esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessing Database Contribution via Distributed Tracing for Microservice Systems
## 3 Methodology
### Aggregating Numbers of Records of the Same Operation  
The method in this paper is oriented to business scenarios. Therefore, to reproduce a business scenario, a typical business process that contains a series of interface inputs or clicks can be executed automatically or manually. At the same time, we use a distributed tracing tool to generate corresponding traces which contain behavioral information on the database table in span tags or span logs. For example, from the span tags in Table 3, we can see the query operation of the current span on the _Catalogue_ table.  
Then, for each business scenario, we aggregate numbers of records based on the following rules:  
* It aggregates the numbers of records of the same operation on the same database. When a database contains multiple tables, the aggregation result can be obtained by counting the number of records of the same operation for each table.
* For parallel behavior in the typical process of business scenarios, the average value of the numbers of the records of the same operation on the same database of them is adopted. For example, as illustrated in Figure 5, there are three steps in a typical process of _purchasing socks_ and two parallel behaviors (i.e., _catalogue menu_ and _browse_) in the first step. Assume that the numbers of records of retrieving operations on the _Catalogue_ for _catalogue menu_, _browse_, and _detail page_ are \(r_{c}\), \(r_{b}\), and \(r_{d}\), respectively, and the third step does not have a retrieving operation on the _Catalogue_. Then, for _Purchasing socks_, the aggregated result of the number of records of retrieving operations on the _Catalogue_ is \(\left(\frac{r_{c}+r_{b}}{2}+r_{d}\right)\).  
Finally, for the \(i\)-th business scenario, an aggregation matrix \(\mathbf{AM_{i}}(i=1,2,\ldots,m)\) can be obtained based on the above rules:  
\[\mathbf{AM_{i}}=\begin{pmatrix}c_{i1}&r_{i1}&u_{i1}&d_{i1}\\ c_{i2}&r_{i2}&u_{i2}&d_{i2}\\ \vdots&\vdots&\vdots&\vdots\\ c_{in}&r_{in}&u_{in}&d_{in}\end{pmatrix}_{n\times 4} \tag{6}\]  
Figure 5: A typical process of _Purchasing socks_ in Sock-Shop.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Key** & **Value** \\ \hline http.method & GET \\ http.url & catalogue/03 fe6 ac-18964 ce8-bdd69-b798f85 ce0b \\ internal.span.format & zipkin \\ span.kind & server \\ \hline \hline \end{tabular}
\end{table}
Table 3: Span tags in a span of _Catalogue_ service. The span ID is _75e9f686f795d70_ and span name is _GET/catalogue/lid_).  
where \(n\) denotes the number of databases in the microservice system; \(c_{ik}\), \(r_{ik}\), \(u_{ik}\), and \(d_{ik}\), respectively, represent the number of records required to create, retrieve, update, and delete on the \(k\)-th (\(1\leq k\leq n\)) database to complete the \(i\)-th business scenario.
# Force Performance Analysis and Numerical Simulation of Assembled Ribbed-Slab Abutments
## 4 Parametric FE Analysis of Assembled Ribbed-Abutments
### Analysis of the Length of Flange Anchoring Steel Plate  
The connection between the flange and concrete structure was primarily related to the length of the steel plate inserted by the flange inside the concrete structure, and the length of the steel plate inside the concrete was an important factor affecting the stability of the joint between the back wall cap and flange. In this subsection, three types of flanges with different steel plate lengths (50 cm, 40 cm, and 30 cm) were set up for parametric analysis, and the corresponding load-deflection curves are shown in Figure 17. The load-displacement curves of the flange model with different steel plate lengths exhibited the same trend. The ultimate displacement of the ARBA with a 50 cm flange length increased by 24.6 and 29.2% compared to ARBA with 40 and 30 cm flanges, respectively. The ultimate load-carrying capacity increased by 3.2 and 6.2% compared to those of the prefabricated ARBA with 40 cm and 30 cm flanges, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Number of Bolts (pcs)**} & **Individual Shear** & **Total Shear Strength** & **Modeled Mid-Span** \\  & **Strength (kN)** & **(kN)** & **Section Shear Capacity** \\ \hline
10 & & 5920 & \\
12 & & 7140 & \\
14 & 592 & 8288 & 6073.83 \\
16 & & 9472 & \\
18 & & 10,656 & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Bolt shear capacity.  
