esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The experimental results showed that the ultrasonic nonlinearity parameter decreased as the surface roughness increased; however, they conducted measurements only 410 Cb stainless steel with both surfaces were rough. [PERSON] et al. [21] experimentally studied the influence of surface roughness on ultrasonic nonlinearity parameter measurements for aluminum and steel specimens. However, to produce specimens with the rough surfaces, the surfaces of both the specimens were processed with the same sandpaper; thus, the nonlinearity parameter of steel was measured at a very low surface roughness range, which does not significantly affect the nonlinearity parameter measurements. In this study, we investigated the influence of the surface roughness on the ultrasonic nonlinearity parameter measurements. For this purpose, Al6061-T6 and SUS304 specimens with different surface roughnesses of 0.5-2.9 \(\upmu\)m on both sides were prepared. Then, Al6061-T6 specimens with one rough surface and one smooth surface were prepared to analyze the influence of surface roughness by varying the surface roughness of the surface where the ultrasound wave is transmitted. Then, the absolute and relative ultrasonic nonlinearity measurement experiments were conducted using the through-transmission technique, and the absolute nonlinearity parameter was measured using the piezoelectric method [13]. Then, the influence of the surface roughness on the ultrasonic nonlinearity parameter measurement for each case was analyzed.
# Hierarchical and Unsupervised Graph Representation Learning with Loukas's Coarsening
## 5 Evaluation
### Supervised Classification
#### 5.2.2 Model Selection  
The relevant hyper-parameters of HG2V are the number of features \(d\in\{16,128,256\}\) at each stage, the receptive field of Truncated Krylov \(a\in\{2,4\}\), and the maximum depth of Loukas's coarsening \(L\in\{3,5\}\). They are selected using a grid search. Five random split of the dataset are generated: _TrainVal_ (80% of the data) and _Test_ (20% of the data). HG2V is trained over _TrainVal_. Then, a C-SVM is trained over _TrainVal_, using 5-cross validation grid search for the selection of its hyper-parameters (C, Gamma). The average validation score of the best C-SVM classifier is used to select the hyper-parameters \((d^{*},a^{*},L^{*})\) of HG2V. The average test score of the best C-SVM classifier (accuracy over _Test_ split) is reported in Table 2. Note that HG2V could be trained on the test set _without_ using labels, due to its unsupervised nature. We decided not doing it, to ensure fair comparison with the other methods.
## 6 Results and Discussion
### Ablation Experiment
#### 6.3.1 The Need for Hidden State-Based Information Enhancement  
To validate the necessity of our information enhancement operation on text embedding during the input phase of the transformer decoder, which utilizes the hidden state of the GCN, we have removed the embedding splicing operation described in Equation (11) from GCLTE\({}_{\text{W/E}}\) (without the enhancement). From the comparison in Table 6 between GCLTE\({}_{\text{W/E}}\) and GCLTE, it is evident that the rating prediction performance of GCLTE\({}_{\text{W/E}}\) remains nearly identical to that of GCLTE after the removal of the information enhancement operation. This outcome results from structuring the rating prediction and reason generation tasks in two distinct, independently functioning modules within the model, thereby preventing any mutual interference during multi-task learning optimization.  
Additionally, we observe a significant decline in the quality of the generated text after removing the information enhancement from the hidden state of the GCN. This decline is due to the loss of personalized user and item information embedded in the GCN's hidden state, which reduces the personalization in the generated text. Specifically, the decline in the quality of the generated text is more noticeable for the Amazon_Music dataset, which is larger than the others despite having a similar number of users. This results in a more informative hidden state of the GCN. When comparing GCLTE\({}_{\text{W/E}}\) with other variants in Table 7, the most significant performance degradation occurs with GCLTE\({}_{\text{W/E}}\), demonstrating that the information enhancement operation significantly boosts model performance.
