esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Impact of Defrosting  
To study the effect of defrosting on the HHP system, configuration 2 was analyzed for both heat pumps with and without a defrosting cycle. The results are presented in Figure 11. When the heat pump operates in reversible mode, defrosting is typically achieved by inverting the cycle. As seen in Figure 11a, defrosting negatively impacts HHP performance across all heat pump sizes, with a nearly 3% reduction in \(\text{SCOP}_{\text{net}}\) at \(\text{T}_{\text{cut-off}}=-3~{}^{\circ}\text{C}\). For higher \(\text{T}_{\text{cut-off}}\) values, the effect of defrosting is reduced. This outcome can be explained by two factors: a reduction in the energy supplied to the building and a decrease in heating demand due to the energy removed by the heat pump in cooling mode, since weather conditions do not force the system to activate defrosting [40]. Additionally, electrical consumption rises because of the energy required to melt or prevent the frost [43]. When the heat pump uses a defrost cycle, the number of on-off cycles for both the heat pump and the boiler increases with the size of the heat pump (Figure 11b). However, the opposite trend is observed for heat pumps that do not use a defrost cycle. This is due to the wider extension of humid and cold working periods for larger sized HP pumps. Looking at Figure 11c, it can be observed that the EWI remains unchanged for the heat pump, regardless of whether a defrost cycle is used. As expected, the operating cost  
Figure 10: The effect of the cut-off temperature and heat pump size on the environmental and economical KPIs: (**a**) EWI, (**b**) OC.  
for a heat pump with a defrost cycle is higher due to the increased electrical consumption needed to melt the frost and the reduced overall efficiency.  
Figure 11: Considering the effect of defrosting cycles for configuration 2: (**a**) SCOPnet, (**b**) on–off cycles, (**c**) EWI & OC.
## 3 Stabilization Problem  
In this section, we suppose that \(v_{in}(t)=V_{in}\). In practical situations, one of the common stabilization problems is to maintain voltage and currents around some specific values. Strictly speaking, defining a classical notion of equilibrium state for systems described by model (12) is meaningless. By contrast, it is possible to define equilibrium points for system described by model (13). Because its trajectories can be arbitrarily approximated by the ones of model (12), the set of equilibrium points associated with (13) is of a particular interest for controls designed from model (12). These relations are particularly analyzed in [30] where the notion of solution for (12) is discussed (Caratheodory, [PERSON]'s solutions, etc.). Introduce the following notations:  
\[B_{0}(x)=\left[\begin{array}{cccc}B_{1}(x(t))&\cdots&B_{M}(x(t))\end{array} \right]\text{ and }D_{0}(x)=\left[\begin{array}{cccc}D_{1}(x(t))&\cdots&D_{M}(x(t))\end{array}\right] \tag{15}\]  
The set of equilibrium points associated with (13) can be expressed as  
\[X_{eq}=\{x_{e}\in\mathbb{R}^{n},\,\lambda_{e}\in\Lambda_{R}:A_{0}\ x_{e}+B_{0 }(x_{e})\lambda_{e}\ =0\} \tag{16}\]and model (12) is given by  
\[\left\{\begin{array}{l}\dfrac{dx(t)}{dt}=A_{0}x(t)+B_{0}(x(t))\lambda(t)\\ y(t)=C_{0}x(t)+D_{0}(x(t))\lambda(t)\quad\lambda:\mathbb{R}\rightarrow\Lambda_{S }\end{array}\right. \tag{17}\]  
Associated with each \(x_{e}\in X_{eq}\), we can define an output \(y_{e}\). The set of admissible outputs is given by  
\[Y_{eq}=\left\{y_{e}\in\mathbb{R}^{p},\ x_{e}\in X_{eq},\ \lambda_{e}\in\Lambda_{R}:y_{e}=C_{0}\ x_{e}+D_{0}(x_{e}) \lambda_{e}\right\} \tag{18}\]  
Related to the practical problem of interest, a boost or buck property of the converter can be invoked. In general, such a property is associated with the gain \(G(\lambda_{e})\) between the voltage \(V_{in}\) and an admissible output \(y_{e}\). Then, the set of the admissible gains of the converter \(G(\lambda_{e})\) can be defined as follows  
\[y_{e}=G(\lambda_{e})V_{in}\ \text{where}\ G(\lambda_{e})\in\mathcal{G}_{eq} \tag{19}\]  
with  
\[\mathcal{G}_{eq}=\left\{G(\lambda_{e})\in\mathbb{R}^{p},\ \lambda_{e}\in \Lambda_{R}:G(\lambda_{e})=-\left[\sum_{i=1}^{M}\lambda_{ei}(C_{0}+C_{i}) \right]\left[\sum_{i=1}^{M}\lambda_{ei}(A_{0}+A_{i})\right]^{-1}\left[\sum_{i =1}^{M}\lambda_{ei}B_{i}\right]\right\} \tag{20}\]  
