esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Analysis of Model Performance
### Analysis of Operation Conditions  
#### 5.1.1 Analysis of Operation Using FEL Strategy  
From Figure 6a, under the FEL strategy, it was found that the electrical power mainly comes from the micro-gas turbine and power grid. The micro-gas turbine power supply is more stable. However, the power grid provides more electricity than the micro-gas turbine power. On the other hand, the micro-gas turbine provides more power than the photovoltaic system. The grid power  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**System** & **Variable** & **Symbol** & **Numerical Value** \\ \hline Micro-gas turbine & efficiency & \(\eta_{mt}\) & 0.29 \\ Waste heat recovery & efficiency & \(\eta_{re}\) & 0.8 \\ Electric chiller & COP & COP\({}_{ec}\) & 3.5 \\ Adsorption chiller & COP & COP\({}_{ac}\) & 0.85 \\ Gas boiler & efficiency & \(\eta_{gb}\) & 0.8 \\ Heat exchanger & efficiency & \(\eta_{the}\) & 0.875 \\ \(CO_{2}\) emission coefficient & Natural gas & \(\mu_{f}\) & 220 \\ Power grid & \(\mu_{e}\) & 969 \\ Battery & Self-discharge rate & \(\eta_{b}\) & 0.02 \\  & Charge/discharge efficiency & \(\eta_{c}\) & 95\% \\ Thermal storage tank & Self-loss coefficient & \(\alpha_{tsl}\) & 0.05 \\  & Heat storage/release efficiency & \(\eta_{bsl,chr}\) & 90\% \\  & & \(\eta_{bsl,dis}\) & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters setting of the CCHP system.  
Figure 5: Cold, hot and electrical load curves.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cost** & **Natural Gas [10]** & \
\begin{tabular}{c} **Electricity Price** \\ **(600-21:00) [4]** \\ \end{tabular} &
\begin{tabular}{c} **Electricity Price** \\ **(22:00-5:00) [10]** \\ \end{tabular} \\ \hline Unit price ($/KWh) & 0.024 & 0.1028 & 0.047 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Time-of-use electricity price and natural gas price.  
purchase is more economic than the micro-gas turbine and photovoltaic power system. From 7:00 to 15:00, when the sunlight is relatively sufficient, the photovoltaic power generation system participates in the power supply task. Nevertheless, its contribution is very small due to the economic cost. From Figure 6b, the heat power is mainly supplied by the micro-gas turbine and the gas boiler. However, the output of the gas boiler varies with the fluctuation of the load. In contrast, the output of the micro-gas turbine is relatively stable. Moreover, the system adopts the operation strategy of FEL. From Figure 6c, the first priority is to meet the demand of electric load. Subsequently, redundant electric energy is produced to cool. Therefore, the AC bears more cold load than the EC. Note that the Eload increases substantially at 6 o'clock. Under the FEL strategy, the electric balance is satisfied, and there is no excess electrical energy to provide EC to generate cold power at this stage. To maintain the cold power required by the users, the Qac therefore increases abruptly.
## 5 Integration Options
### Integration at MEC Service Level  
In this case, the primary domain, Domain A, implements only the MEO function, interconnecting to the MEPM and the VIM of the secondary domain for the orchestration of the applications as provided or enabled by Domain B. Figure 5 graphically depicts this case. Since the secondary domain provides all the capabilities for management of the lifecycle of the applications, this approach can be seen as an outsourcing of all of that functionality from the provider in Domain A to provider in Domain B. Then, the provider in Domain A basically focuses on the commercial relation with the MEC customer (and the end users) and in the decisions about instantiating and running applications in the system.  
The business motivation for this integration model could be that of a main provider acting as the aggregator of MEC systems either to increase coverage or to complement its own offer with additional capabilities or applications. The secondary provider will retain all the logic for handling the lifecycle of the applications, with the main provider triggering instructing what to do in each moment.  
The interaction among providers is done through the management interfaces MD-Mm2, MD-Mm3 and MD-Mm4, then having management interaction from Domain A with the platform and the infrastructure of Domain B.  
