esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Validation  
Based on the dynamic model presented in the section Dynamic Model of The System, a simulation scheme in the Simulink was created for the validation process. The basic physical parameters of the system (mass, length, a moment of inertia and distance from the axis of rotation of the joint to the center of gravity for link 1 and link 2) were exported from the CAD software, which was used to make a 3D model of the system. The coefficients viscous friction coefficient and Coulomb friction magnitude were determined experimentally. An overview of these parameters (with parameter designation, value and unit) is summarized in Table 2.  
The validation outputs were evaluated based on the MAE (Mean Absolute Error) and NRMSE (Normal Root Mean Square Error) statistical indicators. The indicators are used to compare the output of the model with the outputs of the system. The MAE indicator by a numerical value represents the error that occurred during the validation compared to the measured data. The general mathematical formulation of MAE is given in Equation (7), where \(y_{j}\) represents the output of the system on the k-th sample and \(\bar{y}_{j}\) represents the output of the model on the k-th sample. The NRMSE, unlike the MAE, compares the degree of agreement of the simulated output with the measured data (Equation (8)). The desired value is for NRMSE = 100% when the model output corresponds 100% to the system output.  
\[MAE=\frac{1}{n}\sum_{j=1}^{n}\abs{y_{j}-\hat{y}_{j}} \tag{7}\]  
\[F_{gof}=\left(1-\frac{\sqrt{\sum_{j=1}^{n}\left[y_{j}-\hat{y}_{j}\right]^{2}}} {\sqrt{\sum_{j=1}^{n}\left[y_{j}-\frac{1}{n}\sum_{j=1}^{n}y_{j}\right]^{2}}} \right)\times 100\% \tag{8}\]
## 3 Results and Discussion  
The results are divided into three subsections based on the research questions presented. First, it is investigated whether real defects such as voids can be produced in the laboratory. Then, the damage development of initial defects before and after exposure to rain is observed and analyzed. Finally, the feasibility study of active thermography as an alternative measurement method to CT and XRM takes place, including a technique comparison for validation. Due to the large number of tests undergone and hence generated data, the results are focused on a specimen with built-in voids in the coating layer (specimen Type B).
## 6 Conclusions  
In this paper, a laser triangulation displacement sensor for a dispensing robot location system was developed based on the comprehensive characteristics of the actual image waveform of the laser. The \"centroid position-peak intensity\" relationship of static imaging waveforms of different objects was fitted by the least square method, and the optimal peak intensity was solved to eliminate the interference of different measured objects. On the basis of the centroid difference of two dynamic imaging waveforms, the static and dynamic working modes were distinguished, and the peak intensity was adjusted to the ideal interval by the linear iterative method. According to the dynamic test experiment of the dispensing robot, the step response speed of the sensor was 0.5 ms under the dynamic measurement scene, and the repeatability accuracy was 2.7 um for different measured objects. However, the repeatability of the sensor still had room for improvement. In future work, we would increase the sampling frequency to collect more data for an average filter, and research a method to quickly obtain more feature information on the imaging laser spot, in order to improve the repeatability accuracy. Moreover, the LTDS proposed in this paper can also be applied to other high-precision dynamic measurement scenarios, in addition to the dispensing robot.  
Conceptualization, Z.N. and W.T.; data curation, N.L.; methodology, Z.N.; software, H.Z.; writing--original draft, Z.N. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under grant no. 51975374.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Repeatability** & **Dynamic Delay** & \(\sigma\) \\ \hline Only dynamic mode & 16.2 um & 0.5 ms & 0.65 um/Hz \\ Only static mode & 2.9 um & 10 ms & 0.38 um/Hz \\ PID adjustment & 10.9 um & 2 ms & 0.85 um/Hz \\ The method proposed & 2.7 um & 0.5 ms & 0.08 um/Hz \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of different laser adjustment methods.
# Behavioural Verification: Preventing Report Fraud in Decentralized Advert Distribution Systems
## 5 Detailed System Analysis
### Information Components
#### 5.1.3 Service Confirmation Board (SC-Board)  
The Service Confirmation Board (or _SC-Board_ for short) is a digital database which serves as an information sharing platform between all of the system's stakeholders. The indexed entries of the _SC-Board_ represent Request Forms that have been distributed to users and consist of five fields as shown in Figure 5.  
