esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Service Placement for Microservice Architecture in Clouds
## 2 Problem Formulation
### Model Description  
We consider a cloud computer cluster is composed of a set of heterogeneous machines \(\mathcal{M}=\{m_{1},m_{2},...,m_{M}\}\), where \(M=|\mathcal{M}|\) is the number of machines. We consider \(R\) types of resources \(\mathcal{R}=\{r_{1},r_{2},...,r_{R}\}\) (e.g., CPU, memory, disk, etc.) in each machine. For machine \(m_{i}\), let \(V_{i}=(v_{i}^{1},v_{i}^{2},...,v_{i}^{R})\) be the vector of its available resources, where the element \(v_{i}^{j}\) denotes the amount of resource \(r_{j}\) available on machine \(m_{i}\).  
In infrastructure as a service (IaaS) model or container as a service (e.g., Amazon ECS) model, users would specify the resource demands of VMs or containers (e.g., a combination of CPU, memory, and storage) when submitting deployment requests. Thus, the resource demands are known upon the arrival of service requests. We consider a service-based application is composed of a set of services \(\mathcal{S}=\{s_{1},s_{2},...,s_{N}\}\) that are to be deployed on the cluster, and \(N=|\mathcal{S}|\) is the number of services. For service \(s_{i}\), let \(D_{i}=(d_{i}^{1},d_{i}^{2},...,d_{i}^{R})\) be the vector of its resource demands, where the element \(d_{i}^{j}\) denotes the amount of resource \(r_{j}\) that the service \(s_{i}\) demands. Let matrix \(\mathbb{T}=[t_{ij}]_{N\times N}\) denote the traffic between services, where \(t_{ij}\) denotes the traffic rate from service \(s_{i}\) to service \(s_{j}\).  
We model a placement solution as a 0-1 matrix \(\mathbb{X}=[x_{ij}]_{N\times M}\). if service \(s_{i}\) is to be deployed on machine \(m_{j}\), it is \(x_{ij}=1\). Otherwise, it is \(x_{ij}=0\).
## 4 Results
### Electrical Resistivity
#### 4.2.2 Salinity  
The surface water salinity measured using the YSI probe varies from 15 PSU in the west in West Lake to 21 PSU in the east in Seven Palm (Figure 12A). In the Seven Palm system, the surface water salinity measured using the YSI probe varies from 21 PSU in the north to 33 PSU in the south (Figure 12A).  
Figure 11: West–east profile showing the inverted lake-bottom resistivity from the VES surveys. Error bars indicate the lower and upper acceptable limits derived from equivalence analysis. West Lake system is located between 515,000 and 525,000 m and the Seven Palm system is located between 525,000 and 530,000 m.  
Figure 12: Salinity in West Lake and Seven Palm. (**A**) Surface water using YSI probe. (**B**) Surface water from the inverse model. (**C**) Groundwater from the inverse model.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Site**} & \multirow{2}{*}{**Date**} & \multicolumn{2}{c}{**Distance from**} & \multicolumn{2}{c}{**Well Data**} & \multicolumn{2}{c}{**Geophysical Model**} & \multirow{2}{*}{**F**} \\ \cline{3-4} \cline{6-7}  & & **the Well** & & **Surface Water** & **Groundwater** & \(\rho_{w}\) & **Surface Water** & **Lake Bottom** & \(\rho_{f}\) \\ \hline West Lake & 15 July 2019 & 68 & 0.32 & 0.26 & 0.29 & 3.23 & 12.6 \\ \hline Long Lake & 15 July 2019 & 807 & 0.19 & 0.20 & 0.14 & 2.11 & 10.6 \\ \hline Cuthbert Lake & 15 July 2019 & 48 & 0.28 & 0.31 & 0.27 & 2.77 & 9.0 \\ \hline Seven Palm & 10 July 2019 & 41 & 0.23 & 1.12 & 0.22 & 32.85 & _29.41_ \\ \hline \hline \end{tabular}
\end{table}
Table 4: Formation factors in the coastal lakes of ENP.  
The inverted surface water salinity has the same trend and increased from 19 PSU to 27 PSU from west to east and from 27 PSU to 35 PSU from north to south (Figure 12B). In contrast, the inverted groundwater salinity decreased from 42 PSU in the west in West Lake to 2 PSU in the east in Seven Palm. In the Seven Palm system, the groundwater salinity increased from 2 PSU in the north to 49 PSU in the south (Figure 12C).
