esa-sceva/satcom-chunk-collection huggingface dataset:
# Harnessing Spatial-Frequency Information for Enhanced Image Restoration
## 2 Related Works
### Modeling Frequency Information  
Some restoration methods have been proposed to bridge the frequency gap between paired degraded and clean images [10, 11, 14]. Generally, numerous studies have utilized methods that decompose images into different frequency components using various transformation tools, such as the wavelet transform [12, 13, 28] and the Fourier transform [14, 15, 29]. Specifically, [PERSON] [29] proposed restoring the amplitude and phase separately, as each plays a different role. [PERSON] [14] introduced FPE, which consists of spatial interaction and channel evolution for efficient global modeling. Recently, some studies have proposed learnable low-pass filters using pooling techniques [10, 11]. In this study, we directly access the frequency latent space using the DFT tool, similar to methods employing Fourier transforms [14, 29]. However, we model their implicit interactions through complex convolutions instead of processing the real and imaginary parts separately.
## 3 Numerical Analysis and Experimental Verification
### Modulation Due to Belt Defects  
Because abnormal belt wear and local defects inevitably occur over operation time, vibrations at \(f_{b}\) can become more significant. To model this dynamic effect, dynamic torque \(T_{p}\) is introduced in Equation (4). This torque exhibits impulsiveness as local defects or abnormal wear patterns pass through the bellies. This impulsive torque is modelled using a partial sinusoidal function, as follows:  
\[T_{p}=\left\{\begin{array}{cc}0.8\cos(\beta_{b}-0.1),&\beta_{b}<0.2\\ 0,&else\end{array}\right. \tag{22}\]  
where the angular displacement is calculated by \(\beta_{b}=\text{mod}(2\pi f_{b}t,2\pi)\) based on \(f_{b}\).  
Figure 7: Motor current signature analysis results under 50% load: (**a**) time–domain current diagram and (**b**) spectra for current and speed responses.  
Figure 8: Spectra of current (**a**) and speed (**b**) for spindle rotational fluctuation.  
Figure 9: Spectra of current and speed (motor shaft eccentricity and belt’s transverse vibration).  
The simulation results are shown in Figure 10. Both the current and speed spectra exhibit periodic sideband modulation, and the signal complexity increases. It has been observed that there are multiple-order sideband modulations associated with belt-passing frequencies as well as coupling modulations between spectra. Furthermore, both the \(f_{r}\) and \(f_{s}\) sidebands exhibit decreased amplitudes owing to the rotation vibration of the belt, which is caused by the vibrations of the pulley and belt.
## 3 Results and Discussion
### Thermomechanical Properties
#### 3.3.2 Loss Modulus (E\")  
Figure 5b shows the effect of temperature on the loss modulus values of the CPH-UPT composites. In all the cases, the loss modulus curve reached a maximum peak before decreasing at higher temperatures due to an increase in molecular mobility in the polymer chains. The UPT resin gave a broad peak in the temperature range 80-100 \({}^{\circ}\)C with a minimum value of loss modulus which is due to lack of heat dissipation [79] with sample B1 having the highest value. This higher value of sample B1 could be due to better adhesion between fibre and matrix. Inclusion of CPH fibre resulted in flatter peaks at lower temperature ranges. Similar observation has been made by [46] on Bambara nut shell polyester composite. The addition of fibre introduces greater chain segments in addition to greater loose volume and these contribute to a broadening of the loss modulus curve [80]. As the temperature increases, lignocellulosic materials begin to degrade releasing heat. This could explain the higher peak temperature of the pure UPT resin compared to the CPH composites. Loss modulus of the hybrid composite is presented in Figure 5f showing a decrease in E\" with increasing fibre content with irregular glass transition temperature. This could be seen in the double peak of sample D2. However, the values obtained were higher than the UPT resin with the exception of sample E2. Such irregular behaviour was observed by [PERSON] et al. and [PERSON] et al. [46, 81] with addition of clay to Bambara nut polyester composite and inclusion of nanoclay Cloisite15A to glass fibre polyester composite. The decline in the value of E\" could be due to clay agglomeration which loosens the polymeric structure [82].