The first two fields are filled by the Broker when he issues a new \(RF\) and respectively contain the \(ARC\)-\(ID\) and the name of the issuing Ad-Dealer (the Ad-Dealer who forwarded the user's RF-Request) along with the corresponding date. The remaining fields are completed when the report is submitted with the third field keeping the identity of the submitting Ad-Dealer (the Ad-Dealer who forwarded the user's \(ARC\) and \(IH\)) and the date of submission while the forth and fifth contain the Ad-Report Chain \(ARC\) and Integrity Hash \(IH\).  
Indicated in the diagram with a darker shade under the second, third and fourth field, are certain sections which are completed by the issuing Ad-Dealer, the submitting Ad-Dealer, the Broker and the individual Advertisers. These fields serve the purpose of verification checks. In more detail, \(VC\)-\(I\) under the second field is signed by the issuing Ad-Dealer to verify the issue of the new \(ARC\)-\(ID\). In a very similar fashion, the submitting Ad-Dealer signs the third field marked as \(VC\)-\(S\) in order to verify the submission of the \(ARC\) and confirm the correctness of the hash digests \(h(N)\) for each block of the \(ARC\). Recall that in Section 5.1.1, we briefly mentioned that the content of the Ad-Reports are encrypted except for the sequence number \(N\) and hash \(h(N)\) which are still visible to the submitting Ad-Dealer. While the \(ARC\) in the fourth section is published by the Broker after decryption, the submitting Ad-Dealer confirms that the hashes have not been altered by comparing them to his own copy. The individual verification checks, which are marked as \(VC\)-\(I\) to \(VC\)-\(N\) under the \(ARC\), are filled either by the Broker to indicate blocks that have been verified or by the Advertisers to indicate blocks for which the Advertiser has awarded a commission to the Publisher. More details on the exact operation and the reasons behind it are provided in Sections 5.3.3 and 6. Lastly, we need to mention that all fields of the \(SC\)-\(Board\) are visible to Ad-Dealers, Publishers and Advertisers but the  
Figure 5: Service Confirmation Board architecture.  
first field with the _ARC-ID_ also becomes available to the users after submission has been completed. The users only need to have access to the first field in order to verify that their submission has been delivered but cannot see any other information that is published on the SC-Board
# Impacts of Thermal Maturity on the Carbon Isotopes of
## 2 Samples and Experiments
### Experiments  
#### 2.2.1 Compaction Pyrosis Experiment  
The pyrolysis experiments were conducted on a high-temperature and high-pressure semi-closed hydrous pyrolysis system and can simulate various factors that influence the petroleum generation process, including temperature, overlying lithostatic pressure, fluid pressure, mineral composition, and formation of water. The schematic diagram of the device structure is shown in Figure 1. It can control the episodic hydrocarbon expulsion and the generation, discharge, and retention processes of oil and gas [32; 33; 34]. Therefore, this device can simulate the actual underground hydrocarbon generation and expulsion process. The experimental procedures for pyrolysis experiments in the high-temperature and high-pressure semi-closed hydrous pyrolysis system have been well described in previous studies [35; 36]. Here, we give a brief description as follows: Firstly, the shale sample was subdivided into seven equal portions, each weighing approximately 20 g. The samples were then compressed into a dedicated sample compartment under a pressure of at least 5 MPa. The entire sample compartment was subsequently loaded into the reaction vessel. The pyrolysis experiment was conducted at temperatures of 300 \({}^{\circ}\)C, 325 \({}^{\circ}\)C, 345 \({}^{\circ}\)C, 350 \({}^{\circ}\)C, 365 \({}^{\circ}\)C, 375 \({}^{\circ}\)C, and 385 \({}^{\circ}\)C, respectively. The heating procedure involved first increasing the temperature from room temperature to 150 \({}^{\circ}\)C in 30 min, then elevating it to the preset temperature at a heating rate of 60 \({}^{\circ}\)C/h and maintaining this temperature for 24 h. During the experiment, the fluid pressure was controlled by a back-pressure valve and maintained lower than 30 MPa. Once the pressure exceeds the preset value, the petroleum and water will automatically be expelled into the product collector. When the temperature of the oven drops to 120 \({}^{\circ}\)C after pyrolysis, the hydrocarbon expulsion valve will opened. Then, high-pressure fluid will be discharged into the product collector, which was first vacuumized before the pyrolysis experiments. After that, the oven is opened, and the residual samples are removed. Finally, the samples are crushed into 80-100 mesh size and divided into three parts: one for Soxhlet extraction, one for vitrinite reflectance measurement, and the third for geochemical tests.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Formation** & **Depth (m)** & **TOC (\%)** & **\(S_{1}\) (mg/g)** & **\(S_{2}\) (mg/g)** & **\(T_{\text{max}}\) (\({}^{\circ}\)C)** & **HI (mg/g TOC)** & **\(R_{\text{o}}\) (\%)** \\ \hline E\(k_{2}\) & 2090.5–2105.6 & 4.38 & 0.67 & 35.88 & 444 & 819 & 0.31 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic geochemical information of shale sample.
