esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Laser Driver and Its Application in Gas Sensing
## 5 Conclusions  
In this paper, the design of a laser driver implanted with an RTX5 system and emWin GUI was systematically described. Although some similar circuits had been revealed in previous studies, most of them focus on the application, while the stability of the circuit was rarely analyzed in depth. Here, TINA-SPICE software was used to analyze the stability of the laser drive circuit from the working principle of the operational amplifier. The graphical simulation curves intuitively pointed out the potential oscillation risk of the circuit and showed a significant effect of the compensation method. Our study thus offers a feasible strategy to treat, with the problems that may occur in the design, not only the laser drivers but also other circuit modules. Importantly, to make the simulation as realistic as possible, a macro model built upon the laser rate equation was used as the DFB-LD, which differs largely from the diodes involved in general studies. For validation, the home-made laser driver was introduced into the gas sensor which combines logarithmic transformation and TDLAS-WMS. Compared with the classic WMS-2\(t\) technology, because the logarithmic conversion circuit additionally brought a small amount of noise, it seems to have no advantage in terms of the detection limit. However, the symmetrical harmonic waveform and negligible DC offset are obvious evidence that the logarithmic transformation method eliminates the influence of light intensity modulation (IM) on WMS. We firmly believe that by lengthening the optical path, reducing the etalon effect, and optimizing the circuit, the SNR can be further increased and an ideal detection limit can be reached.  
Conceptualization and methodology, M.C.; software and validation, Y.W. and K.Z.; formal analysis and writing, S.Z. and D.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Natural Science Foundation of Inner Mongolia Autonomous Region (China), grant number 2018 BS06004; National Natural Science Foundation (China), grant number 61963031; Scientific Research Project of Inner Mongolia University for the Nationalities (China), grant number NMDYB19065; Intelligent Agricultural equipment & technical team of Inner Mongolia Minzu University.  
Not applicable.  
Data available on request to the corresponding author.  
We would also like to thank [PERSON] for language editing.  
The authors declare that they have no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & \begin{tabular}{c} **Detection Limit** \\ **(ppb)** \\ \end{tabular} & \
\begin{tabular}{c} **Optical Lengths** \\ **(m)** \\ \end{tabular} & **Laser Source** &
\begin{tabular}{c} **Line Position** \\ **(cm\({}^{-1}\))** \\ \end{tabular} \\ \hline
[26] & 500 & 10 & DFB-LD & 6046.95 \\
[27] & 60 & 29.91 & DFB-LD & 6046.95 \\
[28] & 1.4 & 54.6 & DFB-ICL & 3038.50 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Examples of the TDLAS-based CH\({}_{4}\) sensors.
# Design of a Digital Array Signal Processing System with Full Array Element
## 2 System Architectural Design
### System Hardware Architecture Design  
#### 2.1.1 Overall Framework Design  
The hardware architecture of the broadband digital array signal processing system consists of three main parts: the digital array signal processor, the RF front-end, and the antenna array. The hardware architecture is shown in Figure 1.  
The digital array signal processor is mainly responsible for transmitting and receiving IF signals and interacting with the outside world for commands and data. It is designed with the 6U VPX standard structure consisting of four transceiver modules and one master control module; the different modules are interconnected by high-speed serial buses. The transceiver module realizes the processing of baseband signals and the transmission and reception of HIF signals. The main control module realizes external communication, reports the system working status, data processing results, etc., through the 10 Gigabit Ethernet and optical fiber. It also performs task configuration for each module in the processor, coordinates the workflow of the whole processor, and generates specific radar working timing signals.  
The RF front end is responsible for the reception and transmission of RF signals, which consists of an up-conversion module, a down-conversion module, and a calibration module. The frequency conversion module realizes the conversion of the IF signal and RF signal, and the calibration module realizes the online amplitude and phase calibration of multiple transceiver channels.
###### Abstract  
Underwater manipulators are important robotic tools in the exploration of the ocean environment. Up to now, most existing underwater manipulators are rigid and with fixed 5 or 7 degrees of freedom (DOF), which may not be very suitable for some complicated underwater scenarios (e.g., pipe networks, narrow deep cavities, etc.). The biomimetic concept of muscles and tendons is also considered as continuum manipulators, but load capacity and operation accuracy are their essential drawbacks and thus limit their practical applications. Recently, the cable-driven technique has been developed for manipulators, which can include numerous joints and hyper-redundant DOF to execute tasks with dexterity and adaptability and thus they have strong potential for these complex underwater applications. In this paper, the design of a novel cable-driven hyper-redundant manipulator (CDHRM) is introduced, which is driven by multiple cables passing through the tubular structure from the base to the end-effector, and the joint numbers can be extended and decided by the specific underwater task requirements. The kinematic analysis of the proposed CDHRM is given which includes two parts: the cable-joint kinematics and the joint-end kinematics. The geometric relationship between the cable length and the joint angles are derived via the established geometric model for the cable-joint kinematics, and the projection relationship between the joint angles and end-effector's pose is established via the spatial coordinate transformation matrix for the joint-end kinematics. Thus, the complex mapping relationships among the cables, joints and end-effectors are clearly achieved. To implement precise control, the kinematic control scheme is developed for the CDHRM with series-parallel connections and hyper-redundancy to achieve good tracking performance. The experiment on a real CDHRM system with five joints is carried out and the results verify the accuracy of kinematics solution, and the effectiveness of the proposed control design. Particularly, three experiments are tested in the underwater environment, which verifies its good tracking performance, load carrying and grasping capacity.  
