esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Control Algorithm Design
### Attitude Controller Design
#### 3.3.1 Initial Damping Phase  
Considering that the satellite may have uncertain angular momentum after separation from the vehicle (or losing attitude control for a long period of time), the satellite enters the initial damping phase first, during which the 3-axis MTQs work to slow down the 3-axis angular rates of the satellite.  
In the initial damping phase, the desired control torque \(\mathbf{T}_{c1}\in\Re^{3}\) outputted by the controller is given by the following equation:  
\[\mathbf{T}_{c1}=-K_{1}\mathbf{J}\mathbf{\omega}_{bi}^{b}+\mathbf{\omega}_{bi}^{b}\times\left(\mathbf{ J}\mathbf{\omega}_{bi}^{b}\right), \tag{14}\]  
where \(K_{1}\) is the control coefficient greater than zero. The first part of \(\mathbf{T}_{c1}\) is used to slow down the 3-axis angular rates of the satellite, and the second part is used to eliminate the effects of angular rate coupling of different axes.  
The desired control torque \(\mathbf{T}_{c1}\) will be outputted by the 3-axis MTQs, and then the objective of angular rate damping is realized. When the 3-axis angular rates are damped near \(0^{\circ}/\mathrm{s}\), the controller enters the next phase, namely the sun-aligned phase.
## 2 Materials and Methods
### Materials
#### 2.2.1 The Exponential Model  
In situations where the failure rate or intensity \(\lambda\) is constant, the exponential model is used, which, according to the \"bathtub curve\" model, begins at the end of the initial period and ends at the beginning of the final or aging stage, but can also be successfully applied in the modeling of the maintainability of mechanical systems, when the repair rate or intensity (\(\mu\)) can be considered constant.  
For the random variable time, T, with \(t\geq 0\), the probability density of the operating time until failure is:  
\[f(t;\lambda)=\lambda\cdot exp(-\lambda\cdot t), \tag{1}\]  
where \(t>0\) is the distribution parameter.  
The distribution function F(\(t;\lambda\)), namely, the probability of product failure in a time interval (0, \(t\)), will be:  
\[F(t;\lambda)=1-exp(-\lambda\cdot t). \tag{2}\]  
The probability that the product will not fail in the time period (0, \(t\)) will be:  
\[R(t;\lambda)=exp(-\lambda\cdot t). \tag{3}\]  
The failure rate or failure intensity, \(z(t)\), will be:  
\[z(t;\lambda)=\lambda=ct. \tag{4}\]  
The mean value of this distribution, respectively the mean time to failure, will be:  
\[m=MTBF=\frac{1}{\lambda}. \tag{5}\]
# Primary User Traffic Pattern Based Opportunistic Spectrum Handoff in Cognitive Radio Networks
## 3 PUTPOSH Model
### Framework  
Figure 1 shows the proposed primary user traffic pattern based opportunistic spectrum handoff framework. The proposed design contains three different parts; (i) spectrum sensing, (ii) spectrum mobility management, and (iii) spectrum handoff decision. These are connected in Figure 1 and also described in the following subsections respectively.
