esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Adsorption and Desorption Curves of DAC Systems
The loading is defined as the adsorbed CO\({}_{2}\) and sorbent mass ratio, then it is depending on the adsorbent length. Figure 3: Breakthrough curves (CO\({}_{2}\) concentration at the outlet/CO\({}_{2}\) concentration at the inlet) for physisorbents. Figure 4: Breakthrough curves (CO\({}_{2}\) concentration at the outlet/CO\({}_{2}\) concentration at the inlet) for chemisorbents. Figure 5: Loading for physisorbents: (**a**) MIL-101, (**b**) MOF-177, and (**c**) MOF-5. Figure 6: Loading for chemisorbents: (**a**) SI-AEATPMS sorbent and (**b**) APDES-NFC-FD sorbent. Lower values of equilibrium loading are apparent for MOFs. For MOF-177, MOF-5, and MIL-101 this parameter is, respectively, 0.00043 mol/kg, 0.00093 mol/kg, and 0.00039 mol/kg. On the other hand, for SI-AEATPMS and APDES-NFC-FD the equilibrium loading is, respectively, 0.4 mol/kg and 0.85 mol/kg. The above discussion is related to the adsorption stage, when CO\({}_{2}\) is captured by sorbent. Heating is provided for desorption, as in a TSA process. Figure 7 shows CO\({}_{2}\) concentration as a function of time and length for MOFs, while Figure 8 reports the same parameter for chemisorbents. It is possible to see that the CO\({}_{2}\) concentration starts from a value corresponding to the inlet concentration of 0.016 mol/m\({}^{3}\), has a maximum close to the inlet of bed and after that, it decreases towards zero, as also reported in the work of [PERSON] et al. [82]. The maximum value is due to the impact of heating when the bed is still saturated with CO\({}_{2}\). The loading starts from the value at the inlet condition and has a decreasing trend.
## 4 Discussion
### Improving CGAN Performance  
The performance of CGANs can be improved with the help of a more efficient training dataset design. For example, using L8 and S2 scene pairs acquired on the same date can increase network accuracy. Our scene pairs (input L8 and target S2) had some temporal gaps (see Table 1), which could introduce some uncertainty due to the possibility of land surface changes occurring between each image acquisition by L8 and S2. In terms of spatial generalizability, this method can be applied to any region provided there exist observations from both sensors for that region within an acceptable time frame. CGAN's performance could be increased for the region of interest by fine-tuning the model on additional L8-S2 scene pairs acquired from that specific region and over multiple timestamps. Furthermore, instead of using a single L8 spectral band to predict each S2-like RE band, training our CGAN on multiple L8 spectral bands may allow the network to predict each RE band more accurately. To predict the RE bands, we used the spectral bands with the highest correlation with them following the study by [PERSON] and [PERSON] [7]. However, the strength of correlation typically varies across land use/land cover classes and was not addressed in this study. Therefore, measuring the spatial and spectral similarity between L8 and S2 spectral bands over different land use/land cover classes could give a better idea of which L8 spectral bands should be used for predicting the desired S2 bands.
## 2 Nonlinear Dynamics of Delta Robot Model
### The Velocity Second Order Sliding Mode Observer  
The proposed controller defined in (42) requires the knowledge of angular positions and velocities. However, we only use the low-resolution encoders to measure the position and velocity of joints for the experimental Delta robot system (34). Hence, a velocity estimator is essential for the practical controller. Popularly, the velocity values can be calculated by the backward differentiator (BD) technique from the position values.  
\[\dot{q}_{i,BD}(t)=\frac{q_{i}(t)-q_{i}(t-\Delta t_{s})}{\Delta t_{s}} \tag{58}\]  
where \(q_{i}(t)\) and \(q(t-\Delta t_{s})\) are the present and previous angular positions of the \(i\)th joint and \(\Delta t_{s}\) is the time-delay system.  
