esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Improve L1 Optimization to Correct 100% of Corrupted Propagated Errors in Network Coding
### The Organization of Data for L1 Optimization  
A sketch approach is as follows: \(\kappa\in R^{k\times 1}\) is the message needed to be sent where \(k<n\). \(\kappa\) may not be sparse. Add \(n-k\) zeros to \(\kappa\), to form \(x_{0}\in R^{n\times 1}\) which is sparse. Then, to get \(y\in R^{m\times 1}\) based on Equation (1). Divide \(y\) into two parts: \(A\) and \(B\). The part \(A\) is sent through a secret channel. Then, the part \(B\) is sent through the networks where the network coding is performed in the relay nodes. Although adopting complex field may improve performance of network coding [29], we just consider real field rather than complex field.  
L1 optimization is performed in \(R\) field. Thus, all the encodings in relay nodes are performed in \(R\). The decoding of L1 optimization is also performed in \(R\). All the coefficients of network coding are selected in a finite field \(F_{q}\) as usual, as we do in the common network coding.  
\(y\) is sent through the networks with network coding, and polluted by errors of the networks. The received messages are \(Y\) the mixture of \(y\) and errors. The max-flow min-cut \(C\) may be different from \(m\), and usually \(C<<m\). To adapt with transmission through networks, reorganize \(y\in R^{m\times 1}\) to \(y^{\prime}\in R^{C\times\mathbb{E}}\). This process can be expressed as follows, that is  
\[Y=T\times y^{\prime}+\ T_{Z\to Y}\times Z \tag{7}\]  
If the percentage of the \"original error\" is stable, the number of the \"propagated errors\" projected to \(y\) is the same with that of \(y^{\prime}\). For clarity and convenience, we assume that \(C=m\) in this subsection, though it is far from the truth. It is convenient for theory analysis. Therefore, we also adopt Equation (7) to analyze the case \(y\in R^{m\times 1}\).  
\[Y=T\times y+T_{Z\to Y}\times Z \tag{8}\]  
In reality, we have to consider the truth \(C<<m\). In the algorithm, we assume \(C<<m\). This simplified model captures the essence for the error spread. With some abuse of terminologies, we re-define the dimensions in Equation (8). \(T\in C\times C\), i.e., \(T\in m\times m\). \(T\) is the true transfer matrix of \(y\). \(Z\in R^{t\times 1}\) is the error vector. Note that, in our L1 optimization method, there is no need to assume \(t\leq C/2\) as done in previous works. \(t\) is arbitrarily big, and is even equal to the number of all the links and this is contradictory to intuition. However, if \(t\) is equal to the number of all the links, we can increase the sparseness of \(T_{Z\to Y}\). This method will decrease the number of \"propagated errors\" because it is smaller than \(C\). The propagated errors are what the \(t\) original errors are projected to the received messages \(Y\).  
\(Z\)'s all components are nonzero. \(t\) is the number of corrupted packets. \(T_{Z\to Y}\) refers to the linear transform from error edges to the sink. \(T\) is \(C\times C\), \(T_{Z\to Y}\) is \(C\times\text{t}\).
## 3 Analysis of Test Results
### Response Surface Analysis
#### 3.3.1 The Interaction Effect of Multiple Factors on Slurry Density  
The interplay of multiple factors on the slurry density is shown in Figure 12. Based on the contour line trend in Figure 12a, the sequence of the influence of various factors on the slurry fluidity is as follows: fly flash-cement ratio > water-binder ratio > soil-binder ratio > particle size > binder-sand ratio. As seen in Figure 12b-e, the response surface undergoes distortion, and the fly flash-cement ratio and the soil-binder ratio have a certain interaction effect on the density of grouting slurry. Further, more significant interactive impacts are observed between the fly flash-cement ratio and particle size, soil-binder ratio and water-binder ratio, and fly flash-cement ratio and particle size, respectively. Figure 12b and Equation (8) indicate that as the muck content decreases, the influence of  
Figure 11: Relation curve between each factor and slurry compressive strength.  
