esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Cluster Analysis of Cultivars and Traits  
The cluster analysis, as shown in Figure 4, includes yield attributes and yield, root traits, and their respective contributions to the overall genetic diversity. Thirteen rice cultivars' Euclidean distances were calculated with the help of normalized phenotypic parameters/standardized morphological data, and a two-way hierarchical clustering heatmap (cultivars and traits; row and column), along with a dendrogram, is shown in Figure 4. Based on the divergence in traits, the 13 rice cultivars were grouped into three row clusters (Figure 4). Cluster 1 had three (3) cultivars that were closely linked to one another, whereas Cluster 2 and Cluster 3 are each composed of five (5) cultivars. The assessed 20 traits, however, were divided into two column clusters comprising 1 trait for Group1 and 19 traits for Group 2. The trait PH was placed in Group1. All the root traits and yield-contributing traits, such as RV, RL, RN, RP, LAI, RFW, BDW, LDW, NoTT, NoET, TGW, and GpP were placed in trait Group 2. Cluster 3, comprising the cultivars Binadhan-10, Hira-2, BRRI dhan29, BRRI dhan58, and Tejgold, performed better in that it achieved greater standardized values of the measured traits and their outcomes followed by Cluster 2 (BRRI dhan81, BRRI dhan67, SL8H, BRRI dhan74, and Binadhan-8) and Cluster 1(Rata boro, Jagliboro, and Lakhai). The cultivars belonging to Cluster 1 exhibited poor performance in comparison to the cultivars of the other clusters. The cultivars Binadhan-10 and Hira-2 in Cluster 3 outperformed the other studied cultivars, showing higher normalized values of all traits (Figure 4).  
Figure 4: Heatmap with dendrogram (two-way; row and column) illustrating the groupings of the studied cultivars and measured traits. The 13 rice cultivars were divided into three (row) clusters, while 20 measurable attributes were sorted into two (column) groups. The heatmap was generated using the normalized mean values of traits (scale range: \(-\)2 to +2). Trait details: PH—plant height (cm); BY—biological yield (g pot\({}^{-1}\)), GY—grain yield (g pot\({}^{-1}\)); SY—straw yield (g pot\({}^{-1}\)); NoTT—number of total fillers; NoET—number of effective fillers; PL—panicle length (cm); RL—root length (cm); SDW—shoot dry weight (g plant\({}^{-1}\)); LDW—leaf dry weight (g plant\({}^{-1}\)); TGW—1000-grain weight (g); RP—root porosity (%); LAI—leaf area index; LA—leaf area; RN—root number; RV—root volume (cm\({}^{3}\) hill\({}^{-1}\)); GpP—grain panicle\({}^{-1}\); RFW—root fresh weight (g plant\({}^{-1}\)); RDW—root dry weight (g plant\({}^{-1}\)); HI—harvest index (%).
## 3 Implementation and Experiments
### Quality Criteria  
We now briefly describe the measurements used in the next subsection to evaluate the experimental results.  
Obvious criteria for the evaluation are the number of suppressions and the running time. Furthermore, we use the average and the minimum size of the output row types as already done by [PERSON] _et al._[38] and [PERSON] _et al._[23], as well as the number of output row types. The perhaps most difficult to describe measurement we use is \"usefulness\" introduced by [PERSON] and [PERSON] [11]. According to [PERSON] and [PERSON], usefulness \"is based on the following observation: close values [...] enhance usefulness, as they will require a small amount of modification to achieve a \(k\)-anonymisation. [...] A small value in usefulness implies that tuples [= rows] are close together w.r.t.these attributes, hence require less modification to satisfy \(k\)-anonymity\".  
Formally, it is defined as follows.  
**Definition 3** ([11]).: _Let \(M\in\Sigma^{n\times m}\). Let \(1\leq i\leq m\) be an integer, and let \(\Sigma_{i}\subseteq\Sigma\) be the domain of the \(i^{\text{th}}\) column in \(M\), that is, the set of symbols used in the \(i^{\text{th}}\) column. For a subset \(V_{i}\subseteq\Sigma_{i}\) the attribute diversity, denoted by \(d_{A}(M,i,V_{i})\), is_  
\[d_{A}(M,i,V_{i})=\begin{cases}\frac{\max(V_{i})-\min(V_{i})}{\max(\Sigma_{i})- \min(\Sigma_{i})}&\text{numerical attributes}\\ \frac{|V_{i}|}{|\Sigma_{i}|}&\text{non-numerical attributes,}\end{cases}\]  
_where \(\max(V_{i})\), \(\min(V_{i})\), \(\max(\Sigma_{i})\) and \(\min(\Sigma_{i})\) denote maximum and minimum values in \(V_{i}\) and \(\Sigma_{i}\), respectively._  
Informally speaking, the attribute diversity is a measurement of how many symbols of \(\Sigma_{i}\) are in \(V_{i}\). The next definition extends this to diversity for a subset of rows of a given matrix.  
