esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review and Hypothetical Relationships in the Research Model
### Classification of SWDs  
Smart wearable devices encompass an array of electronic apparatuses housing microcontrollers strategically situated in proximity to or upon the dermal surface. These devices are adept at discerning, analyzing, and transmitting data concerning bodily cues, encompassing vital signs and environmental inputs, consequently providing instantaneous feedback to the wearer. Facilitated by the gradual proliferation of artificial intelligence, virtual (augmented and mixed) reality, and allied technologies, wearable devices have transcended their initial singular functionality, evolving into multifaceted entities simultaneously underscored by heightened portability and practicality. From a functional standpoint, these products indispensably encompass functions intrinsic to human-machine interfaces, interconnectivity, and sensorial perception. Notably, they are undergoing a transformative evolution characterized by increased integration, diminutive form factors, and enhanced utility, concomitantly amassing a spectrum of physiological data encompassing visual, tactile, and auditory modalities. These comprehensive attributes render SWDs versatile tools tailored to cater to diverse domains, including leisure, sports, and medical requisites [28; 29; 30; 31; 32].  
SWDs are supported by the wrist, the head, the feet, and non-mainstream products. Common types of SWDs include smartwatches/bands, headsets, glasses, clothes, sports shoes, wearable speakers, etc. Smartwatches are representative products worn on the wrist, containing extended communication functions of mobile operating systems. Today's smartwatches track activities by capturing hand gestures and GPS while displaying specific information such as payments or fitness tracking. Smart head-mounted devices use optical displays for virtual reality experiences, while HUD/VR/AR/MR/smart contact lenses offer new products mainly used for entertainment purposes, such as watching movies or playing games. Smart clothing and shoes adjust the temperature according to external environment conditions while tracking physical fitness/location, among other features [33; 34]. The development of these SWDs not only makes life convenient but also enriches people's leisure style, creating huge market potential requiring study into consumers' behavioral intentions.
## 4 Spatiotemporal Rebalancing Pricing Algorithm
### Critic Network  
As mentioned in Section 4.1, we need a _state-action value function_ to evaluate the current state and action (incentive strategy) to guide the optimization of the actor module. In the critic module, we establish a deep network \(Q_{t}=Q(S_{t},Ac_{t};\Phi)\) to approximate the real state-action value function \(V(S_{t},Ac_{t})\). According to [PERSON] 1 and 2, our deep network should also have the following relationships:  
\[Q_{t}=R_{t}+\gamma Q_{t+1}. \tag{16}\]  
Next, \(Q_{t}=Q(S_{t},Ac_{t};\Phi)\) needs to be calculated. Similarly, facing the state space and action space of the bicycle environment, the dimension problem is still the focus of the solution. [PERSON] et al. proposed an HRA structure based on DQN [18], which decomposes the overall reward function into multiple components and uses the low dimensional function to approximate the overall value function [5]. The value function of each component depends on the feature subset, and it makes the overall value function smoother. [PERSON] et al. [9] proposed an HRP structure [9] based on a DDPG [28] framework combining the factors of time and space. When the hierarchical structure is adopted, and the low-dimensional function is used to approximate the high-dimensional value function, a general formula is proposed to approximate the whole station-action value function, as follows:  
\[V(S,A)\cong\sum_{i=1}^{N}v(s_{i},a_{i})+\sum_{i=1}^{N}f(N(s_{i},a_{i})). \tag{17}\]  
The left side of Equation (17) represents the overall state-action value function [5]. The first part on the right represents the sum of the value functions of each node, and the second part on the right represents the sum of the value functions of local nodes. It can be seen from the formula that the optimization of the overall value function should focus on the sum of the value functions of local nodes.  
