esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Problem Formulation  
The aim of this article is to minimize the total transmission latency and the total transmission energy consumption of MDs. Thus, the function can be written as  
\[U_{j}=\rho T_{j}^{\text{total}}+(1-\rho)E_{j}^{\text{total}}, \tag{21}\]  
where \(\rho\in[0,1]\) is a coefficient that is used to define the relative weight of transmission latency and transmission energy consumption based on the nature of the application. A higher \(\rho\) value prioritizes reducing transmission latency, while a lower value emphasizes lowering energy consumption. Additionally, \(\rho\) can be assigned to different values to align with various objectives and can be easily modified through the application settings, as needed. Next, the optimization problem can be expressed as follows:  
\[\min_{\begin{subarray}{c}C_{x,j},C_{y,i},\\ R_{x,j},R_{y,j},\theta_{j}\end{subarray}}\quad\sum_{j=1}^{N}U_{j}, \tag{22}\]  
\[\text{s.t.}\quad\sum_{j=1}^{N}A_{i,j}=1,\quad\forall i\in M, \tag{23}\]  
\[X_{\min}\leq C_{x,j}\leq X_{\max},\quad\forall j\in N, \tag{24}\]  
\[Y_{\min}\leq C_{y,j}\leq Y_{\max},\quad\forall j\in N, \tag{25}\]  
\[R_{\min}\leq R_{x,j}\leq R_{\max},\quad\forall j\in N, \tag{26}\]  
\[R_{\min}\leq R_{y,j}\leq R_{\max},\quad\forall j\in N, \tag{27}\]  
\[\theta_{\min}\leq\theta_{j}\leq\theta_{\max},\quad\forall j\in N, \tag{28}\]  
\[R_{i,j}\geq R_{\text{th}}, \tag{29}\]  
\[T_{i,j}^{\text{tran}}\leq T_{\text{th}}, \tag{30}\]  
\[E_{i,j}^{\text{tran}}\leq E_{\text{th}}, \tag{31}\]  
where Equation (23) indicates one MD can connect to one UAV-MEC server at a time, to offload its task. Equations (24) and (25) indicate the horizontal and vertical center position deployment constraints for the UAV-MEC server's ellipsoidal trajectory. Equations (26)-(28) are the constraints for the major radius, minor radius, and rotation angle, respectively, which define the size and shape of the ellipsoidal trajectory. Constraint (29) indicates that the data transmission rate should be higher than the predefined threshold value, to ensure the QoS for each MD. Equation (30) is the constraint for the transmission latency, and (31) is the constraint for the transmission energy consumption of each MD, which has to be less than the threshold value.  
To address the problem formulated above, we propose a DQN-based solution in the next section, to obtain nearly optimal results utilizing environmental knowledge.
## 2 Background and Key Features
### UAV-Assisted SAGINs
#### 2.1.3 Key Issues of UAV-Assisted SAGINs
The joint optimization of UAV endurance and resource allocation is often modeled as NP-hard (resource-power) problems, solved via heuristic or AI-based iterative algorithms [28; 29]. * _Latency Tolerance and Low Latency:_ SAGINs must support both latency-tolerant and low-latency critical services. GEO satellites (approx. 250 ms one-way latency) struggle to meet real-time demands [16]. LEO/MEO satellite priority routing reduces latency to 20-40 ms, while inter-satellite links minimize ground relay overhead. For latency-tolerant traffic, non-real-time data are transmitted via edge channels to avoid bandwidth contention with critical services.
## 3 Energy Storage Configuration Model
### Constraints  
**Power balance constraint.**  
\[P_{grid}(\text{t})=P_{load}(t)+P_{ch}(t)U_{ch}(t)-P_{dis}(t)U_{dis}(t) \tag{15}\]  
where \(P_{grid}(t)\) is power purchase from the grid at \(t\)th period (kW), \(P_{load}(t)\) is load power at \(t\)th period (kW).  