Figure 4: Integration of MEC providers at platform level following a PaaS approach with infrastructure owned by Domain B provider.
###### Abstract
LOS pointing control and stabilization techniques for a two-axis platform have already been studied in [9; 14]. An attitude control strategy for a three-axis platform is presented in [15], which provides the stabilization of the camera's LOS to the desired attitude. To the authors' knowledge, the image attitude control technique for a three-axis ISP, which is essential for gap-free coverage in sweep imaging, has not been reported in the literature. In accordance with the rotation coupling laws of the three gimbal axes and the space vector decomposition and transformation theorems, an image attitude control algorithm for a three-axis ISP is proposed in this paper. Image motion [16; 17; 18; 19; 14] is the primary factor that affects the quality of an aerial image. Image motion on the sensor plane will significantly vary with the position while the angle of view for an area camera is large and the area camera tilts remarkably. Image motion variation (IMV) on the sensor plane makes it impossible to completely compensate for the image motion for all pixels. Therefore, the quantity of image motion compensation (IMC) residual resulting from IMV should be quantitatively analyzed, and the exposure parameters should be adjusted correspondingly. [PERSON] and [PERSON] proposed an image motion calculation model and LOS stabilization techniques using a fast steering mirror for aerial imaging [14]. Their LOS stabilization techniques can also be used in three-axis platforms. However, their image motion calculation model is based on the LOS and cannot be used for calculating the IMV. The image motion calculation model for analyzing IMV and the IMC residual computing method for area cameras are proposed, utilizing space position and velocity vector transformation mathematics and derivations. The distribution of image motion vectors on the sensor plane is presented, and the changing laws of the maximum IMV with the image attitude are studied. The layout of the rest of this paper is as follows. Section 2 describes the structure design of the three-axis ISP and the imaging control process. The calculation formulas of the gimbal angles for controlling the imaging attitude are deduced in Section 3. The image motion analysis model is established in Section 4, and the IMV, as well as the residual of the IMC, are quantitatively studied. Section 5 presents the flight test results that verify the validity of the proposed imaging techniques. Finally, conclusions are made in Section 6.
# Nondestructive Surface Crack Detection of Laser-Repaired Components by Laser Scanning Thermography
## 4 Scanning Thermography for Crack Detection of Repair Component  
In order to achieve higher quality detection of surface cracks on the repaired components, we adopt the laser point scanning thermography technique to detect cracks on the real components repaired using iron-based alloy powders. The optimization window of laser thermography parameters is verified by experiments. Meanwhile, the thermal image processing algorithm is optimized to improve the accuracy and efficiency of surface crack detection.
## 4 SMS Configurations Setup
### Testing and Validation
#### 4.3.2 Second Configuration  
In this Second Configuration, we will test out three types of different scenarios. Table 6 shows the values specified for our test experiments with Lead time as a medium value in the second configuration. For all three different test scenarios, a static value of 47.3 was used to indicate the Lead time as a medium value. Then, quality was analyzed with 71.5 as high value, 19.4 as low value and 49.2 as the medium value, respectively, followed by cost as 24.1, which was a medium value, then 35.4 as high value and 17 as in between low value and medium value, respectively.  
