esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Interaction Between the Gut Microbiota and Cardiovascular Diseases
### Non-Bacterial Microbiota and Atherosclerosis
These genes may modulate bacterial metabolism in mechanisms that ultimately influence the host's immunity, intestinal permeability, and the production of inflammatory mediators. In fact, individuals with cardiometabolic diseases, including obesity, type 2 diabetes, and atherosclerosis, have been found to have altered gut phageome composition. For instance, enrichment in phages infecting _Enterobacteriaceae_ and _Streptococcus_ species has been reported among these individuals [17; 142]. In parallel, although through different pathways, eukaryotic viruses have also been implicated in the pathogenesis of atherosclerosis. Various human viruses, including influenza virus, herpes simplex virus (HSV), hepatitis viruses (HAV, HBV, HCV), human cytomegalovirus (HCMV), human papillomavirus (HPV), human immunodeficiency virus (HIV), and SARS-CoV-2, have been associated with this disease through direct and indirect mechanisms, such as chronic systemic inflammation, immune activation, endothelial dysfunction, and oxidative stress. For example, influenza virus can increase LDL uptake by desialylation and induce endothelial permeability and thrombosis. In arterial walls, HCMV has been detected, and this virus has been implicated in the formation of foam cells, the proliferation of smooth muscle cells, and endothelial injury. Finally, cytokine storms and persistent inflammation can be triggered by both HIV and SARS-CoV-2, further contributing to vascular damage and plaque formation [16]. Collectively, these findings suggest that cardiovascular health, and, in particular, atherosclerosis, is actively influenced by the non-bacterial gut microbiota and may act synergistically with bacteria derived effects. To fully understand their involvement in atherogenesis and their potential as therapeutic targets, further studies are required.
# The LHAASO PeVatron Bright Sky: What We Learned
## 3 Overview of the 12 LHAASO Sources
### Lhaaso j2108+5157  
LHAASO J2108+5157 is a unique case because it has no detected TeV counterparts and it was the first source ever detected at UHE [131; 195] (see Figure 15, left). Its maximum energy is about 430 TeV, and it was found to be point-like; however, without the possibility of ruling out a possible extension. It is located in the complex Cygnus region (CygnusOB7 MC), and it is the only source without a bright pulsar found in the surroundings (the first is at 3\({}^{\circ}\)) [26]. The Fermi-LAT extended source 4 FGL J2108+5155e was found in its proximity (\(\sim\)0.13\({}^{\circ}\)) but a physical connection is not obvious because of spectral differences [195].  
Instead, a correlation with the MC [MML2017]4607 seemed to be confirmed [195] and a possible second cloud was proposed in [196], where the authors presented a pioneering study of the COB7 region, with low-resolution \({}^{12,13}\)CO(2\(\rightarrow\)1) observations made with the 1.85 m radio-telescope at Osaka Prefecture University, in order to investigate the nature of the LHAASO sources. This second cloud is [FKT-MC]2022, and the authors proposed it as a candidate source of the UHE from LHAASO J2108+5257, with a hadronic origin favored (MCs illuminated by accelerated CRs). Consequently, we have two MCs that suggest a  
Figure 14: LHAASO J2032+4102. (**Left**): HAWC significance map of the Cocoon Region, after subtraction of the known sources, and with the addition of the LHAASO source centroid (Figure from [103], reproduced with permission from [103], Springer Nature, 2023). (**Right**): the multiwavelength data from the Cygnus cocoon fitted by the lepto-hadronic scenario in [193] (Figure from in [193]). From this model the authors estimated the neutrino flux expected if the source was hadronic.  
hadronic scenario (see also [197]), even if the hard spectral index requires a nonlinear DSA or stochastic acceleration hypothesis.  
The multiwavelength analysis carried on in [198], putting together LST-1, XMM-Newton, and Fermi-LAT results, could not rule out any scenario, leptonic or hadronic (see Figure 15, right). LST-1 and LHAASO data can be explained by the IC scenario but the absence of X-ray emissions implies a weak magnetic field, favoring a TeV Halo scenario over the PWN one. However, there is no evidence of a pulsar. Nevertheless, in their work, a more complete combined analysis of both leptonic and hadronic components in the region is missing, and this could allow obtaining more constraining parameters.  
The estimated neutrino flux in a complete hadronic scenario is within the sensitivity range of future instruments, giving to us a chance to understand this challenging LHAASO source [197].