**Energy storage constraint.** The operational constraints of the battery energy storage mainly include the upper and lower bounds of SOC, charge and discharge power limit constraints, charge and discharge state constraints and energy storage SOC coupling constraints, as well as SOC state regression constraint [21].  
\[\left\{\begin{array}{l}SOC_{\text{min}}\leq SOC(t)\leq SOC_{\text{max}}\\ U_{dis}(t)+U_{ch}(t)\leq 1\\ 0\leq P_{dis}(t)\leq U_{dis}(t)P_{dis\text{max}}\\ 0\leq P_{ch}(t)\leq U_{ch}(t)P_{cl\text{max}}\\ SOC(t+1)=SOC(t)+\frac{P_{dis}(t)P_{ch}}{S_{N}}\Delta t-\frac{P_{dis}(t)}{P_{ dis}\Delta t}\\ SOC(96)=SOC(1)\end{array}\right. \tag{16}\]  
where \(SOC(t)\) is state of charge of energy storage in \(t\)th period, \(SOC_{min}\) is lower limit of SOC and \(SOC_{max}\) is Upper limit of SOC. \(P_{dis\text{max}}\) is maximum discharge power of energy storage and \(P_{cl\text{max}}\) is maximum discharge power of energy storage. \(\eta_{ch}\) is charging efficiency of energy storage and \(\eta_{dis}\) is discharging efficiency of energy storage. \(SOC\) (96) is SOC at 96 th period and \(SOC\) (1) is SOC at 1 st period.  
It can be seen that the proposed metathetic model form a linear optimization problem with constrains. To solve this kind of problem, there are various effective algorithm, such as the branch and bound algorithm, or the cut plane method. At the same time, there are some ready-made software or code packages, such as Cplex, Lpsolve, Yalmip and so on. Cplex possesses the advantages of high efficiency and stability in solving mathematical programming problems, but its modeling statements are complex. On the contrary, Yalmip's modeling statements are simple and can support multiple solvers. Consequently, Yalmip (R20160923, Yale University, New Haven, CT, USA and 2016) is utilized to build the mathematical model shown in equation, then the Cplex solver (Cplex12.5, IBM, Armonk, NY, USA and 2015) is applied to obtain the optimization in the MATLAB (MATLABR2014b, The MathWorks company, Natick, MA, USA and 2014) environment [22; 23].
## 2 The Building, Temporary Instrumentation and Methodology
### Nonparametric Method
#### 2.4.1 Obtaining the Spectral Density  
As previously mentioned, the Fourier transform identifies important frequencies from an engineering point of view and has become the dominant method [29]. Accordingly, new techniques have been created and improved based on the Fourier transform, as in the case chosen for this analysis, for which the Welch power spectral density estimation (\(G_{xx}\)) is used. The Welch power spectrum works with superimposed segments, increasing the frequency resolution and allowing the spectrum to be observed better. Figure 7 shows a spectrum obtained using the Fourier transform superposed onto the Welch spectrum.
# Long-Range Correlations and Natural Time Series Analyses from Acoustic Emission Signals
## 3 Applications
### \"Spaghetti\" Bridge Model
#### 3.2.3 Discussion  
(i) As in the first application, important information was omitted during the first calculation of parameters \(H\) and \(\alpha\). For this reason, such a calculation was repeated for different time scales to further evaluate these parameters' usefulness in monitoring structural damage.  
Figure 15 illustrates \(H\) and \(\alpha\) for signals that were identified at normalized instants 0.59 and 0.65. The red line indicates the overall linear fit for the data, whereas the fits for each subset are shown in blue. The values for \(H\) and \(\alpha\) are high for short-time ranges, i.e., high-frequency signals are persistent as expected from [48], whereas low-frequency ones (\(n\geq 400\)) are anti-persistent.  
Naming the DFA values for \(n<400\) e \(n\geq 400\) as \(\alpha_{short}\) and \(\alpha_{high}\), respectively, their behavior throughout time appears in Figure 16, where the AE signals' amplitudes are out of scale. As in the first application, these parameters have opposite tendencies as a high-AE-activity point is approached. While high-frequency correlations are always persistent, low-frequency ones are persistent for noncritical periods, turning into anti-persistent at critical points, such as at the normalized instant 0.6 and during the subsequent avalanches.  
Figure 15: Details of the Hurst and DFA exponents for (**a**) signal #84 at normalized time 0.59; (**b**) signal #127 at normalized time 0.65.  
(ii) The natural time analysis of the spaghetti bridge's AE results was also performed using \(Q_{k}=E_{AE,k}\), as shown in Figure 17a. Now, two possibly critical points are identified, one at \(t=0.4046\) (F/F\({}_{\rm max}=0.3178\)) and the other at \(t=0.6025\) (F/F\({}_{\rm max}=0.4695\)), where the latter is followed by significant changes in both the order parameters and the two entropy metrics, anticipating the bridge collapse. Figure 17b depicts the normalized power spectrum \(\Pi(\phi)\) versus the natural frequency (\(\phi\)) that was calculated for the AE signal (blue), compared to its expected theoretical value (red). Once again, the criticality parameter \(\Pi(\phi)\) appears as a good predictor of critical stages throughout the test.  