the water-binder on the density intensifies. When the soil-binder ratio falls below 0.6, the water-binder ratio's effect becomes more pronounced, and its linear relationship with the density strengthens as the soil-binder ratio diminishes. From Figure 12c and Equation (9), it is evident that reduced much content diminishes the powder-to-ash ratio's impact on the density. Figure 12d and Equation (10) reveal that for particle sizes below 0.2 mm, the fly flash-cement ratio inversely correlates with the slurry density, following an approximately parabolic curve. Conversely, for sizes exceeding 0.2 mm, a direct proportionality exists, also adhering to a parabolic trend. Lastly, Figure 12e and Equation (11) show that larger particle sizes amplify the influence of the binder-sand ratio on the density, with a linearly inverse proportional relationship.  
\[f_{11}=-0.762x_{1}^{2}-0.109x_{4}^{2}+0.684x_{1}-0.148x_{4}+0.473x_{1}x_{4}+1.581 \tag{8}\]  
\[f_{12}=0.026x_{3}^{2}-0.109x_{4}^{2}-0.109x_{3}+0.3784x_{4}-0.074x_{3}x_{4}+1.754 \tag{9}\]  
\[f_{13}=0.026x_{3}^{2}-0.171x_{3}-0.299x_{5}+0.203x_{3}x_{5}+1.968 \tag{10}\]  
\[f_{14}=0.406x_{5}-0.498x_{2}x_{5}+1.809 \tag{11}\]  
Figure 12: _Cont._
# An IoT System and MODIS Images Enable Smart Environmental Management for Mekong Delta
## 3 Materials and Methods
### Ground Measurement Stations  
Figure 3 displays the sensor station architecture of our proposed system. As shown in the figure, the power supply for the IoT stations is provided by solar panels, considering the park's natural conditions. The data communication between the stations and the data collection module on the cloud is facilitated through the use of a 3G/4G network. Each station collects a variety of parameters, including temperature, moisture, CO\({}_{2}\), and particulate matter (PM10 and PM2.5), among others, through the use of sensors. It is important to note that the list of sensors used can be customized and modified according to the system's specific requirements.  
Figure 4 presents a visual representation of the actual deployment of an IoT sensor station in the study area. For deploying in the Mekong Delta area where the water level may change from 0 m to 2 m every year, we store the sensors and control board inside a waterproof 20 cm \(\times\) 30 cm box. The box is placed on the top of a 2 m concrete column. The large scale of the Mekong Delta requires a comprehensive and efficient approach to data collection for the smart environmental management system. Hence, deploying a combination of IoT stations and using remote sensing images is critical to ensuring the quality of data collected and reducing the building and operating costs of the system.  
Integrating IoT stations and remote sensing images enhances the coverage and accuracy of data collection, allowing for a more comprehensive and reliable representation of the state of the environment. The use of IoT stations ensures that real-time data can be collected and processed. At the same time, the remote sensing images provide a broader  
Figure 2: Study area in Dong Thap province.  
perspective of the study area, allowing for the detection of patterns and trends. The combination of these two approaches enables the smart environmental management system to be a cost-effective and efficient solution for monitoring the environment in the Mekong Delta.
###### Abstract  
This paper evaluates the key factors influencing the design of optical wireless communication (OWC) systems operating in the mid-infrared range. The performed analysis has shown that working in this spectral \"window\", compared to other wavelengths, is more effective in reducing the attenuation of radiation. The main goal was to verify the capabilities of the \"on-shelf\" interband cascade (IC) laser in the context of OWC system construction, considering its output power, modulation rate, room temperature operation, and integrated structure. For this purpose, a lab model of a data link with IC laser has been developed. Based on its main parameters, the estimation of signal-to-noise power ratio versus data link range was made. That range was about 2 km for a case of low scintillation and relatively low visibility. In the experimental part of the work, the obtained modulation rate was 70 MHz for NRZ (non-return-to-zero) format coding. It is an outstanding result taking into consideration IC laser operated at room temperature.  
