esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulation Results  
Each of the PRRC methods presented in Sections 2 and 3 was implemented on the model presented in Section 5 using the OP5700 real-time simulator from Opal-RT Technologies. Simulation results were obtained in two stages. First, three variations of each PRRC method were evaluated for one day. The proposed PDS method was also evaluated considering variations in the prediction window and the accuracy of the prediction. In this stage, the performance indicators were the SOC variations, the SOC at the end of the day, the PRR, the PRR violations, the number of normalized cycles, the battery degradation, and the variance of the output PRR. Second, the best variation of each PRRC method was tested during an entire year to evaluate the PRR violations, the LCOS, and the degradation of the ESS.
## 4 Results and Discussion
### Fluctuating Wind Characteristics  
The fluctuating wind characteristics are concerned with the short-term random fluctuations in wind speed and direction. Fluctuating wind characteristics refer to the rapid and random changes in wind speed and direction within a short period, representing the turbulent component of the wind. These characteristics include turbulence intensity, the fluctuating wind power spectrum, the turbulent integral scale, the coherence function, the probability density function, kurtosis, and skewness, among others. By analyzing the fluctuating wind characteristics, a more accurate prediction of the dynamic response of bridge components can be achieved, enhancing the comfort and safety of bridges.
# Conceptual and Technical Design Aspects of Accelerators for External Injection in LWFA
## 5 Challenges for Beam Stability, Control and Diagnostics
### Stability Requirements and other Technical Aspects in the Machine Design  
There are multiple technical decisions that have a significant influence on the achievable performance. In the following we discuss the solutions adopted at SINBAD-ARES.  
* The most important decision is the use of a modern, state of the art LLRF (Low Level RF) design. A general discussion of LLRF concepts is beyond the scope of this paper. Therefore in the following only a few related issues are discussed.
* A proper grounding network has been designed and installed. Suitable cable trays are chosen to minimize the EMI (Electro-Magnetic Interference) effects.
* The overall facility layout must be chosen to minimize the path lengths of the LLRF signal cables. Within the possibilities of the given overall layout of the old DORIS tunnel, this has been achieved e.g., by placing the LLRF-racks directly below the RF-structures, longitudinally placing the RF-Stations to minimize the waveguide length etc. (see Figure 14).
* The electronic racks for the LLRF have been carefully chosen to provide a humidity- and temperature- controlled environment.
* The type and routing of the LLRF cables has been carefully chosen and active temperature stabilization of the cables is foreseen [98].
* Fs level synchronization between the laser and the RF field of the gun will be achievable by implementing the same methods described in [99].
* The traveling wave structures are chosen to have an external RF-dump in order to allow better prediction and control of the temperature along the structure. Furthermore, multiple RF-pickups and temperature measurement devices along the cavity have been requested to provide the required input for the LLRF-system [100].
* Significant effort has been invested in the precision water cooling of the RF-structures [101]. In order to optimize the performance, short pipe-lengths are required and thus a multi-stage approach was used with the first stage located inside the tunnel right next to the structures.  
Also, dedicated heat-exchangers are used to protect the inner-moist cooling circuit from pressure changes in the facility-wide cooling system.
* within the local capabilities--sources avoided by e.g., adding suspensions to the water cooling pumps.
* Due to budgetary constraints the accelerator tunnel is currently not air-conditioned, but it is expected that the temperature of the tunnel remains relatively constant due to the large air-volume.
# A Distributed Autonomous Mission Planning Method for the Low-Orbit Imaging Constellation
## 3 The Basic CBBA  
### Structure of the CBBA  
CBBA is an auction-based distributed algorithm, where each satellite has the capacity for autonomous decision-making. Each satellite continuously obtains planning information from other satellites through local communication, ultimately reaching a consensus scheme. During the running time, each satellite needs to maintain two lists to record the global bidding information under the satellite's cognition, which are: the highest bid satellite and its bid corresponding to each task, denoted by \(z_{i}=\left\{z_{i1},\ z_{i2},\ldots,\ z_{i|T|}\right\}\) and \(y_{i}=\left\{y_{i1},\ y_{i2},\ldots,\ y_{i|T|}\right\}\), respectively. Additionally, it is necessary to record the timestamp list of other satellite's information obtained by the satellite, represented by \(h_{i}=\left\{h_{i1},\ h_{i2},\ldots,\ h_{i|T|}\right\}\). At the same time, each satellite is required to maintain a task set of its choice, denoted by \(b_{i}\).  
