esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Hepatocellular Carcinoma  
Primary hepatic malignancies are very rare in AYA. Among them, HCC predominates. In developed countries, the incidence of HCC in AYA has been constantly increasing during the last decades [132; 133].  
HCC in AYA has distinct pathological and clinical features compared to older patients. In countries with low rates of hepatitis B virus (HBV) infections, it is usually of a fibrolamellar pathological subtype and not related to cirrhosis or underlying liver disease [220; 221]. However, HCC has been observed in patients with Fanconi's syndrome and metabolic diseases (hereditary tyrosinemia, [PERSON] disease and glycogen storage disease type IA) [222; 223; 224]. As most patients are asymptomatic for a long time, the final diagnosis is delayed and the prognosis is poor [131; 220].  
There are several reports about exosomes in HCC [93; 129; 140; 225; 226; 227; 228; 229]. They suggest that exosomes may be a new biomarker in diagnosis of early-stage HCC and potential target for the treatment of this type of the tumor. However, only single studies concern exosomal Hsps and their role in the biology and aggressiveness of HCC.  
[PERSON] et al. detected Hsp70 expression on the surface of hepatoma G2 (HepG2) cell-derived exosomes. They confirmed that exosomal Hsp70 can be considered as significant molecules connected with angiogenesis and immune response in HCC [93]. Another study found increased release of Hsp70-containing exosomes from HCC cell lines after the treatment with an inhibitor of DNA methyltransferase, 5-Aza-2'-deoxycytidine [141]. [PERSON] et al. reported that release of Hsp60, Hsp70, and Hsp90 was enhanced in HCC cells under antineoplastic treatment. Additionally, secretion of exosomal Hsps caused by HCC cell-resistant anticancer drugs, such as carboplatin and irinotecan hydrochloride, was remarkably higher than sensitive drugs. The authors showed that Hsps released from exosomes can trigger activity of natural killer (NK) cells resulting in augmented cytolytic activity against targeted cells and stronger immune response [129].  
Another group investigated the effect of the epigenetic drug MS-275 on exosomes from HepG2 cells [94]. The study revealed that MS-275 was responsible for higher secretion of Hsp70 in exosomes, resulting in enhancement of the exosome-induced cytotoxic activity of NK cells. Findings reported by [PERSON] et al. and [PERSON] et al. can contribute to the innovative and effective treatment of HCC. Notably, further efforts are required in this field, in particular, studies on the fibrolamellar subtype of HCC, characteristic for AYA patients, are lacking.
## 3 Materials and Methods
### Fourier Transform Infrared Spectroscopy (FTIR Analysis)  
To prepare the samples for FTIR analysis, a freeze-dryer with a vacuum pump, MRC AB Chem SM-450, was used, in which the earthworm tissues were placed in a tray and subjected to a drying process according to the manufacturer's instructions for a period of 24 h. After this time, the tissues were placed in a mortar, crushed gently to a fine, uniform dust, and transferred to glass cuvettes.  
