esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hierarchical Mapping System for Flat Identifier to Locator Resolution Based on Active Degree
## 5 Performance Analysis and Simulation
### Query Failure Rate (QFR)  
Since hosts roam from one access router to another access router, their ID-to-locator mapping items change accordingly by reporting to the IMS. If the cache timeout for a mapping item is too short, an AMS frequently queries the mapping system (IMS), but the new item perhaps does not update. This results in the failure of the query.  
In fact, the query failure rate is impacted by factors including the capacity of the mapping server cache, \(\Delta T\), the definition and update of \(F\), and other factors. We only simulated the capacity of mapping server cache and \(\Delta T\) in this paper because they are the main factors.  
The simulation was completed based on the structure shown in Figure 2. We used PC simulation of the three entities: IMS, NMS, and AMS. The data collected over 30 days can be accessed from UCB1993 [9]. The data can be ordered as destination IP address: communication time. We assume one time of communication corresponding stands for one time of mapping query, the destination IP address stands for AID, and assume that the mapping entries have been stored in the existing IMS. In the simulation, we defined \(F\) as the number of queries that \(F\) adding 1 with a query occurs, and decrease 1 with the time \(\Delta T\) past, which replaces the defined for the activity in Formula (1) with the simplified method.  
We reproduced the communicating process, and the statistic of QFR in AMS for query is shown in Figure 8. Here L/R stands for the rate of AMS local cache size to remote AID numbers.  
Figure 8 shows the QFR increased with the cache size. When the L/R increased to a certain amount, the QFR no longer increased rapidly. We can also conclude that the QFR was correlative to \(\Delta T\). This result can be explained by two points. First, the larger the AMS cache size, the less the query from IMS was, which resulted in more failure. In addition, the larger the \(\Delta T\), the less the query from IMS was, which resulted in more failure too.
# Experimental and Parametric Study on the Pull-Out Resistance of a Notched Perfobond Shear Connector
## 5 Parametric Study
### Influence of Cut Width  
Figure 15 shows the influence of the cut width on the pull-out behavior of notched perfobond shear connectors. The cut ratio \(c_{w}/d_{p}\) in this group of specimens changed between 0 and 1. The conventional perfobond shear connector had a cut width of 0 mm and a cut ratio as 0. The notched perfobond shear connector had cut widths of 15 mm, 30 mm, 45 mm, and 60 mm, with the corresponding cut ratios as 0.25, 0.50, 0.75, and 1.00. It was shown that the increase of the cut width had a significant influence on the pull-out resistance of the notched perfobond shear connector. When the cut width was smaller than 45 mm, the corresponding cut ratio was smaller than 0.75, and the variations in the pull-out resistance of the notched perfobond shear connector were no greater than 11%. However, the notched perfobond shear connector with a cut width of 60 mm and a cut ratio of 0 had only 11% of the pull-out resistance in comparison with other specimens in this group. The reason for this might be that the U-shaped hole could hardly bear the dowel forces of the concrete and the perforating rebar in the hole.  
Figure 14: Influence of hole diameter. (**a**) Load–separation curves; (**b**) influence analysis; (**c**) failure mode.
## 3 Results and Discussion
### Core Diameter  
The most significant difference between the DWP and DWCP scheme was the mode overlap of \(P_{1976}\), \(\Gamma_{1976}\). Moreover, \(\Gamma_{1976}\) was mostly related to the fiber core diameter, so optimizing the fiber core diameter in these two schemes was expected to be quite different. As shown in Figure 5a,b, the optimum points of the DWP scheme were strongly related to the power of \(P_{1976}\), whereas the DWCP scheme was suitable for a larger core diameter, and the optimum points were of lower relevance to the fiber core diameter. These differences can be explained by the different slope of \(\Gamma_{1976}\) versus the fiber core diameter plotted in Figure 5c, with the dotted part corresponding to the multi-mode range that (2) was no longer accurate. \(\Gamma_{1976}\) at the DWCP scheme was overall much lower, and its slope versus fiber core diameter increased as the diameter increased, whereas the slope of another curve decreased. For a certain power of pump, a larger fiber core diameter led to a larger \(\Gamma_{1976}\), and therefore a larger \(P_{1976}\) absorbed rate. Continuously increasing the fiber core diameter after the pump lasers absorbed too much made the output power decrease. So, the \(\Gamma_{1976}\) variety decided the pump absorbed rate and yielded the output power behavior.