Figure 16: DFA results considering short (\(\alpha_{short}\)) and large (\(\alpha_{high}\)) time ranges for the spaghetti bridge. AE signals out of scale.  
Figure 17: (**a**) Results from the natural time analysis of the AE events during the loading test for the spaghetti model bridge, according to the same format described for Figure 7d. (**b**) Evolution of \(\Pi(\phi)\) during the same test.
## 3 The Method Proposed in This Paper
### Signal Online Monitoring Scheme Design  
The implementation scheme of ADMM-1 DNet mechanical equipment component vibration signal reconstruction based on CS can be realized in the following three steps:  
**Step 1** The sensors collect and compress bearing vibration signals \(y\) and then transmit the signals to the local data center.  
**Step 2** The local data center uses the collected historical data to train the ADMM-1 DNet network model, with the training guidelines following the settings in Section 4.2.1.  
**Step 3** The trained network accepts the data from Step 1, reconstructs the compressed measurement values, and outputs the reconstructed signal \(\hat{x}\).  
As shown in Figure 3, the detailed online monitoring scheme for mechanical equipment component signals based on ADMM-1 DNet is provided.  
Figure 3: ADMM-1 DNet-based online signal monitoring scheme.  
Figure 2: Illustration of ADMM\(-\)1 DNet.
## 2 Related Work
### Detection Head  
The object detection head in the object detection model is responsible for detecting objects within the feature pyramid. It typically comprises a series of convolutional, pooled, and fully connected layers. To achieve a better detection performance, studies have proposed different detection heads [13; 16; 26] based on model characteristics. In the YOLOv5 model, the coupled head plays a crucial role in multiscale object detection; it uses a feature map extracted from the backbone network. The design idea of the module is simple, but it requires many parameters and computing resources, and is easy to overfit. The coupled head includes anchors, classification, and object detection.  
Anchors are used to define object boxes of different sizes and aspect ratios in object detection models. These anchor boxes provide reference points for the model to predict the bounding box coordinates and class probabilities. During the preprocessing phase before model training, K-means clustering [37] is applied to group similar object boxes into clusters, and the centroids of these clusters become the anchor box dimensions.  
Classification determines whether a detection box contains an object. The features extracted from the detection box typically pass through a fully connected layer, followed by a softmax activation function. The softmax function assigns class probabilities to each class, and the box is assigned to the class with the highest probability if it exceeds a certain threshold.  
Figure 1: YOLOv7-tiny network structure.  
Object detection regression is used to refine the position and size of each detection box. Typically, this regression is performed using a fully connected layer that learns to predict the necessary adjustments to the initial bounding box coordinates.  
YOLOv7 introduced an auxiliary head to the head side for training. When the auxiliary head is used for training, the loss of the auxiliary and detection head is integrated, and the training of the model is deeply supervised. This is equivalent to performing high-level local model ensemble operations in a network to enhance the overall performance of the model.  
In YOLOX, the use of a decoupled head for classification and localization was designed to effectively reduce the number of parameters and computational complexity, while simultaneously enhancing the model's generalization ability and robustness. This architectural choice improved the efficiency and overall performance of the YOLOX object detection model. For each level of feature pyramid network (FPN) features, a 1 \(\times\) 1 convolutional layer was initially applied to reduce the number of feature channels to 256. This reduction in feature channels helped manage computational complexity while preserving relevant information. Two parallel branches were introduced, each consisting of two 3 \(\times\) 3 convolutional layers. These branches were responsible for the classification and localization. Finally, an additional Intersection over Union (IoU) branch was added to the positioning branch.
