esa-sceva/satcom-chunk-collection huggingface dataset:
# WiFi-Based Gesture Recognition for Vehicular Infotainment System--An Integrated Approach
## 4 Methodology
### Gesture Detection
#### 4.2.2 Feature Extraction  
Let \(H_{c}(s)\) be the first CSI packet detected by our algorithm and \(S_{c}\) stands for the number of subcarriers between a single link of antenna pair \(Tx-Rx\). We can extract \(N_{c}\) successive CSI packets for any activity profile.  
Based on our preliminary experimental investigations and detailed analysis of extracted CSI, we particularly selected six statistical features that is, mean, standard deviation, median absolute deviation, maximum value, 25 th percentile and 75 th percentile. Mathematically, these features are defined as [50]:  
(1) Mean: The mean \(\mu(j)\) is defined as the average CSI of all packets \(H_{c}\) belonging to \(j\)th subcarrier written as:  
\[\mu(j)=\frac{[H_{cj}(s)+H_{cj}(s+1)+\ldots+H_{cj}(s+N_{c})]}{N_{c}} \tag{13}\]  
where \(N_{c}\) is the total number of successive CSI packets for activity profile and \(j\in[1,S_{c}]\).  
(2) Standard Deviation: The standard deviation \(\sigma(j)\) of \(j\)th subcarrier is basically the square root of variance. Assume \(j\in[1,S_{c}]\), then \(\sigma(j)\) can be expressed as:  
\[\sigma(j)=\sqrt{\frac{1}{N_{c}}\sum_{k=s}^{k=s+N_{c}}(H_{cj}(k)-\mu(j))^{2}}. \tag{14}\]  
(3) Median Absolute Deviation: A robust way to quantify CSI variations for any activity segment is Median Absolute Deviation (MAD). Mathematically, median absolute deviation \(MAD(j)\) for \(j\)th subcarrier is defined as:  
\[\begin{cases}MAD(j)=median(|H_{cj}(k)-\widetilde{H}_{cj}|)\\ s.t.\ j\in[1,S_{c}]\ and\ k\in[1,N_{c}]\end{cases} \tag{15}\]  
where \(\widetilde{H}_{cj}\) is the median of \(H_{cj}\).  
(4) Maximum Value: The maximum value is a unique number, that is the highest value of all other values in the CSI data set. Maximum value \(MAX(j)\) of \(j\)th subcarrier with \(j\in[1,S_{c}]\) is mathematically calculated as:  
\[MAX(j)=max([H_{cj}(s)|H_{cj}(s+1)|\ldots|H_{cj}(s+N_{c})]). \tag{16}\]  
(5) 25 th Percentile: If 25 is the ordinal rank, the 25\(th\) percentile of a data set is defined as the value at which 25 percent of distribution is below it. Mathematically, 25\(th\) percentile \(P_{25 th}(j)\) for \(j\in[1,S_{c}]\) is explained as:  
\[P_{25 th}(j)=H_{cj}(\lceil\frac{25}{100}*N_{c}\rceil). \tag{17}\](6) 75 th Percentile: Similarly, 75\(th\) percentile \(P_{75 th}(j)\) can be formulated as:  
\[P_{75 th}(j)=H_{cj}(\lceil\frac{75}{100}*N_{c}\rceil). \tag{18}\]  
In order to differentiate between multiple gesture profiles, we integrate all features into a feature vector. We propose constituting a tuple \(\mathcal{F}\) of integrated features utilizing both magnitude and phase, defined as:  
\[\mathcal{F}=\{f_{1},f_{2},\ldots,f_{n}\}\subset\mathbb{F}, \tag{19}\]  
where \(f_{i}\) is a feature and \(\mathbb{F}\) stands for the dataset consisting of all gesture features. We get twelve features in total (6-amplitude features and 6-phase features).
