}}}\right)V_{i} \tag{11}\]  
\[\text{MHA}(Q,K,V)=\text{Concat}(\text{head}_{1},...,\text{head}_{k})W \tag{12}\]  
where \(d_{k}\) represents the dimension of each attention head, \(h\) is the number of attention heads, and \(W\in\mathbb{R}^{D\times D}\) is the linear projection matrix. MHA captures long-distance dependencies between channels while incorporating multi-scale information representation capabilities. To further optimize the feature distribution, a normalization layer (NL) is applied before MHA to enhance training efficiency and mitigate overfitting. The process is as follows:  
\[Y^{\prime}=NL(Y+\text{MultiHead}(Q,K,V)) \tag{13}\]  
where \(Y\) and \(Y^{\prime}\) represent the embedding sequence and the MHA output, respectively, and \(NL(\cdot)\) denotes the normalization operation. To improve feature selection accuracy, the ECA module optimizes feature weighting using an adaptive weight allocation mechanism, expressed as follows:  
\[\mathbf{O}_{\text{ECA}}=\sigma(\mathbf{Y^{\prime}})\cdot\mathbf{X} \tag{14}\]  
where \(\sigma(\cdot)\) denotes the Sigmoid activation function and \(\mathbf{O}_{\text{ECA}}\) is the output feature after channel attention weighting. By combining MHA's modeling of global channel dependencies with ECA's dynamic weighting of channel features, the MDFT module effectively captures comprehensive feature information from the wide-area fused image. In summary, the generator and discriminator of MDFT-GAN are constructed using multiple MDFT modules, enabling the comprehensive representation of both global and local signal information. This design significantly improves generation quality and discrimination accuracy, providing robust support for high-quality sample generation and precise fault classification.
## 4 Results and Discussion
### Group Pile Lateral Load Capacity
Under the same lateral load, a rigid pile will mobilize the soil to a greater depth. A flexible pile will mobilize the soil to a shallower depth with a larger degree of mobilization [32]. In addition, the 3D method indicates that the lateral resistance will decrease as the soil stiffness rises (Figure 11). However, both methods show the same results in the combination lateral loads (Figure 9), in which the lateral resistance will rise along with the combination lateral loads. This is following the study by [PERSON] et al. [33] that stated the load-deflection alongside the pile would increase with the applied lateral load amplitude. Overall, observations show that the numerical model is reliable for the improvement of the soil\(-\)pile reaction while decreasing the overall flexural stiffness of the pile friction [4]. The average difference in the lateral capacity of the first to the fourth row of piles obtained by 3D group pile analysis compared with single pile analysis is 40% for the first row, 1-10% for the second row, 20% for the third row, and 50% for the fourth row (Table 5). The front row piles have the largest resistance values, followed by the second, third, and fourth row piles. In other words, the 3D finite element model exaggerates the shadow effect behind the leading row [5]. [PERSON] [34] revealed that the overall lateral load is shared among each pile in the group. Each pile pushes against the soil behind it, creating a shear zone in the soil. These shear zones begin to enlarge and overlap as the load increases and the distance between piles becomes adjacent. Overlap between two piles in the same row is called the edge effect, while overlap between piles in different rows is known as the shadow effect. All these group interactions result in reduced lateral resistance per pile. The leading row of piles has the highest resistance in the group as it only suffers from edge effects. Piles in other rows, however, have a lower resistance due to edge effects and shadow effects. The gap that forms behind the pile also reduces the resistance of the pile (Figure 12c).
# Numerical Simulation of the Influence of Spur Dikes Spacing on Local Scour and Flow
###### Abstract  
The spacing of spur dikes is an important consideration for the layout of spur dike channels. This study focuses on the local scour morphology and flow field characteristics of spur dikes with different spacings. The results show that the maximum scour depth is generally found in the vicinity of the first spur dike head. With the increase of the spacing of spur dikes, the shielding effect of the first spur dike is weakened. The maximum velocity in the main flow zone is twice that of the approach flow velocity in the fixed bed. But it is approximately the same as the incoming velocity in equilibrium scouring. The maximum turbulent energy appears to be mainly located in the backflow area of the fourth spur dike in the fixed bed, while the maximum value appears at the second spur dike head in the movable bed. Further, the shear stress decreases as scouring develops. Pearson correlation analysis was carried out between scour depth and shear stress. The analysis results are significantly correlated, indicating that the bed shear stress plays a prominent role in the scouring process. These discoveries can serve as a guide to determine the most reasonable spacing of spur dikes.  
numerical simulation; spur dike spacing; local scour; flow field +
Footnote †: journal: Applied Sciences
# Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview
## 4 CR-Based UAVs
### Hardware Characteristics  
Usually, CR hardware consists of a radio platform, typically an SDR, and a computer platform. The Wireless Open-Access Research Platform (WARP) from Rice University [26] and the USRP from Ettus Research [25] are the most commonly deployed radio platforms for CR [27].  