The CBBA consists of two phases. The first phase is the task bundle construction phase. In this phase, each agent independently and greedily selects the task with the highest bid one-by-one from the task set from which they believe they have bid higher than the current global highest bid. The second phase is the consensus check phase. At this stage, each agent updates its knowledge of global information by interacting with neighboring agents, which is then used to guide the updating and construction of the task bundle. After communicating with neighbors, for the task in \(b_{i}\), when there is a satellite bidding higher, the satellite \(s_{i}\) needs to give up the task. Since tasks are added sequentially to the task bundle in the first phase, each addition will have a cascading effect on the remaining tasks to be scheduled (e.g., the tasks in the bundle set need to satisfy the constraint of Equation (6), and if there is an overlap in the O-VTW of the two tasks, the addition of one task will result in the loss of bidding eligibility for the other task due to the failure to satisfy the constraint). So, if a task is abandoned, all subsequent tasks added to the task bundle will be invalidated. Therefore, we need to discard the task and all subsequent tasks in the task bundle, and initialize the information of all tasks after that task in \(y_{i}\) and \(z_{i}\). This step is called conflict resolution. With the continuous iteration of the two stages, more and more tasks have had the final winner of the bid determined, and a consensus on the task planning has been ultimately reached.  
The process of CBBA from the perspective of single satellite \(s_{i}\) is shown in Figure 4. The inside part of the dotted box represents the satellite's built-in operating program, and the outside part of the dotted box is the satellite's information interaction with the environment.  
Figure 4: Flow chart of CBBA from a single satellite perspective.
# A Location Routing Problem with Time Windows Consideration: A Metaheuristics Approach
## 2 Materials and Methods
### Proposed Method
#### 2.2.2 Initialization  
The computational result is exemplified by a combination of \(n\) customers (1, 2,..., \(n\)), \(m\) prospective depots (\(n+1\), \(n+2\),..., \(n+m\)), and \(N_{dummy}\) zeros. These \(N_{dummy}\) zeros are utilized to arbitrarily dismiss the routes to increase the chance for a better solution. The calculation of \(N_{dummy}\) number is based on the aggregate of division between demand of customers \(i\) and the vehicle capacity \(Q\). This also will ensure that a route will be terminated if the vehicle capacity constraint is violated. During the initialization phase, a number representing a depot will always be the first number in the solution representation. Next, it is followed by a number in {1, 2,..., \(n\)} which represents the next customer to be served. Based on the solution representation, each depot will provide service to the customers located between the depots. Authors attempted not to contravene the time windows of each customer when developing the initial solution. Note that capacity constraints can be violated to search for a better solution. However, the final solution will be made feasible. The detailed algorithm for the initial solution generation can be found from [3; 49]. The solution starts with the initially designated depot, then is followed by customers selected to be served by an open depot. Once all customers have been allotted to the first depot based on the depot capacity, the second depot will be chosen and incorporated to the solution representation. This procedure is carried out until each and every customer has been  
Figure 1: VNSPR framework.  
designated to a depot. Then, \(N_{dummy}\) zeros is randomly added. Finally, the closed depot is affixed to the solution. Figure 2 shows the example of the generated initial solution.  
The first depot in this example is 24, and yet because the following number is 23, which is also a depot, Depot 24 is unutilized. Customers 3, 4, 5, and 6 will be served by Depot 23. Based on Figure 2, Depot 22 will have three routes. The representation solution's number 0 functions as the route termination and indicates that the route is rescinded after serving customer 19. The second route will end once its visited customer 10, and the final route from Depot 23 will only serve customer 15. As Depot 21 is unutilized, Depot 25 will be open and serve customers 7, 11, 13, 14, 16, and 17 according to the sequence shown in Figure 2.
