esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Process Modeling
\[Q_{\rm p}=\sum_{i}^{n}Q_{\rm p_{i}}=\sum_{i}^{n}\left(A\ \cdot TCF \cdot FF\cdot SF_{\rm m_{i}}\cdot\left(p_{\rm f_{i}}-\frac{\mathrm{a}\cdot \left(\frac{Q_{\rm f_{i}}+Q_{\rm f_{i}}}{2}\right)^{\rm b}\cdot 0.07}{2}-p_{\rm p _{i}}-0.0787\cdot(273+T)\cdot\right.\\ \left.\left(\Sigma m_{\rm j}\right)_{\rm f}\cdot\frac{\frac{C_{\rm f _{i}}+C_{\rm b_{i}}}{2}}{\rm C_{\rm f_{i}}}\cdot\mathrm{e}^{0.7\cdot\frac{Q_{ \rm p_{i}}}{\frac{C_{\rm f_{i}}+C_{\rm b_{i}}}{2}}}{\rm 2}+0.0787\cdot(273+T)\cdot\left( \Sigma m_{\rm j}\right)_{\rm p}\right)\right) \tag{1}\]  
\[C_{\rm p_{i}}=B_{\rm j}\cdot\mathrm{e}^{0.7\cdot\left(\frac{Q_{\rm p_{i}}}{ \frac{Q_{\rm p_{i}}}{\frac{Q_{\rm p_{i}}}{2}}}\right)\cdot TCF\cdot\frac{S_{ \rm m}}{Q_{\rm p_{i}}}\cdot\frac{C_{\rm f_{i}}\cdot\left(1+\frac{1}{1-\frac{Q_ {\rm p}}{Q_{\rm f}}}\right)}{2} \tag{2}\]  
If \(T\geqslant 25\)\({}^{\circ}\)C:  
\[ TCF=\exp\left[2640\cdot\left(\frac{1}{298}-\frac{1}{273+T}\right)\right] \tag{3}\]  
If \(T\leqslant 25\)\({}^{\circ}\)C:  
\[ TCF=\exp\left[3020\cdot\left(\frac{1}{298}-\frac{1}{273+T}\right)\right], \tag{4}\]  
where \(Q_{\rm p}\) is the permeate flow of the RO system, \(i\) is the membrane element (\(1...n\)), \(n\) is the number of membrane elements in series, \(Q_{\rm p_{i}}\) is the permeate flow of the membrane element \(i\), \(A\) is the average water permeability coefficient of the membrane, \(TCF\) is the temperature correction factor, \(FF\) is the fouling factor (considered \(=1\), new SWMMs), \(S_{\rm m_{p}}\) is the membrane area, \(a\) and \(b\) are two parameters obtained experimentally to calculate the pressure drop, \(p_{\rm p_{i}}\) is the permeate pressure (considered as 5 psi), \(T\) is the feed temperature, \(m\) is the modal concentration of each ion j, \(C_{\rm b}\) is the concentration in the brine, \(B\) is the average ion permeability coefficient of the membrane. The \(p_{\rm f}\) range considered was between 7 and 20 bar in steps of 0.5 bar, and the \(Q_{\rm f}\) range between \(Q_{\rm r\mbox{-}min}\) and \(Q_{\rm f\mbox{-}max}\) in steps of 10 m\({}^{3}\) d\({}^{-1}\).
## 2 Related Work
More relaxed schedules have been presented, in [10; 11; 12; 44], under window-based implementation for each priority class. In [10; 44], the algorithms have been designed without the need for end systems to be fully synchronized. However, synchronization is still required for all selected switches in the path. [PERSON] et al. [11] proved that complete isolation between TT windows would reduce the available solution space for soft real-time streams. They analytically formulated the worst-case end-to-end latency for TT flows, resulting in an enhancement of the overall solution space compared with that in the full isolation scenario, while satisfying TT flow requirements. However, the authors just made the improvement based on a particular overlapping case without considering how to optimize the overlapping for every networking scenario and every targeted latency deadline. [PERSON] et al. [12] examined the preemption technique's effect on the latency bounds based on the algorithm [11]. However, the proposed scheduling designs, in [10; 11; 12; 44], were implemented under a single node design and did not consider the rational timing difference between the offsets for the same-priority queues in consecutive nodes [13]. These models resulted in high-pessimistic end-to-end latency bounds. Based on the above, [PERSON] et al. [13] improved the worst-case latency bounds for TT traffic by considering the relative positional relationships between the same-priority windows. The introduced results realized in a dramatic enhancement of the latency bounds and reduced the degree of pessimism in [11; 12]. However, the presented analysis did not include the overlapping situation among TT windows. Moreover, we observe that more latency correctness can be done on that model as the technical switching delay has been considered twice in the overall latency derivations. The first is included indirectly in the maximum waiting time calculation, and the second is added directly in the final end-to-end latency expression. Although this delay is considerably slight compared with other latency components, its impact is notable in large-scale network topologies. A summarized comparison between the above algorithms is illustrated in Table 1.  
