esa-sceva/satcom-chunk-collection huggingface dataset:
# A Histopathological Image Classification Method Based on Model Fusion in the Weight Space
## 3 Model Fusion in the Weight Space for Histopathological Image Classification
### Basic Model Training
: _Let \(L:R^{n}\to R^{+}\) be a loss function and \(S\subseteq R^{n}\) be a closed convex set, then \(S\) is a \((\varepsilon,\delta)-\)basin for \(L\) if and only if the following holds:__Let \(U_{S}\) represent the uniform distribution over \(S\), and \(v_{S,L}\) represent the expected value of \(L\) on samples drawn from \(U_{S}\). Then_ \[\mathbb{E}_{w\sim U_{S}}[|L(w)-v_{S,L}|]\leq\varepsilon\] (5)
* \(\forall w_{1},w_{2}\in S\)_, define_ \(h(w_{1},w_{2})=w_{1}+\widetilde{\alpha}(w_{2}-w_{1})\)_, where_ \(\widetilde{\alpha}=\max\alpha|w_{1}+\alpha(w_{2}-w_{1})\in S\)_. Then_ \[\mathbb{E}_{w_{1},w_{2}\sim U_{S},p\sim\mathcal{N}(0,(\delta^{2}/n)I_{n})}[L( f(w_{1},w_{2})+p)-v_{S,L}]\geq 2\varepsilon\] (6)
* _Let_ \(\kappa(w_{1},w_{2},p)=f(w_{1},w_{2})+\frac{1}{||f(w_{1},w_{2})-w_{1}||_{2}}(f (w_{1},w_{2})-w_{1})\)_. Then_ \[\mathbb{E}_{w_{1},w_{2}\sim U_{S},p\sim\mathcal{N}(0,\delta^{2})}[L(\kappa(w_{ 1},w_{2},|p|))-v_{S,L}]\geq 2\varepsilon\] (7)  
The definition of basin implies that, on the one hand, a basin should be relatively flat (small variance), and the value of the loss function that corresponds to each point in it should be close to the expected value. On the other hand, the value of the loss function corresponding to the points around the basin should be higher than the expected loss. Specifically, Equation (6) stipulates that the values of the loss function associated with the points within the basin should be higher than the expected values when Gaussian noise is added. Equation (7) stipulates that if a point within the basin is manually pushed away from the basin by a short distance, the value of its loss function should increase. To validate the effectiveness of the ingredients, we use linear interpolation between two ingredient models parameterized by \(w_{1}\) and \(w_{2}\) to generate \(k\) middle points \(w_{i}(1\leq i\leq k)\), where \(w_{i}\) is given by  
\[w_{i}=\gamma_{i}w_{1}+(1-\gamma_{i})w_{2}\quad\ 0\leq\gamma_{i}\leq 1 \tag{8}\]  
If \(L(w_{i})-\frac{L(w_{1})+L(w_{2})}{2}<\tau\) holds for all \(w_{i}(1\leq i\leq k)\), in which \(\tau\) is a threshold, it can be inferred that \(w_{1}\) and \(w_{2}\) are in the same basin. We report the results of the proposed model in Section 4.
## 3 Verification of the Composite Main Reflector
### Analysis of the Surface Error of the Composite Main Reflector  
The composite main reflector that constitutes the reflector surface of the deployable reflector antenna must be manufactured by precisely simulating the shape of the reflector surface design considering the electrical performance of the antenna. To this end, the surface error of the composite main reflector surface that was finally manufactured and processed was measured and analyzed.  
In order to confirm the dimensional stability of the curved surface of the composite main reflector's surface, the measurement reference coordinates were selected at regular intervals for the entire main reflector, and the dimensions of the product were measured using a CMM (Coordinate Measuring Machine).  
When the main reflector is installed in a shape similar to the orbital environment and measured with a CMM, it was confirmed that the main reflector sagged due to the influence of gravity, resulting in a measurement error of more than 5 mm.  
Because the side of the main reflector is designed to be installed horizontally to the ground, a gravity compensation device was used to install the main reflector vertically, as  
Figure 11: Results of NDT for main reflectors.  
Figure 10: Comparison of the surface of the main reflector based on curing conditions.  
shown in Figure 12. And, 75 points were measured for each case of the manufactured main reflector to analyze the surface error.
## Verification by Simulated Switching Loadings
### Simulation Results  
Figure 5a displays, for Load A, the confidence intervals (normalized to the expected damage) as a function of \(N_{B}\). Very similar trends (not shown) are obtained for Load B and Load C. For any \(N_{B}\), \(E[D(T)]\) (approximated by \(\overline{D}_{val}(T)\)) always falls within the confidence interval. As \(N_{B}\) increases, the interval width diminishes towards a sort of asymptote; this confirms that the prediction error cannot be decreased by subdividing the load states into infinitely many blocks. In fact, block subdivision does not add \"new information\", such as new fatigue cycles, to the original undivided load.  