Figure 16: Load–displacement curves of flanges with different numbers of bolts.  
Figure 17 shows that extending the length of the flange anchoring steel plate effectively increased the stability of the force transmission of the structure by a large amount, and elevated the ultimate load capacity of the structure by a small amount.  
In summary, we recommended the following parameter combinations to ensure that the structural damage mode remained unchanged and the economic and structural stress performance was maintained: the thickness of the stiffening ribs was 30 mm, the number of bolts was 12, and the length of the steel plate bonded to the concrete was 50 cm.
## 3 Results and Discussion
### Sensory Evaluation  
The sensory evaluation was performed with respect to the appearance, taste, softness and hardness, texture, and overall acceptance of soy protein breads. The consumers' acceptance of the sensory properties of breads fortified with different SPI ratios was evaluated on a 9-point hedonic scale, in which 1 = extremely dislike, 3 = moderately dislike, 5 = neutral, 7 = moderately like, and 9 = extremely like. A total of 58 questionnaires were administered, 51 of which were valid. Male and female evaluators accounted for 54.90% and 45.10% of the participants, respectively. Individuals under 20 years of age comprised 3.92%, 21\(\sim\)30-year-olds accounted for 41.80%, 31-40-year-olds comprised 25.49%, and 41-50-year  
Figure 2: Effects of partially substituting high-gluten flour with soy protein isolate on the (**A**) moisture content, (**B**) a\({}_{w}\), and (**C**) pH of bread.  
olds accounted for 25.49% of the participants. Participants above 50 years of age accounted for 3.92% of the total.  
The preferential evaluation results are displayed in Table 3. In terms of appearance, the control had the highest score (6.37), which was not significantly different from that of the 2% SPI-fortified bread (5.65). The mean scores of the 4%, 6%, and 8% SPI-fortified breads were 5.43, 5.25, and 4.33, respectively, all of which were significantly lower than the control. The evaluation results indicate that the appearance scores decreased with increased SPI use.  
In terms of taste, the score of the control (6.53) was not significantly different from the score of the 2% SPI-fortified bread (5.84). The mean taste score decreased significantly (\(p<0.05\)) when the SPI use exceeded 4%. The mean scores of the 4%, 6%, and 8% SPI-fortified breads were 5.59, 5.18, and 4.31, respectively. A possible reason is that SPI contains polyunsaturated fatty acids, which produce volatile compounds during oxygenation, resulting in heavy or grassy flavors. Moreover, polyphenolic compounds, such as isoflav, saponin, and phenolic acids, create an astringent taste in the bread [54]. As such, the consumers felt that the tastes of the \(\geq\)4% SPI-fortified breads were significantly different from that of the control.  
In terms of softness/hardness, the mean score of the control was 6.84 while those of the 2%, 4%, 6%, and 8% SPI-fortified breads were 6.14, 5.56, 4.94, and 3.82, respectively. Consumer preference decreased with increasing SPI substitution, which was significant in the \(\geq\)4% SPI-fortified breads.  
In terms of texture, the mean score of the control was 6.63 while those of the 2%, 4%, 6%, and 8% SPI-fortified breads were 6.02, 5.84, 4.88, and 4.22, respectively. The score of the control was not significantly different with those of the 2% and 4% SPI-fortified breads, but it was significantly different from the 6% and 8% SPI-fortified breads. A possible reason for this is that the SV of bread decreases with increasing SPI use and creates a denser structure. The consumers' preferences for 6% and 8% SPI-fortified breads were significantly lower.