The FTIR apparatus used in this study was a Jasco FTIR Model 6200, operating in the near-infrared (15,000 cm\({}^{-1}\)) and far-infrared (50 cm\({}^{-1}\)) regions. The resolution of the apparatus is 0.25 cm\({}^{-1}\). Each complete, powdered tissue from one individual was divided into four smaller sections, each of which was subjected to FTIR analysis in 5 measurements; the results were summed, giving a total qualitative image for one individual. In this way, all tissues from both species were examined, identifying the plastics that were present. The characteristic peak values and polymer patterns are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
\begin{tabular}{c} **Chemical** \\ **Compound** \\ \end{tabular} & \multicolumn{2}{c}{**Structural Pattern**} & \multicolumn{3}{c}{**Absorption Band**} \\ \hline polypropylene & \(\begin{bmatrix}\text{CH}_{9}\\ \end{bmatrix}\) & 842 cm\({}^{-1}\) & 998 cm\({}^{-1}\) & 1167 cm\({}^{-1}\) & 1377 cm\({}^{-1}\) & 1460 cm\({}^{-1}\) \\ \hline polyethylene & \(\begin{bmatrix}\text{CH}_{1}\\ \end{bmatrix}\) & 720 cm\({}^{-1}\) & 1460 cm\({}^{-1}\) & 2850 cm\({}^{-1}\) & 2920 cm\({}^{-1}\) & \\ \hline polyester & \(\begin{bmatrix}\text{CH}_{9}\\ \end{bmatrix}\) & 845 cm\({}^{-1}\) & 970 cm\({}^{-1}\) & 1126 cm\({}^{-1}\) & 1340 cm\({}^{-1}\) & 1718 cm\({}^{-1}\) \\ \hline poliyaml & \(\begin{bmatrix}\text{CH}_{9}\\ \end{bmatrix}\) & 745 cm\({}^{-1}\) & 1077 cm\({}^{-1}\) & 1125 cm\({}^{-1}\) & 1285 cm\({}^{-1}\) & 1726 cm\({}^{-1}\) \\ \hline polyethylene & \(\begin{bmatrix}\text{CH}_{9}\\ \end{bmatrix}\) & 1050 cm\({}^{-1}\) & 1237 cm\({}^{-1}\) & 1370 cm\({}^{-1}\) & 1744 cm\({}^{-1}\) & 3400 cm\({}^{-1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristic peak values of polymeric materials for FTIR analysis.  
Figure 3: Images (**A**–**D**) of the PA molecule under UV (3) light.  
Figure 2: (**A**,**B**) Images of PET molecule and (**C**,**D**) images of PS molecule under UV (4) light.
###### Abstract  
Physics-informed neural networks (PINNs) have emerged as a transformative methodology integrating deep learning with scientific computing. This review establishes a three-dimensional analytical framework to systematically decode PINNs' development through methodological innovation, theoretical breakthroughs, and cross-disciplinary convergence. The contributions include threefold: First, identifying the co-evolutionary path of algorithmic architectures from adaptive optimization (neural tangent kernel-guided weighting achieving 230% convergence acceleration in Navier-Stokes solutions) to hybrid numerical-deep learning integration (5x speedup via domain decomposition) and second, constructing bidirectional theory-application mappings where convergence analysis (operator approximation theory) and generalization guarantees (Bayesian-physical hybrid frameworks) directly inform engineering implementations, as validated by 72% cost reduction compared to FEM in high-dimensional spaces (\(p<0.01,n=15\) benchmarks). Third, pioneering cross-domain knowledge transfer through application-specific architectures: TFE-PINN for turbulent flows (\(5.12\pm 0.87\%\) error in NASA hypersonic tests), ReconPINN for medical imaging (\(\bigtriangleup\mathrm{SSIM}=+0.18\pm 0.04\) on multi-institutional MRI), and SeisPINN for seismic systems (\(0.52\pm 0.18\) km localization accuracy). We further present a technological roadmap highlighting three critical directions for PINN 2.0: neuro-symbolic, federated physics learning, and quantum-accelerated optimization. This work provides methodological guidelines and theoretical foundations for next-generation scientific machine learning systems.  
partial differential equation; physics-informed neural network; scientific computing; AI for science; artificial intelligence
## 3 Preliminaries and Problem Definition
### Optimal Mathematical Programming Model  
A Mixed-Integer Quadratic Constrained Programming (MIQCP) model is proposed (5) for solving the patients **Scheduling problem**. In this model, the objectives given in Equations (2) and (4) are mixed in a linear cost function with two terms balanced by the value of the parameter \(\beta\):  
\[\begin{array}{ll}\min&\sum\limits_{i=1}^{|\mathcal{B}|}\left[-\mathbf{\mu_{w}} \cdot\mathbf{S}_{i}\cdot(|\mathcal{B}|-i+1)\right.\left.+\right.\\ &\left.\beta\cdot\mathbf{P}_{o}\cdot\mathbf{S}_{i}\cdot(|\mathcal{B}|-i+1)\right]\end{array} \tag{5}\]  
\[\begin{array}{ll}\text{Subject to:}\\ \left\{\begin{array}{ll}P[T_{b_{i}}\leq l(b_{i})]&\geq&Cl,\quad\forall i=1,2, \ldots,|\mathcal{B}|.\\ \sum\limits_{i=1}^{|\mathcal{B}|}\mathbf{S}_{i}[j]&\leq&1,\quad\forall j=1,2,\ldots,|\mathcal{W}|\\ \mathbf{S}_{i}\in\{0,1\}^{1\times|\mathcal{W}|},&\forall i=1,2,\ldots,|\mathcal{B}|.\end{array}\right.\end{array} \tag{6}\]  
The MIQCP model has two sets of constraints. The first one is related to the chance probability constraint given in Equation (3), while the second one imposes that each patient in the waiting list is scheduled at most once. Development of the chance constraint (3) quadratic constraints is obtained [29].  