# Proof-of-Concept of a Millimeter-Wave Integrated Heterogeneous Network for 5G Cellular
## 2 mmWave-Integrated Cellular Networks
### LTE-WLAN Interworking and Aggregation
An RAN-level LTE-WLAN integration architecture [46; 47; 59] is therefore proposed and is shown in Figure 2(b). In this architecture, the LTE eNB and WLAN AP are directly connected for both UP and CP via a coordination interface that directly transfers real-time traffic and radio information. The mobility anchor point resides in the eNB, minimizing interruptions during handover. This architecture enables CP/UP splitting between the LTE and WLAN since a single CP entity in eNB (i.e., RRC) can handle both LTE and WLAN UPs. The LTE-WLAN integration architecture was proposed and discussed during 3 GPP standardization. As a result, two types of LTE-WLAN integration architectures [46; 47] are defined in Release 13: LTE-WLAN aggregation (LWA) and LTE-WLAN radio level integration with IPsec tunnel (LWIP). Figure 4 shows the protocol stacks of these architectures. Figure 4: LTE-WLAN interworking architectures in Release 13: (**a**) LTE-WLAN aggregation (LWA) in a non-collocated scenario and (**b**) LTE-WLAN radio level integration with IPsec tunnel (LWIP). The main difference between the architectures is the layer of the forwarded packets. In LWA, the eNB forwards packet data convergence protocol (PDCP) data units (PDUs) to the WLAN AP. The PDCP PDUs, which are encapsulated in the LWA adaptation protocol (LWAAP), can be sent via WLAN. In this architecture, the PDCP features can be utilized in WLAN access. For example, security protection on WLAN can be simplified because the PDCP provides external ciphering and an integrity protection mechanism. However, the WLAN infrastructure needs to be updated to support WLAAP. In LWIP, the eNB forwards PDCP service data units (SDUs), i.e., IP packets, to the WLAN AP. The forwarded IP packets are encapsulated in the Generic Routing Encapsulation (GRE) protocol and are secured using IP security (IPsec). The setting of an LWIP bearer is therefore more complicated than that of LWA. However, LWIP can be introduced into the existing WLAN infrastructure because the GRE and IPsec tunnels are transparent to the existing infrastructure. Both LWA and LWIP are controlled by eNB based on UE measurement reporting of radio information, so that their handover failure can be reduced dramatically as reported in [59].
## 3 Statistical Methods
### Testing Compliance with Statistical Distribution Laws
#### 3.1.1 Shapiro-Wilk Test  
The Shapiro-Wilk test is one of the most common methods to assess whether a dataset follows the normal distribution, especially effective when the sample size is small. The test statistic \(W\) is calculated as  
\[W=\frac{\left(\sum_{i=1}^{n}a_{i}x_{(i)}\right)^{2}}{\sum_{i=1}^{n}\left(x_{i}- \overline{x}\right)^{2}} \tag{2}\]  
where, \(x_{(i)}\) are the ordered sample data (which is the AC-BDS in this study), \(a_{i}\) are pre-defined constants, \(n\) is the number of samples, and \(\overline{x}\) is the sample mean [19]. In this research, \(\overline{x}\) is the average AC-BDS of epoxy/Al\({}_{2}\)O\({}_{3}\) micro-composites. The statistical significance of the test statistic \(W\) is determined by comparing the \(p\)-value to a significance level of 0.05. In case \(p\)-value \(\leq\) 0.05, the null hypothesis (i.e., the data follows the normal distribution) is rejected. That is, the given data set does not conform to the normal distribution. For the case of \(p\)-value > 0.05, conversely, the null hypothesis is accepted, meaning the data are normally distributed.
# Hybrid-Compliant System for Soft Capture of Uncooperative Space Debris
## 3 Hybrid-Compliant System for a Soft Capture of Space Debris
### Active Compliance Unit (ACU)  
The ACU, as shown in details in Figure 8, is directly linked to the PCU with the same plate shown in Figure 7. This unit comprises four linear actuators linked with their base to a force/torque (F/T) sensor. Active compliance is ensured thanks to the active control of the linear actuators along the capture axis. Details about the controller are presented in Section 4. By actively changing the stiffness of the ACU, it is possible to ensure a sufficient contact time to actuate the other parts of the ADR capturing process. This allows the system to target a wider range of debris without fundamentally changing its conceptual design.  
* **Force/Torque Sensor** The presence of an F/T sensor, as seen in green in Figure 8, helps feed the controller with the force encountered at the impact between the chaser satellite and the debris. Consequently, the linear actuators will be actuated regarding the force sensed by the F/T sensor, providing the required equivalent stiffness of the overall system towards the targeted debris. This means one can change the parameters of the stiffness and damping of the ACU.
