esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Proposed Mechanism  
PI staining analysis was performed to confirm indirectly the mechanism of how drugs are forcibly injected into the cells by ES. PI is a substance that exhibits red fluorescence upon binding to DNA inside the cell. PI is generally used to detect dying or dead cells because the cell membranes of healthy cells do not allow PI to pass. On the other hand, it is also used as a tool to detect the opening of the plasma membrane when a specific stimulus is applied to a cell [39, 40]. We hypothesized that the ES method temporarily increased the permeability of the cell plasma membrane, thereby increasing the influx of drugs into the cells, so PI was administered at increasing time intervals (0, 30, 60, and 120 min) after ES treatment. In our results, numerous red spots resulting from PI, which indicates dying or dead cells, appeared in the group treated with 6 kV ES at 0 min. However, as time passed, the expression of red fluorescence rapidly decreased, and there were almost no red spots at 60 min (Figure 4A). Compared with the control, the amount of red fluorescence in the 6 kV ES treatment group was approximately 20 times higher at 0 min (Figure 4B). After ES treatment, the red fluorescence derived from DNA-bound PI was different at each time interval, indicating that the opening of the plasma membrane was changed. Thus, we indirectly indicated that when a specific substance is delivered to cells using ES, it can facilitate substance transfer by increasing the permeability of the cell plasma membranes. Figure 4C shows the proposed CDES mechanism. The charged droplets generated by ES increase the plasma membrane permeability of the cells and greatly increase the influx of cisplatin.  
In this study, we proposed a new method for inducing apoptosis of cancer cells by delivering cisplatin using an ES method and indirectly proved its mechanism. Several studies have reported the transfer of genes and proteins to cells using ES, but our study is the first to induce apoptosis by delivering anticancer drugs to cancer cells using ES.  
Figure 4: Proposed CDES mechanism. (**A**) Comparison of plasma membrane permeability after electrospray (ES) using propidium iodide (PI) staining (scale bar: 1 mm). (**B**) PI staining analysis over time. Compared to the control, when treated with 6 kV ES, red fluorescence derived from DNA-bound PI was significantly higher, but over time, the expression of PI decreased to a similar level (n = 3, \(p<0.05\). Columns with different letters are significantly different according to one-way ANOVA and Duncan’s test). (**C**) Proposed CDES mechanism. The charged droplets generated by ES increase the permeability of the plasma cell membrane. Then, it increases the influx of cisplatin.  
Recently, a study on the delivery of genes using a miniaturized ES device was reported [36]. If an anticancer drug can be delivered using such a miniaturized ES device, it is expected that a new anticancer treatment method can effectively deliver the drug to a limited target area. In the future, we plan to use technology to reduce the size of skin cancers and in vivo tumors.
## 2 Results and Discussion
### Thermal Analysis Investigations  
The obtained thermoanalytical curves TG/DTG for compounds **1a-d** are presented in Figure 1A-D.  
The thermal treatment of compound **1a** suggests that it is thermally stable up to 194 \({}^{\circ}\)C, when the first mass loss occurs up to 284 \({}^{\circ}\)C (DTG first process between 194-246 \({}^{\circ}\)C, DTC\({}_{\text{peak}}\) at 204 \({}^{\circ}\)C; DTG second process between 246-284 \({}^{\circ}\)C, DTC\({}_{\text{peak}}\) at 267 \({}^{\circ}\)C), with a corresponding mass loss \(\Delta\)m = 15.06%. The main mass loss due to thermolysis takes place in the 284-597 \({}^{\circ}\)C temperature range (DTG third process between 303-597 \({}^{\circ}\)C, DTG\({}_{\text{peak}}\) at 425 \({}^{\circ}\)C), the residual mass being insignificant (less than 1%). Surprisingly, the behavior observed under thermal stress is different when the heating takes place in the furnace of the Setline instrument vs. Optimelt apparatus, a fact that can be explained by the different environments around the solid sample under heating: dynamic air/oxidative atmosphere with considerable flow, vs. static air atmosphere in the capillary glass. However, the browning observed at 201 \({}^{\circ}\)C during the melting point investigation can be associated with the first mass loss process from the TG/DTG curve (Figure 1A).  
