esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Optimization of Supplemented COS Concentration  
Nine types of wine spoilage organism were isolated from the contaminated must, comprising six types of bacteria (_Enterococcus_, _Bacillus cereus_, _Paenibacillus_, _Alkaliphilus_, _Pantoea_ sp., and _Lactococcus_) and three types of fungi (_Aspergillus flavus_, _Aurebosidium pullulans_, and _Penicillium_). Various concentrations of COS (0, 100, 500, and 1000 mg/L) were added into the beef extract-peptone medium or Czapek-Dox medium without agar and then inoculated with a specific type of wine spoilage organism to investigate the antimicrobial effect. The antimicrobial effect of COS was evaluated by measuring the colony counts of the nine types of spoilage organism when applying COS at 100, 500, and 1000 mg/L to the must. Many previous reports have described the antibacterial and antifungal activities of COS against many types of bacteria and fungi [28; 29; 30; 31]. COS can alter the cell membrane permeability, which protects the release of cell components and controls the entry of materials into the cell from the environment. Positively charged COS can then bind to and be absorbed into microbe cell walls, causing DNA penetration and blocking RNA transcription and finally resulting in microbial cell death [30]. However, the antimicrobial activities of COS change depending on various factors, such as the degree of polymerization and microorganism type. Therefore, to efficiently exert the antibacterial activity of COS during winemaking, the COS concentration supplemented into the must should be optimized.  
As shown in Table 1, when 100 mg/L COS was applied to the must, most wine spoilage organisms were inhibited to a certain extent, and the colony countswere decreased, which were ina range of 12%-95% of those without COS. The inhibition rate was calculated according to the following formula: inhibition rate = (colony count (with COS)-colony count (without COS))/colony count (without COS). _Enterococcus_ and _Lactococcus_ were only inhibited by 21% and 12%, respectively. When the COS concentration was increased to 500 mg/L, the inhibition rates of bacterium or fungus reached almost 100%. When the COS concentration was increased to 1000 mg/L, the colony count was further decreased by one or two orders of magnitude, but the inhibition rate was not markedly changed. By considering the economic benefits and the antimicrobial effect, 500 mg/L COS was selected as the optimized concentration.
# Reliable Application Layer Routing Using Decentralized Identifiers
## 4 Design
### The Use of Decentralized Identifiers
#### 4.3.1 Domain Registration  
Each domain owner should interact with a registrar in order to register a domain that they own. The registration request includes also a public key, owned by the domain owner. The outcome of the registration process is the authorization proof, which includes the id claim, an id of the form \(did:<domain>\), and in the controller claim, the public key sent by the owner. The proof is signed by the registrar. Since (as we already discussed) the public keys of the trusted registrars are well-known, anybody can verify the validity of the latter signature.
## 3 Experiments and Results
### Introduce Previous Methods and Evaluation Metrics  
At present, there are two kinds of mainstream methods to solve the speech separation task. One is the separation method based on frequency domain, and the other is to separate directly in time domain.  
* On basis of the deep clustering framework, DPCL++ [12] introduced better regularization, larger temporal context, and a deeper architecture.
* DPCL model was added to realize the end-to-end training of signal reconstruction quality of the first time.
* uPIT-BLSTM-ST [11] added an utterance-level cost function to Permutation Invariant Training (PIT) technology to extend it, eliminating the additional permutation problem that frame level PIT needs to solve in the inference process.  
* DANet [13] created attractor points by finding the centroids of the source in the embedded space, and then utlieze these centroids to determine the similarity between each bin in the mixture and each source, achieving end-to-end real-time separation on different numbers of mixed sources.
* ADANet [14] solved the problem of DANet's mismatch between training and testing, providing a flexible solution and a generalized Expectation-Maximization strategy to determine attractors assigned from estimated speakers. This method maintains the flexibility of attractor formation at the discourse level and can be extended to variable signal conditions.
* Conv-Tasnet [2] proposed a fully convolution time-domain end-to-end speech separation network, it is a classic network structure with outstanding effect.