Moreover, the decision variables \(S_{i}\) are binary, so the computational complexity of the MIQCP model is really high. In order to obtain scheduling in a reasonable time, heuristic approaches are needed.
## 5 Performance Evaluations
### Simulation Results  
#### 5.2.1 Comparison on Increasing Transmission Rate  
Figure 5 shows the average delivery delay with increasing transmission rate. The average delivery delay means the average time delay between the moment the source sends a packet and moment the destination receives this packet. When the transmission rate is 1 packet per second, we can see that the average delivery delay of SPEED is lower than our protocol and GPSR. This is because SPEED always tries to discover a high speed path for forwarding packets. Since the transmission rate increases, the average delivery delay of SPEED increases significantly. This is because congestions occur at the boundary in SPEED. In our protocol, when the packets need to bypass the obstacle, the boundary candidate nodes have a lower forwarding probability than normal nodes. This is because the size of out-degree of boundary nodes is less than that of the out-degree of normal nodes. In the forwarding node selection game, the probability that a great amount of packets are forwarded by the same node is relatively low. Thus, the average delivery delay of our protocol does not significantly increase with an increase in transmission rate.  
Figure 6 shows the energy consumption of the three protocols. For GPSR and SPEED, the source always selects the node closest to the destination in the neighbor set. However, normally the closest node is the local superior decision, not the global optimal decision. This has been proven by lemma 2.  
Figure 5: Average delivery delay with different transmission rates.  
For our protocol, in the forwarding node selection game, if some node has a lesser angle with the line formed by source and destination, it has the high probability to be the forwarding node. Thus, our protocol costs less node energy.  
At the aspect of packet delivery ratio, from Figure 7 we can see that our protocol has better performance than SPEED and GPSR. With the increase of transmission rate, SPEED and GPSR always forward packets along the obstacle boundaries by perimeter approach. This leads to a high probability of packet congestion around the obstacle. In our protocol, since the process of forwarding node selection is a game process, the source has lower probability to make the same candidate gain too much benefit from the game process. This is the reason why the packet delivery ratio of our protocol does not significantly decrease with the increase of transmission rate.  
Figure 6: Energy consumption with different transmission rates.  
Figure 7: Packet delivery ratio with different transmission rates.
## 2 Materials and Methods
### Cell Culture and WSM Treatment  
Mouse calvarial MC3T3-E1 pre-osteoblasts (American Type Culture Collection, Rockville, MD, USA) were seeded at a density of 25 \(\times\) 10\({}^{3}\) cells per well in 24-well plates. The cells were cultured in \(\alpha\)-Minimum Essential Medium (\(\alpha\)-MEM) (Gibco, Waltham, MA, USA) supplemented with 10% fetal bovine serum (FBS) (Gibco, USA), 1% penicillin (100 U/mL) and streptomycin (100 \(\upmu\)g/mL) (Gibco, USA) and maintained at 37 \({}^{\circ}\)C in a 5% CO\({}_{2}\) humidified incubator. To induce osteoblastic differentiation, MC3T3-E1 cells were treated with water-soluble matrix (WSM) at concentrations of 0.025%, 0.05%, and 0.1% \(w/v\). Culture media were changed every 3 days, and osteogenic supplements (ascorbic acid at 50 \(\upmu\)g/mL and \(\upbeta\)-glycerophosphate at 10 mM) were freshly added during each medium change. Positive and negative controls were set with \(\alpha\)-MEM supplemented with ascorbic acid (50 \(\upmu\)g/mL) and \(\upbeta\)-glycerophosphate (10 mM), respectively, as described in previous studies [27; 28; 29]. Each experiment was repeated three times to ensure the reproducibility and reliability of the results.
