esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Performance Evaluation and Optimization Theory for Thermal Microscope Imaging Systems
## 5 Application and Experiment  
### System Design  
After completing the above theoretical research, we designed and built the thermal microscope imaging system, which has good performance and potential applications in the field of nondestructive testing of microelectronic devices.  
Firstly, according to the conclusion of the analysis in Section 4.1, to ensure the system has better temperature resolution, we choose the magnification factor of the system as 2 times.  
Secondly, according to the analysis in Section 4.2, the refrigerated detector has high-temperature resolution in both a medium-wave infrared band and long-wave infrared band. However, the large size and high price of the refrigerated detector greatly limits its application and promotion. Simultaneously, the non-refrigerated detector can also obtain higher temperature resolution through reasonable collocation and scientific operation methods. For example, uncooled detectors have higher sensitivity to the non-uniformity of emissivity in the long-wave infrared band, and it is easier to distinguish the small differences in emissivity of the target to be measured. When the infrared imaging system is used for nondestructive testing, the fault detection is carried out by using the slight difference of the emissivity between the faulty electronic component and the trouble-free electronic components. Therefore, we use a combination of non-refrigerated detector and long-wave infrared band to build a thermal microscope imaging system.  
Finally, the sub-pixel imaging technology based on optical micro-scanning can improve the temperature resolution of the imaging system by reducing the imaging frame rate [34]. Therefore, we have designed a 2 \(\times\) 2 micro-scanning system, and built a new type of micro-scanning thermal microscope imaging system, as shown in Figure 11. This system consists of an infrared magnifying objective (Magnification: 2), a micro-scanner (Model: GIP-101, micro scan step size: 4.5 pixel size), an infrared image acquisition card (Model: Tau2-324), an image processing system (Computer name: LAPTOP-7 DJSLF4O), and a power supply (220 V), as shown in the block diagram of a high-resolution thermal microscope imaging system in Figure 12[35; 36; 37].
# The Latest Advances in Wireless Communication in Aviation, Wind Turbines and Bridges
## 1 Introduction
The possibility of remote, wireless access to information on the current state of the structure allows cost savings related to inspections and periodic reviews. Still, above all, it increases the safety of the structure and its users. The possibility of direct transmission of information on emergency conditions preceding a disaster to often remote administration and infrastructure management centers significantly increases the safety of using the structure. Among civil engineering objects in which SHM systems are implemented, wireless technologies are most often used in bridge structures. Examples of applications in this type of construction are presented in a further chapter of the paper.
## 2 Literature Review
### Literature Identification and Collection  
The literature identification and collection phase comprised three steps, as illustrated in Figure 1.  
Step 1: Define the preliminary outline.  
An outline for the paper was created by exploring the issues associated with IFM, identifying the associated risk factors, and identifying areas to be discussed to ensure that IFM is safe for consumption. The dimensions of IFMSC risk factors were determined by reviewing various sources and articles and adequately using the recurrent concepts and phases of the IFM in different research papers. The risk factors were categorized into three categories: environmental, operational, and quality.  
Step 2: Define the academic sources.  
Library search engines such as Google Scholar, Researchgate, Science Direct, Scopus, and Springer Link were used to conduct the literature search. Book chapters were also used to enhance the search results and add more value to the review. The use of all these search engines provided a diversified knowledge base with a range of relevant concepts, as well as enhanced recognition of risk factors and mitigation strategies.  
Step 3: Identify the keywords.  
We focused our search on secondary sources published in English only, since primary sources were insufficient. Different keywords covering the objectives of the review were identified.  
The keywords were categorized into two areas. The first area looked into the issues with infant formula milk and the risk factors, while the other focused on risk mitigation criteria for ensuring safe infant formula milk. Different search strings were used with different combinations of keywords, such as \"infant formula milk AND milk quality AND infant formula milk supply chain\", \"supply chain risk (OR risks) AND risk mitigation\".