* DPRNN [3] proved that the deep structure of RNN layers organized by dual paths can better model extremely long sequences to improve separation performance.
* SepFormer [4] proved the effectiveness of learning short-term and long-term dependencies using a multi-scale method with dual-path transformers.  
In this paper, scale-invariant signal-to-noise ratio improvement (SI-SNRi) and signal-to-distortion ratio improvement (SDRi) are reported as objective measures of separation accuracy. In addition to the distortion indicators, the quality of the separated mixtures is assessed utilizing perceptual evaluation of subjective quality (PESQ), short-time objective intelligibility (STOI) and mean opinion score-listening quality objective (MOS-LQO).
## 3 Numerical Examples  
Based on the above theory, the dispersion characteristics of longitudinal thermoelastic guided waves in hollow cylinders are calculated. In order to verify the correctness of the theoretical analysis and algorithm, the phase velocity dispersion curves of circumferential thermoelastic waves in an orthogonal anisotropic cylinder are first calculated [19] and compared with the results in this paper. Meanwhile, based on the above theory, the phase velocity dispersion curves of longitudinal thermoelastic waves as well as the displacement versus temperature curves in a hollow cylinder of silicon nitride with an inner radius of 9 mm and a thickness of 1 mm are calculated. Then, the effect of the diameter-to-thickness ratio on the longitudinal mode dispersion characteristics is discussed. Finally, the effect of temperature on the propagation characteristics of longitudinal thermoelastic waves was investigated. The material properties of silicon nitride are shown in Table 1.
# Mixer U-Net: An Improved Automatic Road Extraction from UAV Imagery
## 4 Experiments, Results and Discussion
### Implementation Details  
The proposed models, along with the baselines, were implemented on Python 3.8.12 and Pytorch 1.10. The experiments were performed on a single NVIDIA RTX A5000 GPU with 24 GB memory. An Adam optimizer was used to optimize the models with the initial learning rate set to 0.001. Cosine Annealing learning rate scheduler [45] is applied to control the learning rate during training. All models were trained for 200 epochs with mini-batch size equal to 8.  
Binary Cross Entropy (BCE) (Equation (10)) loss is considered to be stable and works best in the case of equal class distribution [46]. However, our dataset suffers severely from class imbalance. In this case, Dice loss (Equation (11)) is preferred as it does not treat every pixel individually but measures the overlap between two samples. By leveraging the  
Figure 6: The flowchart of methodologies.  
stability of BCE loss and flexibility of Dice loss of class imbalance, we can adopt a Combo loss (Equation (12)) that is the combination of two loss functions [46,47].  
\[L_{BCE}=\frac{1}{N}\sum_{i=1}^{N}(y_{i}\log\hat{y_{i}}+(1-y_{i})\log{(1-\hat{y_{ i}})}) \tag{10}\]  
\[L_{Dice}=1-2\cdot\frac{|Y\cap\hat{Y}|+smooth}{|Y|+|\hat{Y}|+smooth} \tag{11}\]  
\[L_{Combo}=L_{BCE}+L_{Dice} \tag{12}\]  
In Equation (11), a _smooth_ (usually set to 1) variable is added in both the numerator and denominator to ensure that, in the case of \(Y=\hat{Y}=0\), the function is not undefined.