A recent implementation of SDR, however, has been carried out using various methods rather than considering the integration with UAVs. As a consequence, for UAVs, overhead, energy consumption, and time delays associated with conventional SDR are constraints. Alternatively, [PERSON] and [PERSON] [27] built the SKIRL, a simple and low-cost RFIC-based RFCR platform suitable for the experimentation of small radio-controlled UAVs. The HopeRF RFM22B [50] was used as the radio platform, whereas the BeagleBoard-xM [24] single board was deployed as the computer unit. This was done to reduce resource consumption and simplify CR design.  
Figure 4 shows the physical dimensions of the USRP B200, 97 \(\times\) 155 millimeters, and the Hope RF RFM23B, 16 \(\times\) 16 millimeters. For UAVs, particularly for micro UAVs, where physical space is limited, so if a large board is inserted to it, series of changes may be triggered in the aircraft system, configuration, and scale. Thus, this difference in size is critical. Moreover, the energy consumption of the Hope RF RFM22B is just 0.306 Wh in the worst case scenario. The USRP B200, on the other hand, may have up to 4.092 Wh of energy consumption. Because the energy capacity of a LiFe SourceHCAM6426 UAV lithium battery, a typical UAV battery, is just 12 Wh, energy consumption is a key aspect to take into account in this context.
# An Efficient Trust-Based Scheme for Secure and Quality of Service Routing in MANETs
## 3 Techniques and Methods
### Trust Based Secure and QoS Routing Scheme  
We describe the proposed trust-based secure QoS routing scheme (TSQRS) and its implementation in detail in this section. A trust model is employed in TSQRS to improve the cooperative routing and the performance of MANETs through evaluating the trustworthiness of the nodes in the networks. In the trust model, a node promiscuously listens to its neighboring nodes to evaluate the trust of these values. Due to the broadcast nature of MANETs, a node can observe and estimate the resources of a neighboring node through their direct interactions in a passive mode. When direct observation is involved, a complete history of trustworthiness can be provided by a node. Such a history would include information about communication quality of the node. In TSQRS, we include in our scheme the direct observations to derive trust values on neighboring nodes through using the social and the QoS trust parameters. In addition, trust is assessed on a continuous basis with a fixed time interval and trust value is calculated according to the quality of the behavior in packet forwarding by a node. The whole process continues until the destination node is found and an optimal and secure path from the source to the destination is formed. The selection of the intermediate nodes or the forwarding path is determined according to the trust feedback information. Following are the mechanisms that are used in our scheme for trust management:  
* Trust recommendation using HELLO messages.
* Trust update.
* Trust based secure QoS routing strategy.
## 4 Simulations
### Comparison Between the Numerical and Theoretical Results
An interesting research subject is studying the performance of a noncoherent Q-ary PPM system in a fading channel in more detail and improving the BER performance. In this study, the bit error theoretical formulas of a noncoherent PPM suboptimal receiver and optimal receiver in the AWGN channel were derived, respectively, and a numerical simulation was carried out. The numerical simulation results are in good agreement with the calculation results of the theoretical formula, which verifies the correctness of the error code theoretical formula, especially that the deduced error code theoretical formula is suitable for a \(Q\)-ary PPM system including binary PPM. The relationship between the threshold coefficient of the suboptimal receiver and the system error performance is given, which can be used to guide the selection of the threshold coefficient. In addition, the research results show that the larger the \(Q\), the better the bit error performance of the suboptimal receiver and the optimal receiver of noncoherent PPM but the more difficult it is to realize. Therefore, it is necessary to comprehensively consider and select an appropriate \(Q\) value in practical applications. Conceptualization, X.S. ; Formal analysis, M.C. ; Investigation, Y.S. and J.T. ; Methodology, X.S. ; Project administration, J.Z. ; Supervision, Y.L. and N.X. ; Validation, X.S. ; Visualization, M.C. ; Writing (oiginal draft), X.S., J.T. and M.C. ; Writing (review and editing), Y.S. All authors have read and agreed to the published version of the manuscript. This research was funded by CAEP Foundation under Grant CX20200010. Not applicable. The authors declare no conflict of interest.
