esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient SS-MAC Protocol for IEEE 802.15.4-Based WSNs of Cluster Tree Topology
## 6 Conclusions  
The SS-MAC protocol proposed in this paper is an efficient MAC protocol for WSNs with cluster tree topology. The protocol integrates and improves many techniques, such as the hybrid CSMA/TDMA technique, the short address technique and the sharable slot technique. It provides a detailed design of the frame structure and data collection process for each phase of network operation. It has the benefit of reducing competition and energy consumption between nodes, as well as improving network throughput. In addition, it can be applied to large-scale MWSNs. There are three limitations of SS-MAC as follows: first, the topology cannot be rebuilt in time when a node fails; second, the design, so that CHs do not perform sensing tasks; third, lack of direct communication between the sink and the member nodes will limit the protocol use cases. In the future, we aim to address the existing drawbacks of the SS-MAC protocol by implementing a Markov model that takes into account the various states of nodes. This model will enable us to conduct a thorough analysis of the protocol's performances and provide a sound basis for future optimization.  
[PERSON] investigated the literature, established the mathematical models, designed algorithms, completed numerical simulations, was responsible for the original draft preparation, and edited the manuscript; [PERSON] conceived the research concepts, improved the systematic research and analysis methodology, and supervised the completion of the refinement of the paper; [PERSON] edited the manuscript, checked equation deductions and English grammar. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China (61663024) and in part by the Hongliu First Class Discipline Development Project of Lanzhou University of Technology (25-225305).  
Figure 12: Throughput versus offered load.  
Data Availability Statement: The data supporting this article are from previously reported studies and datasets, which have been cited.  
The authors declare no conflicts of interest.
## 2 Mathematical Programming Model
### MILP Model Formulation
Equation (47) indicates the attitude transition time constraints between digital transmission missions on satellites. Equation (48) indicates the attitude transition time constraints between imaging missions on satellites. Equations (49)-(54) indicate the satellite fixed storage capacity constraint. Equation (55) indicates the energy constraint for satellites.
# Analytic Continual Learning-Based Non-Intrusive Load Monitoring Adaptive to Diverse New Appliances
## 4 Experiments and Analysis
### Validation Metrics  
The accuracy (\(ACC\)) and macro-averaged F1-score (\(F_{1}\)-macro) were used to evaluate NILM performance. Compared to the accuracy, F1-macro is more suitable for evaluating tasks with an imbalanced distribution of types, as it fairly considers the contribution of each label. Especially in streaming data, new appliances often have only a few samples. Meanwhile, new and original appliances gradually transform between the majority and minority types during the continual learning experiment in novelty detection. Given the total number of samples \(N\), the number of appliance types \(C\), and the true-positive (\(TP_{i}\)), false-positive (\(FP_{i}\)), and false-negative (\(FN_{i}\)) samples for appliance type \(i\), the ACC and F1-macro are calculated as follows:  
\[ACC=\sum_{i=1}^{C}TP_{i}/N \tag{12}\]  
\[F_{1}\text{-macro}=\frac{1}{C}\sum_{i=1}^{C}\frac{2\cdot Pre_{i}\cdot Rec_{i}} {Pre_{i}+Rec_{i}} \tag{13}\]  
where \(Pre_{i}=TP_{i}/(TP_{i}+FP_{i})\) and \(Rec_{i}=TP_{i}/(TP_{i}+FN_{i})\).  
To track the performance changes in the learned appliances during continual learning, the datasets from D\({}_{0}\) to D\({}_{k}\) were merged after each learning round, with the ACC and F1-macro computed as average performance metrics [28].
# Combining InSAR and GNSS to Track Magma Transport at Bastic Volcano
## 5 Results
### Static Inversion
#### 5.1.4 Relative Weights of InSAR Versus GNSS Data  
We also added campaign and continuous GNSS data to certain inversions. In inversion 05, the GNSS is weighted according to the measurements' standard deviations. Although GNSS data points are less numerous than InSAR data points (\(\sim 80+10\) GNSS points with 3 displacement components for each point versus \(\sim 2000\) InSAR points), they are independent of each other, while InSAR data are spatially correlated. Their variance on horizontal components was smaller (\(\sigma_{d}^{2}=2\times 10^{-4}\) m\({}^{2}\) and \(\sigma_{d}^{2}=9\times 10^{-3}\) m\({}^{2}\) on horizontal and vertical displacements, respectively) than that of InSAR (\(\sigma_{d}^{2}=5\times 10^{-4}\) m\({}^{2}\)). This results in GNSS data accounting for 61% of the total reference misfit \(\chi^{2}_{ref}\) Equation (6) and Table 3.  
