esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result and Discussion
### In Vitro a-Glucosidase Inhibitory Activity of C. operculatus Essential Oil  
In this study, the essential oil of C. _operculatus_ leaves was demonstrated to have \(\alpha\)-glucosidase inhibitory effects three times stronger than those of acarbose, with IC\({}_{50}\) values of 61.82 \(\pm\) 3.91 and 201.4 \(\pm\) 8.46 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, respectively.  
These outcomes are also in line with earlier investigations on C. _operculatus_'s anti-diabetic effects. The leaf extract of C. _operculatus_ has shown \(\alpha\)-glucosidase inhibitory action with an IC\({}_{50}\) value of 0.11 mg/g [22]. Furthermore, [PERSON] et al. (2022) revealed that the plant's fruit extract has a good ability to inhibit \(\alpha\)-amylase and \(\alpha\)-glucosidase, with IC\({}_{50}\) values of 0.42 and 0.23 g/mL, respectively [23]. In addition, pharmacological studies showed that the C. _operculatus_ extracts had positive effects on streptozotocin-diabetic rats, such as reduced blood glucose levels and protective activities on the \(\beta\)-cells of pancreatic islets [24; 25]. The \(\alpha\)-glucosidase inhibitory activity of the essential oil of C. _operculatus_ leaves has never been documented before this investigation. However, both earlier investigations and the findings of the current study support the notion that C. _operculatus_ may be a source of natural compounds with anti-diabetic properties.
## 5 Discussion
In particular, as rock engineering moves towards intelligence and digitization, the application of automatic recognition technology provides fundamental data for the intelligent management of rock engineering. Combined with big data analysis and machine learning, the technology of automatically recognizing structural planes in rock masses not only provides real-time monitoring information but also allows for long-term dynamic analysis, offering real-time support for engineering decision making. In summary, automatically identifying structural planes and accurately obtaining their geometric information can provide a data foundation for engineering rock mass quality grading, rock mass stability assessment and analysis, 3D geomechanical model construction, rock support design, and disaster monitoring and early warning involved in rock engineering disaster prevention and control. This is conducive to solving the dual scientific challenges of the prevention and precise control of rock engineering disasters and intelligent emergency decision making. It should be noted that although considerable progress has been made in the theory and technology of the automatic identification of rock discontinuities, due to the complexity and uncertainty of geological bodies and the urgent need for accurate prevention and control of rock engineering disasters, relevant research is still needed in the future to strengthen the refined and intelligent identification, extraction, analysis, and characterization of discontinuities, so as to obtain complete and true information, such as the occurrence and spatial distribution of structural planes, to improve the theoretical and technical level of disaster prevention and control.
# Development of a Multi-Sensor GNSS-IoT System for Precise Water Surface Elevation Measurement
## 3 Field Experiment
### Ultrasonic Sensor Performance  
Figure 9 presents the ultrasonic sensor readings from 1 February 2024 to 30 May 2024 and provides insights into the data's behaviour and statistical properties. It is important to note that the \"raw data\" presented here have had outliers removed that fall outside the empirical region of [1.40 m, 1.65 m]. Figure 9 depicts the raw and smoothed sensor readings over the specified period, illustrating the fluctuations and outliers in the raw data  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Description** & **Mean** & **STD** & **Min** & **Max** & **Range** \\ \hline WSE variation range in 24 h & 0.011 & 0.018 & 0.001 & 0.156 & 0.155 \\ \hline WSE variation range in 6 h & 0.003 & 0.006 & 0.000 & 0.053 & 0.053 \\ \hline Difference in 24 h and 6 h average & 0.000 & 0.006 & \(-\)0.005 & 0.050 & 0.055 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Descriptive statistics for the WSE data provided by Icon Water (unit: m).  
Figure 8: Icon Water Googong reservoir WSE data from 1 February 2024 to 30 May 2024: **(a)** raw data with 10 min interval and 24 h and 6 h average data; **(b)** WSE variation range in 24 h and 6 h; **(c)** difference between 24 h and 6 h average data.  
due to factors such as the reflective angle of the ultrasonic wave or platform movement. Missing epochs of data are attributed to communication loss. The ultrasonic sensor raw data and smoothed data histograms are shown in Figures 9b and 9c, respectively, both demonstrating a normal distribution.  