# Experiencing LoRa Network Establishment on a Smart Energy Campus Testbed
###### Abstract  
The fundamental properties of long-range (LoRa) performance have been revealed by previous research, but advanced issues remain unresolved. This paper tackles three technical challenges that are confronted when establishing a LoRa network on a smart energy campus testbed in Korea. First, the communication range of LoRa in a combined indoor and outdoor environment has yet to be determined. To address this problem, this study builds a LoRa testbed from which we measure the propagation properties of radio signals in a combined environment. Then, we establish a simplified path loss model for LoRa PHY and compute the communication range. Next, national regulations enforce the use of the Listen-Before-Talk (LBT) scheduling scheme, but its performance has never been examined. This paper enumerates the impacts of LBT, threshold values, back-off algorithms, and application parameters on LoRa Medium Access Control (MAC) performance. Last, the application parameters required by the smart energy scenario influence LoRa performance, but their impacts have been scarcely reported. Based on the application parameters and experimental results, this paper proposes a strategy to deploy LoRa gateway(s) and nodes on the campus. To the best of the authors' knowledge, this is the first work that considers performance issues when establishing a LoRa network on a real-world application testbed.  
LoRa; LoRaWAN; LPWAN; IoT; smart grid; smart energy; wireless communications +
Footnote †: journal:  
_Article_
# Design and Evaluation of an Acoustic Modern
## 4 Autonomous Underwater Vehicle (AUV) Integration Results and Analysis
### Experimental Results and Analysis  
In Figure 11 the waveform and associated spectrum of the AUV communication signal collected during the second trial are presented, from which one can see the background noise of the moving AUV produced by the electromagnetic interference (EMI) of the motor. It is noticeable that the spectrum of AUV noise overlaps with that of the acoustic communication signal, leading to certain interference on communication performance. The channel response of the second trial is shown in Figure 12, which indicates the simultaneous presence of an apparent multipath and Doppler [14].  
The Doppler estimated by the modern is provided in Figure 13a, with the corresponding BER before and after Doppler compensation shown in Figure 13b. From Figure 13a we can observe that the Doppler generated by the AUV movement was in the range of \(-3\) to \(10\) Hz. As displayed in Figure 13b, the proposed modem was capable of accommodating the small Doppler by generating a BER on the order of \(10^{-2}\) without compensation. Meanwhile, the Doppler compensation can reduce the BER to zero under the Doppler of about \(2\) Hz, indicating error-free communication under a relatively small AUV speed.  
Furthermore, under the Doppler of \(10\) Hz, while the BER approached \(40\%\) without Doppler correction, the BER performance of the proposed modem improved by \(7\%\) after Doppler correction and  
Figure 12: Response of the experimental channel for the second trial.  
Figure 13: Doppler and bit error rate curves obtained by the modem onboard AUV. (**a**) Doppler curve, (**b**) BER curve.  
was further promoted to 3% after Doppler correction and channel encoding. Thus, the experimental results obtained by the modem verify the effectiveness of the proposed modem in Doppler mitigation when the AUV experiences a relatively fast movement.
# Radiator Enablers for Wireless Communication Evolution
## 2 Challenges, Enabling Technologies, and Antenna Developments
### Challenges and Enabling Antenna Technologies
#### 2.1.3 mMTC to Massive Communication Challenge  
mMTC entails increased numbers of low-cost IoT devices that are to communicate infrequently, transmitting short sequences of information. Indeed, to enable massive connectivity, the wireless communication system must cope with numerous (low-cost and low power consumption) devices that desire to send small amounts of data at any random instant of time. This fact results in increased overhead and thus decreased spectral efficiency. The major issue in mMTC is the joint channel estimation and activity detection since the access point is immersed in a multi-user (multi-IoT device) environment. To establish communication, each device transmits not only the desired data but also a sequence of metadata (a spreading or device signature that is not unique nor orthogonal to the rest). In turn, this sequence is used in a compressed sensing process where a sufficiently sparse characteristic is employed. This sparsity, jointly based on the infrequent transmissions and device metadata, is seriously mitigated as the device density increases (refs. [14; 15; 16]). In this case, one can restore sparsity, employing a MIMO antenna array at the access point and space division multiplexing utilizing multiple beam formation (ref. [17]).  
