esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Implementation Details
#### 4.1.1 Dataset  
The segmentation datasets used in this paper were Liver Tumour Segmentation (LiTS) and Segmentation of the Liver Competition 2007 (SLiver07).  
LiTS is the public dataset of the MICCAI 2017 Liver Tumor Segmentation Challenge, which contains 131 training sets and 70 test sets. Both of them contain patients' contrast-enhanced 3D abdominal CT scans with a resolution of \(512\times 512\). The in-plane resolution is 0.55\(\sim\)1.0 mm. The training dataset was labeled by experienced clinicians, but the testing dataset was not. Nevertheless, due to the large scale of the dataset and the high quality of the CT scans, it is currently a wildly used dataset in liver and tumor segmentation tasks.  
SLiver07 is an earlier dataset that originated from the Segmentation of the Liver Competition 2007 (SLIVER07). It contains 20 training sets and 10 testing sets, which comprise clinical CT scans. The size of the images is \(512\times 512\), with an in-plane resolution of 0.56\(\sim\)0.8 mm. The training sets are labeled, while the 10 testing sets of CT scans are not, and both sets only contain liver information.  
Since the testing sets of the two datasets were unlabeled, we only used the training set for all experiments. In detail, we used these two datasets for liver segmentation experiments, though only LiTS was selected to conduct tumor segmentation, as Sliver07 does not contain tumor information.
## 3 Scheme Design
### Low-Complexity Diagonalizing Design
#### 3.2.3 Summarization of the Proposed Algorithm  
The low-complexity algorithm based on CP method depicted above is summarized as Algorithm 2.  
```
1. Channel decomposition  Decompose the channel pairs \(\left\{\mathbf{H}_{S_{1}R}^{H},\mathbf{H}_{S_{2}R}^{H}\right\}\) and \(\left\{\mathbf{R}_{RS_{1}},\mathbf{H}_{RS_{2}}\right\}\) using Equations (19) and (20).  Initialize  Define \(\beta_{i}=0.5\), \(\mathbf{F}_{r}=\sqrt{\frac{P_{r}}{\mathbf{F}_{r}}}\mathbf{I}_{N}\), and \(\mathbf{F}_{i}=\sqrt{\frac{P_{i}}{\mathbf{M}_{i}}}\mathbf{I}_{M_{i}}\),  where \(\mathbf{\Psi}_{R}=\mathbf{R}_{\mathbf{F}_{h}}\Delta_{1}\mathbf{\Lambda}_{1}^{H \mathbf{T}_{1}^{2}}\mathbf{I}_{h_{1}^{2}}\mathbf{I}_{h_{1}^{2}}^{H}\mathbf{I }_{h}^{H}+\mathbf{R}_{\mathbf{F}_{h}}\mathbf{E}_{h_{2}}\mathbf{\Lambda}_{2}^{H \mathbf{T}_{2}^{2}}\mathbf{I}_{h_{1}^{2}}^{H}\mathbf{I}_{h}^{H}+\sigma_{r}^{2 }\mathbf{I}_{N_{2}}\).
```  
**Algorithm 2** The low-complexity algorithm based on the channel parallelization (CP) method  
Algorithm 2 is convergent based on the following Property 2.  
Theorem 2: _The low-complexity algorithm based on the CP method is convergent._  
Proof: In the \(k\)th iteration of the proposed algorithm, we first compute \(\mathbf{F}_{r}^{[k]}\) with the given \(\mathbf{F}_{1}^{[k-1]}\), \(\mathbf{F}_{2}^{[k-1]}\), and \(\mathbf{W}_{i}^{[k-1]}\). Since the optimal solution \(\mathbf{F}_{r}^{[k]}\) can be achievable with CVX, we discover that the objective value corresponding to \(\mathbf{F}_{r}^{[k]}\), \(\mathbf{F}_{1}^{[k-1]}\), \(\mathbf{F}_{2}^{[k-1]}\), and \(\mathbf{W}_{i}^{[k-1]}\) is no greater than that to \(\mathbf{F}_{r}^{[k-1]}\), \(\mathbf{F}_{1}^{[k-1]}\), \(\mathbf{F}_{2}^{[k-1]}\), and \(\mathbf{W}_{i}^{[k-1]}\), which means the objective value is descendent. Consequently, the objective value of the original problem monotonically decreases and is lower-bounded by zero, which verifies the convergence of Algorithm 2.
