esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The preparation, characterization and application of metal organic frameworks (MOFs) have attracted considerable attention in the past two decades [1, 2, 3, 4, 5, 6]. The well-known applications of MOFs are gas sorption and separation [7, 8, 9, 10, 11, 12, 13, 14, 15], catalysis [16, 17, 18, 19], luminescence sensing [20, 21, 22, 23, 24] and biomedical usage [25, 26]. MOFs can be used as bioactive framework materials (BioMOFs) for efficient antimicrobial agents [25, 26, 27, 28, 29, 30, 31]. For example, the water-stable Cu-BTC MOF (BTC = 1,3,5-benzetrictarboxylate) inhibits the rate of growth of _C. albicans_[32], and the other Cu-based HKUST-1 MOF incivatives _Saccharomyces cerevisiae_ due to CuII ions released from its structure [33]. A Co-based MOF (Co-TDM) [8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 5  
delivery system with fewer side effects [37]. Fe-MIL-100 was used as a carrier for the fungicide azoxystrobin, and azoxystrobin-loaded Fe-MOFs (AZOX@Fe-MIL-100) exhibited good fungicidal activities against two pathogenic fungi (_Fusarium graninearum_ and _Plytophthora infestans_) [38].  
Recently, the antifungal activities of Cu-MOFs and Co-MOFs have been analyzed [39; 40]. A flexible dicarboxylate ligand and glutamate were used to bridge metal ions to form two-dimensional (2D) sheets, and bipyridyl ligands (bpy = 4.4\({}^{\prime}\)-bipyridine, bpa = 1,2-bis(4-pyridyl)ethane, bpe = 1,2-bis(4-pyridyl)ethylene, and bpp = 1,3-bis(4-pyridyl)propane) can connect these sheets to form three-dimensional (3D) frameworks, Cu-MOFs [41; 42; 43; 44; 45; 46] and interpenetrated 3D and 2D coordination polymers, Co-CPs [39]. Co-CPs can inactivate _C. albicans_ cells and _A. niger_ spores with maximum 99% and 62% inactivation efficiency, respectively [39]. Cu-MOFs show approximately 50-80% inactivation of both fungi in our experimental conditions [40]. However, these antifungal activities of Cu-MOFs and Co-CPs were acquired after a 4-day treatment. The development of MOFs for upgraded antifungal activity will be continuously explored.  
To develop MOFs with improved antifungal activity, we synthesized a modified version of Cu-MOFs that were used in our previous study [40]. 4,4\({}^{\prime}\)-azopyridine (AZPY) is employed for the developed 3D **Cu(AZPY)-MOF** while the bpe ligand has been used for connecting 2D sheets in the previous Cu-MOF, [Cu\({}_{2}\)(Glu)\({}_{2}\)(bpe)] (Scheme 1). The structure of **Cu(AZPY)-MOF** is confirmed by X-ray crystallography, PXRD (powder X-ray diffraction), elemental analysis and TGA (thermograuric analysis) technique. The antifungal activity of **Cu(AZPY)-MOF** were examined against _C. albicans_ and _A. niger_ in this study.
## 2 Seismic Monitoring
### Seismic Network NA: Caribbean Netherlands Seismic Network  
The Caribbean Netherlands Seismic Network has been gradually expanded since 2006 and currently comprises four seismic monitoring stations on Saba (Figure 2), three on St. Eustitius (Figure 2) and one on St. Maarten. All seismic stations are equipped with a Streckeisen sensor that is characterised by a flat response to ground velocity from 120 s to about 10 Hz and a Quanterra datalogger (Table 2) that is configured to continuously record ground velocity at sample rates of 1, 40 and 100 samples per second (sps). The high (100 sps) sampling data streams are generated by the data logger with a minimum phase filter to optimize accurate seismic phase picking, while the lower sampling rate data streams are processed with a linear phase filter to provide undisturbed data for full waveform analysis [20; 21]. Although each site has its own characteristics, the seismometers are installed in similar conditions: on the ground floor of buildings (owned by the local telecommunication company or the government) with concrete floor and walls, in a temperature controlled room without cisterns or open space underneath, and with access to power and Internet connections. Station SEUG, however, is located in a small concrete vault with a concrete plate base (\(2\times 2\) m). The base plate is built upon a small gradient slope and is on private grounds. Both power supply and Internet are facilitated at this site, but there is no temperature regulator (e.g., air conditioning).  