optical wireless communication; interband cascade lasers; optical link; free space optics; MWIR data link +
Footnote †: journal: Physics Letters A  
1
Footnote 1: email: [EMAIL_ADDRESS]
# MSSP: A Blockchain Sharding Protocol Based on Multi-Shard Storage
## 1 Introduction
Finally, experimental verification demonstrates that MSSP has significant advantages over traditional sharding protocols in terms of throughput, latency and security. The contributions of this paper are as follows:  
* Proposed a sharding protocol MSSP based on multi-shard storage for blockchain. Multi-shard storage is a novel storage mechanism in which nodes in the blockchain network store data from multiple shards. The collection of nodes storing the same multiple shards forms a consensus zone. These consensus zones enable direct processing of cross-shard transactions related to the stored shards, eliminating the need for complex coordination between separate shards. Compared to traditional single-shard storage, multi-shard storage significantly reduces the communication overhead and latency associated with cross-shard transactions, thereby improving transaction throughput and overall system performance. * Designed a priority sorting mechanism to ensure the security of the blockchain system. This mechanism sets priorities for consensus zones, automatically adjusts them based on specified conditions, and sorts blocks according to the priorities of consensus zones. This mechanism provides the ability to resist double-spending attacks for the tree-like blockchain structure under the protocol while ensuring fairness. * Introduced a \(P\)-probability return update completion proof mechanism, allowing nodes to probabilistically return a proof indicating that an update has been completed. This mechanism ensures that after cross-shard transactions are completed, all nodes storing relevant account data can be updated promptly and enhances the security of the blockchain. * Conducted a theoretical analysis of the security of MSSP, implementation of a prototype of the scheme, and evaluation of its performance. The results indicate that compared to traditional sharding, this protocol exhibits significant performance and security advantages.
## 4 Conclusions  
Using traditional nitrate extraction methods for biochar may underestimate the nitrate content of the biochar. Increasing the temperature of the extraction to 50 \({}^{\circ}\)C for a period of 24 h results in an extraction efficiency above 90% after one additional rinse. Additional rinses beyond the first rinse did increase the extraction efficiency, although the fraction removed after each rinse was not significant. It is recommended that, to increase nitrate extraction efficiency for biochar, the temperature and duration of extraction should be increased from other methods, particularly those developed for soil matrices. In this case, it is recommended to use 50 \({}^{\circ}\)C for a period of 24 h with one additional rinse to increase nitrate recovery above 90%. However, future studies should investigate if shorter durations would achieve similar results, as there was no significant difference between 24 and 96 h in this study and it may be possible to achieve equilibrium from shorter durations. Additionally, this study was limited in scope to only one biochar and one extractant, thus, future studies should investigate extraction efficiency for biochars produced from different biomasses, temperatures, and modifications to confirm the method's performance, and evaluate other extractant possibilities to see if the current method could be altered for better performance.  
The authors contributed equally to this work. This material is based upon work that is supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture under project numbers 2015-67019-23573 and 2017-67003-26055. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture. The authors would like to acknowledge [PERSON], [PERSON], [PERSON], and [PERSON] for laboratory assistance. The authors declare no conflict of interest.  
Figure 1: Nitrate extraction efficiency from biochar following initial KCl extraction and subsequent rinses. Error bars represent standard deviation between replicates. Capital letters signify a significant difference between nitrate extraction efficiencies for initial and subsequent rinses within each treatment; lowercase letters signify a significant difference between treatments for the extraction step among treatments.
# Short-Term Power Load Prediction of VMD-LSTM Based on ISSA Optimization
## 2 Fundamentals of the Model
### Permutation Entropy  
Permutation entropy (PE), a metric for quantifying the complexity of time-series data, has gained extensive use in nonlinear analyses because of its high robustness and computational efficiency [29]. In this research, PE is utilized to assess the complexity of the VMD components. The optimal decomposition level is determined by taking into account the forecasting horizon and avoiding over-decomposition and under-decomposition. The detailed decomposition principle is as follows:  
Assume there exists a time sequence \(u(1)\), \(u(2)\), \(\cdots\), \(u(N)\) with length \(N\). The sequence is reconstructed and represented by a new sequence \(u^{\prime}(i)\) as follows:  
\[u^{\prime}(i)=[u(i),u(i+\tau),\cdots,u(i+(m-1)\tau)] \tag{8}\]  
where \(\tau\) means the delay time, and \(m\) means the embedding dimension.  
Sort the inner elements of \(u^{\prime}(i)\) incrementally, and then  
\[u^{\prime}(i)=u[i+(k_{1}-1)\tau]\leq u[i+(k_{2}-1)\tau]\leq\cdots\leq u[i+(k_{ m}-1)\tau] \tag{9}\]  
where \(k_{i}\) means the position before permutation. If two values are equal, they are sorted according to the subscript \(i\) of \(k_{i}\). Thus, \(u^{\prime}(i)\) is mapped to \((k_{1},\ k_{2},\cdots k_{m})\). There are \(m!\) permutations in total.  