The statistical summary in Table 5 further elaborates on the characteristics of the ultrasonic sensor readings. The maximum value for raw data is 1.649 m, with a minimum of 1.419 m, a mean of 1.543 m, a median of 1.541 m, a standard deviation of 0.024 m and a range of 0.23 m. In contrast, the smoothed data show a maximum of 1.639 m, a minimum of 1.439 m, maintaining the same mean and median as the raw data, but with a reduced standard deviation of 0.019 m and a range of 0.2 m. This comparison underscores the effectiveness of the smoothing process in reducing variability and improving the reliability of the ultrasonic measurements for subsequent analysis.  
Figure 10 shows both raw and smoothed ultrasonic sensor daily average measurements, which are in close agreement, except for 18 May 2024 when the pontoon position and attitude changes are significantly different due to the impact of strong winds [38]. Moreover, a deviation of about 1 cm in the ultrasonic sensor's daily mean measurement is observed after 22 April 2024. This deviation coincides with the installation of new equipment on the pontoon in the week beginning 22 April 2024, which increased the pontoon's weight and, consequently, its draft. As a result, the ultrasonic sensor's distance measurements became slightly smaller.
## 3 Interference simulation of different bandwidth signaling
### Analysis of the Spatial Distribution of the Noise Field
When the frequency was higher than 4 kHz, the noise mainly came from the surface waves, and the surface fluctuations contributed more significantly to the high-frequency noise field strength of 1 kHz-10 kHz. In the experiment, the array element closest to the surface was placed about 2 m underwater. When the water depth reached 5 m, the noise variation curve had no obvious fluctuations in the depth range covered by the hydrophone array. Thus, after the device was placed at a certain depth, the contribution of high-frequency noise to the fluctuation of the communication signal could be ignored. According to the analysis of the experimental results, the spatial distribution of the signal field and the noise field was basically consistent with the simulation results, and its vertical distribution showed that the intensity of the near-sea surface signal was lower than that of other depth signals.
# Adaptive Navigation in Collaborative Robots: A Reinforcement Learning and Sensor Fusion Approach
## 2 Literature Review
But they admit difficulties in achieving the performance of traditional neural networks in essential tasks. [PERSON] [28] highlights the utility of SNNs in the context of robotics, as SNNs are capable of reducing latency and energy consumption through the use of spiking mechanisms to process sensory data efficiently as well as adapt to environmental changes. Collectively, these studies establish firm groundwork for coupling ultrasonic sensors, GPS, and magnetometers to neuromorphic systems. Real-time map generation, representation, and semantic learning can all be achieved through model-free reinforcement learning; coupled with data-driven approaches, they pave the way to developing adaptive robots capable of continuous learning from their environments and improving navigation while avoiding unexpected obstacles. This integration of event-based sensing with brain-inspired computation and learning systems is a promising approach toward developing autonomous robotic systems capable of efficient operation in complicated, dynamic environments. The paper [29] discusses integrating ethical reasoning into reinforcement learning (RL) agents using a modular normative supervisor. This supervisor employs defeasible deontic logic to ensure that the agent complies with ethical norms while maintaining optimal learned behavior. The approach dynamically checks for compliance, handles conflicting norms, and selects 'lesser evil' actions when no fully compliant option exists. Applications of this approach in Cobots could include avoiding high-risk zones based on GPS or sensor data, limiting speed, or halting movement if predetermined objects, such as pedestrians, are detected. Lastly, the development of a hybrid algorithm integrating classical and heuristic methods for mobile robot path planning has shown enhanced capabilities in dynamic environments [30]. This approach addresses the challenges of real-time navigation in the presence of both static and dynamic obstacles, resulting in superior adaptability and efficiency in navigating complex scenarios. Cobot navigation system implementations reviewed above show methods that help the robot vehicle to navigate using cost-effective sensors like GPS, ultrasonic sensors, and magnetometers, while others use advanced sensors, like LiDAR, etc., along with intelligent algorithms. Our focus is to use cost-effective sensors along with an intelligent algorithm to get an effective performance out of the system to perform desired navigation and obstacle avoidance.
## 2 Materials and Methods
### Structure of the Nondestructive Detection System
#### 2.3.3 Photodetector  
Our system was equipped with a Thorlabs PDA50B2 NIR photodetector (spectral range: 800-1800 nm), which has a Ge photodiode with a surface area of 19.6 mm\({}^{2}\) and a spectral range that encompasses the aforementioned range of wavelengths (min: 1050 nm, max: 1650 nm). Additionally, this NIR photodetector contains a high-gain amplification circuit.
