esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Scheme
### Task-Adaptive Parameter Transformation  
In the meta-learning stage, we consider \(N\) tasks, each consisting of \(n\) animal sound samples with different characteristics (knowledge). When these tasks have very diverse knowledge, MAML may have difficulty in generalizing them into common knowledge. Initial parameters set through such generalization may result in poor ASC performance. To address this, we convert the initial parameters \(\theta\) directly to task-specific parameters \(\overline{\theta}\) according to task suitability. The task-specific parameters can provide better ASC performance compared to the original MAML by considering task-specific knowledge as well as common knowledge.  
Two essential factors to be determined in the process are (i) the suitability of the initial parameters \(\theta\) to each task and (ii) the amount of transformation according to the suitability. In order to assess the suitability of \(\theta\) to the \(i\)-th task \(T_{i}\), we use gradients, \(\
abla_{\theta}\mathcal{L}_{T_{i}}^{D_{T_{i}}}(f_{\theta})\) (Line 9). Although gradients are typically utilized to update parameters via a gradient descent, they also contain information about optimization and parameter quality. Thus, gradients can also be used to represent the meta-information (i.e., suitability) of parameters to a task [20; 21].  
In order to obtain an optimal set of transformations of the initial parameters, we construct regression models. The new models \(g_{\varnothing_{\gamma}}\) and \(g_{\varnothing_{\beta}}\) (parameterized by \(\varnothing_{\gamma}\) and \(\varnothing_{\beta}\), respectively) take the gradients of \(T_{i}\) as inputs and generate transformation variables \(\gamma_{i}\) and \(\beta_{i}\). These variables are used for transforming the task-wide initial parameters \(\theta\) into task-specific parameters \(\overline{\theta}_{i}\) of \(T_{i}\) (Line 10-13). Here, the two models \(g_{\varnothing_{\gamma}}\) and \(g_{\varnothing_{\beta}}\) are two-layer multilayer perceptrons, with ReLU and tanh as the activation functions at the end, respectively. Further, \(\gamma_{i}=\left\{\omega_{i}^{j}\right\}\) and \(\beta_{i}=\left\{\beta_{i}^{j}\right\}\) are sets of the layer-wise transformation variables of \(T_{i}\) for the \(j\)-th layer of the DeepASC model parameter \(\theta_{i}\).  
In the adaptation stage, we transform the task-wise parameters \(\theta\), which are adaptive to \(T_{target}\), with the gradients \(\
abla_{\theta}\mathcal{L}_{T_{target}}^{D_{T_{target}}}(f_{\theta})\) and conduct fine-tuning using Equation (1).
# Empirical Evaluation of TLS-Enhanced MQTT on IoT Devices for V2X Use Cases
## 5 Performance Evaluation  
This section quantifies the resource requirements associated with TLS phases, over both Ethernet and WiFi. For the handshake phase, we focus on measurements of CPU cycles, execution time, energy consumption, and maximum Resident Set Size (RSS), all conducted over Ethernet. WiFi measurements were not performed at this stage, as the low volume and short duration of handshake message exchanges minimize the influence of the underlying network medium. Consequently, the Ethernet-based results are considered a reliable proxy for wireless performance in the handshake phase. In contrast, for the data exchange phase, we systematically evaluate the impact of five distinct message packaging scenarios on CPU cycles, execution time, end-to-end latency, energy consumption, maximum RSS, and goodput--using both Ethernet and WiFi interfaces and for both the Chacha20_Poly1305 and AES_256_GCM cipher suites. These metrics collectively provide insights into the resource and performance implications of different application-layer architectural choices.
# Underestimation of Dry Matter of Anaerobic Media with High Bcarbonate Concentration
## 3 Results and Discussion
### Bicarbonate Interference
#### 3.2.3 Importance and Effect of the Correction for Accurate DM Estimations  
The question that arises is the interest and importance of the correction linked to the decomposition of the bicarbonate; is it significant, in practice, within the margin of error of the technique? To do so, the actual bicarbonate content of the medium must be first determined by an accurate method.  