## 4 TSN Network-Wide Scheduling--Approaches Overview, Challenges, and Future Directions
### Literature Analysis
#### 4.2.3 Diversity of Objectives  
In total, we identified 42 different objectives that different solution approaches aimed to achieve, which we summarized in Figure 3. More than 45% of the papers aim to achieve more than one objective, simultaneously. In around half of those cases, the objectives belong to different groups. The earliest and most commonly used objective is the minimization of latency (objective 2.2 in Figure 3), which appeared already in 2016, as can be seen from Figure 5 and continued to appear until 2022. In total, 15 papers that we surveyed considered it in their approach. Interestingly, more recent works shifted interest to also consider other aspects such as the dynamism of the network. We believe that this is again related to the maturity of TSN development. While in the early days, TSN researchers were looking more into proof of concepts, more recently there can be seen a shift towards practical deployment. As a consequence, the focus shifted more towards developing fast solution heuristics (i.e., objective 1.6 starting to appear around 2020 and was considered in seven works afterward, as seen in Figure 3) and considering domain-specific constraints such as resilience (objective 5.1 in the year 2019 ) or safety (objective 5.3 in the year 2019) related constraints. We also noticed an interest in scheduling optimization for fog computing after the year 2020. This includes aspects such as VM placement and migration.
# Biohydrogel Based on Dynamic Covalent Bonds for Wound Healing Applications
## 2 Materials and Methods
### Effect of PBA-Alg Hydrogel on Wound Healing  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board (ethics committee) of the University of Nigeria Nsukka/Faculty of Pharmaceutical Sciences. Adult Swiss albino rats (170-300 g; average body length (nose tip to tail base) = 19 cm) of both sexes were used to evaluate wound healing using an incision model. The animals were divided into five subgroups (four treatment groups and one control group) of five rats each. The animals were anesthetized with intramuscular ketamine (50 mg/kg) and xylazine (10 mg/kg). The hair on the dorsal region of the animals was shaved off, and a 4 cm paravertebral incision was made through the skin and cutaneous muscles. The incisions for each treatment group were covered with preparations containing 3% \(w/w\) PBA-Alg hydrogel, 4% PBA-Alg hydrogel, 3% PBA-Alg hydrogel/Savlon antiseptic, 3% sodium alginate hydrogel, or neomycin sulfate Vaseline gauze, while the control group received no treatment.  
The wound area was measured on alternating days during a 9-day study period (days 0, 2, 4, 6, 8, and 9). Wound closure time was recorded as the number of days required for the scalp to fall off with no raw wound left behind. Wound closure or size reduction was calculated from Equation (3):  
\[\textit{Wound closure}\ \%=\frac{\textit{initial wound size}-\textit{ wound size at time}\ t}{\textit{initial wound size}}\times 100 \tag{3}\]
## 2 Preliminaries
### Quaternion Operations  
The unit quaternion \(\boldsymbol{q}=\left[\begin{array}{cc}q_{0}&\boldsymbol{q}_{v}\end{array} \right]^{T}\in R^{4}\), \(\|\boldsymbol{q}\|=1\), is used to represent the rotation of the quadrotor in this paper. Several corresponding operations are given as follows.  
