esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design of SROA
### The SROA Overview  
The SROA protocol is proposed to find routing paths for achieving scalability, energy efficiency, and reliability based on on-site architecture in UASNs. The proposed protocol maintains stable with the increasing network size, selecting efficient routes for transmission and avoiding single-point failure through the decentralized mechanism in the network. In addition, a machine learning method is adopted to select optimal routes. The design of the proposed routing protocol is depicted in Figure 2.  
Figure 2: Flowchart of the SROA protocol.  
By monitoring channel conditions, sensor nodes deployed in the underwater environment collect information and their local information tables are kept up to date. Through broadcasting messages, all the sensor nodes are grouped into multiple clusters and elect the cluster heads for each cluster. Sensor nodes in the SROA are divided into three types: Cluster heads (CHs), the potential cluster node (PCHs), and ordinary nodes (ONs). The cluster head node is responsible for aggregating data from ONs and transferring the data packet to the sink node through a multi-hop communication; PCH is used as an assistant for CH and similar to the CH in the basic function. The rest of the sensor nodes are the ONs that collect data and forward packets to cluster head within a single hop. Afterwards, Q-Learning is applied to select relay forwarders. The Q-values of qualified neighboring nodes are calculated by the sender node, employing the Q-Learning technique. Moreover, it should be noted that data packets in SROA are transmitted to sink node through multiple-hop communication using the OR strategy. The candidate forwarding set is constructed by taking energy, latency, and connection quality into account. However, it is not suitable for all the nodes participating in the same packet forwarding, which will result in energy consumption and collisions of packets. Therefore, a waiting mechanism is designed to the coordinate candidate set. The obtained Q-value determines the waiting time of candidate nodes. The greater Q-value implies the higher priority for data transmission; hence the waiting time of that node is shorter. When receiving a packet, the sensor node will first check the packet header. If the receiver is not in the candidate list, it will drop the packet. Otherwise, the packet will be kept by the receiver for the waiting time. Furthermore, if the node overhears other nodes transmitting this packet during the waiting period, it will cease forwarding the packet. Table 1 lists the notations in the SROA protocol.
## 3 Methods
### Feature Extraction  
The connectivity matrix represents the structural complexity of the brain network. A powerful framework for mathematically treating such a complex system is graph theory. Several graph metrics can be computed from the connectivity matrix to describe the topological properties of the brain. Most of these measures are based on the shortest path connecting two nodes of the network. Their relevance rests on the idea that the communication between two nodes takes place through the shortest path connecting them. However, in many real-world networks, such as social networks, information does not necessarily flow along the shortest paths; moreover, it can go back and forward several times before reaching its final destination (e.g., [30; 31]). As a consequence, relying only on shortest path-based models can lead to relevant information loss.  
In order to overcome this drawback, [PERSON] and [PERSON] proposed a new concept of communicability, initially only for binary networks, defining the _communicability_ between two nodes as a function of the total number of walks connecting them, giving more importance to the shorter than the longer ones [14].  
Let \(G\) be a graph of \(N\) nodes and \(A\) the corresponding \(N\times N\) adjacency matrix, then:  
\[(A^{k})_{pq}:=\sum_{r_{1}=1}^{N}\sum_{r_{2}=1}^{N}\cdots\sum_{r_{k-1}=1}^{N}a_ {p,r_{1}}a_{r_{1},r_{2}}a_{r_{2},r_{3}}\cdots a_{r_{k-2},r_{k-1}}a_{r_{k-1},r_{ 4}}, \tag{1}\]  
Figure 1: Image processing pipeline adapted from [16].  
counts the number of walks of length \(k\) starting at node \(p\) and ending at node \(q\). The communicability between \(p\) and \(q\) is given by the total number of walks connecting them, weighted in decreasing order of their lengths:  
\[G_{pq}=\sum_{k=0}^{\infty}\frac{(A^{k})_{pq}}{k!}=(e^{A})_{pq}. \tag{2}\]  
This equation can also be rewritten in terms of the graph spectrum as:  
\[G_{pq}=\sum_{j=1}^{n}\varphi_{j}(p)\varphi_{j}(q)e^{A_{j}}, \tag{3}\]  
where \(\varphi_{j}(p)\) is the \(p\)-th element of the \(j\)-th orthonormal eigenvector of the adjacency matrix associated with the eigenvalue \(\lambda_{j}\).  