Bicarbonate can be determined by CIT, but titrimetric methods are the most used in anaerobic digestion, among others, the FOS/TAC method [22]. However, this method overestimates the bicarbonate values in a wide range, as shown by [PERSON] et al. (2021) [23]. The calculation formulas developed by [PERSON] et al. were applied to the influent and effluent of a 1000 L pilot digester, operating under steady state mesophilic conditions (data not shown), considering a uniform thermal decomposition of 37% of the sodium bicarbonate, measured by the modified FOS/TAC method. The measurements and balances, presented in Table 5, were obtained for a full-residence time (20 days).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Samples Tested** & **Initial Fresh Mass (g)** & **DM** & **Loss at 105 \({}^{\circ}\)C** \\ \hline \multicolumn{4}{c}{Effluent from the reactor treating blue mussel subproducts} \\ Sample 1 & 10.01 \(\pm\) 0.01 & 0.21 \(\pm\) 0.01 & – \\ Sample 1 plus bicarbonate & 12.55 \(\pm\) 0.10 & 0.29 \(\pm\) 0.01 & \(37\pm 1\%\) \\ Sample 2 & 50.00 \(\pm\) 0.01 & 0.95 \(\pm\) 0.01 & – \\ Sample 2 plus bicarbonate & 51.00 \(\pm\) 0.01 & 1.63 \(\pm\) 0.01 & \(32\pm 1\%\) \\ \hline \multicolumn{4}{c}{Effluent from the reactor treating intermediate energy crops} \\ Effluent & 50.00 \(\pm\) 0.01 & 2.39 \(\pm\) 0.01 & – \\ Effluent plus bicarbonate & 52.00 \(\pm\) 0.01 & 3.59 \(\pm\) 0.01 & \(39\pm 1\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Bicarbonate losses of a concentrated solution after heat-drying and lyophilization.  
The results show that when the bicarbonate content is important, these corrections are far from negligible; the DM contents are underestimated and the balances incorrect. The OM conversions are not affected, as discussed below.
## 3 Proposed Scheme
### Proposed SDPCACM Design for NDN
#### 3.1.1 Network Model  
Three planes--the administration plane, the control plane, and the data plane--make up the proposed SDN architecture. Though the control plane manages all actions of a router that are employed to gather and distribute traffic to cope with the performance of the network, the management plane is made up of various APIs created for various objectives and implemented upon devices by means of a northbound user interface. The data plane is made up of forwarding devices which forward packets, together with data and interest packets.  
For the integration of these controllers to work together on the control plane, a mobility management program is needed. Northbound interfaces are used for communication between the SDN controller and the mobility management application. To facilitate the task of dissemination of mobility supervision applications, the communication between the SDN and the central controllers is carried out via an east-west contact interface. The controller, however, has a strong understanding of the status of the mobile consumers (MCs) as connected with the devices to send the packets to the MCs through a southbound interface, which is utilized for the collaboration of the data and control plane.  
Three network elements--the NDN router, the user (MC), which includes an NDN access router (NAR) along with the NDN producer, as well as the controller as illustrated in Figure 4--are included in our planned architecture.
# Application of Artificial Intelligence in Wind Power Systems
## 4 Artificial Intelligence Algorithms
### Algorithms for Wind Turbine Maintenance  
Machine learning classifiers (random forest, extra trees, logistic regression, K-nearest neighbor, naive Bayes) facilitate error detection and classification of sensor data such as vibration signals and the state of the pitch system [24; 75; 77]. Random forest and extra trees, for example, have shown a detection accuracy of around 80% for faults in the blade system, and logistic regression in combination with clustering can detect damage to the rotor blades.  
Deep learning for image segmentation (using CNN and Siamese CNN architectures) is used for drone-based inspection of rotor blades, with segmented images highlighting cracks or surface defects [76].  
Hybrid predictive models combining ML and DL focus on predictive maintenance using SCADA data to predict failures hours in advance [78; 79; 80]. For example, LSTM or XGBoost models can be integrated into statistical process control to detect anomalies in the temperatures of gearboxes or generators. Big data frameworks also support the continuous 10 min detection of anomalies in large turbine fleets [81].