Let us elaborate on that and examine the unlikely event of two IoTs transmitting the same metadata at the same time. When such events become frequent, then the device capacity (Cd) of the mMTC system is reached, and no more devices can be served. When upgrading the BS--reader--access point antenna system to a multi-user MIMO, for the collation event to take place, apart from the same time, the two transitions should originate from IoTs belonging to the same angular sector (or the reader's antenna half-power-beamwidth). Thus, now, referring to Equation (5), it is not the device density but the device density per angular sector that reaches the Cd value. Thus, according to Equation (5), the total density is increased since it is the product of the total density per angular sector (that is, Cd), multiplied by the angular sector number (which is the antenna's field of view divided by the antenna's half-power beamwidth).  
One more issue that MIMO solves is the energy efficiency level for the IoT device. This holds, since using directive beams at the access point decreases the power level needed for the IoT device during transmission. This is the case when multiple antennas are used on the BS side. Focusing on Equation (6), more antennas result in beamforming gain increasing, in turn, the channel gain H, and, of course, the energy efficiency. Increased energy efficiency means that the given transmitted sequences consume less energy, thus increasing the IoT's battery life or inter (re-)charge time duration. Furthermore, one can also examine the case when multiple antennas can be used in a passive IoT site. In this case, retrodirective back scattering (refs. [18; 19; 20; 21]) can be employed.
## 4 Results and Discussion
### Parametric Study on Hydrogen Purification Performance
#### 4.3.2 Effect of Feed Flow Rate  
Figure 10 shows the hydrogen purity and recovery variations under different feed flow rates. As expected, an increase in feed flow rate, while maintaining a constant PSA cycle time, leads to a higher total volume of mixed gas processed by the adsorption bed. This results in an increase in the recovery of product hydrogen. Furthermore, the figure shows that as the feed flow rate increases, the decrease in hydrogen purity becomes more pronounced, while the rate of increase in recovery diminishes. Notably, when the feed flow rate rises from 2 LSTP/min to 3 LSTP/min, purity decreases from 98.59% to 97.03%, and recovery increases from 35.52% to 48.60%. In contrast, when the feed flow rate increases from 4 LSTP/min to 5 LSTP/min, purity decreases sharply from 94.25% to 89.56%, while recovery only increases from 57.46% to 60.40%. The former change can be attributed to the incomplete adsorption of a greater amount of impurities, causing them to overflow from the outlet and increasing the rate of purity decrease. The latter change is likely due to the feed flow rate reaching the processing capacity of the adsorbent per unit mass, resulting in a diminishing rate of increase in recovery.  
Figure 10: Influence of feed flow rate on the purification of PSA.  
Figure 9: Influence of feed pressure on the purification of PSA.
# Relationship between Unilateral Leg Extension Strength and Dynamic Balance in Healthy Young Men
## 2 Materials and Methods  
### Study Design and Participants  
The study was a cross-sectional design to investigate the relationship between strength and dynamic balance in healthy young men. Before the measurements of strength and dynamic balance, participants were asked not to exercise for at least 72 h and to maintain their daily eating habits. The study was completed with measurements taken at 3 different time points. The first time point was used to measure dynamic balance, the second to estimate weight for a maximum repetition (1 RM), and the third to directly measure 1 RM. An interval of 48 h was maintained between laboratory visits to avoid mutual interference of measurements and fatigue. The sample size was calculated a priori with the help of a sample size calculator (G-Power 3.1.9.3). An effect size of 0.59 was considered a reasonable and conservative starting point for determining the sample size [19]. Assuming that the type I error (\(\alpha\)) was 0.05 and the power (1-\(\beta\)) was 0.80, the model indicated a minimum total sample size of 20 subjects, but allowing for a rate of missing participants,we decided to have a total of 33 participants. A total of thirty-three young healthy men (mean age \(\pm\) standard deviation = 21.21 \(\pm\) 1.24 years) were included in the study. To ensure homogeneity of participants, we studied individuals with similar physical activity and no history of systematic strength or power training. None of the subjects had a history of health limitations that might affect their ability to perform strength and balance tests. Inclusion criteria were an age of 18 to 30 years and the ability to have strength and balance to meet the requirements of the test. Exclusion criteria were subjects with chronic ankle instability, who underwent ACL surgery, or who experienced pain during strength and balance exercises. The study was approved by the Inonu University Ethics Committee (protocol number: 2022/3596). After the subjects were informed about the study, the consent forms were signed.