In the bicycle network, the cross-neighbors of stations are defined, and the value function of the feature subset of the neighbors is used to smooth the overall value function. Based on the above theory and practice, we propose a general Equation (17) to approximate the state-action value function. In this module, the formula used is as follows:  
\[V(S_{t},Ac_{t})=\sum_{i=1}^{N}(S_{t}||Ac_{t})*\varphi_{1}+\sum_{i=1}^{N}A \times(S_{t}||Ac_{t})*\varphi_{2}, \tag{18}\]  
where \(\varphi_{1},\varphi_{2}\) are learnable parameters. \(||\) is a parallel symbol, indicating that variables \(S_{t}\) and \(Ac_{t}\) are transfered at the same time. The first item on the right side of the formula represents the sum of the independent value functions for all nodes. The second item on the right side of the formula represents the value function from the combined value function from the local nodes of every node. The processing idea of the critic module in this paper are somewhat similar to those of HRP, but our formula is more widely used than the HRP. When matrix \(A\) represents the neighborhood relationship of the cross, we can solve the problem which the HRP could solve.
## 2 Related Works
### Contrast Enhancement Method
#### 2.2.5 Adaptive Histogram Equalization (AHE)  
This is a method of dividing an image into smaller parts and applying histogram equalization to each part. This method can improve local contrast, but it can amplify noise or sharpen the boundaries between parts [23].  
Among the various adaptive histogram equalization techniques, CLAHE (contrast limited adaptive histogram equalization) is an image processing method that suppresses noise while enhancing the contrast of an image [24]. The CLAHE technique achieves equalization over the entire image by dividing the image into small blocks of uniform size and performing histogram equalization on a block-by-block basis. When the histogram equalization is completed for each block, the boundary between blocks is smoothed by applying bilinear interpolation. The CLAHE method redistributes pixel values above a certain height by limiting the histogram height before calculation. The transformed image has characteristics similar to those of the actual image because it is converted in such a way that it is robust to noise located in low-contrast areas. CLAHE is simple; processed images can be reverted to their original form with the inverse operator, the properties of the original histogram can be preserved, and it is a good way to adjust the local contrast of an image. However, it increases noise when pixel intensities are clustered in very narrow areas, and this can lead to the enhancement of the pixel intensity of missing parts (noise amplification), and it is important to properly set parameters such as tileGridSize and clipLimit [25].  
Each of the above contrast enhancement techniques has advantages and disadvantages, so the selection of an appropriate technique or the use of a combination of techniques is recommended. Recently, research and efforts to improve video images using deep learning technology have been actively conducted [26; 27].
###### Abstract  
This study analyzes the impact of injection condition design factors of (3-glycidoxypropyl) trimethylokinase (GPTMS)-SiO\({}_{2}\) nanofluid on improving wettability and oil recovery through flotation and core flooding tests, respectively. Flotation tests were conducted to assess improvements in wettability that resulted from varying nanoparticle concentration, reaction time, and treatment temperature. The test results demonstrated that the hydrophilic sample ratio increased by up to 97.75% based on the nanoparticle reaction, confirming significant wettability improvement in all samples. Additionally, time-dependent fluid-flow experiments were conducted to validate oil recovery and rock-fluid interactions. In these experiments, for a 24-h reaction time, nanofluid injection caused a decrease in the maximum contact angle (43.4\({}^{\circ}\) from 166.5\({}^{\circ}\)) and a remarkable enhancement in the oil recovery rate by over 25%. Moreover, variations in contact angle and sample permeability were observed as the reaction time increased. Subsequently, the core flooding test revealed a critical reaction time of 24 h, maximizing oil recovery while minimizing permeability. Below this point in time, wettability improvement did not significantly enhance oil recovery. Conversely, beyond this threshold, additional adsorption due to particle aggregation decreased permeability, causing reduced oil recovery. Therefore, GPTMS-SiO\({}_{2}\) nanofluid can be utilized as an injection fluid to enhance oil recovery in high-temperature and high-salinity carbonate reservoirs.  