**Assumption A1**.: _We consider that voltage and currents are accessible for measurements; thus, the complete state is measurable for control._  
Consider an equilibrium point \(x_{e}\in X_{eq}\) and introduce the error signal  
\[e(t)=x(t)-x_{e} \tag{21}\]  
Then, the stabilization problem can be defined as follows:  
**Problem 1**.: _Design a control law \(\lambda:\mathbb{R}\rightarrow\Lambda_{S}\), such that, for any initial condition \(e(0)\in\mathbb{R}^{n}\), the error dynamic is asymptotically stable, ensuring that_  
\[\lim_{t\rightarrow\infty}e(t)=0 \tag{22}\]  
_and, for all \(t>0\), the error \(e(t)\) is bounded implying that the state \(x(t)\) is also bounded with_  
\[\lim_{t\rightarrow\infty}x(t)=x_{e} \tag{23}\]
## 3 Conceptual Design  
This section provides an overview of system hardware and software specifications, and provides details on how the individual components interrelate.  
The focus of this research is on identifying whether AR facilities training. In this case, whether it can help complete beginners train how to play chords on the guitar correctly by demonstrating correct finger placement. In using AR to create interactions between the real and virtual world it is possible to create a more interesting train experience, meaning that training can be delivered in an interesting and satisfactory way [23]. Taking this into account, the research team developed a 3D model of an acoustic guitar that is implemented in AR to display the neck and frets of the guitar. When building this model, the intention was to construct a tool that would allow any novice to identify the strings and frets that needed to be played for a particular chord. To achieve this, a static colour is assigned to each finger. Figure 1 below shows the distribution of colours by finger.  
Using this colour code, learners are instructed how to position fingers on the strings when playing any given chord. As there was a perceived risk that learners might quickly forget or confuse the colours assigned to each finger, researchers also modelled a semi-transparent 3D hand to indicate correct hand and finger placements. This hand was made semi-transparent for improved visibility. Figure 2 below shows how the G chord is rendered by the software. In this figure it is possible to discern both of the aforementioned elements, the colour code used for finger placements and the 3D hand.  
Figure 1: Distribution of colours by finger.  
Figure 2: 3D model showing correct finger positions for the G chord.  
Some chords require more than one string to be pressed down by the same finger (barre chords). For such chords, a vertical line was created that passes through the fret/strings that need to be pressed down simultaneously by a single finger.  
The learner can visualize the model using a mobile device, can zoom in and out at will, and can also freely rotate the model 360\({}^{\circ}\) to allow them to accommodate the model into the position it is easiest for them to observe.  
To measure if a chord or short melody is played correctly researchers use the smartphone app Chord Detector. The time taken to perform chords or chord transitions is measured using a chronometer. Figure 3 below details the scenario design used to test the research hypotheses and measure usability.
## 4 Conclusions  
In this paper, a wide-range and low phase noise VCO is designed with the linear transconductance technique. The presented architecture is implemented in a 40-nm CMOS process, and the VCO core consumes 24 mW power from the power supply of 0.8 V. The proposed VCO achieves a wide range, of 13 GHz to 19 GHz, and offers 37.5% frequency tuning range. The noise of the active part of the VCO is lowered through the transconductance linearization technique, and the passive part noise is reduced by optimizing the switched capacitor array. Besides this, the utilized layout techniques reduce the parasitic resistance and inductance of the VCO routings, thereby improving the phase noise performance. The circuit shows a FoMT performance of 193.76 dBc/Hz, and superior performance compared to recent works. The main application of the proposed VCO is in the structure of Phase-Locked Loops for mm-Wave 5G transceivers.  
