esa-sceva/satcom-chunk-collection huggingface dataset:
# Eco-Efficient Cement-Based Materials Using Biomass Bottom Ash: A Review
## 5 Biomass Bottom Ash in CTGM (Soil Cement) and Soil Stabilization  
Cement-treated granular materials (CTGM) or also called cement soil, are homogeneously compacted mixes of soil or granular materials, cement and water, widely used in structural pavement layers [58].  
In the last decade, many studies have been carried out on the use of CTGM incorporating industrial by-products. The most used waste has been recycled aggregate from construction and demolition waste. [PERSON] et al. [59] reported on the properties of CTGM manufactured with recycled aggregate and different percentages of cement, while [PERSON] et al. [60] carried out research based on a real application in road base construction, using recycled aggregates of different composition such as CTGM.  
The incorporation of other industrial by-products such as steel slag, ash from power plants, blast furnaces as raw material for the manufacture of CTGM or to stabilize marginal soils are being studied. [PERSON] et al. [61] used biomass fly ash as a binder for road stabilization.  
However, there are very few studies in which BBA is used as CTGM or for soil stabilization. [PERSON] et al. [20], studied the possibility of using BBA as a raw material mixed with natural aggregates and recycled aggregates as a road base or sub-base. They reported physical, chemical, and mechanical parameters with very satisfactory results in some of the dosages used. In 2018, [PERSON] et al. [24] presented an experimental investigation of stabilization of expansive soils by removing lime and incorporating BBA. The results showed significant improvements in the mechanical behavior of the studied mixes.
## 5 Conclusions  
In this paper, we investigate the charrelation matrix (the generalized covariance matrix) in DS-CDMA systems for blind user separation and blind chip/speading sequence estimation. The unknown mixing matrix is estimated by joint diagonalization of the charrelation matrix of the observations. Theoretical analysis and simulation results show that the proposed blind separation using charrelation matrix performs better than the conventional scheme in low SNR. Especially, the proposed blind separation method has superior performance than that of the existing classical JADE algorithm-based HOS when the number of samplings is short and the SNR of the received signal is low. Furthermore, we can acquire the chip sequence in the case of low SNR and high accuracy assisted blind separation based on the charrelation matrix, so the proposed method has strong ability anti-interference, which is promising in applications for anti-jamming in military communications and satellite communications.  
This work is fully supported by a grant from the National High Technology Research and Development Program of China (863 Program) (No. 2012 AA01A502), National Natural Science Foundation of China (No. 61179006), Science and Technology Support Program of Sichuan Province (No. 2014 GZX0004).
## 1 Introduction
### Mechanical Behaviour at Elevated Temperatures
[PERSON] [19,20] compared the mechanical behaviour of different PPA and polyamide (PA) types after isothermal ageing. He showed the degradation of the secant modulus as well as the tensile strength for PPA and PA at higher temperatures. He concluded that PPA is superior to PA in terms of tensile strength, creep-rupture life, and stiffness when it comes to high temperature applications especially in the long run [19]. Furthermore, the standard failure mechanisms, which are fibre fracture with matrix crack and fibre pull-out, were not affected by the temperature change but the degree of their contribution to failure. On top ofthat, SEM observations unveiled a dependence of matrix ductility and fibre pull-out length to the surface roughness in creep-rupture tests [20]. [PERSON] et al. [21] investigated how the fatigue performance of previously damaged steel specimens can be influenced by CFRP patches and further investigated the influence of testing temperature. The main findings were that the CFRP patches effectively can enhance the fatigue performance of the specimens and that the elevated temperature (60 \({}^{\circ}\)C) weakens the fatigue performance of the created steel-CFRP specimens significantly. They further describe premature debonding for some specimens at 60 \({}^{\circ}\)C and a shift in debonding mechanisms from CFRP/adhesive layer debonding at lower temperatures to adhesive layer/steel debonding at elevated temperature levels (>45 \({}^{\circ}\)C). [PERSON] et al. [22] investigated the temperature influence of a hybrid bonded-riveted joint connecting CFRP and aluminium. It could be seen that joint strength and energy absorption is temperature-dependent, and it could be observed that higher temperature led to a more severe failure of the CFRP substrate around the rivet hole. [PERSON] et al. [23] developed a fatigue testing procedure for steel-CFRP hybrids with a special look at self-healing effects. They furthermore used simulations to investigate the influence of thermal residual stress (TRS) due to elevated temperatures and stated that an influence of the TRS cannot be neglected.