The concept of communicability was extended to the weighted case by [PERSON] and [PERSON] [32]. The definition provided above is still valid but \(A\) is the \(N\times N\) weighted matrix and the terms \(a_{p,r_{1}}a_{r_{1}r_{2}}a_{r_{2}r_{3}}\cdots a_{r_{k-2}r_{k-1}}a_{r_{k-1},r_{ q}}\) represent the weights of the walks \(i\mapsto r_{1},r_{1}\mapsto r_{2}\), etc. In order to avoid the excessive influence of a node depending on its high weight, the authors proposed a normalization step which consists in dividing the weight \(a_{ij}\) by the product \(\sqrt{s_{i}s_{j}}\), where \(s_{i}\) is the strength of node \(i\). Therefore, the communicability between two nodes \(p\) and \(q\) in a weighted network is defined as:  
\[G_{pq}=(\exp(D^{-1/2}AD^{-1/2}))_{pq}, \tag{4}\]  
where \(D=\text{diag}(s_{i})\) is the \(N\times N\) diagonal strength matrix.
# Connecting the Brain with Augmented Reality: A Systematic Review of BCI-AR Systems
## 4 Results
### Reactive BCI
#### 4.1.7 Evaluation Metrics  
Several evaluation metrics were utilized by the authors in order to measure the effectiveness of their systems. The most common were the classification accuracy presented in 24 studies [14; 15; 16; 19; 20; 22; 23; 24; 25; 27; 29; 33; 35; 36; 37; 38; 39; 41; 42; 44; 45] along with the information transfer rate (ITR) presented in 12 studies [15; 19; 20; 22; 24; 27; 29; 30; 33; 37; 38; 41]. In addition to these common performance metrics, some authors employed further evaluation metrics (EEG and time metrics) that were well suited to their respective systems. Those metrics were measured brain potentials [18], power distribution topography [19], time to complete a robot movement [22], performance [26], amplitude of ERP [28], acquisition time [31], SSVEP competition scores [34], average time to reach target [37], SUS, NASA-TLX questionnaires [43], and time to response [40]. It is worth mentioning that most authors employed more than one of the previously mentioned evaluation metrics for their systems.
# Detection of Carrageenanen in Cheese Using
## 1 Introduction  
Carrageenanen is a polysaccharide that consists of D-galactose and 3,6-anhydro-galactose units that are linked by \(\alpha\)-1,3 and \(\beta\)-1,4 glycosidic bonds. These polysaccharides are obtained from red seaweed (Rhodophyceae) [1, 2, 3]. Carrageenanen is used in the food industry mainly for its gel-forming, thickening, emulsifying, and stabilizing properties in the amount of 0.005-2.0%. The K-, \(\iota\)- and \(\lambda\)-carrageenan are of the greatest commercial importance. They are used, for example, to improve the texture of curd cheese, blanc-mange, dairy deserts, cheese, ice cream, and meat products [4, 5, 6, 7]. The use of carrageenanen in foods is considered safe. However, there are studies indicating that carrageenanen can cause inflammatory reactions, damage the digestive mucosa, or be associated with breast cancer [3, 8].  
Processed cheese is a good source of protein, fat, minerals, and vitamins. The basic raw materials are natural cheese, butter, emulsifying salts, and water [9]. The addition of carrageenan prevents syneresis of dairy products and it has thickening and gelling properties [10]. The strength of the gel depends on the carrageenanen-caseen binding in cheese with the addition of carrageenan. In particular, this bond is formed between the negatively charged sulphate groups contained in the carrageenan and the positively charged region of the casein. This binding depends on parameters such as hydrocolloid and protein concentration, sugar content, temperature, or pH. Carrageenanen stabilizes the fat in cheese and may even serve as a substitute for the emulsifying salt, which may lead to a reduction in the amount of phosphorus and sodium in processed cheese [9, 10, 11].  