# A Survey of Handwritten Character Recognition with MNIST and EMNIST
## 3 EMNIST Database
### State of the Art
[83] used EMNIST for training a optical character recognition system for bank cheques, and [PERSON] et al. [84] used the database for optical recognition in specialized forms filled by students, although in neither case performance on the EMNIST test set was reported. Similarly, [PERSON] et al. [85] designed a system using a CNN with six convolutional layers and two fully connected layers for Bangla handwritten digit recognition, testing it also in EMNIST and reporting accuracies of 90.59% in EMNIST Balanced and 99.79% in EMNIST Digits. Additionally, some researchers have relied on NIST SD 19 in the past as a testbed for their proposals. These works are not directly comparable to EMNIST since datasets are not identical, but can be used as a reference. For example, [PERSON] et al. [86] reported an average accuracy of 98.75% using SVMs with sigmoid function over a test set made only of digits. [PERSON] et al. [87] used particle swarm optimization for evolving the topology of neural networks, obtaining an accuracy over a test set of digits of 96.49%. Moreover, [PERSON] et al. [88] tested a multi-layer perceptron, reporting an accuracy of 98.39% in the same dataset. In other works, letters from NIST SD 19 are also taken into account. For example, [PERSON] et al. [89] optimized a classifier using an annealing-based approach called record-to-record travel reporting an accuracy of 93.78% for letters and 96.53% for digits. [PERSON] and [PERSON] [90] tested a multi-layer perceptron attaining an accuracy of 87.79% in the letters dataset. Also, [PERSON] et al. [91] used hidden Markov models reporting an accuracy of 87% for letters (both uppercase and lowercase) and 98% for digits. Finally, [PERSON] et al. [19] have used this database to test the performance of committees of CNNs, attaining accuracies of 88.12% for the whole database, 92.42% for letters and 99.19% for digits. Surveyed works are summarized in Table 3, with the upper side of the table showing the performance of classical machine learning models and the lower part showing works involving CNNs.
## 6 Security Analysis
### Spa  
Security of the SPA scheme depends on the security of hash functions and digital signatures. In general, hash functions are assumed to be secure which means that given \(y\), it is computationally infeasible to find \(x\) such that \(h(x)=y\). In addition, a BS is assumed to be always secure, thus the BS's private key is never exposed to attackers. Hence, a digital signature signed by the BS is always secure which implies that any attacker cannot forge a valid digital signature of the BS. In the SPA scheme, every packet is authenticated by the previously received hash value. Since a hash function is assumed to be secure, the adversary cannot fabric a malicious packet with the same hash value as the previously  
Figure 7: An example of BFBSA.  
received hash value. In other words, the SPA scheme can authenticate every packet securely. Now let's think about the case where a sensor node is captured and controlled by an adversary, who can access and extract all the materials (e.g., \(h_{i,j}\)) in the node. However, the only thing that the adversary can do is to drop packets since it is infeasible to fabric a malicious packet to have \(h_{i,j}\). If he modifies packets or injects malicious packets, it will be detected immediately through an invalid hash value. DoS attacks in the hash-chain based authentication scheme refers to the situation where the adversary try to make the buffer of receivers full by sending invalid packets repeatedly so that receivers get crippled. The SPA scheme is vulnerable to DoS attacks since it uses a hash chain containing the hash value of the next packet. If one packet is missing, all the subsequent packets cannot be authenticated and thus the buffer becomes full.
## 2 Methods: Modeling and Numerical Calculation Method
### Selection of the Wear Model  
The expression of the wear prediction model used in the computation of the surface wear of the mining pump through-passage components can be expressed as shown in Equation (3) [32]:  
\[R_{erosion}=\sum_{n=1}^{N_{particles}}\frac{m_{p}C(d_{p})f(\theta)v^{b(v)}}{A_{face}} \tag{3}\]  
where  
\(R_{erosion}\): erosion rate;  
\(C\big{(}d_{p}\big{)}\): particle size function of the solid-phase particles;  
\(\theta\): impact angle between the particle and the wall;  
\(f(\theta)\): function of the impact angle;  
\(v\): relative velocity between the particle and the wall, m/s;  
\(b(v)\): function of the relative velocity between the particle and the wall;  
\(A_{face}\): unit surface area of the wall, mm\({}^{2}\);  
\(N\): the number of particles impacting the cell surface area;  
\(m_{p}\): mass of a single particle;  
\(d_{p}\): particle size; and  
\(n\): number of particles.  