The quaternion multiplication:  
\[\boldsymbol{q}\otimes\boldsymbol{\sigma}=\left[\begin{array}{cc}q_{0} \sigma_{0}-\boldsymbol{q}_{v}^{T}\boldsymbol{\sigma}_{v}\\ q_{0}\boldsymbol{\sigma}_{v}+\sigma_{0}\boldsymbol{q}_{v}-\boldsymbol{S( \sigma}_{v})\boldsymbol{q}_{v}\end{array}\right] \tag{3}\]  
The relationship between rotation matrix \(\boldsymbol{C}_{A}^{B}\) and unit quaternion \(\boldsymbol{q}\) is represented by:  
\[\boldsymbol{C}_{A}^{B}=(q_{0}^{2}-\boldsymbol{q}_{v}^{T}\boldsymbol{q}_{v}) \boldsymbol{I}_{3}+2\boldsymbol{q}_{v}\boldsymbol{q}_{v}^{T}+2q_{0}\boldsymbol {S(\boldsymbol{q}_{v})} \tag{4}\]  
Take the time derivative of Equation (4), we could obtain  
\[\dot{\boldsymbol{C}_{A}^{B}}=-\boldsymbol{S(\omega)}\;\boldsymbol{C}_{A}^{B} \tag{5}\]  
The derivative of a quaternion and the quaternion error \(\boldsymbol{q}_{e}\) are provided, respectively, as  
\[\dot{\boldsymbol{q}}=\left[\begin{array}{c}\dot{q}_{0}\\ \dot{\boldsymbol{q}}_{v}\end{array}\right]=\frac{1}{2}\boldsymbol{q}\otimes \left[\begin{array}{c}0\\ \boldsymbol{\omega}\end{array}\right]=\frac{1}{2}\left[\begin{array}{c}- \boldsymbol{q}_{v}^{T}\\ \boldsymbol{S(\boldsymbol{q}_{v})}+q_{0}\boldsymbol{I}_{3}\end{array}\right]\boldsymbol {\omega} \tag{6}\]  
\[\boldsymbol{q}_{e}=\boldsymbol{q}_{d}^{*}\otimes\boldsymbol{q} \tag{7}\]where \(q_{d}\) represents the desired quaternion whose conjugate is \(q_{d}^{*}=\left[\begin{array}{cc}q_{d0}&-q_{dp}\end{array}\right]^{T}\). Moreover, \(\omega\) denotes the angular rate of the system.  
\[\omega_{e}=\omega-C_{d}^{b}\omega_{d} \tag{8}\]  
\[\dot{q}_{e}=\frac{1}{2}q_{e}\otimes\left[\begin{array}{c}0\\ \omega_{e}\end{array}\right]=\frac{1}{2}\left[\begin{array}{c}-q_{eo}^{T}\\ S(q_{eo})+q_{eo}I_{3}\end{array}\right]\omega_{e} \tag{9}\]
# Detection and Characterization of Physical Activity and Psychological Stress from Wristband Data
## 2 Materials and Methods
### Signal Processing  
In this work, a single non-invasive wrist-worn device, the Empatica E4 wristband (Empatica E4, Empatica Inc., Cambridge, MA, USA [48]) was used. The E4 is capable of recording and streaming the photoplethysmogram (PPG) signal, reporting blood volume pulse (BVP) at a frequency of 64 samples/s, data from a triple-axis accelerometer (ACC) measured at a frequency of 32 samples/s, heart rate derived from the BVP signal using a propriety algorithm and reported at a frequency of 1 samples/s, and ST and GSR recorded at a frequency of 4 samples/s. In this section, signal processing methods are introduced for the five different biosignals (ACC, HR, BVP, GSR, and ST).
# Gaussian Quantum Trajectories for the Variational Simulation of Open Quantum-Optical Systems
## 3 Kerr Bistability and the _Xp_-Gaussian Methods
Relation Equation (17) remains fulfilled at a jump up to order \(\left|\alpha\right|^{-5}\), so that only in systems where there is a significant jump rate at zero density, it is better to evolve \(\left\langle\hat{\delta}^{\dagger}\,\hat{\delta}\right\rangle\) explicitly with Equation (15). In Figure 1, we show a comparison between single trajectories that were obtained with the numerically-exact evolution of the wave function and the Gaussian variational ansatz, where for the jumps in both simulations, identical random numbers were used. Initially there is a very strong correspondence with the \(XP\)-Gaussian variational method, while at later evolution times an offset in time emerges and the good correspondence is lost. Qualitatively though, the behavior remains similar so that approximate correspondence in the weak sense (for the whole ensemble) can remain fulfilled. To verify this, in Figure 2 densities and density correlations are plotted as a function of \(F\) throughout the bistability regime of the Kerr model. We see that the \(XP\)-Gaussian methods can provide reasonable to very good predictions of the exact correlation functions from [59]. In this regime of relatively low density, these \(XP\)-Gaussian methods are mostly outperformed by the TWA method, though. It is also seen that the predictions on the statistics of the ensemble as a whole depend (weakly) on the choice of unraveling, whereas this is independent for exact trajectories. We should note that, because of the low density, simulation with exact trajectories can also still easily be performed. It is the opposite, high-density, limit where Gaussian ansatzes will provide a better description, as well as where exact trajectories become computationally unfeasible.
