esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Forest Tree Losses in Cote d'Ivoire Using Drone Aerial Images
## 2 Materials and Methods  
### Study Area  
The study area is the classified forest of Bossematie. It covers an area of approximately 220 km\({}^{2}\) and is located between latitudes 06\({}^{\circ}\)21\({}^{\prime}\)30\({}^{\prime}\) and 06\({}^{\circ}\)32\({}^{\prime}\)30\({}^{\prime\prime}\) north and longitudes 03\({}^{\circ}\)24\({}^{\prime}\)30\({}^{\prime\prime}\) and 03\({}^{\circ}\)34\({}^{\prime}\)30\({}^{\prime\prime}\) west in the southeast of Cote d'Ivoire in the Indenie-Djuablin Region (Figure 1). The classified forest of Bossematie is located in the former cocoa belt--named due to the high cocoa production here in the 1980s [12]. It is a classified forest of Cote d'Ivoire that is still well-preserved despite being subject to very strong pressures [12]. Moreover, it hosts the last population of forest elephants (_Loxodonta africana_) of the southeast. The vegetation is that of the Guinea domain characterized by dense evergreen rainforest.  
Dense forests represented 83% of the classified forest in 2016, while degraded forests represented 8%, and perennial crops of cocoa, _Thebroma cacao_ (Sterculiaceae); coffee, _Coffea arabusca_ and _Coffea caenphora_ (Rubiaceae); and rubber, _Heeva Brasiliensis_ (Euphoriaceae), represented 9% at the same date [12]. Within this forest, lianas are less numerous; the herbaceous stratum is generally well represented, with a predominance of Poaceae and Acanthaceae. The vegetation is of the semideciduous type as defined by [30] with rare epiphytes. This vegetation can be described as Malvaccae and Cannabaceae [31]. It is populated in abundance by _Celtis mildbreadii_ (Cannabaceae), _Nesogordonia papaverifera_ (Malvaccae), _Triplochiton scleroylon_ (Malvaccae), and _Mansonia altissima_ (Malvaccae).
###### Abstract
Recent use cases for wearable technology have explored the ability to encourage the functional use of the MI upper limb in chronic home settings [25, 26, 27, 28, 29, 30, 31] and subacute IRF settings [32, 33, 34, 35]. Much of the prior work aimed at promoting increased upper limb movement poststroke has relied on prompts or cues to encourage patients to engage their affected limbs. For example, [PERSON] et al. (2018) prompted stroke survivors to perform pre-selected impaired upper limb tasks with vibration cues delivered to a motion sensor system placed at the wrist [32]. Wearers were prompted once every 1-4 h to encourage the practice of purposeful impaired arm movements, resulting in a total of 3-12 cues per day. [PERSON] et al. (2019) [33] tested the efficacy of prompting activities with 5 repetitions cued every 10 min for 3 consecutive hours, resulting in a total of 90 cues per day. These studies highlight the variability of cue dosage delivery using wearables in IRFs, with intervals ranging from several hours to minutes between each cue. To our knowledge, no study has investigated the effects of delivering larger dosages of cues at higher frequencies (i.e., more than 100 repetitions per day with less than a minute between cues), a strategy more closely aligned with the principles of high-repetition task training. In this study, we evaluate the feasibility and user experience of a graded-challenge exercise program and associated wearable technology that delivers a high dosage of activity cues and monitors upper limb movement in stroke survivors in an IRF setting. Previous pilot testing conducted with a small cohort of chronic stroke survivors showed positive reception of the system in home settings [27]; here, we evaluated the system in the early days and weeks after stroke. The wearable system aims to promote high-repetition, graded-challenge activities (60 cues per 30 min session, three times per day) that can be tailored to the needs of individual survivors of hemiparetic stroke. The clinician-developed exercise program can be used by patients with a broad spectrum of upper limb impairments, including those who are moderately to severely impaired. This is important because many current interventions to address upper limb function poststroke, such as CIMT, cannot be performed with these individuals. This study captured user experience using validated quantitative surveys that measure motivations to engage with the system, perceptions of system usability, and experiences with the technology. Quantitative movement data were collected using motion trackers worn on both wrists to estimate compliance with the cue responses.
