esa-sceva/satcom-chunk-collection huggingface dataset:
# SMM: Leveraging Metadata for Contextually Salient Multi-Variate Motif Discovery
## 2 Related Works
### Multi-variate Motifs  
The state-of-the-art approaches for multi-variate (multi-dimensional) motif discovery often belong to one of two categories. (a) _Symbolization:_ For searching uni-variate motifs, first convert the input time series data into a symbolic form and then apply symbolic mining techniques to discover motifs [18]. This can also be extended to multi-variate series by applying symbolization onto vectors. In [6], the multi-variate time series is first projected into a single dimension using PCA; then, the SAX representation is extracted and, finally, the motifs on this uni-variate series are discovered using minimum description language (MDL [26]). (b) _Direct search_ avoids the symbolization process and motifs are searched directly on the time series data. In [27], once single variate motifs are identified, multi-variate motifs are constructed combining those single variate motifs based on their overlaps in time and variates. In [6; 28; 29], the authors leverage logical (AND and OR) operators to concatenate collision matrices extracted from sub-sequence matching in each variate. This helps discover motifs of same length, but has poor accuracy if a pattern can be stretched or contracted. Other approaches include _similarity matrix-based approaches_ and _density-estimation-based techniques_[30; 31].  
Motif search in sub-spaces is an expensive process, as the potential number of subspaces in which a pattern can occur increases exponentially with the number of variates. A possible simplification of the multi-variate motif search is to seek concurrent motifs across all dimensions. In [7], the motif grouping process is based on a predefined temporal proximity using neighbor distance. After the identification of neighbor data points using scope information along the temporal dimension, motifs computed from these neighbor points are stored in a _motif bag_ (treated as potential multi-dimensional motifs). An alternative model is to track recurring groups of temporal relations among co-occurring motifinstances where SAX is first applied on each dimension, followed by a grammar induction approach to identify re-occurring patterns [8].  
In [9], the concept of matrix profiles introduced in [25] was extended to multi-variate time series. The algorithm essentially constructs matrix profiles separately for each variate; then, it clusters, in a bottom-up fashion, temporally overlapping pairs of sub-sequences across different variates to find the most similar multi-variate subsequences in the data. While this algorithm is quite efficient, it has several limitations. Firstly, the algorithm returns pairs of similar sub-sequences and, to identify the most frequent patterns, one still has to do additional post-processing. Secondly, the algorithm takes the length of the sub-sequence as given; further, it is limited with patterns that are of the same length and are re-occurring on the same set of variates.
## 1 Introduction  
External fixation is one method of treatment for fractures, especially for patients who are at high risk for systematic complications [1]. They are frequently applied for combat and disaster related casualties [2; 3; 4], but also to manage bone defects [5]. Since World War II, design and wearing comfort of the external fixators has been improved significantly. The main biomechanical principle of external fixation remains almost the same, i.e., providing sufficient stability at the fracture focus [6], but nowadays it is well known that micromovements in bone callus are also important to the bone consolidation process [7; 8; 9; 10]. Hence, the challenge for external fixation research on fracture healing is to improve the biomechanics of fracture fixation, and therefore, after good initial fixation stability, a fracture heals as fast as possible. In fact, fixator stability and stiffness are very important since they influence not only the amount of interframed movement (IFM) and alignment at fracture focus, but also affect pin-screw loosening [11; 12; 1].  
Although there is a great variety of external fixator devices and fixator configurations, the Orthofix Limb Reconstruction System (LRS) was developed fkor the following three main indications: bone loss with shortening; bone loss without shortening; and deformity, with and without shortening, and extreme shortening. Previous studies have recommended its use in the treatment of bone defects since it offers several advantages over others [5; 13; 14; 15], such as ease of application, versatility, stronger fixation, less fixator related complications, early weight bearing and primary bony union, patients satisfaction, etc. Nevertheless, there are also reports of higher complications and less patients' satisfactions when it is used for femoral lengthening [16; 17]. In fact, LRS is a telescopic device in which joints are locked in order to assure a rigid fixation or unlocked to allow load sharing, or even to carry out day-to-day lengthening or transport by the patient himself. Normally, under this condition, the external fixator is extremely loaded during the treatment and pin-tract infections, as well as pin angulation is the commonest complications.  