The principle of lectin histochemistry lies in the lectin-saccharide bond. Lectins are proteins and glycoproteins that bind to specific carbohydrates [12]. Lectin histochemistry uses this principle, by which glycan residues can be detected directly or indirectly. Directmethods are based on the binding of lectin that is conjugated to a particular carbohydrate by a fluorochrome or enzyme. Indirect methods involve the binding of a labeled antibody to lectin that binds to a given carbohydrate. In addition, methods based on the avidin-biotin bond can be used, in which biotinylated lectin binds to the saccharide. In the next step, the avidin-biotin complex, which is conjugated to a fluorochrome or enzyme, is attached to it [12; 13; 14].  
Carrageenan is an additive that can be used to adulterate foods. For this reason, it is necessary to be able to detect them. There is currently no official method for their detection in food. In this respect, the use of lectin histochemistry in food analysis, which has not yet been used in this field, offers great potential. The aim of this paper is to validate the detection of carrageenan in cheese using lectin histochemistry and comparing the results of Human Inspection measurements and CIE L*a*b* measurements.
# A Study on Wireless Charging for Prolonging the Lifetime of Wireless Sensor Networks
###### Abstract  
Wireless charging is an important issue in wireless sensor networks, since it can provide an emerging and effective solution in the absence of other power supplies. The state-of-the-art methods employ a mobile car and a predefined moving path to charge the sensor nodes in the network. Previous studies only consider a factor of the network (i.e., residual energy of sensor node) as a constraint to design the wireless charging strategy. However, other factors, such as the travelled distance of the mobile car, can also affect the effectiveness of wireless charging strategy. In this work, we study wireless charging strategy based on the analysis of a combination of two factors, including the residual energy of sensor nodes and the travelled distance of the charging car. Firstly, we theoretically analyze the limited size of the sensor network to match the capability of a charging car. Then, the networked factors are selected as the weights of traveling salesman problem (TSP) to design the moving path of the charging car. Thirdly, the charging time of each sensor node is computed based on the linear programming problem for the charging car. Finally, a charging period for the network is studied. The experimental results show that the proposed approach can significantly maximize the lifetime of the wireless sensor network.  
mobile charger; network size; moving path; optimization problem; wireless energy transfer; wireless sensor network +
Footnote †: journal: Sensors
## 3 Results and Discussion
### Test Conditions and Test Scheme  
#### 3.1.1 Test Conditions  
The entire testing process was divided into three stages from 13 to 15 December 2018. The weather condition was usually cloudy during tests. Each stage was arranged in one day to complete three groups of experiments. Each group of experiments targeted one application rate combination of wheat and compound fertilizer and was repeated once. The three target application rate combinations were as follows: 225 and 300 kg/ha, 300 and 375 kg/ha, and 375 and 525 kg/ha, respectively, as listed in Table 1. During the tests, the tractor's driving system was fixed in a certain gear, where the operation speed changed within a range of 2 km/h to 4 km/h because of the changing field resistance or different depth on the accelerator pedal during operation. After effectively traveling for 300 m, the test was deemed complete. Because the field in which we performed the test was relatively small, we needed to turn around the tractor several times to reach a 300 m testing distance. The driving speed, the seed, and the fertilizer application rates used during tests are commonly adopted operation parameters in practice in Jiangsu Province in China. The recording interval for all measurement parameters was 0.5 s during the tests.