# Effects of Vibration on Adjacent Pipelines under Blasting Excavation
## 2 Experimental Design of Blasting Vibration
### Monitoring Results Analysis  
Ten separate blasts were surveyed on-site. Figure 5 shows the recording signal typical waveform at T5. The results are shown in Table 2. It can be observed the maximum _PPV_ occurred at point T5 at 1.36 cm/s, and the minimum occurred at point T10 at 0.72 cm/s. The _PPV_ distribution characteristics on both sides of the blast source show the _PPV_ decreased by 0.15 cm/s, 0.13 cm/s, 0.14 cm/s, and 0.23 cm/s in three directions and composite vectors above the excavated area. In comparison, it decreased by 0.4 cm/s, 0.4 cm/s, 0.3 cm/s, and 0.64 cm/s above the unexcavated area, respectively. The _PPV_ attenuation above the excavated area was significantly lower than the _PPV_ attenuation in the unexcavated area, which indicates that the ground vibration was amplified when the blasting vibration was propagated in the excavated cavity area. Considering the maximum _PPV_ was only 1.36 cm/s, the damage or destruction to the buried pipeline due to blasting excavation was limited.  
Figure 4: Monitoring locations.  
Figure 5: Vibration velocity curve in each direction at ground-soil-monitoring point T5.
## 1 Introduction
What's the impact of MOT on the stations' WRMS and long-term velocity? These are the first focus of this paper. In the spectral domain, it is well known that there is strong correlation between stations' seasonal variation, especially the vertical annual displacement and the surface displacement induced by redistribution of environmental loads [22]. However, recent publications have demonstrated that the imperfect GPS data processing strategy could also produce spurious seasonal signals in the long GPS time series. For example, the unmodeled or mismodeled diurnal and semidiurnal ocean tides could produce spurious signals with periods of nearly fortnightly, semi-annual and annual variations [23; 24; 25; 26], while [PERSON] and [PERSON] found that neglect of semidiurnal and diurnal atmospheric tides would also introduce anomalous signals with periods that closely match the GPS draconitic annual (\(\sim\)351.4 days) and semiannual period (\(\sim\)175.7 days) [27]. These kinds of spurious signals would interfere with the embedded environmental signals, thus resulting in wrong geophysical interpretation of the GPS coordinate time series. [PERSON] _et al._, also found that unmodeled subdaily signals would bias low-degree spherical harmonics estimates of geophysical loading at the level of 5%-10% [28]. What's the impact of MOT on the spectrum of global GPS coordinate time series? This is another motivation of this research. Finally, [PERSON] _et al._, found that there existed an anomalous harmonic with period as 1.04 cycle per year (cpy) in the stacked global GPS time series, and the possible origin of this anomalous harmonic was from GPS technique errors, e.g., the repeating geometry of the GPS constellation [29]. Whether the coupling between MOT and the 11 main ocean tides would cause these kinds of anomalous harmonics or not is another issue to be resolved. In this paper, we first determine the magnitude and spatial distribution of global IGS station's displacement caused by MOT. The OTL modeling method including the MOT correction is then implemented in GAMIT by expanding the 11 main ocean tides into 342 constituents. Based on both the original and the modified GAMIT software, the GPS data of 109 globally distributed IGS stations spanning from June, 1998 to December, 2010 has been reprocessed with state of the art models according to IERS Conventions 2010. Finally, quantitative analyses have been done on two sets of GPS coordinate time series in both time and frequency domains to evaluate the contributions of MOT to global GPS coordinate time series. Results of this paper may provide numerical support to the recommended data processing strategy in the IERS Conventions for crustal movement and interpretation of geophysical signal, as well as the target accuracy of ITRF to achieve 1 mm in position and 0.1 mm/a in velocity [30].
## 3 Results
### Muscle Size  
Muscle size was measured in 10 selected studies (13 cases). A total of 196 participants from the LLBERT group and 204 who underwent the control strategies were included. Thigh thickness, circumference, lean mass, and quadriceps cross-sectional area were used to assess the muscle size of the affected lower limbs. In contrast, wrist and forearm circumferences were used to evaluate the effects of the interventions on the upper-limb muscles. Although four studies [33; 45; 46; 48] (six cases) reported increases in muscle volume after LLBFRT, the results of our meta-analysis revealed no significant effects (SMD = 0.34; 95% CI: \(-\)0.28 to 0.95; heterogeneity: \(\chi^{2}\) = 96.42, df = 12, l\({}^{2}\) = 88%; and overall effect: Z = 1.08, \(p\) = 0.28) (Figure 4). In fact, five other studies [40; 41; 42; 43; 44] (6 cases) found significant effects favoring control interventions, while the remaining study did not show any effect [50].  