# Recent Lipid Membrane-Based Biosensing Platforms
## 5 Practical Applications of Lipid Membrane Devices in Food, Environmental, and Clinical Analysis
### Applications of Lipid Film Devices Based on Polymeric Lipid Membranes  
The stable in air lipid film devices were applied for the determination of carbofuran using flow injection analysis (FIA) [14]. The technique was based on the degree of inhibition and reactivation of enzyme upon substrate injections. Carbofuran could be detected between \(10^{-7}\)-\(10^{-9}\) M. Interference studies were made and involved proteins and lipids that are food components. No interferences were noticed from these compounds. The device was evaluated/validated using real food samples (i.e., fruits, vegetables, and dairy products). The recovery studies have shown satisfactory results between 96% and 106%, which shows that there are no interferences from the sample matrix.  
A synthetic \"receptor\" (calaxarene) was prepared and immobilized on lipid membranes on glass microfiber filters. Calixarene was inserted into the lipid structure and provided a signal which was adequate to rapidly determine insecticides rapidly, with a sensitive and selective response, and was used to determine these compounds in real samples of fruits and vegetables [80]. Similar devices were constructed to selectively and rapidly determine food hormones (i.e., naphthalene acetic acid) in fruits and vegetables [81] and a zinc in water [82].
# Automatic Identification of Bridge Vortex-Induced Vibration Using Random Decrement Method
## 4 Application to the Xhiounen Bridge
### Bridge Wind Field  
As shown in Figure 11, the Xhiounen Bridge spans the narrow waterway between Cezi Island (North) and Jintang Island (South). According to its geographical location, the Xhiounen Bridge will not only encounter low velocity wind but also typhoons with much higher wind velocity. Therefore, the bridge encounters a large wind velocity range. As the largest suspension bridge in China at the time of 2019, it is very flexible and has multiple low natural frequencies, which are sensitive to dynamic wind action.  
Figure 18 showed the wind rose diagram of the 10 min mean wind velocity at the quarter span of the bridge, measured in January, May, and August 2015. It can be seen that the 10 min mean wind velocity in three months is less than 20 m/s, and the wind direction distribution varies in different months. The northwest wind mainly occurs in winter and the southeast wind in summer. The wind direction distribution is the most dispersed in August. In the northwest direction, the wind direction varies greatly; for the southeast direction, most of the wind direction is almost perpendicular to the bridge. In general, the wind direction is likely to be perpendicular to the bridge, and this direction easily causes VIV. Additionally, the wind velocity changes in range, which may cause different modes of VIV in the bridge.
## 5 Statistical Analysis
### Regression Analysis
#### 5.5.1 Hypothesis 1: The Agile Approach Has a Positive Impact on the Success of a Project  
For Hypothesis 1, the regression analysis results are summarized in Table 6. This table, shows a strong relation between Agile Methodologies' Effectiveness and project success, because the value of \(\beta\) is 0.690 significant value of \(p\leq 0.001\). Hypothesis 1 is acceptable since the results indicate that there is a 0.698 unit change in project success when there is a 1 unit change in Agile methodology effectiveness.
## 4 Discussion  
The development of the digital twin system underscores the revolutionary impact of digital technology in the solid-wood-panel-processing industry, proposing a path toward achieving intelligent production. The digital twin system integrates physical and virtual layers, provides real-time operational monitoring alongside production process simulation, and offers unprecedented control and transparency. Moreover, the simulation platform, tailored for solid-wood-panel processing, employs a dynamic Gantt chart reflecting the real-time manufacturing process. This interactive tool allows for the alteration of variables such as AGV speed, thereby offering a vivid depiction of the production process and modeling potential outcomes of adjustments in the production parameters. Combined with real-time mapping, this simulation aspect facilitates comprehensive monitoring and control of the operations, rendering the production process transparent and easily manageable.  