For latency evaluation, analytical models are statically designed to cover all system corner cases, which are incredibly significant for real-time systems [26], especially in strict automated designs and automotive driving systems. Although there are several analytical approaches used to implement latency models, network calculus (NC) theory [45] is preferred in most studies as it induces less pessimistic and safe worst-case latencies compared to other approaches [46; 47]. Accordingly, we used the NC approach to calculate WCD bounds.
# Automatic Method for Bone Segmentation in Cone Beam Computed Tomography Data Set
## 2 Materials and Methods  
### Data Acquisition  
A retrospective study was performed by using 40 CBCT data sets from the database of the [PERSON] Orthognathic Surgery clinic. Before the study, all CBCT data sets were anonymised in order to protect the patients' data. Half of them (n = 20) were preoperative (obtained one week before the surgery) and another half (n = 20) were postoperative (obtained about one week after surgery) scans of the same patients' group. All scans were done using the i-CAT FLX V17 machine. All patientsundertook double jaws correction. CBCT data sets were acquired with a resolution of an isotropic voxel of 0.3 mm, 230 mm \(\times\) 170 mm field of view (FOV), the time of exposure was 7 s, tube voltage 120 kV and tube current 5 mA. Preoperative and postoperative CBCT data sets were segmented by an experienced oral and maxillofacial surgeon using ITK-SNAP software (Version 3.4.0) selecting a global threshold value for each case individually. Mandible and lower parts of skull (including maxilla, zygomatic bone) were used to perform the segmentation. Therefore, mentioned anatomical regions were selected as the segmentation target assessing the importance of them to perform a surgery. The study framework is presented in Figure 1.
# Effectiveness of Decision Support to Treat Complex Regional Pain Syndrome
## 1 Introduction
Despite countless rehabilitation protocols reported in current literature, at present, no universal treatment for all forms of CRPS emerged; rather, rehabilitation protocol should be tailored to each patient's symptoms and clinical phase to obtain the most from every approach. Again, treatments should be complementary and sequential to move patients away from each CRPS phase [17; 18; 19]. To date, suggested pain management approaches comprise mirror therapy for brain functional reprogramming, physical therapy with transcutaneous electrical nerve stimulation (TENS), pain desensitization techniques, and the pulsating electromagnetic field technique [15; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31]. Edema management involves compressing, bandaging, and therapeutic masses [32; 33; 34; 35; 36; 37]. Furthermore, functional re-education is commonly achieved with active hand mobilization, together with paraffin thermal therapy and custom-made braces with thermo-modeling materials for both static and dynamic use [35; 36; 37]. Given the complexity of CRPS clinical presentation and treatment, we developed a decision tree for deciding upon the most effective treatment strategy. Therefore, the primary goal of the present study is to propose and evaluate the effectiveness of the rehabilitation protocol for CRPS we employed for tailoring treatments to each patient. In particular, patients' dexterity, hand disability, edema, and pain severity were evaluated.
# The Subsea Micro-Drilling Vehicle's Dynamic Analysis during Landing
## 2 Landing Process and Establishment of MBD Model
### Analysis of the Effect of Landing Water Resistance  
The RecurDyn software only accounted for the free-fall height of a multi-body system subject to the force of gravity without considering other potential factors that could impact the descent. During the landing process of the SMDV, the combined effect of water resistance and buoyancy has a particular effect on the acceleration and landing impact of the SMDV when it lands on the substrate, so it is necessary to analyze and calculate the motion of the SMDV when it lands on the substrate. Considering the water resistance and buoyancy on the SMDV [26; 27], the kinetic equation of the SMDV landing process is given by:  
\[mg-(\frac{1}{2}C_{d}\rho v^{2}A+\rho gV)=m\frac{dv}{dt} \tag{3}\]  
The specific parameters in the formula are represented by Table 3, and this differential equation can solve the landing speed corresponding to a certain release height according to the initial conditions of the SMDV's release fall.  