###### Abstract  
This research paper provides an in-depth examination of the mechanical characteristics of 3D-printed specimens made from acrylonitrile butadiene styrene (ABS) and resins akin to ABS, with a focus on two widely used 3D printing methodologies: fused deposition modeling (FDM) and stereolithography (SLA). The study investigates how variations in 3D printing technology and infill density impact mechanical parameters such as Young's modulus, tensile strength, strain, nominal strain at break, maximum displacement, and maximum force at break. Tensile testing was conducted to assess these critical parameters. The results indicate distinct differences in mechanical performance between FDM- and SLA-printed specimens, with SLA consistently showing superior mechanical parameters, especially in terms of tensile strength, displacement, and Young's modulus. SLA-printed specimens at 30% infill density exhibited a 38.11% increase in average tensile strength compared to FDM counterparts and at 100% infill density, a 39.57% increase was observed. The average maximum displacement for SLA specimens at 30% infill density showed a 14.96% increase and at 100% infill density, a 30.32% increase was observed compared to FDM specimens. Additionally, the average Young's modulus for SLA specimens at 30% infill density increased by 17.89% and at 100% infill density, a 13.48% increase was observed, highlighting the superior mechanical properties of SLA-printed ABS-like resin materials. In tensile testing, FDM-printed specimens with 30% infill density showed an average strain of 2.16% and at 100% infill density, a slightly higher deformation of 3.1% was recorded. Conversely, SLA-printed specimens at 30% infill density exhibited a strain of 2.24% and at 100% infill density, a higher strain value of 4.15% was observed. The comparison suggests that increasing the infill density in FDM does not significantly improve deformation resistance, while in SLA, it leads to a substantial increase in deformation, raising questions about the practicality of higher infill densities. The testing data underscore the impact of infill density on the average nominal strain at break, revealing improved performance in FDM and significant strain endurance in SLA. The study concludes that SLA technology offers clear advantages, making it a promising option for producing ABS and ABS-like resin materials with enhanced mechanical properties.  
Keywords:additive manufacturing; FDM; SLA; ABS filament; ABS resin; mechanical parameters +
Footnote †: journal: Applied Sciences
# Inactivation of Contaminated Fungi in Rice Grains by Dielectric Heating
## 2 Materials and Methods
### Experimental Desing for Dielectric Heating System
#### 2.3.2 Determining the Optimal ISM Frequency Bands  
Specific frequency range of RF and MW were studied at 40 to 8500 MHz based on the industrial, scientific, and medical (ISM) frequency [44]. Then, the dielectric constant (\(\varepsilon^{\prime}\)) and loss (\(\varepsilon^{\prime\prime}\)) at 40.68 MHz of RF, and 915 and 2450 MHz of MW were measured and analyzed. The optimal frequency was calculated using the ratio between dielectric properties of _Aspergillus_ sp. BP17 and rice samples, and the highest of the ratio was chosen as optimal ISM frequency (MHz).
# Transparent Redirections in Content Delivery Networks
## 3 SDN Based Transparent Redirection  
The state-of-the-art chapter in this article introduced existing transparent redirection methods; however, each redirection method requires certain modifications on the endpoints, which prevents ISPs and content providers using legacy closed source CDN surrogate servers. The next section describes our proposed solution which allows transparent redirection of requests. The solution does not require any additional changes to the TCP/IP stack or the operating system kernel, which can be achieved using SDN.  
Software Defined Networking (SDN) is a new approach toward computer networking. SDN's main idea is to separate the control plane from the data plane to create a centralized controller. This centralized controller controls multiple data plane network devices, called forwarders in SDN terminology. Forwarders are fast packet forwarding devices that are not capable of making any forwarding decisions until they are configured by the controller. The idea of this approach to eliminate the problems with compatibility of different vendors. It supports BYOD (Bring Your Own Device) and vendor independence. In today's agile and elastic networks devices must respond quickly and adapt to the changes. Implementing new services requires an enormous amount of effort with legacy devices and often results in a need to replace the existing infrastructure to newer devices [25]. SDN improves network automation by using a common API to abstract the underlying network details from the orchestration and provisioning systems and applications. The centralized environment brings a better network reliability and security, uniforms the policy enforcement and decreases the number of configuration errors [26].
## 4 Results and Discussion
### Internal Cavity Pressure and Excess Pore Pressure at the Cavity Wall  
In this section, the characteristics of the cavity expansion curve (i.e., variations of the total and effective internal cavity pressure, \(\sigma_{a}\) and \(\sigma^{\prime}{}_{a}\)) and the variations of excess pore pressure at the cavity wall were analyzed. Note that the results of \(R_{s}=1.0\) are equivalent to that of Chen and Abousleiman [22] (i.e., the results in the MCC model), which would be taken as a benchmark to highlight the influences of the initial structure and subsequent destruction of soils on these variations.  