## 7 Translocation of Micro and Nanoplastics to Liver  
MP/NPs cross the intestinal barrier, travels through the bloodstream to the organs such as liver and kidney [125,173]. In the liver, oxidative stress determines enzymatic and metabolic changes [125]. Histologically, in the liver, MPs particles were detected in the cytoplasm of the hepatocytes ranged between 1416 and 1634 um, with moderate lipid-like vacuolar degeneration translated into an enlargement of the hepatocyte cytoplasm due to the presence of one or several vacuoles that displaced the nuclei to the periphery. Congestion, sinusoidal dilatation, glycogen-depletion [107] and cellular necrosis were also found [107,126]. Signs of inflammation and lipid accumulation or lipid profile changes were found in African catfish, _C. gariepinus_ by [PERSON] et al. [145]. MPs has been observed into the livers of _Engraulis encrasicolus, Sardina pilchardus_ and _Clupea harengus_ after degradation of the hepatic tissue and in the livers cryosections observed in polarized light microscopy [174]. The livers contained relatively large MPs that ranged from 124 um to 438 um. High levels of bisphenol A and analogous compounds in numerous fish species of commercial interest were found. The potential relationship between MPs and bisphenol contamination of fish was investigated [175]. Higher concentrations of bisphenols were found in fish with microplastic while they were absent in liver samples of fish without microplastics. It has been established that a large part of the quantities of microplastics ingested are translocated to fish liver [176]. Different opinion about the size of the microplastics that would be able to move into the intestine, from gut to circulatory system before further translocation to liver bare present in literature. Some authors refer that 20 \(\upmu\)m microplastics cannot translocate to fish liver in contrast to microplastics less than 5 \(\upmu\)m [125]. In zebrafish, 5 \(\upmu\)m diameter MPs accumulated in fish liver were found while 20 \(\upmu\)m diameter MPs were not found in the liver but only in the gills and intestines. Histopathological analysis showed inflammation and lipid accumulation in zebrafish liver also. Alterations of metabolic profiles in fish liver and alteration of lipid and energy metabolism were also found. However, the exact route through which MPs reach the liver is still unknown.
## 2 Methods
### Instantaneous Correlation Coefficient Method for Detecting R Peaks
#### 2.1.2 Instantaneous Correlation Coefficient  
Let \(x(n)\) be the measured ECG signal, with sampling frequency \(f_{s}\), signal length \(N\), and reference heart rate \(HR\). We scale the dimensionless heart beat model according to \(HR\) and resample according to \(f_{s}\), with \(h(k)\) denoting the reference heart beat, where \(k=1,2,\cdots,K\). \(K\) is the reference heart beat length.  
\[K=60f_{s}/HR \tag{1}\]  
Firstly, the baseline wandering of \(x(n)\) is removed and the high-frequency noise is pre-denoised; then, the instantaneous correlation coefficient \(R_{x}(n)\) is calculated.  
\[R_{x}(n)=\frac{\sum\limits_{k=1}^{K}x(n-K/2+k)h(k)}{\sqrt{\sum\limits_{k=1}^{ K}\left(x(n-K/2+k)\right)^{2}\sum\limits_{k=1}^{K}\left(h(k)\right)^{2}}} \tag{2}\]  
Figure 1: Dimensionless heart beat model.  
Here, \(n=1,2,\cdots,N\). When \(n-K/2+k\) is smaller than 1 or larger than \(N\), \(x(n-K/2+k)\) is equal to 0.
## 2 System Model
### Architectures for Forwarding NB-IoT Frames over a Satellite Relaying Node  
In the coming years, the feeder links of satellite systems will start to introduce optical wireless technology to cope with the capacity demand that new 5G/6G services will require, such a the OTA applications [6]. However, the throughput of the optical links can be increased even further by adding more advanced spectral-efficient communication techniques. Specifically, this paper considers that time-packing can be one the enablers to enlarge the end-to-end throughput of HTS systems. Nevertheless, the architecture of the optical receiver placed on the satellite payload is also important to increase the HTS throughput. Some of these architectures have been already considered in the deployment of non-terrestrial networks, resulting in bent-pipe and fully-regenerative satellite configurations [3]. However, this paper extends the possibilities of fully-regenerative satellites (i.e., Decode-and-Forward) by considering the partial regeneration of NB-IoT frames until the modulation level (i.e., Detect-and-Forward), and by considering the encapsulation of the NB-IoT modulated symbols in DVB-S2(X) frames. Specifically, this paper analyzes the throughput of the following satellite architectures, namely: (i) Case 1: NB-IoT modulated symbols detected at the satellite and forwarded to the corresponding IoT terminal; (ii) Case 2: NB-IoT frames decoded until the bit-level, re-encoded, re-mapped at the satellite, and re-sent to the IoT terminal; and (iii) Case 3: NB-IoT modulated symbols are encapsulated in DVB-S2(X) frames and re-sent to the IoT terminal (see Figure 3 for more details).  