Based on the above steps, the occurrence probability of each \(u^{\prime}(i)\) is denoted as \(P_{1},P_{2},\cdots,P_{n}\), where \(n\leq m!\). Subsequently, the permutation entropy of the time series \(u(1)\), \(u(2)\),...., \(u(N)\) is expressed as  
\[H(m)=-\sum_{k=1}^{n}P_{k}\text{ln}\,P_{k} \tag{10}\]  
For the ease of calculation, permutation entropy \(H(m)\) is generally normalized to obtain its normalized value as  
\[H_{p}=\frac{H(m)}{\text{ln}(m!)} \tag{11}\]  
Permutation entropy (PE), validated through experiments as a metric for quantifying time-series complexity, shows that a higher regularity in a time series corresponds to a lower PE, while greater complexity leads to a higher PE.
## 6 Results and Discussion
### The Impact of the BA Filler on the Results of Indirect Tensile Strength/Ratio--ITS/ITSR  
The indirect tensile strengths of the conditioned dry samples (ITSd) and water-saturated samples (ITSw) were determined in accordance with the HRN EN 12697-23 standard [50]. The results of the indirect tensile strength of the dry samples (ITSd) and the water-saturated samples (ITSw), together with the ITSR results, can be found in Figure 4.  
\begin{table}
\begin{tabular}{l c c} \hline \hline \
\begin{tabular}{c} **Characteristic,** \\ **(Unit of Measurement)** \\ \end{tabular} & **Results** &
\begin{tabular}{c} **Max. Allowed** \\ **Concentration** \\ \end{tabular} \\ \hline Fluorides (mg/kg dry matter) & 0.433 & 10 \\ \hline Sulfates (mg/kg dry matter) & 13.3 & 1000 \\ \hline Chlorides (mg/kg dry matter) & \textless{}12.0 & 800 \\ \hline Content of total dissolved solids (TDS) (mg/kg dry matter) & \textless{}12.0 & 4000 \\ \hline Dissolved organic oxide (DOC) (mg/kg dry matter) & 30.7 & 500 \\ \hline Arsenic (As) (mg/kg dry matter) & \textless{}0.005 & 0.5 \\ \hline Barium (Ba) (mg/kg dry matter) & 0.077 & 20 \\ \hline Cadmium (Cd) (mg/kg dry matter) & \textless{}0.005 & 0.4 \\ \hline Total chromium (Cr) (mg/kg dry matter) & \textless{}0.005 & 0.5 \\ \hline Copper (Cu) (mg/kg dry matter) & \textless{}0.005 & 2 \\ \hline Mercury (Hg) (mg/kg dry matter) & \textless{}0.002 & 0.01 \\ \hline Molybdenum (Mo) (mg/kg dry matter) & \textless{}0.005 & 0.5 \\ \hline Nickel (Ni) (mg/kg dry matter) & \textless{}0.005 & 0.4 \\ \hline Lead (Pb) (mg/kg dry matter) & 0.016 & 0.5 \\ \hline Antimony (Sb) (mg/kg dry matter) & \textless{}0.005 & 0.06 \\ \hline Selenium (Se) (mg/kg dry matter) & \textless{}0.005 & 0.1 \\ \hline Zinc (Zn) (mg/kg dry matter) & \textless{}0.005 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Test results of the submerged asphalt sample eluate (cylinder).  
Figure 4: Results of indirect tensile strength (ITS) and ratio (ITSR).  
The ratio of the indirect tensile strengths of the wet and dry asphalt samples with BA (ITSR\({}_{\text{BA}}\)) and without BA (ITSR) are  
\[\text{ITSR}_{\text{BA}}=\text{ITSw}/\text{ITSd}=1850/1870=98.9\%\]  
\[\text{ITSR}=\text{ITSw}/\text{ITSd}=1900/2080=91.4\%\]  
The prescribed condition, according to the technical regulations [51], is ITSR \(\geq\) 90% in the most demanding category, which confirms that the asphalt concrete with 50% bio-ash in the filler achieves a good resistance to the action of water, better than asphalt with 100% mineral filler, with an increase of 8.2% achieved.