After combining the above formula and the particle motion trajectory for analysis, the wear on the surface of the pump's various through-passage components caused by the movement of solid-phase particles in the computational domain can be quantitatively predicted. The impact angle function, \(f(\theta)\), can expressed using a piecewise polynomial, as follows:  
\[f(\theta)=b\theta^{2}+c\theta \tag{4}\]  
When \(\theta>15^{\circ}\), then:  
\[f(\theta)=x\cos^{2}\theta\sin(w\theta)+y\sin^{2}(\theta)+z \tag{5}\]  
The values of the constants in the above formula are shown in Table 1.
## 2 Materials and Methods  
### Study Region  
To explore the relationships of SIF and vegetation indices and the sensitivity of SIF to meteorological data in different ecosystems, three typical ecosystems in Northeast China, forest, grassland, and cropland, were selected in this research. The study region is located in northeastern China and contains the three provinces of Heilongjiang, Jilin, and Inner Mongolia (111\({}^{\circ}\)04\({}^{\prime}\)17\({}^{\prime\prime}\)-130\({}^{\circ}\)44\({}^{\prime}\)56\({}^{\prime\prime}\) E, 41\({}^{\circ}\)15\({}^{\prime}\)10\({}^{\prime\prime}\)-53\({}^{\circ}\)35\({}^{\prime}\)11\({}^{\prime\prime}\) N). It is bounded by Russia to the north and Mongolia to the west.  
The Greater and Lesser Khingan Mountains represent the forest ecosystems, the Songnen Plain represents the cropland ecosystem, and Hulunbuir grasslands and Xilin Gol grasslands represent the grassland ecosystems. These land cover types, with a 30 m resolution [30], were selected to explore the different responses of land cover types to changes in SIF (Figure 1).  
The Greater Khingan Mountains are part of the coniferous forests of northern Eurasia and constitute the largest virgin forest in China. The zonal vegetation is typical taiga type composed principally of Dahurian larch (_Larix gmelinii_), deciduous broadleaf bir  
Figure 1: Geographic location of the study area consisting of forest, cropland, and grassland ecosystems (Reference. frame: GCS_WGS_84, EPSG: 4326).  
(_Betula platphyllla_), and aspen (_Populus davidiana_ and _Populus suaveolens_) [6; 8]. Elevations in the area range from 260 m to 1700 m. The Greater Khingan Mountains experience a cold temperate and continental monsoon climate with an annual average temperature of \(-2.8\)\({}^{\circ}\)C and a mean annual precipitation of 350-500 mm [31; 32].  
The cropland ecosystem is located in the central Songnen Plain, which is one of the 'black soil regions' of the world. The Songnen Plain is one of the most important grain production areas in China, and this region has the largest acreage of corn and also small area of rice. Mean annual precipitation in the Songnen Plain is 350-700 mm. The area typically experiences mean temperatures in the range of 21-26 \({}^{\circ}\)C in the month of July and \(-24\) to 9 \({}^{\circ}\)C in January.  
The Hulunbuir and Xilin Gol grasslands, which are part of the Inner Mongolia typical steppe, have an annual mean temperature range from \(-2\) to 26 \({}^{\circ}\)C and have a mean annual precipitation of approximately 300 mm [33].
## 3 Heuristic Scheduling Algorithm for Deep Reinforcement Learning
### Training Methods  
The HCNN model is a decision model, and we apply a policy gradient method and the actor-critic (AC) algorithm (Algorithm 2) [23] to train the parameters of this decision model.  