# A Novel Active Polyphase Filter Employing Frequency-Dependent Image Rejection Enhancement Technique
## 2 Circuit Analysis
### Frequency-Dependent Image Rejection Enhancement Technique Based on Secondary Pole Compensation  
In view of the unavoidable negative influence of \(\omega_{P}\) in the APPF, a novel frequency-dependent image rejection enhancement technique based on the secondary pole compensation is proposed from the perspective of frequency correlation, instead of pursuing the ultimate optimization of parasitic parameters. After considering the gain and pole frequency mismatches caused by \(\omega_{P}\) and \(C_{P}\), the IRR of the APPF can be rewritten as  
\[IRR(\omega_{IF})=\frac{|H(s=j\omega_{IF})|}{|H(s=-j\omega_{IF})|}=\frac{\left|A_ {L}\frac{\omega_{L}\big{[}-\frac{1}{\omega_{P}-\omega_{H}}\omega_{IF}^{2}+ \frac{\omega_{P}}{\omega_{P}-\omega_{H}}\omega_{H}-\frac{A_{H}}{A_{  
Phase imbalance is more difficult to deal with than amplitude imbalance because the latter can be eliminated by adding circuits such as limiters, buffers, etc., before the APPF. Figure 8 shows the processing mechanism of the proposed enhancement technique in response to the phase imbalance of the input signal. There are two possible cases where the phase difference between the I channel and Q channel signals at the input of the APPF is less than or greater than \(90^{\circ}\). Compared with traditional schemes, the proposed enhancement technique can better overcome the degradation of the image rejection ability of the APPF when the phase difference of the input quadrature signal is not equal to \(90^{\circ}\). Because the proposed technique can simultaneously handle the phase imbalance \(\theta_{IF}\) and the phase imbalance \(\theta\) and can relatively well handle the amplitude imbalance \(\Delta A\). Based on the above theoretical analysis, the proposed technique can effectively improve the image rejection ability of APPFs, especially at high operating frequencies.
###### Abstract  
We demonstrate wavelength-wavelength correlations of classical broad-band amplified spontaneous emission (ASE) photons emitted by an erbium-doped fiber amplifier (EDFA) in a wavelength regime around 1530 nm. We then apply these classical correlated photons in the framework of a real-world ghost spectroscopy experiment at a wavelength of 1533 nm to acetylene (C\({}_{2}\)H\({}_{2}\)) reproducing the characteristic absorption features of the C-H stretch and rotational bands. This proof-of-principle experiment confirms the generalization of an ASE source concept offering an attractive light source for classical ghost spectroscopy. It is expected that this will enable further disseminating ghost modality schemes by exploiting classical correlated photons towards applications in chemistry, physics and engineering.  
ghost modalities; ghost imaging; ghost spectroscopy; correlated photons; amplified spontaneous emission (ASE); spectroscopy; spectral correlations; coherence; photon correlation modalities; erbium-doped fiber amplifier; quantum optics +
Footnote †: journal: Applied Physics Letters B
# Joint Power and Time Allocation in Hybrid NoMA/OMA IoT Networks for Two-Way Communications
## 3 Numerical Efficient Solution  
In order to obtain a numerical efficient solution to the problem (5), we first transform the original problem to a difference of convex/concave (DC) programming problem, and then reformulate the DC programming problem to be a convex one relying on the successive convex approximation (SCA) method [21; 22]. On this basis, the numerical efficient solution is finally obtained by using an iterative routine. We begin with the non-convex objective function and transform it to a convex one. By introducing auxiliary variables \(q_{\text{U}}\) and \(q_{\text{D},k}\), and the additional constraints  
\[\frac{q_{\text{U}}^{2}}{\tau}\geq\sum_{k=1}^{K}p_{\text{U},k}, \tag{6}\]  
\[\frac{q_{\text{D},k}^{2}}{\tau_{k}}\geq p_{\text{D},k}, \tag{7}\]  
the objective function can be replaced by its upper bound of \(q_{\text{U}}^{2}+\sum_{k=1}^{K}q_{\text{D},k}^{2}\). It is easy to see that the newly defined objective function and the terms on both sides of the constraints (6) and (7) are convex [23], and thus the associated constraints are in DC form. Now we convert to the non-convex constraint C1 and express it to be an equivalent form as  
\[\log_{2}\left(\sum_{j=k}^{K}\left|h_{j}\right|^{2}p_{\text{U},j}+ \sigma_{\text{AP}}^{2}\right)- \tag{8}\] \[\log_{2}\left(\sum_{j=k+1}^{K}\left|h_{j}\right|^{2}p_{\text{U},j }+\sigma_{\text{AP}}^{2}\right)\geq\frac{\bar{R}_{\text{U},k}}{\tau}.\]  
In the constraint (8), the terms on the left-hand side are the differences in two concave functions and the term on the right-hand side is convex, so the constraint also owns a DC form.