When the track lands on the substrate, the landing speed is crucial in SMDV fall impact. The same landing velocity in free fall, referred to as the equivalent release height in this paper, can be obtained by applying the principle of equal fall velocity and solving the underwater fall velocity using the formula above. Figure 5 illustrates the relationship between the SMDV's landing velocity and the release fall height. The SMDV's landing speed is substantially slower than the free-fall speed, as indicated in the figure, and this difference becomes more and more noticeable as the release height increases due to buoyancy, particularly water resistance. Due to the influence of water resistance, the release height has a decreasingly significant impact on the landing velocity. When the release height reaches  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Data Type** & **Test Data** \\ \hline Soil cohesive deformation modulus (kN/mn + 1) & 3.12 \\ Soil frictional deformation modulus (kN/mn + 2) & 40.5 \\ Sinkage index & 0.53 \\ Cohesive force (KPa) & 12.715 \\ Shearing resistance angle (\({}^{\circ}\)) & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical parameters of the deep-sea sediment soil.  
4 m, the SMDV experiences zero vertical acceleration and reaches its maximum and stable falling velocity. The simulation software can apply this method to simulate the effect of hydrodynamic action on SMDV.
## 4 Methods
### The DOB Method  
This is proposed for estimating and canceling disturbance (as explained in Section 2) and is applied to both the translational and rotational motions of the cart. To utilize the disturbance observer, the terms in (4) and (6) are considered as time-persistent disturbances, i.e., \(\dot{d}_{T}=\dot{d}_{R}=0\). The low-pass filter plays a significant role in DOB, and it takes time to compensate for the disturbance due to the phase delay. While rotating on a slope, the disturbance varies rapidly as the cart rotates, resulting in the conditions \(\dot{d}_{T}\
eq 0\) and \(\dot{d}_{R}\
eq 0\). Thus, the movement of rotating on a slope was not covered in this study.  
Figure 8 shows a block diagram of the whole system, where \(P_{1}\), \(P_{2}\), \(Q_{1}\), and \(Q_{2}\) are the real plants and low-pass filters for the translational and rotational movements, respectively. Different from the traditional method, a low-pass filter of \(\frac{1}{0.1s+1}\) was used to make the inputs of the disturbance observer, such as force and velocity smoothing. Since it is impossible to measure the weights of the load and friction in daily life accurately, the parameters for the real plants \(P_{1}\) and \(P_{2}\) are unknown but limited. As mentioned in [34], the behavior of a real system can be made similar to the nominal one by using the disturbance observer. Apart from the time-persistent disturbances \(d_{T}\) and \(d_{R}\), the DOB method also estimates and cancels the difference between the real plant and the nominal one. The design of the low-pass filter in DOB is extremely significant, and the filter was chosen as  
\[Q_{*}(s)=\frac{1}{\tau_{*}s+1}\,\ \ast=1,2\, \tag{11}\]  
where \(\tau_{*}\) corresponds to the cut-off frequency (a small \(\tau_{*}\) leads to a fast response). It should be noted that a response that is too fast may make the user feel uncomfortable, and thus, different values of \(\tau_{1}\) and \(\tau_{2}\) for the translational and rotational movements need to be properly selected. In this study, the value of \(\tau_{1}\) brought about a faster response than that of \(\tau_{2}\).