## 3 Parametric Study
### Effects of \(\tau^{\prime}\) on the Load-Deflection Characteristics  
The nonlinear load-deflection characteristic of slotted disk springs has been confirmed by previous studies [17; 34]. The desirable mechanical properties can be obtained by adjusting parameters such as the ratio of free height to thickness [20]. The LGT slotted disk springs provide a new approach to the regulation of load-deflection characteristics and a critical method for lightweight design.  
Neglecting the friction contribution (\(\mu_{e}=0\)), Figure 7 presents the effect of the parameter \(\tau^{\prime}\) on the load-deflection relationships of LGT slotted disk springs. The baseline material and geometric parameters are listed in Table 1. This finding reveals that the thickness variation has a strong influence on the load capability. As shown in Figure 7a, the load capacity decreases significantly as \(\tau^{\prime}\) increases in the range of 0 to 0.75. However, the load capacity increases as the value of \(\tau^{\prime}\) changes from 0 to \(-0.75\), as presented in Figure 7b.  
Figure 5: Variations in the parameter \(\tau\) with \(\tau^{\prime}\) and (**a**) parameter \(C_{1}\); (**b**) parameter \(C\).  
Based on previous studies, the nonlinear stiffness characteristic is obtained by connecting the negative stiffness system in parallel with the positive stiffness system in the structure [41]. Compared with the aforementioned methods, it is easier to obtain the nonlinear stiffness characteristic by using LGT slotted disk springs. The stiffness formula \(K(f)\) of LGT slotted disk springs can be expressed as  
\[K(f)=\frac{dP}{df}=\frac{1}{df/dP}=\frac{1}{df_{2}/dP+df_{3}/dP} \tag{26}\]  
As calculated in Section 2, the equivalent stiffness \(dP/df_{3}\) is approximatively constant when the material and geometric parameters are determined. The equivalent stiffness of the slotted segment increases significantly with increasing \(\tau^{\prime}\).  
Figure 8 illustrates the effect of parameter \(\tau^{\prime}\) on the stiffness characteristics. It is illustrated that the effects of parameter \(\tau^{\prime}\) on the stiffness are related to deflection. The stiffness decreases as \(\tau^{\prime}\) increases from 0 to 0.75, but it initially decreases and then increases when the deflection is close to the free height, as shown in Figure 8a. Conversely, the stiffness increases significantly when \(\tau^{\prime}\) decreases from 0 to \(-0.75\), as shown in Figure 8b. Neglecting the deflection \(f_{3}\), the relationships between the stiffness and normalized deflection (\(f_{1}/h_{0}\)) are shown in the insert of Figure 8. A comparison of the above two conditions shows that the deflection caused by the bending of tongues has a stronger influence on the stiffness when \(\tau^{\prime}\) is negative.
## 3 Results
### Differences in Averages
#### 3.2.3 Differences According to Technical Quality  
A Mann-Whitney U test was performed to determine whether differences were established in any of the acceleration axes depending on the technical quality defined by the trainer; however, no significant differences were found (\(p>0.05\)) (Table 6).  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **N** & **Average (G)** & **Average (m/s2)** & **SD (G)** & **SD (m/s2)** \\ \hline Acceleration AP RMS & 9 & 1.3337 * & 13.079 & 0.11175 & 1.096 \\ Acceleration ML RMS & 9 & 1.7023 ** & 16.694 & 0.08483 & 0.832 \\ Acceleration SI RMS & 9 & 1.1884 & 11.654 & 0.17284 & 1.695 \\ \hline \end{tabular} Note: * Indicates a significant difference (\(p<0.05\)) between AP-ML and AP-SI. ** Significant difference (\(p<0.05\)) between the ML-SI. AP = Anterior–posterior. ML = Mediolateral. SI = Superior–inferior. RMS = Root mean square. G = Gravity force. m/s2 = meters per second squared. SD = Standard deviation.  