# Challenges and Solution Directions of Microservice Architectures: A Systematic Literature Review
## 5 Identified Challenges and Solution Directions
### RQ2. What Are the Identified Solution Directions?
#### 5.2.5 Communication and Integration  
Study [AI] focuses on complex microservice data flows and communication. To contribute to the solution of this problem, an event-driven lightweight platform called Beethoven for microservice orchestration is proposed. The platform is formed of reference architecture and an orchestration language. To prove its practicality, an example application has been implemented.  
Study [R] focuses on the network pressure increase because of inter-microservice communication. The networking of containerized microservice is inefficient. This paper proposes a high-performance user-space networking solution for containerized microservices called DockNet and provides a master-slave threading model to decouple execution and management. This model uses Data Plane Development Kit (DPDK) and customized lightweight IP (LwIP) as the high-performance data plane and TCP/IP stack. Thus, in order to improve network performance, a robust and fast channel between microservices is built. Various experiments are conducted to validate it and as a result of these experiments, DockNet delivers over 4.2\(\times\), 4.3\(\times\), 5.5\(\times\) higher performance compared to existing networking solutions.  
To sum up, with the widespread use of MSA, the need for communication and integration between microservices has become a challenging point. However, there are not enough studies proposing solutions. We identified only two studies figuring out some solutions to the challenging part of communication. These studies come up with solutions to complex microservice data flows and network performance.
# Fractional Frequency Reuse Optimal SINR Threshold Selection Using NIR and ISODATA
## 6 Conclusions  
This paper targeted to improve throughput and fairness in FFR schemes. This was achieved using global thresholding techniques, i.e., ISODATA and NIR to set the SINR threshold. The results were validated by comparing throughput and fairness of the two approaches with static FFR. Previous approaches have lacked the dynamic aspect of continuously updating the SINR threshold. Our approach periodically updated the SINR threshold after every 50 TTIs. Scheduling was done using the PF scheduler. We set up simulations using the MATLAB-based Vienna LTE system level simulator. We used scripts and functions to implement the thresholding algorithms, as shown in the methodology. We varied the number of UEs per cell to demonstrate varying cell loads. We concluded from the ECDF curves that NIR had the highest probability of achieving the best throughput for a targeted minimum network throughput value. We also concluded that NIR outperformed both ISODATA and static FFR in terms of edge throughput, as shown in Figure 12. This edge throughput represented the highest throughput achieved by a subscriber in the cell edge. We also concluded that NIR and ISODATA perform similarly when it comes to average throughput. NIR and ISODATA significantly outperformed static FFR in terms of throughput. However, when it comes to fairness, NIR consistently outperformed ISODATA and static FFR. Overall, despite ISODATA marginally outperforming NIR in average throughput in the case of 20 UEs per cell, we can conclude that NIR has superior performance. For the case of static FFR method, we opine that even if other values of SINR threshold and \(\beta_{FR}\) were set, the results would still be sub-optimal. These values would not continuously adapt to the changing cell-load conditions and fluidity or movement of the subscribers. Future work can involve considering thresholding techniques that set multiple thresholds for cases of FFR schemes that have more than two regions.  