Compound **1b** has good thermal stability up to 181 \({}^{\circ}\)C when a small mass loss (\(\Delta\)m = 0.19%) takes place up to 232 \({}^{\circ}\)C (DTG first process between 181-232 \({}^{\circ}\)C, DTG\({}_{\text{peak}}\) at 197 \({}^{\circ}\)C); in the 232-309 \({}^{\circ}\)C temperature range, the first considerable mass loss takes place (\(\Delta\)m = 19.38%, DTG process between 232-308 \({}^{\circ}\)C, DTG\({}_{\text{peaks}}\) at 258 and 285 \({}^{\circ}\)C, respectively), followed by another considerable mass loss process in the 309-624 \({}^{\circ}\)C temperature range (\(\Delta\)m = 80.41%, DTG\({}_{\text{peaks}}\) at 396 and 501 \({}^{\circ}\)C, respectively).  
Thermal analysis of compound **1c** reveals that is thermally stable up to 238 \({}^{\circ}\)C when a multistep mass loss takes place, according to the DTG curve; the first mass loss process occurs in a narrow temperature range (238-266 \({}^{\circ}\)C, \(\Delta\)m = 14.44%, DTG\({}_{\text{peak}}\) at 252 \({}^{\circ}\)C--in agreement with the melting indicated by the Optimelt apparatus), followed by several other processes, up to 678 \({}^{\circ}\)C (\(\Delta\)m = 83.4%, DTG\({}_{\text{peaks}}\) at 287, 315 and 647 \({}^{\circ}\)C).  
Similar thermal behavior to that observed for the **4c** derivative is observed for the **1d** compound. This triazole is stable up to 232 \({}^{\circ}\)C, when a multistep mass loss takes place, according to the DTG curve; the first mass loss process occurs in a narrow temperature range (238-261 \({}^{\circ}\)C, \(\Delta\)m = 13.64%, DTGpeak at 252 \({}^{\circ}\)C--in agreement with the melting indicated by the Optimelt apparatus), followed by several other processes, up to 750 \({}^{\circ}\)C (\(\Delta\)m = 86.34%, DTGpeaks at 264, 276, 330, 448 and 583 \({}^{\circ}\)C).
###### Abstract  
Background: Little evidence has been provided regarding the effects of carrying standardized load equipment and foot parameters during quiet standing. Therefore, the main purpose of the study was to examine whether a load carriage might impact static foot parameters in police recruits. Methods: Eight hundred and forty-five police recruits (27.9% women) were tested in 'no load' vs. standardized '3.5 kg load' conditions. Foot characteristics during standing were assessed with the Zebris FDM pedoaborraphic pressure platform. Results: Carrying a 3.5 kg load significantly increased the 95% confidence ellipse area (\(\Delta\) = 15.0%, \(p\) = 0.009), the center of pressure path length (\(\Delta\) = 3.3%, \(p\) = 0.023) and average velocity (\(\Delta\) = 11.1%, \(p\) = 0.014), the length of the minor axis (\(\Delta\) = 8.2%, \(p\) < 0.009) and the deviation in the X (\(\Delta\) = 12.4%, \(p\) = 0.005) and Y (\(\Delta\) = 50.0%, \(p\) < 0.001) axes. For relative ground reaction forces, a significant increase in the left forefoot (\(\Delta\) = 2.0%, \(p\) = 0.002) and a decrease in the left hindfoot (\(\Delta\) = -2.0%, \(p\) = 0.002) were shown. No significant changes in relative ground reaction forces beneath the forefoot and hindfoot regions for the right foot were observed (\(p\) > 0.05). Conclusions: The findings suggest that spatial and temporal foot parameters may be more prone to change while carrying heavy loads, especially the center of pressure characteristics.  