In inversions 04a and 04b, the covariance matrix was modified in order to give GNSS data the same weighting as each interferogram. This decreased the weighting of GNSS data to the total misfit from 61% in inversion 05 to 20% in inversions 04a and 04b (Table 3). However, the best model geometry remained the same regardless of the weighting and a similar fit of GNSS data was obtained (Table 1).  
Model family F1 (inversion 04a) explains the GNSS data better (Table 1) than family F2 (inversion 04b). However this is done at the expenses of the InSAR fit giving the same total fit. Overall, all models explain the InSAR and GNSS data equally well and the addition of GNSS data does not provide any critical new information. With four different LOS, the displacement field is well characterized and any ambiguity is due to the non-uniqueness of inversions. Thus a criteria solely based on the misfit minimization is not sufficient to choose between the two equally well fitting families of models.
## 2 Materials and Methods  
### Materials  
The cement used in this study was ordinary Portland cement (OPC) produced by Schwenk, conforming to EN 197-1: 2011. Table 1 illustrates its material characteristics. The type of OPC used was CEM 1 42.5 N with an average bulk density of 0.9 to 1.5 g/cm\({}^{3}\), ignition loss of 5.0%, early referencing compressive strength after 2 days over 10.0 MPa, and 28-day standard strength varying from 42.5 to 62.5 MPa. Normal tap water was utilized for the preparation of LWCM samples. FAC of Grade 1 as a substitute for ordinary river sand was adopted as fine aggregates to fabricate LWCM. As is shown in Table 2, the color of FAC varies from grey to white, and the shape is a hollow sphere. The bulk density is 0.37 to 0.40 g/cc, and the pH value is 7-8. The morphology of FAC under the microscope is exhibited in Figure 1. As can be seen from the micrograph, the size of FAC varies from 30 to 300 \(\upmu\)m and the particles are of both smooth and rough surfaces.  
CNT and NS in the form of powder were used in the investigation to modify the mechanical properties of LWCM. Multi-walled CNTs were selected over single-walled CNTs, for they are of lower cost and have less tendency of agglomeration, promoting a better-quality suspension [20]. CNTs were synthesized via the chemical vapour deposition method which is a most commonly used approach for both laboratory and factory.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Material Property** & **Values** \\ \hline Initial setting time/min & \textgreater{}60 \\ Loss on ignition/\% & \textless{}5.0 \\ Bulk density/(g/cm\({}^{3}\)) & 0.9–1.5 \\ Sulphate content/\% & \textless{}3.5 \\ Chloride content/\% & \textless{}0.1 \\ Early compressive strength (2 days)/MPa & \textgreater{}10.0 \\ Standard compressive strength (28 days)/MPa & 42.5–62.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material characteristics of OPC used in the experiments.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Material Property** & **Values** \\ \hline Size/micron & 40–300 \\ pH value & 7–8 \\ Bulk density/(g/cm\({}^{3}\)) & 0.37–0.40 \\ True density/(g/cm\({}^{3}\)) & 0.6–0.7 \\ Thermal conductivity/(w/mk) & 0.036–0.060 \\ Compressive strength/MPa & 20–40 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material characteristics of FAC used in the experiments.  
Figure 1: Microstructure of FAC.  
The specific area of CNTs was 233 m\({}^{2}\)/g, and the diameters as well as lengths varied from 6-15 nm and 4-13 nm respectively. In order to obtain preferable workability, as well as to facilitate the homogeneous dispersion of nanofillers, Melment F 10 manufactured by BASF Corporation was used as superplasticizer at an amount of 0.5% by cement weight (bcw). It is a powdery sulfonated polycondensation product based on melamine and is usually used as superplasticizer for cement and calcium-sulphate-based materials. The color of the powder is white, and the bulk density is 500 to 800 kg/cm\({}^{3}\).