hyper redundancy; cable-driven manipulator; mechanical design; kinematics +
Footnote †: journal: Applied Mechanics and Physics Letters  
_Applid_  
_Appl. Sci._**2019**_9_, 1142; doi:10.3390/app9061142
## 5 Results and Discussion
### Academic Contribution  
In previous studies of cultural heritage, cultural heritage was distinguished into tangible and intangible cultural heritage. Although the concept of 'cultural heritage as a service' has been established internationally, the content and mode of cultural services are severely misconstrued due to the opposition of different standpoints [2]. Such studies presented an 'intangible' perspective for cultural heritage, but they did not investigate how cultural heritage generates cultural value. To generate cultural value, actors in historical blocks must extract cultural meaning from cultural heritage and then present common value propositions, thus co-creating value for historical blocks. This study analysed how actors in historic districts interactively extract and increase the cultural values of historic districts and subsequently develop cultural service ecosystems for historical blocks through resource integration and value co-creation. The findings of this study provide an innovative approach for the sustainable development and economic revival of historical blocks. Today, developing the local economy through the revitalisation of cultural heritage has become a mainstream mode of city promotion worldwide. However, such attempts are mostly limited to the provision of cultural services and rarely focus on developing cultural service ecosystems for historical blocks. This study analysed the operation of cultural service ecosystems of cultural heritage. The findings of this study contribute to the examination of the organisational effectiveness of revitalisation and cultural innovation activities in historical blocks and provide a reference for policymakers pursuing sustainable development of the local cultural economy.  
According to the service-dominant logic, actors extract tangible and intangible resources from cultural heritage through active participation and service exchange to increase the cultural value of innovation, whereas cultural service ecosystems are created for cultural heritage. This process also facilitates the creation and development of cultural services, or more specifically, actors co-create services on a consensus basis for their mutual benefit. To ensure that historical blocks are revitalised to provide sustainable cultural services and promote the communication of cultural brands, actors must be able to integrate and share resources. A cultural service system is self-adaptive through mutually beneficial interactions and resource integration [4], thus promoting resource integration [29; 30; 45]. The degree of resource integration and value co-creation determines whether the revitalisation of historical blocks leads to sustainable operation and whether cultural heritage becomes a cultural brand through the cultural services provided by actors, thus promoting local economic development.
## 3 Results and Discussion
### Charging Experiment Data Collection  
A battery charging experiment was conducted to validate the charging algorithm. In this study, we used a 48 V/100 AH lithium titanate battery pack comprising two parallel and 15 serial single 50 AH/3.2 V aluminum-shell battery cells.  
Charging was carried out with sufficient photovoltaic power and a lithium titanate battery pack with a rated voltage of 48 V. Therefore, the charging process only underwent the constant-current, constant-voltage, and floating charging stages. The charging voltage and charging current data were recorded every 20 min, with a total charging time of 405 min, and the battery temperature was kept below 30 \({}^{\circ}\)C. Table 2 lists the recorded data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Charging Time** & **Charging Current** & **Charging Voltage** & **Charging Time** & **Charging Current** & **Charging Voltage** \\
**(min)** & **(A)** & **(V)** & **(min)** & **(A)** & **(V)** \\ \hline
0 & 19.99 & 47.91 & 220 & 20.12 & 51.41 \\
20 & 20.12 & 49.94 & 240 & 20.09 & 51.61 \\
40 & 20.12 & 50.48 & 260 & 20.08 & 52.01 \\
80 & 20.11 & 50.76 & 280 & 19.98 & 53.32 \\
100 & 20.11 & 50.8 & 300 & 16.41 & 53.96 \\
120 & 20.11 & 50.83 & 320 & 7.55 & 54.05 \\
140 & 20.12 & 50.87 & 340 & 3.15 & 54.15 \\
160 & 20.12 & 50.97 & 360 & 2.32 & 54.16 \\
180 & 20.11 & 51.13 & 380 & 1.82 & 53.07 \\
200 & 19.99 & 51.23 & 400 & 1.15 & 52.98 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Constant-current and constant-voltage charging/discharging voltages and currents.  