# Emerging Biomedical Applications of Carbon Dot and Polymer Composite Materials
## 4 Emerging Applications of Carbon Dots and Their Composite Materials
### Treatments of Neurodegenerative Disease  
Neurodegenerative diseases (NDDs) are significant problems that affect movement, coordination, speech, and other organ functions that receive nerve supply [110]. Scientists have extensively studied the pathobiology and diagnosis of NDDs. However, this treatment remains challenging [111]. CDs have recently been employed for the treatment and diagnosis of NDDs. The blood-brain barrier (BBB) is a key obstacle in the delivery of drugs to the brain. A group of researchers was able to breach the BBB using CDs synthesized by pyrolysis from \(\mathrm{D}\)-glucose and \(\mathrm{L}\)-aspartic acid [112, 113]. Fluorescent-glucose CDs produced intriguing findings in vitro and in vivo, indicating a bright future for CDs as drug delivery systems for brain imaging and therapy. In zebrafish and rat models, GluCDs traverse the BBB without the need for conjugation to a specific ligand for receptor-mediated transport. GluCDs can transport cargo to the CNS, as illustrated in Figure 6[114]. Furthermore, the absorption of GluCDs or glucose is critical in yeast models [114]. CDs derived from sodium citrate have also been utilized to treat Alzheimer's disease, a disease in which amyloid cells, which play a crucial role in neuronal development and repair, are severely damaged [115]. In addition, high-oxygen-content CDs obtained from \(\mathrm{L}\)- and \(\mathrm{D}\)-cysteine are able to suppress the protein fibrillation that may prevent the fibril formation associated with neurodegenerative diseases [116]. CD/polymer composites have also been used to treat neurological problems. For instance, CDs generated from chitosan by carbonization were coupled with chitosan polymers to prepare dopamine-encapsulated CD/chitosan nanoparticles (NPs). CD/chitosan NPs were used as drug depots with sustained release capabilities of dopamine in neurological illness [117].  
Figure 5: The possible role of carbon dots (CDs) in counterfeit detection. The Figure was created with BioRender.com (accessed on 1 September 2022).
# Benefits of Auditory Training with an Open-Set Sentences-in-Babble-Noise
###### Abstract  
Auditory training (AT) has limited generalization to non-trained stimuli. Therefore, in the current study, we tested the effect of stimuli similar to that used in daily life: sentences in background noise. The sample consisted of 15 Hebrew-speaking adults aged 61-88 years with bilateral hearing impairment who engaged in computerized auditory training at home four times per week over a two-month period. Significant improvements were observed in sentences comprehension (Hebrew AzBio (HeBio) sentences test) with both four-talker-babble-noise (4 TBN) and speech-shaped-noise (SSN) and in words comprehension (consonant-vowel-consonant (CVC) words test), following one month of AT. These improvements were sustained for two months after completing the AT. No evidence of spontaneous learning was observed in the month preceding training, nor was there an additional training effect in the additional month. Participants' baseline speech perception abilities predicted their post-training speech perception improvements in the generalization tasks. The findings suggest that top-down generalization occurs from sentences to words and from babble noise to SSN and quiet conditions. Consequently, synthetic training tasks focusing on sentence-level comprehension accompanied by multi-talker babble noise should be prioritized. Moreover, an individualized approach to AT has demonstrated effectiveness and should be considered in both clinical and research settings.  
auditory training hearing impairment; aging; speech perception; generalization
## 5 Simulation Results and Analysis
### Analysis and Interpretation of Simulation Results  
#### 5.2.1 Movement Velocity Analysis  
(1) Overall flow velocity: The simulated mudslide velocities exhibited relative stability across different scenarios. Under typical rainfall frequencies, velocities within the tributary gully generally ranged from 0 to 1.5 m/s, while velocities in the upstream section of the main gully, characterized by a steeper gradient, ranged from 0 to 3 m/s. Peak velocities were observed at the confluence of the tributary and main gullies. Downstream, velocities decreased as the terrain gradient flattened, ultimately leading to deposition at the gully outlet.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Section**} & \multirow{2}{*}{
\begin{tabular}{c} **Catchment** \\ **Area (km\({}^{2}\))** \\ \end{tabular} } & \multicolumn{3}{c}{**Rainstorm Intensity (mm/h)**} & \multicolumn{3}{c}{**Peak Flood Discharge (m\({}^{3}\)/s)**} \\ \cline{3-8}  & & \(p\) = 1\% & \(p\) = 2\% & \(p\) = 3\% & \(p\) = 1\% & \(p\) = 2\% & \(p\) = 3\% \\ \hline Main gully & \multirow{2}{*}{0.179062} & \multirow{2}{*}{125} & \multirow{2}{*}{115} & \multirow{2}{*}{100} & \multirow{2}{*}{1.87} & \multirow{2}{*}{1.72} & \multirow{2}{*}{1.49} \\ catchment & & & & & & & \\ \hline Tributary gully & \multirow{2}{*}{0.10916} & \multirow{2}{*}{125} & \multirow{2}{*}{115} & \multirow{2}{*}{100} & \multirow{2}{*}{1.14} & \multirow{2}{*}{1.05} & \multirow{2}{*}{0.91} \\ catchment & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Calculation of peak flood discharge (\(Qp\)) in the main gully and tributary gullies for various rainfall frequencies.  