The outcome in Figure 5a, though very promising, only represents one observation, and it cannot be used to draw general conclusions on the correctness of the method. In fact, a confidence interval, by definition, may or may not enclose the expected value \(E[D(T)]\); a set of 20 confidence intervals (for the case \(N_{B}=10\)) is displayed in Figure 5b to show an example in which \(E[D(T)]\) is not enclosed by the confidence interval.  
If the construction of the 95% confidence interval described so far is repeated in a series of independent analyses, in the long run it is expected that 95% of the computed confidence  
Figure 5: Load A: (**a**) confidence intervals versus number of blocks \(N_{B}\); (**b**) sample of 20 confidence intervals for case \(N_{B}=10\).  
intervals will enclose \(E[D(T)]\). To prove this statement, it is necessary to repeat the above procedure a multitude of times, and then count how often the confidence interval encloses \(E[D(T)]\)--theoretically, it should be 95 out of 100 times. While the validation set is kept unchanged, switching loads are iteratively simulated \(2\cdot 10^{5}\) times, and for each iteration, the confidence interval is constructed. The fraction of confidence intervals enclosing \(E[D(T)]\) represents the estimated confidence level \(100(1-\hat{\alpha})\%\). As an example, for \(N_{B}=10\), the obtained values are 94.9% (Load A), 95.6% (Load B) and 95.2% (Load C), which are almost coincident with the theoretical value of 95%. Only in the limit case \(N_{B}=2\), the estimated confidence becomes larger (e.g., 98.1% for Load C), but it rapidly reduces as \(N_{B}\) increases; it is then recommended to divide the stationary states into as many blocks as possible, compatibly with a minimum number of cycles in each block. In summary, simulations results have confirmed the accuracy of the proposed approach.
## 3 Results and Discussion
### CH/CNC and CH/CD Edible Films
#### 3.1.1 Thickness and Physicochemical Properties  
Both the formation of edible films and coatings and their application in foods, are affected by physicochemical properties [44]. The effect of CNC or CD incorporation on the thickness and physicochemical properties of the CH samples is reported in Tables 1 and 2. The physicochemical properties measured were density (\(\rho\)), viscosity (\(\eta\)), consistency index (k), flow behavior index (n), pH, surface tension and moisture. Thickness and moisture were studied in the final formed edible films, while the rest properties were studied in the primary solutions. Results show that the standard CH solution exhibited high surface tension (52.017 mN/m, Table 1), which is desirable for a coating, but, on the other hand, it presented a significantly high viscosity (167.34 mPa\(\cdot\)s, Table 1), which may hinder its handling. In general, the combination of high surface tension and low viscosity is considered ideal for a coating [45]. The high viscosity of the CH sample explains the high values of the other two rheological parameters (k and n). As it is obvious, it is required to reduce the viscosity of the solutions in order to improve the coating process. The addition of CNC or CD to the CH solutions changed their physicochemical properties and, especially, their rheological parameters (Table 1). In particular, they both reduced the consistency of the CH solutions and led to reduced viscosity values (from 167.34 mPa\(\cdot\)s to 90.67-54.83 mPa\(\cdot\)s), which facilitates handling and processing. This can be easily explained as CNC and CD form solutions with lower viscosity than CH. Furthermore, the surface tension presented small changes and remained in high values, which is beneficial for the coating procedure [45]. The final formed edible films with only CH had relatively high moisture (11.0875 g H\({}_{2}\)O/100 g film, Table 2), probably because chitosan is a hydrophilic substance and holds a considerable amount of water in its three predominant absorption sites: hydroxyl group, amino group and polymer chain end [14]. High moisture can favor the growth of microorganisms acting negatively on the antimicrobial activity of the edible films and coatings and consequently on the shelf life of the food product it is applied to [46]. On the other hand, the thickness of the CH films was relatively low (0.04 mm, Table 2), which is a positive attribute. It should be noted that edible films with thickness less than 0.25 mm are considered thin [47]. In contrast to the addition of CNC to CH, which maintained the moisture content of the final formed edible films at the same levels, the addition of CD to CH reduced films' moisture. This can be explained by the final structure of the CH/CD films. More specifically, they consist of hollow truncated cone structures with an external hydrophilic character and an internal hydrophobic character, which means that the interactions between CD and CH include hydrophobic forces [48]. When compared to the studies of [PERSON] et al.