Conceptualization, A.O. and S.M.; methodology, A.O.; software, A.O.; validation, P.K. and A.O.; formal analysis, A.O.; investigation, A.O.; resources, A.O.; data curation, A.O.; writing--original draft preparation, A.O.; writing--review and editing, A.O.; visualization, A.O.; supervision, P.K. and S.M.; project administration, A.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
## 3 Results and Discussion
### Chemical Properties of Organosolv Lignin  
Qualitative and quantitative analysis of obtained organosolv lignins from E-S-OL were performed via pyrolysis-GC-MS analysis, and the results are listed in Table 7. Organosolv lignin obtained under steam explosion conditions of 200 \({}^{\circ}\)C and 5-10 min exhibited more than 14 peaks. Phenolic compounds, such as toluene (1), phenol (2), guaiacol (5), syringol (8), and isoeigenenol (10), were the major compounds. As shown in Table 7, the number of chemical compounds and the overall peak size in the organosolv lignin decreased by increasing residence time. This might be explained by the fact that the lignin compounds can be condensed in harsher conditions and less decomposed by heat.  
\begin{table}
\begin{tabular}{c c c} \hline  & & **Crystallinity Index (\%)** \\ \hline Raw & & 58.3 \\  & 180-10 & 35.9 \\ E-S-CS & 200-10 & 47.8 \\  & 220-10 & 60.0 \\  & EA-S 2.0\% & 47.8 \\  & EA-S-CS 2.0\% & 57.0 \\ \hline \end{tabular}
\end{table}
Table 6: X-ray diffraction of (**A**) E-S-CS and (**B**) EA-S and follow-up EA-S-CS.  
Figure 4: X-ray diffraction of (**A**) E-S-CS and (**B**) EA-S and follow-up EA-S-CS.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**No.**} & **Compound** & \multicolumn{2}{c}{**Concentration (Area/IS Area)**} \\ \cline{3-5}  & & **220-5** & **220-10** \\ \hline
1 & Toluene & 0.45 & 0.15 \\
2 & Phenol & 4.66 & 1.67 \\
3 & \(\rho\)-Cresol & 0.44 & 0.16 \\
4 & \(p\)-Cresol & 0.54 & 0.19 \\
5 & Guaiacol & 0.51 & 0.33 \\
6 & Creosol & 0.53 & 0.25 \\
7 & 2-Methoxy-4-vinylphenol & 0.40 & 0.12 \\
8 & Syringol- & 1.01 & 0.61 \\
9 & 3,5-Dimethoxy-4-hydroxytoluene & 1.05 & 0.33 \\
10 & Isouegenol & 0.30 & 0.10 \\
11 & 2,6-Dimethyl-3-(methoxymethyl)-benzoquinone & 0.55 & 0.12 \\
12 & (E)-4-Propenylsyringol & 0.68 & 0.19 \\
13 & n-Hexadecanoic acid & 1.07 & 0.36 \\
14 & Octadecanoic acid & 0.33 & 0.15 \\ \hline \end{tabular}
\end{table}
Table 7: Qualitative and quantitative analysis of chemical compounds in the organosolv lignin from E-S-OL.
## Materials and Methods
### Laser-Assisted Extraction Installation Description  
Technological studies were done on an extraction installation (made by the SMI project partner in the study) following the principles of the classical extraction at reflux temperature, adapted for laser irradiation (Figure 2). Specifically, a classic steel bio-reactorof 2 L total capacity, and 1.5 L effective, extraction capacity was built (1). On the cover of the bio-reactor there were placed two connections, two windows of 3.50 cm diameter each (2); one ZnSe window with observation and sample collection purpose, and one quartz window through which the laser irradiation was done. The laser kit device (3), assembling the (six) monochrome laser beam in the study (4), was designed in a manner to be connected to a telescope (5). The telescopic system collects the radiation from the lasers through an optical fiber connector and delivers an uniform, circular, 15 mm diameter laser beam almost similar for every wavelength. The telescope was placed at about 16 cm from the liquid surface. Since the beam is collimated, this distance does not influence the power density (W/m\({}^{2}\)), providing a large area of irradiation (approximately 176 mm\({}^{2}\)) with perfect uniform and stable laser beams. The radiation was continuous, with 360 s total time of irradiation. Briefly, the bio-reactor (1) was charged with 50 g plant powder and 1000 mL distilled water, after that the vegetal mixture was homogenized with a glass rod for about 5 min. After closing the cover of the bio-reactor, the telescope (5) was placed in front of the quartz window, after that the other devices, specific to the classic reflux extraction, were gradually put into operation: specifically, the cooling system (6), the agitation system (7), the heating system (8.9). In the end, the punctual laser radiation in the study was fixed (4). In the end of the extraction process, the extraction devices were closed in the reverse order of starting, after that the cover was removed, and the vegetal mixture was filtered through a double cotton cloth filter. The extracts were processed as described in plant extraction preparation section.