This system has multiple practical applications. It enhances production efficiency, optimizes resource use, and reduces production costs and energy usage. The system's real-time monitoring and intelligent scheduling capabilities allow for the immediate detection and resolution of production issues. Optimization strategies can be rapidly implemented to minimize production interruptions, enhancing the manufacturing operations' reliability and sustainability. Additionally, the convenience of optimizing production procedures and minimizing waste and energy consumption yields a reduction of adverse environmental impacts.  
Figure 22: Convergence comparison chart.  
Now shifting focus to DSAGA, this methodology's superiority over conventional algorithms can be clearly observed. As evidenced in Table 3, DSAGA improves maximum completion time, reducing it to 5403 s, compared to GA, which typically takes 99 min.  
The percentage deviation of the maximum completion time was calculated to demonstrate the superiority of the proposed method over the alternative algorithms. To evaluate the performance of DSAGA, Equation (22) was introduced as a measurement criterion [51].  
\[P=\frac{T_{x}-T_{0}}{T_{x}}\times 100\% \tag{22}\]  
Here, \(T_{x}\) is the maximum completion time of GA, and \(T_{0}\) is the maximum completion time of DSAGA. With an increase in \(P\), the efficiency of the production line employing the algorithm declines, suggesting a decrease in algorithmic performance. As observed in Table 4, the algorithms demonstrate efficiency, with DSAGA surpassing the traditional version, resulting in an 8.93% enhancement in efficiency.  
This study establishes a foundation for intelligent production systems in the solid-wood-processing industry, driving the move towards further digitization and innovation in manufacturing. By integrating digital twin technology with DSAGA, this effort charts a course for transforming and elevating China's wood-processing industry. This approach aims to boost competitiveness while promoting environmental sustainability.
## 2 Materials and Methods
### Data Processing
#### 2.3.1 Carbon Sinks and Aerosol LiDAR Data Processing
In this study, the RH metric was calculated based on the end position of the effective waveform segment. The RH calculation consists of the three following steps:  
Step 1: Cumulative energy calculation: The waveform amplitude \(f(i)\) is integrated from _SigEnd_ to _SigBeg_ to obtain the total energy \(E_{i}\):  
\[E_{i}=\sum_{i=SigEnd}^{SigBeg}f(i) \tag{2}\]  
where \(SigEnd\) denotes the end position of the effective waveform segment (i.e., the starting point for cumulative energy calculation) and \(SigBeg\) represents the beginning position of the effective waveform segment (i.e., the endpoint for cumulative energy calculation). Step 2: Percentile position determination: The sampling position \(PRHn\) (where \(n\) ranges from 0 to 100) is identified when the cumulative energy reaches \(x\)%:  
\[PRHn=\arg\mathit{persent}_{n}\{E_{i}\} \tag{3}\]  
where arg \(\mathit{persent}_{n}\) is a function that calculates the position of the specified percentile in an array. Step 3: RH value conversion: \(PRHn\) is converted to height (in meters):  
\[PRHn=(PRHn-PRH0)\times\mathit{C}/2/K \tag{4}\]  
where \(K\) is the TECIS full-waveform signal sampling frequency (1.2 GHz) and \(c\) is the speed of light in a vacuum. The calculated RH metrics start from RH0 to RH100 for a total of 101 RH metrics. Figure 4 shows some of the RH metrics (RH25, RH50, RH75, and RH100), which are sub-tabulated to represent the relative elevation of the vegetation corresponding to 25%, 50%, 75%, and 100% of the cumulative energy of the waveform for each footprint.