As shown in Table 6, cost justification of 25, 40, and 16.1 were obtained, which indicates that Test 1 is deemed to be reasonable, followed by Test 2 result as high cost and Test 3 result as low or reasonable cost, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Cases** & **Lead Time** & **Quality** & **Cost** & **Cost Justification** \\ \hline
1 & 47.3 (M) & 71.5 (H) & 24.1 (L) & 25 (R) \\
2 & 47.3 (M) & 19.4 (L) & 35.4 (M) & 40 (H) \\
3 & 47.3 (M) & 49.2 (M) & 17 (L) & 16.1 (L/R) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Second Configuration Test Scenarios.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Value Chart**} \\ \hline
**Variables** & **Low** & **Medium/Reasonable** & **High** \\ \hline Lead time & 0–30 & 30–60 & 60–90 \\ Quality & 0–30 & 30–60 & 60–90 \\ Cost & 0–20 & 15–35 & 30–50 \\ Cost Justification Value & 0–20 & 15–35 & 30–50 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Value Chart.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Cases** & **Lead Time** & **Quality** & **Cost** & **Cost Justification** \\ \hline Scenario 1 & 12.7 (L) & 77.4 (H) & 23.7 (M) & 25 (R) \\ Scenario 2 & 12.7 (L) & 13.9 (L) & 42.9 (H) & 40 (H) \\ Scenario 3 & 12.7 (L) & 46.3 (M) & 8.51 (L) & 17.5 (L/R) \\ \hline \hline \end{tabular}
\end{table}
Table 5: First Configuration Test Scenarios.
# Improving the Design of MEMS INS-Aided PLLs for GNSS Carrier Phase Measurement under High Dynamics
## 2 Tracking Error Analysis of INS-Aided PLLs
### Tracking Error Analysis  
The tracking error testing set is shown in Figure 2. To test tracking errors quantitatively, a GPS/INS hardware simulator was used. A GPS/IMU data synchronous recorder, developed by ourselves, was connected to the simulator to sample GPS intermediate frequency (IF) data and IMU raw data. The raw data was processed using our GNSS/INS deeply-coupled system. Since the tracking errors caused by thermal noise and clock were analyzed in literatures, we focused on analyzing the impact of the aiding information on tracking error [24]. To highlight the tracking error caused by aiding information, the impact of thermal noise and clock should be reduced as much as possible. Hence, an oven controlled crystal oscillator (OCXO) was used in the recorder to reduce the clock impact on the tracking error, and the carrier to noise ratio (CNR) of the GPS signal was set to 100 dB-Hz, to minimize the thermal noise effect. The IMU parameters set in the simulator were based on a typical MEMS IMU STIM300 [25]. Figure 3 shows the vehicle dynamics in the motion scenario. The accelerations were set to less than 10 m/s\({}^{2}\) before 392,915 s. The dynamic had an acceleration of 1000 m/s\({}^{2}\), and a jerk of 2000 m/s\({}^{3}\) was set at 392,915-39,298 s.  
To tolerate high dynamics, the INS-aided PLLs with a bandwidth of 20 Hz, and an integration time of 1 ms, was used. Figure 4 shows the INS velocity error and PLL tracking error. When the vehicle acceleration in the scenario was less than 10 m/s\({}^{2}\), the INS velocity error affected by dynamics was not obvious. However, when the vehicle acceleration was 1000 m/s\({}^{2}\), the INS velocity error was approximately 1.7 m/s. Since the velocity error enters PLLs with the aiding information, the tracking error increased with the velocity error increased. The tracking error increased to 22\({}^{\circ}\) when the velocity error was 1.7 m/s.  
To analyze the aiding information impact on the tracking error thoroughly, Figure 4 is zoomed in as Figure 5. The INS velocity error had a step change at each integer second. This was because the INS velocity error was modified by the coupled filter output at each integer second. The INS velocity error modification was helpful to the navigation results, but harmful to assist the PLL operation. The tracking error amplitude was proportional to the step change at each integer second, which could not be ignored even under low dynamics. Therefore, the step change can deteriorate the carrier phase accuracy, and even lead to loss of lock of carrier phase. This problem was only mentioned in the analysis to the impacts of IMU in deep integration by [PERSON] et al. (2016). However, the solution of this  
Figure 3: Vehicle dynamics in the simulation scenario.  
Figure 2: Tracking error testing set.  
problem was not yet reported to the best of the authors' knowledge. The improved INS-aided PLL structure is proposed to eliminate the impact of the step change in next section.