However, this scheme does not suffer only from the quantization noise due to the low-resolution encoder but also the time-varying delay system in different iterations. To effectively estimate the velocity for this kind of system, a combination of low pass filter (LPF) and second-order exact differentiation via the sliding mode (SOSM) [44; 45] is introduced in the experiment results of this paper. The velocity estimator algorithm for the \(i\)-th joint is presented as Equations (59)-(61) below.  
\[\left\{\begin{array}{l}\dot{q}_{i,SOSM}(t)=v_{i}(t)\\ v_{i}(t)=v_{1,i}(t)-\eta_{i}|\hat{q}_{i}(t)-q_{i}(t-\Delta t_{s})|^{\frac{1}{2 }}\text{sign}(\hat{q}_{i}(t)-q_{i}(t-\Delta t_{s}))\\ \dot{v}_{1,i}(t)=-\alpha_{i}\text{sign}(\hat{q}_{i}(t)-q_{i}(t-\Delta t_{s}) )\end{array}\right. \tag{59}\]  
\[\hat{q}_{i}(t)=\hat{q}_{i,SOSM}(t-\Delta t_{s})\Delta t_{s}+\hat{q}_{i}(t- \Delta t_{s}) \tag{60}\]  
\[\hat{q}_{i}(t)=\frac{\dot{q}_{i,SOSM}(t)+\frac{1}{f_{\text{c}}\Delta t_{s}}\dot {q}_{i}(t-\Delta t_{s})}{\frac{1}{f_{\text{c}}\Delta t_{s}}+1} \tag{61}\]  
where \(\dot{q}_{i,SOSM}\) is the SOSM velocity, \(\dot{q}_{i}\) is the recursive angular position estimation from \(\dot{q}_{i,SOSM}\). \(\dot{q}_{i}\) is the estimated velocity, \(f_{\text{c}}\) is the cut-off frequency of the low pass filter, and \(\eta_{i}\), \(\alpha_{i}\) are the parameters of the SOSM estimator.  
**Remark 4**: _It can be noticed from equation (59) that the velocity estimator using SOSM can achieve finite time error convergence. Boundedness conditions and stable tracking errors with the desired trajectory satisfy the finite-time stability of the sliding mode differentiator [44; 46]. Thus, the proposed controller and observer can be designed separately. In the practical assembly system, the angular velocity signal is always quantized by encoder values and contaminated by time-varying delays during estimation, so the control effort leads to chattering. A combination of SOSM and LPF can reduce the chattering phenomenon from the standard (first-order) sliding mode observer [47; 48] and preserve the advantage features of the robust exact differentiator._
## 3 Results and Discussion
### Analysis of Orthogonal Experiment Results  
#### 3.4.1 Range Analysis  
Range analysis uses range R\({}_{\text{i}}\) to determine the sensitivity order of each experimental parameter and find the optimal level combination. The range value of a factor intuitively reflects the fluctuation range experienced by the experimental indicator when the factor changes at different levels. The higher the range value, the greater the influence of the factor on the experimental indicators, which can be used to evaluate the sensitivity of each factor and further determine the optimal parameter combination. The results are shown in Table 7.  
Figure 9: Adhesion test of shotcrete coating: (a) artificial trampling test; (b) artificial rainfall test; (c) wind test. According to the data in the table above, the light transmittance values measured for all nine experimental groups were 0, indicating that the slurry coating layer can completely block light. Previous studies have shown that a 5 cm sand covering is sufficient to maintain the strength of geotextiles [20]. Using the thermal insulation performance of a 5 cm sand layer as a benchmark, while maintaining the upper surface temperature at 60 \({}^{\circ}\)C, the thermal insulation temperature of the sand layer's upper and lower surfaces was measured to be around 25 \({}^{\circ}\)C. In comparison, the thermal insulation temperature of the slurry-sprayed geotextile ranged from 15.22 \({}^{\circ}\)C to 18.67 \({}^{\circ}\)C, achieving 61% to 75% of the thermal insulation effect of the 5 cm sand layer. This demonstrates that the slurry-spraying process can effectively block most of the heat. Analysis of the thermal insulation temperature data reveals that the influences for each factor are ranked as follows: slurry thickness (C) > geotextile thickness (A) > clay content (B). Therefore, the sensitivity of thermal insulation temperature to each factor is in the order of C--A--B. The optimal parameter combination is a geotextile surface density of 500 g/m\({}^{2}\) (corresponding to a thickness of 3.4 mm), a clay content of 18.18%, and a slurry thickness of 3 layers (6 mm). To more clearly illustrate the trend of each factor's influence on the thermal insulation temperature, a trend chart was used for analysis, with thermal insulation temperature on the vertical axis and the different levels of each factor on the horizontal axis, as shown in Figure 10. The trend of the experimental indicators shows that the degree of influence of each factor follows the order [clay content < geotextile thickness < slurry thickness], and the thermal insulation temperature increases with the level of each factor.