## 3 Results and Discussion
### Effect of Genotype on the Anti-Inflammatory and Antimelanogenic Activities
The antimelanogenic properties of the leaf extract obtained from BT (IC\({}_{50}\) = 185.56 \(\pm\) 16.17 \(\mathrm{\SIUnitSymbolMicro g}\)/mL), NT (IC\({}_{50}\) = 185.81 \(\pm\) 5.24 \(\mathrm{\SIUnitSymbolMicro g}\)/mL), and NG (IC\({}_{50}\) = 195.32 \(\pm\) 5.62 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) were significantly greater than those of other genotypes (Figure 4f). Correlations were identified between TSC and antimelanogenic activity (R\({}^{2}\) = \(-\)0.511 ''; '' \(\leq\) 0.05). This indicates that the leaf of _A. triphylla_ has the potential for development as a whitening agent. Nevertheless, the antimelanogenic activities of _A. triphylla_ exhibit diversity among various genotypes. In addition, we discovered a strong correlation between various biological activities of _A. triphylla_, such as DPPH-radical scavenging activity, anti-inflammatory activity, and antimelanogenic activity, and its TSC. Triterpenoid saponin from _A. triphylla_ suppressed the growth of human gastric cancer cells via inhibition of the extracellular (ERK) signal-regulated kinase signaling pathway [34], which is also known to play crucial roles in melanogenesis and inflammation [35; 36]. As a result, it is reasonable to hypothesize that the anti-inflammatory and antimelanogenic activities of _A. triphylla_ leaf extract (Figure 4) may be attributed to the inhibition of the ERK signaling pathway, which requires further investigation. Figure 4: Comparison of anti-inflammatory and antimelanogenic activities among the five A. triphylla genotypes. Effects of each genotype (150 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) on NO production (**a**) and cell viability (**b**) in LPS-stimulated RAW264.7 cells. (**c**) IC\({}_{50}\) values of the LPS-induced NO production of each extract. The effect of extracts (200 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) obtained from each genotype on melanin production (**d**) and cell viability (**e**) was analyzed in IBMX-treated B16F10 cells. (**f**) IC\({}_{50}\) values of the IBMX-induced melanin production of each extract. Different letters indicate significant differences at \(p\leq\) 0.05 (means \(\pm\) SE from three independent replicate experiments).
## 5 Impacts of Salinity on Soil
### Soil Biological Activity  
Salts are an important component of soil, and certain salts (e.g., nitrates, sulfates, phosphates) are critical for soil fertility [118]. Salt-impacted soils contain high concentrations of soluble salts in both the micro and macro-pores [119]. Therefore, soil salinity inhibits soil respiration and C mineralization, as demonstrated by lower the CO\({}_{2}\)-C evolution rates of saline soils in comparison to non-saline soils [120]. Similarly, the osmotic potential of the soil solution increases and thus minimizes the potential plant and microbial water availability [121]. Saline soils have higher permeability to soluble salts and lower potential assimilation rates of nutrients like N [122]. Additionally, the absorption of nutrients is inhibited by ion competitiveness and higher pH, whereas the weak soil structure generated by high sodium levels adversely affects soil water quality and plant growth [123].
# A Survey of the Applications of Text Mining for the Food Domain
## 8 Challenges and Future Directions
### Challenges  
The food domain possesses a vast amount of unstructured text data, including recipes, menus, and reviews, which are valuable assets. Text mining involves preprocessing and feature extraction, followed by machine learning and deep learning algorithms for tasks like classification, clustering, and sentiment analysis. However, challenges arise due to the varied quality, diversity, and complexity of multi-source text data in the food domain. Ensuring semantic understanding is hindered by inconsistent quality, including misspellings and grammar errors in reviews. Authenticating reviews and dealing with potential false information further complicate the analysis. Additionally, the lack of structured meta-data limits semantic understanding, despite efforts to generate structured training data through manual annotation. Data diversity and complexity necessitate effective methods for integration, cleaning, and management, especially concerning different languages, formats, and sources [141]. Ethical and legal concerns, such as privacy protection, also arise, balancing open application with information security [142]. Overall, while text mining offers promising applications in the food industry, addressing challenges of data quality, representativeness, completeness, privacy, and skill requirements is essential for its effective implementation and social value creation.