GPTMS; carbonate reservoir; enhanced oil recovery; flotation test +
Footnote †: journal:  
Article  
Enhancing Oil Recovery and Altering Wettability in Carbonate Reservoir Rocks through (3-Glycidoxypropyl)
###### Abstract  
The increase in the throughput of digital television and radio broadcasting (DVB) channels can be achieved due to application of signals with a compact spectrum and a relatively small peak-to-average power ratio (PAPR). The reason is the usage of traveling wave tubes (TWT) for amplifying and transmitting signals from a satellite repeater in DVB-S2X systems. At the same time, given that the bandwidth allocated for transmission should be used as efficiently as possible, a high reduction rate of out-of-band emissions level is required. The most effective solution in this direction is the transition to spectrum-economic signals, such as optimal Faster-Than-Nyquist (FTN) signals, which can provide a certain reduction rate of the out-of-band emissions level and minimum acceptable PAPR. This article proposes a method for obtaining optimal FTN pulses, which have symmetry in time domain, with specified PAPR and reduction rate of out-of-band emissions for the quadrature phase shift keying (QPSK) and offset quadrature phase shift keying (OQPSK). The possibility of synthesizing signals with OQPSK modulation is presented theoretically for the first time. Optimal FTN signals can provide PAPR reduction by at most 3 dB and outperform known root raised cosine (RRC) pulses. The simulation model adopts an architecture for quadrature generation of optimal FTN signals with OQPSK modulation with blocks for adjustable pre-amplification, clipping, and power amplification. The proposed signals can be used to increase the spectral and energy efficiencies of satellite broadcasting systems, such as DVB-S2/S2X, as well as low-rate return channels of interactive broadcasting systems with a frequency resource shortage.  
Faster-Than-Nyquist signaling; optimization methods; constraint; peak-to-average power ratio; out-of-band emissions; spectral efficiency +
Footnote †: journal: Applied Optics and Photonics
# A Simplistic Downlink Channel Estimation Method for NB-IoT
## 4 Measurement Setup  
To evaluate the performance of the proposed channel estimation method, a series of test waveforms were captured and processed. An R&S(r) CMW500 radio communication tester [31] was used as an NB-IoT downlink signal source, as shown in Figure 5. Test signals were generated for 2 channel types: AWGN (without multipath fading) and EPA 5 Hz (EPA multipath profile with 5 Hz maximum Doppler shift). For both channel types, 4 SNR values were considered: \(-5\) dB, 0 dB, 5 dB and 10 dB. The radio tester was connected to the Ettus Research USRP X310 software defined radio platform, which was used to capture the waveforms of the test signals. For every [channel type, SNR] pair, a series of waveforms was recorded. They were further passed through a dedicated software-defined NB-IoT UE receive path developed in MATLAB environment.  
The algorithm for processing waveform files is shown in Figure 6. Each iteration starts with the frequency and time synchronization step which also provides information about NB cell ID and frame number modulo 8. The synchronization is considered successful if the decoded NB cell ID matches the one set in the tester and the absolute frequency shift does not exceed 1 kHz. Once the waveform is properly aligned in time and frequency, further processing steps are conducted, with a view to decode the Master Information Block (MIB-NB) whose transmission is repeated every 64 frames. If MIB-NB reception is successful, i.e., CRC verification is passed, the block is decoded in order to extract the information necessary to retrieve the subsequent System Information Block Type 1 (SIB1-NB). Receiving SIB1-NB requires processing 256 consecutive frames. If MIB-NB and SIB1-NB are both decoded successfully, bits of their transport blocks are used to calculate channel estimation quality indicators, according to the procedure shown in Figure 3.  
The waveform processing loop proceeds to the next iteration either after successful decoding of SIB1-NB or in the case of synchronization/decoding failure. In each iteration, waveform processing starts from the sample which is offset by 640 ms in relation to the previous iteration. Such time shift corresponds to the transmission time interval (TTI) of MIB-NB (64 frames \(\times\) 10 ms) [32] which ensures that a different MIB-NB is processed at every iteration. In other words, each iteration is an independent trial of synchronization followed by MIB/SIB1 decoding. The success rate of the latter may be considered an additional, higher-level measure of channel estimation quality. The better the channel estimation is, the fewer failures of MIB/SIB1 reception should occur.  
Figure 5: The test bed for generating NB-IoT downlink signals and capturing waveforms.