## Appendix A Experimental Basis for Configuration Selection
### Aggregation Strategy
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline \multirow{2}{*}{**Minimum Overlap**} & \multirow{2}{*}{**Valid-Area Threshold**} & \multirow{2}{*}{**Aggregation**} & \multicolumn{3}{c}{**NCB + FPE (Baseline)**} & \multicolumn{3}{c}{**CBS + RPE (Ours)**} \\ \cline{5-10}  & & & **Accuracy** & **Macro F1** & **Kappa** & **Accuracy** & **Macro F1** & **Kappa** \\ \hline \multirow{5}{*}{0\%} & \multirow{5}{*}{5\%} & vote & 0.5656 & 0.5390 & 0.5129 & 0.7419 & 0.6423 & 0.6962 \\  & & sum & 0.5673 & 0.5377 & 0.5144 & 0.7436 & 0.6438 & 0.6981 \\  & & product & 0.5677 & 0.5393 & 0.5148 & 0.7441 & 0.6445 & 0.6987 \\ \cline{2-10}  & & vote & 0.6958 & 0.6169 & 0.6508 & 0.7802 & 0.7033 & 0.7435 \\
10\% & 10\% & sum & 0.6950 & 0.6156 & 0.6500 & 0.7813 & 0.7049 & 0.7446 \\  & & product & 0.6957 & 0.6168 & 0.6506 & 0.7811 & 0.7043 & 0.7444 \\ \cline{2-10}  & & vote & 0.5681 & 0.5467 & 0.5181 & 0.7799 & 0.6770 & 0.7410 \\
15\% & & sum & 0.5710 & 0.5483 & 0.5210 & 0.7819 & 0.6802 & 0.7433 \\  & & product & 0.5698 & 0.5485 & 0.5199 & 0.7815 & 0.6799 & 0.7431 \\ \hline \multirow{5}{*}{5\%} & \multirow{5}{*}{5\%} & vote & 0.5654 & 0.5387 & 0.5127 & 0.7419 & 0.6421 & 0.6962 \\  & & sum & 0.5675 & 0.5381 & 0.5146 & 0.7438 & 0.6439 & 0.6983 \\  & & product & 0.5677 & 0.5393 & 0.5149 & 0.7442 & 0.6446 & 0.6988 \\ \cline{2-10}  & & vote & 0.6957 & 0.6167 & 0.6507 & 0.7803 & 0.7033 & 0.7435 \\  & & sum & 0.6949 & 0.6156 & 0.6498 & 0.7813 & 0.7048 & 0.7446 \\  & & product & 0.6956 & 0.6168 & 0.6506 & 0.7811 & 0.7043 & 0.7444 \\ \cline{2-10}  & & vote & 0.5680 & 0.5466 & 0.5180 & 0.7799 & 0.6770 & 0.7410 \\
15\% & & sum & 0.5707 & 0.5482 & 0.5207 & 0.7818 & 0.6801 & 0.7432 \\  & & product & 0.5697 & 0.5483 & 0.5198 & 0.7816 & 0.6799 & 0.7431 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parcel-level classification performance (accuracy, macro F1, and Kappa) on the test set under the same parameter combinations as Table 11. These results verify the robustness and generalization ability of the proposed method under domain shift.
## 2 Experimental Methods  
### Water Channel and Flow Velocity  
The experiments were conducted in the recirculating water channel at the State Key Laboratory of Hydraulic Engineering Simulation and Safety of Tianjin University (SKL-HESS), as seen in Figure 1. The water channel includes a tank, a power section, a wide section, a bend section, a contraction section, and a narrow section. The water in the channel is forced to circulate by a tubular pump powered by a 90 kW motor. A frequency conversion controller (FCC) is used to achieve variation in the flow velocity. The experimental setup is established at the narrow section. The length of test section is approximately 15 m, the width is 1 m, and the depth is 1.5 m. The water depth is 134 cm.  
In this section, the increasing and decreasing flow velocities are compared. The velocity varied by adjusting the frequencies of the FCC. The velocities were measured using the Pitot tube with a differential pressure transmitter. The accuracy of the differential pressure transmitter is within \(\pm\)0.1% of the linear available range 6 kPa, and the resolution is within 0.01% FS (Full Scale). The test results of the increasing and decreasing velocities are plotted in Figure 2.  
Figure 1: Layout of recirculating water channel system.  
Figure 2: Comparison of increasing and decreasing flow velocity.  
It can be observed that the flow velocity increases linearly with the increase of the frequency of FCC, and the maximum flow velocity is approximately 1.80 m/s. Moreover, the curves of flow velocity against increasing and decreasing frequencies of FCC basically coincide, indicating that it is reasonable to vary the flow velocity by adjusting the frequencies of FCC.