## 1 Introduction  
The past few decades have witnessed the rapid development of computer technology and internet technology, as well as the great convenience they have brought to social life. As an important technology and application extension, the Internet of Things (IoT) is significantly bettering the human condition, augmenting enterprises, and benefiting society through broader automation, connectivity, efficiency, security, data insights, and asset optimization [1, 2]. The IoT, which is closely related to 5G and 6G, is indispensable for continued progress and expansion of the digital economy into all realms of life and industry [3, 4]. Additionally, the world is becoming highly integrated, automated, and data-driven thanks to the power and promise of IoT [5, 6, 7].  
Thanks to the large number of widely deployed and diverse sensing terminal devices, the IoT can timely and comprehensively obtain the dynamic situation of the target area, and can provide managers with good decision-making and feedback interaction. However, the IoT also faces greater security risks at the same time, as the large number of terminals poses challenges for the access management of the IoT gateway [8, 9, 10, 11]. Specifically, if the terminals cannot be authenticated in a timely and reliable manner, the IoT may face problems of low efficiency and network attacks. Eventually, the network will be paralyzed, and sensitive data will be lost. For the IoT, due to the defects in identity authentication, the types of network attacks have become diverse, such as eavesdropping, man-in-the-middleattacks, denial of service attacks, data loss, and data tampering, etc. These problems show that efficient and reliable identity authentication and real-time terminal status monitoring are important for the IoT [12; 13; 14].  
In general, cryptographic methods can reliably authenticate user terminals. Specifically, the IoT gateway and terminals can rely on symmetric encryption or asymmetric encryption methods to achieve one-way or two-way authentication. However, these related methods generally have the characteristics of complex protocol processes, pre-shared keys, and high computational complexity, which make the methods difficult to apply to IoT scenarios where the computing and storage capabilities of terminals are weak. By comparison, identity authentication methods founded in physical layer attributes tend to be lightweight and streamlined. These characteristics are expected to make them an important support for the identity authentication process in IoT scenarios [15].
## 2 Methodology
### Fatigue Experiment
#### 2.2.1 Specim Description  
In this paper, all specimens were cast using ordinary Portland cement No. 42.5 (R) in the laboratory. Fine aggregate refers to two kinds of granular quartz sand, namely 10-20 mesh and 40-70 mesh; apparent density is 2650 kg/m\({}^{3}\); coarse aggregate corresponds to a limestone crushed stone with 5-20 mm particle size. The additive was a polycarboxylic acid water-reducing agent. The mix proportions by weight were presented as follows: 1 cement, 2.697 sand, 3.881 gravel, 0.390 fly ash, and 0.013 additive. The water/cement ratio was 0.552. The average compressive strength (C) of the concrete, using a standard 100 mm cube according to GB50080-2002, at 28 d was 42.3 MPa. The size of the fatigue specimens was cuboid (100 mm \(\times\) 100 mm \(\times\) 300 mm). Before the test, the dynamic elastic modulus (\(E\)) and the shear modulus (\(G\)) were calibrated by using an ETM (Emodumeter-TM) for a non-destructive test. The average dynamic elastic modulus (\(E\)) and the shear modulus (\(G\)) were 41.8 and 16.9 GPa, respectively (Table 4).  
Figure 5: Time-history curve of the train load.  
Fatigue tests were conducted under tensile cyclic loading with their minimum stress levels, according to the initial stress value of cross tunnel and the dynamic amplitude equal to the stress increment, due to the running train. The cycle load was simulated by utilising MTS with a loading frequency of \(f_{\text{P}}=10\) Hz. The fatigue test was stopped either upon the fracture failure of the specimen or after cyclic number \(N=2\times 10^{6}\) load cycles.  
The strains were analysed in the area of the concrete fatigue. Therefore, the strains selected in this paper were installed on the specimen surface at the theoretical maximum tensile and compressive zone. The dynamic strain gauge adopted a foil resistance strain gauge, and the test accuracy was \(2.18\%\pm 1\%\).