Each seismic station is connected to Internet and can be monitored, accessed and controlled remotely from the Netherlands. Each datalogger is configured (a) to transmit data in real-time to KNMI and the Tsunami Warning Center at Puerto Rico using the SeedLink protocol (www.seiscomp3.org/doc/applications/seedlink.html) and (b) to store data locally at the site to serve as a buffer to prevent data loss in case of long duration Internet failures. Data streams are synchronised in time by GPS time signals. All data streams are openly available to the research community through FDSN  
Figure 5: Example recordings of the vertical ground velocity (mm/s) by all eight NA stations of the magnitude 4.3 earthquake on 14 March 2017 at 18:35 UTC at a depth of 87 km just east of St. Eustius. Stations are ordered by epicentral distance.  
standardized webservices at KNMI (rdsa.knmi.nl/fdsnws/dataset/1/), as well as through the standard data services provided by the Observatories and Research Facilities for European Seismology (ORFEUS) and by the European Integrated Data Archive (EIDA) (www.orfeus-eu.org).
## 4 Feature Extraction
### Acoustic Features
In the chromagram, each pitch class (also known as a chroma) embraces a set of pitch bands separated by octaves (e.g., the chroma C consists of {C1, C2, C3, C4,...}). The CQT chromagram further mirrors the human auditory sensation through logarithmic transformation, while the CENS chromagram smooths the local deviations and hence shows better robustness to dynamical and timbral variations [49]. Moreover, this study also included the tonal centroid (tonnetz) features following [50], which indicates the harmonic change of music. Specifically, based on the harmonic network, collections of pitch classes were projected to a six-dimensional space as tonal centroid points, which can be visualized on the circles of major thirds, minor thirds, and fifths. The tonnetz features are particularly useful for chord recognition [50]. The aforementioned acoustic features were all extracted using a specialized Python library for music and audio signal analysis, Librosa [51], with the default parameter settings (e.g., sample rate: 22050, hop length: 512). Unless otherwise specified, the aggregate statistics (i.e., mean, standard deviation) of the frame-wise feature vectors were used to form the clip-level feature representation.
# A Random Subcarrier-Selection Method Based on Index Modulation for Secure Transmission
### Motivation and Related Work  
It can be derived from [17; 18] that the key of combining FDA with OFDM, while mitigating the coupling effect, was to randomly distribute a set of orthogonal subcarriers to each antenna array element. The authors of [18] gave two practical random subcarrier-selection methods: QSS Plus RP and PSS Plus RP. The QSS was defined as a set whose subcarrier index along the antenna array is a non-linear function of the corresponding antenna element index. The PSS was defined as a set whose subcarrier index is prime. Moreover, in order to destroy the regular order and produce a more random subcarrier index distribution over the transmitting antennas, the authors designed a block interleaving randomization procedure and repeated it until the randomization metric was larger than the predefined threshold.  
However, this method brought about new problems. First, the repetition of the block interleaving operation increased system complexity, and it was hard to obtain a proper predefined threshold. Also, it generated a large number of unused subcarriers that greatly wasted spectrum.  
To alleviate the above problems, this paper proposed a random subcarrier-selection method based on index modulation. On one hand, the bitstream was a random binary sequence, which could guarantee the randomness of the selected subcarriers; on the other hand, similar to IM [19], the indices of the subcarriers could also carry the information, which mitigated the waste of subcarriers. Moreover, our proposed scheme avoided the repeated block interleaving, achieving lower complexity. The proposed scheme could be used in wireless communications where the FDA is required to provide secure transmission.
