esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dataset Construction of Landslides in Yiyang City
### Extraction of Landslide Predisposing Factors
#### 3.2.2 Topographical Factors  
A 30 \(\times\) 30 m digital elevation model (DEM) is utilized to derive topographic factors, such as slope, aspect, and elevation, which are widely employed in landslide susceptibility analysis. In the study area, the terrain exhibits slopes ranging from 0\({}^{\circ}\) to 65\({}^{\circ}\), with over 50% of landslides occurring in areas characterized by slopes exceeding 15\({}^{\circ}\) and elevations below 200 m. Furthermore, plan curvature and profile curvature allow for the depiction of geometric features within the regional terrain, aiding in the identification of potential landslide areas. Therefore, these two factors are also given due consideration. Figure 5a-e display elevation, slope, aspect, plan curvature, and profile curvature, respectively, along with other terrain factors.  
Figure 4: Geological factors.
## 3 Nonlinear Dynamic Inversion Control Law  
NLDI is a control strategy that uses the model of a system to control it and through that eliminates the need for gain scheduling and improves performance. Consequently, NLDI controller is used for achieving high maneuverability associated with a high level of accuracy [6]. The attitude control is generally conducted using two loops: the first rotation control loop for fast-timescale state dynamics, and attitude control loop for slow-timescale state dynamics. The two-timescale NLDI approach used to achieve the desired angle of attack, side-slip angle and the desired bank angle \((\alpha_{c},\beta_{c},\phi_{c})\) are shown in Figure 3, which reduces the controller order to simplify the design process [20].  
The inner loop corresponds to the fast state dynamics roll, pitch and yaw angular rates \((p,\ q,\ r)\), which are controlled by control surface commands aileron, elevator, and rudder \((u=[\delta_{a},\ \delta_{e},\ \delta_{r}]^{T})\), respectively. The outer loop corresponds to the slow-state dynamics \((\alpha,\ \beta,\ \phi)\), which are controlled by angular rates \((p,q,r)\). Regarding the outer loop, the transient dynamics of the fast states occur quickly so that they have a negligible effect on the slow states. In the inner loop process, slow-timescale variables are assumed to remain constant. The fast loop controller attempts to track the desired angular rates \((p_{c},q_{c},r_{c})\).
# TDM-PON PAM Downstream Transmission for 25 Gbit/s and Beyond
## 1 Introduction
Upstream and downstream transmissions do not have the same constraints in term of cost and having a Mach-Zehnder modulator is not reasonable in each Optical Networks Unit (ONU) at the user premises. The complexity of the decoder also has an impact on the desired transmission direction. Furthermore, the choice of wavelength will determine the effect of the channel. For instance, chirp intensity modulation might not have the same effect with negative or positive chromatic dispersion. Here, we focus our study for downstream transmissions on a directly modulated laser which reduces the overall cost by sharing the optoelectronics at the Optical Line Terminal (OLT) but requires a linear electrical driver for the PAM4 signal. The complexity of the DSP and/or equalization techniques could prevent real-time applications in access networks due to the cost of the equipment and the potentially high entailed latencies (for instance, emerging from the processing time needed for a high number of equalization taps). In that sense, in the O band, low-cost, low-latency, high-throughput transmissions would better be reached with either PAM4 on 20-25 GHz optics or higher PAM formats such as PAM8 while reusing 10 GHz optics without DSP. In a previous work, we demonstrated a real-time 25 Gbit/s PAM4 downstream transmission based on 10 G optics, achieving a 29 dB Optical Budget (OB) corresponding to a \(-18.5\) dBm receiver sensitivity up to 40 km in O band [11]. We use our previous experimental work to fine-tune the proposed simulation model, and we then extrapolate the model results to assess higher bit rates by either adding more amplitude levels on the signal while keeping the same optical bandwidths or by using optics with higher bandwidths to achieve 37.5 Gbit/s and 50 Gbit/s throughputs, which are aligned with target bit rates currently investigated in the normative framework [3]. The novelty in this paper is the extrapolation of a real-time setup to higher bit rates and higher modulation format. This study leads to focusing on or excluding some future research schemes. Most importantly, we focus on keeping simple Intensity Modulation-Direct Detection (IMDD) without signal processing other than Forward Error Correction (FEC), which is essential to assure the interoperability of the physical layer in access networks while allowing low-cost customer premises equipment (CPE).