* **Electromechanical Linear Actuators** The four linear actuators are an essential part of the ACU. They are represented in Figure 8 with the static part in gray and the dynamic part in yellow. Although a single linear actuator would have performed the task properly, a failure in that system can generate mission failure. In that regard, it is important to ensure better reliability of that part of the system by using four redundant electromechanical linear actuators. The linear motion of  
Figure 8: General view of the ACU. The CDU is composed of one force/torque sensor, four electromechanical linear actuators and one plate.  
Figure 7: General view of the PCU, composed of one plate, six articulated legs, and six gecko-inspired dry adhesive pads.  
the actuators makes them act as a spring and damper system in a controlled way. As a result, active compliance is created with the F/T sensor in a control loop.
## 2 Mechanism of Microbial-Induced Calcite Precipitation (MICP)
### Application of MICP in Engineering  
The MICP technology comes from the development of microbiology and geotechnical engineering, and is a novel technology with ecologically friendly and sustainable advantages [23]. Now, the technology has achieved good results in engineering applications, including soil treatment, seepage prevention, and cracking repair.  
Many scholars have proved that MICP plays a significant role in soil treatment through experimental tests [71; 72; 73]. For example, compared with untreated expansive soil samples, it found that the swelling rate of soil samples after MICP mixing treatment was significantly reduced [25]. [PERSON] et al. [74] found that the unconfined compression resistance of treated clay samples increased nearly three times through the MICP pressure grouting treatment test of clay. [PERSON] et al. [75] used MICP technology to strengthen the sand and premixed method. The so-called premixing method refers to the use of the mixing method and unconfined compressive strength test to make the reaction liquid better contact with the soil. Compared with other methods, the advantage of this method is that it can make CaCO\({}_{3}\) uniformly distributed in the soil.  
The CaCO\({}_{3}\) generated by MICP technology can also fill the pores of the solidified soil and further reduce permeability to achieve the purpose of plugging [76]. A plugging test of MICP was conducted on fly ash modified concrete using giant backillus [28]. The results suggested that the calcite precipitation between the aggregate and the bio-cement mortar was a primary reason for reducing the permeability of samples.  
[PERSON] et al. [77] also demonstrated that as the CaCO\({}_{3}\) precipitate is induced by MICP, it plays a role in bio-cementing soil samples, since it fills cracks among soil particles. [PERSON] and [PERSON] [78] used an energy dispersive spectrometer to observe the status of concrete cracks repaired by MICP, and their results showed that a crack of the width of 0.46 mm was completely healed after 100 days of repair, which was much larger than that of 0.18 mm in the control group. [PERSON] et al. [79] found that the MICP has an advantage in the self-healing of cracks in bio-cement slurry by the theoretical model and microscopic test.  
In general, the usage of MICP technology can effectively repair cracks, but it is undeniable that the time period for treatment and repair is long. Subsequent research should be carried out to develop a more efficient and faster MICP technology in the material source and reaction processes.
# Deformation and Stress Analysis of Pile-Supported Immersed Tunnels under Seismic Loads
###### Abstract  
The stress and deformation of pile-supported immersed tunnels under seismic loads is a critical issue in tunnel design. This paper utilizes ABAQUS (version 2020) finite element software to analyze the seismic load response of the sand compaction pile-immersed tunnel-seawater pressure (SIS) system, which is verified by a physical model. The study shows that the suppression effect of the seawater on the vertical frequency of the tunnel increases with depth. When the replacement rate of the piled foundation reaches 50%, the deformation of the tunnel \"H-shaped\" structures increases, which also changes the vertical frequency of the tunnel. However, the presence of the suppression effect causes resonance injury at the far end of the tunnel from the earthquake source, resulting in a shift of the peak stress point. It was also found that seawater pressure affects the resistance-deflection (_p-y_) at the tip of the pile more than at the end of the pile. The slenderness ratio (\(\gamma\)) of the pile affects the _p-y_ value at the end of the pile more than at the tip of the pile. The connection between the piled foundation and the tunnel is most stable when \(\gamma\) is in the range of 9.25 to 15.  
s amed compaction pile; immersed tunnel; frequency; slenderness ratio; replacement rate; stress and deformation +
Footnote †: journal: Applied Sciences
## 4 Simulation Results and Discussions
### BER under Measured Underwater Channel  
In this subsection, the performance in terms of the BER of BC-UAC-TD and BC-UAC-FS is verified under the measured underwater channel.  