**Definition 4** ([11]).: _Let \(M\in\Sigma^{n\times m}\) be a matrix, and let \(R\subseteq M\) be a matrix containing a subset of rows of \(M\). The tuple diversity of \(R\), denoted by \(d_{T}(M,R)\) is_  
\[d_{T}(M,R)=\sum_{i=1}^{m}d_{A}(M,i,\alpha(R,i)),\]  
_where \(\alpha(R,i)\) denotes the domain of the \(i^{\text{th}}\) column of \(R\)._  
With these notations, one can define the usefulness measure.  
**Definition 5** ([11]).: _Let \(M\in\Sigma^{n\times m}\) be a matrix and let \(M^{\prime}\in\{\Sigma\cup\star\}^{n\times m}\) be a \(k\)-anonymous matrix obtained from \(M\) by suppressing entries. Let \(\mathcal{R}^{\prime}=\{R^{\prime}_{1},\ldots,R^{\prime}_{\ell}\}\) be the row types of \(M^{\prime}\). Further, denote with \(\text{original}(R^{\prime}_{i})\) the set of rows in \(M\) that form, after having applied the suppression operations, the output row type \(R^{\prime}_{i}\)._  
_The usefulness of this partition \(M^{\prime}\) is:_  
\[\text{usefulness}=\frac{1}{\ell}\sum_{i=1}^{\ell}d_{T}(M,\text{original}(R^{ \prime}_{i}))\]  
Roughly speaking, the _usefulness_ is the average tuple diversity of all output row types. In general, small usefulness values are better, and the values lie between zero and the number \(m\) of columns.
# Synthesis of Hydrogel-Based Microgels and Nanogels Toward Therapeutic and Biomedical Applications
## 4 Paradigm Shift: Bulk Hydrogels to Hydrogel Micro/Nanoparticles
### Hydrogel Nanoparticle Synthesis
#### 4.3.3 Emulsification and Membrane Extrusion  
Membrane-mediated emulsification is a highly effective approach for producing nanogels and particularly suitable for large-scale synthesis. In this technique, a preformed dispersed phase (emulsion) is forced through a microporous membrane under high pressure, where each emulsion experiences a high level of shear stress and deformation, resulting in overall size reduction. The size of nanodroplets generated in this method is dictated by the pore size of the membrane, applied inlet pressure, and initial size of emulsions [57; 186; 244].  
Subsequent stabilization is imperative because emulsions become increasingly unstable as they become smaller due to the increased surface area and mechanical disruption accompanied with membrane extrusion. Nano-emulsion destabilization can occur through three mechanisms: coalescence, flocculation, and Ostwald ripening [245]. Coalescence occurs when emulsions merge to form larger droplets, which is a spontaneous phenomenon to reduce the total interfacial area. It is easily driven by the insufficient stabilization of the nano-emulsion interface, eventually leading to macro-phase separation. flocculation describes the aggregation of emulsions without merging, as if they are stuck together. The fate of flocculation is governed by inter-droplet forces; however, it is generally seen as a \"delayed\" mechanism leading to macro-phase separation. Ostwald ripening involves the diffusion of a smaller emulsion into larger ones as if they are absorbed. This phenomenon is driven by differences in Laplace pressure, where a smaller radius of curvature exhibits a larger value, making it very challenging to maintain smaller groups of emulsions. Over time, this process causes the average size of emulsion to increase and the size distribution to broaden, destabilizing nano-emulsions.  
Despite the fundamental challenges, the membrane extrusion coupled with emulsification offers tight control over particle size with minimal energy compared to conventional nano-emulsion synthesis methods. This technique has been widely adapted for encapsulating bioactive reagents, including protein, nucleic acids, and small drug molecules.