To the best of our knowledge, data comparing the mechanical performance of this fixator at different configurations and, especially, with unlocked joint, is not available. Therefore, the aim of this work is to study the behavior of the LRS fixator and optimize the distance between the external fixator frame and the bone, as well as the number of pins used to connect the external fixator to the bone, in order to promote ideal external fixator stability and decrease healing time. A load cell-based system was developed to identify and quantify the load value that is transferred to the fracture focus, when the bone is being solicited under compression. Experimental and numerical analyses were completed and the results were compared. Good correlations between the values were obtained.
## 3 Results
### Analysis of Population Parameters  
The estimated nucleotide diversity parameters for different genomic RNAs of ASaV revealed that the ORF1 C-proximal showed a higher value of nucleotide diversity (\(\pi\) = 0.05957 \(\pm\) 0.00775) and Watterson's Theta (\(\theta_{W}\) = 0.05758 \(\pm\) 0.02187) in comparison to the other genomic RNAs, followed closely by RNA5 (\(\pi\) = 0.05552 \(\pm\) 0.00318; \(\theta_{W}\) = 0.05183 \(\pm\) 0.01678). RNA3 showed moderate to high nucleotide diversity, and RNA4 showed the lowest nucleotide diversity. The haplotype diversity ranged from 0.03282 \(\pm\) 0.01034 to 0.05758 \(\pm\) 0.02187 for all genomic RNA segments, denoting moderate haplotype diversity. An estimation of the ratio of nonsynonymous substitutions to synonymous substitutions (\(\mathrm{Pi_{a}}\)/\(\mathrm{Pi_{s}}\)) or selection pressure on different genomic regions of the ASaV genome revealed that all studied regions are under strong purifying selection pressure (\(\mathrm{Pi_{a}}\)/\(\mathrm{Pi_{s}}\) ranged from 0.00874 to 0.0417) (Table 2). However, this pressure was not distributed equally across different genomic RNAs or within different regions of the same genomic RNA. The tree based on the deduced amino acids of the ORF1 N-proximal forms two clades (Supplementary Figure S8). Clade II contains two isolates, E62033 (Melzower forest, Germany) and E62017 (Berlin, Germany). The conserved amino acids in these two isolates (S\({}_{308}\), L\({}_{347}\), T\({}_{389}\), I\({}_{416}\), I\({}_{426}\), and K\({}_{494}\)) differ from those in all isolates of clade I, where the corresponding residues are A\({}_{308}\), S\({}_{347}\), A\({}_{389}\), V\({}_{416}\), L\({}_{426}\), and M\({}_{494}\). A site-specific selection pressure analysis indicated that L\({}_{347}\) was under positive selection, and other residues were under negative selection. Multiple alignments of deduced amino acid sequences of ORF3 revealed seven conserved residues (G\({}_{9}\), A\({}_{13}\), T\({}_{18}\), I\({}_{41}\), M\({}_{109}\), E\({}_{157}\), and K\({}_{163}\)) among all isolates belonging to clade ORF3-I. At the same sites, the conserved amino acids in the clade ORF3-III isolates are T\({}_{9}\), V\({}_{13}\), A\({}_{18}\), V\({}_{41}\), L\({}_{109}\), K\({}_{157}\), and A\({}_{163}\), and in the clade ORF3-II isolates, by a slight difference of clade ORF3-III, are S\({}_{9}\), V\({}_{13}\), A\({}_{18}\), V\({}_{41}\), L\({}_{109}\), K\({}_{157}\), and E\({}_{163}\).