## 2 Materials and Methods
### Background
#### 2.1.4 Communication Performance Metrics  
Link quality is characterized by received signal strength indicator (RSSI), but varies greatly among chipset manufacturers [27]. When measured on a logarithmic scale, in decibels (dB) or dBm, values closer to zero are indicating a better signal [16]. RSSI is sometimes used during evaluations to characterize performance as a function of distance or environmental conditions [1], while within LoRa, its value from the messages that arrive at the GW is used for the selection of a specific spreading factor [1]. Generally, existing work has shown that a high RSSI can be used as an indicator of a very good link [13] and depends on the distance between the GW and the ED, according to a path loss model [1]. However, in a lot of cases RSSI does not correspond linearly with the distance between receiver and transmitter. SNR is a key factor to evaluate and analyze the overall performance of the network and can have a value between \(-20\) dB and \(20\) dB, under the highest spreading factor (SF12) or under optimum environment, respectively [27]. On evaluation there are some other metrics that are also used. Packet error rate (PER) or PDR are used as scalability performance metrics where PER is defined as the rate of the packet not successfully received by the GWs, while PDR is the probability that an ED has a successful transmission to the GW. A scalable technology is one that provides low PER or high PDR. Another metric that counts the average number of packets transmitted successfully by an ED is energy efficacy (EE), which is a performance metric for power consumption and measured for every 1 unit of transmission energy. A serious factor that affects power consumption is the packet transmission time which is measured by time on air (ToA) which is the total time between ED transmission and GW reception. Higher ToA is influenced by the spreading factor of the transmission and the size of the payload [27]. Because LoRaWAN uses link adaptation, it is shown that ToA varies from 22 ms to 860 ms depending on the coupling loss [25]. Furthermore, to measure the overall network behavior and scalability, and not for each ED independently, the data extraction rate (DER) is used, which is computed in a given time period, and represents the ratio between messages that have correctly been received by all the GWs and the transmitted messages from all EDs [1].
## 3 Results  
A total of 130 muscle injuries were recorded during the six-year period with 42 of them defined as ruptures and 88 as DOMS (Table 1). Structural muscle injuries (i.e., ruptures) sidelined players from team training and competition for 25.8 days on average, while DOMS resulted in 7.5 days of average absence. Approximately every fifth muscle injury was re-injury, and the vast majority were described as non-contact.  
Injury incidence was stable during the observed period as the injury incidence rate ratio (IRR) was 1.01 (95%CI: 0.99-1.03), with 3.13 (95%CI: 3.09-3.17) muscle injuries per 1000 h of exposure in the first season and 3.06 (95%CI: 3.02-3.1) in the sixth season (Tables 1 and 2, and Figure 1). A significantly higher risk for muscle injuries was present in the games as the incidence for the whole observed period was 7.38 (95%CI: 7.29-7.47) compared to 2.25 (95%CI: 2.24-2.26) in the training.  
The majority of the injuries occurred in the lower body, with hamstrings (N = 43) and quadriceps (N = 41) being injured most frequently. Besides these two groups, U15 players suffered significant amounts of adductor injuries (N = 25), while only six calf injuries were reported in the observed period (Figure 2).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Season** & **Overall** & **Muscle Injuries Training** & **Match** & **All Injuries** \\ \hline
2016/2017 & 3.13 (3.09–3.17) & 2.73 (2.69–2.77) & 6.56 (6.34–6.76) & 11.92 (11.84–11.99) \\
2017/2018 & 2.51 (2.47–2.54) & 1.93 (1.9–1.96) & 9.84 (9.59–10.1) & 6.33 (6.27–6.38) \\
2018/2019 & 2.86 (2.82–2.89) & 2.15 (2.12–2.18) & 8.20 (7.97–8.43) & 6.83 (6.78–6.89) \\
2019/2020 & 2.11 (2.07–2.14) & 1.69 (1.66–1.72) & 6.90 (6.68–7.11) & 5.06 (5.01–5.11) \\
2020/2021 & 2.66 (2.62–2.69) & 2.22 (2.18–2.25) & 6.45 (6.25–6.65) & 5.31 (5.26–5.36) \\
2021/2022 & 3.06 (3.02–3.1) & 2.90 (2.86–2.94) & 4.92 (4.74–5.09) & 6.65 (6.59–7.61) \\ All & 2.73 (2.71–2.74) & 2.25 (2.24–2.26) & 7.38 (7.29–7.47) & 7.02 (6.96–7.08) \\ \hline \end{tabular}
\end{table}
Table 2: Muscle injuries/1000 h of exposure.  
Figure 4 shows that the incidence rate of players suffering a muscle injury had a downward trend until the 2019/2020 season (i.e., from 46.7% to 28.3% of players with muscle injuries), with that trend being altered and rising up to 41.9% in the last season. This implies that in the U15 team of 20 players, approximately 8 of them can expect muscle injury over the one-season period.