## 3 Results and Discussion
### Assessment of Agricultural Residues for Exoperoxidase Production  
The high cost of production is a major challenge to the industrial application potentials of peroxididases. Hence, it is imperative to find cost-effective means of peroxidase production. The use of cheap substrates as alternative carbon sources for fermentation has been suggested as an efficient means of reducing enzyme production cost [56]. The abundance, availability and renewable nature of lignocellulosic materials confer on them the status of perfect candidates of cheap carbon sources. Agro-residues including sawdust, wheat straw, corn cobs, rice straw, peapods etc have been utilized for production of various lignocellulosic enzymes through solid state and submerged fermentation processes [25; 57; 58; 59; 60]. However, SSF is perhaps, more promising for optimum utilization of agricultural residues for enzyme production as SSF is reported to be economical [61]. SSF is also characterized by high yield, low wastewater output and decrease in energy demand [62]. This study assessed selected agricultural residues (sawdust, wheat straw and corn stover) for peroxidase production by _Ensifer alhaerens_ NWODO-2 under SSF and the results are presented in Table 1.  
Figure 3: Exoperoxidase production by _Ensifer alhaerens_ NWODO-2 over a time course. (**a**) Enzyme production and protein concentration. (**b**) Specific productivity and cell growth.  
The results revealed that peroxidase production by the test organism differ significantly (\(p<0.05\)) when cultivated on the different substrates, with sawdust supporting the highest production (\(37.50\pm 0.00\) U mg\({}^{-1}\)) while the lowest production was recorded with corn stover (\(3.76\pm 0.00\) U mg\({}^{-1}\)). This result is in agreement with previous similar investigations [25; 57; 63], where high peroxidase activity was induced by sawdust as solid substrate under SSF. This finding could be linked to the inductive effect of the phenolic and non-phenolic constituents of sawdust. Nonetheless, enzyme activity was not detected in the absence of hydrogen peroxide when grown on sawdust and wheat straw but a specific extracellular enzyme activity of \(1.23\pm 0.21\) U mg\({}^{-1}\) was detected in the absence of hydrogen peroxide when grown on corn stover. The extracellular enzyme activity observed in the absence of hydrogen peroxide when _Ensifer ahaerens_ NWODO-2 was grown on corn stover indicates likely lacase production. This suggests that corn stover may possess phenolic compounds with inductive lactose effect [64; 65].
## 3 The SemDaServ Approach
### Process
#### 3.2.3 Specify Data Needs  
The goal of the Specify data needs process step is the final specification of the data requirements of the smart service. This involves all roles of the Core Team as well as the Data Analytics Specialist. If necessary, domain experts can be involved upon request of the Data Analytics Specialist. The sub-processes explained below and shown in Figure 7 are carried out.  
The goal of the Derive data needs sub-process step is to convert the information model into a data model. The data model describes which data are needed to meet the information requirements described in the information model. For this purpose, variables are defined (or documented in the case of existing data) and the information quality described is supplemented by figures, data, and facts from the field of data quality management. The data types are also defined in the process.  
The goal of the Evaluate existing data sub-process step is to complete a technical fit-gap analysis comparing the data requirements described in the data model and the existing data. This involves assessing the extent to which the existing data meets the data needs. In addition to searching for relevant data, this also requires a technical assessment of the data quality. Here, the Data Analytics Specialist can be supported by the Information Service Provider (Provides supplementary data from external sources [35]), the Data Center Operator (Operates the IT infrastructure [35]), the Cloud Platform Provider (Operator of application-independent (external) cloud components [35]), and the Connectivity Provider (Responsible for technical interface (e.g., mobile network) between (smart) product and IT infrastructure [35]) are supported. The result of this process step is a qualitative assessment of the gap between data requirements from the domain expert's perspective and the existing data. Nevertheless, the economic perspective is still not yet included in this final definition of the data requirements but is taken into account in the next step of the data specification process.  