# RBC Storage Lesion Studies in Humans and Experimental Models of Shock
## 7 Longer-Stored RBCs in Hemorrhagic Shock  
After establishing that older stored RBCs did not harm uninfected controls but were associated with increased lung injury and mortality in a canine model of septic shock from _S. aureus_ pneumonia, we next sought to determine if longer stored RBCs would produce similar abnormalities during shock and inflammatory injury in the absence of infection. Therefore, 2-year-old purpose-bred beagles (n = 12) underwent controlled hemorrhage to produce shock and 2.5 h afterward were transfused with similar quantities of either RBCs stored for 42 days or RBCs stored for 7 days [14]. Canines transfused with longer stored RBCs had increases in CFH (\(p<0.0001\)) and iron (\(p=0.004\)) levels and an unexpectedly lower mortality (18% vs. 50%) from the hemorrhagic shock. The increased levels of CFH with longer-stored RBC transfusion were associated with a more favorable hemodynamic response. With hemorrhage-reperfusion, longer-stored transfused RBCs lowered exogenous norepinephrine requirements (\(p<0.05\)) and this lowered cardiac outputs (\(p<0.05\)). This hemodynamic effect is consistent with the ability of CFH to cause vasoconstriction by scavenging NO without the cardiac stimulatory properties of norepinephrine. By lowering norepinephrine requirements, elevated CFH in this clinical situation decreased cardiac output which resulted in slower reperfusion after resuscitation which is known to lessen reperfusion injury.  
These data in various shock states suggest that host factors mainly dictate if harm, benefit, or no effect will result from the increased hemolysis and liberation of CFH and iron associated with transfusion of older RBCs. In our canine model of septic shock, during transfusion of older blood, the transient increase in iron levels and the rapid removal of iron from circulation is associated with increased lung damage and mortality. These preclinical data are consistent with the notion that during septic shock iron derived from hemolyzed transfused older RBCs disappears more rapidly because iron is utilized by bacteria as a nutrient and growth factor, worsening the infection and increasing mortality. During hemorrhagic shock, elevations of in vivo iron levels persist longer after transfusion of old blood than during infection. Furthermore, in hemorrhagic shock, increased levels of iron are not associated with worsened outcomes but the elevations of CFH lower the need for cardiac stimulatory drugs, which is beneficial. In normal controls, the CFH and iron from transfused longer stored RBCs has no harmful effects. Thus, it is the underlying host factors such as infection or hemorrhagic shock, not the presence of increased CFH and iron alone, that appear critical to determine the outcome from longer-stored transfused RBC.
## 3 Results and Discussion
### Impact of Dosage and Metal Ions  
The dosage effect of the extracted biopolymer on flocculation was evaluated. The biopolymer displayed the peak flocculation (95%) at the least dosage and revealed the lowest activity (92%) at 0.8 mg/mL. However, there were no differences (\(p>0.05\)) in the activities shown by all used concentrations (Table 3). The decline in the flocculating capability with the increase in the dosage was owed to the formation of the high viscosity, which hindered the interaction between the effective structures of the biopolymer and kaolin [47; 48]. The profound activity at the low dosage size of the biofocculant implied its economic advantage. Our observations contrasted those reported by [PERSON] et al. [49], where the flocculating activity of the biofocculant produced by _Bacillus pumilus_ was maximum (96.5%) at the low dosage of 0.1 mg/L. However, they were in conformity with those observed by [PERSON] and [PERSON] [50], where the dosage size of 0.2 mg/mL revealed the maximum flocculation.  
\begin{table}
\begin{tabular}{c c} \hline
**Elements** & **Composition (\%) (\(w\)/\(w\))** \\ \hline Potassium & 0.5 \\ Oxygen & 17.5 \\ Nitrogen & 1.1 \\ Carbon & 61 \\ Magnesium & 2.8 \\ Chlorine & 0.9 \\ Phosphorus & 8.1 \\ Sodium & 1.7 \\ Calcium & 6.4 \\ \hline \end{tabular}
\end{table}
Table 2: The elemental composition of the biofocculant as illustrated by EDX analysis.  
