esa-sceva/satcom-chunk-collection huggingface dataset:
# Creep-Fatigue Behavior and Life Prediction of Medium-Si-Mo Ductile Iron
## 3 Results and Analysis  
### Low-Cycle Fatigue Properties  
#### 3.1.1 Stress-Strain Characteristics Under Cyclic Loading  
Under constant strain amplitude loading, the accumulation of microscopic dislocations and plastic deformation within the material leads to changes in its plastic flow characteristics, which manifest macroscopically as cyclic softening and hardening phenomena [23]. During the cyclic softening and hardening process, if a stable hysteresis loop is not formed, the hysteresis loop at the half-life point is typically adopted in engineering as the nominal stable hysteresis loop for further analysis [24]. The first-cycle and nominal stable hysteresis loops of the ductile iron assessed in this study are shown in Figure 3 at various temperatures and under various strain loads (solid lines represent the first-cycle loops, and dashed lines represent the stable loops).  
QTRSi4 Mo exhibits cyclic hardening at low and medium temperatures but cyclic softening at high temperatures, based on a comparison between the first-cycle and stabilized hysteresis loops. The hysteresis loops at different temperatures and strain amplitudes generally display symmetry, which indicates similar material properties during tension and compression. Furthermore, the shapes of the hysteresis loops show that as the temperature increases, the nominal stabilized loops gradually become flatter and wider, with a decrease in the peak stress and an increase in the stress valley under the same strain amplitude. This  
Figure 1: Load setup for low-cycle fatigue test and creep–fatigue interaction test. (**a**) Low-cycle fatigue test waveform and (**b**) creep–fatigue interaction test waveform.  
Figure 2: Low-cycle fatigue specimen. (**a**) Size diagram of fatigue specimen and (**b**) physical photo of fatigue specimen.  
suggests that elevated temperatures enhance this material's plastic deformation capability, which is consistent with the softening and hardening characteristics of other similar grades of ductile iron [1].  
The cyclic stress amplitude response curve of this material at a given temperature can be obtained by connecting the vertices of different hysteresis loops at the same temperature. According to Equation (1), this curve can be described mathematically as follows [25]:  
\[\varepsilon_{a}=\varepsilon_{e}+\varepsilon_{p}=\frac{\sigma_{a}}{E}+\left( \frac{\sigma_{a}}{K^{\prime}}\right)^{\frac{1}{n^{\prime}}} \tag{1}\]  
In this equation, \(\varepsilon_{a}\) represents the total strain amplitude, \(\varepsilon_{e}\) denotes the elastic strain amplitude, \(\varepsilon_{p}\) is the plastic strain amplitude, E is the elastic modulus, \(\sigma_{a}\) is the stress amplitude, \(K^{\prime}\) is the cyclic strength coefficient (in MPa), and n\({}^{\prime}\) is the strain hardening exponent.  
In Table 2, the cyclic strength coefficient and strain hardening exponent values of the material are presented. They were calculated by fitting the cyclic stress-strain curve provided by Equation (1).
# Efficient Data Gathering in 3D Linear Underwater Wireless Sensor Networks Using Sink Mobility
## 5 Simulation Results
### Throughput  
The amount of data sent to the sink depends on the number of nodes that are alive and participating in sensing data. 3D-SM is designed in such a way that the MS efficiently covers the areas and gathers data from nodes at the least distance. Nodes send their data to the MS or CNs directly, which results in a network with higher throughput. In Figure 9, throughput is shown; 3D-SM performs better due to the longer NLT and the mobility pattern of the MS. CNs frequently receive data from the nodes and send these data to the MS; this results in greater throughput. Figure 9 shows that initially, both schemes, 3 DSM and AUV-PN, have almost the same throughput, but with the passage of time, 3D-SM takes the lead. The energy depletion of nodes directly affects the throughput of the network. In AUV-PN, when the stability period is over, the throughput also decreases, due to the decrease in the number of nodes present in the network.  