# Aerostat-Based Observation of Space Objects in the Stratosphere
## 3 Aerostat-Based Observation Installation for Space Objects  
The observation installation based on the aerostat for space objects in near space can be represented by the space objects floating observation system (SOFOS) shown in Figure 6. It includes the high-altitude balloon detection system and the ground support system. The former consists of a high-altitude balloon (including a parachute for pod recovery) and a pod that integrates with a power unit, control unit, application loads, and servicing devices. The latter consists of an observation planning and data processing subsystem, a release and recovery subsystem, and a control and data transmission subsystem.
## 4 Results
### Prescribed Burning Emissions
#### 4.3.1 Emission Comparison between FINN and BlueSky  
The daily total emissions for the southeastern United States were compared between FINN and BlueSky. A linear regression without an intercept was conducted to understand the agreement between FINN and BlueSky emissions (Figure 6). Particulate matter and CO estimated from the two different methods were highly correlated (R\({}^{2}\) = 0.96). PM\({}_{2.5}\) and PM\({}_{10}\) emissions from FINN were 74% and 96% of the respective BlueSky emissions. CO emissions from FINN were 16% higher than BlueSky CO emissions.  
Figure 5: A linear regression between the FINN burned area and permitted burn area (unit: acres) of prescribed burns matched over 4 km grid cells in Florida, Georgia, and South Carolina. The black line is a 1:1 line and the red line is the regression line. Uncertainty of linear regression parameters was reported with a 95% confidence interval.  
Figure 6: A comparison between estimated FINN and BlueSky daily total prescribed burning emissions in the southeastern United States. The black line is a 1:1 line and the red line is the regression line. Uncertainty of the linear regression parameters was reported with a 95% confidence interval.  
Although daily total emissions from FINN and BlueSky were consistent with each other, a comparison of emissions for individual fires showed discrepancies. FINN statistically has higher CO and particulate matter emissions from a single fire event compared to the BlueSky framework (Figure 7).  
For regional chemical transport model simulations, typical grid-based emissions are provided for the model. We compared the FINN and BlueSky emissions under a 4 km grid definition to understand the differences in emission inputs (Figure 8). The R\({}^{2}\) between estimated FINN and BlueSky emissions was higher than 0.55. FINN had higher CO and lower PM\({}_{2.5}\) emissions than the BlueSky method, while PM\({}_{10}\) emissions were close.
## 3 Suspension Design and Modelling
### Suspension Design of the Tracked Chassis  
Figure 3 shows the structure of the cylinder-piston assembly and the suspension status in two operation modes. The suspension is mainly composed of an upper pneumatic chamber and a lower hydraulic chamber. The hydropneumatic cylinder-piston assemblies connect the load wheels to the tracked frame. The lower end (piston) is mounted on the U-shaped shaft with a load wheel. The upper end (cylinder) is fixed with the tracked frame. It should be emphasized that the design of the U-shaped shaft increases the total stroke of the suspension compared to the traditional unbent shaft. This design overcomes the height limitation of the tracked chassis and significantly reduces the risk of suspension breakdown.  
Figure 3: Tracked chassis suspension status in two operation modes (**a**) Rail operation mode (**b**) Road operation mode.  
Figure 2: Operation mode switching process (**a**) Climbing process (**b**) Lifting process (**c**) Rotating process (**d**) Switching complete.  
As shown in Figure 3a, the whole tracked chassis is wholly suspended during the rail operation mode. The oil is injected into the hydraulic chamber as the filling medium and push the piston to the top of the cylinder so that the load wheel does not produce vertical vibration affecting the driving stability of the vehicle.  
During the road operation mode as shown in Figure 3b, the pneumatic chamber is filled with nitrogen as an elastic medium, which provides a non-linear suspension stiffness. The hydraulic chamber is filled with oil, which provides a suspension damping. When driving on the ballasts or the terrains beside the rail, the bounce displacement of the load wheel is small, the suspension stiffness is low, and the vehicle has good driving stability. When crossing the barriers such as the rail and the ramp, the load wheel's stroke becomes large, so strong shock absorption capacity and high reliability could be achieved.  
The hydropneumatic suspension system of the in-plane model is illustrated in Figure 4. When valve B opens and valves A, C, D close, the hydraulic chamber is directly connected to the oil tank and the suspension system is working in road operation mode. When valves A, C open and valves B, D close, the oil pump supplies oil to the hydraulic chamber and the tension cylinder simultaneously. The gas in the pneumatic chamber is compressed and the load wheels are lifted.  