###### Abstract  
Convolutional neural networks (CNN) are widely used for structural damage identification. However, the presence of environmental disturbances introduces noise into the acquired acceleration response data, impairing the performance of CNN models. In this study, we apply empirical mode decomposition (EMD) and variational mode decomposition (VMD) to denoise the data from a steel truss bridge. By comparing the smoothness and convergence of the obtained modal functions (IMFs) using EMD and VMD, we confirm the effectiveness of VMD in smoothing and denoising the bridge structure signals. Additionally, we propose a convolutional self-attention neural network (CSANN) model to extract features and identify damage in the denoised data using VMD. Comparative analysis of the CNN, LSTM, and GRU models reveals that the VMD-CSANN model outperforms the others in terms of damage localization and identification accuracy. It also exhibits excellent performance when handling noise-contaminated data with a noise level of 10%. These findings demonstrate the efficacy of the proposed method for identifying internal damage in steel truss structures, while maintaining smoothness and robustness during processing.  
Keywords:modal signal decomposition; variational mode decomposition (VMD); self-attention mechanism; convolutional neural network; structural damage identification +
Footnote †: journal: Medical Imaging and Biomedicine
# Modeling and Speed Tuning of a PMSM with Presence of Fisure Using Dragonfly Algorithm
## 5 Sensitivity Analysis  
The proposed tuning algorithm, using the dragonfly algorithm, searches for gains and the subsequent simulation of them in a defined domain. In order to limit the search domain, a sensitivity analysis is proposed for the PMSM speed control system. The sensitivity analysis will determine the variations in each of the gains involved, defined as the \(k_{j}\) parameters [26], where  
\[k_{j}=\left[\begin{array}{cccc}k_{1}&k_{2}&k_{i}&k_{p}&k_{d}\end{array}\right] \tag{38}\]  
In addition, the study aims to ensure that this domain is optimal in terms of the consumption of the runtime of the algorithm. An energy function is defined, which aims to analyze the sensitivity of the PMSM model, defined as  
\[E_{T}=E_{C}+E_{P} \tag{39}\]  
where \(E_{T}\) is the total energy, \(E_{C}\) is the kinetic energy and \(E_{P}\) potential energy. The previous two energy functions can be defined as follows [1]  
\[E_{C}=\int T_{e}\omega_{r}dt \tag{40}\]  
\[E_{P}=\frac{1}{2}\Bigg{[}\frac{i_{d}^{2}}{L_{d}}+\frac{i_{q}^{2}}{L_{q}}+\frac {P}{2J}\omega_{r}^{2}\Bigg{]} \tag{41}\]  
The total energy is calculated using (1) and (2)  
\[E_{T}=\int T_{e}\omega_{r}dt+\frac{1}{2}\Bigg{[}\frac{i_{d}^{2}}{L_{d}}+\frac {i_{q}^{2}}{L_{q}}+\frac{P}{2J}\omega_{r}^{2}\Bigg{]} \tag{42}\]Equation (42) can be expressed as a differential equation as  
\[\frac{dE_{T}}{dt}=T_{e}\omega_{r}dt+\left[\frac{i_{d}}{L_{d}}\left(\frac{di_{d}}{ dt}\right)+\frac{i_{q}}{L_{q}}\left(\frac{di_{q}}{dt}\right)+\frac{P\omega_{r}}{2J} \left(\frac{d\omega_{r}}{dt}\right)\right] \tag{43}\]  
The energy cost function G, for the temporary evaluation period, can be defined as  
\[G=\int E_{T}dt \tag{44}\]  
The speed of change in the cost function in differential form, in the analysis time interval, can be described as  
\[\frac{dG}{dt}=E_{T} \tag{45}\]  
The set of differential equations that determines the behavior of the sensitivity of the parameters of the PMSM system is determined by combining the mathematical model of the machine and the energy cost function, and is expressed as  