## 6 Conclusions  
An approach to model round-off noise in general cascade filter structures has been studied. This round-off noise depends on the used scaling method and on the ordering of the stages. These general results are used to study and optimize the round-off noise behavior of MFIR filters.  
It has been shown that the round-off noise performances of the MFIR pole approximations indeed depend on the used scaling method. L\({}_{2}\) bound scaling results in the best performance, followed by infinity and absolute bound scaling.  
In general, it can be concluded that in the region of interest (approximating pole behaviors with magnitudes \(r>0.9\)) the MFIR approximations perform better than the approximated IIR filters. Evenoutside the region of interest, the performance generally does not differ too much from the corresponding IIR filters (max 6 dB).  
The analysis presented in [1] suggests forward ordering as an optimal ordering in case no scaling is applied. In case of L\({}_{2}\) bound scaling the ordering has no impact on the round-off noise performance. The analysis presented here, extends these results to other practical scaling methods and concludes that in these cases, reverse ordering performs better than forward ordering for most pole approximations. However, it should be noted that special attention to the stage ordering is required  
* when approximating a complex-conjugate pole pair having a pole angle in the neighborhood of \(\pi\)/2 and the cascade structure has been used;
* when approximating the squared magnitude response of a complex-conjugate pole pair filter in case the pole angles are situated in the neighborhood of \(\pi\)/4, \(\pi\)/2 and 3\(\pi\)/4 and the linear phase cascade structure has been used.  
Further research is required to determine if alternative orderings can be found which would yield a better noise performance. Note that the orderings considered here are not the only possible orderings. For best performance, one must determine the \"declining amplification\" order for every pole pair that is approximated. At the moment, no systematic approach has been found, so trial and error is required. Research will have to prove if an optimal ordering can be calculated. If not, a heuristic approach as in [8] for the pole zero pairing or an iterative optimization algorithm [17] or another near optimal ordering technique [16] could also be interesting.
# An Adaptive Security Framework for Internet of Things Networks Leveraging SDN and Machine Learning
## 6 Performance Analysis  
Detailed simulations assess the Enhanced SCAFFOLD protocol's efficacy and efficiency in Performance Analysis. Results include detection accuracy, attack mitigation delay, channel latency, and energy usage. Current performance evaluation constraints and problems are also discussed here.  
OPNET Modeler thoroughly simulated the Enhanced SCAFFOLD protocol for testing. IoT network design had three distributed SDN controllers, twenty switches, and 300 nodes. The nodes had sensors, actuators, computers, smartphones, and embedded devices. Machine learning modules, including the attack detection ensemble classifier, were not simulated in the present performance investigation. Wired, LTE, WiFi, and other features were available on the network. A variety of background traffic patterns were simulated, including video conferences, VoIP conversations, HTTP, FTP, SSH sessions, SMTP, and custom IoT application protocols. Additionally, a variety of attacks were included, such as malware infections, remote code execution, man-in-the-middle (MITM), distributed denial-of-service (DDoS), and reconnaissance probes. During the simulations, dynamic traffic loads were imposed, and the nodes were designed with mobility patterns.  
It is important to note that the current performance analysis focuses on evaluating the Enhanced SCAFFOLD protocol in isolation, without a direct comparison to other existing SCAFFOLD protocols. While a comparative analysis would provide valuable insights into the relative performance and advantages of Enhanced SCAFFOLD, it is beyond the scope of this paper due to the differences in design choices, target environments, and security objectives among various protocols. The current evaluation aims to demonstrate the effectiveness and efficiency of Enhanced SCAFFOLD's unique features, such as the ensemble machine learning classifier, SDN-based mitigation, and lightweight cryptographic mechanisms. Future studies can build upon this foundation to conduct comprehensive comparative analyses with other state-of-the-art SCAFFOLD protocols.
## 3 Background
### The Unstructured Mesh Computational Motif  
Computations defined on unstructured meshes form an important basis for many engineering calculations commonly used in PDE discretizations, such as finite elements of finite volumes. An unstructured mesh is characterized by a number of sets (vertices, edges, cells, etc.) with explicit connectivity information between them (e.g., edges to vertices). Computations are commonly expressed as a parallel loop over a set, with computations accessing data either directly on the iteration set or through an indirection. For example, a common operation in computational fluid dynamics is to compute fluxes across faces (edges), and then increment/decrement state variables defined on connected cells. The key motif here is the edge-centered computations indirectly incrementing cell data, which then gives rise to non-determinism when the order of execution of the edges is relaxed for the sake of parallelism. Another common pattern is the global reduction, often conducted in a non-deterministic order, where the result is then used in subsequent computations. For example, in the conjugate gradient algorithm, the results of dot products are used as weights in the next step.  