## 5 Experimental Results and Discussion
### Performance Evaluation and Analysis
#### 5.3.3 Decoding-Complexity Reduction and the Impact on Video Quality
It can be observed that despite the PSNR drops listed in the Table 3, the bit streams generated by the proposed algorithm retain a visual quality level similar to that of the bit streams prepared by the HM 16.0 encoder.
# Integrated Predictor Based on Decomposition Mechanism for PM2.5 Long-Term Prediction
## 2 Distributed Decomposition Model
### Gated Recurrent Unit (GRU) Network: Deep Prediction Network for Periodic and Residual Components  
GRU is widely used for modeling because of its outstanding performance in sequence modeling. In order to solve the long-term dependency problem, the GRU architecture introduces a gating mechanism that can maintain the state of the cell for a long time. This method can solve the gradient problem of disappearing or exploding. The input data of GRU is the period component \(S_{t}\) and residual component \(R_{t}\).  
The GRU contains these two gates:  
* The update gate controls the degree to which the state information at a previous time is brought into the current state. The larger the value of the update gate, the more status information from a previous moment is brought in.
* The reset gate decides how much information is written to the current candidate activation \(\widetilde{h}_{t}\) in the previous state. The smaller the reset gate, the less information of the previous state is written.  
Output from each GRU cell, \(\widetilde{h}_{t}\), is computed as [35]:  
\[\begin{array}{l}z_{t}=\sigma(x_{t}U^{2}+h_{t-1}W^{2}+b^{2})\\ r_{t}=\sigma(x_{t}U^{t}+h_{t-1}W^{r}+b^{r})\\ \widetilde{h}_{t}=\tanh(x_{t}U^{h}+(h_{t-1}\circ r_{t})W^{h}+b^{h})\\ h_{t}=(1-z_{t})\circ\widetilde{h}_{t}+z_{t}\circ h_{t-1}\end{array} \tag{8}\]  
where \(x_{t}\) is the input; \(z_{t}\), \(r_{t}\), \(\widetilde{h}_{t}\), and \(h_{t}\) stand for the update gate, reset gate, current candidate activation, and activation of the GRU at time \(t\), respectively; \(U^{z}\), \(U^{r}\), \(U^{h}\), \(W^{z}\), \(W^{r}\), and \(W^{h}\) are weight matrices to be learned during model training; \(\circ\) is an element-wise multiplication; and \(\sigma\) and \(\tanh\) are commonly used nonlinear activation functions, whose mathematical form are as follows:  
\[\sigma(x)=\frac{1}{1+e^{-x}} \tag{9}\]  
\[\tanh(x)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}} \tag{10}\]  
Using the known input and output data, the network is trained by the stochastic gradient descent algorithm, and the optimal weight can be obtained. The GRU network is trained by the decomposed period and residual components. The GRU network consists of multiple GRU cells, and usually has several layers of structure. Shown as Figure 4, \(x_{t}(t=1,2,\ldots,M)\) is the input of the GRU network, and \(y_{S}(S=1,2,\ldots,N)\) is the output.  
Figure 4: Network structure of the gated recurrent unit (GRU) network.