The experiment was carried out in the Zhoushan sea area in October 2020. In the experiment, the transmitter was fixed on the ship and the receiver was fixed on the wharf. The depth of the seawater was about 60 m, the depths of the transmitter and the receiver were about 15 m and 5 m, respectively.  
The impulse response of the measured underwater channel was estimated using the IOMP algorithm, as shown in Figure 14. From Figure 14, one finds that there are about 11 paths in the measured underwater channel, where the largest time-delay of the multipath is about 29.7 ms, and its corresponding amplitude is about 0.12.  
The impact of the symbol length (\(T_{\text{sym}}\)), the frequency offset and the modulation order (\(M\)) on the performance of the proposed BC-UAC method in terms of the BER under the measured underwater channel is shown in Figures 15-17, respectively. The other simulation parameters are the same as in Table 4.  
Figure 12: The impact of the frequency offset on the BER of the BC-UAC method under the simulated channel. (**a**) BC-UAC-TD (\(\Delta f\)); (**b**) BC-UAC-FS (\(\Delta f_{0}\)).  
Figure 13: The impact of \(M\) on the BER of the proposed BC-UAC method under the simulated channel. (**a**) BC-UAC-TD; (**b**) BC-UAC-FS.  
Figure 16: The impact of the frequency offset on the BER of the proposed BC-UAC method under the measured channel. (**a**) BC-UAC-TD (\(\Delta f\)); (**b**) BC-UAC-FS (\(\Delta f_{0}\)).  
Figure 14: The impulse response of measured underwater channel.  
Figure 15: The impact of \(T_{\text{sym}}\) on the BER of the proposed BC-UAC method under the measured channel. (**a**) BC-UAC-TD; (**b**) BC-UAC-FS.  
From Figures 15 and 16, we observe that the performance of the proposed BC-UAC method in terms of the BER can be improved by increasing \(T_{\text{sym}}\), \(\Delta f\) in BC-UAC-TD and \(\Delta f_{0}\) in BC-UAC-FS. Moreover, the influence of \(T_{\text{sym}}\) is relatively small, while the influence of the frequency offset is relatively large.  
From Figure 17, we observe that as the performance of the proposed BC-UAC method, in terms of the BER, decreases as \(M\) increases. However, the performance is improved using the VTRM equalization module at the receiver. Moreover, the influence of \(M\) on the performance of BC-UAC-TD is larger than that of BC-UAC-FS.  
Comparing Figures 12-14 with Figures 15-17, shows the results obtained under the simulated underwater channel are in accordance with those under the measured underwater channel.
## 2 Materials and Methods
### Ultra-Weak Luminescence  
The test method consists of measuring the photons resulting from the secondary emission from the analyzed biological material. A prototype equipment was used to measure the photon emission level as an ultra-weak luminescence. It was determined by the measurement of fluorescence decay using the time-correlated method counting single photons using the TCSPC technique (Time-Correlated Single Photon Counting). The device consists of, among others, from 8 photomultipliers placed above the measuring chamber. The measuring system enables automatic recording of test results and the LabView-based application software enables quick adjustment of the measurement process characteristics to the specificity of biological material. The fruit after picking was kept at 18-20 \({}^{\circ}\)C for 48 h and at 4 \({}^{\circ}\)C (\(\pm\)0.1 \({}^{\circ}\)C) for the next 48 h. Before the measurement itself, the fruits were kept in the dark for about 1 h until they reached 18 \({}^{\circ}\)C (\(\pm\)0.1 \({}^{\circ}\)C). Samples were excited by exposure to standard light with an intensity of 300 lx (wavelength 555 nm) for 600 s at the distance of 12 cm between the samples and the light source. The weight of a single sample was 5 g (\(\pm\)0.1 g) of intact fruits with a temperature of 18 \({}^{\circ}\)C (\(\pm\)0.1 \({}^{\circ}\)C) and relative humidity less than 40%. Prepared samples were irradiated in the aforementioned conditions and then each of them was placed in the measuring chamber in a manner enabling standardization of the measurement. Before the measurements, the intensity of background photon emission was measured, the value of which for all laboratory measurements ranged from 50 to 90 units. Background measurements according to an internal procedure were always performed after 10 appropriate measurements on a given day and at the beginning of the following day. The results of the photon emissivity given in the paper are reduced by the background emissivity. After commenced measurement system, it was stabilized for the first 120 s to prevent disturbances resulting from temporary destabilization of standard conditions. The initial phase was followed by the main (measuring) phase with a time interval of 600 s, and the sampling frequency was 1 Hz [14]. The result was provided in conventional photon emission units depending not only on the actual photon emissivity from the analyzed samples but also on the device and measurement methodology. Obtained results enable a relative analysis of the photon emissivity under identical experimental conditions.