# Detect Overlapping Community Based on the Combination of Local Expansion and Label Propagation
## 5 Conclusions  
In this paper, we propose an overlapping community detection algorithm named \(LELP\) that is a combination of local expansion and label propagation. It first conducts local expansion based on _closeness_ to generate some immature communities, which are pruned to retain a dense structure called the dense region. These dense regions are very close to the center of the expected communities and so are taken as the source for label propagation. The experiments in diverse synthetic networks and real-world networks show that \(LELP\) is excellent, compared with other algorithms. The results in the synthetic networks demonstrate that \(LELP\) is better than the current mainstream algorithms in general. The performance of \(LELP\) is less affected by the network size, mixing degree and community size. The proportion of overlapping nodes has a great influence on \(LELP\), which is similar to the contrast algorithms. The accuracy of \(LELP\) is high at the node level. Most of the true overlapping nodes are correctly detected; a few nodes may be misjudged as being overlapping nodes. Almost all non-overlapping nodes are correctly detected. In the real-world networks, \(LELP\) also performs better than other algorithms. It can be applied to most real-world networks as the result of its stability in the face of different network structures. The number of overlapping nodes detected by \(LELP\) is in an appropriate range for the detection to be of practical significance.  
Figure 6: Sub figure (**a**–**c**) respectively represent overlapping modularity \(Q^{e}_{ov}\), the proportion of overlapping node \(O_{n}\)/N and average number of community memberships \(Q^{m}\) of different algorithms in 6 real-world networks.  
**Author Contributions:** Conceptualization, X.L. and Q.S.; methodology, X.L. and Q.S.; software, X.L.; validation, X.L.; investigation, X.L. and Q.S.; resources, X.L. and Q.S.; writing--original draft preparation, X.L.; writing--review and editing, X.L. and Q.S.; visualization, X.L.; supervision, Q.S.; project administration, Q.S.; funding acquisition, Q.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Beijing Social Science Fund No. 11 JGB063 and Social Science Research Project of Ministry of Education No. 11 YJA630109.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Analysis
### Thickness and Microhardness of Boronizing Layer with Rare Earth Addition
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Surface Depth** & **(CeO\({}_{2}\)) 2\% Hardness/HV** & **(CeO\({}_{2}\)) 4\% Hardness/HV** & **(CeO\({}_{2}\)) 6\% Hardness/HV** \\ \hline
20 \(\upmu\)m & 1595.93 & 1571.97 & 1411.97 \\
40 \(\upmu\)m & 1412.10 & 1529.93 & 1509.04 \\
60 \(\upmu\)m & 714.29 & 1670.02 & 1173.15 \\
70 \(\upmu\)m & 662.47 & 1546.32 & 1125.45 \\
100 \(\upmu\)m & 686.60 & 652.42 & 624.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Boronizing layer hardness (boronizing temp. 950 \({}^{\circ}\)C, time 4 h). Figure 2: Hardness curves of different rare earth CeO\({}_{2}\) contents. away from the surface of the boronizing layer. This shows that when the content of rare earth CeO2 is 4%, the hardness of boronizing layer is high and relatively stable [18]. The analysis of the above results preliminarily shows that when adding rare earth CeO2 for boronizing, the ideal boronizing layer thickness and microhardness can be obtained when the boronizing temperature is 950 \({}^{\circ}\)C, the boronizing time is 4 h, and the rare earth content is 4%.
## Materials and Methods
### UV Spectrophotometric Titrations  
Stock solutions of the tested compounds were prepared in EtOH at 100 \(\upmu\)M concentration. UV-visible spectra were recorded for compounds **6a-h** in order to determine proton dissociation constants (pK\({}_{\rm a}\)) in the pH range 1.8-10. The compounds had absorption only in the UV range. Measurements were accomplished at a constant ionic strength (0.10 M KCl) and at 25.0 \(\pm\) 0.1 \({}^{\circ}\)C in aqueous phase (with 1% (\(v\)/\(v\)) EtOH). For the titrations, a carbonate-free KOH solution (0.10 M) was used, the concentration of which was determined by pH-potentiometric titrations. The initial volume of the samples was 10.0 mL and the spectrophotometric titrations were performed on samples containing 10 \(\upmu\)M of tested compounds. Purified argon was bubbled through the samples for deoxygenation circa 10 min prior the measurements. The UV spectra of the individual species in the various protonation states and the proton dissociation constants were calculated with the computer program PSEQUAD [24]. Experimental titration data measured in the absence of any precipitate in the solution (pH = 1.8-5 (or 3.5 in case of **6h**)) were used for the calculations. In the interval 200-800 nm, a diode array spectrophotometer (Hewlett Packard 8452A) was applied to record the spectra. The length of the path was 2 cm.  