## Chemical Constituents of _U. tometosa_  
Previous studies reported the chemical constituents of several _Uncaria_ species and recognized the different molecules present in different parts of the plant. It is worth noting that more than 50 phytochemical molecules have been identified and isolated from _U. tometosa_, some of them are considered new to that species [12]. _U. tometosa_ leaves contain higher oxindole alkaloid content than that present in stem bark and branches. This result is compatible with a study previously described by [PERSON] et al. [13], who documented the accumulation of speciophylline and uncarine F (the main oxindole alkaloids) in leaves that can occur as tetracyclic oxindole alkaloid (TOA) or pentacyclic oxindole alkaloid (POA) derivatives. Both TOA and POA are liable to isomerization that depends mainly on medium polarity, pH, and temperature [13].  
A recent study about the chemical variation of a wild population of cat's claw from Peru reported the existence of three specific chemotypes that producing different alkaloid constituents [14]. Chemotype I is mainly composed of the POA with the intersection of D/E ring, chemotype II consists primarily of POA with _trans_ D/E ring junction, while chemotype III consists primarily of TOA derivative. Uncarine C and uncarine E are two POA stereoisomers, while mitraphylline, rhynchophylline, and isorhynchophylline are TOAs found in cat's claw. On the basis of these results, the U.S. Pharmacopeia revealed that dried raw material of cat's claw included 0.05% (_w/w_) of the TOA concerning the POA amount, whereas cat's claw powdered dried extract, tablets, and capsules contained up to 25% (_w/w_). Cat's claw contains several active compositions including ajmalicine, campesterol, carboxyl alkyl esters, akuammigine, sitosterols, rutin, chlorogenic acid, speciophylline, catechchin, cichonain [15], corynoxine, harman, daucosterol, epicatechin, hirsuteine, corynantheine, hirsutine, loganic acid, mitraphylline, iso-teropodine, oleanolic acid, ursolic acid, lyaloside [16], rhynchophylline, palmitoleic acid, pteropodine quinovic acid glycosides, procyanidins [10], stigmasterol, 3,4-dehydro-5-carboxystictosidine, vaccenic acid, uncarine A thru F, and strictosidines [10; 17]. Moreover, other reports revealed that various compounds other than oxindole alkaloids such as rotundifoline and isorotundifolune, coumarins, flavonoids, quinovic acid glycosides, and triterpenes may be responsible for the cat's claw medicinal effects [18; 19].
## 2 Basic Theoretical Analysis
### Frequency Estimation
The PSO algorithm is an evolutionary computing technology, which describes the foraging behavior of birds and other social organisms and was proposed by [PERSON] and [PERSON] [PERSON] and [PERSON] (1973). In the PSO algorithm, a population is constructed by \(M\) particles. Each particle \(i\,,\,(i=1,2,\cdots,M)\) has a position vector \(X_{i}=[x_{i,1},x_{i,2},\cdots,x_{i,L}]\) and a velocity vector \(V_{i}=[v_{i,1},v_{i,2},\cdots,v_{i,L}]\), where \(L\) represents the dimension of solution space of a optimization problem. The position vector \(X_{i}\) of each particle \(i\) is a potential solution in the problem-solution space. The iterative learning process is shown as:  
\[V_{i}^{t+1}=\omega V_{i}^{t}+c_{1}\times rand\times(pbest_{i}-X_{i}^{t})+c_ {2}\times rand\times(gbest-X_{i}^{t}) \tag{16a}\] \[X_{i}^{t+1}=X_{i}^{t}+V_{i}^{t+1} \tag{16b}\]  
where \(t\,\in\,[1,T]\) is the number of iterations; \(T\) is the maximum number of iterations; \(rand\) is a random real number uniformly distributed between 0 and 1. \(pbest_{i}=[\,pbest_{i,1},\,pbest_{i,2},\cdots,\,pbest_{i,L}]\) is the personal best position of particle \(i\) from the initial moment to current iteration \(t\). \(gbest=[gbest_{1},gbest_{2},\cdots,gbest_{L}]\) is the best position of whole population until now. The self-cognition factor \(c_{1}\) controls the strength that the particle \(i\) affected by its personal best position \(pbest_{i}\) during the evolution. On the contrary, the group cognition factor \(c_{2}\) controlsthe strength that particle \(i\) affected by the population best position \(gbest\). \(\omega\) is the inertial weight which represents the memory strength of the previous velocity and can balance the global search and local search ability [30]. The large inertial weight \(\omega\) means the PSO has better global search ability and weaker local search ability. The smaller \(\omega\) means the local search ability is better. Thus, in the initial stage of iteration, the PSO needs to have better global search ability (large \(\omega\)) to explore more solution space as possible and converge to the global optimum as soon as possible. When the iteration reaches the later stage, the particles gradually converge to the global optimum. At this time, PSO should have better local search ability (small \(\omega\)) and enable PSO to search accurately near the global optimum. Therefore, the linear decreasing inertial weight \(\omega\) strategy is adopted:  
\[S[k]=DFT\{s[n]\}=\sum_{n=0}^{N-1}s[n]e^{-j\frac{2\pi nk}{N}} \tag{12}\]0  
where the \(\omega_{max}\) and \(\omega_{min}\) are 0.9 and 0.4, separately. The inertial weight \(\omega\) decreases gradually as the number of iterations \(t\) increases. The pseudo-code of the frequency estimation method is shown in Algorithm 1. \[S[k]=DFT\{s[n]\}=\sum_{n=0}^{N-1}s[n]e^{-j\frac{2\pi nk}{N}} \tag{12}\]1 The spectrum of a simulation signal: (**a**) DFT result of the simulation signal; (**b**) refined spectrum \(|X(f)|\) in frequency range \([\,(p_{k}-0.5)\frac{f_{k}}{N}\,,\,(p_{k}+0.5)\frac{f_{k}}{N}\,]\).