## 2 Materials and Methods
### Sample
#### 2.2.2 Dataset Creation and Data Augmentation  
A total of 140 photographs containing oysters comprise the raw dataset. In real production lines, oysters may present various orientations. Thus, a different number of oysters were selected each time and placed in random positions for image acquisition. During the shooting process, the camera was perpendicular to the bottom plate on which the oysters were placed. The resolution of the captured image was 4032 \(\times\) 3024 pixels, and images were stored as.tiff files. To ensure randomness, the captured images were shuffled. Image labeling was performed manually using the roLabelImg v1.8.5 software ([[https://github.com/cgvict/roLabelImg](https://github.com/cgvict/roLabelImg)]([https://github.com/cgvict/roLabelImg](https://github.com/cgvict/roLabelImg)) (accessed on 20 June 2024). The oyster region was selected by drawing a bounding box and then rotating it to form the minimum bounding rectangle around the oyster. The quadrilateral coordinates and angle outputs were saved in a TXT file using the DOTA format which was passed into a training process together with the input images after conversion. This conversion was implemented using a custom Python v 3.9 script based on the official DOTA coordinate transformation protocol.  
To increase the amount of training data, a data augmentation method was employed. The roLabelImg tool, which allows users to determine the minimum bounding rectangle, also facilitates the easy generation of JavaScript Object Notation (JSON) txt files for subsequent data augmentation. Therefore, images generated by the roLabelImg tool were used to augment the training dataset. Geometric transformations were performed on the image, including the random flipping and translation of object data, the adjustment of image brightness, and the addition of salt and pepper noise. These techniques aim to improve the generalization ability of the model (as shown in Figure 4). As a result, the original set of 140 images was expanded to 700 images, which was then divided into two subsets: a training subset consisting of 564 images and a testing subset comprising 136 images. Finally, all outputs were converted to TXT files required for training the detection model.  
The green box is the rotation box, the central green point is the anchor point of the rotation box, and the red is the coverage area of the rotation box.
## Appendix A  
For a non-singular matrix \(A\), if there is a new column and a new row added to the matrix and get \(E\) as as followings:  
\[E=\left[\begin{array}{cc}A&b\\ b^{T}&c\end{array}\right].\] (A1)  
Figure 8: Performance comparison among no prediction (last uplink measurement), the ALD-KRLS algorithm, and the proposed SSW-KRLS algorithm. \(\beta=0.97\), \(v=120\) km/h.  
Suppose the inversion of \(E\) is formulated as:  
\[E^{-1}=\left[\begin{array}{cc}D&e\\ e^{T}&f\end{array}\right]. \tag{10}\]  
Then the \(E^{-1}\) can be calculated by solving:  
\[\begin{cases}AD+be^{T}=I\\ Ae+bf=0\\ b^{T}e+cf=1\end{cases}, \tag{11}\]  
where \(E^{-1}\) can be obtained as:  
\[E^{-1}=\left[\begin{array}{cc}A^{-1}\big{(}I+bb^{T}A^{-1H}f\big{)}&-A^{-1}bf \\ -\big{(}A^{-1}b\big{)}^{T}f&f\end{array}\right], \tag{12}\]  
where \(f=\big{(}c-b^{T}A^{-1}b\big{)}^{-1}\).