esa-sceva/satcom-chunk-collection huggingface dataset:
# Health Impact Assessment from Rice Straw Production in Cambodia
###### Abstract  
Rice is a staple crop best known for its crucial role in feeding humans. In Cambodia, the human population depends on rice as its staple food. Increased rice production results in an increase in straw residue. The extensive residue caused by straw burning significantly contributes to high concentrations of air pollution, which are associated with critical health issues. As such, our objective in this study was to assess the human health impact of rice straw production in Cambodia. In this assessment, we focused on primary and secondary fine particulate matter formation (PM\({}_{2.5}\), NH\({}_{3}\), NO\({}_{x}\), and SO\({}_{2}\)) from fertilizer, combustion of fuel in rice production processing, and burning of straw. We assessed endpoint impact by the following methods: (1) We estimated the proportion of rice straw from the annual rice production of the Ministry of Agriculture, Forestry, and Fisheries (MAFF) of Cambodia subjected to open burning in all 25 Cambodia provinces. (2) We calculated air pollutants emissions in terms of kilograms of pollutants per ton of rice produced by following the air pollutant emission inventory (EI) in the EMEP/EEA Guidebook for 2019, using data for fertilizer and fuel combustion that was applied in the field, based on a face-to-face survey of farmers. (3) We analyzed health impact in terms of DALYs using the characterization factor (CF) adapted from the ReCiPe 2016 v1.1 method. The results showed that the total health impact of rice straw production was 13,093.50 DALYs. Our analysis showed that open burning alone contributed 98.55% of the total health impact considered in this study, while fertilizers and combustion contributed only 0.12% and 1.33%, respectively. As the major of the total health impact arose from open burning, a policy of zero open burnings should be an effective way to reduce health impacts. These findings provide information for policymakers on how to alleviate air quality issues caused by the practice of open straw burning by adopting alternative techniques of rice straw management.  
Keywords:health impact assessment; rice straw; air pollutants; emission inventory +
Footnote †: journal: Applied
## 3 Results and Discussion
### X-ray Diffraction  
The powder diffraction patterns of raw materials, filaments, and 3D printed composites are shown in Figure 6. Patterns containing BT show a high-intensity peak at 31.5\({}^{\circ}\) assigned to (110) planes. In the case of HAp/BT composites, the above peak suppresses the diffraction peaks assigned to the presence of HAp since the highest intensity peak of HAp appears at 31.7\({}^{\circ}\). It is interesting to note that in both types of samples, BT exhibits the characteristic double peak in the range of 44\({}^{\circ}\) to 46\({}^{\circ}\). This indicates that after two thermal treatments of BT that correspond to the preparation of the filaments and the 3D printing objects, the transition of tetragonal to a cubic structure at 120 \({}^{\circ}\)C is reversible.  
Figure S2 shows the XRD patterns of all polymeric materials without the presence of fillers. The pattern of PLA shows a broad peak at about 15-16\({}^{\circ}\) indicating that PLA exist in orthorhombic structure [46; 47; 48]. It is interesting to note that PLA shows the same broad peak in filaments as well as in the printed objects, indicating that thermal treatment does not influence its crystallinity. The PCL pattern depicts two intense peaks at 21.4\({}^{\circ}\) and 23.8\({}^{\circ}\) that correspond to the (100) and (200) planes, respectively, belonging to the orthorhombic structure [49; 50]. The above diffraction peaks of PCL are also present in the filaments and in the printed objects.  
Figure 5: SEM images and digital photos (insert) of PCL/PLA (**a**) and PCL/PLA/20-20 (**b**) fabricated scaffolds.
## 2 Preliminaries  
Throughout this paper we let \(\ell\) be a fixed non-zero natural number. A _structural fragment_ is a sequence of \(\ell\) 3D-points. The _mean square distance (\(MS\))_ between two structural fragments \(f=(f[1],\ldots,f[\ell])\) and \(g=(g[1],\ldots,g[\ell])\), is defined to be  
\[MS(f,g)=\min_{R\in\mathcal{R},\tau\in\mathcal{T}}\sum_{i=1}^{\ell}\parallel f [i]-(R\cdot g[i]+\tau)\parallel^{2} \tag{2}\]  
where \(\mathcal{R}\) is the set of all rotation matrices, \(\mathcal{T}\) the set of all translation vectors, and \(\parallel x-y\parallel\) is the Euclidean distance between \(x,y\in\mathbb{R}^{3}\).  