## 1 Introduction  
Pulsed electric field (PEF) technology has stimulated intense research as an alternative processing technique to achieve cellular disruption since the 1960s [1, 2]. This processing applies short (micro- to milli-seconds) pulses of high voltage across food materials placed between electrodes [3, 4]. PEF processing at electric field strengths lower than 2 kV/cm can modify the structure and texture of plant tissue, which can enhance mass transfer, thereby facilitating extraction of phytochemicals and improving the accuracy and energy efficiency of cutting and slicing [5, 6]. However, most of these phenomena have been studied on mechanically fragmented tissue sections such as tissue disks [7, 8, 9], tissue slices [8, 10], tissue cubes [11], and tissue cylinders [12, 13]. PEF treatment has been shown to produce non-uniform effects on the microstructure of intact tubers (e.g., potato and kumara) [14, 15]. Thus far, limited information is available on how PEF treatment influences the volatile profile of intact plant materials.  
Onions (_Allium cepa_ and _Allium fistulosum_) are commonly used for culinary purposes [16] and are rich in _trans_-S-propenyl cysteine sulfoxide (PECSO), S-propyl cysteine sulfoxide (PCSO), and S-methyl cysteine sulfoxide (MCSO). These compounds act as volatile flavor compound precursors [17, 18].  
The volatile compounds in onions are produced when cell damage occurs facilitating the mixing of the enzyme alliinase and the flavor precursors, which are present in different compartments of the cell, namely in vacuole and cytoplasm, respectively [19].  
In the literature, the volatile compounds in onions are typically characterized on completely disrupted tissue, where the enzymatic reactions have been allowed to occur completely over a defined time period. This study aimed to assess the changes in the volatile profile in intact onions immediately after PEF treatments and 24 h after PEF treatment in order to understand the impact of PEF processing and storage on an onion's physiochemical properties.
# Review of Fast Computation Methods for Finite-State Predictive Control of Multi-Phase Drives
## 2 MPC for Current Control of MPS
### VSI States and Voltages  
In a power-converter-supplied drive, the phase voltages are determined by the VSI state. This state can be represented as a vector \(u=(K_{a},K_{b},\cdots,K_{c})^{\top}\), where the values \(K_{h}\) indicate the state of the corresponding VSI switch for phase \(h\). For a five-phase VSI, there are 32 possible configurations, ranging from \(u_{0}=(0,0,0,0)^{\mathsf{T}}\) to \(u_{31}=(1,1,1,1,1)^{\mathsf{T}}\). Each configuration results in a specific set of phase voltages. The stator voltages corresponding to each VSI state can be expressed as \(V(k)=V_{DC}TMu(k)\), where \(V_{DC}\) is the voltage supplied to the DC-link, and \(T\) and \(M\) are derived from the VSI structure [18].  
The stator voltages can be mapped to the \(\alpha\)-\(\beta\) and \(x\)-\(y\) subspaces using the Clarke transformation. The resulting voltages in the \(\alpha\beta xy\) coordinate system are referred to as voltage vectors (VVs). Figure 2 illustrates the distribution of VVs for a five-phase VSI.  
Figure 1: Diagram of FSMPC for a five-phase IM.