## 1 Introduction
As far as we know, little previous research has investigated the large lift-off (>30 mm) detection approach in MFL nondestructive field. Aiming at the challenges and problems mentioned above, the requirements of the sensor head, which can detect wire rope defects at a large distance (>30 mm) with the MFL detection method, are proposed: High sensitivity; High stability; High signal-to-noise ratio (SNR); High amplitude. A novel 30 mm detecting distance testing device for wire rope based on an open magnetizer is proposed in this article. By comparing traditional small lift-off detection, it can be deduced that if a large lift-off detection method can be obtained, weak defect signals can be collected when the magnetizer, sensors, and signal processing system are improved. In this paper, verification of the proposed method is further investigated by established 3D finite element analysis and relevant pre-test. Aiming at the related difficulties of detecting steel wire rope with a magnetic flux leakage sensor in large lift-off (>30 mm), a magnetic sensing and coupling method of the weak signal is proposed by combining simulation and a large number of experiments. In the experiments, the characteristics and parameters of magnetic sensitive elements are analyzed and compared, and their array arrangements are reasonable. Firstly, the noise such as strand signal is differentially processed at the extraction of magnetic leakage field, which improves the SNR of the signal source, reducing the difficulty of subsequent signal processing and indirectly improving the sensitivity of sensitive elements. Secondly, the appropriate frequency response space is designed by using the analog signal processing method of weak signals. Finally, the detection system is tested, and the test shows that the defects of steel wire rope can be detected in large lift-off (>30 mm). Besides, the magnetic sensing and coupling method and the analog signal processing method of the weak signal are expanded to other applications of minor damage detection. This article is organized as follows. Section 2 of this paper will model and simulate magnetizer sensor detection at a large lift-off distance, and a preliminary experiment verifies the feasibility. Section 3 analyses the induction coils from 4 different perspectives, including type, array, array coupling, and structure parameter. Then the analog circuit design and circuit simulation are performed in Section 4. Furthermore, the test bench is constructed and the MFL detection system is assembled to start an experiment. The experiments compare different coil arrays and coil structure parameters mentioned in Section 3 to determine the best type of coil sensors, and accompanied by soft magnetic material, permalloy in Section 5. Finally, a discussion and conclusion about the advantages, limitations, and what to do in the future are presented in Sections 6 and 7.
# Erasure Codes for Cold Data in Distributed Storage Systems
## 3 NewLib Code
### Decoding in the N-Schedule Scheme  
When a node leaves or data corruption occurs, a decoding operation is required to recover the lost data. In the following, we consider a case where an optimized decoding scheme is derived using an example.  
Figure 4: The aligned request.  
Figure 5: The non-aligned request.  
Suppose \(k=7\) and \(w=7\). Suppose the information on data nodes \(D_{1}\) and \(D_{2}\) is lost; in order to recover the lost data, we take the first 14 rows of the check matrix and transpose them. With the obtained transposed matrix and the remaining data nodes, we can obtain the data on \(D_{1}\) and \(D_{2}\), which is shown in Figure 6.  
The XOR operation number is equal to the number of ones minus one. The number of ones is 316 in 14 rows of the decoding matrix; hence, the number of XOR operations is 316 minus 14 which is equal to 302. Now, let us compare the number of ones in the first line with the number in the eighth line to calculate \(D_{1,0}\) and \(D_{2,0}\), respectively. The number of ones is 30 in the first line, while it is 25 in the eighth line, which means the number is the sum of 29 and 24. We can calculate one of the two lines from the XOR operation; then, the other line can be obtained from the first line using the erasure code method. We can formulate the equation shown as Equation (3).  
\[d_{0,0}=d_{1,0}\oplus d_{2,0}\oplus d_{3,0}\oplus d_{4,0}\oplus d_{5,0}\oplus d _{6,0}\oplus p_{0} \tag{3}\]  
There are only four XOR operations in Equation (3), which is the number of XOR operations to obtain \(D_{1,0}\).  
In summary, the other nodes are calculated by rationally utilizing the results that have been obtained, which can effectively reduce the XOR operation number. Let us continue with the example we just used, and with this optimized approach, the recovery process is as follows:  
Calculate \(d_{2,4}\): seven XORs.  