## 3 Nonlinear Material Equations and the Energy-Based Method for the Inverse Analysis  
In continuum mechanics, a Eulerian frame is used for a viscous fluid, where coordinates, \(\mathbf{x}\), denote the spatial positions. By using Cartesian coordinates, the velocity of a fluid particle currently at \(x_{i}\) at the present time \(t\) is denoted by \(v_{i}=v_{i}(x_{j},t)\). Since the velocity of each fluid particle is challenging to monitor separately, in a rheometer measurement, the measurand of a system is the energy, \(E\). The temperature is held constant such that the process is isothermal. In an isothermal process, the energy can be calculated in a domain of interest, \(\Omega\), herein within the sample, for a period of time, \(\tau\), as follows:  
\[E=\int_{\tau}\int_{\Omega}\sigma_{ij}v_{j,i}\,\mathrm{d}v\,\mathrm{d}t\, \tag{1}\]  
Figure 1: Output from the oscillatory measurement. Left: Frequency variation by fixed amplitude, \(\sigma_{0}=40\) Pa. Blue squares denote the storage moduli, and red dots indicate the loss moduli. Right: Amplitude variation by fixed frequency, \(\
u=1\) Hz. Blue squares denote the storage moduli, and red dots indicate the loss moduli. where a comma indicates a partial derivative in space, and we understand a summation over every repeated index, known as [PERSON]'s summation convention. [PERSON]'s stress, \(\sigma_{ij}\), is symmetric for non-polar materials such that we can rewrite the energy:  
\[E=\int_{\tau}\int_{\Omega}\sigma_{ij}d_{ij}\,\mathrm{d}v\,\mathrm{d}t\,,\ d_{ij}=\frac{1}{2} \big{(}\frac{\partial v_{i}}{\partial x_{j}}+\frac{\partial v_{j}}{\partial x_{ i}}\big{)}\, \tag{2}\]  
since the antisymmetric part of the velocity gradient vanishes by contracting it to the symmetric stress tensor. The symmetric part of the velocity gradient, \(d_{ij}\), and the symmetric Cauchy stress, \(\sigma_{ij}\), are related by a material (or constitutive) equation. We aim at constructing this material equation and then at determining its coefficients. In a rheometer, the energy is monitored precisely by measuring the electric power from the wall (minus the standard induction motor's losses). The rheometer shears the sample, say, on the \(x_{1}x_{2}\)-plane, and holds the distance of the plates fixed such that \(v_{3}=0\). Hence, the velocity gradients read:  
\[d_{ij}=\left( \begin{array}{ccc}0&d_{12}&0\\ d_{12}&0&0\\ 0&0&0\end{array} \right). \tag{3}\]  
We know that for stress, more than the shear stress occurs in many materials. However, the velocity gradient has only shear terms even in the nonlinear case. Instead of using this energy, in linear rheology, the storage and loss moduli are introduced. We will briefly explain their use and suggest a strategy to extract more information out of them. In the case of a stress-steered rheometer, a harmonic input for the shear component of stress is applied, written in an exponential form (in order to include sinus and co-sinus functions together):  
\[\sigma_{12}=\sigma_{0}\exp(\mathrm{i}\omega t). \tag{4}\]  
Usually, it is assumed that the material response is collinear such that only a shear component of the symmetric velocity gradient appears.
## 6 Results
### Assessment of the Research Model
#### 6.2.1 Assessment of the Measurement Model  
[PERSON] [82] stated that the measurement model adequacy could be specified by obtaining item reliability, internal consistency reliability, convergent validity, and discriminant validity.  
Item Reliability  
According to [80], the threshold value for individual item loadings should be above 0.7 to determine item reliability. In this study, all the items (46 items) exceeded the threshold value indicating that the survey instrument was adequate for measuring each construct individually. Therefore, all 46 items were kept for further analysis. Their item loadings, as presented in Figure 4, ranged from 0.865 to 1 and all indicated statistical significance.  
The internal consistency reliability was examined by determining the composite reliability (CR) values. In this study, the CR values were all above 0.9 (over the minimum 0.80) (See Table 3), which indicated that the constructs measures had high internal consistency reliability.  
The three conditions laid down by [PERSON] [83] concerning the measures, convergent validity was used. First, the item factor loadings have to be over 0.70 (item reliability) and second, each construct should have composite reliability (CR) value of over 0.80 (internal consistency reliability). Lastly, the construct's average variance extracted (AVE) should be more than 0.50. Previous sections confirmed that the entire items factor  
Figure 3: Respondents by experience.  
loadings exceeded 0.70 and the entire composite reliability values exceeded 0.80. With regards to AVE values, they ranged from 0.83 (over 0.50) as presented in Table 3, which confirmed their convergent validity.  
Discriminant Validity was evaluated for each construct by observing the AVE square root (diagonal line in Table 4), which must be higher compared to the correlations between the construct and other model constructs. The discriminant validity of the constructs is presented in Table 4.