## 2 System Architecture
### Sensor Interface Unit  
Pd nanowire grid fabricated on a silicon wafer has been used for gas sensing. Each grid consists of 14 Pd nanowires, half of which are covered with a passivation layer to prevent hydrogen from reaching the nanowire [8]. These coated nanowires, which are only sensitive to temperature, have been used as reference nanowires, while the remaining nanowires, which are sensitive to both temperature and H\({}_{2}\) concentration, have been used as sensing nanowires. This circuit measures the conductance change of the sensing nanowires in comparison to the reference nanowires. In addition to eliminating the effects of temperature by first order, the measurement is only sensitive to the ratio of conductance of  
Figure 2: Deliverable power of the photovoltaic (PV) module in different lightning conditions.  
nanowires, instead of their absolute values, and as a result, a higher accuracy is achievable. Although the temperature effect is eliminated by first order, there are still second order effects, as the temperature coefficient of nanowire resistance changes according to H\({}_{2}\) concentration. In order to compensate this second order effect, temperature is measured using an integrated temperature sensor [9]. H\({}_{2}\) sensing accuracy can be increased by incorporating the measured temperature during sensor calibration. An incremental analog to digital converter (ADC) [13] converts the measured temperature and H\({}_{2}\) concentration to 12-bit digital values. Power consumption and conversion time of this ADC can be reconfigured according to the energy stored in the battery to match the amount of available power, resulting in an adaptive, autonomous sensor. The power management circuit dynamically reduces the operating frequency of digital circuits and the bias currents of analog circuits in this sensor interface circuit under reduced light intensity. However, ADC conversion time increases as a result.
# Efficient Resource Allocation for Beam-Hopping-Based Multi-Satellite Communication Systems
###### Abstract  
With the rapid growth of data traffic, low earth orbit (LEO) satellite communication networks have gradually ushered in a new trend of development due to its advantages of low latency, wide coverage, and high capacity. However, as a result of the limited on-board resources and rapidly changing traffic demand, it is increasingly urgent to design an efficient resource-allocation scheme to satisfy the traffic demand. In this paper, we propose two resource allocation algorithms in the multi-satellite system based on beam-hopping technology. In the offline case, it is assumed that the channel gains in all time-slots are known in advance, and we propose a heuristic algorithm to allocate time and frequency resources, and a successive convex approximation (SCA) algorithm to allocate power resources. In the online case, it is assumed that only the instant channel gains information is known; therefore, we apply the dynamic programming (DP) algorithm to maximize the system throughput. The simulation results prove that the proposed resource-allocation algorithms based on beam-hopping technology have better performance than the traditional average allocation method, and the online algorithm has acceptable performance loss compared with the offline algorithm.  
beam-hopping: resource allocation; successive convex approximation; dynamic programming Article
# Comparison of the Biological Potential and Chemical Composition of Brazilian and Mexican Propolis
## 3 Discussion
### Propolis: Color, Botanical Sources, and Chemical Composition
In Mexico, among the botanical sources described for red propolis, in the Gulf, Pacific Coast, and Peninsula of Yucatan regions are _Bursera simaruba_ ([PERSON], _Dalberga glabra, Cordia alliodora, Cardiospermul halicacabum, Dombeya vallichii, Antigonon leptopus, Sapindus saponaria_, and _Dalbergaic species_[54; 55; 56]. [PERSON] et al. [57] refer to brown-green-ocher (chestnut-green) propolis that differs in composition from the Brazilian green propolis, despite the similar color-based nomenclature. Distinctive chemical compounds are present due to botanical sources such as _Encelia farinosa_, _Ambrosia deltoidea_, _Ambrosia ambrosioides_, _Bursera laxiflora_, _Populus fremontii S. Prosopis laevigata_, and _Acacia greggii_. However, this propolis contains compounds also found in other types of propolis, such as gallic acid, cinnamic acid, p-cumaric acid, naringenin, quercetin, luteolin, kaempfer, apigenin, pinochemrin, pinobanksin 3-acetate, CAPE, chrysin, galangin, acacetin, and pinostrobin [29].