Figure 3: Block diagram of the GEO satellite forward link for the three relaying configurations under analysis: (1) Detect-and-Forward with NB-IoT (green blocks with solid edges); (2) Decode-and-Forward with NB-IoT (orange blocks with dotted edges); and (3) Detect-and Forward with NB-IoT/DVB-S2(X) (blue blocks with dashed edges).
## 3 Solution Procedure
### Transfer Matrix Method  
The TMM is one of the most commonly used methods to deal with discontinuous dynamical systems, owing to its simplicity and accuracy in calculation. Herein, the TMM is also employed to calculate the BG regions, and the results are further compared with those by the SEM.  
According to the continuity between the \(n\)th and \((n+1)\)th cells, and that between segment A and segment B in the \((n+1)\)th cell of the periodic pipe, their deflection, slope, bending moment and shear force at the interfaces are equal, respectively, that is  
\[\begin{array}{cccccccc}\left[\begin{array}{cccc}w&\theta&M&Q\end{array} \right]_{n+1\mathbf{A}}^{\mathrm{T}}\left[\begin{array}{cccc}(0,t)\\ (0,t)\end{array}\right.=\left[\begin{array}{cccc}w&\theta&M&Q\end{array} \right]_{n,\mathbf{B}}^{\mathrm{T}}\left[\begin{array}{cccc}(1,t)\\ (1,t)\end{array}\right.,\\ \left[\begin{array}{cccc}w&\theta&M&Q\end{array}\right]_{n+1\mathbf{A}}^{ \mathrm{T}}\left[\begin{array}{cccc}(1,t)\\ (1,t)\end{array}\right.=\left[\begin{array}{cccc}w&\theta&M&Q\end{array} \right]_{n+1,\mathbf{B}}^{\mathrm{T}}\left[\begin{array}{cccc}(1,t)\\ (1,t)\end{array}\right..\end{array} \tag{31}\]  
Based on Equation (31), the amplitude relationship between adjacent cells can be described by the transfer matrix \(\mathbf{T}\), and consequently that between the first and \(n\)th cells can be determined by \(\mathrm{T}^{n-1}\), by which the frequency response and vibration shape can be obtained.  
In order to calculate the BG region, [PERSON]'s theorem is applied again, leading to the following eigenvalue problem  
\[\mathrm{det}\Big{(}\mathbf{T}-\mathrm{e}^{\mathrm{i}d/\mathrm{i}\mathrm{i} \mathrm{c}}\;\mathbf{I}\Big{)}=0, \tag{32}\]  
where \(\mathbf{I}\) is the identity matrix. Thus, the relationship between the incident frequency and wave vector can be determined.  
In terms of the above derivations, it is found that although in the SEM, the matrix \(\mathbf{S}_{n}\) shown in Equation (25) becomes complicated when the number of substructures gets high, the calculation and time consumed will be generally acceptable since it is essentially a large sparse matrix with most zero elements, which is a quasi-diagonal matrix. Actually, compared with other traditional methods, the SEM has superior characteristics. It is readily seen that by using the SEM, not only the BG characteristics but also the natural frequency and vibration mode of the PC pipe conveying fluid can be achieved. However, some other methods, e.g., the homogenization method and Galerkin method, can hardly solve the BG of such PC structures, while the TMM is difficult to treat the natural frequency and mode shape though the BG can be obtained.
## 3 Analysis by FEM and BEM  
To check the efficacy of LPM, the BAR is also analyzed by FEM and BEM. In the electromagnetic simulation part, the BAR is modeled with a different deformation, i.e., different displacement. Varying current is then input to the coil. The magnetic vector potential is defined as zero on the surface of the surrounded air box which is shown in Figure 9. Finally, the flux density is solved through FEM simulation. The electromagnetic FEM simulation governing equation is demonstrated by the following equation:  
\[\
abla\times\left(\frac{1}{\mu_{r}}\
abla\times\mathbf{A}\right)=\mathbf{J}\mu _{0}+\mu_{0}\
abla\times\mathbf{H}_{c} \tag{9}\]  
Figure 8: Equivalent circuit of BAR.  