In DDRP, when the MS starts the data gathering tour, it stays at different sink stops. When it stays at the first stop, it broadcasts a control packet that has information about its stop location, sensing range, time of stay, _etc_. The nodes that are present in the sensing radius of the MS attach themselves to the MS. The nodes that do not receive a control packet broadcast a message to find the route towards the MS. The nodes that are a one-hop neighbor of the MS send their data directly. The nodes that are two or more hops away send data packets through multi-hop communication. In multi-hop communication, the chances for packet drops increases, which leads to low throughput. In this scheme, an MS gathers data from the whole network and randomly moves in the field. Due to the random mobility, the MS may not receive all of the information from the field. DDRP achieves higher NLT as compared to AUV-PN, however, at the cost of throughput. Initially, the behavior of the plot is linear, as in all three protocols, all of the nodes are alive.  
Figure 8: Network lifetime. (**a**) when all nodes die; (**b**) when first node dies.
###### Abstract  
By leveraging the advantages of the uniform transmission line, this manuscript presents a broadband high-selectivity filter range starting from 2.5 GHz to 16.8 GHz, utilizing a simple uniform transmission line structure loaded with three-quarter-wavelength stubs. The proposed UWB filter is studied using the ABCD network parameter method. After that, a shorted T-shaped stub-loaded resonator is coupled with the transmission line of the UWB filter to obtain dual-notch features at 4.4 GHz (for long distance wireless ISPs (WISPs), 4G/5G operator for LTE backhaul) and 7.5 GHz (for X-band downlink communication). The overall footprint is specified as 22.5 mm \(\times\) 12 mm or 1.12 \(\lambda_{8}\times 0.6\)\(\lambda_{g}\), where \(\lambda_{g}\) represents the wavelength at the central frequency. The operating principle of such a filter is explained, and its controllable broadband response, as well as controllable stopband frequencies, are optimized to show some of the attractive features of the new scheme, such as a super wideband response of about a 148.18% fractional bandwidth; an out-of-band performance up to 25 GHz; five single-resonator transmission poles filtering behaviour at different frequencies, with highly reduced radiation losses greater than 10 dB; a simple topology; a flat group delay; a low insertion loss of 0.4 dB; and high selectivity. Additionally, the filter is fabricated and evaluated, and the results show a good match for experimental validation purposes.  
widespread filter; wireless communication; dual stopband filter; low insertion loss; uniform transmission line; high selectivity +
Footnote †: journal:
# Edge-Guided Hierarchical Network for Building Change Detection in Remote Sensing Images
## 3 Results
### Experimental Dataset  
In order to fully validate the effectiveness of the proposed algorithm on building datasets, the WHU Building Change Detection dataset [29] and the LEVIR-CD Dataset [30] are selected for experiments. The WHU dataset and the LEVIR-CD dataset were selected due to their high relevance to building change detection tasks and their high temporal and spatial resolutions. Both datasets are specifically designed for building change detection, containing multiple different building types, and are rich in detail. Ensuring that our research will better validate the performance of the building change detection model in a wider range of scenarios.  
The WHU dataset covers remotely sensed images of Christchurch after a magnitude 6.3 earthquake in 2011 and its reconstruction four years later. The two images of these data are the remote sensing image containing 12,796 buildings in April 2012 and the remote sensing image containing 16,077 buildings in the same area in 2016, with a spatial resolution of 0.3 m. In this experiment, the whole image is cropped into image blocks of 256 \(\times\) 256 size, and 20% of the data are randomly selected as the test set, while the rest is used as the data for the training set.  
The LEVIR-CD Dataset consists of 637 pairs of 1024 \(\times\) 1024 pixel bi-temporal images, divided into training, validation, and test sets. These bi-temporal images are provided by Google Earth and come from 20 different areas in Texas. In order to be consistent with the experimental setup on the WHU dataset, we cut each pair of images into non-overlapping image chunks of 256 \(\times\) 256-pixel size, which are set up as training, validation, and test sets according to the original dataset distribution. In particular, the main challenges of this dataset are the very uneven distribution of samples and the architectural change detection. Figure 4a-c shows examples from the LEVIR-CD dataset.