## 4 Assessment Validation
### Quantitative Evaluation
#### 4.2.1 Methods
To control for multiple comparisons, \(p\)-value adjustments according to the Holm-Bonferroni method are conducted [43]. Next, we empirically evaluated the Q-matrix with the stepwise Wald method [21]. This method provides suggestions for modifications to Q-matrix entries based on the amount of explained variance in the success probabilities for different attribute profiles. This procedure is based on the data at hand, and whether the suggested modifications should be incorporated should be subject to the judgment of domain experts. Therefore, we carefully evaluated the suggested modifications in relation to theoretical considerations and the results from the think-aloud study to make decisions about the final Q-matrix. We then examined attribute behavior at the item level. This refers to the compensatory nature of the attributes (i.e., whether nonmastery of an attribute can be compensated by mastery of another). The Wald test is used to evaluate whether, for items that require at least two attributes, the LCDM can be replaced by a reduced model without significant loss of fit, reflecting different attribute behaviors at the item level [44]. This can result in higher classification accuracy [45]. We end with an evaluation of the final model. Several fit statistics are evaluated, namely, the \(M_{2}\) statistic [46], the \(RMSE_{2}\), and \(SRMSR\)[47], and the maximum transformed correlation and log-odds ratio [48]. Finally, the attribute-level classification accuracy index from [49] and the reliability measurement from [50] are reported.
## 2 Establishment of Coupled Vehicle-Bridge Vibration Equations
### Road Surface Unevenness Model  
Bridge deck irregularities can be viewed as a normal stochastic process with anisotropic ephemerality [30]. The international description of its irregular elevation is usually expressed using the frequency domain method and power spectral density function. This is shown in the following equation:  
\[G_{q}(n)=G_{q}(n_{0})\bigg{|}\frac{n}{n_{0}}\bigg{|}^{-w} \tag{3}\]  
Figure 1: Four-axis space vehicle model of degrees of freedom.  
In Equation (3), \(n_{0}=0.1\) m\({}^{-1}\) is the spatial reference frequency, \(G_{q}(n_{0})\) is the power spectral density of a particular unevenness class of pavement, \(w\) is the frequency index which is generally taken as \(w=2,n\) denotes a particular frequency among the valid frequencies.  
According to the national regulations on pavement irregularity [20], pavement unevenness can be divided into eight levels from A to H. This paper's study on pavement unevenness only considers the three levels from A to C.  
The unevenness of the bridge deck can be regarded as a random function. This function is subject to a normal distribution, so in the generation of different levels of unevenness, data can be used to generate the unevenness of the bridge deck samples. According to the power spectrum density function of each level of the unevenness of the bridge deck, its unevenness samples can be expressed as:  
\[r(x)=\sum_{j=1}^{m}\sqrt{2G\left(n_{j}\right)}sin\left(2\pi xn_{j}+\theta_{j}\right) \tag{4}\]  
In Equation (4), \(x\) is the displacement along the cis-bridge direction, \(m\) is the number of segments divided by spatial frequency, \(G\left(n_{j}\right)\) is the \(j\) frequency spectral density corresponding to the median spatial frequency of the segment, \(n_{j}\) is the median spatial frequency of segment \(j\), and \(j\) is a uniformly distributed random variable over \([0,2\pi]\).  
As the level of unevenness increases, the undulation of the road surface becomes greater. When performing a vehicle-bridge coupling analysis, the degree of unevenness of the bridge deck significantly influences the excitation caused by the vehicle on the bridge deck. Hence, the degree of unevenness of the bridge deck is an influencing factor that cannot be ignored in a vehicle-bridge coupling analysis.
## 3 Theoretical Formulation  
In this section, firstly, the kinetic energy and potential energy of the beam are given. Then, a Chebyshev spectral approximation, proposed by [PERSON] et al. [28] and modified by the authors [29], is briefly reviewed, which is employed to approximate all the spatially varying parameters. By using the Chebyshev spectral method, the discretization of a system of partial differential equations with variable coefficients can be more easily performed and implemented. Moreover, compared with the finite element method and finite difference method, the Chebyshev spectral method adopts high-order shape functions, and can give a better approximation of the gradually varying material properties by setting fewer nodes, so it is accurate and has fast convergence compared to the FEM [30; 31; 32]. Finally, the discrete equations governing the bending and vibration of the beams are derived from [PERSON]'s equation, and a projection matrix method is introduced to apply boundary conditions.  
Figure 1: A non-uniform AFG beam on an inhomogeneous viscoelastic foundation subjected to a non-uniformly distributed moving load.