(2) Influence of rainfall intensity: Both mean and peak mudslide velocities increased slightly with increasing rainfall intensity. Mudslide velocity is primarily controlled by equally morphology and rainfall conditions. While increases in rainfall intensity resulted in only minor increases in flow velocity, the extended duration of high-velocity flow led to a significant increase in sediment transport capacity, thereby enhancing the destructive potential of the mudslides. Simultaneously, downward and lateral erosion deepened and widened the guilty, respectively, replenishing the sediment supply for future mudslide events and further increasing their magnitude and destructive capacity.
# Interconnecting Haptic Interfaces with High Update Rates through the Internet +
## 7 Conclusions  
The interconnection of haptic interfaces with high update rate through the Internet is feasible. Haptic data should be transferred by specific timely delivery protocols. Haptic transport protocols should be standardized. Strict QoS requirements should be enforced by Internet Service Providers in order to transfer the haptic feeling over the internet. Simulation and real world experiments revealed which transport protocols should be used for real-time haptic data delivery with high update rates over the Internet. Real-time, lightweight, unreliable, protocols outperform heavyweight reliable protocols. UDP, RTP and ETP protocols are more suitable for interconnecting haptic interfaces than the TCP, the DCCP and the SCTP protocol.  
Conceptualization, G.K., K.E.P., M.R., and Y.I.; Methodology, G.K., S.K., and P.N.; Software, G.K., S.K.; Validation, M.R., K.E.P., and P.N.; Formal Analysis, G.K., and S.K.; Investigation, G.G., and K.E.P.; Resources, K.E.P., P.N., and Y.I.; Data Curation, G.K., and S.K.; Writing-Original Draft Preparation, G.K., and S.K.; Writing-Review & Editing, K.E.P., M.R., and Y.I.; Visualization, G.K., and P.N.; Supervision, K.E.P., Y.I., M.R., and P.N.; Project Administration, G.K., K.E.P., and M.N.  
This research received no external funding.  
The authors declare no conflict of interest.
## 4 Game Clustering Model
### Utility Function  
The game model includes two strategies, \(D\) and \(ND\), and the probabilities of choosing strategy \(D\) and \(ND\) for a node are assigned as \(P\) and \(1-P\), respectively. We assume that the cluster that node \(i\) locates at has \(N_{m}\) nodes, each node competes with other \(N_{m-1}\) nodes, and the cluster payoff relies on the number of choices of strategy \(D\). If strategy \(ND\) is selected for all \(N\) nodes, the benefit for all nodes is 0. Suppose at least one node chooses strategy \(D\) and the income of the node is \(V(i)-C_{CH}(i)\), then the number of nodes that select strategy \(ND\) is \(N_{m-1}\) and the revenue of each node is \(V(i)-C_{CM}(i)\). The total payoff of these \(N_{m-1}\) nodes can be expressed as follows:  
\[U_{ND}(i)=[V(i)-C_{CM}(i)]\Big{[}1-(1-p)^{N_{m}-1}\Big{]} \tag{18}\]  
Furthermore, the average income of node \(i\) is obtained by  
\[\overline{U(i)} =p\times U_{D}(i)+(1-p)\times U_{ND}(i) \tag{19}\] \[=p[V(i)-C_{CH}(i)]+(1-p)[V(i)-C_{CM}(i)]\Big{[}1-(1-p)^{N_{m}-1} \Big{]}\]Subsequently, \(\overline{U(i)}^{\prime}\) is achieved by taking the derivative of \(\overline{U(i)}\) to compute the maximum benefit as follows  
\[\overline{U(i)}^{\prime}=C_{CM}(i)-C_{CH}(i)+N_{m}[V(i)-C_{CM}(i)](1-p)^{N_{m}-1 }=0 \tag{20}\]  
\[p_{i}^{*}=1-\left(\frac{C_{CH}(i)-C_{CM}(i)}{N_{m}[V(i)-C_{CM}(i)]}\right)^{ \frac{1}{N_{m}-1}} \tag{21}\]  
where \(p_{i}^{*}\) is the optimal probability function for node \(i\) to select strategy \(D\).