Regarding the lower limbs, significant increases in quadriceps CSA [48], thigh thickness [33], and thigh circumference [45] were observed after LLBFRT programs of at least 6 weeks (12 sessions) using 80% AOP. The same effects were found on upper limbs since wrist and forearm circumferences increased after 4-weeks of LLBFRT programs in which incremental AOP (40-80%) was performed [46]. In contrast, the effects on muscle size were not significant for LLBERT-based interventions compared with similar programs (8 to 16 weeks of low-load resistance training) without blood flow restriction. Specifically, the effects on thigh lean mass [40], thigh circumference [43; 44], thigh thickness [41], and quadriceps CSA [42] were evaluated.  
Figure 3: Forest plot displaying the difference in muscle strength between the experimental (LLBERT) and control groups [40; 41; 42; 43; 44; 45; 46; 48; 49; 50]. Letters (a–c) indicate different outcome assessments in the same study (cases).
## 4 Results and Discussion
### PCL-GO Dataset
Regarding the proposed nonlinear approach, the RMSE values have been calculated using the low dimensionality feature map, obtained with the AANN, and the reconstructed feature map obtained using Equation (9), where \(\Phi_{\mathrm{z}}(\mathrm{i,j})\) and \(\mathrm{E}_{\mathrm{z}}\) are respectively the endmembers and the fractional abundances derived using the linear approach in the feature space. Table 2 reports the RMSE values obtained after reconstructing the feature and the original data maps. Figure 7 graphically represents the per-pixel root mean square error (RMSE) obtained after reconstructing the Raman Spectral maps, using the linear approach and the proposed nonlinear approach, respectively. As can be seen, the proposed nonlinear method improves the quality of the map reconstruction. \begin{table}
\begin{tabular}{c c c} \hline
**RMSE** & **Linear Approach** & **Nonlinear Approach** \\ \hline PCL-GO & 2.24 & 0.0096 \\ \hline PLA-GO & 1.012 & 0.0164 \\ \hline \end{tabular}
\end{table} 
Table 2: RMSE values derived between the original map and the reconstructed one, using linear (left) and nonlinear (right) approaches. Figure 6: Comparisons between reference spectra (GO on the **left**, PCL on the **right**), and the spectra most similar in terms of SAD. Figure 7: RMSE reconstruction errors for linear (**left**) and nonlinear (**right**) unmixing algorithms applied to the PCL-GO structure.
## 3 Temperature-Aware Routing Protocols
### HPR (Hotspot Preventing Routing)  
The Hotspot Preventing Routing, or HPR, algorithm [24] is an improvement of LTR and ALTR. In LTR and ALTR, reducing the network average temperature does not necessarily mean preventing any node from having a very high temperature. However, HPR not only prevents the formation of hotspot nodes in the network, but also prevents the packets from taking suboptimal paths and, thus, reducing the average network delay. This protocol achieves both objectives of preventing hotspots and reducing delay by means of the shortest hop algorithm and a threshold value.  
There are two phases in HPR: setup phase and routing phase. In the setup phase, nodes exchange information about the shortest path and initial temperatures and build a routing table based on that information. In the routing phase, first, the nodes use the shortest hop algorithm as long as no hotspots appear in the path. A hotspot is dynamically determined using a threshold value that is derived from both the average temperature of neighboring nodes and the nodes' own temperature. If the next hop node's temperature exceeds the summation of temperature of the sender node and threshold, then the packet will be forwarded to the \"coolest\" neighbor. Similar to LTR, HPR uses the MAX HOPS parameter, which drops packets that exceed this threshold value, and a list of recently visited nodes to prevent any routing loops.  
Figure 6: An example of Least Total-Route Temperature (LTRT).  
This protocol differs from its predecessors on how it defines when a node is marked as a hotspot. The previous protocols use a predefined temperature threshold value, and if a node exceeds this value, it is marked as a hotspot. However in HPR, the threshold value is dynamically calculated by each node considering the neighbors' temperature and its own.
# Development of Enabling Technologies for Ku-Band Airborne SATCOM Phased-Arrays
## 4 Developed Devices
### Transmit/Receive Active Module Design  
Each radiating element is directly connected with an active circuitry that provides the functionality of the Tx/Rx module. The Tx/Rx module (intellectual property of Airbus Italy [9]) is able to realign the linear polarization in receive and in transmit or, alternatively, may allow the generation of a circular polarization. The phase shifters used for the polarization control are also able, at the same time, to form the antenna beam imposing the correct relative phase delay between the elements. The active modules are composed by the receive device and the transmit device (the assembled Tx/Rx device is shown in Figure 12).  