## 4 Dataset Development
### Shape Deformations
#### 4.4.1 Surface Generation  
The surface generation process involved modifying a base planar surface by integrating various shapes, such as hemispheres, to resemble geometric structures and by applying parametric deformations to create deformation forms. Each surface was assigned a specular material to mimic realistic lighting interactions, crucial for representing real-world scenarios.  
1. **Shape Integration:** To generate the _Geometric Surfaces_ set, multiple convex shapes, primarily hemispheres, were superimposed onto a base planar surface \(\tilde{S}\) at randomized positions and scales.
2. **Parametric Deformations:** To generate the _Deformation Surfaces_ set, the planar surface was deformed using a set of parametric functions \(\Pi\), such as parabolic and sinusoidal transformations. These functions control the depth and curvature of the deformation, sculpting the surface into various forms.  
**Randomization:** To enhance diversity, the parameters involved in both geometric shape integration (size \(\theta_{1}\), position \(\theta_{2}\), and the number of hemispheres \(\theta_{3}\)) and deformations were randomized, resulting in a wide range of surface topologies \(\mathcal{S}_{\theta}\), featuring variations from subtle indentations to pronounced curvatures.
4. **Specular Material Assignment:** All generated surfaces \(\mathcal{S}\) were assigned a specular material to ensure that the generated data accurately represented real-world scenarios, where surface reflectivity significantly impacts depth perception.
# Modeling and Analysis of Staged Constellation Deployment from a Single-Unit System
## 4 Case Studies
### Case Study 1: The PQ8 Satellite
#### 4.1.2 Disengagement Velocity and PQ1 Insertion Points  
The initial angular velocity of each satellite increased linearly with respect to an increase in disengagement velocity, as illustrated in Figure 8. The PQ1s that have experienced multiple positive \(\Delta v\) instances exhibit higher initial separation rates. For example, PQ17, having undergone three successive positive, exhibits the highest initial angular velocity. PQ16, PQ15, and PQ13 follow accordingly, while the remainder experienced lower separation rates due to fewer positive \(\Delta v\) increments.  
The insertion points represent the \(\left[\theta\;\dot{\theta}\right]_{0}^{T}\) values for each PQ1 satellite in the PQ1 phase. These values depend on both the disengagement velocity and the relative positions of the satellites at the time of disengagement, as illustrated in Figure 9. The separation rates are grouped similarly to those described by the disengagement velocity in Figure 6. Notably, oscillations in the plots highlight the influence of the epoch at disengagement on the resulting relative angular velocities.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Satellite**} & \multicolumn{2}{c}{**Maximum**} & \multicolumn{2}{c}{**Minimum**} \\ \cline{2-4}  & **Ram Surface** & \(\beta\) **(kg/m2)** & **Ram Surface** & \(\beta\) **(kg/m2)** \\ \hline PQ4 & & 57.2 & 28.6 \\ PQ2 & & 57.2 & 28.6 \\ PQ1 & & 28.6 & 14.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: PQ8 ballistic coefficients.  
Figure 8: PQI initial angular velocities.  
Figure 7: (**a**) PQ satellite relative motion: relative angle, (**b**) angular velocity, (**c**) angular acceleration, and (**d**) average acceleration.
# Novel Small-Molecule Treatment and Emerging Biological Therapy for Poriasis
## 3 Biomacromolecules  
### Protein and Peptide  
#### 3.1.1 Protein and Peptide Delivery  
The potential of proteins for treating psoriasis has been explored as a promising strategy in recent years. Previously, the C-terminal peptide of chemokine-like factor 1 (CKLF1) has demonstrated protective effects against psoriasis by inhibiting the MAPK pathway and reducing inflammation [90]. Similarly, a selenium-enriched yeast peptide fraction (SeP) has demonstrated anti-inflammatory effects in psoriasis treatment by inhibiting both the MAPK and NF-kB pathways, although the specific molecular targets of these peptides have yet to be fully elucidated [91]. Moreover, the signal transducer and activator of transcription 3 (STAT3) participated deeply in regulating the pathogenesis ofposoriasis and can be targeted to inhibit its development. [PERSON] et al. reported a water-soluble STAT3-inhibiting peptide, caveolin-1 (CAV-1), which subsequently suppresses cytokines, such as IL-22, and inhibits angiogenesis [92]. Another peptide identified for alleviating psoriasis symptoms is Sprouty1 (SPRY1). SPRY1 functions as an inhibitor of receptor tyrosine kinases (RTKs), thereby attenuating various proinflammatory signaling pathways, including MAPK and STAT3 [93]. A nanoparticle-based peptide APTstat3-9R that inhibits STAT3 was recently reported [94]. Additional preparations have also been reported. [PERSON] et al. developed liposome-encapsulated antimicrobial peptides (AMPs) that demonstrate antibacterial and anti-inflammatory properties [95]. Similarly, [PERSON] et al. designed a dual-functional microneedle incorporating M-CSF and IL-13, which exhibited potential for immunotherapy and multi-drug release to modulate macrophage phenotypes [96]. In summary, most current strategies utilizing proteins and peptides focus on inhibiting the inflammatory response.