## 3 Millimeter Wave Link Budget  
Following the Friis line of sight transmission formula [3], incorporating atmospheric losses, the overall link budget can be expressed by:  
\[\frac{P_{r}}{P_{t}}=\left|S_{2,1}\right|^{2}=G_{r}\left(\frac{c}{4\pi d\cdot f} \right)^{2}\cdot G_{t}\cdot\frac{1}{L(f)} \tag{9}\]  
where \(P_{r}\) and \(P_{t}\) are the received and transmitted power, respectively, \(G_{r}\) and \(G_{t}\) are the gains of the transmitting and receiving antennas, \(c\) is the light speed, and \(d\) is the propagation distance. The graph in Figure 2 presents the power ratio Pr/Pt as a function of frequency for distance of 1 km. Here, Omni directional antennas are assumed (\(G_{r}=G_{t}=0\) dBi), and multiple levels of visibilities are considered. The graph in Figure 3 presents the power ratio Pr/Pt as a function of distance d for a link operating at a frequency of 28 GHz, in several visibility scenarios.  
Figure 2: Graphs of the power ratio Pr/Pt as a function of frequency expected at distance of 1 km, assuming omni-directional antennas for different visibility levels.
# Prediction Interval Adjustment for Load-Forecasting using Machine Learning
## 3 Materials and Methods
### Prediction Interval Estimation  
A prediction interval (PI) is the estimation of a range in which a load value will fall with a certain probability [21]. PI estimation is an important part of a forecasting process and it is intended to indicate the expected uncertainty of a point forecast. Also, PIs allows us to offer a set of values in which a future value will fall given a probability, thus, creating a probabilistic forecast result. The following is a general form of a \(100(1-\alpha)\%\) confidence prediction interval expression:  
\[\hat{V_{t}}(p,h)\pm z_{\alpha}\sqrt{Var[\epsilon(p,h)]} \tag{4}\]  
where \(\hat{V_{t}}(p,h)\) is the point forecast of the period \(p\) in the horizon \(h\), \(z_{\alpha}\) is the \(z\)-_score_ of an empirical distribution given the probability \(100(1-\alpha)\%\) and \(\epsilon(p,h)\) is the empirical distribution of errors of the forecast method in the period \(p\) and horizon \(h\). In Equation (4), the \(z\)-_score_ is the parameter that allows us to modify the prediction interval coverage [22]. In Figure 5, an example of how the \(z\)-_score_ modifies the prediction interval coverage.  
Figure 4: Graphical representation of a delayed time series.  
It is worth mentioning that \(z\)-_score_ value depends on the \(\alpha\) value. Specifically, \(z\)-_score_\(=Z(100(1-\alpha))\) where \(Z\) is a function that estimates the \(z\)-_score_ value.  
Prediction interval estimation using Equation (4) requires estimation of a set of prediction errors of a forecast model. In this paper, we use Artificial Neural Networks to generate a prediction model for each period of \(p\).