Figure 7: Equivalent circuit of cavity.  
where **A** is the magnetic vector potential, **J** is the current density, **H\({}_{c}\)** is the permanent magnetic intensity, **\(\mu_{0}\)** is the permeability in the vacuum. After solving the governing equation, the flux density of every node in the modeling can be obtained. By post process, the force factor, inductance, and cogging force are obtained, which become the input of the vibro-acoustic simulation.  
The governing equation of the mechanical FEM simulation is listed in equation  
\[[M]\ddot{u}+[C]\dot{u}+[K]u=[F_{i}] \tag{10}\]  
where [\(M\)], [\(C\)], [\(K\)], [\(F_{i}\)], and u denote the mass matrix, damping matrix, stiffness matrix, vector of current force, and displacement. The surround part of the diaphragm is fixed which means that the displacement is zero. With a given input force, the displacement of every node can be solved.  
In the acoustic domain, the velocity of the tube surface is defined as zero, which means a rigid wall and is depicted in Figure 10. The sound pressure on the test point is solved by solving the Helmholtz governing equation.  
\[\
abla^{2}p-k^{2}p=-j\rho_{0}wq \tag{11}\]  
where \(p\), \(k\), \(\rho_{0}\), \(w\), and \(q\) are sound pressure, wave number air density, angular frequency, and volume velocity. The volume velocity is from the displacement result obtained in the mechanical FEM simulation. The details are outlined in Tables 2 and 3.  
Figure 10: Modeling of acoustic BEM simulation.  
Figure 9: Modeling of electromagnetic FEM simulation.  
Table 2 shows the simulation detail. ANSYS is taken advantage of in the electromagnetic and mechanical system and Virtual Lab is adopted to simulate the acoustic system. In order to obtain an accurate result, the numbers of nodes in the three systems are 146817, 525006, and 1329. Based on the same computer and the listed node number in Table 2, the computation time by FEM and BEM is 36 h while, the time by the LPM method is 1 hour. So the computation time is improved.
## 5 Experimental Results
### MTPA Trajectory  
To find MTPA trajectory and torque production at current references, under-load test was conducted on the condition where rotational speed of target motor is constantly controlled by dynamometer. After getting the system into steady state, the data of generated torque were measured from torque sensor at given d, q-axis current. The rotational speed was controlled to be 1000 r/min so as to minimize the attenuation in torque production caused by iron loss. Current references range from 1 to 20 A\({}_{\rm peak}\) (approximately equal to 14 A\({}_{\rm rms}\)) at an interval of 1 A\({}_{\rm peak}\). Measured current controlled by DSP is given in Figure 17 at 10, 15, and 20 A\({}_{\rm peak}\).  
MTPA trajectories verified from the experiment and FEA are shown in Figure 18a. Figure 18b summarizes the procedure of under-load test to find out the current angle of MTPA. Torque productions at 10, 15, and 20 A\({}_{\rm peak}\) are evaluated as 3.7 Nm, 5.5 Nm, and 7.18 Nm from FEA and 3.6 Nm, 5.4 Nm, and 6.98 Nm from the measured data. Errors are less than 2.87%. Since FEA did not take consideration of the attenuated torque production due to iron loss, torque production and current angle for maximum torque at each current reference are considered to coincide with design intent in Section 4.3. Efficiency was measured in range from 800 r/min to 8000 r/min and 1 Nm to 6 Nm. The highest efficiency was 94.7% at 3 Nm and 5000 r/min, and the lowest efficiency was 56% at 6 Nm and 800 r/min.  
Figure 16: Comparison between FEA results and measured results on back-EMF: (**a**) waveforms of phase voltage at 6000 r/min and (**b**) RMS values of fundamental magnitude at 1000–6000 r/min.  
Figure 17: Phase current in U-phase at 1000r/min: (**a**) 10 A\({}_{\rm peak}\), (**b**) 15 A\({}_{\rm peak}\), (**c**) 20 A\({}_{\rm peak}\).
# Slope Stability Prediction Based on Incremental Learning Bayesian Model and Literature Data Mining
## 4 Discussion
### Limitations  
The ILB model boasts strong classification performance, robust generalization, and high computational efficiency, yet it also has certain limitations. First, our study focuses solely on six variables influencing slope stability, whereas the actual factors affecting slope stability are more diverse than those considered here. Additional variables such as topography, precipitation, support structure type, seismic intensity, and wind speed could enhance the model's accuracy and reliability.  