The distributed and parallel execution of unstructured mesh algorithms is a well-established field [42; 43; 44; 45]. For distributed memory execution, the mesh is partitioned using one of many established libraries, such as PT-Scotch or ParMetis [46; 47]. It is important to note here that an unstructured mesh is a hypergraph, consisting of multiple \"vertex\" types, whereas most partitioners only partition a simple graph, and the rest of the hypergraph is usually partitioned in a greedy way through connections to the simple graph. This is then related to how computations are executed, an \"owner-compute\" approach is commonly utilized, where all computations associated with a given element are performed on the process that owns that element. So, for instance, in the earlier example, the process that owns a given cell will execute all the edges that increment that cell, even if some of those edges are not owned by it. This requires communicating all the data needed to execute those edges as well. This often leads to redundant computations around partition boundaries. Depending on the exact implementation the deterministic order of execution for elements is often relaxed at this point to allow shared memory parallelization and powerful optimizations such as overlapping computations and communications.  
To enable shared-memory parallel execution of unstructured mesh computations, one needs to address the issue of race conditions when indirectly incrementing/updating data. Virtually all execution schemes used in the literature rely on the associativity of these operations, for example, by using atomic updates, a staging of increments in an auxiliary array and their separate sum, or a coloring scheme [48; 49]. We are not aware of related works that explicitly aim to maintain an ordering of operations whilst enabling shared memory parallel execution.
# CSNOMA: Carrier Sense Non-Orthogonal Multiple Access
## 4 Carrier Sense Non-Orthogonal Multiple Access (CSNOMA)
### Area Spectral Efficiency Analysis  
According to the authors of [44], if the carrier sensing range is \(r_{s}\), the transmitting node density is \(\lambda_{t}\) is  
\[\lambda_{t}=\frac{1-\exp\left(-\lambda\pi r_{s}^{2}\right)}{\pi r_{s}^{2}}. \tag{4}\]  
The additional transmitting nodes are located within the distance \(  
Now, we can derive ASE of the proposed scheme (CSNOMA) using Equations (6)-(8). ASE \(\eta\) is  
\[\eta \leq\log_{2}\left(1+\beta\right)\left\{\lambda_{t}\exp\left(- \lambda_{t}\pi r_{c}^{2}\right)p_{s}^{\text{non-IC}}\right. \tag{9}\] \[\quad+\lambda_{t}\left(1-\exp\left(-\lambda_{t}\pi r_{c}^{2} \right)\right)p_{s}^{\text{IC}}\] \[\quad\left.+\lambda_{t}\left(1-\exp\left(-\lambda_{t}\pi r_{c}^{2 }\right)\right)p_{s}^{\text{add}}\right\}.\]  
The reason for this is as follows. If no other nodes are within the region of radius \(r_{c}\) of the carrier sensing based transmitting node, the transmission success probability is determined by \(p_{s}^{\text{non-IC}}\). This situation happens with probability \(\exp\left(-\lambda_{t}\pi r_{c}^{2}\right)\). On the other hand, the probability that the nodes exist within the region of radius \(r_{c}\) of the given transmitting node is \(\left(1-\exp\left(-\lambda_{t}\pi r_{c}^{2}\right)\right)\). Moreover, the transmission success probabilities of the carrier sensing-based transmitting nodes and additional transmitting nodes are \(p_{s}^{\text{IC}}\) and \(p_{s}^{\text{add}}\), respectively.  
As the carrier sensing range expands, all transmit success probabilities, \(p_{s}^{\text{non-IC}}\), \(p_{s}^{\text{IC}}\), and \(p_{s}^{\text{add}}\), increase. At the same time, however, the transmitting node densities \(\lambda_{t}\) and \(\lambda_{a}\) are reduced. This means that an optimal operating point exists in regarding of the carrier sensing range. We will discuss about an optimal carrier sensing range in the next subsection.