Attempts to determine the distribution coefficients (D\({}_{7,4}\)) and permeability values have been made in \(n\)-octanol/buffered aqueous solution by using the traditional shake-flask method at pH 7.40 (20 mM phosphate buffer, 0.10 M KCl), as described earlier [25], and by the PAMPA method using a Corning Gentest pre-coated PAMPA Plate System [26], respectively.
# Spintronic RF-Direct on-off Keying Modulation Using a Frequency Division Multiplex
## 3 Design of the Spin Modulator  
A spintronic RF-direct OOK modulation technique using an FDM is designed by actively exploiting the strengths of the STNO, which can be easily fabricated with an array structure in a small form factor. Figure 4 shows the proposed spintronic RF-direct OOK modulation technique using an FDM concept realized with an STNO array. In particular, we designed near field communication with low power consumption by not amplifying the signal at the low power level of the STNO.  
For realizing spintronic RF-direct OOK modulation using an FDM, one antenna is used in the transmitter. For easy verification, the receiver has one antenna per channel. Figure 4 shows a block diagram of wireless spintronic communication applying spintronic RF-direct OOK modulation using an FDM.  
Here, \(\mathrm{Dn}\), \(\mathrm{I_{DCn}}\), and \(\mathrm{fn}\) are \(\mathrm{nth}\) data, current, and frequency, respectively; STNOn is the \(\mathrm{nth}\) STNO, and \(\mathrm{MNn}\) is a matching network for the \(\mathrm{nth}\) frequency at the \(\mathrm{nth}\) STNO.  
We executed experiments with two STNOs to check whether wireless communication can be realized with spintronic oscillation, and also assessed whether spintronic RF-direct OOK modulation using an FDM concept was practicable. We simply modulated the signal of a spintronic oscillator with two STNOs in the array. Accordingly, the STNO, which has conditions of a magnetic field of 125 Oe for 3.5 GHz and 175 Oe for 4.2 GHz, generates nearly maximum output peak power of \(-71\) dBm with a minimum linewidth of 130 MHz, especially has conditions of a rotation field angle of 45 degree for 3.5 GHz and 0 degree for 4.2 GHz at the same magnetic field, 175 Oe; the STNO under these conditions will be used for wireless communication in this work.  
Figure 3: The signal level and linewidth of the spintronic oscillation depending on magnetic field at the condition of IDC = 1.5 mA. (1 Oe (Oersted) = 1 Gauss in air = 0.1 millitlesla).
## 3 Test Results and Analysis
### Freeze-Thaw Cycle Effects  
#### 3.1.1 Backbone Curve  
The average value of the hysteresis circle vertices of the 4 th, 5 th, 6 th, and 7 th turns of each loading level is selected, and the average value of the stress-strain vertices under eight levels of loading is connected to obtain the backbone curve under the working condition. The backbone curves of the soil under the influence of freeze-thaw cycles at the four sand blending rates are shown in Figure 5. After the freeze-thaw cycles, the backbone curves of the soil still met the nonlinear characteristics. With the increase of freeze-thaw cycles, the dynamic strain gradually increases under the same dynamic stress amplitude, and the backbone curve shows a downward trend in position, and the soil strength decreases. The first freeze-thaw cycle had the most obvious effect on the backbone curve with the largest decrease; after the third freeze-thaw cycle, the decreasing trend slowed down; after the sixth freeze-thaw cycle, the stress-strain curve stabilized, which was very close to the backbone curve of the soil sample after the ninth freeze-thaw cycle.  
The ultimate dynamic stress amplitude \(\sigma_{\text{dmax}}\) in the modified Hardin-Drnevich hyperbolic model can reflect the dynamic strength of the soil to some extent. So, in order to quantitatively analyze the effect law of soil dynamic properties under freeze-thaw cycles, the relationship curve between the fitted parameter \(\sigma_{\text{dmax}}\) and the number of freeze-thaws is made, as shown in Figure 6a. As can be seen from the figure, after one freeze-thaw cycle, the ultimate dynamic stress magnitude of the four weathered sand content soil samples decreased by 11.62%, 16.36%, 9.30%, and 17.75%, respectively, with an average decrease of 13.76%; when the number of freeze-thaw cycles was three, six, and nine, the  
Figure 5: Variation curve of dynamic stress amplitude of weathered-sand-modified expansive soil under different freeze–thaw cycles.  
average decrease of the ultimate dynamic stress magnitude was 12.68%, 3.33%, and 1.77%, respectively, compared with the last time; the ultimate dynamic stress magnitude of the soil samples that experienced nine freeze-thaw cycles decreased by 20.37% compared with that of the unfrozen soil samples.