\end{table}
Table 4: Descriptive statistics and differences between the three axes.  
Figure 2: Description of the EMG of each leg by subject. Each color represents the research subjects with their dominant and non-dominant legs.
# MIMO Channel Estimation in an SDR Platform for Evaluation of D&F Relay Nodes
## 3 System Model  
In Figure 2, we show a general MIMO transceiver corresponding to the 3 GPP LTE downlink system model [22; 23] used in the developed SDR platform. The modulated complex symbols are mapped to multiple transmission layers on the antenna ports. Then, the Space Frequency Block Coding (SFBC) scheme is performed on each layer and mapped to Resource Elements (REs) for each antenna port, after which the pilot symbols are inserted. Finally, the Resource Grid for each antenna port is modulated through an OFDM Modulator block and, prior to being transmitted over the wireless channel from the \(i\)-th antenna, \(i\in\{1,2,...,N_{Tx}\}\) is inserted as the CP (Cyclic Prefix). RG in LTE is employed to depict the frequency/time space [24; 25].  
At the receiver, the CP samples are first extracted, which were inserted into the transmitter side. Then, the resultant signal is de-modulated by the OFDM De-modulator block. Therefore, the complex baseband model considered for the wireless MIMO channel between eNB and RN with \(N_{Tx}\) transmitter and \(N_{Rx}\) receiver antennas can be expressed as  
\[\mathbf{y}_{c,s}^{j}=\sum_{i=1}^{N_{Tx}}\mathbf{H}_{c,s}^{i,j}\mathbf{x}_{c,s }^{i}+\mathbf{n}_{c,s}^{j}, \tag{1}\]  
where \(\mathbf{y}_{c,s}^{j}\) is the received signal at the \(j\)-th (\(j\in\{1,2,...,N_{Rx}\}\)) antenna of the receiver and \(s\)-th OFDM symbol at subcarrier \(c\in\{0,1,2,...,N-1\}\), while \(\mathbf{H}_{c,s}^{i,j}\) is the channel frequency response between the \(i\)-th transmitter and the \(j\)-th receiver antennas. Complex data symbols at the \(i\)-th transmitter antenna are denoted by \(\mathbf{x}_{c,s}^{i}\). Further, \(\mathbf{n}_{c,s}^{j}\) represents the Additive White Gaussian Noise (AWGN) vector at the \(j\)-th receiver antenna, with \(N\sim(0,\sigma_{n}^{2})\). On the other hand, the received Resource Grid, taking into account the expression (1), can be rewritten as  
\[\mathbf{Y}=\mathbf{H}\mathbf{X}+\mathbf{N}. \tag{2}\]  
Figure 2: MIMO Scheme in the core network of the SDR Platform.  
All employed parameters in the system model are described in Table 1.
# Multiple Mobile Sinks for Quality of Service Improvement in Large-Scale Wireless Sensor Networks
## 3 Methodology
### Sink Mobility Patterns
Mobility Model is similar to the RW Mobility Model if pause time is zero and [min-speed, max-speed] = [speed-min, speed-max]. Figure 1c shows an example path of a sink, which begins in the center of the RoI, using the RD Mobility Model. In this model, the sink chooses a random direction to travel, similar to the RW Mobility Model. The sink then travels to the border of the simulation area in that direction. Once the RoI boundary is reached (represented by dots in the figure), the sink pauses for a specified time, chooses another angular direction (between 0 and 180 degrees) and continues the process. Figure 1d illustrates an example traveling pattern of a sink using the GM Mobility Model; the sink begins its movement in the center of the RoI and moves for 1000 s. The Gauss-Markov Mobility Model was designed to adapt to different levels of randomness via one tuning parameter. Initially, the sink is assigned a current speed and direction. At fixed intervals, n, movement occurs by updating the speed and direction. Specifically, the value of speed and direction at the nth instance is calculated based on the value of speed and direction at the n-1 instance and a random variable. This model can eliminate the sudden stops and sharp turns encountered in the RW Mobility Model by allowing past velocities (and directions) to influence future velocities (and directions).