Calculate \(d_{1,4}\) from \(d_{2,4}\): six XORs.  
Calculate \(d_{2,5}\) from \(d_{1,4}\): seven XORs.  
Calculate \(d_{1,5}\) from \(d_{2,5}\): six XORs.  
Calculate \(d_{2,6}\) from \(d_{1,5}\): seven XORs.  
Calculate \(d_{1,6}\) from \(d_{2,6}\): six XORs.  
Calculate \(d_{2,7}\) from \(d_{1,6}\): seven XORs.  
Calculate \(d_{1,7}\) from \(d_{2,7}\): six XORs.  
Figure 6: The decoding in the N-Schedule scheme.  
Calculate \(d_{2,1}\) from \(d_{1,7}\): seven XORs.  
Calculate \(d_{1,1}\) from \(d_{2,1}\): six XORs.  
Calculate \(d_{2,2}\) from \(d_{1,1}\): six XORs.  
Calculate \(d_{1,2}\) from \(d_{2,2}\): seven XORs.  
Calculate \(d_{2,3}\) from \(d_{1,2}\): six XORs.  
Calculate \(d_{1,3}\) from \(d_{2,3}\): seven XORs.  
With this optimized decoding method, the XOR operation number required is 91, versus 302 in the original method. It can be seen that the efficiency of the decoding operation has been greatly improved.
## 3 Methodology
### Path Planning Model
#### 3.3.6 PLC Selection  
The kernel function, the spatial distance between the PLCs and the regular activity hotspot \(\varphi\), the spatial distance from the receiving point, the weight-volume-urgency ratio of the express delivery, and other factors are included in the PLC selection decision. In the process of logistics distribution, we should not only consider whether the user's path is along the way, because the goods are divided into heavy goods and bubble goods, and the PLCs also have the limits of weight and volume, so the characteristics of delivery goods should also be considered. This study innovatively harnesses the inherent properties of kernel density estimation to seamlessly integrate the distinctive parcel characteristics into the estimation framework. In order to better incorporate these influencing factors into the PLC selection decision, this paper constructs the PLC selection model as shown in Formula (17) by reconstructing the kernel function and finally selects the PLCs \(r^{\prime}\).  
\[MAX(P)=\sum_{\sigma=1}^{R}H_{r}\big{(}p_{r}(r)+p_{z}(r)\big{)} \tag{17}\]  
\[\text{s.t. }\ \ \ 0<L\leq h \tag{18}\]  
\[0<T_{d}\leq 86,400 \tag{19}\]  
\[0<RT_{W}\leq  
where \(D\big{(}w_{\varphi},w_{r}\big{)}\) represents the spatial distance of the PLC \(r\) from the selected active hotspot.  
\[\mathrm{P}_{x}(r)=\frac{1}{2\mathrm{h}^{2}}\frac{1}{\sqrt{2\pi}}\mathrm{e}^{- \frac{1}{2}\frac{L(W_{\mathrm{v}}X_{Z})^{2}}{\mathrm{h}^{2}}}*\mathrm{RT}_{ \mathrm{WV}} \tag{26}\]  
where \(RT_{WV}\) represents the weight-volume-urgency ratio, and the calculation formula is shown in Equation (27). \(RT_{W}\) and \(RT_{V}\) represent the ratios of the weight and volume of express packages to the limit values of the weight and volume of the parcel locker, to ensure that selected PLCs can accommodate the physical dimensions of parcels, aligning with the practical limitations of self-collection facilities. \(\alpha\) represents the urgency of parcel delivery.  
\[\mathrm{RT}_{\mathrm{WV}}=(\mathrm{RT}_{\mathrm{W}}+\mathrm{RT}_{\mathrm{V}})* \alpha;\mathrm{RT}_{\mathrm{W}},\mathrm{RT}_{\mathrm{V}}\in(0,1] \tag{27}\]  
In the above, \(RT_{W}\) represents the ratio of the express weight to the weight of the maximum load of the PLC, and \(RT_{V}\) represents the ratio of the express volume to the maximum capacity of the PLCs.