## 2 Why the Electroencephalogram (EEG)?
### qEEG and Vegetative Status/Autonomic Nervous Systems (ANS)  
Several studies have demonstrated the association between qEEGs and ANS [155, 156, 157, 158]. It seems that the brainstem mediates a functional coupling between the ANS and the central nervous system (CNS) assessed by qEEG [159, 160, 161]. A theoretical concept of the integration between the ANS and CNS was presented by Jennings and Coles [162]. The coordination and communication in and between the autonomic vegetative systems and the brain occur with tuned frequencies in the range of qEEG oscillations, suggesting the existence of resonant links in the brain with all organs of the body (for a review and discussion, see [5]; see also [163]). Basar [5] suggested that such mutual resonances form a coordinated dynamic system that maintains survival functions such as blood pressure, respiratory rhythms, cardiac pacemakers, and body temperature (see also [155, 156, 159, 160, 161, 162]).  
Since the dynamics of the physiologic variables (autonomic system) and the dynamics of brain activity depend on each other, it is reasonable to hypothesize that reduced variability in the activity of the neural networks should cause a concurrent decrease in the variability of autonomic physiologic functions. Indeed, it was demonstrated that a widespread brain injury that causes a derangement in neural networks leads to a reduced complexity of qEEG (measured by entropy, the dynamic repertoire of the probable qEEG states, and operational architectonics) [164, 165, 166] and reduced heart rate variability [167] in unresponsive patients compared to healthy subjects.  
It seems that decreased qEEG variability is coupled with a decrease in the variability of other physiologic variables (autonomic system), which results in reduced physiological adaptability. In turn, reduced physiological adaptability can contribute to stress and weakened immunity, which may further impact the qEEG pattern, creating a downward spiral.
# Mobility-Multihoming Duality
## 6 Results of Evaluation
### Transport Layer Multipath Connectivity
Note how evenly the traffic is distributed for TCP with ILThe issue of the scheduling behaviour for the MP-TCP transmission is a long-standing point of discussion and research. For example, different scheduling has been proposed to improve MP-TCP behaviour for web traffic [53], and yet there is another approach to make the scheduling of MP-TCP transmissions more suitable for mobile networks [54]. A further discussion is presented in Section 7.2. One of the other issues with MP-TCP is potential security threats. As described in [55], MP-TCP has several security threats, such as session hijacking. Therefore, the current application of MP-TCP has been limited to known good servers with known good clients, such as Apple's mobile platforms' notification delivery [56]. Overall, MP-TCP was designed with different objectives from ILNP. A primary focus of MP-TCP is _resource pooling_ at the transport layer to make the best use of capacity across multiple paths and specifically for applications that use TCP. However, ILNP works at the network layer to enable mobility-multihoming duality for _any_ transport layer protocol, not just TCP. Figure 16: _Cont._  
Figure 15: Box plot showing MP-TCP flow for 20 runs with no added delay on path. While the individual interfaces may exhibit ‘bursty’ behaviour due to the way multipath congestion control algorithm distributes traffic, it satisfies the target load requirement of 10 Mbps.
## 4 Results
### Post-Fire Regeneration within Burns  
We analyzed post-fire regeneration on burns located within the northern (continuous permafrost) and southern (discontinuous and patched permafrost) areas of the larch (_L. gmelini, L. sibrica_) range (study areas #1-3 in Figure 1).  
(a) Post-fire regeneration in the continuous permafrost zone (study area #1)  
Forests within study area #1 are formed of _Larix gmelini_. In this area, larch regenerated successfully within burns. The seedling population within burns was composed mostly of _L. gmelini_ with an admixture of birch (_Betula_ sp.) (Table 2). Siberian pine (_Pinus sibrica_) species occasionally populated burns which are located southward of the Arctic Circle. The amount of regeneration was dependent on the burn severity. Thus, if on-ground vegetation moss and lichen was mostly burned out, the number of larch seedlings may exceed 500,000 per ha (Figure 11d). Otherwise, the seedling population is considerably lower (3000 or less). Under the mother canopy, the number of seedlings is also considerably lower (ca. 600 or less specimen per ha (Figure 11a-c; Table 2)). Shrubs within burns are represented mostly by alder (_Duschekia fruticosa_), birch (_Betula_ spp.), and willow species (_Salix_ spp.). The population of small shrubs included _Vacciniium_ sp., _Empetrum_ sp., _Lydum_ sp., and some other species.  
* Post-fire regeneration at the south of the larch range (study area #2)  
The study area is located within the mid-mountain belt (1000-1600 m a.s.l.). Forests are represented by larch-dominant stands with an admixture of Siberian pine and spruce (_Pica obovata_). Observations were conducted within burns caused by surface fires (test sites number N = 32). Meanwhile, one observation was conducted within the burn that was caused by the crown fire. Generally crown fires are rare in the larch forests.  