## 2 Materials and Methods
### Films Characterization
#### 2.3.3 Thermal Analysis  
DSC analysis was carried out by employing a TA Instruments Q100 equipment (TA Instruments, New Castle, DE, USA) working in nitrogen atmosphere (50 mL min\({}^{-1}\)). Film samples (5.0 mg) were added in aluminium pans (70 \(\upmu\)L) and the applied thermal programme was: Heating from 25 \({}^{\circ}\)C to 300 \({}^{\circ}\)C (3 min hold), cooling to 0 \({}^{\circ}\)C (5 min hold), and heating to 300 \({}^{\circ}\)C; all steps at 10 \({}^{\circ}\)C min\({}^{-1}\). DSC were obtained from the first heating scan. Crystallisation and melting temperatures (T\({}_{c}\) and T\({}_{m}\)) were calculated at peak temperatures of the corresponding transitions, while the crystallisation and fusion enthalpies (\(\Delta\)H\({}_{c}\) and \(\Delta\)H\({}_{m}\)) were obtained from the area of the corresponding peaks. The crystallinity index (\(X_{c}\)) of LDPE was obtained by using the Equation (2):  
\[X_{c}=\left(\Delta\)H\({}_{m}\)/\(\Delta\)H\({}^{\circ}\)\({}_{m}\)W\({}_{LDPE}\right)\times 100 \tag{2}\]where \(\Delta\)H\({}_{\rm m}\) is the melting enthalpy per unit mass of the sample, \(\Delta\)H\({}_{\rm m}^{{}^{\circ}}\) is the theoretical value of the melting enthalpy per unit mass of 100% crystalline PE (293 J/g) [23], and W\({}_{\rm LDFE}\) is the weight fraction of the LDPE matrix.  
The OIt was determined by using the same DSC equipment described above. Five mg of multilayer films (ML and ML/LDPE_CAR) were enclosed in a 70 \(\mu\)L aluminium pans and they were heated to 200 \({}^{\circ}\)C at 10 \({}^{\circ}\)C min\({}^{-1}\) in nitrogen atmosphere (50 mL min\({}^{-1}\)). After that, samples were maintained at 200 \({}^{\circ}\)C during 30 min under the air atmosphere. The beginning of material oxidation was clear due to an increase of the slope of the exothermal heat flow. The time before switching to air was removed and OIt was the time in which the slope starts to increase [24].  
Thermogravimetric analysis (TGA) experimental determinations were performed by employing a TGA/SDTA851e/SF/1100 Mettler Toledo (Mettler Toledo, Schwarzenbach, Switzerland) equipment. Films (5.0 mg) were heated from 30 to 700 \({}^{\circ}\)C at 10 \({}^{\circ}\)C min\({}^{-1}\) in nitrogen atmosphere (50 mL min\({}^{-1}\)). The initial degradation temperature (T\({}_{\rm ini}\)), calculated at 5% of weight loss, and the temperature of maximum decomposition rate (T\({}_{\rm max}\)) were obtained. Triplicate DSC and TGA analysis of all the samples were carried out.
## 6 Conclusions  
We have briefly described the theory, development, and demonstration of a new prototype instrument for measuring GHGs in the atmosphere. Several advantages over exiting methods for both ground-to-ground and space-to-ground have also been discussed. The LAnTeRN prototype instrument successfully demonstrated the potential for this open-path LAS measurement concept to provide high precision measurements over long path lengths, and at very low received power levels. The prototype measurements also served to validate a high-fidelity physics-based performance model that can be used to optimize future developments for specific applications. Further testing is planned for a detailed evaluation of instrument precision and accuracy. Other further developments will include locking rather than measuring the transmitter wavelengths, as would be required for a space-based approach, and building a more complete model by including other noise sources such as the laser stability and intensity fluctuations.  
The instrument has a wide range of potential applications, which were only briefly touched on here. The range of applications spans ground-to-ground and space-to-ground implementations. The measurement capability compliments current ground-based _in situ_ instrumentation by providing an integrated path measurement, both day and night, that is not biased by heavy aerosol concentrations in the path, and can be implemented to provide concentration and time-varying flux maps over a large area. Furthermore, the space-to-ground application complements current and planned space monitoring of GHGs by adding high spatial and temporal resolution measurement capability which, through averaging and the ability to evaluate and determine bias, will enable measurements to the precisions and accuracies required to distinguish anthropogenic and biogenic sources and sinks. The authors look forward to discussing further developments towards these and other applications in future publications.