# Evaluation of the Long-Term Performances of SMA-13 Containing Different Fibers
###### Abstract
[PERSON] [27] concluded that the addition of basalt fiber is the main reason of improving the mixture performance. [PERSON] [28] found that basalt fiber can effectively reduce the damage degree of asphalt mixture in freeze-thaw cycle test. Many useful conclusions have been achieved in the study of fresh BF asphalt mixtures. However, more and more attention has been paid to its long-term performance. Although [PERSON] [29], [PERSON] [30] have evaluated the long-term performance of glass fiber asphalt mixtures, more studies on the effect of different fibers on the long-term performance of SMA are needed. Therefore, this paper focuses on the study on the long-term performances of SMA containing different fibers. BFSMA-13(defined as the SMA-13 containing BF) and LFSMA-13 (defined as the SMA-13 containing BF) were designed for test. The high-temperature performance, low-temperature property, water stability, and anti-cracking ability of mixtures under different aging degrees (unaged, short-term aged, and long-term aged) were compared and analyzed. SEM tests were adopted to analyze the distribution of BF and LF in the mixture to reveal the strengthening mechanism of the fibers. Since fibers are needed in SMA-13 to stabilize free asphalt [8], hence the SMA-13 without fibers are not manufactured in this study, and LFSMA-13 can be treated as standard SMA-13 in this article. The findings of this study help to further understand the performance changes of SMA-13 mixtures during the service life and to guide the selection of fiber additives for SMA-13 mixtures.
## 6 Simulation Experiment and Analysis
### Simulation Results
#### 6.2.3 OFDM Signal Identification Simulation  
With the increase in SNR, the accuracy of modulation identification for each subcarrier of the OFDM signal continues to improve, as shown in Figure 10. The accuracy change curve for SNRs ranging from 0 to 15 dB is plotted. It can be seen that the average identification rate exceeds 90% when the SNR is greater than 5 dB. Among them, the trend of identification rate growth for the five modulation schemes is similar. When the SNR is greater than 7 dB, the identification rate of all signals exceeds 90%, and for QAM signals, which have a variety of signal types with similar amplitude and phase characteristics, it is more difficult to distinguish. When the SNR is greater than 9 dB, the identification rate of all modulation signals exceeds 95%.
## 5 Experiment and Analysis
### Field Trials  
#### 5.2.1 Trial Conditions  
To verify the regulation effect of the Coupling System's adaptive algorithm, a field trial was conducted in July 2022 in Yintang Township, Shuangfeng County, Loudi City, Hunan Province, China. The test area was 400 m\({}^{2}\), and a buffer zone of 5 m was set up. We investigated the field conditions in the area before the test. The rice variety was Jingliangyou 1125, with plant rows spaced about 30 cm apart and distributed evenly. It had an average height of about 970 mm, no overturning, and approximately the same water content. The terrain was flat, with no obvious potholes. The harvesting trial site is shown in Figure 16.
# Advanced Microbubbles as a Multifunctional Platform Combining Imaging and Therapy
## 2 Advanced Microbubbles for Imaging and Therapy
### Blood-Brain Barrier Disruption
This study demonstrated that the gene-loaded MBs with FUS-induced BBB opening was an effective approach and might be a therapeutic strategy for neurodegenerative diseases such as Parkinson's, Alzheimer's and Huntington's diseases [75]. [PERSON] applied repeated scanning ultrasound (SUS) for treatments of the mouse brain to remove Ab which is implicated in the pathogenesis of Alzheimer's disease [76]. Their findings suggest that repeated SUS treatment could induce microglia to effectively cleanse Ab and may be potentially promising for Alzheimer's therapy. [PERSON] et al. further tested the impact of periodic FUS in combination with liposomal doxorubicin (DOX) in a rat glioma tumor [77]. They found a pronounced improvement in median survival time by 100% of rats that received FUS + DOX compared to rats only treated with DOX or FUS, and approximately 75% of tumors appeared almost completely cured in the FUS + DOX group. Multiple sessions using FUS to enhance the delivery of liposomal doxorubicin had a prominent effect in a rat glioma model. The study on BBB opening is not confined to animal experiments, but has been translated to clinical trials to some extent. A first clinical test on US-assisted chemotherapy for malignant brain tumors has been carried out at the University of Toronto in 2015 [78]. [PERSON] et al. also applied Sonocloud as an US device for treatment with carboplatin patients with recurrent glioblastoma (GBM) in a clinical trial. The BBB opening using Sonocloud combined with monthly systemic microbubble injection proved to be safe and well tolerated in patients, and acceptable treatments have been effective according to current clinical data [79].