The root of the \(MS\) measure, \(RMS(f,g)=\sqrt{MS(f,g)}\) is a measure that has been extensively studied. Note that \(R\in\mathcal{R}\), \(\tau\in\mathcal{T}\) that minimize \(\sum_{i=1}^{\ell}\parallel f[i]-(R\cdot g[i]+\tau)\parallel^{2}\) to give us \(MS(f,g)\) will also give us \(RMS(f,g)\), and vice versa. Since given any \(f\) and \(g\), there are closed form equations [2; 3] for finding \(R\) and \(\tau\) that give \(RMS(f,g)\), \(MS(f,g)\) can be computed efficiently for any \(f\) and \(g\).  
Furthermore, it is known that to minimize \(\sum_{i=1}^{\ell}\parallel f[i]-(R\cdot g[i]+\tau)\parallel^{2}\), the centroid of \(f\) and \(g\) must coincide [2]. Due to this, without loss of generality we assume that all structural fragments have centroids at the origin. Such transformations can be done in \(O(n\ell)\) time. After such transformations, in computing \(MS(f,g)\), only the parameter \(R\in\mathcal{R}\) need to be considered, that is,  
\[MS(f,g)=\min_{R\in\mathcal{R}}\sum_{i=1}^{\ell}\parallel f[i]-R\cdot g[i] \parallel^{2} \tag{3}\]  
Suppose that given a set of \(n\) structural fragments \(f_{1},f_{2},\ldots,f_{n}\), we are to find \(k\) structural fragments \(g_{1},\ldots,g_{k}\), such that each structural fragment \(f_{i}\) is \"near\", in terms of the \(MS\), to at least one of the structural fragments in \(g_{1},\ldots,g_{k}\). We formulate such a problem as follows:  
\begin{tabular}{|c l|} \hline \multicolumn{2}{|c|}{\(k\)-Consensus Structural Fragments Problem Under \(MS\)} \\ Input: & \(n\) structural fragments \(f_{1},\ldots f_{n}\), and a non-zero natural \\  & number \(k<n\). \\ Output: & \(k\) structural fragments \(g_{1},\ldots g_{k}\), minimizing the cost \\  & \(\sum_{i=1}^{n}\min_{1\leq j\leq k}MS(f_{i},g_{j})\). \\ \hline \end{tabular}  
In this paper we will demonstrate that there is a PTAS for the problem.  
We use the following notations: Cardinality of a set \(A\) is written \(|A|\). For a set \(A\) and non-zero natural number \(n\), \(A^{n}\) denotes the set of all length \(n\) sequences of elements of \(A\). Let elements in a set \(A\) be indexed, say \(A=\{f_{1},f_{2},\ldots,f_{n}\}\), then \(A^{m!}\) denotes the set of all the length \(m\) sequences \(f_{i_{1}},f_{i_{2}},\ldots,f_{i_{m}}\), where \(1\leq i_{1}\leq i_{2}\leq\ldots\leq i_{m}\leq n\). For a sequence \(S\), \(S(i)\) denotes the \(i\)-th element in \(S\), and \(|S|\) denotes its length.
## 6 Results: Simulations of Extended Scenarios
### Modes of Tracer Motion  
Two additional types of motion were introduced to the GATE model: a non-sinusoidal oscillatory motion and a spiral motion. For the non-sinusoidal oscillatory motion, the motion is described by  
\[X(t)=\frac{2Q}{\pi}\arcsin\left(\sin\left(2\,\pi\,f\,t\right)\right), \tag{2}\]  
where the parameters include frequency \(f\) and the amplitude of the motion \(Q\)[48]. A plot showing the motion of Equation (2) can be seen in Figure 9.  
The shorthand notation for the simulations of oscillatory motion is Oscillatory [\(Q\) (mm), \(f\) (Hz)]. The oscillatory motion was used previously to explore the ability of different location algorithms for PEPT to track the tracer at the turning points of a path [49] and offers an alternative test to the circular path used in other works (e.g., [18; 19; 29]). The oscillatory motion was chosen for this work to give insight into the ability to track tracer motion at the turning points of a path. The tracer particle has infinite acceleration at the turning points, a feature that occurs in high-frequency fluctuations as could be expected in turbulent multiphase flow paths.  
Figure 8: Location data for the NRW-100 PointTracer [591 \(\upmu\)Ci, 400 \(\upmu\)m, \({}^{68}\)Ga] undergoing Circular [27.64 mm, 19.74 Hz, \(C_{x}\) = 1.14 mm, \(C_{z}\) = 1.73 mm] in the \(XZ\) plane from PEPT experiments (red dots) and GATE simulations (blue dots). Subplots (**a–c**) show the locations with time in the \(X\), \(Y\), and \(Z\) dimensions respectively.  