# Marginal Contribution-Based Distributed Subchannel Allocation in Small Cell Networks
## 2 System Model  
We consider a downlink subchannel allocation in an OFDMA-based SCN, as shown in Figure 1, that consists of \(N\) transmitting SBSs and \(M\) receiving MSs where \(N\leq M\). Let \(\mathcal{N}=\{1,\ldots,N\}\) be the set of SBSs, and \(\mathcal{M}=\{1,\ldots,M\}\) be the set of MSs. Each SBS takes care of a non-overlapping set of MSs. Then, \(\forall n\in\mathcal{N}\), \(\mathcal{M}=\bigcup_{n=1}^{N}\mathcal{M}_{n}\), and \(M_{n}=|\mathcal{M}_{n}|\) is the number of MSs associated with SBS \(n\). Here, a link \(i\in\mathcal{M}\) is between one transmitting SBS and one receiving MS. Hence, the total number of links is equal to \(M\). The spectrum bandwidth \(B\) of the OFDMA network is divided into \(K\) subchannels. The set of subchannels is denoted as \(\mathcal{K}=\{1,\ldots,K\}\), with each subchannel having a bandwidth of \(B/K\). Figure 1: System model. SBS: small cell base station; MS: mobile station. Consider \(h_{ii}(k)\) to be the channel gain of transmission on link \(i\), i.e., the channel gain from the transmitting SBS of link \(i\) to the receiving MS of link \(i\), on subchannel \(k\in\mathcal{K}\). Hence, \(h_{ji}(k)\) represents the channel gain of interference from the transmitting SBS of link \(j\) to the receiving MS of link \(i\) on subchannel \(k\). The transmit power of link \(i\) which is using subchannel \(k\) is \(P_{i,k}\). We assume all links are active simultaneously and each link utilizes only one subchannel. Hence, if the number of links transmitted by SBS \(n\) is \(M_{n}\), then \(P_{i,k}=P_{\max}/M_{n}\), where \(P_{\max}\) is the maximum transmit power of each SBS. If the link \(i\) does not use subchannel \(k^{\prime}\in\mathcal{K}\) then \(P_{i,k^{\prime}}=0\). In this work, we use the physical interference model which takes into account interference produced by all other links using the same subchannel. Hence, the downlink signal-to-interference-plus-noise ratio (SINR) for link \(i\) on subchannel \(k\) is given by  
\[\gamma_{i,k}=\frac{P_{i,k}h_{ii}(k)}{I_{i,k}+\sigma^{2}} \tag{1}\]  
where \(I_{i,k}\) is the downlink co-channel interference to link \(i\) on subchannel \(k\), i.e., \(I_{i,k}\) is the sum of the co-channel interference from all other links using the same subchannel \(k\), \(I_{i,k}=\sum_{j\in M_{i},ji}P_{j,k}h_{ji}(k)\), and \(\sigma^{2}\) is the additive white Gaussian noise (AWGN) power. From [PERSON] theory, the capacity of link \(i\) using subchannel \(k\) is  
\[C_{i}=\frac{B}{K}log_{2}(1+\gamma_{i,k}) \tag{2}\]  
The total capacity of the network is \(C=\sum_{i=1}^{M}C_{i}\). The notations used in this paper are summarized in Table 1.
## 2 Materials and Methods
### DPIA Methodologies  
In this context, various jurisdictions and sectors have developed their own DPIA frameworks. Such frameworks include the UK Information Commissioner's Office (ICO) DPIA Guidance and Template [40], the International Association of Privacy Professionals (IAPP) DPIA Tool [41], and the French Data Protection Authority (CNIL) DPIA Tool [42]. These tools provide comprehensive guidelines, templates, and methodologies to help organizations systematically approach the assessment process, highlight potential risks and implementation measures to protect individual privacy rights effectively.  