The goal of the Decide final data needs sub-process step is the final specification of data requirements. However, after the domain experts finish specifying the data and information needs, maybe the costs for data acquisition exceed the expected revenue of the smart service. In this case, the smart service would be a loss-making business. Therefore, at this point, the profitability of the smart service is reviewed and optimized-if necessary by reducing the data requirements or removing variables. This process step is guided by the Project Sponsor. It involves the Core Team, the Data Analytics Specialist, and domain experts, as needed. This process step relies heavily on the data decision matrix (see Section 3.3.3). The final determination of data requirements concludes the data specification process. The artifacts described below add up to the semantic data specification.  
Figure 7: Process steps to specify data needs.
# High-Performance Digital Devices Design by the ASMD-FSMD Technique for Implementation in FPGA
## Appendix B  
This appendix presents an example of a Verilog code of the sequential multiplier in the form of the FSMD, which implements the multiplication algorithm d.  
module mult_d_Mealy_FSMD  
#(parameter N = 4)  
(input clk, reset, run,  
input [N-1:0] a,b,  
output [2*N-1:0] p,  
output reg done);  
reg [2*N:0] rp;  
reg [N-1:0] ra,rb;  
reg [N_cnt-1:0] cnt;  
localparam N_cnt = clogb2(N - 1);  
function integer clogb2(input [N - 1:0] v);  
for (clogb2 = 0; v > 0; clogb2 = clogb2 + 1)  
v = v >> 1;  
endfunction  
localparam [0:0] s0 = 0, s1 = 1;  
reg [0:0] state;  
always @(posedge clk, negedge reset)  
if(!reset) state <= s0;  
else  
case (state)  
s0: if(run)  
begin  
rp = 0; cnt <= 0; done <= 0;  
ra <= a;  
rb <= b;  
state <= s1;  
end  
else  
state <= s0;  
s1: begin  
if(rb[0]) rp = rp[2*N - 1:0] + [ra,[N[1*0]]];  
cnt <= cnt + 1*b1;  
rb <= rb >> 1;  
if (cnt == N - 1)  
begin  
rp = rp >> 1;  
done <= 1*b1;  
state <= s0;
## 4 Discussion
### Characterization and Testing of the Sensors Integrated into the Smart Garment
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Test**} & \multicolumn{2}{c}{**Carbon Monoxide**} & \multicolumn{2}{c}{**Formaldehyde**} & \multicolumn{2}{c}{**Hydrogen Sulfide**} & \multicolumn{2}{c}{**Oxygen**} \\  & **CO (ppm)** & \multicolumn{2}{c}{**CH2O (ppm)**} & \multicolumn{2}{c}{**H2S (ppm)**} & \multicolumn{2}{c}{**O2S (\%Vol)**} \\ \hline  & **Garment** & **Reference** & **Garment** & **Reference** & **Garment** & **Reference** & **Reference** \\ \hline
1 & 1.9 & 2.1 & 0 & 0 & 0.9 & 1.0 & 21.1 & 20.9 \\
2 & 1.8 & 1.7 & 0 & 0 & 1.9 & 2.0 & 21.2 & 21.0 \\
3 & 2.7 & 2.6 & 0 & 0 & 0.5 & 0.5 & 21.2 & 21.3 \\
4 & 3.0 & 3.2 & 0 & 0 & 1.6 & 1.7 & 21.3 & 21.8 \\
5 & 3.2 & 3.1 & 0 & 0 & 1.7 & 1.8 & 21.1 & 21.2 \\ \hline \end{tabular}
\end{table} 
Table 4: Results of field tests carried out on the developed smart garment, comparing the acquired measurement with those provided by portable gas detectors. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Parameters**} & **ME2-CO** & **ME2-CH2O** & **ME2-O2** & **ME3-H2S** \\ \hline Sensitivity & 0.025 \(\mu\)A/ppm & 0.55 \(\mu\)A/ppm & 0.25 mA/Vol & 0.85 \(\mu\)A/ppm \\ \(\tau_{R}\) [s] & 18 & 22 & 12 & 18 \\ \(\tau_{F}\) [s] & 19 & 27 & 14 & 24 \\ \hline \end{tabular}
\end{table} 
Table 3: Parameters of the electrochemical gas sensors extracted from the transient characteristics reported above. The reported comparison shows that other devices reported in the literature can acquire similar parameters but with different sensor technologies [25]. The use of electrochemical gas sensors allows reducing the garment's power consumption. Furthermore, a key feature of the proposed device that is not exploited in the compared systems is the combination between a wearable sensing system and a harvesting unit, enabling the significant extension of the device lifetime and making it energetically autonomous in practical operating scenarios.