The spiral motion was a constant linear velocity (CLV) spiral in the \(XY\) plane, which has a constant tangential velocity independent of the radius of the spiral. The polar position (\(\gamma\), \(r\)) has two coordinates as a function of time \(t\),  
\[\gamma=\sqrt{\frac{V\,4\pi\,t}{dr}} \tag{3}\]  
and  
\[r=\sqrt{\frac{V\,dr\,t}{\pi}}. \tag{4}\]  
The parameters describing the spiral are \(dr\), the spiral separation or sampling pitch along the radial direction, and \(V\), the instantaneous velocity [50].  
Thus, the coordinates of the \((X,Y)\) position of the tracer on the spiral at a given time \(t\) are  
\[(X,\,Y)=(r\cos\gamma,\,r\sin\gamma), \tag{5}\]  
with a constant \(Z\) value.  
A shorthand notation for the simulation of the spiral motion is Spiral [\(dr\) (mm), \(V\) (ms\({}^{-1}\))]. This motion was chosen to test the ability of PEPT measurements with the HR++ scanner to track the structure of eddies of different length scales that appear in turbulent, multiphase fluids.
## 4 BP Neural Network and Pearson Correlation Analysis
### Computation Analysis
#### 4.2.4 Comparison Analysis with Existing Results  
Referring to Table 1, existing results from short-circuit experiments in refs. [2; 10; 11], as well as those related to gas jet blowing in refs. [8; 9], were analyzed and compared with this study, as presented in Table 8:  
* In ref. [2], Table 8 indicates that the relative errors of peak pressure in ballast tanks, directly associated with the drainage percentage, ranged from 0.53% to 39.17%, significantly exceeding the results shown in Table 7 of Section 4.2.2;
* In refs. [10; 11], the small-scale short-circuit experimental test bench focused on the flow rate of high-pressure gas cylinder groups closely linked to the ballast tank drainage percentage. Their relative error was found to be 8%, considerably larger than the maximum relative error reported in Table 7 of Section 4.2.2;
* In refs. [8; 9], using a gas jet blowing-off method similar to short-circuit blowing resulted in a relative error for the drainage percentage below 5% and individually below 10%. However, these values still exceeded those presented in Table 7 of Section 4.2.2;
* From this comparative analysis, it could be inferred that the BPNN method demonstrates significantly higher prediction accuracy than traditional numerical modeling; furthermore, no statistical correlation studies were identified between the manipulation factors and the blowing process in Sections 3.1, 3.2 and 4.2.3.
# Two-Layer Network Caching for Different Service Requirements
## 2 Background
### Networks of Caches  
In this subsection, we consider the aspects related to both content replication strategies and coordinated cache management. A hierarchical cache organization, rooted at a single content repository, is presented in [22]. Cooperative cache management algorithms for minimizing bandwidth occupancy and maximizing hit ratio are proposed. The Leave Copy Everywhere (LCE) content replication strategy [5; 6], consists in replicating the contents on all the caches present in the path followed by the content for its delivery to the requesting customers. An LCE variant is the so-called Conditional LCE (CLCE), for which a cache node stores an arriving content if it satisfies a qualifying condition [21]. Another strategy is the Leave a Copy Down (LCD) [23], which consists of replicating contents only downward the location of a cached copy on the path to the requesting client. Move Copy Down (MCD) [6] is a strategy consisting of moving a cached content from the cache where is found to the connected downward cache. Leave Copy Probability (LCP) [6] is a strategy according to which a content copy is cached with a given probability \(p\) in the caches staying in the path to the requesting customers. In the strategy Centrality-based caching [6], a content is stored in the cache node having the highest betweenness centrality. A significant strand of research on cache networks is related to coded caching, which is worth of mention although it is not in the scope of our proposal. In [24] the authors investigated coded caching in a multiserver network of different types. Servers are connected to multiple caches implemented in clients. The Multi Queue caching algorithm [25] consists of a hierarchical organization of LRU queues in a cache. Each hierarchical level is associated with an expected lifetime. The position of a content at a given hierarchy level is determined by its request counts within the expected lifetime and the contents at the lowest hierarchy level are candidates for eviction. The a-NET was proposed in [26]. It approximates the behavior of multi-cache networks by making use of other approximation algorithms designed for isolated LRU caches. An analytical model of an uncooperative two-level LRU caching system is presented in [27]. A Time-to-Live based policy is proposed in [28] and performance is determined for both a linear network and a tree network with a root cache and multiple leaf caches with infinite buffer size. In the context of _content centric networking_, content placement schemes and request routing are tightly coupled. The relevant optimization problems typically consists maximization of a submodular functions subject to matroid constraints [29; 30]. Such mathematical framework is also used in this paper.