Figure 2: FTIR spectrum of the biofocculant from _B. megaterium_ BMBF.  
The influence of metal ions on flocculation was assessed. The subsequent addition of Li\({}^{+}\), Ca\({}^{2+}\) and Ba\({}^{2+}\) into the biofoculant solution led to an improvement in the flocculating rate, while the addition of Na\({}^{+}\) led to the decrease in the flocculating performance. Ca\({}^{2+}\) gave the peak flocculating efficiency of 96%; however, the activity was statistically equal to those obtained when Li\({}^{+}\) and Ba\({}^{2+}\) were utilised (Table 3). This implied that, of Li\({}^{+}\), Ca\({}^{2+}\) and Ba\({}^{2+}\), any of the three metal ions could be used during the application of this biofoculant, as they effectively neutralised and stabilised the charges of the functional structures of the kaolin and the biopolymer, consequently leading to an improved flocculation process. Our observations aligned with the discoveries by [PERSON] et al. [51], in which Ca\({}^{2+}\) effectively stimulated the rate of flocculation.
# An Aquatic Mobile Sensing USV Swarm with a Link Quality-Based Delay Tolerant Network
## 5 Link Quality Characterization for Simulation Support
When the antenna is at the drone level, the Achievable Throughput is lower. The Bandwidth decreases with the distance, but has a better sensitivity to higher distances, as opposed to the SSI. The Achievable Throughput obtained through experimentation was used to estimate a delay for each packet in the simulator. The delay was computed by dividing the number of bytes to send by the Achievable Throughput at a certain distance. Once again, using the measurements obtained with the antenna in the p15 position, we can model the Achievable Throughput for this environment using a 4 th degree function with a _MAE_ of 0.738, given by Equation (7). Other fits were tried, such as cubic fit with a _MAE_ of 1.602, a quadratic with 1.602, and a logarithmic with 1.964. The delay also plays a significant role in the link quality estimators, because it is used to determine the bit rate of the connection to be assumed in simulation environments. \[AT(x)=-0.00017\ x^{4}-0.01603\ x^{3}+0.472862\ x^{2}-4.20788\ x-14.6655,\quad x \in[5,40]. \tag{7}\]  
Estimating the Achievable Throughput with a single peer to peer connection may not represent the AT when the channel is being shared by more than one transmitter. Knowing this, in simulation environments, we have to adapt the AT depending on the number of USVs transmitting at the same time and on their distances, which may result in smaller throughputs, and, consequently, higher delays.
# Real-Time Communication Support for Underwater Acoustic Sensor Networks +
## 6 Computing the Minimum Frame with a Genetic Algorithm
### Effective Period  
Although the frame length defines the system period, it is possible to reduce the transmission period of each node by overlapping each frame with the one from the next period.  
**Definition 1**.: _The effective period is defined as the minimum period between messages transmission from the same node that is possible by overlapping frames._  
Overlapping is not always possible, because it depends on which slots are allocated at the beginning and at the end of the obtained frame. In some cases, a frame of greater length than theoptimal frame may have a shorter effective period. In order to take this advantage into account, it is possible to modify the objective function of the problem that contains the Algorithm 1 in a way that instead of returning the length of the computed frame, it is returned the maximum possible overlapping slots, or what is the same, the effective period of the frame. In this way, the optimization heuristic can find frames of greater length, but of minimum effective period increasing the network throughput.