Conceptualization, A.H.; Formal analysis, A.H. and Y.P.; Investigation, A.H. and Y.P.; Project administration, K.-Y.L.; Software, A.H.; Supervision, K.-Y.L.; Writing: original draft, A.H. and K.-Y.L.; Writing: review & editing, Y.P. and K.-Y.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The EDA tool was supported by the IC Design Education Center (IDEC), Korea. The authors declare no conflict of interest.
## 3 Results and Discussion
### Phase Transition Characteristics
Although the conventional TES tank transmitted more power during the charging cycle, the bio-inspired TES tank transferred more power during the discharging cycle. This might result from the PCM's ability to disperse heat from the bio-inspired TES tank more quickly than it would in a shell and tube arrangement because of the increased heat transfer surface area created by replicating the biological structure of the human body. A similar kind of research was conducted by the authors of [25]; however, it compared the thermal performance of commercially available finned shell-and-tube TES tanks with commercially available shell-and-tube TES tanks. It was found that during the phase transition, the finned shell-and-tube TES tank transferred around 25.1 to 27.7% more power than the conventional shell-and-tube TES tank during the discharging cycle. Nonetheless, in this instance, the power transferred by the bio-inspired TES tank was 208% greater than that of the conventional shell-and-tube TES tank. When compared to the finned shell-and-tube TES tank, the power transfer percentage enhancement was considerably larger. However, the amount of PCM was maintained the same in all the TES tanks.
## 4 Application and Results
### Diagnostic Analysis
#### 4.4.2 Violin Plot  
As a further diagnostic tool, the violin plot is utilized in Figure 13(a),b to assess the predicted results of the developed models for DO concentration. The violin plot, which has the ability to indicate the probability distribution of an observed and predicted dataset, is categorized as a box plot with the integration of the kernel density plot [95]. Based on the legends of Figure 13(a), the median of the observed data was predicted by the MLP-III accurately (6.173 vs. 6.403), while the 25 th and 75 th percentiles in the CCNN-III had a better fit than the MLP-III. In addition, the MLP-III model overestimated the minimum, 25 th percentile, median, and 75 th percentile range of the DO concentration, whereas the CCNN-III model underestimated the 25 th percentile, median, and 75 th percentile range of the DO concentration. Overall, the violin plots indicated that the CCNN-III model performed better than the MLP-III model. Figure 13(b) explains that the VMD-MLP-III (\(K\) = 4 and \(\alpha\) = 10) model  
Figure 13: Taylor diagrams for evaluating the performance of the applied models.  
overestimated the minimum, 25 th, and median of the DO concentration, while the DWT-MLP-III (S6) underestimated the minimum, median, 75 th percentile, and maximum of the DO concentration. Overall, the violin plots indicated that the VMD-MLP-III (\(K\) = 4 and \(\alpha\) = 10) model performed slightly better than the DWT-MLP-III (S6) model.
# User Trust Inference in Online Social Networks: A Message Passing Perspective
## 4 Experiments
### Performance Comparisons
#### 4.3.2 The Second Set of Experiments with Reduced Feature Data  
From a quick look at the experiment setup, the second set of privacy-aware experiments conducted with reduced feature data revealed some similarities with the first set of experiments in the training set and test set split configuration. It is true that if we are to conduct another group of experiments in which users choose to opt out of usages of all their profile data and UGC data and even some of users deny the usage of their trust relationships in trust inference tasks, the experiments will be exactly the same as the ones in the first set of experiments using only the first category of auxiliary features (\(\mathcal{F}_{\text{TAUX}}\)) with specific training and test datasets.  