## 7 Networks
### Synchronization
#### 7.1.2 Dead-Beat Synchronization  
\"Lead-beat\" synchronization, first introduced by De Angeli et al. [131], corresponds to the situation in which the error between the drive and response systems converges to zero in finite steps.  
Grassi [132] studied the propagation of projective synchronization in a series connection of \(N\) chaotic discrete-time drive systems and N response systems. By exploiting an observer-based approach, he demonstrated that dead-beat projective synchronization (i.e., exact synchronization in finite time for any scaling factor) is achieved between the \(n\)th drive and \(n\)th response systems. In particular, he showed that projective synchronization starts from the innermost (\(N\)th) drive-response system pair and propagates toward the outermost (first) drive-response system pair. He illustrated the propagation of different types of chaos synchronization in a series connection consisting of three chaotic attractors: the Gingerbread man map, the Lozi map and the generalized Henon map.  
The Gingerbread man map [133] (a conservative version of the Lozi map) is defined as  
\[\left[\begin{array}{c}x_{1,1}(k+1)\\ x_{1,2}(k+1)\end{array}\right]=\left[\begin{array}{cc}0-1\\ 1\end{array}\right]\left[\begin{array}{c}x_{1,1}(k)\\ x_{1,2}(k)\end{array}\right]+\left[\begin{array}{c}1\\ 0\end{array}\right]|x_{1,1}(k)|+\left[\begin{array}{c}1\\ 0\end{array}\right]+\left[\begin{array}{c}0\\ 1\end{array}\right]\epsilon_{1}\varphi_{1}(k). \tag{98}\]  
The Henon map is generalized as follows:  
\[\left[\begin{array}{c}x_{2,1}(k+1)\\ x_{2,2}(k+1)\\ x_{2,3}(k+1)\end{array}\right]=\left[\begin{array}{cc}0&0-0.1\\ 1&0&0\end{array}\right]\left[\begin{array}{c}x_{2,1}(k)\\ x_{2,2}(k)\\ x_{2,3}(k)\end{array}\right]+\left[\begin{array}{c}1\\ 0\\ 0\end{array}\right](x_{2,2}(k))^{2}+\left[\begin{array}{c}1.76\\ 0\\ 0\end{array}\right]+\left[\begin{array}{c}-1\\ 0\\ 1\end{array}\right]\epsilon_{1}\varphi_{2}(k). \tag{99}\]  
and the Lozi map for \(a=1.4\) and \(b=0.3\) is  
\[\left[\begin{array}{c}x_{3,1}(k+1)\\ x_{3,2}(k+1)\end{array}\right]=\left[\begin{array}{cc}0&1\\ 0.3&0\end{array}\right]\left[\begin{array}{c}x_{3,1}(k)\\ x_{3,2}(k)\end{array}\right]+\left[\begin{array}{c}-1.4\\ 0\end{array}\right]|x_{3,1}(k)|+\left[\begin{array}{c}1\\ 0\end{array}\right]+\left[\begin{array}{c}-1.4\\ 0\end{array}\right]\epsilon_{3}\varphi_{3}(k), \tag{100}\]  
where \(\varphi_{1}\) is an arbitrary perturbation  
\[\varphi_{2}(k)=|x_{1,1}(k)|+[0\quad-1][x_{1,1}(k)\quad x_{1,2}(k)]^{T}+ \epsilon_{1}\varphi_{1}(k)\]  
and  
\[\varphi_{3}(k)=(x_{2,2}(k))^{2}+[0\quad 0\quad 1][x_{2,1}(k)\quad x_{2,2}(k)\quad x _{2,3}(k)]^{T}+\epsilon_{2}\varphi_{2}(k).\]  
Synchronization was obtained after few iterations.  
In [134], Lian et al. analyzed various scalar coupling signals with several classical chaotic attractors (Logistic, Henon, Lozi, etc.) to show that the connected systems achieve dead-beat synchronization.