# A Novel Disturbance Rejection Control of Roll Channel for Small Air-to-Surface Missiles
## 3 Controller Design Base on Extended State Observer
### Stability Analysis  
In Equation (23), \(z_{1}\) is the observation value of \(x_{1},z_{2}\) is the observation value of \(x_{2}\), and \(e1=z_{1}-x_{1},e_{2}=z_{2}-x_{2}\), which can be obtained from Equations (22) and (23):  
\[\left\{\begin{array}{l}\dot{e}_{1}=e_{2}-\beta_{1}(e_{1}),\\ \dot{e}_{2}=-\beta_{2}\text{fal}(e_{1},\alpha_{x},\delta_{x}).\end{array}\right. \tag{26}\]  
Presently, divide the \((e_{1},e_{2})\) plane into five regions [33] as shown below:  
\[\left\{\begin{array}{l}G_{1}=\left\{(e_{1},e_{2})|e_{1}>r_{0},0\leq e_{2}\leq \beta x_{1}\right\}\\ G_{2}=\left\{(e_{1},e_{2})|e_{2}>0,e_{2}\geq\frac{\beta}{2}(e_{1}+r_{0}),e_{2 }\geq\beta e_{1}\right\}\\ G_{3}=\left\{(e_{1},e_{2})|e_{1}<-r_{0},0\geq e_{2}\geq\beta e_{1}\right\}\\ G_{4}=\left\{(e_{1},e_{2})|e_{2}<0,e_{2}\leq\frac{\beta}{2}(e_{1}-r_{0}),e_{2 }\leq\beta e_{1}\right\}\\ G_{0}=\left\{(e_{1},e_{2})|e_{2}|<r_{0},\frac{\beta}{2}(e_{1}-r_{0})\leq e_{2 }\leq\frac{\beta}{2}(e_{1}+r_{0})\right\}\end{array}\right. \tag{27}\]  
For \(G_{i}(i\in 1,2,3,4)\), respectively, construct the following discontinuous piecewise smooth Lyapunov positive definite functions \(V_{i}(e_{1},e_{2})\):  
Figure 1: Structure of attitude control system.  
\[V_{i}(e_{1},e_{2})=\left\{\begin{array}{l}\frac{\beta}{2}(e_{1}-r_{0}),(e_{1},e_{ 2})\in G_{1}\\ e_{2}-\beta e_{1},(e_{1},e_{2})\in G_{2}\\ -\frac{\beta}{2}(e_{1}+r_{0}),(e_{1},e_{2})\in G_{3}\\ -e_{2}+\beta x_{1},(e_{1},e_{2})\in G_{4}\end{array}\right. \tag{28}\]  
If parameters \(r_{0}\), \(\beta_{1}\), \(\beta_{2}\), \(\beta\), \(\sigma\) can be selected for fixed \(\alpha\), so that the derivative of function \(V_{i}(e_{1},e_{2})\) along the trajectory of system (26) is less than zero outside the region \(G_{0}\)[34]. It can be proved that the system (26) is stable according to the multiple Lyapunov function theorem.
# Location Detection and Numerical Simulation of Guided Wave Defects in Steel Pipes
## 2 Theoretical Basis
### Multi-Node Fusion and Modal Projection Algorithm  
In order to extract ultrasonic guided wave signals of different modes, m calculation nodes are evenly distributed around the pipeline, and the signal of each node is expressed as \(S_{i}(t)\). The steps for extracting modal signals are as follows:  
1. Signal preprocessing  
The time domain signal \(S_{i}(t)\) is transformed into the time-frequency domain using the wavelet transform algorithm. The signal components of different frequency bandsare separated, which lays the foundation for subsequent modal extraction. The converted signal is expressed as \(S_{i,\omega}(t)\).
2. Calculation of the modal basis function  
Based on the properties of a steel pipe and the dispersion curve, the basis function \(B_{m}(\theta)\) of each mode is determined, where m represents the mode number.
3. Modal projection  
Using the basis function \(B_{m}(\theta)\), the signal of each node is modal-projected, and the corresponding modal signal is extracted. The mth mode of signal projection is calculated on the modal basis function through angle integration, and the projection operation is defined as follows:  
\[S_{m,i}(t)=\int_{0}^{2\pi}S_{i,\omega}(t,\theta)B_{m}(\theta)d\theta\]
4. Multi-node signal fusion  
The weighted average or weighted sum operation is performed on the modal projections of all node signals, and the global \(m\)-th modal signal \(S_{m}(t)\) is obtained. The specific formula is as follows:  
\[S_{m}(t)=\frac{1}{M}{\sum_{i=1}^{M}w_{i}S_{m,i}(t)}\]  
where \(w_{i}\) is the weight factor of the sensor, which depends on the sensor position or signal-to-noise ratio.