# Model-Based Analysis and Improvement of Vehicle Radiation Emissions at Low Frequency
## 5 Application and Analysis  
In Sections 3 and 4, a systematic approach is proposed to model, diagnose and improve the vehicle radiation problem. To realize this process in practice, some specific methods or techniques are required. In this section, the proposed strategy is adopted to diagnose and improve of a radiation emission problem of an EV in the frequency range from 15 kHz to 30 MHz to show some details for example and validate its effectiveness. The radiation emission is measured according to SAE J551-5 [23] and some results are shown by Figure 5. The experiment is conducted in the 10 m semi-anechoic chamber, the electric field and magnetic field are measured by the rod and loop antennas respectively, which are located 1 m away from the vehicle body. During the testing, the vehicle running state and test system layout are consistent with the regulation, the electric field is measured with vertical polarization, but the magnetic field is measured with three antenna orientations. It is found that, at around 13.85 MHz, the electric field exceeds the limit by about 7 dB\(\mu\)V/m.  
Figure 4: Hierarchical evaluation of improvement solution.  
Since the vehicle layout is delivered and fixed, it is almost impossible to change the component arrangement and the design of the electrical system. A feasible way is to find out and improve the main interference source. One traditional way to identify the source is by comparing the emission under different combinational running states by switching on/off different components. This is very resource-consuming and cannot provide an efficient improvement measure. Here, the systematic approach presented in Sections 3 and 4 is adopted to identify the main interference source and determine the final solution.
# A Survey on Hardware Accelerators for Large Language Models
## 6 In-Memory Hardware Accelerators
### ReTransformer  
In 2020, [PERSON] et al. proposed an in-memory framework for the acceleration of Transformers, called ReTransformer [33]. ReTransformer is a ReRAM-based in-memory architecture for Transformer acceleration that not only accelerates the scaled dot-product attention of the Transformer using ReRAM-based in-memory architecture but also eliminates some data dependency by avoiding writing the intermediate results using the proposed matrix decomposition technique. Furthermore, ReTransformer proposes a new sub-matrix pipeline design for multi-head self-attention.  
ReRAM is used to perform matrix multiplication and scaling operations in Transformer networks. ReTransformer also optimizes softmax by incorporating in-memory logic to maximally exploit on-chip ReRAM crossbar arrays in the processing sub-arrays. Specifically,ReTransformer uses ReRAM-based compare-and-select logic to identify the maximum values and uses look-up tables to perform the exponential and logarithm functions.  
The performance evaluation shows that--compared to GPU--ReTransformer can achieve a speedup of up to 23.21\(\times\) while the corresponding overall power is reduced by 1086\(\times\).
# Fatigue Reliability Analysis of Rib-To-Deck Joints Using Test Data and In-Situ Measurements
## 2 Fatigue Tests of Rib-To-Deck Joints
### Test Setup
Based on the above analysis, the distance between the strain gauges and the loading point can be assumed to be the same in the two types of specimens. Besides, penetrant testing (PT) checks were also performed to identify the cracking of RD joints during the test. At first, the PT check is conducted after every 50,000 cycles. After any fatigue crack is observed, the interval of PT checks is narrowed to every 10,000 cycles.
## 3 Results and Discussion
### Effect of HP Treatment on pH Values of Meat Products  
Initial pH value of all meat products was approximately 6.5; HHP treatment did not alter the pH value of the vacuum-packed meat products. During storage, all control samples exhibited pH decrease, while for HP treated ones the pH values were slightly affected. Reduction of pH value in foods and more specifically in meat products is usually correlated to lactic acid bacteria population [(30; 31)]. After 60 days of storage at 0 \({}^{\circ}\)C, the pH values of control and HP treated samples were 5.7 and 6.1, respectively, for cooked ham. For the ham cubes at 10 \({}^{\circ}\)C, pH of HP treated samples was above 6.2 after 18 days of storage, while for control samples, the values decreased to 5.2. For Bratwurst sausages, after storage at 0 \({}^{\circ}\)C for 60 days, the pH values of control and nitrite-made samples were 5.7 and 5.8, respectively, while for the HP-treated samples, the pH value was 6.1.  
Figure 5: Growth of lactic acid bacteria of diced cooked ham (ham cubes) during storage at 0, 5, 10, and 15 \({}^{\circ}\)C, for control (**a**) and HP treated samples at 600 MPa and 25 \({}^{\circ}\)C for 5 min (**b**).