# Research on Improved OEE Measurement Method Based on the Multiproduct Production System
## 4 MPSE
### Calculation Steps of MPSE  
OEE is a useful measurement method in the industrial management. Similarly, MPSE is the application of OEE in multiproduct production system, thus, it should be expressed by a formula. Here, the detailed calculation steps are displayed.  
Indices  
\(i\) Index for the products, \(1\leq i\leq I\)  
\(j\) Index for the manufacturing procedures, \(1\leq j\leq J\)  
Parameters  
\(y_{i}\) Theoretical bottleneck speed of the \(i\)th product in multiproduct production system, \(s_{ij}\) Theoretical production speed of the \(i\)th product in the \(j\)th procedure  
\(x_{i}\) Actual bottleneck speed of the \(i\)th product in multiproduct production system  
\(s_{ij}^{\prime}\) Actual production speed of \(i\)th product in the \(j\)th procedure  
\(z_{i}\) Theoretical load time of the \(i\)th product  
\(PWT_{i}\) Planned working time of the \(i\)th product  
\(PD_{i}\) Planned downtime of the \(i\)th product  
\(t_{i}\) Actual load time of the \(i\)th product  
\(UD_{i}\) Unplanned downtime of the \(i\)th product  
\(PC_{i}\) Production capacity of the \(i\)th product  
\(q_{i}\) Quantity of defective products within the process of manufacturing the \(i\)th product  
\(MPSE\) Multiproduct Production System Effectiveness  
1. To measure the theoretical bottleneck speed of each product:  
\[y_{i}\ =\ min\big{(}s_{ij}\big{)};\,i\ =\ 1\ldots I;\,j\ =\ 1\ldots J \tag{12}\]
2. To measure the actual bottleneck speed of each product:  
\[x_{i}\ =\ min\Big{(}s_{ij}^{\prime}\Big{)};\,i\ =\ 1\ldots I;\,j\ =\ 1\ldots J \tag{13}\]
3. To calculate theoretical load time of each product:  
\[z_{i}\ =\ PWT_{i}-PD_{i};\,i\ =\ 1\ldots I \tag{14}\]
4. To calculate the actual load time of each product:  
\[t_{i}\ =\ z_{i}-UD_{i};\,i\ =\ 1\ldots I \tag{15}\]
5. To calculate the production capacity:  
\[PC_{i}\ =\ \frac{x_{i}\ \times\ t_{i}-q_{i}}{y_{i}\ \times\ z_{i}};\,i\ =\ 1\ldots I \tag{16}\]
6. To integrate all \(PC_{i}\) into a complete arithmetic:  
\[MPSE\ =\ \frac{\sum_{i=1}^{n}(x_{i}\ \times\ t_{i}-q_{i})}{\sum_{i=1}^{n}(y_{i} \ \times\ z_{i})};\,i\ =\ 1\ldots I \tag{17}\]
## 3 Method and Experimental Setting
### Implementation Details  
The RNN, the CNN and the MLP were all chosen with simple architectures due to the fact that the features in input are of low dimensionality, the dataset is not of vast size and the inputs themselves are the outputs of two relatively more complex deep models. Be that as it may, more complex architectures were attempted but with no discernible improvements in accuracy and worsening overfitting. The RNN network, shown in Figure 2, is composed of a layer of eight GRU bidirectional cells, followed by a dense layer with 16 neurons and a softmax output layer. The CNN network, shown in Figure 3, is composed of 16 filters, a kernel size of 8 and a stride of 1 in both the dimensions, and the inputs were zero padded so as to preserve input dimensionality at layer output. Following the convolutional layer are a 2D global average pooling layer and a softmax output layer. The MLP had a single hidden layer with 256 hidden units. Dropout was applied on the hidden, convolutional and recurrent layers in the MLP, CNN and RNN, respectively.  
The dataset was split into test and train layers by actors: actors 5, 15 and 18 were randomly chosen for testing, and the rest were used for training and validation. Splitting by actors instead of sampling from the combined data offers a more objective measure of performance. Had parts of the data of a particular actor been present in both the test and train datasets, it would have provided the opportunity for the models to learn actor-specific attributes and potentially display higher classification accuracy on the test data.  