As shown in Figures 8 and 9, both the total internal cavity pressure \(\sigma_{a}\) and the excess pore pressure \(\Delta u\) increase sharply at the initial stage of cavity expansion, but then become slowly with an increase in \(a/a_{0}\). When \(a/a_{0}\) is greater than 5.0, both \(\sigma_{a}\) and \(\Delta u\) reach constant values. The comparisons between these results for soils with different \(R_{s}\) indicate that the structure has significant influences on the variations of \(\sigma_{a}\) and \(\Delta u\). Specifically, the greater the \(R_{s}\), the larger \(\sigma_{a}\) is required and the larger \(\Delta u\) will be generated when the cavity expands to the same \(a/a_{0}\). In addition, for heavily over-consolidated soils (\(R=10\)), a negative excess pore pressure is generated at the instant of cavity expansion, but it dissipates immediately with the further expansion, as shown in Figure 9.  
The variations in the effective internal cavity pressure \(\sigma^{\prime}{}_{a}\) with \(a/a_{0}\) are plotted in Figure 10. For \(R_{s}=1.0\), the variation trends of \(\sigma^{\prime}{}_{a}\) along \(a/a_{0}\) are consistent with those of the total internal cavity pressure \(\sigma_{a}\). For \(R_{s}=1.2\) and 1.5, \(\sigma^{\prime}{}_{a}\) first increases to the peak values, and then decreases gradually with a further increase in \(a/a_{0}\). Finally,  
Figure 8: Relationship between the total internal cavity pressure \(\sigma_{a}\) and the cavity radius \(a/a_{0}\).  
Figure 9: Relationship between the excess pore pressure \(\Delta u\) and the cavity radius \(a/a_{0}\).  
reaches the same value as that of the corresponding reconstituted soils (\(R_{\rm s}=1.0\)), although the peak value of \(\sigma^{\prime}{}_{a}\) increases with \(R_{\rm s}\). The softening behavior of structured soils can be attributed to the release of accumulated stress due to the degradation and crushing of the structure. The same effective stress state is reached for the structured and the corresponding reconstituted soils, which indicates that the structure of soils at the cavity wall was completely destroyed. In fact, \(\sigma^{\prime}{}_{a}\) can be calculated from the measured values of \(\sigma_{a}\) and \(\Delta u\), which can be used to estimate the degree of destructuration for structured soils, as mentioned by Sivasithamparam and Castro [31].
# Recent Progress Trend on Abrasive Waterjet Cutting of Metallic Materials: A Review
## 3 Abrasive Waterjet Cutting Process Parameters and Influences
### Abrasive Waterjet Cutting Input Process Parameters Functions and Influences
#### 3.1.1 Hydraulic System  
The hydraulic system is the waterjet pressure denoted by (P) and measured in MPa or PSI. A continuous flow of pressurized water generated from the AWJM water pressure pump drives the cutting head controlled by an accumulator and pressure tubing [3,6].  
**Impacts of waterjet pressure:** Waterjet pressure affects the distribution of water and jet abrasive particles during material erosion processes. [PERSON], [PERSON] et al. [82] have recommended that high-level pressure, with a value of 399 MPa, can acquire superior surface quality in cutting brass-360. Additionally, [PERSON] et al. [83] have utilized an ultra-high-pressure (UHP) waterjet cutting system in evaluating deformation on materials with the same composition but different thicknesses such as Al-6061 aluminum alloy, brass-353, AISI 1030, and AISI 304 steel materials. Their experiment showed that a very high waterjet pressure negatively affects the surface roughness as the thickness of the material decreases. A high-level water pressure produces high velocity, resulting in a stronger impact of abrasive particles [49]. Ultra-high-pressure (UHP) AWJM pumps provide water pressure to the cutting head at continued pressures from 40,000 psi (276 MPa) to 87,000 psi (600 MPa) and have progressed its industrial application since its commercialization due to its wide range of application, i.e., 2D shape cutting, surface grounding, weight reduction of space-borne mirrors, and various machining tasks tasks [84]. [PERSON] et al. [85] have observed that geometrical tolerances were obtained by increasing waterjet pressure to a value of 300 MPa when cutting Hardox 400 steel. The achieved results denote that by the increase of jet pressure, the surface quality improves and the geometrical errors are reduced. This is similar to [PERSON] et al. [79] as they have concluded that a jet pressure of 240 MPa can yield a high material removal rate for AWJ cutting of EN24 Steel 14. These previous works established that waterjet pressure is directly proportional to the depth of jet penetration and the material removal rate.