special population; foot characteristics; center of pressure; statics; equipment; changes 2020
## 3 Results and Analysis
### Variation in Apparent Friction Coefficient  
The ratio of shear force to normal force is defined as the apparent friction coefficient in the unloading period. Different from conventional direct shear tests, the temporal variation in the apparent friction coefficient in unloading shear tests is artificially designed. According to Equation (4), since \(F_{s}\) is a constant and \(F_{N}\) linearly decreases with unloading time, the friction coefficient will increase inversely and reach the maximum value when the normal unloading stops. As illustrated in Figure 8a,b, at the maximum sliding velocity of 3.7 mm/s, the corresponding friction coefficient increased with the increase in unloading rate, and decreased with a larger applied shear force.  
In test Group B (with no intermission during the sliding), since the unloading rate is a constant (=0.24 kN/s), the apparent friction coefficient is a function of time and shear force,  
Figure 8: (**a**) Apparent friction coefficient when the sliding velocity reaches 3.7 mm/s (i.e., ultimate value in the test) for different unloading rates and different shear force. (**b**) Apparent friction coefficient versus unloading rate and shear force at sliding velocity of 3.7 mm/s. (**c**) The variation in friction coefficient versus normal unloading time for different shear force (the recorded data in Group B). The different colors represent different single tests.  
as formulated in Equation (5). Its corresponding surface in the space is an inclined concave surface. Obviously, after the same normal unloading time, the smaller the shear force, the smaller the friction coefficient. Because the sliding friction coefficient exists only during the stage of continuous slip, the range of friction coefficient variation is limited. This is reflected by the inverse proportional function curves from \(t_{i}\) to \(t_{j}\) in Figure 8c. The points beyond these lines are the values that the coefficient of friction has not reached in our tests. It can be seen that the greater the shear force, the narrower the range of the friction coefficient will be. The initial value of the friction coefficient is lower than that of the conventional direct shear test, and it changes according to the inverse proportional function. Also, the ultimate value of the friction coefficient is significantly greater than the tan \(\varphi\) value in Equation (3), which matches the result in Figure 6b when the normal force is fairly low.  
\[\mu=\frac{F_{\mathrm{S}}}{F_{\mathrm{N}}}=\frac{F_{\mathrm{s}}}{F_{\mathrm{ N}0}-at} \tag{4}\]  
\[\mu=\mu(F_{S},t)=\frac{F_{\mathrm{s}}}{20-0.24t},t\in(t_{i},t_{j}) \tag{5}\]
# DDR4 BER Degradation Due to Crack in FBGA Package Solder Ball
###### Abstract  
This paper measures bit error rate degradation in DDR4 due to crack in fine pitch ball grid array (FBGA) package solder ball. Thermal coefficient mismatch between the package and printed circuit board material causes cracks to occur in solder balls. These cracks change the electrical model of the solder ball and introduce parallel capacitance in the electrical model. The capacitance causes higher frequency attenuation and closes the data eye. As the data rate of the DDR4 increases there are more data eye closures. The data eye closure causes bit error rate (BER) degradation as the timing margin and voltage margin decreases. This degradation reduces the reliability of the system and causes more intermittent errors. DDR4 data line is loaded with a parallel capacitive element to mimic a crack in solder ball. The measured data eye shows a decrease in eye width. Bathtub plots are created for comparison of cracked solder ball and intact solder ball. The bathtub plots show the BER degradation due to crack in solder ball.  