## 5 Comparative Evaluations
### Database and Conditions  
We selected a total of 100 utterances: 50 semi-randomly selected utterances from LibriSpeech [17] and 50 semi-randomly selected utterances from Voice Cloning Toolkit (VCTK) [18]. The selections were semi-random so that we could obtain utterances from a specific number of speakers. LibriSpeech was originally sampled at 16 kHz to study automatic speech recognition, and VCTK was originally sampled at 48 kHz to study speech synthesis. The sampling frequency for both databases was standardized to 16 kHz.  
Inaudibility was evaluated by performing a sound quality test. Robustness was evaluated by carrying out basic robustness tests of ten processes as non-malicious interference [19]: normal (no interference), downsampling to 12 kHz (Resample-12), upsampling to 24 kHz (Resample-24), bit compression to 8 bit (Requant-8), bit expansion to 24 bit (Requant-24), conversion to Ogg format (Ogg), conversion to G711 coding (G711), conversion to G723.1 coding (G723.1), conversion to G726 coding (G726), and conversion to MP4 format (MP4). Blind-detectability was evaluated by carrying out frame synchronization tests and bit detection tests. Confidentiality was evaluated by performing bit detection tests when the embedding process and detection process of the front-side form and back-side form were switched. The bit rates of 4, 8, and 16 were used for evaluation.
## 4 Experimental Results  
The Tirano South database, shown in Figure 2, was reanalyzed in order to expose the uncertainties associated with prediction patterns. The stepwise procedure for generating and assessing prediction patterns, proposed by [PERSON] et al. (2017) [27], was also followed here, i.e., (1) use models to obtain prediction patterns from likelihood ratios, (2) cross-validate the patterns, (3) interpret the cross-validation results, and (4) obtain and compare the target and uncertainty patterns via equal-area ranks. Furthermore, step (5) was added, namely, analyze target, uncertainty, and 50% combination pattern relationships.  
Table 1 shows the likelihood ratios for the ISPs computed for the Tirano South study area. For the ISP units and value ranges, only the ratios \(\geq\)2 or \(\approx\)2 are shown in the table. A ratio of 2 represents a normalized frequency in the presence of occurrences that is twice that in their absence. The arbitrary value of 2 was used as an empirical rule of thumb to separate the supportive ISP units or value ranges from the unsupportive ones of the proposition in (1). A ratio of 1 represents that the frequency in the presence of occurrences is the same as the one in their absence, i.e., null support of the proposition. Only the  
maximum values of the supportive ratios and respective units and value ranges are shown in Table 1: two land-use classes (2.29 and 4.48); eight lithology units (5.19 to 5.52); three permeability classes (1.92, 2.06 and 1.91), (categorical ISPs); one range of aspect angles (maximum ratio 2.07); two curvature ranges (11.07 and 5.62); two elevation ranges (4.24 and 2.31); one internal relief range (5.97); and one slope angle range (4.26). All prediction patterns generated by the five models have been based on the supports provided by all the ratio values of which, for simplicity, only the most supportive are listed in Table 1.
# Service-Aware Clustering: An Energy-Efficient Model for the Internet-of-Things
## 2 The Energy-Efficient Internet-of-Things Model
### The SAC Algorithm
#### 2.4.4 Step 4: Backbone Construction  
This stage determines how the multi-hop routes are set up by the cluster heads to create a sensor network backbone. The cluster heads use their radio to create the sensor backbone as follows.  
* In order to optimize the routing process, each cluster head uses the closest cluster head as the next hop to the base station, as determined by the signal strength emitted by all neighboring cluster heads, including the base station.