When the load wheels reach the limit position, valve A closes. The pressure in accumulator A ensures that the load wheels maintain the limit position; The tension cylinder is stretched to ensure that the pedrails are not slack. When the tension force reaches a limit value, valve C closes and accumulator B will hold the tension pressure.
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 3 Instruments for TD-NIRS
### Other New TD-NIRS Systems
#### 3.3.4 TD-NIRS Systems Using SPADs  
[PERSON] et al. [58] proposed and developed a new TD-NIRS system incorporating single-photon avalanche diodes (SPADs) operating in a fast-gating mode into a TCSPC unit for use in TD-DOT. Through phantom experiments, they compared the results with and without time-gating and concluded that the proposed method not only extended the dynamic range of the detector but also improved the  
Figure 12: Photo of tNIRS-1.  
Figure 13: Photo of the compact dual-wavelength TD-NIRS system developed by the group in Italy. Modified from Reference [56].  
depth sensitivity due to the higher sensitivity to late photons. [PERSON] et al. [59] also developed a system using fast-pulsed vertical cavity surface emitting lasers and fast-gated SPADs, both embedded into probes with SD distances of 5 mm and 30 mm, indicating the feasibility of application to wearable imaging instruments which should be compact, inexpensive, and quantitative. Furthermore, [PERSON] et al. [60] modified the above system using an SC laser with an AOTF for the light source and fast-gated SPADs for the detector, aiming the application at non-contact measurements. They performed phantom experiments to evaluate the performances of the system and discussed the possibility of TD-DOT imaging with non-contact measurements.  
[PERSON] et al. [61] proposed an early photon approach to improve the spatial resolution of TD-DOT images using a developed TD-NIRS system based on the TCSPC technique incorporating SPADs. To enhance the detection efficiency of early photons with less scattering events, measurements were made with a detection rate much higher than that leading to counting losses due to the dead time.  
[PERSON] et al. [62] reported a TD-DOT system using a state-of-the-art SPAD camera chip for detection, which is an array of 32 \(\times\) 32 SPADs including a time-to-digital converter, and a super-continuum laser. The source light can be guided to 24 channels at most, and a total of 1024 TOF distributions at the maximum can be obtained without bulky instruments using conventional TD-NIRS systems.
## 5 Modern Technologies for Ascorbic Acid Biofortification in Tomatoes
### Biofortification through Novel Regulatory Genes and Transcription Factors
Overexpression _ALO_ in tomato increased leaf and green fruit AsA contents by 1.5-fold and 1.25-fold, respectively, possibly by pulling carbon flux towards AsA biosynthesis, while AsA turnover was also induced as indicated by the high DHA levels, probably allowing feedback responsive regulation of AsA synthesis [128]. Malate dehydrogenase (MDH) is the enzyme responsible for the reversible reduction of oxaloacetate to malate. Antisense expression of a mitochondrial _MDH_ in tomato has led to significant induction of AsA accumulation in leaves, and simultaneously, a repressed rate of flux through the TCA cycle, a reduced rate of respiration, and an increased photosynthetic rate in the transgenic plants compared to WT plants [139]. The tomato _High-Pigment-1 (hp1)_ mutants are functionally deficient in the negative regulator of UV-Damaged DNA-Binding Protein 1, exerting enhanced carotenoids [162] and flavonoids [163], and reduced AsA levels [140], compared to WT plants. The decreased AsA accumulation at various stages of fruit development and ripening has been attributed to the differential expression of several structural genes from the AsA biosynthetic and recycling pathways in the _hp-1_ fruit. In particular, through the course of fruit development and ripening, the expression of _GMP_, _GME_, and _GPP_ was higher in _hp-1_ than in WT plants, while the expression of _MDHAR_ was lower.
# Nanomaterials for Healthcare Biosensing Applications
## 4 Organic Nanomaterial Applications for Healthcare Biosensing
### Dendrimers
The nanohybrid was assembled on a GCE covered with GO-carboxymethyl cellulose (CMC) on a layer-by-layer basis to obtain a nanostructured electrode, which was used to immobilise xanthine oxidase (XOx) for fabricating a biosensor for the determination of xanthine, an alkaloid bronchodilator used to treat asthma symptoms. The sensor achieved a broad detection range of 50 nM-12 \(\upmu\)M and the LOD was 13 nM. Dervievic's group developed a neoteric amperometric biosensor for urea, relying on the self-assembled immobilisation of urease on CYA-modified PAMAM grafted MWCNT [258]. They performed experiments using first-generation (G1) to G5 PAMAM dendrimers and concluded that G5 PAMAM presented an exceptional performance with a rapid detection time of just 3 s, wide detection range of 1-20 mM, and an LOD of 0.4 mM.