Figure 6: Task observation arrangement process.  
Figure 7: Observation arrangement process for tasks deleted due to constraint violation.  
```
1:Initialize actor network parameters \(\theta\)
2:Initialize critic network parameters \(\theta_{c}\)
3:Initialize the priority list \(L\)
4:for\(iteration\gets 1,2,\cdots\)do
5: reset gradients: \(d\theta\gets 0\), \(d\theta_{c}\gets 0\)
6: generate \(N\) problem instances from \(\phi\)
7:for\(k=1,2,\cdots N\)do
8: step counter \(t\gets 0\)
9: while not terminated do
10: select next variable \(x^{k}_{t+1}\) according to the probability distribution \(P(y^{k}_{t+1}|Y^{k}_{t},X^{k}_{t})\)
11: update new state \(X^{k}_{t+1}\) to \(X^{k}_{t}\)
12:\(y_{t+1}\) is added to \(L\)
13:\(t\gets t+1\)
14:endwhile
15: calculate the reward \(R^{k}\) by algorithm \(TAA\) to \(L\)
16:endfor
17:\(d\theta\leftarrow\frac{1}{N}\sum\limits_{k=1}^{N}\left(R^{k}-V(X^{k}_{0};\theta _{c})\right)\
abla_{\theta}\log P(Y^{k}|X^{k}_{0})\)
18:\(d\theta_{c}\leftarrow\frac{1}{N}\sum\limits_{k=1}^{N}\
abla_{\theta}(R^{k}-V(X^ {k}_{0};\theta_{c}))^{2}\)
19: update \(\theta\) using \(d\theta\) and \(\theta_{c}\) using \(d\theta_{c}\)
20:endfor
```  
**Algorithm 2** Actor-critic algorithm.  
The objective of reinforcement learning is to maximize the reward from the environment. However, the output of the HCNN model is a sequence of task priority, which is not an explicit reward. To evaluate the model and obtain a reward appropriately, the profit of the TAA after the HCNN model is selected to be the reinforcement reward function value.  
During the training, the parameters \(\theta\) of the HCNN model are updated in each iteration for approximating the optimal parameters \(\theta^{*}\) as closely as possible, which could render the reinforcement reward function value to become the optimal \(R^{*}\).  
In the training process of each iteration, for each case, the task scheduling sequence is generated by the current HCNN network parameters. Then, the profit of this scheduling sequence is calculated by the TAA algorithm to get the reward value, which reinforces learning needs. The calculation process shown in step 17 is used to update the actor-network, where \(V(X^{k}_{0};\theta_{c})\) is the output value of the critic with the current parameter \(\theta_{c}\). After observing the difference between the estimated and the true value, the critic network is updated at step 18.
# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 3 Issues Faced by RPL during Mobility  
Following, we discuss the issues faced by the RPL when the devices are non-stationary.
## 3 Electromagnetic Characteristic Analysis
### Transient Analysis  
In this paper, the transient solver in the 3D module of Maxwell is employed to analyze the motor characteristics of the axial flux permanent magnet motor under different operating conditions. The material settings for each component of the axial flux permanent magnet motor are shown in Table 1.  
First, the initial angle of the motor is set. It is necessary to calculate the angle through which the rotor needs to rotate to align the direct axis of the motor with the centerline of the A-phase winding. Then, this angle is set as the initial angle. Next, the band domain, which should enclose the rotor core and the permanent magnets, is configured. The band domain settings for the motor model are shown in Figure 10.  
Figure 8: The magnetic flux density distribution of the air gap at the average radius of the motor air gap, which is 31.8 mm.  
Figure 7: The magnetic flux density distribution of the air gap at the innermost diameter of 26.4 mm of the motor air gap.  
Figure 9: The magnetic flux density distribution of the air gap at the outermost radius of 36.2 mm of motor air gap.  