## 2 Materials and Methods
### Scenario S1  
In scenario S1, the energy flows of each house are simple. In batteries, the state of charge (SOC) must be continuously monitored. All batteries have an \(SOC_{L}\) up to which discharge is allowed. An \(SOC_{M}\) can also be defined as the maximum \(SOC\) that should not be exceeded, which for this study is set at \(SOC_{M}=100\%\). If generation is greater than demand, the surplus energy is available to charge the ESS with an efficiency \(\eta_{\text{c}}\) corresponding to the charging efficiency. If the ESS cannot be charged because it has reached \(SOC_{M}\), the available energy that could have been generated becomes a new amount of ENU. On the other hand, if generation is lower than demand, the deficit of energy being actually demanded can be supplied from the ESS with the efficiency \(\eta_{\text{d}}\) corresponding to the discharging efficiency. Under these conditions, if the ESS cannot be discharged because it reaches \(SOC_{L}\), then there is a certain amount of ENS that will affect the studied indices. The operation of this scenario and the different situations that can take place are described in sufficient detail in [38]. Figure 2 shows the different energy lows and efficiencies in this scenario.
# Machine Learning for Child Oral Health: A Scoping Review
## 3 Results
### Temporal Trends and Machine Learning Techniques  
Over the three decades, there was an increasing trend in the use of machine learning for prediction in pediatric and dental public health, predominantly from HICs (Figure 3). In the initial decade (2001-2010), neural networks were the preferred ML algorithm (Figure 4A). Between 2011 and 2020, in addition to previously used algorithms, researchers started employing deep learning, random forest, and regression techniques. In the current decade (2021-2023), random forest and regression continued to rise, while newer techniques like gradient boosting rapidly emerged. When analyzed across dental specialties, deep learning and neural networks were commonly utilized (Figure 4B). When examined across economic regions, HICs consistently showed the most robust adoption of newer techniques, integrating gradient boosting and natural language processing for advanced applications. UMICs exhibited a gradual shift toward deep learning and decision trees, reflecting an expansion in technical capacity. LMICs, on the other hand, maintained a focus on simpler yet effective models like neural networks and decision trees, reflecting resource-driven constraints.  
Figure 4: (**A**) Use of different ML algorithms over time. (**B**) Use of different ML algorithms according to dental specialties.
## 5 Conclusions  
In this paper, we propose a distributed mobility management scheme in NDN designed to support producer mobility and ensure low-latency content delivery for UAVs as a use case. Our scheme is based on three components: (1) a new forwarding approach that combines default and location-based forwarding strategies; (2) a location prediction strategy to predict the location of the MP prior to handoff; (3) distributed Anchors that proactively track and forward the consumer Interest packets toward the MP when handoff events occur. Through simulation, we evaluated the performance of the proposed strategy using a real scenario and compared it with two popular schemes, which represent both Anchor-less and Anchor-based approaches. The results obtained showed that the proposed scheme can effectively minimize communication delay and Input loss with low overhead signaling under producer mobility. These promising results make the proposed strategy the best-suited producer mobility management for seamless data delivery.  
Our future work includes: (1) further improving the distribution of Anchors by finding the optimal number and optimal placements of Anchors able to handle the network traffic; (2) improving the prediction accuracy by leveraging deep learning techniques by focusing on the producer mobility's related features, which we expect to present even better prediction results; and (3) conducting further experiments for different network topologies and environments.  
Conceptualization, M.B., N.N. and A.J.; methodology, M.B.; software, M.B.; validation, M.B., N.N. and A.J.; formal analysis, M.B.; investigation, M.B.; resources, M.B.; writing--original draft preparation, M.B.; writing--review and editing, M.B.; visualization, M.B.; supervision, N.N.; project administration, N.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data can be shared up on request. The data are not publicly available due to private issues.  
The authors would like to acknowledge [PERSON] and [PERSON] for the funding help.  
The authors declare no conflicts of interest.