* UK Information Commissioner's Office (ICO) DPIA Guidance and Template [40]: The ICO provides comprehensive guidance and a template for conducting DPIAs. The inclusion of a template simplifies documentation, but may not fully accommodate the intricacies of diverse processing activities, possibly necessitating further resources for complex scenarios.
* International Association of Privacy Professionals (IAPP) DPIA Tool [41]: The IAPP offers resources and tools for privacy professionals, including a DPIA tool designed to streamline the process of conducting impact assessments. This tool typically includes checklists and questionnaires to help identify and assess privacy risks associated with data processing activities. With its practical checklists and a supportive privacy professional community, it offers a flexible approach to DPIA. However, access to some IAPP resources is not open.
* French Data Protection Authority (CNIL) DPIA Tool [42]: The CNIL provides an open-source DPIA tool and methodology to guide organizations through the assessment process. This tool is designed to help identify processing operations that require a DPIA, evaluate the risks to the rights and freedoms of natural persons, and determine measures to mitigate those risks (it should be pointed out, though, that emphasis is given to security risks). The CNIL's approach to DPIAs includes detailed guidance on when a DPIA is required, how to carry it out, and what elements it should contain. The CNIL also provides templates and supporting documents to facilitate the DPIA process, making it more accessible and manageable for organizations of all sizes.  
All these frameworks are designed to be broadly applicable; therefore, they do not specifically address the unique challenges and risks associated with AI systems.
# Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation
## 4 Carbon Flux Data Assimilation and Inversion Studies
### Summary of Assimilation Research  
After decades of development, the assimilation inversion method has achieved numerous achievements in the estimation of carbon sources and sinks. New inversion methods and observational data are continuously being incorporated into carbon source and sink estimation research, greatly promoting progress in this field. Despite significant research achievements and advancements in global carbon-assimilation studies, there are still many challenges. To further improve the accuracy and precision of assimilation systems, coupling ecosystem, and atmospheric transport models, optimizing the accuracy and efficiency of atmospheric transport models, and speeding up the assimilation algorithm framework are all urgent issues to be addressed in carbon flux-assimilation inversion systems. In the future, surface carbon flux-assimilation inversion systems will develop in the directions of model assimilation, joint assimilation of satellite and ground-based data, regional assimilation, and simultaneous assimilation of multiple gases, aiming to better apply and support carbon-assimilation systems in the field of carbon cycling.
## 2 Basics of QDI Circuits
### Types and Characteristics of QDI Circuits  
QDI circuits are classified as strong indication, weak indication, and early output circuits. Their input/output behavior are captured via a representative illustration in Figure 3.  
Figure 3: Input/output behaviors of strong indication, weak indication, and early output circuits with respect to: (**a**) RTZ handshaking, and (**b**) RTO handshaking.  
Strong indication (QDI) circuits [25] will wait to receive all the primary inputs (data/spacer), and after receiving them would commence the processing to produce the required primary outputs (data/spacer). Weak indication (QDI) circuits [25] can commence the processing after receiving a subset of the primary inputs (data/spacer) and are able to produce all but one of the required primary outputs (data/spacer). However, they are constrained by the rule that only after receiving the last primary input (data/spacer) can they process and produce the last primary output (data/spacer). Early output (QDI) circuits [26] are very relaxed in terms of the timing compared to strong indication and weak indication circuits since they are allowed to process and produce all the primary outputs (data/spacer) after receiving a subset of the primary inputs (data/spacer). This characteristic of early output circuits implies that some of the late arriving primary inputs (data/spacer) to an early output circuit may not be acknowledged, which might give rise to wire orphans, and wire orphans are unacknowledged signal transitions occurring on the primary input wires. However, wire orphans are quite easily overcome in early output QDI circuits. This is because although an early output circuit may not acknowledge the late arriving primary inputs (data/spacer), the completion detector preceding the early output QDI circuit would properly acknowledge them. Thus, there does not arise any issue with respect to acknowledging the arrival of all the primary inputs (data/spacer) to an early output circuit. In addition, there are two sub-types of early output circuits called the early set type and the early reset type. If an early output circuit produces the primary output data early, it is said to be of early set type. Contrarily, if an early output circuit produces the spacer early, it is said to be of early reset type. The early set and reset behaviors of early output circuits with respect to RTZ and RTO handshaking are depicted within the dotted ovals in Figure 3.