## 4 Simulation and Prediction Methods
### Finite Element Model
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Natural Frequencies/Hz**} & \multicolumn{4}{c}{**Deflections of Gider/cm**} & \multicolumn{6}{c}{**Longitudinal Stress of CSS/MPa**} \\ \cline{3-13}  & & & & & & \multicolumn{3}{c}{**Upper Plate**} & \multicolumn{3}{c}{**Bottom Plate**} \\ \hline
**Mode Shapes** & **Simulation** & **Measurement** & **Sections** & **Simulation** & **Measurement** & **Test Points** & **Simulation** & **Measurement** & **Test Points** & **Simulation** & **Measurement** \\ \hline V1 & 0.757 & 0.756 & CSE & \(-1.02\) & \(-0.9\) & SU1 & \(-5.41\) & \(-4.11\) & SD1 & \(-5.78\) & \(-6.14\) \\ V2 & 1.274 & 1.318 & CS4 & 0.93 & 0.9 & SU2 & \(-5.37\) & \(-4.01\) & SD2 & \(-5.81\) & \(-4.28\) \\ V3 & 1.464 & 1.465 & CSS & 1.32 & 1.2 & SU3 & \(-5.32\) & \(-5.38\) & SD3 & \(-5.87\) & \(-4.90\) \\ T1 & 1.256 & 1.245 & CS6 & 0.95 & 1.0 & SU4 & \(-5.27\) & \(-4.72\) & SD4 & \(-5.92\) & \(-5.87\) \\ T2 & 1.421 & 1.392 & CS8 & \(-1.05\) & \(-0.8\) & & & & & & \\ T3 & 1.474 & 1.489 & & & & & & & & \\ \hline \multicolumn{13}{c}{Notes: \(V-\)Vertical bending mode shape: \(V-\)T\(-\)Terosial mode shape: \(V+-\)Tensile stress/Upward displacement: \(V-\)\(-\)Compressive stress/Downward displacement.} \\ \hline \hline \end{tabular} Notes: \(V-\)Vertical bending mode shape: \(V-\)T\(-\)Terosial mode shape: \(V+-\)Tensile stress/Upward displacement: \(V-\)\(-\)\(-\)Compressive stress/Downward displacement.  
\end{table} 
Table 1: Verification of the finite element model.
# Global Regulatory Challenges for Medical Devices: Impact on Innovation and Market Access
## CE Marking
### Unique Device Identification  
The Unique Device Identification (UDI) system is a standardized identification mechanism designed to enhance the traceability and monitoring of medical devices within the EU. Introduced under Regulation (EU) 2017/745, the UDI system aims to improve patient safety and simplify the management of medical devices throughout their lifecycle. It represents a comprehensive approach to enhancing the traceability, safety and regulatory compliance of medical devices within the EU, aligning with international standards and promoting efficient healthcare management [11].  
The UDI must be placed on the device label or on its packaging, ensuring it is visible and easily identifiable. It is an alphanumeric code that includes standardized information, enabling the unique identification of each medical device in the market. It consists of two parts: the UDI Device Identifier (UDI-DI) and the UDI Production Identifier (UDI-PI). The UDI-DI includes details about the manufacturer and the device model, which remain consistent across all devices of the same model. When multiple individual units of a medical device are packed together in a larger box or container, each higher level of packaging must possess its own UDI-DI, except for shipping containers, which are exempt from this requirement [48, 49]. The UDI-PI contains information specific to each unit of device production, such as the serial number, batch number, manufacturing and/or expiration date and software identifier. This part of the code varies for each individual unit produced [49]. Figure 5 provides an example of an UDI in the EU.
## 2 Geological Background and Sampling  
### Geological Background  
The study area is located in the Ningzhen region, which is part of LYRMB in eastern China. The LYRMB is one of the most important metallogenic belts in China, containing over 200 Cu-Fe-Au-S deposits. This belt comprises the eastern portion of the Yangtze block in central-eastern China and spans approximately 600 km from Hubei Province in the west to Jiangsu Province in the east. The LYRMB includes a Paleozoic basement and a sequence of Mesozoic sedimentary rocks [27]. The exposed stratigraphic sequences range from Silurian to Triassic and consist of shallow marine sandstones, shales, quasi-molasse and lacustrine sediments. Cretaceous volcanic rocks overlay the sediments, and the region was subsequently covered with red-bed clastic sedimentary rocks, with minor basalt.  