# Energy Sustainability in Wireless Sensor Networks: An Analytical Survey
## 3 Hardware-Based Energy Sustainability in WSNs
### Hardware-Based Methods for Energy Sustainability
#### 3.2.2 Energy Harvesting
Such devices include piezoelectric, electromagnetic, or electrostatic mechanisms in order to scavenge energy created by vibrations, stress-strain and pressure [33; 36]; Human-based energy harvesting is performed in Wireless Body Area Networks (WBANs) in which nodes are either deployed on human bodies or implanted in human bodies. In such networks of this type, human-based energy harvesting is ideal for energy supply. It refers to the scavenging of the energy created during various activities or processes of human body, such as walking, finger movements, blood flow, and body heat. Electroactive materials, miniscule thermoelectric, piezoelectric, or triboelectric generators, and tiny rotary devices may be used for this purpose [34; 39; 40].
# Improving GNSS PPP Convergence: The Case of Atmospheric-Constrained, Multi-GNSS PPP-AR
## 3 Concepts of Functional and Stochastic Atmospheric Constraints
### Existing Atmospheric Products  
#### 3.1.1 Existing GIM Products  
Dual-frequency GNSS observations from ground networks are used to generate ionospheric delay models which are useful for both precise GNSS positioning and ionosphere study. Utilizing global or regional network of stations, ionosphere delay models generated are dependent and correlates with the scope of coverage of the reference networks. GIM is a typical example and is in the form of spherical harmonic functions [32]. The assumption made is that the electron density of the atmosphere is concentrated on an atmospheric layer at a fixed height, usually 350 to 450 km, in the global model recovery. Regarding this assumption, the slant ionospheric delays are expressed by a combination of the VTEC and a mapping function. The VTEC is represented by the estimations of the coefficients of the spherical harmonic function. The VTEC is then mapped to obtain the slant ionospheric delay through a mapping function after the ionosphere pierce point (IPP). Over the past two decades, VTEC estimates and their associated rms estimates, have been provided, originally for meteorological purposes, to the GNSS community through GIMs. As part of the estimation process, satellite and receiver differential code biases (DCBs) with their rms values, are provided as a by-product. An average of 21 GIM products currently exist. These products are either post-processed, predicted or combined from other analysis centres (ACs) and generated from dual-frequency GNSS measurements. Shown in Table 2 are the unique current products available to GNSS users. Of interest is the varying numbers of receiver stations and GNSSs used in the generation of the products. Figure 2 shows the results of how different current GIM products affect the performance of ionospheric constraining in multi-GNSS PPP solutions. Using 3 days of 24-h observations from MGEX multi-GNSS stations mapped out in Figure 1, solutions were obtained for both dual- and triple-frequency PPP processing modes. It must be pointed out that only post-processed products were used for the sake of uniformity - the rationale being to demonstration whether after 24 h of processing,
# Exploring Hybrid-Multimodal Routing to Improve User Experience in Urban Trips
## 4 User Experience Model
### Core  
Let \(r\in R\) be a route option to a given trip of a person (decision-maker). \(R\) is the set of available options created with a heuristic. \(r\) is a tuple \(\langle t_{r},c_{r},l_{r},S_{r}\rangle\), the route's duration, cost, length, and set segments in different transport modes, respectively. One way of comparing aspects of route options of a trip is evaluating the opportunity cost of these aspects. For instance, the Opportunity Cost Equation [31] (Equation (4)) evaluates the opportunity cost for selecting a route in the set \(R\) as the loss due to selecting this option and not the best possible option.  
\[copp_{t}(r)=t_{r}-\min_{\forall r\in R}t_{r}. \tag{4}\]  
where \(r\) and \(r^{\prime}\) are the routes taken from the set \(R\) with duration \(t_{r}\). \(copp_{t}(r)\) denotes the opportunity cost of duration of a given route \(r\). The opportunity costs of other route aspects are given by analog equations evaluated on \(c_{r}\), \(l_{r}\), and so on.  
A typical use of the opportunity cost in transport engineering is to evaluate the Value of Time Travel Savings (VTTS). It represents the monetary cost of the extra time that is spent by selecting a given route instead of the fastest route.  