# Validation of Cable-Driven Experimental Setup to Assess Movements Made with Elbow Joint Assistance
## 3 Results
Figure 7 shows the surface plot of the moment generated, \(M_{C}\), at the anchor point attachment \(C\)--refer to Figure 3--with anchor point positions \(d_{1}\) and \(d_{2}\). Figure 7a is the surface plot of \(M_{C}\) at \(\theta\) = 90\({}^{\circ}\) for a load of 0.5 kg. It is observed that \(M_{C}\) decreases with an increase in \(d_{2}\) and increases with an increase in \(d_{1}\). From Equation (16), \(M_{C}\) depends on cable tension \(T_{s}\) which acts on either side of the anchor attachment \(E\)--refer Figure 3--and the attachment point \(C\). When \(d_{2}\) is at the farthest position, and \(d_{1}\) is closest to the joint, \(M_{C}\) is minimal because of the reduction in cable tension, \(T_{s}\), along \(\overrightarrow{EM\dot{o}}\) and \(\overrightarrow{ED}\), as the cable is nearly parallel to the upper bar and resultant tension decreases. Conversely, if \(d_{2}\) is at the closest to the joint and \(d_{1}\) is farthest, then the cable tension, \(T_{s}\), is higher compared to the previous case, because of an increase in resultant tension. Figure 7b,c are the plots of the moment, \(\overrightarrow{M_{C}}\), at \(\theta\) = 90\({}^{\circ}\) and loads of 1 kg and 2 kg. With the increase in mass, \(T_{S}\) increases; thus, we observe an increase in \(M_{C}\) with a similar gradient. Figure 7d is surface plot for \(M_{C}\) at \(\theta\) = 120\({}^{\circ}\) for a load of 0.5 kg. The resultant tension will further reduce with an increase in  
Figure 6: (**a–**f**) Surface plots of the simulated moment at the anchor position \(A\), by varying \(d_{1}\) and \(d_{2}\), at joint positions 90\({}^{\circ}\) and 120\({}^{\circ}\) and masses 0.5 kg, 1 kg, and 2 kg. Discrete dots along the surface plot represent the moment at \(A\) calculated using the experimentally recorded tension. joint angle 120\({}^{\circ}\); thus, \(M_{C}\) decreases. Similar observations are reported in Figure 7e,f for a load of 1 kg and 2 kg, respectively. With the results, the physical variables measured with the experimental setup are validated using a static model. Thus, the equations of cable tension, joint reaction forces, and moments can be used to optimize the anchor point position.
## 1 Introduction  
_Strobilanthes crispus_, known as \"pecah beling\" or \"Hei Mian Jiang Jun\", is native to various countries, such as Malaysia and Indonesia. Its leaves are used as traditional medicine in the form of infused fresh or dried leaves. The intake of its herbal preparation became popular as an alternative treatment to prevent diseases and increase overall well-being. [PERSON] et al. (2015) showed that _S. crispus_ extract is high in antioxidant activity because of various phenolic constituents, such as caffeic acid, ferulic acid, kaempfer, and luteolin (1; 2; 3).  
Drying is an effective preservation step that involves reducing the moisture content of a freshly harvested material, thereby extending a product's shelf life. Conventional drying methods, such as convective drying (CD) and freeze-drying (FD), are characterized by low drying rates, especially in the falling rate period (4). New drying technologies, such as vacuum microwave drying (VMD) and convective pre-drying followed by vacuum microwave finish drying (CPD-VMFD), can address the limitations of conventional drying. Both drying methods generated products of high bioactive retention, essential oil content, and antioxidant activity in the drying of basil (5), oregano (6), thyme (7), and beetroot (8). VMD is a hybrid drying technology that combines microwave and vacuum drying. During VMD, microwaves interact with water molecules in the whole volume of a product, leading to volumetric heating. A large vapor pressure formed at the central region of a material facilitates quick moisture removal (8; 9).  
In VMD combined with CD, a material is pre-dried with CD and completely dried through VMD. This combined method, known as CPD-VMFD, maximizes the benefits of both drying methods. VMFD is usually applied at the end stage of drying to remove bound water from a product during the falling rate period, thereby effectively reducing drying time (9).  