* Each cluster head selects the strongest signal strength neighbor as its next hop to the gateway and adjusts its routing table accordingly by recording the IDs of its pre-hop node and next-hop node. Note that as currently designed, this backbone could lead to a tree-like or mesh topology.
# Retransmission Avoidance for Reliable Data Delivery in Underwater WSNs
## 2 Related Work
### Depth based Routing using Multiple Paths
\begin{table}
\begin{tabular}{|c|c|c|c|} \hline
**Protocol Name** & **Features** & **Achievements** & **Limitations** \\ \hline \multirow{3}{*}{DBR [6]} & \multirow{3}{*}{Multi-hopping} & \multirow{3}{*}{High PDR and less delay} & \multicolumn{2}{c|}{Void holes} \\  & & & waste energy and \\  & & & increase retransmissions \\ \hline \multirow{2}{*}{WDFAD-DBR [7]} & \multirow{2}{*}{Multi-hopping} & \multicolumn{2}{c|}{Increases PDR and} \\  & & energy efficiency & Delay of network increases \\ \hline \multirow{2}{*}{R-ER\({}^{2}\)R [8]} & \multirow{2}{*}{Multi-hopping} & \multicolumn{2}{c|}{Maximize network} \\  & & lifetime and balanced \\  & & energy consumption & \multicolumn{2}{c|}{Void regions} \\ \hline \multirow{2}{*}{VAPR [9]} & \multirow{2}{*}{Multi-hopping} & \multirow{2}{*}{Energy efficiency} & High delay \\  & using depth information & & and low PDR \\ \hline D-DBR [10] & Multi-hopping & Less delay and high PDR & High energy consumption \\ \hline GFGD [11] & Multi-hopping & Energy efficiency & No mechanism \\  & among small cubes & and less delay & to avoid void holes \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of Single Path Routing Protocols. Energy balanced strategies for maximizing the lifetime of sparsely deployed UASNs are proposed in [16]. In this research work, authors proposed energy balanced hybrid (EBH) data propagation algorithm which efficiently utilizes energy of the network. All the sensor nodes can switch their state among direct transmission and multi-hopping which reduces packet drop. Initially, the sensor nodes send data packets through multiple hops towards the destination. When a node's energy depletes, it propagates control message to the higher depth nodes. These high depth nodes start direct transmission with the sink node. EBH balances energy consumption, however, direct transmission and void holes increase energy consumption and delay. The limitations and achievements are summed up in Table 2.
## 4 Experimental Results
### Improved UDN
#### 4.1.3 Analysis of Multiple Channel Inputs  
Figure 10 shows the experimental results of investigating the influence of the different channel combinations introduced in Section 3.1. When the input data is the original three channels of RGB, the LAMR is 44.61%. When the input data is the first channel, the LAMR is 46.13%. When the input data includes both the first and the second channels, the LAMR is reduced by 4.77%. When the input data includes all three channels, the LAMR is reduced furtherly by 2.63%.  
The comparison results show that the original RGB image has the highest miss rate, which is lower than using the first channel alone. Combining more feature descriptions with the first channel, a much lower miss rate is obtained. Thus, the multi-channel feature input method can enhance the feature learning ability of the network and improve the recognition performance of the network.
# Learning-Assisted Rain Attenuation Prediction Models
### Contributions of This Paper  
In this article, we extensively investigate all the published works to the best of our knowledge of rain attenuation based on either the artificial neural network (ANN) or machine learning (ML) techniques. We qualitatively compared the accuracy of the LARA models. The major contributions of this article are:  
* The importance of switching from the traditional frequency to a higher frequency spectrum is previewed. Additionally, the reasoning is presented regarding why it needs to regulate the transmitting power at the end of the transmitter in an optimal manner.
* The LARA model's wide range of input features and up-to-date model's learning algorithms are focused on.
* We classified the LARA models based on the number of input features and model structure. Besides, a general scenario of the LARA models, including all the probable input features, is presented.
* A comprehensive picture of all the LARA models is presented. In contrast, the different properties of the models are tabulated: The model's link supports, experimental dataset, supporting frequency bands, model-specific input, optimization techniques used, and performance assessment criteria.