# Energy Efficient Routing in Wireless Sensor Networks: A Comprehensive Survey
## 6 Conclusions
Therefore, negotiation based protocols should not be utilized for applications that require reliable data packet delivery such as intruder detection, due to the fact that some nodes might not be interested to obtain new data, thus obstructing critical information of reaching its destination. Energy efficient routing protocols of Network Structure category were classified as either Flat or Hierarchical. Flat protocols treat all network nodes as equal entities. They perform well in networks with few sensor nodes, but they have extremely low scalability. Thus, they can be effective only in small scale applications as machine fault diagnosis. On the other hand, in Hierarchical protocols, network nodes are treated according to the place they hold within the hierarchical structure of the network. In this way, data aggregation is enabled, the communication overhead is reduced, and great scalability is achieved. Thus, hierarchical protocols are suitable for applications that require large scale WSNs with heavy load and wide coverage area. Energy efficient routing protocols belonging to Topology category use position related information in order to route data. They were further classified into three subcategories which namely are: Location based, Mobile Sink based, and Mobile Agent based. In Location based protocols, every network node is aware of the positions of both its neighbors and the destination node during data routing. Thus, the most energy efficient routing paths are identified, and no flood is caused. They can be used in applications that utilize location information of a specific node, as a free parking space in a parking lot facility. Mobile agent based routing protocols use a mobile agent, which is a processing function that travels among the network nodes in order to collect their data. In mobile sink protocols, the data sinks or base stations themselves are able to travel within the network field, by using movable platforms, in specific paths or patterns to gather data from the nodes deployed. Military operations, such as battlefield surveillance, are typical applications where protocols of this kind are suitable to be used in. Energy efficient routing protocols of Reliable Routing category protocols were classified into two corresponding subcategories depending on whether they pursue QoS metrics or perform routing via multiple paths. Specifically, QoS based protocols take into consideration not only the energy consumption, but also other metrics such as end to end delay and data quality. Operations which involve the transmission of multimedia data are typical examples of applications of such protocols. Multipath based protocols route data from nodes to sinks via various paths, in order to perform load balancing, overcome route failures, and decrease end-to-end delay. Their main weakness is that they involve high processing and communication overhead.
## 3 Search Most and Remove Useless Algorithm
### The Charging of Sink Nodes  
The SMRU algorithm can calculate the energy consumption of the sink nodes. Under the premise of supplying enough energy to the sink nodes, the SMRU algorithm can reduce the number of chargers deployed to charge sink nodes. It also does not affect the number of chargers that need to charge sensors. First, the sensor will transfer the data to the sink that is closest to itself. After the SMRU has deployed the charger, it checks the amount of energy consumed by each sink. Next, the SMRU checks the charger within the charging distance of the sink and checks the number of sensors charged by the charger. If the number is equal to one, the charging angle is turned to sink. Therefore, the charger will charge one sensor and one sink. If the number of charging sensors is greater than 1, the charger rotates 360\({}^{\circ}\) again to search for the best angle, and the calculation includes the sink node. If the charging target can be covered more than the previous number (meaning that the previously charged sensors are covered; it also covers the sink node), the charging angle is updated; otherwise, the charger maintains the original charging angle. Finally, according to the energy that the sink still lacks, the SMRU deploys additional chargers around the sink to charge the sink. The number of chargers deployed can supplement enough energy as the minimum requirement. Consequently, all sink nodes can have enough power to receive the data sent by the sensor. The SMRU can make good use of the existing charger resources to charge the sink, so it can reduce the number of additional charger deployments. The algorithm is shown in Algorithm 3.  
```
1:Input: Locations of all sensors, locations of all chargers, and locations of all sinks
2:Output: Locations of new chargers
3:Step 1 Check how much energy is consumed by each sink.
4:Step 2. Find the charger, and if its distance from the sink node is less than the charging distance r.
5:Step 3. If the number of sensors \(n\) covered by the charger is greater than one, rotate 360\({}^{\circ}\) to check whether the charging of the sink node can be increased without affecting the originally charged sensors. If possible, update the angle of the charger. If not, maintain the original charging angle of the charger. If the number of sensors \(n\) covered by the charger is equal to 1, then the charging angle of the charger is turned to the sink node.
6:if (\(n>1\)) then
7: Search 360\({}^{\circ}\) and find the best angle that can cover most of the sensors;
8: Check whether the new covered number (ncn) is bigger than the original covered number (ocn);
9:if (ncn > ocn) then
10: Record the new charging angle;
11:endif
12:elseif (\(n==~{}1\)) then
13: Update charging angle;
14:endif
15:Step 4. Check the lack of energy for each sink node. Around the sink nodes, randomly select the position of the chargers to deploy the number of chargers needed to supplement the energy required by the sink nodes.