FBGA; crack; solder ball; data channel; data eye; bit error rate; BER degradation; DDR4 +
Footnote †: journal:  
_Acticle_  
DDR4 BER Degradation Due to Crack in FBGA Package
# Energy-Efficient Connected-Coverage Scheme in Wireless Sensor Networks
## 5 Algorithm for the Energy Saved Maximization Problem
### Improved DEEC Protocol
#### 5.2.3 The Communication Way of Sensor Nodes around the Sink  
When CH in the network is selected and the CH broadcasts a message to other nodes, the non-cluster head node \(s_{i}\) finds the nearest CH according to the received signal strength, as shown in Figure 4. If the sensor node \(s_{i}\) joins the cluster with CH as the cluster head, the message will go through multi-hop or long-distance communication, which will consume a lot of energy. When comparing the distance \(d(i,sink)\) from the sensor node \(s_{i}\) to the sink and the distance \(d(i,CH)\) from sensor node \(s_{i}\) to its CH, if \(d(i,sink)<d(i,CH)\), sensor node \(s_{i}\) will communicate with sink directly. Thus, the energy that is consumed of sensor nodes around the sink can be reduced.  
The time complexity of the I-DEEC is bounded in \(\mathrm{O}(n^{2}+r\cdot n\cdot c)\), Where \(r\) is the number of rounds, \(n\) is the number of sensor nodes, and \(c\) is the number of cluster heads. First of all, we need to judge the distance between every two of the \(n\) sensor nodes, which is executed \(((n-1)+(n-2)+\cdots+2+1)\) times in total, so the complexity of the first step is \(\mathrm{O}(n^{2})\). The network runs a total of \(r\) rounds. We decide whether the sensor node joins the cluster group according to the distance between the sensor node and cluster head. There are a total of \((r\cdot n\cdot c)\) operations, so the complexity of the second step is \(O(r\cdot n\cdot c)\). These two operations run independently, and their sum is I-DEEC complexity, so the complexity of I-DEEC is bounded in \(\mathrm{O}(n^{2}+r\cdot n\cdot c)\). Algorithm 2 outlines more details.  
Input: C A set of sensor nodes in \(\mathbf{s}_{redCover}\) from Algorithm 1.  
Output: The path that consumes the least energy for data transmission.
```
1:Calculate \(d(i,sink)\)
2:for For \(i=1\) to \(r\)do
3:\(p_{i}=\frac{E_{i}(r)}{E(t)}\)
4:\(T(CH_{i})=T(n)*((\alpha*\frac{E_{i}(r)*E(i)}{E(r)*E_{n}}+\beta*D)\)
5: where \(D=(1-\frac{d(i,sink)-d_{\min}}{d_{\max}-d_{\min}})\).
6:\(t\)=Random number
7:if\(T(CH_{i})<t\)then
8:\(CH\gets n_{i}\)
9:\(R_{c}=(1+\alpha*\frac{E_{i}(r)}{E(r)}-\beta*D)*R_{r}\)
10: Calculate \(d(i,CH)\)
11:endif
12:if\(d(i,CH)<d(i,sink)\)then
13: Select \(CH\) and join the cluster
14:else
15: Nodes do not participate in the cluster
16:endif
17:endfor
```  
**Algorithm 2** I-DEEC.
# A Review of Passive Constant Flow Regulators for Microfluidic Applications
## 3 Mechanical Passive Constant Flow Regulators
### Flow Regulator with Passive Membrane Valve in PDMS
**(a)** Concept of the microchannel structure of the device at rest. **(b)** In the forward mode, the check valve is open and a flow regulating valve controls the liquid flowing through the microchannel. **(c)** In the reverse mode, the check valve is closed, and the flow is stopped (reprinted from Reference [46], Copyright MDPI). by [PERSON] et al. [51] and manufactured by Cordis Corporation. The valve labeled Orbis-Sigma(r) was first characterized in 1987 [51] and the later versions of this valve were included in many shunt evaluation campaigns (see, e.g., References [52; 53; 54; 55]). A schematic drawing that illustrates the functioning principle of the valve is shown in Figure 10.  