5. Modal signal reconstruction  
Based on the extracted modal signal \(S_{m}(t)\), the original modal signal is reconstructed using back projection or deconvolution techniques, and the pure signals of each mode are reconstructed. The reconstructed signal can be expressed as follows:  
\[S_{m}(t)=\int_{-\infty}^{\infty}S_{m}(\omega)e^{i\omega t}d\omega\]
# Stream Data Load Prediction for Resource Scaling Using Online Support Vector Regression
## 3 Task Description
### Time Series Prediction of Stream Data Load  
As the data source, the Producer sends data to Kafka in real time in time window \(t\), and the stream data load is a continuously changing data sequence in time period \(t\). Stream data load is modeled as time series \(X{=}\left\{x_{1},x_{2},x_{3},\ldots,x_{n}\right\}\) in this paper. The first core of Figure 1 predicts the stream data load time series and can reduce the potential SLA violation and the delay of resource distribution. The time series prediction is based on the sequence \(X\) of an observation value of the past and finds a function \(f\) (*) that conforms to the law of the change of the system. According to this function, the past values are used as input to predict the future value and output. Given the time series of stream data load as \(X\), the prediction of stream data load in next time window \(t\) can be described as \(x_{n+t}=f(x_{n},x_{n-1},\ldots,x_{n-m+1})\).  
The Online SVR method can learn and update the data online. It is suitable for solving the problem of complex data characteristics and forecasting of dynamic updating of the model. In this paper, the improved online SVR algorithm is used to predict the input load of the stream data in the next period. That is, an improved online SVR is used to fit \(f\)(*) and predict the future value.
## 2 Related Work  
### Deep Learning-Based Approaches for Pharmaceutical Identification  
Recent advancements in deep learning have significantly enhanced pharmaceutical identification systems, addressing the limitations of traditional manual pill recognition methods. Deep learning models, particularly convolutional neural networks (CNNs), have been widely adopted for image-based medication classification due to their high accuracy and adaptability. Various research studies have explored the efficacy of CNN architectures in distinguishing pharmaceutical compounds, with notable contributions in the field.  
[PERSON] et al. [4] developed a pill identification system integrating YOLOv5 for object detection, ResNet32 for image recognition, and RNN models for text-based character recognition. Their system achieved an accuracy of 85.65% on Korean datasets (MFDS) and 74.46% on American datasets (NLM), demonstrating the potential of combining deep learning with natural language processing (NLP) for comprehensive pill recognition. However, the system struggled with variations in pill imprints, environmental lighting conditions, and real-world image distortions, leading to misclassification in certain cases.  
Similarly, [PERSON] et al. [4] introduced an automated pill recognition framework utilizing YOLOv5 and ResNet-32, achieving 85.65% and 74.46% accuracy on standard datasets, with 78% accuracy for consumer-captured images. Their approach incorporated a character correction module, improving accuracy by 3.3-11.3%. Despite its efficiency, the system exhibited limitations in processing lowercase characters and special symbols on pill imprints, affecting classification performance in real-world scenarios.  
[PERSON] et al. [2] highlighted the deficiencies of conventional pill identification models, which primarily rely on limited datasets such as ePillID and CURE, making them less adaptable to real-world environmental variations. To overcome these constraints, they developed PGPNet (priori graph-assisted pill detection network), which integrates graph neural networks (GNNs) with multimodal data fusion techniques. This model demonstrated enhanced context-aware medication recognition, improving classification accuracy in diverse illumination conditions and backgrounds.  
While these studies contribute valuable insights into pharmaceutical image classification, existing models still encounter practical challenges in real-time deployment, accuracy under varying conditions, and user accessibility. Building on these advancements, our research aims to develop a mobile-based medication identification system leveraging ResNet101, achieving a classification accuracy of 98.51%, outperforming other CNN architectures such as DenseNet, EfficientNet, MobileNet, SqueezeNet, and VGGNet. The proposed system integrates real-time inference capabilities, heatmap-based visual explanations for model interpretability, and an optimized user interface tailored for elderly users, ensuring practical usability and patient safety in homecare and clinical environments.
# Assessing the Capacity and Coverage of Satellite IoT for Developing Countries Using a CubeSat
## 5 Conclusions  
Small satellites in LEO can provide global coverage and access to any remote topography. The utilization of CubeSat offers a cost-effective solution in developing countries where data collection from remote areas using terrestrial networks is impractical. This paper explored the coverage and capacity of a single satellite with a SIoT mission capability and multiple low-cost GSTs developed locally to provide services to developing countries. The SIoT satellite payload for KITSUNE was designed and developed to be accommodated within the dimensions and power budget of a 1-unit volume of the 6U CubeSat. The GSTs that can measure any kind of sensor data and transmit the data directly to KITSUNE were developed for the ground segment. The GSTs are simultaneously being developed in 11 mostly developing countries, and they will be placed in remote locations to measure environmental parameters. The involvement in the GST network provided the participating countries with access to space technologies without launching their own satellites.  