## 3 Results
### Critical Test Scenario Selection Example Based on an Existing Vehicle
The lower match values can also be attributed to the fact that in these projects, there were instances where many very similar scenarios were applied, leading to the exclusion of related cases due to their similarity. In addition, we examined the filtered results not only in terms of quantity but also from a content-based perspective. We found that the majority of the matching scenarios were among the more well-defined and critical cases--those that had not undergone significant modifications in earlier stages and where the likelihood of collision with test objects was higher. This is likely due to the fact that, in our previous work, these well-defined test runs required fewer adjustments, making them easier to transfer from ADAS testing procedures to the evaluation of the ADS. The results can be further refined by adjusting the threshold values and WCS weighting, but this requires additional investigation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Vehicle**} & \multicolumn{4}{c}{**Number of Test Runs**} \\ \cline{2-5}  & **In the Previous** & **Matching with the** & **Filtered Using Euclidean** & **That Are** \\  & **Test Plan** & **Previous Test Plan** & **Distance (ED)** & **Irrelevant** \\ \hline Higher-speed, more complex autonomous people mover vehicle & 46 & 21 & 65 & 2 \\ \hline Lower-speed, less complex autonomous people mover vehicle & 25 & 14 & 48 & 6 \\ \hline
\begin{tabular}{c} Automated highway driving function \\ passenger car \\ \end{tabular} & 38 & 17 & 76 & 13 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of utilizing the developed method on test plans of pervious projects. Figure 6: Graphical representation of the results obtained by applying the developed methodology to test plans from previous projects.
# Effects of Nano-TiO\({}_{2}\) Mediated Photocatalysis on _Microystis aeruginosa_ Cells
## 3 Results and Discussion
### Morphology of the M. aeruginosa Cells  
Since the cell wall and membrane were exposed to the free radicals in the medium, we also investigated the damage on the outermost layer of _M. aeruginosa_ cells caused by nano-TiO\({}_{2}\) mediated photocatalysis. Figure 4 shows the morphology of the _M. aeruginosa_ cells after nano-TiO\({}_{2}\) mediated photocatalysis. The control cells exhibited regular sphericity with a diameter of 2.5 \(\upmu\)m, and regular wrinkles appeared on the cell surface at the nano-level (Figure 4A). After nano-TiO\({}_{2}\) mediated photocatalysis for 1 h and 2 h, the surface became much rougher than that of the control cells (Figure 4B,C). Photocatalysis for 4 h deformed the surface morphology of the _M. aeruginosa_ cells (Figure 4D). After photocatalysis for 6 h, the cells were disrupted (Figure 4E).  
Figure 4: _Cont._  
Figure 4: SEM images of _M. aeruginosa_ cells after nano-TiO\({}_{2}\) mediated photocatalysis. (**A1**,**A2**): the control; (**B1**,**B2**): 1 h treatment; (**C1**,**C2**): 2 h treatment; (**D1**,**D2**): 4 h treatment; (**E1**,**E2**): 6 h treatment.  
The cell wall and cell membrane is the outermost layer of the _M. aeruginosa_ cells. They consist of extracellular polymeric substances, and they are responsible for the adsorption of nutrients and the defense against environmental toxins [22]. When the cells were exposed to UV irradiation in the presence of nano-TiO\({}_{2}\) for 4 h, the cell wall and cell membrane were seriously damaged, but the cells still remained spherical and did not show major morphological changes. However, when the irradiation was extended to 6 h, the cells were erupted, most likely due to the failure of the cell wall. A previous report found that the main mechanism of degradation involves hydroxyl substitution, which could occur repeatedly [23]. When _M. aeruginosa_ cells were exposed to free radicals generated by nano-TiO\({}_{2}\), hydroxyl substitution of extracellular polymeric substances may also have occurred repeatedly, which ultimately led to the photolysis of organic molecules.  