## 2 Matrix-Based Object Representation
The negative of the distance is chosen to be consistent with the other utilized metrics, which are all maximized in the optimization. Therefore, the objective functions for the rectangles (\(f_{DBL}^{r}\)) and circles (\(f_{DBL}^{c}\)) can be expressed as follows:  
\[f_{DBL}^{r}=i_{r}-j_{r},\quad f_{DBL}^{c}=i_{c}-j_{c} \tag{11}\]  
This objective is only used with sequential packing. 3.1.4. Adjacent Ones (A1)  
The number of adjacent ones is introduced as the last objective function, which correlates with the overall contact within the domain. This metric promotes efficient utilization of the available space and it is formulated by taking advantage of the raster representation. The objective function to be maximized (\(f_{A1}\)) is simply defined as the sum of the entities of matrix \(F\), which is the elementwise multiplication of four submatrices of the current domain matrix \(D\):  
\[ \begin{split} F=D(1:H-1,1:W-1)\circ D(2:H,1:W-1)\\ \circ D(1:H-1,2:W)\circ D(2:H,2:W)\\ f_{A1}=\sum_{i=1}^{H-1}\sum_{j=1}^{W-1}F_{ij}\end{split}  \tag{12}\]  
In Figure 4, the principle behind the developed contact maximization technique demonstrated for two example domains containing rectangles (a) in contact and (b) separated by zeroes. The proposed adjacent ones metric provides several advantages:  
* It outperforms other metrics in solving certain problems;
* It is versatile as it delivers satisfactory results for several different cases;
* It is applicable for both concurrent and ordered optimization strategies;
* It can be used with or without explicit constraints while performing well in many unconstrained cases.
## 3 Results and Discussion
### Water Activity (a\({}_{w}\))  
When developing new formulas for food products, determining the water activity (a\({}_{w}\)) is essential in the recipe. Water activity (a\({}_{w}\)) is connected with control of the quality and stability of food products in terms of physical properties, texture, and microorganisms growth [50]. The results of the conducted research are presented in Figure 3.  
Figure 3: Effect of different amounts of dried vegetable powders on water activity of fat-free dairy desserts. Letters (a–e) indicate significant differences at \(p<0.05\) ([PERSON]’s HSD test).  
Figure 2: Effect of different amounts of dried vegetable powders on back extrusion values of fat-free dairy desserts. Letters (a–f) indicate significant differences at \(p<0.05\) ([PERSON]’s HSD test).  
The water activity of different samples of fat-free dairy desserts with vegetable powders decreased significantly (\(p<0.05\)) in products with beetroot and carrot powder addition, along with higher concentrations. The opposite tendency was noticed in products with the addition of championon and onion powder. The highest values (\(p<0.05\)) were noted in the samples with 5% onion (0.996), championon (0.997), 1% (0.992), and 3% of beetroot (0.989), respectively. The lowest water activity was noted in the dairy dessert with 5% beetroot powder (0.868). To describe a dairy product as shelf-stable, its water activity should be as low as possible (\(>\)0.85). Sometimes, even when this requirement is fulfilled, this product often has a relatively short storage life [51]. [PERSON] et al. (2005) tested the features of yogurt fortified with calcium and fiber. They noticed that the product's water activity decreased due to the higher amount of solids in the product [52]. The opposite tendency in the two series of tested products was observed (champignon and onion), which may be related to the ability to bind water by vegetable powders used in the experiment.
## 2 Dead-Time Effect of the Auxiliary Resonant Snubber Inverter (ARSI)
### Compared with the Hard-Switching Inverter  
According to the analysis in Section 2.1, the essential principle of ZVS both in the light and heavy load condition is that the resonant capacitors that are parallelly connected to the next turn-on switches are discharged to zero-voltage and then the body diodes conduct the current. Thus, the switches can be turned on with zero-voltage. The dead-time consists of the resonant stage and diode clamping stage. However, the dead-time effect only exists in the resonant stage, which is caused by the finite rise- and fall-times of the voltage. Figure 4 shows the dead-time effect of a single pole, S1 and S3, both in the HLM and LLM where \(v_{a}\) is the actual pole voltage and \(v_{a,id}\) is the ideal pole voltage. In order to summarize the dead-time effect both in the HLM and LLM, all the rise- and fall-times are considered to be linearly changed in Figure 4.  
As for the hard-switching inverter, the dead-time effect is different from that of the ARSI. To analyze the dead-time effect, the assumption is the same as that of the ARSI. Figure 5 shows the operating stages and key waveforms of the hard-switching inverter with a single pole.  