However, the fact is that they are not the same from the management perspective. The split training set and test set configuration is for model verification. On one hand, the aforementioned experiments are only particular ones in trust relationship inference in this paper. On the other hand, real-world privacy settings vary a lot from service to service, OSN to OSN, and it is worth exploring how a model performs in close to real conditions.  
Overall performance results  
Figures 7 and 8 show the results of the second experiment set with reduced feature data evaluated by the Accuracy and the F\({}_{1}\) score metrics, respectively. In general, for different feature set combinations, the performance of the proposed model and the comparison methods decreases when the ratio of removed features increases.  
Similarly to the results of experiments with full feature data, the proposed model works well with UGC features and outperformed other methods greatly in these experiments. This is the answer to the early question about the model's performance with partially available UGC data.  
A second peek at Recall with reduced feature data  
This set of experiments were a simulation of the real-world scenario where obtainable online social network datasets are incomplete due to various reasons. In particular, one whole category or certain types of data from a certain number of users are unavailable, when the users choose to limit the usage of their data by online social services through privacy settings. Although such an experimental setup in this paper does not cover all possible scenarios, it may shed some light on the study about how a model might work when some of the privacy-restricted data are unavailable.  
As previously discussed, a higher recall with acceptable precision achieved by a method to infer trust in OSNs is one of the ideal objects for online social services. In all 30 experiments from the second set, our proposed model achieved an average recall of 0.9409; 27 in 30 recalls were \(\geq\)0.90. The results for SVM, Decision Tree and random forest were, respectively: 0.7682 (2 recalls were \(\geq\)0.90), 0.7444 (none was \(\geq\)0.90), and 0.7844 (two recalls were \(\geq\)0.90). Refer to Supplementary Materials for the full results.
# Numerical Analyses of Ultrasonic Atomization Utilizing Acoustic Effects of a Beam Diaphragm
## 3 Numerical Analyses
### Finite Element Models  
One of the finite element models in the present study is shown in Figure 7. The analyses were conducted by finite element solver [PERSON] (MSC Software, Newport Beach, CA, USA). First, the eigenvalue problem of the diaphragm was solved, and then the harmonic response analyses of the whole oscillating body, with consideration of interaction between the diaphragm and water film.  
The diaphragm and water film were both modeled from thick shell elements using the Mindlin theory, in which out-of-plane components of shear stress are calculated [31]. A flat lattice-shaped mesh was created from quadrature shell elements. The number of elements and nodes were 3423 and 3200, respectively. Since the present model calculates steady-state vibration under harmonic excitation, the thickness of the water film and the position of material points in the water film were assumed not to change. Therefore, the constant thickness of the water film was assigned to the shell elements. The thicknesses of the water film for the cases of hydrophobic and hydrophilic diaphragms were set as 0.5 mm and 0.3 mm, respectively, based on approximate measurements of experimental results. Nodes on the water film were rigidly connected to corresponding nodes on the surface of the diaphragm by MPCs, as shown in magnified form in Figure 7.  
A boundary condition of fixed displacements was defined at the top of the diaphragm. A harmonic load was applied at the bottom of the diaphragm. In the experiment, the diaphragm was indirectly excited by the ultrasonic transducer, and the source of the excitation to the diaphragm was considered as a sub-harmonic wave of the primary wave with a frequency of 2.4 MHz from the transducer. To incorporate this condition into the model, the magnitude of the harmonic load was set as a dependent variable of frequency, with a relationship in which the magnitude was proportional to the square of the frequency.  
Table 2 shows the material properties used in the analyses. Coefficients of Rayleigh damping for the diaphragm were chosen as \(\alpha=-2.0\)\(\times\)\(10^{2}\) and \(\beta=1.0\)\(\times\)\(10^{-7}\), based on attenuation of the experimental results. Numerical damping \(\gamma\) = 5.0 \(\times\)\(10^{-3}\) was used. As already mentioned in Section 3.2, the viscous damping effect due to the interaction between the diaphragm and water film was incorporated through the user subroutine UCOMPL. The water film adhered to the surface of the diaphragm in the case of the hydrophilic diaphragm according to a formula of Newtonian fluids, while it freely slid on the surfaces in the hydrophobic sample. Therefore, the viscosity effect was only considered in analyses with the hydrophilic diaphragm.  