* Finally, open research topics are summarized.
# Bar-Wave Calibration of Acoustic Emission Sensors
## 3 Results and Discussion
### AE Sensor Response
#### 3.2.12. PAC F30a Sensor  
This sensor is a new design based on a composite piezoelectric element, giving a flat frequency response over 200-800 kHz in terms of the normally incident wave displacement sensitivity, as shown by the green dash curve in Figure 20. This differs substantially from the blue curve in this figure that represents bar-wave displacement calibration data. The dotted blue curve is for the bar-wave velocity calibration. The blue curve reaches the maximum of 14 dB at 212 kHz, but the peak of 75 dB for the velocity curve is achieved at 60 kHz. A dip in the spectrum exists at 1.1 MHz, but it is difficult to assess if this relates to the aperture effect without construction details.  
Figure 19: Receiving sensitivity of PAC R15a (BD72). The displacement receiving sensitivities are shown in blue curves and velocity sensitivities are in red. Solid lines are for the bar-wave data, while dashed curves are for the sensitivities to normally incident waves. Green curve is the velocity calibration to normally incident waves from PAC (converted from \(\upmu\)bar to m/s unit).  
Figure 20: Receiving sensitivity of PAC F30a (AA20). The displacement receiving sensitivity to bar waves is shown in solid blue curve. Green curve is the displacement receiving sensitivity to normally incident waves [4]. The velocity sensitivity corresponding to the solid blue curve is given in dotted blue curve.  
This case most clearly demonstrates different spectral characteristics for the two types of incident waves, normally incident and guided waves, requiring a suitable selection of a sensor in an AE application.
## 2 Data and Methods  
### Study Area  
The Tibetan Plateau, situated in Central Asia, holds the distinction of being the largest and highest plateau globally, covering much of southwestern China and most of the Tibet Autonomous Region. Its geographic coordinates range from 25\({}^{\circ}\)N to 40\({}^{\circ}\)N in latitude and 70\({}^{\circ}\)E to 105\({}^{\circ}\)E in longitude. Boasting an average elevation exceeding 4000 m, the TP surpasses many regions, leading to unique climatic and ecological characteristics. Because of its low latitude, high elevation, and intricate topography, the TP is characterized by its cold temperatures and scant precipitation. From 2001 to 2022, the mean annual temperature varied from \(-\)14 \({}^{\circ}\)C in the northern part to 24 \({}^{\circ}\)C in the southern part (Figure 1b). The majority of the area is subject to arid or semi-arid climates, with the total annual precipitation typically falling below 1000 mm in most regions (Figure 1c). The vegetation types and distributions in the TP are diverse, enriched and complicated by the variations in geographical conditions and altitudes. Alpine steppe and alpine meadows represent the predominant vegetation types, constituting 27.94% and 25.57% of the region, respectively. They are mainly distributed across the western, midland, and eastern regions of the TP. The vegetation primarily consists of shrubs in the eastern and southern regions, while alpine sparse vegetation is scattered throughout the entire area.
## 1 Introduction
Specifically, we firstly align a part of the intra-cell interference to the desired signal subspace at the CCUs by designing the processing matrices of relays. Then, the intra-cell and a part of the inter-cell interference are aligned to the same subspace at the CEUs. On the basis of that, both the intra-cell and inter-cell interference are mitigated simultaneously in two sub-time slots, whereas the other conventional methods need three sub-time slots. Numerical results show that the proposed scheme achieves higher SDoF and sum rate than other conventional interference management schemes. _Notations_: Lowercase and uppercase in bold such as **a** and **A** denote vectors and matrices, italics such as \(a\) and \(A\) stand for scalars, and calligraphic letter such as \(\mathbb{A}\) denote set. \(\mathbb{A}^{-1}\), \(\mathbb{A}^{H}\), \(rank(\mathbb{A})\), \(span(\mathbb{A})\), and \(null(\mathbb{A})\) represent the inversion, conjugate transpose, rank, the space spanned by the column vectors and null space of the matrix \(\mathbb{A}\).