This study aimed to evaluate the effects of different drying methods, namely conventional and new drying methods, on the drying kinetics, phenolic content, volatile and phytosterol content, antioxidant activity, water activity (a\({}_{w}\)), and color of dried _S. crispus_. The fatty-acid profile was also determined to verify the active constituents in _S. crispus_. VMD methods are yet to be utilized to dry _S. crispus_. As such, this study also aimed to confirm the potential of this new innovative technology. Determining good drying applications is important for manufacturing new dried forms of _S. crispus_ with high bioactivity. The drying kinetics were modeled with thin-layer models to integrate experimentally obtained data into industrial applications. Drying is also one of the most energy-intensive operations in the food industry. This study also considered the relationship of drying kinetics with the energy input of a drying operation and used specific energy consumptions to provide an indication of the cost effectiveness of _S. crispus_ drying which is yet to be reported.
## 5 Experimental Results and Analysis  
### Parameter Setting of Morfeco-Ms  
In the MOFECO-MS algorithm, several parameters require optimization, including the population size \(N\), the termination conditions of the algorithm \(c_{max}\), the crossover probability \(p_{c}\), mutation probability \(p_{m}\) for updating elements, the number of cycles \(q\), the number of elements \(L\) allocated in each cycle, and the parameter \(l\) determining the ratio of exploration to exploitation within each cycle. To identify the optimal values for these parameters, we conducted experiments individually for each parameter, all performed on the \(a280\_n279\) instance and each set of experiments was run 15 times independently.
# Valorization of Spent Osmotic Solutions by Production of Powders by Spray Drying
## 3 Results
### Particle Morphology and Particle Size
\begin{table}
\begin{tabular}{c|c|c|c|c|c|c|c|c} \hline  & D50 & PDI & MC & \(a_{w}\) & H & **WSI** & DL & **DT** & **HR** \\  & [\(\upmu\)m] & & [\(\%\)] & \(a_{w}\) & [\(\%\)] & [\(\%\)] & [\(\upmu\)m\({}^{3}\)] & [\(\upmu\)m\({}^{3}\)] \\ \hline \multirow{2}{*}{SUC} & \(22\pm 5\) & \(1.8\pm 0.1\) & \(1.2\pm 0.2\) & \(0.040\pm\) & \(17.5\pm 0.2\) & \(97.2\pm 1.2\) & \(0.62\pm 0.02\) & \(0.79\pm 0.01\) & \(1.27\pm 0.05\) \\  & a\({}^{*}\) & b & b & a & b & a & b & b \\ \hline \multirow{2}{*}{BWH} & \(111\pm 4\) & \(1.5\pm 0.2\) & \(1.9\pm 0.2\) & \(0.115\pm\) & \(27.3\pm 0.2\) & \(94.1\pm 3.2\) & \(0.62\pm 0.02\) & \(0.78\pm 0.02\) & \(1.28\pm 0.05\) \\  & b & c & b & d & a & b & b & b \\ \hline \multirow{2}{*}{ACH} & \(717\pm 15\) & \(0.9\pm 0.1\) & \(2.1\pm 0.5\) & \(0.141\pm\) & \(25.0\pm 0.1\) & \(96.8\pm 3.2\) & \(0.88\pm 0.02\) & \(0.92\pm 0.01\) & \(1.05\pm 0.01\) \\  & c & a & c & c & a & c & c & a \\ \hline \multirow{2}{*}{APP} & \(896\pm 10\) & \(0.8\pm 0.1\) & \(2.8\pm 0.2\) & \(0.169\pm\) & \(24.7\pm 0.2\) & \(96.3\pm 2.1\) & \(0.86\pm 0.02\) & \(0.86\pm 0.02\) & \(1.00\pm 0.04\) \\  & d & a & c & d & c & a & c & c & a \\ \hline \multirow{2}{*}{CHO} & \(802\pm 38\) & \(0.8\pm 0.1\) & \(2.8\pm 0.6\) & \(0.127\pm\) & \(25.0\pm 0.2\) & \(97.9\pm 0.5\) & \(0.87\pm 0.02\) & \(0.90\pm 0.01\) & \(1.04\pm 0.01\) \\  & d & a & c & c & c & a & c & c & a \\ \hline \multirow{2}{*}{CHE} & \(734\pm 44\) & \(0.9\pm 0.1\) & \(4.8\pm 0.5\) & \(0.106\pm\) & \(26.0\pm 0.2\) & \(94.5\pm 1.8\) & \(0.82\pm 0.02\) & \(0.87\pm 0.02\) & \(1.05\pm 0.02\) \\  & c & a & d & b & c & a & c & c & a \\ \hline \multirow{2}{*}{MAN} & \(20\pm 1\) & \(15\pm 0.1\) & \(0.4\pm 0.1\) & \(0.208\pm\) & \(0.5\pm 0.1\) & \(97.4\pm 3.0\) & \(0.45\pm 0.02\) & \(0.60\pm 0.00\) & \(1.34\pm 0.06\) \\  & a & b & a & e & a & a & a & a & b \\ \hline \end{tabular} \({}^{*}\) a-e: Different letters and colors of the cells in columns indicate statistically significant differences between mean values (HSD Tukey, \(p<0.05\)).  
\end{table} 
Table 2: Physical properties: median particle size (D50), polydispersity index (PDI), moisture content (MC), water activity (aw), hygroscopicity (H), solubility (WSI), loose bulk density (DL), tapped bulk density (DT), and Hausner Ratio (HR) of powders obtained from different raw materials— spent osmotic solutions: sucrose (SUC), honey buckwheat (BWH), acacia honey (ACH), apple juice concentrate (APP), chokeberry juice concentrate (CHO), cherry juice concentrate (CHE), and mannitol (MAN).
# Reliable Application Layer Routing Using Decentralized Identifiers
## 6 Discussion
### Alternative DID Methods  
In our system, we defined our own DID method. In this section, we discuss how our DID method compares to other similar methods that could have been considered in a solution similar to ours.  
did:web [40] is a DID method that stores DID documents in web servers accessed over HTTPS. The location of the web server is defined in the DID itself; for example, the DID document that corresponds to \"did:web:example.com\" can be located at \"[[https://example.com](https://example.com)]([https://example.com](https://example.com))\" Our DID method uses a similar DID document resolution and it relies on the same security primitives (did:web requires a valid HTTPS certificate, our method requires a valid authorization proof). The main difference between the two methods is that the did:web method does not include the \"prefix\" property for assertion keys, and therefore, it is not straightforward as to how to delegate \(Resource_{\mathit{URL}}\) prefixes to other entities.  
did:key [41] is a simple DID method where DID documents are implicit. In particular, a DID in did:key is the encoding of a public key (e.g., using an encoding such as base-58). Then, this key is used for all verification methods. Using did:key implies that content name prefixes are also public keys (hence, they are not human-readable). Additionally, because DID documents are implied, delegation cannot be implemented. On the other hand, did:key does not require a trusted registrar.  
did:self [19] is a DID method that also uses public keys as DIDs. The private key that corresponds to a DID is used for signing the corresponding DID document. Therefore, did:self can achieve delegation. Moreover, similar to did:key, it does not require a registrar. However, did:self does not support human-readable names. Moreover, in case the private key that corresponds to a DID is breached or lost, the corresponding DID cannot be used any more; therefore, if that DID is used as a prefix, all content items must be updated to use a new prefix.