16:return
```  
**Algorithm 4** Charging energy of the sink nodes
## 3 Application Scenario and System Model
### System Model
#### 3.2.3 Static Sensor Model  
The positions of the static sensor nodes are given by their corresponding coordinates, and this position is assumed to have been established during system deployment and does not change during system operation. It is also assumed that static sensor nodes know their own position, which is possible by means of a Global Positioning System (GPS) [26] device or any other positioning mechanism, such as algorithmic solutions [27]. These nodes have their communication capabilities defined by a communication range \(r_{c}\).  
The operation of static ground sensor nodes is based on an energy saving model [28], in which the nodes sleep most of the time, which means that they turn off all or almost all their devices. A duty cycle mechanism defines the periods in which the nodes wake up (represented by \(t_{w}\)), and stay awake for a time window \(t_{a}\) during which the nodes perform their activities, _i.e._, they turn on their processing, communication and sensor devices to process information, sample the environment and exchange messages with other nodes. Figure 2 presents the finite state machine (FSM) describing the ground sensor nodes' behavior, with the two possible states, active and inactive (sleep), and the transitions between them.  
Figure 2: Finite state machine for ground sensor nodes.
## 2 Flexural Formulation  
### Constitutive Models Used  
The stress-strain relationships for the concrete and the reinforcing steel are assumed to be independent of each other. The axial stress in steel would be only a result of the axial strain in the steel. Additionally, shear stresses in the steel bars on a plane perpen  
Figure 1: [PERSON] and [PERSON] (2006) proposed concrete tension stiffening model. (\(\text{f}_{\text{r}}\) is the concrete tensile strength and \(\varepsilon_{\text{cr}}\) is the concrete cracking strain).  
dicular to their longitudinal axis are assumed to be negligible. The steel axial stress-axial strain relationship is idealized by a bilinear function, which is the same in tension and compression--see Figure 2.  
\[f_{s}=E_{s}\varepsilon_{s}\leq f_{y} \tag{1}\]  
\[f_{s}=f_{y}+E_{s}^{\prime}(\varepsilon_{s}-\varepsilon_{y}),\ for\ f_{s}>f_{y} \tag{2}\]  
where (\(f_{s}\)) is the steel stress corresponding to the axial strain (\(\varepsilon_{s}\)), (\(E_{s}\)) is the modulus of elasticity of steel, (\(f_{y}\)) is the yielding strength of steel. The steel is assumed to start hardening once exceeding the yielding strength (\(f_{y}\)) according to Equation (2). (\(E_{s}^{\prime}\)) is the slope of steel hardening line after yielding. It is determined based on an equal area under the strain hardening region in the actual experimental curve and the analytical model. Table 1 shows the relationship between the estimated (\(E_{s}^{\prime}\)) and the corresponding (\(f_{y}\)) as correlated by [PERSON] (1990) [10].  
The concrete stress-strain relationship is assumed to follow the [PERSON]'s curve [11], Equation (3), for compressive stresses (\(f_{c}\))--see Figure 3.  
\[f_{c}=f_{c}\prime\left(2\frac{\varepsilon_{c}}{\varepsilon_{c}\prime}-\left( \frac{\varepsilon_{c}}{\varepsilon_{c}^{\prime}}\right)^{2}\right) \tag{3}\]  
where (\(f_{c}\)) is the concrete compressive stress and (\(f_{c}\)) is the concrete compressive strength, (\(\varepsilon_{c}\)) is the concrete compressive strain and (\(e_{c}^{\prime}\)) is concrete compressive strain corresponding to (\(f_{c}\)). A linear relationship with a slope equal to the concrete modulus of elasticity (\(E_{c}\)) up to cracking strain (\(\varepsilon_{c}\)) in tension is assumed. This linear relationship is then followed by a descending curve as a function of (\(f_{r}\)) and the steel yielding strain (\(\varepsilon_{y}\)), which will be further developed in the paper--see Figure 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**f\({}_{y}\) (MPa)** & **E\({}^{\prime}\)/E\({}_{s}\)** \\ \hline
40–45 & 0.3–0.7\% \\
45–50 & 0.7–1.2\% \\
50–63 & 1.2–2.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The Relationship between the estimated (E\({}^{\prime}\)s) and the corresponding (f\({}_{y}\)).  
Figure 2: Steel bilinear stress–strain curve.  
The concrete tensile rupture strength (\(f_{r}\)) is taken as the lower bound value given by ACI 318-14 [12].  
\[f_{r}=7.5\lambda\sqrt{f_{c}t}\ \ (\text{psi}) \tag{4}\]  
where (\(\lambda\)) is taken as (1.0) for normal weight concrete, 0.85 for sand-lightweight concrete and 0.75 for all-lightweight concrete.