###### Abstract
[PERSON] et al. used synergy-based linear regression models to estimate wrist joint angles with and without grip motion [29]. The results showed that the predictive performance of the synergy-based linear regression model was similar to that of the traditional EMG-based MSM, with a CC of 0.7891 \(\pm\) 0.0844. Whether the central nervous system employs this low-dimensional embedding to generate motor behavior remains an open question; these innovative studies certainly show the potential of muscle synergy in predicting potential human limb movements [30]. Notably, the aforementioned studies aimed to use muscle synergy to develop prediction strategies that focus on wrist movements (flexion/extension and radial/ulnar deviation). The use of muscle synergy to predict finger movements has not been well studied, and the performances of these models remain unclear. To facilitate the clinical application of muscle synergy-based neurorehabilitation techniques, we propose an ICA-based muscle synergy extraction method that is introduced into the MSM (Synergy-MSM) to estimate the metacarpophalangeal (MCP) angles of the thumb, index finger, and middle finger. The accuracy of prediction using the proposed method was also tested for the wrist joint. This study evaluated whether using muscle synergy extracted from ICA data can improve the predictive performance of the MSM and compared the developed model with two traditional prediction methods: the linear regression model (LRM) and support vector regression (SVR). Data were collected using a 32-channel array EMG system, and the signals were analyzed using ICA. Non-negative matrix decomposition (NMF) was then used to extract muscle synergies from selected independent components (ICs) representing the extensor and flexor muscle groups. The effects of these choices on the prediction accuracy were also evaluated.
# Dynamic Graph Convolutional Crowd Flow Prediction Model Based on Residual Network Structure
## 4 Experiments and Analysis
### Baseline Methods and Comparative Experiments
Figure 7 shows the loss curves of two loss functions generated during the training of the BikeNYC dataset. It depicts a comparison of our model's Huber loss function and RMSE loss function's loss variation. It is evident that the Huber loss function achieves the lowest and most stable loss curve. In terms of the obtained loss values, the Huber loss function outperforms the RMSE loss function in terms of model performance. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{2}{c}{**BikeNYC**} & \multicolumn{2}{c}{**TaxiNYC**} & \multicolumn{2}{c}{**Training**} & \multicolumn{1}{c}{**Number of**} \\  & **MAE** & **RMSE** & **MAE** & **RMSE** & **Time/s** & **Parameters** \\ \hline HA [4] & 9.97 & 17.05 & 47.33 & 88.98 & 0 & 0 \\ ARIMA [26] & 10.41 & 19.14 & 46.78 & 85.25 & 0 & 0 \\ STGCN [13] & 6.49 & 11.73 & 42.58 & 75.47 & 272 & 454,000 \\ DCRNN [27] & 5.88 & 9.85 & 39.64 & 67.57 & 10756 & 372,353 \\ STGNN [28] & 5.79 & 9.80 & 37.30 & 65.12 & 2995 & 617,985 \\ MVGCN [10] & 5.65 & 9.64 & - & - & - & - \\
3D-DGCN [11] & 4.81 & 7.76 & 33.06 & 60.23 & 3130 & 145,677 \\ Res-DGCN & 4.55 & 7.38 & 29.93 & 54.92 & 6321 & 149,867 \\ \hline \(\Delta\) & 5.2\% & 4.8\% & 9.4\% & 8.8\% & - & - \\ \hline \end{tabular}
\end{table} 
Table 2: Model performance comparison. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**SA**} & \multirow{2}{*}{**sCondConv**} & \multirow{2}{*}{**Huber**} & \multicolumn{2}{c}{**BikeNYC**} & \multicolumn{2}{c}{**TaxiNYC**} \\  & & & **RMSE** & \(\Delta\) & **RMSE** & \(\Delta\) \\ \hline - & - & - & 7.76 & 0 & 60.23 & 0 \\ \(\surd\) & - & - & 7.64 & 1.5\% & 56.4 & 6\% \\ - & \(\surd\) & - & 7.52 & 3\% & 58.11 & 3.5\% \\ - & - & \(\surd\) & 7.68 & 1\% & 58.3 & 3.2\% \\ \(\surd\) & \(\surd\) & - & 7.58 & 2.3\% & 57.93 & 3.8\% \\ \(\surd\) & - & \(\surd\) & 7.58 & 2.3\% & 55.76 & 7.4\% \\ - & \(\surd\) & \(\surd\) & 7.43 & 4.2\% & 56.51 & 6.1\% \\ \(\surd\) & \(\surd\) & \(\surd\) & 7.38 & 4.8\% & 54.92 & 9.4\% \\ \hline \end{tabular}
\end{table} 
Table 3: Results of ablation experiments.