# Multi-Temporal Sentinel-1 Backscatter and Coherence for Rainforest Mapping
## 5 Results  
The experiments were conducted by splitting the twelve stacks of Table 2 into validation and training swaths. The former were selected in order to cover a strip of about 250 km \(\times\) 1000 km crossing the Rondonia state, as shown in Figure 4, and they correspond to the stacks of the relative orbit 83, marked with asterisks in Table 2. The other swaths were chosen for training the Random Forests algorithm: in particular, we selected 5 million pixels for each of the considered classes. According to the Random Forests common issues for the training stage [27], these samples were randomly selected from all the training swaths, with the exception of the pixels of the class ART, artificially replicated because of the poor data availability. In this way, we were able to generate a well-balanced training data set. In this paper, we first concentrate on the algorithm performance assessment over a large-scale area located along the S-1 relative orbit plane number 83, and, more specifically, comprising stacks 6, 7, 8, and 9 in Table 2. Secondly, we analyze four significant patches of 512 \(\times\) 512 pixels, selected within the swath corresponding to stack 7 in Table 2.
## 4 Simulation Results  
The HIC-GWA has been applied on the IEEE 30 and 118 bus power systems to solve 12 different cases of OPF problems. The maximum number of iterations is 500 for IEEE 118 bus power system, and 100 for the IEEE 30 bus power systems. Power systems parameters are given in Table 1. The setting of the proposed HIC-GWA approach can be found in Table 2. MATLAB 8.3 (R2014a) has been used to implement simulations on a personal computer with i7 CPU 3.0 GHz 8.0 GB RAM [45; 46].  
The initial population is represented by \(N_{country}\). Each population contains one vector with \(N_{var}\) components, including bus voltage and active power of the power plant, transformer tap changers, and shunt power injection compensator. The parameter \(N_{var}\), given in Table 1 is different for each case.  
Solutions using the proposed approach will be compared with recently published OPF solutions using different optimization methods and objective functions shown in Table 3.
# Modified Generalized Feistel Network Block Cipher for the Internet of Things
## 4 Statistical and Cryptographic Analyses
### Avalanche Effect Test  
The avalanche effect is a useful measure for analyzing the confusion and diffusion properties of a lightweight block cipher. This test evaluates the robustness of a cryptographic algorithm by monitoring changes to the output caused by modifications in the input. The avalanche effect test was performed by generating 64-bit plaintext and ciphertext  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \(i\) & **0** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** & **14** & **15** \\ \hline \(P[i]\) & 4 & 11 & 18 & 7 & 23 & 19 & 2 & 25 & 14 & 26 & 1 & 28 & 20 & 8 & 29 & 0 \\ \hline \(i\) & **16** & **17** & **18** & **19** & **20** & **21** & **22** & **23** & **24** & **25** & **26**inputs using a pseudorandom bit generator. The permutation methods then processed these inputs to obtain the corresponding outputs. The avalanche effect was subsequently determined using Equation (1), as described in [55]. The ideal value for the avalanche effect should be 50% of the total number of cipher bits [56]:  
\[Bit\ Error\ Rate=\frac{cd}{TC} \tag{1}\]  
where \(cd\) is the number of ciphertext bit differences and \(TC\) is the total number of cipher-tex bits.  
This study conducted two types of avalanche tests: the bit error test and the key sensitivity test. The bit error test is utilized to evaluate the sensitivity of the MGFN lightweight block cipher to changes in the plaintext. It involves modifying one bit of the plaintext and observing its resulting ciphertext changes. The key sensitivity test is a means of observing how modifications to a key affect a ciphertext [57]. Even a minor change to the key can result in significant changes to the ciphertext. A single bit of the key is replaced to perform this test, starting from the first-bit position, and ending at the last.  
The test outcome from both avalanche tests are determined using the bit error rate equation. Ideally, a sound block cipher should have a test result falling within the range of 0.5, or 50%, for modifications to the ciphertext bits, as calculated by the bit error rate formula.  
Using the bit error rate equation, the outcome of both avalanche tests is calculated. A sound block cipher should have a test result within the range of 0.5, or 50%, as computed by the bit error rate formula, for modifications to the ciphertext bits.