## 6 Geometric Distribution Algorithm Performance Assessment  
To assess the performance of the GD algorithm, we designed three experiments for network simulation with two node patterns: uniform random and fixed, as depicted in Figures 3 and 4, respectively. Following this, we employed the LoRaSim simulator to generate the uniform random node pattern and to compute DER values for all experiments. Experiment 1 was the evaluation of the GD algorithm for both uniform random and fixed patterns. Experiment 2 was the comparison of the optimal SF assignment and corresponding DER values obtained from the GD algorithm and other previous works for both uniform random and fixed patterns. The parameters for experiments 1 and 2 were configured according to the specifications outlined in Table 5. The third experiment was the comparison of the GD algorithm-based network performance of the fixed pattern with the default ADR scheme [11] and previously reported algorithms--namely, EXPLoRa [14], QCVM [19], and SD [20]--by considering key parameters, namely, coverage area (2 to 5 km), payload size (10 to 255 bytes), and the total energy consumption during the simulation time from 1 to 12 h. Note that, since ADR is a default mechanism deployed by LoRaWAN, it serves well as a baseline for comparison with the GD algorithm. The EXPLoRa-SF or EXPLoRa, QCVM, and SD SF allocation schemes were selected due to their algorithmic simplicities being similar to the proposed GD algorithm. In particular, the QCVM and SD are also grounded in statistical techniques, making them suitable for comparison.
Rigid instruments (especially those that are manually operated) work at the speed of a surgeon's hands, which is vital to ensure that the intuitiveness of a surgeon's actions is not compromised. Any response delay between a surgeon's hand movement and a soft robot's end effector movement therefore needs to be mitigated--a challenge given the complex kinematics of soft robot arms and the requisite computational software. Soft Robots, because of their compliance and infinite degrees of freedom, have low positional accuracy. This is clearly a major handicap in surgical work where precision is absolutely key. To achieve this, we need to enhance the robot's pose (position and orientation) accuracy by embedding appropriate, soft shape-sensing sensors into the body of the soft robot arm. These sensors, combined with camera-based observation of the space, will allow us to obtain a good appreciation of the robot within its work environment. Unwanted contact with healthy organs can thus be detected and avoided. Additionally, soft tactile sensors could be integrated into the robot's outer surface to help estimate a contact point and interactional forces with the environment. That said, the need for \"environment-interaction\" sensors is less critical as the proposed soft devices complianty adapt to their soft environments without exerting the excessive force that their rigid-bodied counterparts might. Although tactile perception has its merits, including the provision of haptic feedback to the surgeon, it is of lesser importance in the context of MIS and beyond the scope of this paper. The interested reader may refer to [4; 5; 6; 7] for a more in-depth treatment of tactile perception in soft robotics. It is noted that, even today, surgeons are generally more accustomed to performing MIS with standard surgical instruments. Despite the tremendous hype--and indeed research--in robotic solutions for MIS over the past twenty years or so, the technology  
Figure 1: Mesorectal excision restricted by rigid straight-light laparoscopic tool. Lateral completion on the right side (**A**), the left side (**B**) and the anterior side (**C**) [3]. has generally failed to achieve more substantial outcomes than standard laparoscopy in the treatment of a range of diseases [8]. There have been many comparative studies between traditional laparoscopy and robotic surgery, from both patients' and surgeons' perspectives, and the findings largely suggest that the much hoped for advantages of robotic technologies have not translated into tangible results [1; 9; 10; 11]. Although there has been recent progress, there is general consensus that the treatment of diseases requiring more complex procedures is not achievable with existing systems, to a large extent, because they are made of rigid parts [1].
# A Brief Review on the Recent Experimental Advances in Thermal Rectification at the Nanoscale
## 2 Experimental Study  
This section mainly introduces the recent experimental achievements in thermal rectification. The rapid advancement of nanofabrication, which has enabled the synthesis of nanostructures with a variety of materials, has been used in these studies. Significant thermal rectification phenomena have been observed, revealing different physical mechanisms.
# Intelligent Control System to Irrigate Orchids Based on Visual Recognition and 3D Positioning
## 3 Results and Discussion
### 3D Localization  
There are two conditions for the experiments of 3D localization. Under Condition 1, the central point of the target is considered the measurement point and departs from the optical center of the left lens by 40 cm, as displayed in Figures 20 and 21.  
Under Condition 2, a sample potted plant with the diameter of 8 cm, the length of 31 cm and the width of 7 cm, as labeled in Figure 22, is utilized for experiments in several 3D positions. They are given by the four coordinates (10, 30, 13.5), (10, 35, 13.5), (25, 30, 13.5) and (25, 35, 13.5) in the unit of centimeters, which are called Points 1 to 4, respectively, for the triggering areas in the field of view of the left lens, as demonstrated in Figure 23.  