## 3 Methodology
### System Configuration  
The first step in a reliability study is to understand the system's operations and the functionality of different subsystems/components. A functional block diagram and a list of components are to be prepared based on their functionality and operations to be performed. The applications and operating conditions of the HVAC system under consideration, and similar systems, need to be studied to identify possible faults, failure modes, causes, and the effects of failures. The typical HVAC system in a passenger car consists of various components, including a compressor, condenser, evaporator, and expansion device, as depicted in Figure 3[39]. The system can heat and cool the air while maintaining its quality and based on specific requirements. Specifically, the compressor plays a crucial role in the HVAC system as it compresses the refrigerant gas, transforming it into a gas with high pressure and temperature. This gas is then directed to the condenser for further cooling. The condenser's role is to cool down the high-pressure, high-temperature gas the compressor produces. It achieves this by dissipating heat into the atmosphere using the vehicle's cooling system. Once the refrigerant gas is cooled in the condenser, it moves on to the expansion  
Figure 1: Keyword co-occurrence in the HVAC system reliability and fault detection literature.  
value. Here, the pressure of the gas is reduced. In response to this reduction, the refrigerant gas expands and cools down, becoming a gas with low pressure and temperature. The next destination for this gas is the evaporator. The evaporator is responsible for absorbing heat from the cabin air, resulting in the cooling of the cabin. The low-pressure, low-temperature refrigerant gas from the expansion valve passes through the evaporator, which takes in heat from the cabin air. Finally, the now-cooled air is then distributed into the cabin through the vents. Table 2 reports the fifteen subsystems considered for the purpose of analysis. Based on the collected field failure data, and after discussion with the experts in the field, the failures/faults were classified into fifteen subsystems.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sr. No.** & **Subsystem** & **Abbreviation** & **Sr. No.** & **Subsystem** & **Abbreviation** \\ \hline
1 & A/C evaporator front side & AEFS & 9 & HVAC heater core & HHC \\ \hline
2 & Blower front side & BFS & 10 & A/C condenser front side & ACCS \\ \hline
3 & A/C gas leak at joints & AGLAJ & 11 & Recirculation motor & RM \\ \hline
4 & A/C compressor & ACC & 12 & A/C evaporator rear side & AERS \\ \hline
5 & A/C hose cut/leaking & AHCL & 13 & Wire mesh (HVAC Unit) & WM \\ \hline
6 & Blower resistor & BR & 14 & A/C pressure switch & APS \\ \hline
7 & HVAC control panel & HCP & & & \\ \hline
8 & All AC louvers & AAL & 15 & Blower switch & BS \\ \hline \hline \end{tabular}
\end{table}
Table 2: Subsystems of an HVAC system and their abbreviations.  
Figure 2: Proposed methodology for reliability and failure analysis of an HVAC system.
# Spatial Shape-Aware Network for Elongated Target Detection
## 1 Introduction
The right panel illustrates the issue of significant differences in anchor box potential caused by the content enclosed within the anchor boxes for high-aspect-ratio targets in complex scenarios. These challenges form the foundation of the research questions tackled in this study. network. This reduces the algorithm's complexity. The detector uses this module to improve feature extraction and achieve better feature coverage. BGSF uses ground truth box boundary information along with classification information to guide the training and optimization of the anchor box offsets and positions. At the instance level, in addition to considering the traditional IoU factor, we incorporate the overall shape of the target into the assignment process. We introduce a Shape-Sensing module (SSM) that calculates a shape coefficient derived from the aspect ratio properties of the ground truth box, which serves as a factor for determining the threshold for positive sample assignment. This threshold is dynamically adjusted according to new statistical features from the generated positive samples. Furthermore, at the comparison level, we introduce a Potential Evaluation Module (PEM) that estimates the relative position of anchor boxes on the target based on statistical information from the predicted samples. This module assesses the potential of each sample and reflects the importance of different potential anchor boxes in the loss function. Our contributions are as follows:  
1. We introduce a Boundary-Guided Spatial Feature Perception Module in the detection head to adjust the output of the backbone network for generating class and position information. 2. We propose a shape-sensing sample selection module that adjusts the discrimination threshold based on aspect ratio, allowing low-IoU anchor boxes with regression potential to be temporarily retained. 3. We introduce a Potential Evaluation Module that evaluates the potential of positive samples and provides stronger training feedback for high-potential positive samples.