Figure 10: Schematic drawings of Orbis-Sigma II® valve (left) and cross-section of the orifice between the membrane and the pin (right). The cerebrospinal fluid (CSF) flow is indicated by an arrow. (**a**) Differential pressure stage, with a low hydraulic resistance up to 9 mbar. (**b**) Flow regulating stage, with a progressive narrowing of the orifice as the pressure increases from 9 to 36 mbar. (**c**) Safety stage beyond 36 mbar, with a low hydraulic resistance to restore normal ICP. This passive flow-regulated valve has a flexible silicone membrane with a narrow orifice surrounding a pin of changing diameter. This three-stage valve has a fluidic characteristic similar to that of the passive high-flow regulator shown in Figure 2, with a regulated flow rate of about \(0.3\) mL/min \(\pm\) 15% in the pressure range [9; 36] mbar [55]. A low flow version (Integra NPHT(tm)) designed to meet the special needs of normal pressure hydrocephalus (NPH) patients requiring a lower flow rate of about \(0.2\) mL/min, throughout a similar pressure range, is also marketed by Integra LifeSciences [56]. In the first stage, also called the differential pressure stage, the valve operates similarly to standard differential pressure valves with low hydraulic resistance. At higher pressure, in the flow regulating stage, the membrane goes down and the pin progressively reduces the orifice to maintain the flow constant. The third stage of the valve, in high-pressure conditions (above 36 mbar), corresponds to the movement of the membrane beyond the pin. In this safety stage, the lowering of the hydraulic resistance enables a higher rate of CSF drainage to restore normal ICP.
# Sustainable Plant-Based Diets and Food Allergies: A Scoping Review Inspired by EAT-Lancet
## 4 Discussion
### Limitations  
This review presents an integrated view of the current evidence but is subject to several limitations. First, the majority of the included studies were narrative reviews, observational cohorts, or preclinical experiments, which limits causal inference. The heterogeneity of study designs, outcome measures, and dietary definitions (e.g., \"plant-based\", \"vegan\", and \"Mediterranean\") further complicates direct comparisons and may reduce internal validity. Second, much of the mechanistic evidence--particularly regarding SCFA production, polyphenol-protein interactions, and probiotic effects--derives from in vitro models or animal studies, such as murine sensitization protocols or Caco-2 assays. These models, while informative, may not fully recapitulate human immunophysiology, and their generalizability remains limited. Third, food processing methods are inconsistently defined across the literature. While this review distinguishes between minimally and ultra-processed products, operational definitions vary widely, and their clinical translation is often unclear. Additionally, many studies fail to report standardized endpoints such as serum IgE, ba sophil activation, or validated clinical symptom scores. Moreover, although we selected only studies that explicitly referred to sustainable plant-based diets or EAT-Lancet-inspired patterns, we acknowledge the existing definitional heterogeneity across the literature. This variability may hinder standardization and comparability across studies. However, rather than representing a methodological flaw of this review, we consider it a reflection of a broader gap in the scientific literature. At present, direct comparisons between specific subtypes of sustainable plant-based diets--such as vegan versus flexitarian or EAT-Lancet-aligned models--in the context of food allergies remain extremely limited. This reinforces the need for future research to refine and standardize dietary definitions in allergy-related studies. Finally, this review did not assess risk of bias quantitatively across all included studies, given the scope and heterogeneity of the data.
## 5 Computational Results
### Algorithm Results
This will permit analyzing the impact on LAHCM when larger \(l\) are considered and thus better exemplify the trade-off between that parameter and solution quality. For classes A and B for all instances the method with \(l=100\) was able to find the same or better solutions compared to CPLEX in shorter computational times. For class B, we can observe the discussed trade-off, that is, as the solution quality improves for \(l=100\), the computational times are higher than for CPLEX. For class C, out of the 10 instances where the method could not provide better values for \(l=50\) (see Table 12), LAHCM was able to find eight equivalent results in less time than CPLEX for \(l=100\). In this regard, the average number of iterations regarding the instances presented in Table 13 for finding the best solution is 73.21 iterations, which also means that a list length of 100 should have been sufficient to find better or optimal results for all instances. Therefore, we can state that the selection of the strategy has a much higher influence than increasing the list length. On the one hand, strategy \(\alpha_{3}\) needs the longest computation time as it allows the most rescheduling decisions. On the other hand, this strategy is very effective to escape local optima while the other strategies are useful to create fast neighborhood solutions.