# Gene Expression Space Shapes the Bioprocess Trade-Offs among Tier, Yield and Productivity
## 2 Materials and Methods
### TRY Performance Indices  
Three measures are commonly used to determine the performance of a bioprocess [31]: titer \(T\), volumetric productivity rate \(R\) and yield \(Y\). We evaluated them for the production of the protein \(A\) in \(E\). _coli_ under the three modes of operation of the bioreactor and for different points covering the expression space (promoter and RBS strength).  
The titer \(T\), i.e., the concentration of the molecule of interest at the end of the bioprocess, was measured in units of grams per liter and was calculated as:  
\[T=\frac{V^{f}n^{f}m_{A}^{f}+M_{A_{out}}^{f}}{V^{f}+V_{out}^{f}}, \tag{19}\]  
where the superscript \(f\) indicates the final time of the fermentation, \(m_{A}\) is the amount of protein \(A\) mass in one cell and \(n\) is the concentration of cells in the bioreactor.  
The average volumetric productivity rate \(R\), i.e., the production of the molecule of interest (protein \(A\)) per time unit, was measured in units of grams per liter per hour and calculated as:  
\[R=\frac{T}{t^{f}}, \tag{20}\]  
where \(t^{f}\) is the final time of the fermentation.  
Finally, the yield, i.e., the conversion factor of substrate into the product (protein or metabolite) of interest, was measured in units of product grams per substrate grams and was calculated as:  
\[Y=\frac{T(V^{f}+V_{out}^{f})}{s^{0}V^{0}-sf^{f}V^{f}+s_{feed}V_{feed}^{f}-s_{ out}^{f}}, \tag{21}\]  
where the superscripts \(\{0,f\}\) indicate the initial and final time of the fermentation process, \(V\) is the culture volume in the bioreactor, \(s\) the substrate concentration in the bioreactor and \(s_{feed}\) is the substrate concentration in the feed stream.  
We used the nominal substrate concentration \(s_{n}=3.6\cdot\mathrm{L}^{-1}\) to evaluate the nominal values of titer, productivity rate and yield as a function of the expression space determined by the parameters \(N_{A}\omega_{A}\), \(k_{b}^{A}\) and \(k_{u}^{A}\).
## 5 Factors Affecting the Anaerobic Digestion Process and Biogas Production
### Organic Loading Rate  
This is another crucial operational parameter in the biogas production process. This parameter is defined as a measure of the substrate's amount being added to a constant digester system per unit of volume per day. OLR is frequently presented as grams of total solids, chemical oxygen demand, or volatile solids per litre digester volume per day [94]. This parameter can be calculated according to Equation (14)  
\[\text{OLR}=\frac{\text{COD}_{\text{feed}}\times\text{Q}}{\text{V}_{\text{r}}} \tag{14}\]  
where \(\text{COD}_{\text{feed}}\) is the substrate strength in terms of COD concentration (mg/L), Q is the flow rate of the substrate (L/day), and V\({}_{\text{r}}\) (L) is the working volume of the reactor [95].  
Literature studies have looked at how this factor affects the biogas production process, for example, [PERSON], et al. [96] explored its effects on the acidogenesis of food waste. This study was focused on the OLR effects at individual AD steps and it illustrated that high OLR favoured the acetate and valerate percentages while propionate and butyrate percentages were low under the same OLR conditions. Similarly, [PERSON], et al. [97] conducted a comparable study for three OLR 5, 9, and 13 g/L d and observed that the highest OLR led to a very vicious fermentation broth and reactor became unstable with a lesser yield compared to the lower OLR values in the same study. Both studies agree that higher OLR may lead to an accumulation of unused solid food waste in the reactor and, therefore, lead to a reactor failure.