## 3 Results
### The Effect of LT on PAPE
However, there was no significant difference in VJ performance at 4 min (ES, \(-\)0.03, 95% CI (\(-\)1.01, 1.34), \(p\) = 1.000) and 8 min (ES, \(-\)0.14, 95% CI (\(-\)0.23, 2.07), \(p\) = 0.467) after the 60 cm drop jump, while VJ performance of 12 min was significantly lower than the pre-test (ES, \(-\)0.21, 95% CI (\(-\)0.14, 2.97), \(p\) = 0.046), indicating that although the 60 cm drop jump failed to induce PAPE, performance was not affected by LT at 8 min and declined at 12 min. In summary, under LT conditions, PAPE was not induced by drop jumps of different heights. As shown in Table 3 and Figure 3a-c, compared with RT conditions, there was no significant difference in VJ performance before the 30 cm (\(p\) = 0.438), 60 cm (\(p\) = 1.000), and 90 cm (\(p\) = 1.000) drop jumps at LT conditions, indicating that the preparation activities at LT conditions achieved the same warm-up effect as the RT conditions. However, VJ performance after 4 min (30 cm, \(p\) = 0.003; 60 cm, \(p\) < 0.001; 90 cm, \(p\) < 0.001), 8 min (30 cm, \(p\) = 0.023; 60 cm, \(p\) < 0.001; 90 cm, \(p\) < 0.001), and 12 min (30 cm, \(p\) = 0.032; 60 cm, \(p\) = 0.001; 90 cm, \(p\) = 0.004) of the 30 cm, 60 cm, and 90 cm drop jumps were lower at RT conditions, indicating that LT can significantly affect the induction and maintenance of PAPE, with a decrease in VJ performance at all time points after the 30 cm, 60 cm, and 90 cm drop jumps.
# Sizing Methodology of a Fast Charger for Public Service Electric Vehicles Based on Supercapacitors
## 2 Materials and Methods  
### Electrical Analysis of an SC Working in a Fast-Charging System  
In this section, the study of the evolution of current in the charging station, as shown in Figure 2, will be presented. For this purpose, the RC series model of SCs will be used. This model is the most widely used for sizing [22; 23; 24; 25; 26; 27] and provides simple analytical expressions when studying constant power and charging or discharging processes [28; 29; 30]. The SC resistance and capacitance values are provided by the manufacturers from standardized tests. The SC bank operating as a generator shall be considered to have an internal resistance and capacitance of R\({}_{1}\) and C\({}_{1}\) (variable), and the vehicle SC array, operating as a load, shall be considered to have a resistance and capacitance of R\({}_{2}\) and C\({}_{2}\) (constant). An inductor must be installed between them to limit the current when the charger is switched on. The value of its internal resistance will be taken to be R\({}_{L}\). It will be assumed that the charger starts from a fixed initial voltage, U\({}_{01}\), and that the load starts from an initial voltage, U\({}_{02}\), which is lower than the previous one and can be variable, since the vehicle during its journey, depending on the number of passengers or the distance traveled, may have consumed more or less energy.
# Challenges Associated with Implementing 5G in Manufacturing
## 3 Use Cases of 5G in Manufacturing
### Artificial Intelligence (AI) and Fast Decision Making  
The best-run processing plants depend on immense information pools to decide--with inescapable deferrals as information is gathered, cleaned, and investigated. 5G accelerates the choice process duration, permitting enormous measures of data to be ingested, handled and actioned close to real-time. In a few substantial enterprises, for instance, producers have had the option to sell overabundance vitality back to the matrix when machines are not running, and costs are reasonable. 5G will permit the gushing of information progressively to the cloud, and the utilization of live video examination. For instance, a surveillance camera could see an unsettling influence, recognize if there is a fast-approaching risk or threat via AI, and dispatch an automaton or alarm a laborer to examine.  
Modern-day manufacturing is based on multiple technologies working together, which translates to the creation of large data sets being created. This large increase in data collection can be transmitted and analyzed faster with 5G's low latency coupled with its high bandwidth capacity. The expansion of multiple sensors to machine monitoring by industrial facilities is creating more information than in the past. Transmission through wired systems is costly compared to wireless networks. 5G can help by empowering the utilization of up-to-date information at scale [18]. While the production floor is the obvious point at which manufacturing can avail of the benefits of 5G there are also other examples of how 5G can contribute to the manufacturing environment in a non-direct manner. Examples of areas within an organization that could benefit indirectly are listed in Table 3.