## 2 Literature Review
[23] integrated the LSTM structure into the deep learning Transformer algorithm framework to address the limitations of LSTM in capturing long-distance sequence information, thereby achieving the complementary advantages of long-range dependencies in temporal and spatial features. In ship trajectory prediction tasks, most researchers have employed traditional recurrent neural networks as the model foundation. Although recurrent neural networks are proficient at learning the temporal characteristics of ship trajectory sequences, their model inference processes are typically sequential [24]. This means that calculating the feature points for the next sequence requires waiting for the completion of the computation of the previous feature point. Additionally, recurrent neural networks are prone to losing information from the beginning of long trajectory sequences. Therefore, in the proposed method of this paper, we have also targeted to add a multi-head attention mechanism. Compared to traditional recurrent neural networks (RNNs), where features are passed sequentially at each time step, the multi-head attention mechanism allows the features at each time step to perceive the global temporal features and their relationship with the current node data. The advantage of multi-head attention is the ability to consider the effect of global temporal features on the current node features from different dimensions.
# Advanced Technologies in Food Processing--Development Perspective
## 2 Application of Non-Thermal Plasma in Food Technology
### Application of Cold Plasma in the Meat Industry
Lipid oxidation has been observed in meat and fish due to the presence of reactive oxygen forms in a cold plasma-controlled atmosphere, accelerating the oxidative rancidity process, which in turn degraded the quality of the products and shortened their shelf life [53]. However, it is worth noting that cold plasma is a relatively new technology, and studies are currently underway on its effectiveness and safety in the context of food production. Appropriate examinations and tests must be carried out prior to the possible use of this technology in meat processing to ensure that it does not adversely affect the quality and safety of the final product [47].
## 2 Materials and Methods  
### Air Monitoring Shelter  
A custom aluminum shelter (4-foot width \(\times\) 6-foot length \(\times\) 8-foot height) was constructed to house the air monitoring instrumentation and associated power system. It was fabricated from welded structural aluminum members and included a 24-inch-wide entry door with locking mechanism and weatherproof gaskets. A 22-foot detachable telescoping fold-over aluminum mast was installed on the opposite side of the entry door where the wind turbine and meteorological sensors were deployed. A total of six watertight inlet tube fittings were fixed to the roof of the shelter to hold the sampler inlet tubes, the bases of which were further fastened to the interior walls. A data logger (Campbell Scientific CR1000) was integrated into the station and was programmed to log measurements from the station's meteorological sensors and to monitor battery compartment temperature, as well as to control sampling events. All sampling events were initiated and controlled by the data logger according to a defined sampling schedule. The data logger was programmed with the sampling schedule, data averaging intervals, and file output formats using LoggerNet (Campbell Scientific, Logan, UT, USA).  
Power for the off-grid air monitoring station was provided by three 150 W solar panels and one wind turbine with a peak output of 200 W. All power-producing components utilized a 12 VDC operating system. A total of six regenerative power batteries were equipped with the peak reserve capacity of 672 Ah. The batteries were connected to a maximum power point tracking (MPPT) charge controller to maximize solar and wind collection efficiency. A remote battery temperature sensor connected to the controller was used to optimize charging. A second controller was integrated within the power supply system to divert excess wind power. Individual batteries had a nominal rating of 6 VDC. An array of three batteries in parallel by two sets in series provided the required 12 VDC, which reduced the risk of charging imbalances and allowed the system to function with fewer than six batteries. All cables were rated for \(-40\)\({}^{\circ}\)C.