Under the canopy of a mature larch-dominant forest, seedling composition was represented mostly by non-larch species, and the regeneration amount was low (Table 3). After fire, the major pioneer grass species within burns are fireweed (_Eplobium angusifolium_) (\"a fireweed burn\") and woodreed (_Calamagrostis_ sp.) (\"woodreed burn\"). The regeneration rate is significantly dependent on the type of burn. Thus, on the \"fireweed\" burn, the regeneration amount is strongly (by a factor of 15) increased and represented mostly by larch seedlings (ca. 90%). However, within the \"woodreed type\" burn, i.e., the area with a dense grass ground cover, the regeneration amount is negligible (Table 3).
## 4 Experiments and Results
### Obtaining a Feature Ranking from SA Algorithm  
As a first experiment, the EventTracker SA algorithm was run on the datasets S055, S075, S085, and S140 individually. We looked for indications of correlation among several SA algorithm runs.  
For example, Figure 11 shows the raw importance scores (SI as in Table 3), i.e., all Sensitivity Index values obtained for all features obtained from the channel C0108 (mdBit) and CodeOS labels. The correlation of SA importance values among several boreholes can be easily seen. Similar results have been observed for other channels and their deduced features. This motivates the assumption that sorted SA importance values, or, in other words, importance rankings, can be averaged over several boreholes. Thus, we calculate the mean normalized importance values for each of the 10 base channels and 6 base features and take the 2 most significant channels as \"SA-selected\" features for all of the following experiments. Table 7 shows the most and second-most important features selected by the SA algorithm for several boreholes.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**State Code** & **Comments** \\ \hline DrISld & Drilling Sliding \\ DrIRot & Rotary Drilling \\ MakeCN & (Dis)Connect a Drill String \\ CircHL & Mud Circulation in Borehole \\ MoveUP & Move Up Drill String \\ MoveDN & Move Down Drill String \\ WashUP & Move Up/w Circulation \\ WashDN & Move Down/w Circulation \\ CleanUP & Move Up/w Circulation \& Rotation \\ CleanDN & Move Down/w Circulation \& Rotation \\ \hline \hline \end{tabular}
\end{table}
Table 6: Possible Operational States Labels.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Well**} & \multicolumn{5}{c}{**Hole Depth, m**} & \multicolumn{5}{c}{**Bit Depth, m**} \\ \cline{2-10}  & \begin{tabular}{c} **Run** \\ **Description** \\ \end{tabular} & **Duration** & **Samples** & **Min** & **Max** & **Span** & **Min** & **Max** & **Span** \\ \hline TDL-S055 & \begin{tabular}{c} Run-5, \\ Drilling \\ \end{tabular} & 34.1 h & 12,281 & 1407.4 & 1775.8 & 368.4 & 16.6 & 1775.8 & 1759.1 \\ TDL-S075 & \begin{tabular}{c} Run-2, \\ Drilling \\ \end{tabular} & 35.5 h & 12,792 & 158.9 & 601.9 & 443.0 & 21.6 & 601.9 & 580.3 \\ TDL-S085 & \
\begin{tabular}{c} Run-3, \\ Drilling \\ \end{tabular} & 17.7 h & 6361 & 354.2 & 624.4 & 270.2 & 24.0 & 624.3 & 600.4 \\ TDL-S140 &
\begin{tabular}{c} Run-5, \\ Drilling \\ \end{tabular} & 36.7 h & 13,202 & 368.5 & 1229.0 & 860.5 & 21.3 & 1229.0 & 1207.7 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Data Subsets used for Feature Selection based on Neural Network Classification.
## 5 Results and Discussion
### Classification Experiments
As the model test time increases, ResNet exhibits a better accuracy. Ultimately, the highest accuracy was achieved using ConvNext's network structure, but at the same time, the model testing time was also the highest, as shown in Figure 25.  
Finally, we performed comparisons of the network structure in terms of accuracy, the model testing time and the number of parameters. The accuracy was obtained by averaging the recognition results for the combination of models using this module. This is shown in Table 2.  