## 1 Introduction
Understanding the mechanisms underlying the age-related decline in ASC stemness is crucial for developing strategies to mitigate its impact. Furthermore, investigating the potential therapeutic interventions that can preserve or restore the stemness properties of ASCs may hold promise for combating age-related adipose tissue dysfunction and its associated metabolic disorders. Nicotinamide adenine dinucleotide (NAD) and its related co-enzymes, including NADH, NADPP\({}^{+}\), and NADPH, play pivotal roles as central mediators in various metabolic processes. NAD itself is indispensable for maintaining metabolic homeostasis, as it acts as a receptor for hydride groups, accepting electrons from other metabolites in diverse metabolic pathways to form NADH. The transfer of electrons facilitated by NAD/NADH is crucial for fundamental cellular functions such as DNA synthesis, mitochondrial respiration, energy production, and overall metabolism [23]. NAD levels tend to decline with age in multiple tissues and organs. The disruption of NAD metabolites can occur due to different metabolic stressors, including overnutrition, type 2 diabetes, circadian disruption, DNA damage, postpartum conditions, heart failure, and tissue injuries [23; 24; 25]. In light of this, NAD precursors have recently emerged as promising therapeutic agents for age-related regenerative decline and associated diseases. The administration of NAD precursors, nicotinamide mononucleotide (NMN), which decreases with age in mammals, effectively restores NAD levels in aged mice tissues and successfully reverses age-related stem cell dysfunction in various organ systems, including skeletal muscle, brain, skin, and endothelium. However, clinical use of NMN is challenged by the dose-dependent adverse he hepatotoxicityoxicity [26]. Notably, the NAD precursor nicotinamide riboside (NR) has been found to be clinically safe and to decrease the overall metabolic activity of hematopoietic stem cells (HSCs) and muscle [26]. However, the metabolic, functional, and molecular effects of NR treatment on ASCs and its potential to restore a more youthful function remain unresolved questions of critical importance. In this manuscript, we aim to explore the effect of NR, on the differentiation potential and stemness characteristics of ASCs. By elucidating the underlying mechanisms involved, we hope to contribute to the development of novel strategies for regenerative medicine and adipose tissue engineering, ultimately enhancing the prospects for therapeutic applications in the context of aging and metabolic diseases.
## 2 Method
### Modeling
#### 2.2.2 EAT System Model  
Modeling of the EAT system is focused on LO-SCR. The LO-SCR model was constructed in GT-Power(GT-SUITE version 2021), as shown in Figure 5. Related SCR parameters are given in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline No. & **Reactions \(k^{\prime}=AT^{b}\)exp\((-E/RT)\)** & **A** & **b** & **E (k/mol)** \\ \hline R9 & NO + 0.5O\({}_{2}\leftrightarrow\) NO\({}_{2}\) & \(5.1\times 10^{7}\) & 0 & 56 \\ R10 & NO + CO \(\rightarrow\) CO\({}_{2}\) + 0.5N\({}_{2}\) & \(2.857\times 10^{9}\) & 0 & 52.374 \\ R11 & 9 NO + 1C\({}_{3}\)H\({}_{6}\)\(\rightarrow\) 3 CO\({}_{2}\) + 4.5N\({}_{2}\) + 3H\({}_{2}\)O & \(2.994\times 10^{11}\) & 0 & 80.063 \\ R12 & NO + H\({}_{2}\)\(\rightarrow\) H\({}_{2}\)O + 0.5N\({}_{2}\) & \(7.88\times 10^{10}\) & 0 & 69.237 \\ R13 & CO + 0.5O\({}_{2}\)\(\rightarrow\) CO\({}_{2}\) & \(1.183\times 10^{12}\) & 0 & 81.33 \\ R14 & H\({}_{2}\) + 0.5O\({}_{2}\)\(\leftrightarrow\) H\({}_{2}\)O & \(9.83\times 10^{4}\) & 0 & 15.31 \\ R15 & C\({}_{3}\)H\({}_{6}\) + 4.5O\({}_{2}\)\(\rightarrow\) 3H\({}_{2}\)O + 3 CO\({}_{2}\) & \(1.566\times 10^{19}\) & 0 & 159.4 \\ R16 & H\({}_{2}\)O + CO \(\leftrightarrow\) H\({}_{2}\) + CO\({}_{2}\) & \(1.8\times 10^{5}\) & 0 & 56.72 \\ \hline \end{tabular}
\end{table}
Table 4: DOC reactions.  