The next step is to apply current excitations to the motor model. The following current source excitations are applied to each of the three-phase windings of the axial flux permanent magnet motor:  
\[\left\{\begin{aligned} \mathit{PhaseA:\sqrt{2}I_{i}sin\left(\frac{\pi n }{30}pt\right)}\\ \mathit{PhaseB:\sqrt{2}I_{i}sin\left(\frac{\pi n}{30}pt-\frac{2}{3}\pi \right)}\\ \mathit{PhaseC:\sqrt{2}I_{i}sin\left(\frac{\pi n}{30}pt+\frac{2}{3}\pi \right)}\end{aligned}\right. \tag{1}\]  
where \(n\) is the rotational speed of the motor, \(I_{i}\) is the effective value of the loaded current, \(p\) is the number of pole pairs, and \(t\) is the simulation time.  
Finally, when setting the solution conditions, the stop time for the solution is set to two electrical cycles, and the solution step size is set to perform 100 calculations per electrical cycle.  
After completing the above steps, the transient field analysis of the axial flux permanent magnet motor is carried out.  
The external characteristic curve of the motor, analyzed based on the working conditions of the quadruped robot, is shown in Figure 13. The rated torque is achieved at a speed of 1080 rpm, and the peak torque is reached at a speed of 900 rpm.
# A Multi-Analytical Approach to Investigate Fresco Paintings in a Hypogeum Environment
## 2 Materials and Methods  
Thermographic analyses were carried out inside the frigidarium environment, specifically surveying the walls at the two investigated frescoes. A FLIR T540 thermal imaging camera was used and the following parameters were set: Field of view: 25\({}^{\circ}\)\(\times\) 19\({}^{\circ}\); Minimum focusing distance: 0.40 m; Thermal sensitivity: 0.045 \({}^{\circ}\)C to + 30 \({}^{\circ}\)C; IR resolution: 320 \(\times\) 240 pixels; Temperature range: \(-\)20 \({}^{\circ}\)C to +350 \({}^{\circ}\)C.  
Hygrometric investigations were carried out with an MO257 moisture meter from EXTECH Instruments (Boston, MA, USA), which detects relative humidity in contact with the surface by means of a spherical probe avoiding surface damage. The measuring points identified on the surface of the fresco \"Madonna in trono con bambino\" are arranged along a vertical band measuring 20 \(\times\) 200 cm, in correspondence with the figure of the [PERSON] on the right side of the fresco, measuring the humidity values every 20 cm. The values measured with the hygrometer were then processed with the software IC Capture 2.4 from the Imaging Source Europe GmbH (Bremen, Germany), which made it possible to obtain a'mapping' of the humidity distributed on the surface (hygrometric tomography).  
Infrared Reflectography (IR-R) analyses were carried out with an IRIS DIGITAL BASIC instrument from Bresciani S.r.l. (Milano, Italy) consisting of an objective with interchange-able pass-band filter lenses, capable of recording responses in the infrared spectral range up to 1100 nm. The lenses are interchangeable and were used in sequence (715 nm; 850 nm; 1000 nm) to detect the presence of any retouches or pre-paintings carried out on the \"Madonna in trono con bambino\" fresco.  
Figure 2: Photo of a portion of “Teoria di Santi” fresco, where salt efflorescences are evident; the red square highlights the area of the fresco that has been cleaned with a soft brush.  
X-Ray Fluorescence (XRF) Spectrometry measurements for elemental pigment analysis on the two frescoes (Madonna in trono con Bambino\" and \"Teoria di Santi\") were performed using a Hitachi X-MET 8000 Expert GEO handheld XRF spectrometer (Hitachi High-Tech Analytical Science, Tokyo, Japan), equipped with a Rh anode X-ray tube. The following acquisition parameters were set:  
- Light element detection: voltage of 8-15 kV, current of 100-150 \(\upmu\)A;  
- Detection of heavy elements: voltage of 15-45 kV, current of 100-33 \(\upmu\)A;  
- Acquisition time equal to 30 s;  
- Spot size equal to 3 mm.  
Figure 3 shows the measuring points for the two frescos.