\[\frac{di_{d}}{dt}=\frac{V_{d}}{L_{d}}-\frac{r_{s}}{L_{d}}i_{d}+\frac{L_{q}}{L_ {d}}a_{r}i_{q} \tag{46}\]  
\[\frac{di_{q}}{dt}=\frac{V_{q}}{L_{q}}-\frac{r_{s}}{L_{q}}i_{q}+\frac{L_{d}}{L_ {q}}\omega_{r}i_{q}-\frac{\lambda_{m}}{L_{q}}\omega_{r} \tag{47}\]  
\[\frac{d\omega_{r}}{dt}=\frac{P}{2J}[T_{e}-T_{1}-\beta a_{r}] \tag{48}\]  
\[\frac{dE_{T}}{dt}=T_{e}\omega_{r}dt+\left[\frac{i_{d}}{L_{d}}\left(\frac{di_{ d}}{dt}\right)+\frac{i_{q}}{L_{q}}\left(\frac{di_{q}}{dt}\right)+\frac{P \omega_{r}}{2J}\left(\frac{d\omega_{r}}{dt}\right)\right] \tag{49}\]  
\[\frac{dG}{dt}=\int T_{e}\omega_{r}dt+\frac{1}{2}\left[\frac{i_{d}^{2}}{L_{d}} +\frac{i_{q}^{2}}{L_{q}}+\frac{P}{2J}\omega_{r}^{2}\right] \tag{50}\]  
From the system of Equations (46)-(50), the behavior of the cost function is obtained for the defined simulation time interval. The sensitivity of the system \(S_{j}\), for each parameter of interest \(k_{j}\), is as in  
\[S_{j}=\frac{dG}{dk_{j}} \tag{51}\]
# A New Orbiting Deployable System for Small Satellite Observations for Ecology and Earth Observation
## 3 Study Cases
### Application 4: Posidonia Oceanica Monitoring
oceanica_, with peak reflectivity of the leaves at 0.53 \(\upmu\)m; (iii) 'Red' (0.63-0.69 \(\upmu\)m), which is strongly absorbed by the water column; (iv) 'Near InfraRed 1' (0.77-0.895 \(\upmu\)m), the wavelengths of which have the lowest water absorption, while being strongly reflected by the meadows' leaves; (v) 'Coastal Blue' (0.4-0.45 \(\upmu\)m), whose penetrating power into the water and its additional contribution for the atmospheric correction would allow for the identification of coastlines and the analysis of bathymetry; (vi) 'Yellow' (0.585-0.625 \(\upmu\)m), which better renders the natural colors of images, and thus allows to optimize the classification processes; (vii) 'Red Edge' (0.705-0.745 \(\upmu\)m), which is fundamental for the in-depth analysis of the health of the vegetation covering the wavelength range most affected by the chlorophyll content; and (viii) 'Near InfraRed 2' (0.86-1.04 \(\upmu\)m), which is poorly affected by atmospheric conditions, and therefore extremely useful in supporting the analysis of vegetation, as it allows to increase the discriminatory capacity with other habitats. The latest generation of sensors onboard large (e.g., QuickBird 2 [220; 221] and WorldView [221; 222]) and medium (e.g., Ikonos [223] and Ikonos-2 [211; 220]) satellites have spectral and spatial resolution sufficiently high to obtain a very detailed map of the _P. oceanica_ (down to the meter-size resolution, e.g., [201]). However, their time resolution is not sufficient for mapping the spatio-temporal changes of the meadows or to systematically study the coastal and estuarine bio-physical dynamics (e.g., [201]). A short revisiting time (daily to weekly order) does not guarantee the observability of the study area, caused by cloud coverage and/or water turbidity after rainfalls. Therefore, the shorter the revisiting time, the better and more detailed the mapping of _P. Oceanica_, to detect possible dangerous regressions. The constellation of SmallSats can thus be the best solution to satisfy such multi-temporality characteristics, although current small satellites, such as the RapidEye constellation [201; 222; 224; 225], still might not reach the satisfactory spatial resolutions, and new design should be considered for the next SmallSat generation (e.g., deployable optical systems).