All the videos are converted into 25 fps from the original 30 due to LipNet having been trained on that frame rate, and the frames at which the models are trained are in the range of 5-65 in 5 frame increments. This corresponds to a range from 0.2 s to 2.6 s of input duration in increments of 0.2 s.  
All the learning parameters were shared across all the models: learning rate 0.001, dropout probability 0.5 and batch size 32. All models were trained for 50 epochs. The loss function was categorical cross entropy, and the optimizer was Adam [48].
## 2 Vehicle Dynamics Model and Model Verification
### Fourteen-Degree-of-Freedom Vehicle Model
#### 2.1.2 Body Motion Equations  
When the vehicle is moving, the motion of the vehicle body can be regarded as a rigid body. According to rigid body motion theory and angular momentum theory, the mass of a car body in motion satisfies the following differential equations:  
\[\begin{split} I_{yys}\dot{q}+(I_{xxs}-I_{zxs})pr\\ =-L_{1}(\frac{F_{y\text{l1}}}{D_{\text{l1}}}+\frac{F_{x\text{l1}}}{ D_{r\text{l1}}})+L_{2}(\frac{F_{y\text{l2}}}{D_{\text{l2}}}+\frac{F_{x\text{l2}}}{D_{ \text{l2}}})+h(F_{x\text{l1}}+F_{x\text{r1}}+F_{x\text{r2}})\end{split} \tag{4}\]  
Roll motion equation:  
\[\begin{split} I_{xxs}\dot{p}+(I_{zxs}-I_{yys})qr\\ =(A_{\text{l1}}F_{x\text{l1}}+A_{r\text{l1}}F_{x\text{r1}}+A_{l2} F_{y\text{l2}}+A_{r\text{2}}F_{x\text{r2}})+m_{s}\dot{q}h^{t}\sin\varphi\\ -m_{s}h^{t}(\dot{v}+ru-pw)\end{split} \tag{5}\]  
\[D_{ij}=\frac{a_{ij}+b_{ij}}{b_{ij}} \tag{6}\]  
\[A_{ij}=\left\{\begin{array}{ll}-n_{lj}+(n_{lj}-b_{lj})\frac{a_{lj}}{a_{ij}+ b_{lj}},&\text{on the left wheel}\\ n_{rj}-(n_{rj}-b_{rj})\frac{a_{ij}}{a_{ij}+b_{rj}},&\text{on the right wheel}\end{array}\right. \tag{7}\]  
where \(h\) is the height of the mass center. \(F_{x\text{r}ij}\) is the vertical force of each wheel. \(I_{xxs}\), \(I_{y\text{s}}\), and \(I_{z\text{z}s}\) are the moment inertia of the vehicle sprung mass around the X-axis, Y-axis, and Z-axis, respectively. \(g\) is the acceleration of gravity. \(A_{ij}\), \(D_{ij}\), \(a_{ij}\), \(b_{ij}\), and \(n_{ij}\) are the geometry parameters related to the suspension connection structure of each wheel. \(\varphi\) is the roll angle of the vehicle.  
Figure 1: A 14-degree-of-freedom vehicle model (including longitudinal, lateral, vertical, roll, pitch and yaw direction of vehicle, and rotation and jump of each wheel).
## 4 Exploring Research Gaps and Opportunities for Remote Sensing Applications in Almond Crop Studies  
A comprehensive review of RS applications in almond crops has enabled the identification of research gaps and promising opportunities for future research in this domain. This systematic review, covering the period from 2010 to 2023, not only provides an overview of the current landscape but also highlights unexplored areas that deserve attention in the field of RS for almond crops. By synthesizing a wide array of literature, an up-to-date overview of the current landscape is presented, but areas that remain largely unexplored are also highlighted, thereby delineating paths for further exploration in the realm of RS for almond crops. The analysis reveals a concentration of research efforts in studies emphasizing WM, representing a critical area of interest in almond cultivation [2]. However, a significant research gap exists in other crucial applications, such as HMDD, YP, and P. It is imperative for future research endeavors to address these gaps, providing a more holistic understanding of almond orchard dynamics. An examination of the geographical distribution of RS studies highlights a concentration in leading almond-producing regions, such as the United States and Spain. However, major producers like Australia receive comparatively less attention in the current research landscape. Future investigations should prioritize broadening the geographical focus to include emerging almond-growing regions and adapting methodologies to regional specificities. This approach holds the potential to yield valuable insights for sustainable almond cultivation on a global scale.  