## 4 Trajectory Generation
### Constraint Settings
#### 4.2.3 Time Allocation  
To allocate time, we assume HAUVs travel with trapezoidal velocity: HAUVs always move at maximal acceleration. To enable an HAUV to cover all the target spots, we allow the duration to be as flexible as possible, assuming the HAUV accelerates from 0 m/s at each spot. For each target spot \(p_{i}\), we quickly search the widths of corridors by the former methods. Let \(D_{i}=\frac{2\sigma_{max}^{2}}{a_{max}}\) be a criterion that demonstrates the distance the HAUV covers when it moves at maximum velocity and acceleration. If \(dist_{i}\geq D_{i}\), the HAUV accelerates until maximum velocity and then slows down to 0 m/s at the destination. It cannot cover the whole lengths of most trips that way, so we assume that the HAUV moves at maximum velocity for a while and then slows down. Otherwise, the HAUV will not have enough time to accelerate to full velocity, so we assume it keeps moving at maximum acceleration. Therefore, reasonable corridors and time duration have been set for trajectory generation. Details are in Algorithm 5.  
```
Input: The sequence of spots \(\Phi\); The maximum velocity,\(v_{max}\),accelerate\(a_{max}\); The nearest distances between each spots and obstacles\(dist_{1},dist_{2},...,dist_{n}\); Output: Time duration \(T_{1},T_{2},...,T_{n}\)for each trip; \(n_{seg}\leftarrow\) length of \(\Phi\); for i=1 to nsegdo calculate \(D_{i}=2*_{max}v^{2}/a_{max}\) if\(D_{i}\)>\(dist_{i}\)then \(t_{i}=v_{max}^{2}/a_{max}\); else \(t_{i}=2\sqrt{dist_{i}/a_{max}}\) return\(t_{1},t_{2},...,t_{i}\);
```  
**Algorithm 5**Arbitrary time duration \(t_{1},t_{2},...,t_{n}\) for HUAV to travel through spots.
# Multi-Information Fusion Indoor Localization Using Smartphones
## 4 Fusion Localization Architecture
### Improved Adaptive Step Length Estimation  
The step length estimation is the key algorithm in PDR localization. Researchers have developed multiple mathematical models to perform related research, including the Weinberg [49], Scarlet [50], and Kim models [51], which are established through the relationship between acceleration and step length during walking:Scarlet method:  
\[s_{m}=K*\frac{\sum_{i=1}^{N}|A_{i}|}{N}-A_{m}^{valley} \tag{16}\]  
Kim method:  
\[s_{m}=K*\sqrt[4]{\frac{\sum_{i=1}^{N}|A_{i}|}{N}} \tag{17}\]  
[PERSON] method:  
\[s_{m}=K*\sqrt[4]{A_{m}^{peak}-A_{m}^{valley}} \tag{18}\]  
where \(s_{m}\) is the step length of the \(m\)-th step; \(A_{m}^{peak}\) and \(A_{m}^{valley}\) are the peak and valley values of acceleration in the \(m\)-th step, respectively; \(A_{i}\) is the \(i\)-th acceleration value; and \(N\) is the number of accelerations.  
These models have been widely used for step length estimation. However, we must extract more accurate information during high precision localization. The models cannot satisfy the requirements of application. During movement, the pedestrian state in the current time is correlated with the current and previous states. Therefore, during step length estimation, we must consider the pedestrian states at the current and previous times. Different devices also have different errors.  
Inspired by the three models [50; 51; 52; 53], we propose a nonlinear adaptive step length estimation model, which is characterized as follows:  
\[s_{m}=b_{1}*s_{m-2}+b_{2}*s_{m-1}+b_{3}*K*\sqrt[4]{A_{m}^{peak}-A_{m}^{valley }}+Bias+Comp \tag{19}\]  
where \(s_{m-2}\), \(s_{m-1}\) are the step length of the \((m-2)\)-th and \((m-1)\)-th steps, respectively; \([b_{1},\ b_{2},\ b_{3}]\) is the weight vector; \(Bias\) is the offset error, which is measured in the stationary state; and \(Comp\) is the accelerometer compensation of different devices.  