Magmatic rocks are well-developed in this region, mainly forming during the Yanshanian period ([PERSON] et al., 2022). As shown in Figure 1, moving from west to east, these ore clusters are: (1) Edong Cu-Fe-Au district, (2) Jiurui Cu-Fe-Au-W district, (3) Anqing Guichi Cu-Mo-Au district, (4) Luzong Fe-Cu district, (5) Tongling Cu-Au-Fe-S district, (6) Nanjing-Xuancheng district ([PERSON] et al., 2022), (7) Ningwu Fe district and (8) Ningzhen Cu-Mo-Fe-W-Pb-Zn district. Most of the deposits are associated with Yanshanian magmatic rocks and are skarn-porphyriny, strata-bound type deposits. The Tongshan skarn Cu-Mo deposit is located in the Ningzhen region. The structural framework of the Ningzhen region comprises three anticlines and two synclines in the west-east direction. The magmatic rocks in this region are primarily early Cretaceous, 20 Ma younger than other ore regions in LYRMB [28]. There are two skarn-porphyriny type Cu-Mo deposits, e.g., Anjishan medium-sized porphyy Cu deposit and Tongshan skarn Cu-Mo deposit (Figure 2). The magmatic rocks in this district are mainly quartz diorite, named Anjishan, Funiushan, Xiyingkeng, Tongshan, Hongshuidang, and Xiashu plutons.
## 2 Applications  
In the last decade, researchers have presented numerous practical and potential IoUT applications [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29]. We classify the applications into five types: (1) environmental monitoring; (2) underwater exploration; (3) disaster prevention; (4) military; and (5) others (Figure 2).
## 3 Results  
Tables 1 and 2 show the patients' demographics, background, US findings, and treatment results. Table 1 describes the characteristics of the patients, US findings, and treatment results. Table 2 shows the results of the statistical analysis. The study included 17 children, of whom 12 (70%) were boys and 5 (30%) were girls. The mean age of the children at the time of the procedure was 6.7 years and ranged from 3 to 16. All the children were generally healthy; 1 had asthma. The affected parotid gland was the left in seven children (41%), right in nine children (53%), and bilateral in one child (6%). A pre-operative US was performed in 21 glands, 14 of which were symptomatic and 7 asymptomatic. Typical US findings were of a heterogenic appearance and strictures or dilation of the ducts. Most of the children were treated with sialoendoscopy only once. Only two children were treated twice, due to a continuation of symptoms, and one child was treated with 4 endoscopies. The majority, 15 of the 17 children, were treated under general anesthesia. Treatment under local anesthesia was performed on two children, aged 13 and 16 years. The average follow-up time was 45.17 months and ranged from 5 to 81 months. Complications were not seen in any of the patients. Immediate short-term post-operative swelling of the parotid gland was expected and considered an innocent surgical sequel that resolved spontaneously and was not counted as a complication. All the patients were discharged on the same day as the procedure. A full resolution was defined as a complete lack of symptoms after the sialoendoscopy and was seen in 10 children (59%). One of these patients had a single event of swelling, 70 months after the endoscopy procedure. Due to the long period of time that had passed, this patient's treatment result was defined as a full resolution. Full resolution with episodes of pain but without any swelling was seen in an additional two patients. Full resolution with a single event of swelling was seen in two additional patients, one of whom suffered from a single event of swelling 11 months after the procedure, and the second patient suffered from a single swelling event 1 month after the procedure, which was accounted as a natural post-operative course, and another single event of swelling 4 months after the procedures. One patient is still suffering from recurrent swellings, yet at a smaller frequency, and the parents report a subjective improvement and are not interested in additional treatment. All the above-mentioned patients, a total of 15 children (88%), are considered to have a successful treatment result.