## 2 Architecture Design  
Figure 1 presents the detailed architecture of the optical receiver with four parallel lanes. PIN_Kx (\(x\) = 1, 2, 3, 4) are the output pads of the four-lane on-chip low dropout regulators (LDOs), which are connected to the cathodes of the photodiodes by bonding wires to provide clean bias. PIN_Ax (\(x\) = 1, 2, 3, 4) are the input pads of the four lanes, which are all connected to the anodes of the photodiodes by bondwires. Furthermore, the series inductance introduced by the bondwire helps to isolate the photodiode capacitor from the TIA input capacitor [15]. Additionally, Voutx (\(x\) = 1, 2, 3, 4) is the differential pad for the output of lane \(x\).  
Each lane of the optical receiver contains a TIA, a single-ended-differential amplifier (STD), a VGA, an output amplifier, a DCOC loop, and an RSSI module. At the input stage, the TIA is designed to preamplify the small current signal to a large voltage signal. Then, the STD stage converts the single-ended voltage signal to a differential signal. The VGA amplifies the voltage signal to a level which is sufficient for the reliable operation of other subsequent implements [16]. Finally, the output amplifier at the output stage drives the off-chip load.  
The DCOC loop is implemented to eliminate the output DC offset due to process variation [17]. Additionally, the RSSI module is applied to control the adjusting resistors in the VGA, which changes the photodiode's DC current provided by the LDO on the chip. Furthermore, adjustment of the output swing, gain, and bandwidth is realized by controlling the current sources of the STD, the VGA, and the  
Figure 1: A block diagram of the optical receiver.  
output amplifier via the power mode controller. The four lanes share only one bandgap module on the chip in order to minimize the chip area.
## 1 Introduction  
The expansion of the Industry 4.0 concept is forcing business partners throughout the supply chain to continually explore opportunities to implement modern technologies for improvement processes. This is particularly evident in production companies, where the primary measure of efficiency is the production precision and quality of the performed processes. The increasing importance of the use of modern technology in production processes is indisputable. Many solutions can be found on the market that optimize production processes and internal logistics, which are responsible for material flow, in various areas. In this paper, the authors have attempted to assess the extent to which innovative solutions are being used in business practice at present, as well as the chances of new trends developing in the near future. The statistical analyses presented in this paper are based on the results of surveys in 68 companies in Poland, conducted in Q2 2021 and Q1 2022. This research sample was assessed as representative in accordance with the research methodology presented in the publication [1]. The main objective of the ongoing research is to analyze the impact of digitization on process improvement in manufacturing companies.  
The literature analysis points to numerous problems for the implementation of supply chain digitalization solutions [2; 3; 4]; however, barriers to the implementation of these solutions have not been clearly defined in the application aspect [5]. The barriers mainly relate to business process problems or, as GSI's research indicates [6], a lack of awareness of the benefits of implementing digitalization solutions. In the literature, it is difficult to point to conducting an analysis of the impact of the implementation of a solution on business development. For this reason, the research was conducted in accordance with hypotheses:  
**Hypothesis 1 (H1)**. _The impact of digitalization on process improvement in production companies is not determined directly, but is modulated by trends in software development_.  
**Hypothesis 2 (H2)**. _There are characteristics of the enterprise that affect the absorption of digital solutions and their implementation in operational processes._  
The above means that the implementation of the digitalization of processes in a company is only possible with the use of additional software, which generates subsequent implementations and related costs.  
The authors set three main aims of the conducted research:  
A1: Identification of trends affecting the development of operational processes of production companies.  
A2: Identification of the characteristics of manufacturing enterprises that affect the absorption and implementation of digital solutions into operational processes.  
A3: Identification of specific solutions affecting the development of an operational process of production enterprises.  
The research has two analytical dimensions:  
* based on PLS path modeling in order to verify a H1 hypothesis;
* aiming to explore the relationship between digitalization, software, and process, using group comparisons to verify the H2 hypothesis.