Second, while there is no pronounced correlation among the six selected variables, a correlation coefficient of 0.499 between two variables indicates a departure from the assumption of variable independence in the naive Bayesian model. Future work should address this issue.  
Third, the dataset in our study was limited in both size and temporal sequence of slope cases, hindering the ILB classifier's performance. In future research, a larger, more balanced, temporally ordered database may improve the ILB model's classification efficacy.  
Last, our comparison only includes four batch learning models, which may not comprehensively assess the ILB model. Considering these models' calculation time constraints and limited hyperparameter search ranges, future studies should explore a wider range of representative batch learning models and use parameter optimization methods for a more rigorous and compelling comparison with the ILB model.
## 3 Methods
### Development of the MODIS Time-Share Two-Stage CI Product  
#### 3.2.1 Statistical Analysis of Phenological Stage Classification  
As a crucial structural parameter reflecting the degree of nonrandomness in the spatial distribution of canopy foliage, the CI, in theory, should exhibit distinct phenological variations. Previous studies have confirmed that the MODIS LSP product (MCD12Q2, V061) has high accuracy and good generalizability in monitoring land surface phenology [48; 49; 50; 51]. A diagram of the phenometrics retrieved for a single hypothetical vegetation cycle of the MODIS LSP product is shown in Figure 3. Therefore, in this study, a vegetation cycle was divided into four phenological stages based on the four phenological dates (greenup, midgreenup, midgreendown, and dormancy) provided by the MODIS LSP product: the leaf-off season (LFS, after dormancy and before greenup of the next vegetation cycle), the leaf-on season (LOS, from greenup to dormancy), the leaf-scattering season (LSS, from greenup to midgreenup and from midgreendown to dormancy), and the leaf-gathering season (LGS, from midgreenup to midgreendown), where the LOS includes the LSS and LGS. [PERSON] et al. estimated seasonal CI values via this classification method, which improved the high GPP estimates of the TL-LUE model to some extent [65]. In this study, we estimated the mean CI values of different IGBP classes for each phenological stage globally in 2020 and used one-way analysis of variance (ANOVA) and [PERSON]'s post hoc test to comparatively assess the variability of the mean CI values within each phenological stage, which in turn comprehensively considered the feasibility of dividing a vegetation cycle into two phenological stages (LFS and LOS) or three phenological stages (LFS, LSS and LGS). The results are expressed as the means \(\pm\) standard deviations. In addition, \"*\" indicates significant differences in the mean CIs for the same IGBP classes and different phenological stages (\" \(p<0.05\), \" \(p<0.01\), \" \(p<0.001\)).
# Analyzing Port State Control Data to Explore Future Improvements to GMDSS Training
## 4 Discussion
### Which Criterion Best Highlights the Worst Performance in Communications Safety of Inspected Ships?
Other studies confirm that tonnage [47] is significant for casualties as following a negative relationship (the smaller the vessel, the higher the risk). However, the GMDSS requires ships to carry various types of communications equipment depending upon the voyages of the ship rather than the gross tonnage. For this reason, this characteristic has not been taken into account in this paper. The importance of the ship age when evaluating the probability of detention is confirmed by several authors referring to different areas of inspection. Although some authors refer to the little relevance of the problems related to maritime radio communications [28] in detentions (with a correlation coefficient between 0.55 and 0.65), the importance of the findings of this study confirms this relationship between static risk factors and the probability of detention. Generic statements about the relationship between vessels 20 years and older and the deficiencies regarding radio communications had previously been made [52]. This is not the first time that this link between the probability of detention and port states has been detected [47; 53; 54; 55]. In turn, analyzing what is behind these specificities between countries in terms of detected deficiencies and detention of vessel probabilities within the Paris MoU ports can be attributed to two areas. The first is about the training and experience of the PSCO, and the second is about the local characteristics of the fleet, for example, ferry traffic connecting ports outside the Paris MoU with ports inside the memorandum. In the present work, it is interesting to note that of the total of 1667 ships involved, 244 were inspected more than once, with the same result of detention. When analyzing the proportion of these MoU port inspections, country by country, some significant data targets the Mediterranean ports (Table 13).