Nevertheless, in order to obtain the user experience in the trip, we must consider both the value of the user's time and the actual paid value, instead of only the duration of \(t_{r}\). We can use the Generalized Cost Equation [32] (Equation (5)), also used in transport economics when evaluating the attractiveness of a route option [33], to combine the two variables. The generalized cost of a route option is the addition of the paid monetary cost to the generalized journey time, which was found via a utility function.  
\[g(r)=c_{r}+u(r). \tag{5}\]  
where \(g(r)\) is the generalized cost, \(c_{r}\) the monetary cost of the route, and \(u(r)\) the utility function to estimate a generalized time. The generalized time \(u\) is estimated using the Value of Time Travel (VTT) of the user. \(u\) varies with the context of the decision-maker, such as route conditions and his income/hour. One way of evaluating \(u\) is by multiplying the trip duration by the VTT of the decision-maker, as \(u(r)=\tau t_{r}\), where \(\tau\) is given in [USD/h]. This may be used to compare the routes with similar characteristics. However, when comparing multiple transportation modes, the impressions of the decision-maker might vary among different modes. One way of contemplating this phenomenon is by using a Generalized Time Function [34] that considers the user perceptions to adapt the measured time. For instance:  
\[u(r)=\tau T(r). \tag{6}\]Subsequently, to define the general time of the complete route \(T(r)\), we sum the general time of all segments \(s\in S_{r}\):  
\[T(r)=\sum_{\forall s\in S_{r}}T(s), \tag{7}\]  
where \(T(r)\) is the generalized time in the complete route, and \(s\in S_{r}\) is a route segment in a specific transportation mode, which has a duration, cost, length, and its mode \(s=\langle t_{s},c_{s_{r}}l_{s},m_{s}\rangle\).
## 6 Conclusions and Future Work  
In this paper, a dynamic placement approach based on a hybrid recommendation system was presented that adapts to real-time workload fluctuations. This method integrates intelligent resource management mechanisms to ensure the efficient use of the computing and storage capacities available in Fog environments. The hybrid approach combines three recommendation techniques: content-based filtering, collaborative filtering, and a hybrid strategy. The proposed system was tested using an IoD scenario. The results show that the hybrid approach achieves better outcomes than some solutions based on ODMA or ICAs. Future directions for improvement include enhancing system evaluation results, particularly in terms of response time and overall latency. Additionally, other Fog service placement objectives such as service cost and energy consumption will be explored. Potential limitations related to scalability will also be addressed, especially the communication overhead and responsiveness challenges that may arise in large-scale IoT environments. Ensuring QOS becomes critical in the presence of faulty Fog nodes necessitates attention to fault tolerance in service placement. Finally, due to the reliance on user history and preferences, particular attention will be paid to safeguarding sensitive data and ensuring system security.  
Conceptualization, H.B.R., L.S. and A.D.; Methodology, L.S., H.Z. and A.D.; Software, H.B.R.; Validation, L.S., R.B.D. and A.D.; Formal analysis, H.Z. and R.B.D.; Investigation, R.B.D.; Data curation, H.B.R.; Writing--original draft, H.B.R.; Writing--review & editing, L.S. and A.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original dataset used in this study, the San Francisco Taxi Dataset, is publicly available at \"[[https://doi.org/10.15783/C7J010](https://doi.org/10.15783/C7J010)]([https://doi.org/10.15783/C7J010](https://doi.org/10.15783/C7J010))\". The adapted dataset generated during the study is not publicly available due to project restrictions.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} FC & Fog Computing \\ SPP & Service placement problem \\ IoT & Internet of Things \\ IoD & Internet of Drones \\ HRS & Hybrid recommendation system \\ RS & Recommendation system \\ S & Second \\ QOS & Quality of Service \\ SLA & Service-level agreement \\ GA & Genetic algorithm \\ ICA & Imperialist Competitive Algorithm \\ HAPs & High-Altitude Platforms \\ LAPs & Low-Altitude Platforms \\ CSA & Cuckoo search algorithm \\ ACU & Admission control unit \\ \end{tabular}