## 3 Role of Small RNA in Stress Response
### DsrA  
There are two global regulators that play a key role in the stress response in _E. coli_. The first is the stress-induced sigma factor RpoS that equips the pathogen with necessary adaptations to cope with the stress. The second is H-NS, a nucleoid-associated protein in bacteria akin to histones in eukaryotic systems that binds across the genome and acts as a global transcriptional repressor, silencing genes involved in stress responses and virulence. Although several transcription factors regulate the expression of RpoS and H-NS, an 87-ntsRNA called DsrA regulates the expression of both RpoS and H-NS antagonistically. DsrA activates the expression of _rpoS_ posttranscriptionally by binding to 5' UTR and disrupting inhibitory secondary structures of RpoS transcript [153]. Alternatively, it represses the expression of _lns_, causing de-repression of H-NS silenced genes that are involved in stress response.  
DsrA promotes acid resistance by activating genes involved in the acid stress response including the _hdeAB_, _gadAX_, and _gadBC_ operons in _E. coli_ K-12 strains, as well as those in the pathogenic _E. coli_ strain O157:H7, contributing to bacterial endurance in acidic environments such as the stomach [154]. In _Salmonella enterica_ serovar Typhimurium, DsrA influences central carbon metabolism (CCM) and NAD(H) homeostasis by altering the expression of genes involved in glycolysis, pyruvate metabolism, the TCA cycle, and NADH-dependent respiration. A GFP-fluorescence-based reporter assay identified _pflB_, encoding pyruvate-formate lyase, as a direct target of DsrA repression. DsrA base pairs with _pflB_ mRNA, recruiting RNase E to degrade it, thereby reducing _pflB_ stability. This repression lowers the NAD\({}^{+}\)/NADH ratio, helping maintain redox balance [155]. Additionally, DsrA plays a key role in prophage \(\lambda\) induction by relieving the repression of _rcsA_ through antagonizing H-NS. This leads to increased capsular polysaccharide (_cps_) expression and RecA-independent prophage induction. Unlike RcsA, which requires RcsB for induction, DsrA functions independently of RcsB in stationary phase, suggesting an alternative pathway for prophage activation [156; 157]. Recent findings suggest that DsrA also plays a role in replication initiation, requiring Hfq, Dps, and OxyR [158].
# Primary User Traffic Pattern Based Opportunistic Spectrum Handoff in Cognitive Radio Networks
## 3 PUTPOSH Model
### Framework
#### 3.2.3 Spectrum Handoff Decision  
The spectrum handoff decision chooses on a suitable handoff class between reactive and proactive w.r.t. the lowest overall service time of the CRU. The overall service time of the CRU is based on the following times: sensing time, processing time, waiting time and transmission time. According to Figure 1 (in the spectrum handoff module), ReA time and PrA time represent the overall service time of the reactive and proactive handoff judgments respectively. In this paper, the ReA and PrA time values are assumed to find the threshold and also to compare different approaches. The overall minimal service time is implemented for the spectrum handoff decision. Subsequently, the CRU can maintain its transmission on the recently selected channel.  
The Spectrum handoff module in Figure 1 shows that the proposed PUTPOSH strategy begins with the recognition of the PU interarrival by spectrum sensing and forecasts PU dwelled period in the assigned channel. Hence in this step, the CRU either dwells on the recent channel or moves into the SH decision stage.
## 3 Test Data Analysis
### Process Optimization
Figure 12 resembles 3D plots of time versus various factors revealing the positive impact of the duration of adsorption on the extent of adsorption. The advancement of time supported by the pH and the concentration of BG increment improvises adsorption. The maximum period of 180 min showed the maximum adsorption. The optimum temperature is 29 \({}^{\circ}\)C and an increase in time with increased time motivates the capacity of a process. As the temperature increases with time, the adsorption capacity increases. The increase in pH beyond 1 decreases the adsorption capacity even if the time is increased. Graph (Figure 12c) plotted against time, and adsorbent dosage indicates that adsorbent measurements ve an adverse effect on adsorption. Nonetheless, expanded time can work on the course of sorption. Figures 12e and 13a,b 12 h for temperature against other autonomous factors, demonstrate that the temperature has a beneficial outcome with any remaining variable on the reaction. Figure 13c-e plotted for the initial dye concentration against other variables indicate that an increase in the initial concentration has a positive effect on adsorption capacity. The process optimization studies enabled us to estimate the peak adsorption extent by employing a quadratic model. It further provided a tool to comprehend the relationships between the independent variables that play a significant role in the adsorption studies. Using statistical optimization studies, the value of adsorption was enhanced from 156 mg/g to 203 mg/g.