# Numerical Investigation of the Collapse of a Steel Truss Roof and a Probable Reason of Failure
###### Abstract
There has been experimental research focusing on the mechanical behaviour of some specific components and/or structures that are used in trusses [15; 52; 53; 54; 55]. In past studies by various researchers, real roof structures were put through experimental testing to analyse their mechanical behaviour [15; 56; 57]. However, experimentation is not always possible and/or feasible. Therefore, numerical studies are conducted to predict the mechanical behaviour as well as to predict plausible failures. For truss-type structures, the finite element method is usually employed since it is one of the most powerful tools in engineering [14; 22; 57; 58]. [PERSON] et al. studied the collapse of a spatial truss roof of a gym building in North Italy during a moderate snowfall by using the finite element method [59]. They reported that the elements and connections made by ductile or brittle materials influenced the bearing capacity of the roof structures and created geometric imperfections. On the other hand, not all the failures occur slowly. As mentioned earlier, some collapses occur suddenly under excessive, impulsive loadings. This study aimed to study a case in which a sudden partial failure of a steel truss roof of a factory built in July 2011 happened during heavy weather conditions on 22 October 2012 in Cerkezkoy Tekirdag in the northwestern part of Turkey [8]. The main focus was on developing a plausible theory for the failure by employing numerical analyses performed using the finite element method, which covers various loading conditions and their combinations. Not only the truss roof but also the combination of the truss roof with the column supports were analysed. Thus, the results of numerical analyses were used to compare the obtained failure modes to the real failure, considering different criteria for the failure. The extreme loads due to the ponding of rainfall or snow accumulation and the temperature shock due to the thunderstorm were considered in the analyses. Even under these potential mechanical overloads, the observed damage was different from the actual damage: The damage caused by these excessive loads occurred in different regions than those in which the actual damage occurred. While the studies in the literature mostly describe failures due to vertical loads caused by water/snow accumulation or horizontal loads caused by wind, this study showed that the critical regions created by temperature change are similar to the actual damaged zones.
## 1 Introduction
NWSB and NTB are the lower and upper limits of temperature that the plant canopy would reach, respectively, related to different VPD values. NWSB refers to a non-limiting water condition when the crop is transpiring at the highest rate and NTB refers to non-transpiration conditions with extreme water stress. Recently, several authors have reported a new index, degrees above the non-stressed canopy (DANS), defined as the difference between the actual temperature of the canopy and the NWSB [14, 15]. It is much simpler than CWSI and has been successfully used as the water status indicator in different crops. It is yet to have been used for woody crops; thus, it is important to evaluate the feasibility of using DANS for sweet cherry trees. Thermal and multispectral cameras have been used over recent years for water stress monitoring with unmanned aerial vehicles (UAVs). However, the difficulty and high cost of using UAVs regularly has meant that their use is reduced to specific events in the crop phenology. [PERSON] et al. [20] recommended that care should be taken when using instantaneous remote sensing indicators to evaluate moderate water deficits in deciduous fruit trees, and more severe/longer water stress conditions are probably needed to detect significant differences. Low-cost thermal cameras could be an alternative and robust means of obtaining satisfactory thermal information instead of high-resolution cameras, due to their price (around 20-fold cheaper), user familiarity and ease of implementation in the farm context as a precision irrigation tool [21, 22]. Furthermore, this technology can be integrated into intelligent sensor systems to use appropriate image-segmentation algorithms, which are capable of identifying regions of interest [23]. However, the lower sensor resolution must be an impediment for remote acquisition or establishing plant water status at larger scales, such as row-level, due to the pixel size [24, 25]. The objectives of the present study were (i) to test the feasibility of low-cost thermal imaging using several thermal indicators (Tc, \(\Delta\)T, CWSI and DANS) to detect and quantify the water status of young and mature sweet cherry trees subjected to water stress; (ii) to define the non-water-stressed baseline (NWSB) and non-transpiration baseline (NTB) for both cultivations; and (iii) to assess the relationship between thermal indicators and midday stem water potential by linear correlation analysis.