Figure 19: Object detection for the testing set, junctions between stems and leaves are framed.  
Figure 18: The coordinate system of the three-axis motion platform.  
Figure 21: Target of Condition 1, target is located at the center (cross mark).  
Figure 22: Target of Condition 2.  
Figure 20: View of Condition 1, 40 cm far from the target to the optical center of the left lens of camera.  
In the first experiment, Condition 1 provided the measuring method, and the results of measurement errors were recorded in Figure 24, corresponding to the Z-axis distance ranging from 20 to 80 cm. When the Z-axis distance ranges from 20 to 35 cm, the error is obviously large. Until the Z-axis reaches farther than 80 cm, the error starts to rise again, but negatively. Figure 25 clearly indicates that the best working distance of this binocular camera is from 35 to 75 cm, within which the absolute values of average, maximum, and minimum errors are 0.22 cm, 0.42 cm, and 0.02 cm, respectively.  
Figure 23: The schematic view of Condition 2, there are 4 measurement points in triggering area.  
Figure 24: Z-axis error vs. Z-axis distance diagram (20 to 80 cm).  
Figure 25: Z-axis error in working range vs. Z-axis distance relationship diagram (35 to 75 cm).  
Additionally, Condition 2 was used for the second experiment. The measurement results were addressed in Table 1. The maximum absolute errors of the X-, Y-, and Z-axes of the four measurement points are 0.18 cm, 0.2 cm, and 0.32 cm, respectively. On the other hand, the minimum absolute errors of the X-, Y-, and Z-axes of the four measurement points are 0.1 cm, 0.12 cm, and 0.18 cm, respectively. It is shown that the resulted four measurements in Figure 23 deviate from the accurate positions with mm-scale errors, which is relatively little in comparison with the nozzle size. Therefore, it can be guaranteed that the nozzle still waters on the correct position, despite the presence of the measurement errors presented in Table 1.
## 4 Results
### Risk Evaluation  
#### 4.4.1 Risk Map  
Statistical analysis of fluoride-lithology data may be hence used to map geological fluoride risk zones (Figure 9). Both augen gneiss and hornblende-biotite gneiss lithologies host >60% risk (68.8%  
\begin{table}
\begin{tabular}{c c c c} \hline
**Question Asked** & \multicolumn{2}{c}{**Lithology**} & **Total** \\ \hline “Does anyone in your household suffer & & & Charnockitic \\ from brown-black staining of & Augen & Hornblende-biotite & gneiss and \\ the teeth?” & gneiss & grainulite & \\ \hline Total responses & 2070 & 134 & 4600 & 6804 \\ “Yes” responses & 853 & 54 & 1022 & 1929 \\ \% yes & 41\% & 40\% & 22\% & 28\% \\ \hline \end{tabular}
\end{table}
Table 2: Summary statistics of dental fluorosis indicator data and corresponding geology.  
Figure 8: Box and whisker plot of fluoride concentrations (5 th, 25 th, 75 th and 95 th percentiles) in the various lithological units (augen gneiss (\(n=16\)), hornblende—biotite Gneiss (\(n=3\)) and charnockitic gneiss and granulite (\(n=20\))).  
and 66.7% respectively) of producing drinking water in excess of the WHO standard (1.5 mg/L) and are thus mapped as Grade 2 (highest risk). Statistically, charnockitic gneiss and granulite displayed the least risk (\(<\)20%) and were mapped as Grade 1. The blank areas were mapped as Grade 0 as no hydrochemical data were available with which to perform statistical analyses. Red zones (Grade 2), therefore, represent areas where there is \(>\)60% risk of abstracted groundwater used for supply producing enough fluoride to cause dental fluorosis (assuming regular consumption from the same water point). Such geology-based risk maps, calibrated to fluoride occurrence, may provide the foundation for the development of risk maps in other areas of similar mapped geologies for which groundwater fluoride data may not exist. With increased groundwater fluoride occurrence data coverage of the presently Grade 0 (unknown) areas it is anticipated greater resolution of the grading system may be possible with increased numbers of grades to characterise the system.