An area that is ripe for exploration is the incorporation of innovative data collection and analysis methods that have yet to be thoroughly investigated. Future research endeavors are poised to derive significant advantages from the integration of various sensors, including RGB, MSP, TIR, HSP, and LiDAR, harnessing the strengths inherent in each sensor type [17]. This advanced integration has the potential to improve the precision, efficiency, and depth of almond crop monitoring. Moreover, conducting studies based on time series data from different RS platforms emerges as a critical strategy to enable more accurate predictions and a deeper understanding of almond growth dynamics [117]. This temporal dimension can provide valuable insights into how almond crops evolve over time, allowing for more informed and nuanced decision-making in agricultural practices. Given the escalating impact of climate change on agricultural productivity [8], this review suggests that future research should explore the interplay between RS applications and changing environmental conditions affecting almond crops. Understanding how RS technologies can contribute to adaptation strategies in the face of evolving climate patterns presents a fruitful subject for exploration.
## 2 Modeling the Problem
Given these points, we can now introduce the constraints that the simulation optimization must respect:  
* Each position \((i,j)\) in the yard and \((r,c,b)\) on the ship must be occupied by at most one container. * No \"holes\" are allowed in the stacking area, meaning that if there is a container in position \((i,j+1)\), then the lower position \((i,j)\) must also be occupied. Likewise, if there is a container is in position \((r,c+1,b)\) on the ship, then the lower position \((r,c,b)\) must also be occupied. * The last in, first out (LIFO) policy must be respected, meaning that if container \(n\) is below container \(q\), then container \(n\) must be moved before container \(q\). * No container can be relocated to a position in the same column. * In the yard, a container in position \((i,j)\) can only be moved if the position \((i,j+1)\) above it is empty. Likewise, on the ship, a container in position \((r,c,b)\) can only be moved if the position \((r,c+1,b)\) above it is empty. * The containers are removed from the yard one at a time and then loaded onto the ship. * Container \(n\), after being loaded onto the ship, does not change its position while the ship is still stopped at the same port. * When a container is unloaded and loaded back onto the ship, these operations are counted as one relocation movement. * The containers relocated outside the ship are always loaded back onto it before the export containers at the port yard. Figure 3: Loading flow at port 2. Any readers interested in the mathematical formulation of these constraints can reef to [35]. In the next section, the methodology used here is described in detail.
## 3 Sample Environments
### Electromagnetic Fields
One of the surprising findings is that on-line strong magnetic fields have so far rarely been used as a sample environment in soft matter-related neutron research. In most neutron scattering laboratories, there is an abundance of resistive and superconducting magnets available for research on magnetic materials. One would have expected that several potential user groups would have taken advantage of this opportunity and used these magnets for soft matter experiments as well. For soft matter, one clearly requires higher sample temperatures than the cryogenic temperatures most commonly used in magnet research and most often a configuration where neutron beam and magnetic field are at 90 degrees, thus requiring a split coil magnet over a simpler solenoid. In liquid crystal and liquid crystalline polymer research, the study of material response to magnetic fields is common, but often only moderate fields (<2 T) are required. An example of this is the interplay between ordering and microphase separation of liquid-crystal polystyrene-poly(methyl methacrylate) block copolymers bearing a chiral biphenyl ester side group linked to the backbone by a dodecyl spacer [92]. The applied magnetic field causes the polymer backbone to partially orient. Experiments on another liquid crystalline block copolymer to map out the temperature-magnetic field phase diagram have also been reported [93]. When doped with lanthanides, lipid membranes can also be oriented [94]. Since the interaction of soft matter often is via diamagnetism, the required fields to be able to observe a relevant alignment interaction are >5 T, i.e., greater than the fields produced by permanent and electromagnets. The requirement to use superconducting magnets is a severe complication. Some static on-line X-ray experiments are published [95; 96] and even some dynamic studies, where the sample was rotated inside the magnetic field and observed whilst rotating back [97; 98]. For neutron experiments, occasionally an off-line magnet was used to produce aligned samples from fibrous biological macromolecules [99; 100] and more recently the use of an on-line 8 T magnet to measure alignment by SANS of biocelles complexed with lanthanides was reported [101]. Transportable higher field on-line superconducting solenoids (17 T) have been used, but so far, no results on soft matter samples have been published [102]. For higher continuous fields, one has to resort to more permanent installations like Bitter magnets, very large superconductors, or hybrids of these two. Pulsed magnetic fields using capacitor banks are often transportable and split coils can reach up to 30 T [103; 104; 105] and solenoids up to 40 T [106], but the short pulses (msec) and the slow repetition rates (several shots/hour) make these magnets unrealistic options for soft matter research.