Figure 19:0 CTC versus Attn. (Model testing time.) Figure 19:1 CTC versus Attn. (Number of parameters.) Figure 19:2 Comparison of feature extraction networks. (Number of parameters.) \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Stage** & **Model** & **ACC.\%** & \begin{tabular}{c} **Time** \\ **ms/Image** \\ \end{tabular} & \
\begin{tabular}{c} **Norm.ED** \\ \end{tabular} &
\begin{tabular}{c} **Params \(\times\) 10\({}^{6}\)** \\ \end{tabular} \\ \hline \multirow{4}{*}{Feat.} & VGG & 65.93 & 4.48 & 0.830 & 5.55 \\  & RCNN & 66.93(+1.00) & 8.72 & 0.835(+0.005) & 1.86 \\  & ResNet & 76.71(+10.78) & 7.23 & 0.905(+0.075) & 44.26 \\  & ConvNeXt & 78.13(+12.20) & 9.97 & 0.920(+0.090) & 44.26 \\ \hline \multirow{2}{*}{Seq.} & None & 63.08 & 6.85 & 0.810 & N/A \\  & BiLSTM & 80.77(+17.69) & 7.47 & 0.932(+0.122) & 2.89 \\ \hline \multirow{2}{*}{Pred.} & CTC & 58.01 & 4.24 & 0.795 & 0.01 \\  & Attn & 85.83(+27.82) & 10.96 & 0.950(+0.155) & 1.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The comparison of recognition accuracy, normalized edit distances, and number of parameters of the network. Figure 25: Comparison of feature extraction networks. (Model testing time.) We find that in the feature extraction phase, as the number of parameters and the testing time of the feature extraction network increase, the network becomes increasingly complex and the accuracy of recognition increases. On the other hand, although the recognition results using ResNet and ConvNeXt as the feature extraction network are better in terms of the accuracy of recognition, they are accompanied by sudden increases in the number of parameters and time. In the sequence modeling stage, using the BiLSTM network to process text features is much better than removing BiLSTM directly. Although [PERSON] et al. [10] suggested that removing BiLSTM would improve the network performance, in the case of Uyghur recognition, the processing of text features is quite important, and more attention needs to be paid to the long- and short-term memory. In the final prediction stage, using the attention mechanism can significantly increase the recognition accuracy with a minimal increase in the number of model parameters, and the convergence speed of prediction using the attention mechanism is much faster than that using CTC. This also illustrated in the fact that the Uyghur language requires more attention to the semantic information of the text due to its long words and sticky nature with similar characters.
# Multi-Lidar System Localization and Mapping with Online Calibration
## 2 Front-End Laser Odometry and Online Calibration
### Realization of Multi-Lidar Online Calibration
#### 2.2.1 Online External Parameter Calibration
Similarly, the distance between point \(i\) and plane \(j_{lm}\) is deducible by dividing the volume by the area of the bottom surface, resulting in the optimization equation for surface characteristics:  
\[d_{h}=\frac{\left|\left(X_{(k+1,i)}-X_{(k,j)}\right)\cdot\left(\left(X_{(k,j) }-X_{(k,l)}\right)\times\left(X_{(k,j)}-X_{(k,m)}\right)\right)\right|}{\left| \left(X_{(k,j)}-X_{(k,l)}\right)\times\left(X_{(k,j)}-X_{(k,m)}\right)\right|} \tag{8}\]  
Based on (7) and (8), the residual equation for feature association can be unified as follows:  
\[\text{loss}=\sum_{i=1}^{N_{c}}d_{ei}+\sum_{i=1}^{N_{h}}d_{hi}=D\left(\tilde{X} _{k+1,i}^{L}\right) \tag{9}\]  
The total loss function can be obtained by combining (6) and (9):  
\[d=F\left(\tilde{X}_{k+1,i}^{L},T_{k+1}^{L}\right)=D\left(G\left(\tilde{X}_{k+ 1,i}^{L},T_{k+1}^{L}\right)\right) \tag{10}\]  
Substituting into (11), we evaluate as follows:  
\[\Delta T=-\left(JJ^{T}+\lambda D^{T}D\right)^{-1}J^{T}d \tag{11}\]  
The gradient descent is then applied using the following equation:  
\[T_{k+1}^{L}\gets T_{k+1}^{L}-\left(JJ^{T}+\lambda D^{T}D\right)^{-1}J^{T}d \tag{12}\]  
Ultimately, the refined pose transformation matrix serves as an accurate extrinsic parameter matrix used to transform the feature point cloud of the auxiliary lidar, thereby yielding the calibration result.
## 4 Discussion  
The objective of this narrative review was to present the published cases of patients suffering from neuromuscular diseases characterized by physical fatigue and to evaluate the effect, positive or negative, of sporting activity on their medical condition. Decrementalresponse in RNS, a neurophysiological analogue of muscle fatigue, was the most frequently reported measure in the reviewed cases. This review elucidates two primary findings. Firstly, the total number of reported cases was very limited. Secondly, all documented cases reported successful participation in sports, with no adverse events described. To our knowledge, no other similar review has ever been published.  