The nano-TiO\({}_{2}\) also experienced changes during the photocatalytic process. At the beginning, nano-TiO\({}_{2}\) particles were suspended well in cell solution. After some time, nano-TiO\({}_{2}\) particles tended to coagulate, but there were no Ti\({}^{4+}\) ions detected in solution. The nano-TiO\({}_{2}\) particles showed a particle size of 25 nm and were easy to aggregate due to high surface energy. In the photocatalytic process, the particle size increased gradually. However, nano-TiO\({}_{2}\) is chemically stable and Ti\({}^{4+}\) ions would not be released from nano-TiO\({}_{2}\) particles into solution.
# Solutions to Increase PV Hosting Capacity and Provision of Services from Flexible Energy Resources
## 2 Simulation Model and Study Cases
### Simulated Study System
#### 3. Focus on  
* Multi-objective ANM scheme
* Different RPW-limits (Fingrid and ENTSO-E) and control of reactive power flow between HV and MV networks
* Effect of different OLTC setting values (20.7 or 20.0 kV)
* Mutual/conflicting requirements between WT control functions) (QU-droop 2 and Q-control to fulfill RPW-limits)
* Very low and high load situations  
In this paper, the studies were undertaken by utilizing Fingrid's (Finnish TSO) Reactive Power Window (RPW)-limits (see [39]) in scenarios with more PVs and BESSs (Figure 1) than in [39]. In the studied cases, WT alone is responsible for fulfilling the Fingrid's RPW-limits (Figure 1). In addition, during normal operation WT close to HV/MV substation does not need to have _QU-_ or _PU-_droops. This also prevents potential mutual effects between control functions, which were reported in [39]. However, if WT would be connected to network in some back-up feeding configuration (e.g., during Sundom HV/MV primary transformer maintenance), then suitable _QU-_ and _PU-_droops could be used. The main focus areas of previous studies in [39] and research work in this paper are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Previous Studies [39]** & **Research Work in the Paper** \\ \hline \hline \multicolumn{3}{c}{_0. Initial choices (based on [39] findings)_} \\ \(-\) & No QU- and _PU-_droops on WT \\ \(\bullet\) & Only Fingrid RPW-limits used which are \\ fulfilled by the control of WT reactive power \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main focus areas of previous studies [39] and research work done in this paper.
## 1 Introduction
### Acoustic Equation in Solid  
Analyze the movement of the small body element in the \(x\) direction separately in Figure 3. A similar analysis can be performed for the \(y\) and \(z\) direction. The external force that the small body element suffers in \(x\) direction can be divided into a normal stress perpendicular to the \(x\)-axis and a tangential stress perpendicular to the \(y\)-axis and \(z\)-axis.  
The force in the \(x\) direction acting on the surface perpendicular to the \(x\)-axis is:  
\[F_{x}^{\prime}=(T_{xx}+\frac{\partial T_{xx}}{\partial x}dx-T_{xx})dydz \tag{4}\]  
The force in the \(x\) direction acting on the surface perpendicular to the \(y\)-axis is:  
\[F_{x}^{\prime\prime}=(T_{yx}+\frac{\partial T_{yx}}{\partial x}dx-T_{yx})dxdz \tag{5}\]  
Figure 3: Stress analysis of small body element.  
The force in the \(x\) direction acting on the surface perpendicular to the \(z\)-axis is:  
\[F_{x}^{\prime\prime\prime}=(T_{zx}+\frac{\partial T_{zx}}{\partial x}dx-T_{zx})dxdy \tag{6}\]  
The sum of the three stresses is the stress in the \(x\) direction:  
\[F_{x}=(\frac{\partial T_{xx}}{\partial x}+\frac{\partial T_{yx}}{\partial y}+ \frac{\partial T_{zx}}{\partial z})dxdydz \tag{7}\]  
According to [PERSON]'s second law, Equation (8) represents the equation of motion in \(x\), \(y\), and \(z\) direction, where \(\rho\) is the density of the substance.  