# Spread Spectrum Image Watermarking Through Latent Diffusion Model
## 2 Method
### Loss Function  
Although watermarking is performed in the noisy latent space, the loss of image quality is still inevitable. In order to minimize the loss of image quality, the mean squared error (MSE) between \(l_{ori}\) and \(I_{gen}\) is used to optimize the trainable latent vector:  
\[\mathcal{L}_{\mathcal{I}}=\frac{1}{CHW}\big{\|}I_{ori}-I_{gen}\big{\|}_{2}^{2}\, \tag{10}\]  
where \(C,H,W\) are the image channels, height, and width. Additionally, SSIM loss [21] is also included to achieve structural similarity to the original image  
\[\mathcal{L}_{\mathcal{S}}=1-\text{SSIM}(I_{ori},I_{gen}). \tag{11}\]  
To get closer to human perception, perceptual loss [22] is also included,  
\[\mathcal{L}_{\mathcal{P}}=\frac{1}{C_{j}H_{j}W_{j}}\big{\|}\phi_{j}(I_{ori})- \phi_{j}(I_{gen})\big{\|}_{2}^{2}\, \tag{12}\]  
where \(\phi_{j}(\cdot)\) represents the output of the \(j\)-th layer of a pre-trained VGG model [23], \(C_{j},H_{j},W_{j}\) are the number of channels, height, and width of the feature map in the \(j\)-th layer. The total loss is formulated as  
\[\mathcal{L}=\gamma_{1}\mathcal{L}_{\mathcal{I}}+\gamma_{2}\mathcal{L}_{ \mathcal{S}}+\gamma_{3}\mathcal{L}_{\mathcal{P}}, \tag{13}\]  
where \(\gamma_{1}\), \(\gamma_{2}\), and \(\gamma_{3}\) are weight factors.
## 5 Discussion and Concluding Remarks  
To enhance the on-orbit safety of manned spacecraft and avoid the threat of constellations, it is necessary to consider equipping them with phased array radar systems for large-scale search and tracking in the future. However, traditional scheduling methods for phased array radar have low scheduling efficiency when facing task requests generated via a large number of satellites and space debris. To enhance the resource allocation capability and efficiency of space station protection radar, this paper investigates adaptive scheduling methods. This paper analyzes the time pointer task scheduling method and proposes a scheduling method based on time shifting constraints and pulse interleaving to address the problems of a large average time shift ratio and idle task waiting period in this method. By interleaving radar tasks using pulse interleaving technology, the proposed method makes more efficient use of time resources and effectively reduces task execution time shifting through shifting constraints. In conclusion, simulation experiments demonstrate that the method proposed in this paper effectively reduces the average time shift ratio  
Figure 9: ASTR of targets with different threat degrees. (**a,b**) respectively show the average time shifting ratios of targets with different threat levels after scheduling using the traditional method and the improved method. In (**b**), the blue curve represents the upper bound of the time shifting ratio corresponding to targets of different threat levels.  
while significantly improving task scheduling success ratios and other performance metrics. Consequently, the space station protection radar achieves superior performance when confronted with a large number of targets requiring tracking and monitoring.  
Conceptualization, G.Z. and H.Y.; methodology, G.Z. and H.Z.; software, G.Z. and J.H.; validation, G.Z. and G.X.; formal analysis, G.Z. and H.Z.; investigation, J.H. and G.X.; resources, G.Z.; data curation, G.X.; writing--original draft preparation, G.Z. and D.W.; writing--review and editing, G.Z. and D.W.; visualization, H.Z.; supervision, H.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding  
No new data were created or analyzed in this study.  
The authors declare no conflicts of interest.