Figure 7: A finite element model of an oscillating body composed of the diaphragm and water film.
## 3 Results
### Qualitative Evaluation  
In order to evaluate the performance of the proposed method based on human visual perception and its qualitative enhancement ability, visual comparisons were made on examples representing different underwater image characteristics. Due to the refraction, absorption and scattering of light in the underwater environment, images are often distorted in hazy, greenish, bluish or yellowish tones. Therefore, scenes representing different types of distortion were selected from both Test-C60 and OceanDark datasets. Figure 2 shows three different distortion examples from the Test-C60 dataset: the first image is distorted in yellowish, the second in bluish and the third in greenish tones. These scenes illustrate common distortions specific to the underwater environment such as chromatic aberration and loss of contrast. Figure 3 presents scenes with low-illumination conditions and heavy haze effects from the OceanDark dataset. In both figures, the outputs of twelve successful underwater image enhancement methods are shown comparatively with the proposed method.  
In the comparison in Figure 2, [PERSON] et al.'s, HFM and the proposed method are seen to be superior to other methods in color accuracy capture and contrast enhancement. Among these three methods, HFM is slightly behind in correcting yellowish tones and balancing contrast in bluish images; [PERSON] et al.'s method produced better results in removing color tones and increasing contrast compared to HFM. The proposed method, on the other hand, exhibited the most successful performance in terms of both color accuracy and detail enhancement in all tones; it made background details more distinct especially in yellowish images, and stood out in both color and detail richness in bluish and greenish images.  
Similarly, in Figure 3, [PERSON] et al.'s, HFM and the proposed method stand out as the three most successful methods. The proposed method provided the best balance in haze removal and contrast enhancement, providing perceptually superior results compared to [PERSON] et al.'s and HFM. In general, the evaluations of Figures 2 and 3 clearly show that the proposed method provides the best performance in terms of color balance, contrast enhancement, detail enhancement and perceptual improvement. The obtained quality metrics and Wilcoxon analysis results also confirm this success.
## 3 The Integrated Sensing and Communication Platform
### LoRaWAN Communication Platform: LoRa Node, GW and Server
#### 3.2.3 Chirpstack LoRaWAN Server  
We consider ChirpStack open-source LoRaWAN Network Server stack [28] for the server side. Chirpstack provides open-source components to form the network infrastructure. Any instance of each component can be installed locally or in a cloud platform to construct the overall network infrastructure. Moreover, this infrastructure provides a user-friendly web-interface for device management and Application Programming Interfaces (APIs) for integration.  
It is also important to highlight that by default Chirpstack uses Message Queue Telemetry Transport (MQTT) protocol for publishing and receiving application payloads. MQTT is used by ChirpStack GW Bridge, ChirpStack Network Server, and ChirpStack Application Server. Figure 7 provides the overall network architecture including the sensor node/ED described in the previous subsection.
## 3 Optimization Problem Formulation
### Objective Function
#### 3.1.3 Customer Costs  
A major objective from the customer perspective is to minimize charging costs and maximize discharging intervals. However, battery life will deteriorate if there is excessive discharging, and so this price (\(w_{p}\)) must be included in the optimization problem and is calculated using the method described in [44]. The objective functions for customers are given by Equations (3) and (4).  
\[F_{3}=Minimize\sum_{n=1}^{N}\left((P_{EV\_Cbg(n,t)}\times C_{t})-(P_{EV\_Dbg(n,t)}\times R_{V2G})\right)\times\Delta t \tag{3}\]  
The minimization of \(F_{3}\) will reduce the charging costs by managing charging/discharging in proper time.  
\[F_{4}=Minimize\sum_{n=1}^{N}\left(P_{EV\_Cbg(n,t)}\eta_{ch}+\frac{P_{EV\_Dbg( n,t)}}{\eta_{Dch}}\right)w_{p}\times\Delta t) \tag{4}\]  
The minimization of \(F_{4}\) will help customers to decrease the battery degradation costs.