\begin{table}
\begin{tabular}{c c c c c} \hline No. & **Reactions \(k^{\prime}=AT^{b}\)exp\((-E/RT)\)** & **A** & **b** & **E (k/mol)** \\ \hline R1 & NH\({}_{3}\) + S \(\rightarrow\) NH\({}_{3}\)-S & \(6.68\times 10^{7}\) & 0 & 0 \\ R2 & 2 NH\({}_{3}\)-S + 1.5O\({}_{2}\)\(\rightarrow\) N\({}_{2}\) + 3H\({}_{2}\)O + 2S & \(5.56\times 10^{16}\) & 0 & 178.8 \\ R3 & NO + 0.5O\({}_{2}\)\(\leftrightarrow\) NO\({}_{2}\) & \(5.1\times 10^{7}\) & 0 & 56 \\ R4 & 4 NH\({}_{3}\) + 4 NO + O\({}_{2}\)\(\rightarrow\) 4N\({}_{2}\) + 6H\({}_{2}\)O & \(7.08\times 10^{13}\) & 0 & 89.1 \\ R5 & 2 NH\({}_{3}\) + NO\({}_{2}\) + NO \(\rightarrow\) 2N\({}_{2}\) + 3H\({}_{2}\)O & \(1.0\times 10^{18}\) & 0 & 77.1 \\ R6 & 4 NH\({}_{3}\) + 3 NO\({}_{2}\)\(\rightarrow\) 7/2N\({}_{2}\) + 6H\({}_{2}\)O & \(1.96\times 10^{17}\) & 0 & 136.3 \\ R7 & 2 NH\({}_{3}\)-S + NO\({}_{2}\)\(\rightarrow\) N\({}_{2}\) + NH\({}_{4}\)NO\({}_{3}\) + H\({}_{2}\)O + 2S & \(2.28\times 10^{8}\) & 0 & 43 \\ R8 & NH\({}_{4}\)NO\({}_{3}\)\(\rightarrow\) N\({}_{2}\)O + 2H\({}_{2}\)O & \(1.25\times 10^{8}\) & 0 & 41.5 \\ \hline \end{tabular}
\end{table}
Table 3: Related SCR reactions.
## 2 Material and Methods
### Unmanned Aerial System: Drone  
The drone is equipped with a payload able to transport and deliver the sensor upon the preselected location, with a smooth release on the ground via the gripper transport system (Figure 3), that serves as well for recuperating the sensor after the end of the recording time or to change the batteries. It is composed by a standard unmanned aerial vehicle compliant along with the UTM (Unmanned Traffic Management) services allowing to fly in BVLOS mode (Beyond Visual Line of Sight). The payload embeds two main features, RF transmitter with directive antenna in charge of the resonant circuit stimulation (wakeup device) inside the sensor lying on the ground, and Bluetooth Low Energy (BLE) transceiver enabling the connection inside the sensor after the wake-up step. Thus, the host in the payload acts as a client and requests the upload of stored data (Figure 4).  
Figure 2: Schematic illustration of the data logger high temperature box (11.5 cm length \(\times\) 6.5 cm width \(\times\) 4 cm height \(\times\) 190 g weight), open and closed with the identification of the inner sealed transducer and its outside hole on the box, along with the main board and the batteries as energy source for active data collection.  
Two versions of drone payload can be provided depending on the \"Pick & Release\" feature and the integration with the drone tightly or loosely coupled. The full featured version fitting on middle size drones, and the light version that can be installed on small drones.  
The full featured payload needs to be connected with the drone, by both electrical/mechanical and logical connection (proprietary DJI SDK or open Pixhawk platform). Inside the full featured payload, the main devices are represented by a gripper to transport and release the sensor in the preselected location, a host computer on board able to upload, through BLE wireless local network, data collected and stored in the sensor. Moreover, a radio transmitter, equipped with a directive antenna pointing down, to be enabled on the site where sensor is supposed to lie; and a GNSS (Global Navigation Satellite System) receiver, aimed to identify the geographic position to place the sensor and to retrieve data when needed (Figure 5).  