The manufactured modules are presented in Figure 13: In particular, the control board sides are shown. Their dimensions are 20.6 by 7 by 37 mm\({}^{3}\), with a weight of 15 g.  
Figure 11: E-plane and H-plane radiation patterns for Rx at 10.7 GHz (**a**) and (**b**) and at 12.75 GHz (**c**) and (**d**); for Tx at 14.25 GHz (**e**) and (**f**). Continuous blue line: measured results, dashed red line: numerical simulations.  
Figure 14a shows the channel gain behavior vs. the frequency when the attenuator and the phase shifter are set at the nominal value (zero). Figure 14b shows how all the SSPAs (Solid State Power Amplifier) power is moved between the two output ports when the relative phase between the two channels is properly set: The correct rotation of the linear polarization is provided by the opportunely balanced power.  
Figure 14: Tx/Rx active module performance: (**a**) Rx active module measured gain; (**b**) Tx active module control power allocation measured between the two output ports.  
Figure 12: Tx/Rx active module connected to the radiating element.  
Figure 13: Tx/Rx active module: (**a**) Tx and Rx RF active board; (**b**) Tx and Rx digital control board.
# Development of a Novel Concrete Curing Method Using Induction Heating System
###### Abstract  
This study aimed to develop an accelerated concrete curing method based on induction heating (IH) technology. The proposed curing method provides improved heating efficiency and safety since it directly heats only the metallic forms in a non-contacting manner. It also has the advantage of being capable of heating the concrete according to a desirable heating scenario. The effects of several parameters on its performance were evaluated using a finite element method (FEM)-based thermal analysis and heating performance tests. The FEM analysis revealed the steel form to be appropriate for the IH system. The analysis also revealed that equally spaced three-turn coils yielded increased temperature uniformity in the steel form, which was verified by results of the steel form heating experiments. Furthermore, the minimum temperature generated in the form was sufficient for concrete curing. The efficiency of the use of IH for concrete curing and the effects of curing parameters were further investigated through compression tests after applications of various curing methods and by examining the temperature distributions during curing. The test results revealed early strength development even under water freezing conditions. This demonstrated the effectiveness of IH for concrete curing in cold weather. However, the efficiency decreased when the cross-sectional dimension of the specimen increased. The test results also verified that the maximum temperature and duration of induction heat curing affect the early age strength of concrete.  
induction heating; concrete; curing; cold weather; uniform temperature +
Footnote †: journal: Medical Engineering  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Development of a Novel Concrete Curing Method Using Induction Heating System. _Appl. Sci._**2021**, _11_, 236. [[https://doi.org/10.3390/app11010236](https://doi.org/10.3390/app11010236)]([https://doi.org/10.3390/app11010236](https://doi.org/10.3390/app11010236))
## 8 Structural Tests for Self-Repairing Efficacy Evaluation
### Comparison of Assessment Methods  
Multiple techniques were used for diverse assessments (Table 5). Measurement of the autonomous healing efficacy of a material through its macro-mechanical properties is the method adopted by most researchers. Moreover, this method is considered to be optimum because it focused on the macrostructure properties and durability, making the findings more dependable. To further improve the dependability of the findings, some studies carried out the durability tests followed by a microscopy to examine the widths of the formed crack. More dependable results were obtained by the microscopic observation in tandem with the microstructures assessment. To ascertain the self-healing effectiveness, macro- and microscopic mechanical tests were also performed by a few researchers (Table 2). Micro- and macro-structural assessments were conducted to determine the autonomous healing efficacy, wherein, sometimes, the former was performed in tandem with the durability tests. The sole use of durability tests to ascertain the autonomous healing efficacy was observed to be a less dependable method. Furthermore, two investigations were made for nano-level assessments to ascertain the autonomous healing efficacy (Table 2). A comprehensive overview of the literature indicated that the macro-mechanical and durability tests, nanostructure and microstructure assessments have never been utilized simultaneously. Increased dependability in determining the autonomous healing efficacy can be deduced in the future studies by forming an approach that integrates the abovementioned versatile techniques.