## Case Study
### Results and Discussion
In addition, the REs between the calculated values and the measured values of displacements are 0.116-4.661 and 0.19-4.015 for the monitoring points D4 and D6, respectively. The comparison between the calculated values and the measured values of D4 and D6 displacements is shown in Figure 13. Combining with Figure 13 and the REs of the displacements of each measuring point, we can note that the accuracy of the surrounding rock mechanical parameters obtained by the proposed back analysis method can meet the actual engineering requirements. From Figures 14-16, we note that the displacements of monitoring points No. 1 (Node 8), No. 2 (Node 69), No. 3 (Node 13), No. 4 (Node 70), No. 5 (Node 17), and No. 6 (Node 20) on tunnel primary support structure present an approximate upward concave parabolic change with rheological time. The displacements of the monitoring point No. 1 at the vault have the largest among them. Figure 14: Contour map of tunnel displacement at rheological time 721 h.  
Figure 8:0 Variation of perimeter displacement with elastic modulus of primary support. From Figure 16, we also note that the large deformation of the initial support of the tunnel has undergone the following five stages of evolution:  
(1) At the first stage, the displacement rate is very small, for example, its average displacement rate of monitoring point NO.1 is 0.226 cm/d from 0 h to 200 h of rheology time. (2) The average displacement rate at the second stage is much larger than that at the first stage. For example, the average displacement rate of monitoring point NO.1 at the second stage is 0.915 cm/d from 200 h to 400 h of rheology time. (3) The average displacement rate at the third stage is much larger than that at the second stage. For example, the average displacement rate of monitoring point NO.1 at the third stage is 1.390 cm/d from 400 h to 542.5 h of rheology time. The displacement of the primary support structure of the tunnel reaches the reserved deformation design value 20 cm when the surrounding rock force rheology time is 542.5 h.  
(4) The average displacement rate at the fourth stage is very much larger than that at the third stage. For example, the average displacement rate of monitoring point NO.1 at the fourth stage is 2.336 cm/d from 542.5 h to 800 h of rheology time. (5) The average displacement rate at the fifth stage is very much larger than that at the fourth stage. For example, the average displacement rate of monitoring point NO.1 at the fifth stage is 3.695 cm/d from 800 h to 850 h of rheology time. From Figure 17, we also note that the displacement velocities of the monitoring points increase approximately linearly from the sixth day to the twentieth day after rheology begins, and then it increases rapidly, especially after 28 days of rheology. That is to say, it is most scientific and reasonable to reinforce and dispose of the large deformation disaster of the tunnel within 20 days after the initial support.
# Research on Energy Harvesting Mechanism and Low Power Technology in Wireless Sensor Networks
## 2 Related Work
### Energy Harvesting Technology  
There are two main energy supply methods for the network nodes: wireless charging technology and energy harvesting technology. However, the wireless charging technology has limitations in terms of short charging distances and is not suitable for powering WSNs deployed in remote areas [12; 13]. Fortunately, the energy harvesting technology provides an ideal solution for meeting the energy demands of WSNs [14]. Currently, there exist numerous well-developed energy harvesting technologies. Of all these technologies, solar energy harvesting stands out as the earliest and most advanced method. The solar harvester has the ability to generate milliwatts of energy per square centimeter, making it suitable for powering wireless sensor nodes. However, its performance is affected by environmental factors such as darkness and precipitation. On the other hand, vibration energy is readily abundant in the natural environment and has a high energy density, making it a perfect candidate for environmental energy harvesting technology [15]. Among the different types of vibration energy harvesting devices, piezoelectric devices have garnered considerable interest in recent years [16; 17]. This is primarily due to their exceptional attributes, such as high energy conversion efficiency, utilization of lightweight materials, simple yet robust structure, and seamless integration. Currently, the utilization of piezoelectric devices is predominantly based on the implementation of cantilever, cymbal, and cylindrical structures for energy harvesting. Among these structures, the cantilever structure has garnered considerable interest in both domestic and international research circles due to its straightforward implementation and resonance capabilities in low-frequency natural environments [18; 19].