## 4 Ad Hoc Routing: Configuration, Scenarios, and Results
### Configuring BATMAN-ADV Protocol  
Since the nodes in the TestBed are based on Raspberry Pi, which operate on a Linux Debian distribution (Raspbian Stretch, in this case), the starting point had to be the configuration of the operating system on Pies through raspi-config, and basic ad hoc routing in Linux. Once the basic installation and configuration of Raspberry Pi OS and the ad hoc network [35] was done, respectively, BATMAN-ADV protocol was configured on all Pies. The hands-on settings for configuring BATMAN-ADV is available at the project Git repository in [36].
# Energy Aware and Quality of Service Routing Mechanism for Hybrid Internet of Things Network
## 5 Performance Evaluation
Taking Figure 4 as an example, the shortest path algorithm can use 80% of the nodes in the mesh network to support 10 submitted flows, whereas the proposed algorithm and the aggregation algorithm require 54% and 50% of the nodes in the network, respectively, to provide the same service. The aggregation algorithm slightly outperforms the proposed algorithm in terms of the overall energy consumption reduction, but it provides very poor performance in terms of delay. However, the slight decrease in energy performance in the proposed algorithm is due to the fact that the proposed algorithm is able to achieve the best energy consumption reduction. Figure 6: The maximum path length variation according to the number of set of flows generated for wireless capacity equal to C = 10 Mbps. Figure 7: The maximum path length variation according to the number of set of flows generated for wireless capacity equal to C = 15 Mbps. proposed algorithm is at the expense of guaranteeing and limiting the delay of flows to the gateway (Figures 5-7). Another point, which is noticeable from Figure 2, is that we submitted more than six sets of flows, but the algorithms do not accept flows of more than six sets of flows. This shows that all the algorithms have the same flow acceptance rate. As can also been seen from Figures 5-7, the aggregation algorithm allows flows to take longer path lengths (in terms of number of hops) to the gateway, which makes it have a longer delay compared with the proposed algorithm and the shortest path algorithm to the gateway. As it is shown in Figure 7, the aggregation algorithm uses a maximum path length of 15 nodes to reach to the gateway, whereas the proposed algorithm and the shortest path algorithm use a maximum route length of 10 nodes to reach the gateway. From the above discussion, it can be concluded that there is a clear tradeoff between QoS and energy saving. For video surveillance applications that have strict QoS requirements, the proposed approach reduces a significant amount of global energy consumption while guaranteeing QoS in terms of bandwidth and delay.
# Green Communication for Wireless Body Area Networks: Energy Aware Link Efficient Routing Approach
## 3 Green Computing for WBANs
### Path Cost Estimation  
According to the Dijkstra algorithm [38], selecting the path with a lower number of hop-counts to the BNC is an effective approach. However, the path with a greater number of hop counts using the higher energy of intermediate nodes may be considered better for balancing energy consumption among the nodes. Link efficiency (LE) between the nodes directly affects the energy consumption. The route with low link efficiency may lead to packet loss and retransmission attempts which consume high energy. The existing routing protocols always attempt to choose the shortest path based on the distance to the BNC. However, unlike other routing protocols, this work considers link efficiency as well as shorter path for selecting the next-hop for data transmission. This framework introduces a novel path cost function (_PCF_) based on residual energy (_RE_), link efficiency (_LE_), hop-counts (_HC_), and distance (_d_) to the BNC. The BSN with the least value of the PCF is chosen as the next-hop for packet forwarding. The value of path cost function is calculated in Equation (4).  
\[PCF=\sum_{\forall N\in N}\Big{[}\alpha\times\frac{1}{RE}+\beta\times\frac{1}{ LE}+\gamma\times HC+\delta\times d\Big{]} \tag{4}\]where \(\alpha,\ \beta,\ \gamma,\) and \(\delta\) denote the weighting factors for the residual energy \((RE)\), link efficiency \((LE)\), number of hop counts \((HC)\), and distance to the BNC \((d)\) respectively. Each weighting factor is assigned a value according to its priority so that \(\alpha+\beta+\gamma+\delta=1\). In order to assign the priority to each parameter in the next-hop selection, the weighting factors are assigned the following values.  
\[\alpha =0.4\] \[\beta =0.3\] \[\gamma =0.2\] \[\delta =0.1\]