Regarding the dead-time \(t_{1}\)-\(t_{3}\), the body diode D3 conducts the current after S1 is turned off at \(t_{1}\). The actual pole voltage is clamped to zero, which equals the ideal pole voltage. As for the dead-time \(t_{4}\)-\(t_{6}\), the current is diverted from S3 to its body diode D3 rather than D1 after the S3 is turned off, because of the positive load current. The pole voltage is clamped to zero, whereas the ideal pole voltage is \(V_{s}\). Therefore, the voltage loss occurs during the dead-time \(t_{4}\)-\(t_{6}\). Only after S1 is turned on can the current be diverted to S1. The dead-time causes voltage loss when the output current is positive. However, when the output current is negative, the dead-time leads to voltage gain.  
The dead-time effect of the hard-switching inverter is related to the output current polarity, which causes the blanking delay error, whereas the dead-time effect of the ARSI has no relation to the current polarity. Only the rise- and fall-error occurs in the ARSI, rather than the blanking delay error. Essentially, the voltage error caused by the finite commutation time of the voltage also occurs  
Figure 4: Dead-time effect of the ARSI.  
in the hard-switching inverter due to the junction capacitances of the switches. However, compared with blanking delay error, this error can be neglected. Regarding the ARSI, the lower the resonant capacitances, the smaller the voltage error. The dead-time effect can be fully eliminated if the resonant capacitances are zero and the junction capacitances are not considered.
## 4 Discussion  
### Evaluation Based on RCEI Sample Images  
In Figure 11, the ecological environmental quality of Jincheng City in 2022 is presented through six carefully selected sample points covering the urban areas, coal mine production area, mine living area, national forest park, and national ecological wetland area. The mean RCEI values of these sample points (0.9089, 0.3874, 0.3863, 0.1363, 0.6711, and 0.8688, respectively) were calibrated in Figure 11a,b based on the RCEI images and the RGB satellite images. Parts c through h of Figure 11 show in detail the RGB imagery of these areas, corresponding to (c) Lishan National Forest Area--Qinshui County; (d) Yicheng Coal Mine--Yangcheng County; (e) Sihe Coal Mine--Qinshui County; (f) Gu Mine--Urban Area; (g) Fenghuangling Forest Wetland--Zezhou County; and (h) Chessing Mountain National Forest Wetland--Lingchuan County. These images cover the details of the five counties of Jincheng City, ensuring a broad and diverse sample, thus eliminating the possibility of interactions. The analytical results in Figure 11 confirmed the close connection between the Resource-Based City Ecological Environment Index (RCEI) and the distribution of mining areas, urban development, and forest ecosystems, and they validated the RCEI as a reliable indicator of ecological change in resource-based cities.  
Figure 10: Land use mapping of Jincheng City in 1990–2022. (**a**) City land use classification. (**b**) Land use changes.
## 4 Case Study
### Construction of the Evidential-Network-Structure Model  
Space-navigation-and-positioning system of systems capabilities include navigation and positioning capability, system survivability, system anti-jamming capability, and security and confidentiality. The corresponding evidence network structure was established using the causality-diagram method, as shown in Figure 1.
# A Spectral Clustering Algorithm for Non-Linear Graph Embedding in Information Networks
## 4 Experiment
### Benchmark Algorithms  
To demonstrate the effectiveness of \(SEG\_SC\), the following methods were used as benchmark algorithms. A brief description of each method is found below as follows:  
* PCA [20]: A common matrix decomposition method used for dimensionality reduction or feature extraction. PCA defines a loss function based on the distance between the original sample and the reconstructed sample after projection. PCA is a linear graph embedding method. The clustering result is then obtained using the K-means algorithm.
* SC [2]: Spectral clustering consists of an anchor graph based on set-to-set distances derived from local covariance matrix representation.
* GraphEncoder [13]: A method for learning graph representations. This method explores non-linear graph embedding using deep learning and obtains clustering results by running the K-means algorithm on the embedding.
* DCN (deep clustering network) [28]: The DCN performs joint dimensionality reduction and clustering. Reduction is achieved by training a deep neural information network to approximate any non-linear function.