# Applications of Nanomaterial Coatings in Solid-Phase Microextraction (SPME)
## 3 Range of Compounds to Which SPME Has Been Applied
### Food  
Evaluating nutritional value, guaranteeing freshness, processed products' quality control, and detecting dangerous pollutants and food additives all depend heavily on food analysis [30]. Analysis is difficult since dietary components constitute a complex matrix. Since analytes are frequently present in trace levels, an optimal approach should combine isolation, preconcentration, and precise quantification [23]. With its expandingapplications, SPME is becoming more and more significant in food sample pretreatment. Solid-phase extraction (SPE), Continuous-Steam Distillation Extraction (SDE), headspace (HS), Nitrogen Purge and Trap (NPT), and Supercritical Fluid Extraction (SFE) are some of the conventional extraction techniques over which SPME provides a number of clear advantages [31].  
[PERSON] et al. identified 44 chemicals in a study that used HS-SPME/GC and/or GC/MS to analyze 67 juices from mandarin, clementine, and hybrids. According to their findings, mandarin juice had higher levels of \(\gamma\)-terpinene (21.1%) and lower levels of limonene (66.3%) than clementine juice, which was highest in limonene (90.0%) and lowest in \(\gamma\)-terpinene (1.2%). The limonene/\(\gamma\)-terpinene ratios of hybrid juices varied, but their profiles were similar. According to their findings, 7% of hybrids displayed abnormal profiles because of para-cytene or terpinen-4-ol, 48% resembled clementine, and 45% were more like mandarin [32].  
Using cutting-edge methods (HS-SPME and GC-O (gas chromatography-olfactometry)), [PERSON] et al. investigated the effects of giving fresh silage to nursing cows prior to milking. They discovered that using butane-2-one and dimethyl sulfide levels as markers may result in milk having an unpleasant flavor. While [PERSON] et al. encouraged more research using absolute VOC concentrations, they also stated that these data might support a diagnostic tool as an alternative to sensory testing. They determined that rather than specific molecules, off-flavors are caused by changes in concentrations of common aromatic compounds (AACs), and they recommended more research to measure these AACs and look into the effects of bacteria on milk volatiles [33].
## 4 Discussion
In view of this, it is also important to highlight the value of employing public databases, since this allows the reproducibility of experiments by other researchers and facilitates direct comparisons (provided the same experimental setup). Also, while RQA systematically calculates specific recurrent patterns, combining it with other techniques like entropy and fractal analysis could provide complementary insights and enhance the characterization of nonlinear dynamics in OSA events. These methods can generate additional images, which, when combined with recurrence-based images, can be fed into DL models. Since DL has shown superior performance compared to traditional machine learning approaches, future research should explore this fusion of diverse visual representations to potentially improve the accuracy and comprehensiveness of OSA diagnostic models, ultimately benefiting clinical practice. Future WorkFuture work can explore advanced feature engineering techniques, such as higher-order statistical measures or nonlinear dynamics, to capture subtle physiological variations beyond standard HRV features, aligning with [PERSON] et al. 's 2021 insights [88]. Integrating multimodal data (e.g., EEG, PSG) offers potential to enhance the detection of subtle changes and broaden understanding of conditions like sleep apnea. Additionally, adopting deep learning architectures like attention mechanisms or transformers could improve the modeling of complex time-series dependencies. Efforts should also prioritize larger, more diverse datasets and robust preprocessing techniques to handle noise or artifacts, further enhancing model generalization and applicability in clinical and research contexts. Moreover, one of the challenges in DL-based approaches is the phenomenon of model degradation over extended periods of use. This issue arises due to changes in data distributions, sensor drift, or evolving application scenarios, which can lead to a decline in model performance. Even though the proposed method was developed and validated under the assumption of stable data characteristics exposed to CNN-based structures with no recurrent connections within the network, the proposed method is not completely exempt from this limitation. Thus, a potential solution to address this issue is the periodic re-training of the model with updated data, as suggested in related works. For instance, [PERSON] et al. explore re-training as a strategy to maintain model Ac over time [89]. In the context of our work, integrating such re-training strategies could improve the robustness and long-term applicability of our method, particularly in scenarios where signal properties may evolve or where datasets are expanded with new populations. Future studies could explore combining our synchronization framework with adaptive learning mechanisms, such as transfer learning or domain adaptation, to counteract the effects of model degradation. This would not only enhance performance but also ensure the reliability of the system in dynamic environments.