Figure 4: Data collection of HT box sensors from a vineyard using a far wake-up device represented by a drone.  
Figure 3: Illustration of the gripper transport system of the drone served to pick and release the sensor on the field.  
In easy-to-reach field crops or orchards, the sensors could be placed manually on the site by an operator so that the payload can be reduced in dimensions and weight (no pick and release function) and can be installed on small drones. In this case, the payload is a small board, with its own embedded battery, including both the gateway (BLE to memory card) and the wake-up device. No electrical or logical connection are provided between the payload and the drone, only a simple mechanical connection is provided by Velcro or clamps.
# Evaluating the Effects of Deep Excavation on Nearby Structures Through Numerical Simulation
## 3 Numerical Simulation Analysis and Verification
### Numerical Simulation Model and Input Parameters  
Based on the aforementioned model establishment procedure, the detailed content was as follows. This study used PLAXIS 3D for full-section analysis. Three soil mechanics models--Mohr-Coulomb model (MC), hardening soil model (HS), and hardening soil small strain model (HSS)--were used for the calculations. These models (MC, HS, HSS) were applied to analyze all soil layers. Boundary conditions extended to five times the excavation depth, with soil layers set to a depth of 50 m according to geological borehole  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Excavation Stage** & **Excavation Depth (m)** & **Water Level (m)** & **Support Size** & **Support Level (m)** & **Preload (t)** \\ \hline
1 & \(-1.7\) & \(-2.7\) & \(\mathrm{H300\times 300\times 10\times 15}\) & \(-1.0\) & 30 \\ \hline
2 & \(-3.9\) & \(-4.9\) & \(\mathrm{H350\times 350\times 12\times 19}\) & \(-3.2\) & 80 \\ \hline
3 & \(-7.0\) & \(-8.0\) & \(\mathrm{2}\)-H350 \(\times 350\times 12\times 19\) & \(-6.3\) & 120 \\ \hline
4 & \(-10.1\) & \(-11.1\) & \(\mathrm{2}\)-H400 \(\times\) 400 \(\times\) 13 \(\times\) 21 & \(-9.4\) & 150 \\ \hline
5 & \(-13.2\) & \(-14.2\) & \(\mathrm{2}\)-H400 \(\times\) 400 \(\times\) 13 \(\times\) 21 & \(-12.5\) & 150 \\ \hline
6 & \(-15.8\) & \(-16.8\) & \(-\) & \(-\) & \(-\) \\ \hline \end{tabular} The first excavation.  
\end{table}
Table 1: The excavation design data.  
data. The numerical model setup is shown in Figure 2. Soil parameters in the PLAXIS program were divided into four layers:  
1. Layer 1: GL 0 to \(-\)5.8 m, clay (CL), undrained conditions;
2. Layer 2: GL \(-\)5.8 m to \(-\)16.4 m, sand (SM), drained conditions;
3. Layer 3: GL \(-\)16.4 m to \(-\)36.4 m, clay (CL), undrained conditions;
4. Layer 4: GL \(-\)36.4 m to \(-\)50 m, sand (SM), drained conditions.  
Parameters used for each layer are listed in Appendix A, Table A1, where \"ref\" indicates values corresponding to the reference stress (\(p^{ref}=100\) kPa).
# A Novel Adaptive Transient Model of Gas Invasion Risk Management While Drilling
## 3 BHP Control Model Development
### Proportional-Integral-Derivative (PID) Controller
Therefore, the correspondence between throttle opening and throttle pressure can be obtained by solving Equation (14), and the correspondence between the throttle opening and BHP can be established so that the BHP can be controlled by adjusting throttle opening, and then the gas invasion accident can be handled. After specifying the target parameters and the control object, this paper searches for the tuning parameters of the PID controller by the grid search method, including \(K_{\mathrm{P}}\), \(K_{\mathrm{i}}\), and \(K_{\mathrm{d}}\), and the search range and step size are shown in Table 4.  