Figure 21: Current regulation accuracies at different set currents. (**a**) 5 A set current; (**b**) 10 A set current; (**c**) 20 A set current.  
Figure 20: Measured charging currents with different set currents. (**a**) 5 A set current; (**b**) 10 A set current; (**c**) 20 A set current.  
The above data were then curve-fitted using EXCEL; Figure 22 shows the results. The charging process is consistent with the designed four-stage charging algorithm, indicating that this algorithm is effective and feasible.
## 3 Data and Methods
### Mechanical Tests  
Before simulating the in situ stress, it is essential to analyze the mechanical parameters, which are critical for the accuracy of the simulation results. Reliable simulation of in situ stress fields can only be achieved by conducting numerical simulations based on precise mechanical parameters. Currently, the analysis of the mechanical properties of rocks under different temperature and pressure conditions typically uses triaxial compression tests [26; 27]. In this research, 15 samples without fractures from different depths of the Fengcheng Formation were collected for triaxial compression tests. According to international rock mechanics experimental standards, the diameter of the cylindrical sample is 2.5 cm, and the height is 5 cm.  
Besides triaxial compression tests, acoustic emission tests are another essential method for a better understanding of the in situ stress state (Figure 5A). Acoustic emission tests can be categorized into uniaxial and triaxial acoustic emission tests [28], both of which can determine the magnitude and stage of paleostress by identifying the Kaiser point [29; 30]. However, samples in uniaxial acoustic emission tests often undergo damage before the Kaiser point appears. Consequently, the collected signal may not be a genuine Kaiser signal but rather a rock sample rupture signal [31; 32; 33]. Therefore, increasing the confining  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Ant Tracking** & **Initial Ant** & **Ant Track** & **Ant Step Size** & **Illegal Steps** & **Legal Steps** & **Stop Criteria** \\
**Parameters** & **Boundary** & **Deviation** & **Allowed** & **Required** & **Stop Criteria** \\ \hline Range & 1–30 & 0–3 & 2–10 & 0–3 & 0–3 & 0–50 \\ Passive ant & 7 & 2 & 3 & 1 & 3 & 5 \\ Aggressive ant & 5 & 2 & 3 & 2 & 2 & 10 \\ \hline \end{tabular}
\end{table}
Table 1: Ant tracking parameters of different types.  
Figure 4: Cross-section of the northern Mahu Sag by applying the method of ant tracking. The location a-a’ is shown in Figure 2 (P1f, P2f, P2x, and P2w are members of the Permian period.).  
pressure and determining the stress through triaxial acoustic emission tests is preferable. The sampling method is illustrated in Figure 5B, with three samples selected vertically and nine selected horizontally. Based on well-established test principles and selective samples, the vertical stress and horizontal principal stresses of the research target can be acquired.
# A Compact Instrument for Temperature-Programming-Assisted Capillary-Nanoliquid Chromatography
## 3 Results
### Performance of the Temperature-Programmable Capillary-NanoLC Oven  
The thermal power provided by the oven is directly proportional to the effective voltage applied to the heating element. However, when the oven is in operation, the thermal power required at any given moment is the sum of the power naturally dissipated by the oven and the energy needed to heat the column at the user-specified rate (heating slope), following the correlation P = P\({}_{\text{dissipated}}\) + P\({}_{\text{slope+adjusted}}\). To determine these values, experiments were necessary to measure the dissipated power across a temperature range of 0 to 100 \({}^{\circ}\)C and the power required to achieve different heating slopes. These parameters were experimentally determined and incorporated into the control software. When the user programs a temperature or temperature ramp, the system automatically calculates the required power for the heating element.  
The oven's performance was assessed under both isothermal conditions and temperature ramping. Figure 6a shows the results of 25 min isothermal runs at 60 \({}^{\circ}\)C to assess temperature control precision. The system maintained a temperature accuracy of 0.001 \({}^{\circ}\)C, demonstrating high precision. Similar experiments were conducted across a temperature range from 0 to 140 \({}^{\circ}\)C, with standard deviations of recorded temperatures consistently below 0.01 \({}^{\circ}\)C. Figure 6b illustrates the linear relationship between the user-programmed temperature and the experimentally measured temperature, confirming the oven's accuracy.  
Figure 6: Performance assessment of the developed temperature-programmable oven: (**a**) example of temperature control during an isothermal run at 60 \({}^{\circ}\)C; (**b**) correspondence between the temperature set by the user and the experimentally measured temperature; (**c**) relationship between the heating power required and the temperature ramp slope (\({}^{\circ}\)C/min); (**d**) an example of the oven’s operation during a run includes both isothermal and temperature gradient segments.  