## 3 Results and Discussion
### Effect of CNC Contents on the Photopolymer Composites
#### 3.2.2 Morphological Properties of CNC/Photopolymer Composites with Various CNC Loading Levels  
In a photopolymer composite without CNCs, no impurities or cracks were found, giving a smooth and clean surface (Figure 15a). This is a common brittle fracture failure found in pure photo-polymerized composites. In the composite fracture surface containing 0.25 wt% CNC, dispersed CNCs were the smallest in size and number, so a smooth and flat surface was still observed (Figure 15b). On the fracture surface of the composite with relatively higher amounts of CNC (i.e., 0.5 and 1.0 wt%), the sizes of the aggregated CNCs were larger, and their distribution frequency was higher (Figure 15c,d). As the CNC loading levels increased, larger shapes and reduced dispersions of CNC aggregates were observed, which may support the results of the previous mechanical strength properties.  
Figure 15: SEM images (\(\times\)200) of CNC/photopolymer composites with various CNC loading levels: (**a**) 0 wt%, (**b**) 0.25 wt%, (**c**) 0.5 wt%, and (**d**) 1.0 wt%.
# A Modified Bearing Capacity Model for Inclined Shallow Anchor Cable with Experimental Verification
## 1 Introduction
Laboratory experiments play a crucial role in validating the theories of anchor cables [25]. While previous experimental studies have primarily focused on vertical or near-vertical anchors [26], there has been increasing interest in understanding the behavior of inclined anchors. [PERSON] [27] performed pull-out tests on anchor plates with different inclination angles; the result show that the failure models of anchor plates are different with different inclinations. [PERSON] and [PERSON] [28] presented a series of experiments of circular multi-plate anchors with different inclinations for the bearing capacity. [PERSON] et al. [29] presented pull-out tests of inclination shallow plate anchors. These experimental studies show that the bearing capacity and the failure behavior of the anchor cable were directly affected by inclination [30; 31]. This paper proposes a modified bearing capacity calculation method for shallow-embedded anchor cables with inclination, based on the inverted conical failure mode and the limit equilibrium theory. Subsequently, indoor pull-out tests were conducted to study and analyze the bearing capacity and failure modes of shallow-embedded anchor cables with different inclinations. The proposed method for calculating the ultimate bearing capacity was then validated through experimental results.
## 4 System Model
### Computation Model  
In this subsection, the computation offloading model is introduced. Firstly, as mentioned above, our simulation has a a single base station connected with \(\mathbb{N}\) set of VR game devices in which each device has \(\mathbb{M}\) independent computation tasks that needed to be completed. For each computation task \(j\), we use a tuple \(\{a_{i,j},b_{i,j},c_{i,j},\tau_{i,j}\}\) to represent the computation task requirement, where \(a_{i,j}\) and \(b_{i,j}\) represent the size of input and output data that need to be transmitted and received, respectively. Whereas \(c_{i,j}\) and \(\tau_{i,j}\) represent the total number of CPU cycles and the completion deadline that are required for task \(j\) of VR device \(i\). The values of \(a_{i,j}\), \(b_{i,j}\) and \(c_{i,j}\) can be obtained through carefully profiling of the task execution [37; 38; 39].  
Consequently, the computation overhead in terms of execution time and energy consumption for local, edge and cloud execution approaches will be discussed later in detail.