Different combinations of regulation parameters were evaluated according to the regulation efficiency and overshoot, and it was found that when \(K_{\mathrm{P}}=4.00\), \(K_{\mathrm{i}}=0.02\), and \(K_{\mathrm{d}}=3.00\), the PID controller of BHP had the highest regulation efficiency and almost zero overshoot, so this combination was selected as the result of the adjustment of the bottom hole pressure PID controller parameters. As shown in Figure 4, the PID controller of BHP established in this section has a significantly higher regulation efficiency compared to the existing PI controller of BHP. Because of the addition of the differential control part, the overshoot of the BHP disappears, which makes the BHP regulation process more stable and more reliable. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Search Parameters** & **Search Scopes** & **Search Steps** & **Optimization Results** \\ \hline \(K_{\mathrm{P}}\) & 1.00–8.00 & 0.50 & 4.00 \\ \(K_{\mathrm{i}}\) & 0–0.06 & 0.01 & 0.02 \\ \(K_{\mathrm{d}}\) & 0–6.00 & 0.50 & 3.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: PID control grid search in the parameter setting process. The pool level reached the warning value 960 s after the gas invasion occurred. The PID controller and PI controller started to regulate the BHP at the same time. The PI controller regulated the BHP within the safety pressure window after 87 s, while the PID controller only took 53 s to regulate the BHP within the safety pressure window, which was 39% more efficient. On the other hand, the PI controller takes 110 s to regulate the BHP to the target BHP, while the PID controller takes only 73 s, saving 34% of the regulation time. During the entire BHP control process, the root mean square error (RMSE) of the PI controller is 0.31 MPa, and the integral absolute error (IAE) is 3250.39 MPa-s. The RMSE of the PID controller is 0.24 MPa, and the IAE is 1870.05 MPa-s. According to the control principle of the PID controller, the function of the differential partial override control and suppression of overshoot is the main reason for the efficiency improvement and the disappearance of the overshoot.
## 4 Proposed System
### Activation and Loss Function  
The activation layer is used to normalize the output of the convolution layer. ReLU is computed according to non-linearity function Equation (5)  
\[G_{x}^{(l)}=f\Big{(}G_{x}^{(l-1)}\Big{)} \tag{5}\]  
where \(l\) defined the non-linearity layer and normalized feature \(G_{x}^{(l)}\) is generated from the feature map \(G_{x}^{(l-1)}\) of the previous layer \((l-1)\). The main activity of this layer is to set zero for the negative value and return the maximum value according to Equation (6)  
\[G_{x}^{(l)}=max\Big{(}0,G_{x}^{(l-1)}\Big{)} \tag{6}\]  
The loss function categorical cross-entropy (CCE) [63] produces a high loss value when the actual observation is 1, but the predicting probability is 0.011 and is considered a bad result. Minimized score and perfect cross-entropy value are 0. In our study, we considered an M-pairs training set consisting with \(\{(x_{1},c_{1}),(x_{2},c_{2}),\ldots,(x_{M},c_{M})\}\), where \(x_{j}\in R^{M}\) defined the \(M\) dimensional \(j\)th input vector, \(c_{j}\in R^{N}\) defined the corresponding targeted class and \(y_{j}\in R^{N}\) defined the output. In our study, CCE loss calculated for M-dimensional input \(x_{j}\) to one of N class categories using (7).  
\[Loss=-\frac{1}{M}\sum_{j=1}^{M}\sum_{c=1}^{N}y_{j,c}\text{log}(p_{j,c}) \tag{7}\]  
where \(N\) defined the number of classes, \(y_{j,c}\) defined binary indicator function if the class label \(c\) is the correct classification for \(j\)th training observation. \(p_{j,c}\) defined the predicted probability for \(j\)th training observation is of the class label \(c\). The target \(y_{j,c}\) able to be compiled as true and the predicted probability output \(p_{j,c}\) for the \(j\)th observation belonging to class \(c\). This function mainly computed the average of the sum between the actual probability and the predicted probability for all classes in the study.