Despite the limited medical literature on the subject of sports participation in cases where muscle fatigue was the main clinical symptom, some general inferences can be drawn. In MG, physical exercise appears to be safe, when appropriate precautions are followed. In myotonia, physical exercise appeared to be safe, and in most cases, it was the key to diagnosis. On the other hand, in MAD, exercise increases the risk of rhabdomylysis, and thus most patients are advised against it.  
The absence of any reported adverse events from sports participation in neuromuscular patients is a matter of debate. It could be argued that only positive outcomes (\"success stories\") find their way into publication. Furthermore, patients afflicted with more severe forms of the disease are unable to engage in sporting activities, let alone competitive ones.  
Decremental RNS is the neurophysiological analogue of transitory weakness occurring after exercise. However, the extent of the reduction does not necessarily correlate with the degree of fatigue. Decremental RNS is the hallmark of MG and other neuromuscular junction disorders, such as Lambert-Eaton myasthenic syndrome and Botulism [32]. In such instances, synaptic transmission is disrupted, occurring either at the pre- or post-synaptic level. Therefore, following repeated stimulation or prolonged exercise, fatigue ensues, thereby impeding patients' ability to sustain their activities.  
RNS is also often described abnormal in the rare cases of altered muscle membrane excitability. In cases on myotonia congenita, mild weakness appears transiently during rest after exercise, while these patients also show a decremental response in RNS. In myotonia, the decrementing response is not due to impaired neuromuscular transmission but due to prolonged after-depolarization attributed to potassium accumulation [34]. The same applies in cases of McArdle Disease, where potassium accumulates due to low concentrations of sodium-potassium ATPase pumps and subsequent depolarization of muscle membrane [35].
# Design of a Baseband Signal for the 406 MHz Satellite Emergency Radio Transmitter Based on STM32
###### Abstract  
The baseband signal of the 406 MHz COSPAS-SARSAT emergency beacon is a 144-bit or 112-bit data with 0 and 1 code, including the information bits and error correcting check bits. In order to improve the coding efficiency of the baseband signal for the 406 MHz transmitter in the STM32 micro-controller, this paper proposes a calculation method for the first BCH error correcting code (BCH-1) and the second BCH error correcting code (BCH-2) of the COSPAS-SARAST transmitter. First, conduct the data coding process by 64-bit integral data; then, compute the BCH (127, 106) code and the BCH (63, 51) code to obtain the respective error correcting codes for improving coding rate; next, generate the baseband signal according to the GB 14391-2021 coding format; finally, modulate the baseband signal into a 406 MHz-RF signal, send it out and complete the task of distress alerting. The experimental results show that the proposed STM32 microcontroller-based baseband signal generation method is effective, and the EPIRB (Emergency Position Indicating Radio Beacon) designed based on this method has reached the industry standard.  
C 2023, 12, 2717. [[https://doi.org/10.3390/electronics12122717](https://doi.org/10.3390/electronics12122717)]([https://doi.org/10.3390/electronics12122717](https://doi.org/10.3390/electronics12122717))
# Adsorption of Hexavalent Chromium Ions Using Pine Sawdust Cellulose Fibres
## 3 Results and Discussion
### Adsorption Studies
#### 3.8.4 Effect of Concentration  
Figure 14 presents the results of the adsorption capacity of untreated pine sawdust and cellulose fibres for Cr (VI) concentrations ranging from 20 to 100 mg/L. Through experimentation, the impact of chromium ions was investigated using diverse initial concentrations, which ranged from 20 to 100 mg/L. As observed in Figure 13, there was a rise in the adsorption capacity of both adsorbents at the outset. As the initial concentration increased, the adsorption reached equilibrium, probably due to the saturation of adsorption sites on the adsorbent surface [44]. The metal ions can bind at low concentrations with the vacant sites, resulting in maximum adsorption. Moreover, the removal efficiency is more significant when the initial concentration of the metal being removed is lower because the ratio of the available surface area to the initial concentration of the metal ion plays a more critical role [53; 54]. As a result, the concentration gradient was more favourable at higher  
Figure 13: Effect of time on hexavalent chromium ions’ adsorption by untreated pine sawdust (UPSD) and cellulose fibres (CF). (Adsorbent dose of 2 g/L, agitation speed of 200 rpm, pH of 6, and adsorbate concentration of 100 mg/L).  
concentration levels, which served as a strong driving force to overcome the mass transfer resistance of the metal ions between the solid and liquid phases [49].