Additionally, Figure 6c presents the calibration curve for estimating the power required to perform temperature ramps with different slopes, measured in \({}^{\circ}\)C/min. The developed system exhibited precise and reproducible temperature control during ramping experiments. Figure 6d shows an example of a run comprising both isothermal and temperature gradient segments, with a standard deviation of less than 0.0026 \({}^{\circ}\)C in temperature measurements. This demonstrates the system's ability to execute complex temperature programming reliably.
## 7 Proposed Technique and Algorithm
* Each node on the main path should have an adjacent node and be ready to receive other data traffic from the selected node if it receives or broadcasts packets by mistake. Received packets are considered normal, and will not enter the buffer for the selected path even if they come by mistake, and they will be forwarded directly to the adjacent node that is not on the selected path. * The adjacent node must know that any packets received from the selected node on the path should pass from another node and should not be returned again, to avoid loops in the network. This can be achieved by informing all adjacent nodes by sending small inquiry packets informing all adjacent nodes that a node is busy, and that any packets received will be directly forwarded to the adjacent nodes, which should not return them until they receive information packets signaling availability, and the session is terminated. This mechanism comprises two tasks: looking and checking all paths in the main routing table between nodes; and constructing a path from the main routing table (if possible), or using the same path (if using a pre-active protocol such as AODV). Algorithm 1 explains the main functionality using buffering. Based on the link-state database, we find the selected nodes from the full topology \(T_{r},s,d\) between source and destination. Each graph has edges and vertices, and the edges have weights, but the routing protocol will construct the routing table with the best (shortest) path between any source and destination. Once the session is open, all emergency packets start to be forwarded hop-by-hop \(p_{a}(s,NextHop)\leftarrow\mathcal{O}\) until they arrive at the destination. The algorithm governs and controls packets from the source, to avoid swimming around the networks.
# Analysis of Score-Level Fusion Rules for Deepfake Detection
## 3 Ensemble Methods for Deepfake Detection
### Non-Parametric Fusion Methods  
As in our previous study [10], we explored various non-parametric fusion methods by applying a set of rules on the scores (i.e., the probability values of belonging to the fake class) predicted by the single classification models. Hence, the final score obtained from each sample represents a combination of the single predicted scores.  
We introduce here a common notation for easing the understanding of such fusion rules. Let us consider the fusion of \(N\) classifier scores, where \(P_{i}(\text{deepfake}|x)\) represents the score of the \(i\)-th classifier on the same frame \(x\) (i.e., the estimated posterior probability that the analyzed frame contains a deepfake). According to this notation, it is possible to compute the fusion scores of the \(N\) classifiers with the following formulas:  
\[avg\_score=\frac{1}{N}\sum_{i=1}^{N}P_{i}(\text{deepfake}|x) \tag{1}\]  
\[bay\_score=\frac{\prod_{i=1}^{N}P_{i}(\text{deepfake}|x))}{\prod_{i=1}^{N}P_{ i}(\text{deepfake}|x))+\prod_{i=1}^{N}\left(1-P_{i}(\text{deepfake}|x)\right)} \tag{2}\]  
\[\text{\emph{prod\_score}}=\sqrt[N]{\prod_{i=1}^{N}}P_{i}(\text{deepfake}|x) \tag{3}\]  
\[\text{\emph{max\_score}}=max_{i}(P_{i}(\text{deepfake}|x)) \tag{4}\]  
\[\text{\emph{min\_score}}=min_{i}(P_{i}(\text{deepfake}|x)) \tag{5}\]  
Therefore, such a set of rules represents, in order, the simple average between the scores obtained from the single detection systems, the Bayesian average between them, the \(N\)-th root of their product, the maximum between them and the minimum between them.
# Response of Grassland Vegetation Growth to Drought in Inner Mongolia of China from 2002 to 2020
## 4 Discussion
### Uncertainties  
While this study provides valuable insights into NDVI, EVI, GOSIF, and GPP trends and their responses to droughts in Inner Mongolia grassland, several limitations should be considered. Firstly, the GOSIF dataset, while offering continuity in spatiotemporal distribution, features a relatively coarse resolution (0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\)). Additionally, GOSIF is derived from a machine learning method based on OCO-2 satellite data, which introduces certain internal uncertainties [23; 33]. Secondly, previous studies have found that the grasslands in Inner Mongolia may experience compound droughts [71]. This study focused on the response to individual drought events, and further research should investigate how grassland vegetation responds to these more complex and compounded drought scenarios (such as integration of soil moisture and SPEI). Moreover, this study assessed the impact of SPEI on grassland vegetation. The vapor pressure deficit (VPD), soil moisture, and human activities can also affect vegetation growth [72; 73; 74; 75], and ignoring these variables will inevitably result in some uncertainties in the presented results. Therefore, these questions will need to be discussed and analyzed in future studies.