In addition, the previous two steps have some influence on the current situation during walking. However, in the step estimation, the influence factor is sometimes too large. In this paper, the maximum weight factor \(Bmax\) is set to ensure the importance of the step length in the current state. If the weight factor \(b_{1}\) is greater than \(Bmax\), we assume that the weight \(W\) is equal to the difference of the weight factor minus the maximum weight factor; thus, \(W=b_{1}-Bmax\). Then, the weight vector \([b_{1},\ b_{2},\ b_{3}]\) updates as follows:  
\[\left\{\begin{aligned} & b_{1}=Bmax\\ & b_{2}=b_{2}+W/2\\ & b_{3}=b_{3}+W/2\\ \end{aligned}\right. \tag{20}\]
## 4 Measurement System  
### Mount Design  
For this study, we designed an original sensor mount with installed microphones and electric devices: an amplifier, an analog-digital converter, a battery, an inverter, and a laptop computer. Figure 4 depicts the design and assembled mount. We used 20 mm square aluminum pipes for the main frame. The terminals were joined with L-shaped connectors, bolts, and nuts. To set the electrical devices, acrylic boards were used for the plate located at a 575 mm height. Microphones were installed on a bracket arm as an array for a straight 1200 mm long line with a 30 mm gap.  
As for the installation of microphones, a spatial foldback occurs in high-frequency signals if the gap is wide. In general [98], the distance \(d\) between microphones and the upper frequency \(f\) where no foldback occurs is defined as:  
\[d\leq\frac{c}{2f} \tag{22}\]  
where the constant \(c\) is the speed of sound. We confirmed from our preliminary experimental results that the power of mallard calls was concentrated in the band below 5 kHz. Therefore, we installed the microphones in 30 mm intervals because the upper limit is approximately 5.6 kHz for \(d=0.030\). In addition, the directivity is reduced for low frequencies, if \(d\) is narrow.  
The height from the ground to the microphones was 1150 mm. We used cable ties to tighten the microphones to the bracket arm. We also labeled the respective microphones with numbers that made it easy to check the connector cables to the converter terminals.
# Effect of Particle Size on Composet Analysis by Portable X-ray Fluorescence
## 3 Results and Discussion
### Effect of the Sample Grinding on the Elemental Concentration Determined by pXRF  
The elemental concentrations before and after grinding the whole compost samples are shown in Table 4. Upon grinding, significantly higher concentrations of the elements Rb, Zr, and P were recorded in the sewage compost, Sr and P in the MSW compost, and Al in the alperujo compost. The highest variation was 23.5% for the Sr concentration in the MSW compost. Actually, the elements Sr, Rb, and Zr are of little interest in the compost because they have no incidence at the level of plant nutrition or from an environmental point of view. The P concentration increased when the grinding was 15.2% in the MSW compost, and in the sewage compost, it was only 2.9% (percentages with respect to the value of the ground sample). On the contrary, the readings of Zn in the MSW and alperujo composts and Cu in the sewage and MSW composts decreased significantly. The reductions were 34% for the Cu concentration in the MSW compost and approximately 20% for the Zn concentration in both the MSW and alperujo composts. With the exception of the cases mentioned, the concentrations of most elements did not show significant differences and, in many cases, both of the values for each pair of the results were very similar. The reason that few differences were observed may be that the grinding process that was carried out had little effect on the finest particles, <0.5 mm, which represent an important weight fraction of the complete sample (57.5% and 29.0% in the case of the sewage compost and the MSW compost, respectively). This fraction was able to dilute the differences and mask possible grinding effects. To overcome this equalizing effect of the finer particles, the same comparisons between ground and unground samples were carried out for the size fractions greater than 0.5 mm (that is, three paired replications for the 2-5 mm and three paired replications for the 0.5-2 mm). The statistical comparisons for these fractions are shown in Table 5. As can be seen, the number and the magnitude of the differences between some elemental concentrations in the ground and unground samples were more evident. In the ground samples, very significant higher readings were obtained for the major elements K, P, Si, and Al as well as for V and Ti. The variations were important, with the values in the ground samples reaching +46.7% of the Si concentration in the MSW compost. The reason for the high difference in the Si concentration could be related to the presence of glass particles in the urban waste. These particles, which were dense, could have settled below the surface layers when the XRF cups were prepared for scanning, leaving them out of range of the incident XR beam. In the sewage and alperujo composts, Si concentration increases close to +27% were also recorded with the grinding process. In the sewage compost, higher readings were obtained in the ground samples for the elements Al, Si, K, Ti, and V. In