\[\begin{array}{l}\rho\frac{\partial^{2}\xi}{\partial t^{2}}=\frac{\partial T _{xx}}{\partial x}+\frac{\partial T_{yx}}{\partial y}+\frac{\partial T_{zx}}{ \partial z}\\ \rho\frac{\partial^{2}\eta}{\partial t^{2}}=\frac{\partial T_{xy}}{\partial x }+\frac{\partial T_{yy}}{\partial y}+\frac{\partial T_{zy}}{\partial z}\\ \rho\frac{\partial^{2}\xi}{\partial t^{2}}=\frac{\partial T_{xx}}{\partial x }+\frac{\partial T_{yz}}{\partial y}+\frac{\partial T_{z}}{\partial z}\\ \end{array} \tag{8}\]  
Since the performance values of the object measured in different directions are exactly the same, according to the generalized [PERSON]'s law of isotropic media and combined with Equation (1), the following can be obtained:  
\[\begin{array}{l}\rho\frac{\partial^{2}\xi}{\partial t^{2}}=(\lambda+\mu) \frac{\partial\Delta}{\partial x}+\mu\
abla^{2}\xi\\ \rho\frac{\partial^{2}\eta}{\partial t^{2}}=(\lambda+\mu)\frac{\partial\Delta} {\partial y}+\mu\
abla^{2}\eta\\ \rho\frac{\partial^{2}\xi}{\partial t^{2}}=(\lambda+\mu)\frac{\partial\Delta} {\partial z}+\mu\
abla^{2}\zeta\\ \end{array} \tag{9}\]  
where \(\Delta=\frac{\partial\xi}{\partial x}+\frac{\partial\eta}{\partial y}+\frac{ \partial\zeta}{\partial z}\), \(\
abla^{2}=\frac{\partial^{2}}{\partial x^{2}}+\frac{\partial^{2}}{\partial y ^{2}}+\frac{\partial^{2}}{\partial z^{2}}\), and \(\lambda,\mu\) are the Lame coefficient.  
Take \(\overset{\rightarrow}{s}=\xi\overset{\rightarrow}{i}+\eta\overset{ \rightarrow}{j}+\zeta\overset{\rightarrow}{k}\) to represent the particle displacement, Equation (9) can be written in vector form:  
\[\rho\frac{\partial^{2}\overset{\rightarrow}{s}}{\partial t^{2}}=(\lambda+ \mu)grad\Delta+\mu\
abla^{2}\overset{\rightarrow}{s} \tag{10}\]
# Autonomous Intersection Management by Using Reinforcement Learning
## 3 Methodology
### Deep Reinforcement Learning for Intersection Control
#### 3.4.3 Reward  
The final key element and also the most important one in reinforcement learning is the reward. After the agent selects and executes an action, the agent will receive a reward corresponding to the action just performed. The reward definition in this research is \(r=r_{q}-\max_{i}w_{t+1}^{i}\), which tries to balance between the efficiency and fairness, where \(r_{q}\) is the change in total queue size and \(\max_{i}w_{t+1}^{i}\) is the maximum average waiting time at lane \(i\) at time \(t+1\). The change in queue size is the difference in queue size of an intersection at time \(t\) and at time \(t+1\). Note that the values of \(r_{q}\) can be positive or negative. The reason for using \(r_{q}\) is to enhance the system's efficiency. The value of queue size should be the less, the better. In other words, if the value of total queue size at time \(t+1\) is more than that at time \(t\), the value of \(r_{q}\) would be negative, representing a penalty. The reason for using \(\max_{i}w_{t+1}^{i}\) is to enhance the system's fairness. If the vehicles of lane \(k\) have waited in the queue for a long time, the value of \(\max_{k}w_{t}^{k}\) will be large, which leads to choosing an action to let vehicles in lane \(k\) pass to eliminate the penalty of lengthy waiting, and the rewards of choosing the other actions will be very low.