The capacity of the SIoT mission for providing services to 11 mostly developing countries was evaluated based on the actual performance of the designed receiver and the ground tests results, simulations and on-orbit results that were performed to determine the parameters that affect the performance of the designed system. The tests included antenna-radiation pattern test for both the payload antenna and the GST, receiver-sensitivity tests for fully integrated satellites, and link budget calculations based on the on-orbit parameters.  
KITSUNE was launched to ISS in February 2022 and deployed into orbit in March 2022. The functionality of the payload receiver was tested but the on-orbit results are not in the scope of this paper since the GSTs are being prepared to be deployed in participating countries. On-orbit data collection from the GSTs, validation of the ground testing results with the on-orbit performance and further analysis will be performed. Future work will involve comprehensive testing of the GSTs made by 11 mostly developing countries with KITSUNE. The on-orbit results will be beneficial for developing countries as a first step towards space utilization for solving domestic issues.  
Conceptualization, P.L., T.D.M., N.C.O., M.A.P., F.D., M.K., H.A.E.-M. and M.C.; methodology, P.L., T.D.M. and N.C.O.; software, P.L. and T.D.M.; validation, P.L., T.D.M., N.C.O. and M.C.; formal analysis, P.L., T.D.M. and N.C.O.; investigation, P.L., T.D.M. and N.C.O.; resources, M.C.; data curation, P.L.; writing--original draft preparation, P.L.; writing--review and editing, P.L., N.C.O. and M.C.; visualization, P.L. and N.C.O.; supervision, M.C.; project administration, M.C.; funding acquisition, M.C. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by JSPS Core-to-Core Program B: Asia-Africa Science Platforms (JPJSCCB20200005).
# A New Hybrid Dynamic FMECA with Decision-Making Methodology: A Case Study in an Agri-Food Company
## 2 Methodology
### Evaluation of the Factors O, S, D, and C  
The first innovation point of EN-B-ED Dynamic FMECA analysis concerns the criteria used. We added the cost, C, in addition to the three traditional FMEA criteria, O, S, and D, to consider the costs arising from the fault occurrence. This term considers:  
* Costs of non-production \[C_{MP}=\left(C_{hp}*N_{p}*T_{i}\right)+\left(Q_{p}*P_{p}*T_{i}\right)+\left(C_ {hps}*N_{p}*T_{i}\right)\] (2)  
Figure 1: Flowchart of the Entropy Best Evaluation Distance Dynamic FMECA (EN-B-ED Dynamic FMECA) methodology.  
where \(C_{hp}\) is the hourly cost of the production labour, \(N_{p}\) is the number of workers, \(T_{i}\) is the time of the maintenance intervention to restart the machine, \(Q_{p}\) is the average quantity produced per hour, \(P_{p}\) is the average price of the finished product, \(C_{hips}\) is the hourly overtime cost of the production labor. Therefore, the first term takes into account the cost of the production unable to work during downtime, the second term takes into account the hidden costs resulting from the lack of production, and the last term instead takes into account the cost to be incurred if it is decided to pay overtime to recover lost production.
* Labour costs \[C_{MDO}=(C_{hm}*N_{m}*T_{i})\] (3) where \(C_{hm}\) is the hourly cost of maintenance workers, \(N_{m}\) is the number of maintenance workers, and \(T_{i}\) is the maintenance intervention time.
* Costs of spare parts used \[C_{R}=\sum_{i=1}^{n}C_{i}Q_{i}\] (4) where \(C_{i}\) is the cost of the i-th spare part and \(Q_{i}\) is the number of spare parts used.  
\[C=C_{MP}+C_{MDO}+C_{R} \tag{5}\]
# On Maximizing the Probability of Achieving Deadlines in Communication Networks
## 6 Simulation Implementation
### Wireless Channel Model  
The wireless propagation medium was simulated using a lognormal shadowing model with an exponent of 2.4, a reference distance of 1 m to the far field of the antennas, and a path loss of \(-55\) dB at that distance. A lognormal random shadow fading with a standard deviation of 4 dB was generated for every link. Additionally, quasi-static Ricean fading with a \(K\) factor of four was employed to simulate small-scale block fading, a value found in many different environments.