esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Changes in Sphenocyte Proliferation Rate by ConA  
Lymphocyte proliferation induced by ConA is a commonly used method for detection of T lymphocyte immunity in vitro [11; 30]. The splenocytes collected and cultured on the final day following euthanasia in the CPA control group showed a significant (\(p<0.01\)) decrease in ConA-induced cell proliferation compared to splenocytes in the normal vehicle control group. However, a significant (\(p<0.01\)) increase in ConA-induced cell proliferation was observed in all test substance administered groups, including EAP, compared to spleen cells in the CPA control group. In particular, the AR groups showed a dose-dependent increase in cell proliferation rate, and the AR (100 mg/kg) administered group showed an inhibitory effect on cell proliferation reduction comparable to that of EAP (200 mg/kg; Figure 3).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Groups**} & \multicolumn{4}{c}{**Body Weights at:**} \\ \cline{2-5}  & **First Test Substance** & **First CPA** & **Second CPA** & **At Sacrifice [B] * & **Body Weight** \\  & **Administration [A] * & **Treatment** & **Treatment** & **At Sacrifice [B] * & **Gains [B–A]** \\ \hline \multicolumn{5}{c}{Controls} \\ \hline Intact & 32.21 \(\pm\) 2.24 & 40.56 \(\pm\) 2.00 & 40.82 \(\pm\) 2.01 & 37.93 \(\pm\) 2.16 & 5.72 \(\pm\) 0.84 \\ CPA & 32.30 \(\pm\) 1.57 & 40.69 \(\pm\) 1.65 & 40.81 \(\pm\) 1.54 & 37.77 \(\pm\) 1.54 & 5.47 \(\pm\) 0.61 \\ EAP\({}_{200}\) & 32.09 \(\pm\) 1.66 & 40.30 \(\pm\) 1.80 & 40.32 \(\pm\) 1.78 & 37.37 \(\pm\) 1.96 & 5.28 \(\pm\) 0.80 \\ \hline \multicolumn{5}{c}{Test materials} \\ \hline AR\({}_{400}\) & 32.34 \(\pm\) 1.64 & 40.78 \(\pm\) 1.81 & 40.97 \(\pm\) 1.75 & 37.99 \(\pm\) 1.76 & 5.65 \(\pm\) 0.87 \\ AR\({}_{200}\) & 32.24 \(\pm\) 2.12 & 40.51 \(\pm\) 2.23 & 40.59 \(\pm\) 2.36 & 37.65 \(\pm\) 2.42 & 5.41 \(\pm\) 0.85 \\ AR\({}_{100}\) & 31.96 \(\pm\) 1.42 & 40.62 \(\pm\) 1.96 & 40.60 \(\pm\) 2.07 & 37.61 \(\pm\) 2.09 & 5.65 \(\pm\) 1.15 \\ \hline \hline \end{tabular} CPA = cyclophosphamide administered group; EAP\({}_{200}\) = _A. pullulans_ purified copolymer administered group at a dose of 200 mg/kg; AR\({}_{400}\), 200, and 100 = Astragalial Radix (Roots of _A. membranaccus_ Bunge) extract administered mice groups at doses of 400, 200, and 100 mg/kg, respectively. * All animals were fasted overnight (approximately 18 h; water was not restricted) before sacrifice.  
\end{table}
Table 1: Body weight gain in intact and CPA-induced immunosuppression mice.  
Figure 2: Effects of AR on the normal splenocytes’ viability. Values are expressed as means \(\pm\) S.D. of six independent experiments; AR = Astragalial Radix (Roots of _A. membranaccus_ Bunge) extract; cells were treated with 0 (control), 0.01, 0.1, 0.5, 1, 2.5, 5 and 10 mg/mL of AR dissolved in media and incubated for 72 h.
# Optimizing Herbicide Use in Fodder Crops with Low-Cost Remote Sensing and Variable Rate Technology
###### Abstract  
The current Common Agriculture Policy (CAP) foresees a reduction of 50% in the use of herbicides by 2030. This study investigates the potential of integrating remote sensing with a low-cost RGB sensor and variable-rate technology (VRT) to optimize herbicide application in a ryegrass (_Lolium multiforum_ Lam.) fodder crop. The trial was conducted on three 7.5-hectare plots, comparing a variable-rate application (VRA) of herbicide guided by a prescription map generated from segmented digital images, with a fixed-rate application (FRA) and a control (no herbicide applied). The weed population and crop biomass were assessed to evaluate the efficiency of the proposed method. Results revealed that the VRA method reduced herbicide usage by 30% (0.22 1 ha\({}^{-1}\)) compared to the FRA method, while maintaining comparable crop production. These findings demonstrate that smart weed management techniques can contribute to the CAP's sustainability goals by reducing chemical inputs and promoting efficient crop production. Future research will focus on improving weed recognition accuracy and expanding this methodology to other cropping systems.  
mediterranean climate; low-cost sensor; spatial analysis; machine learning; RGB licenses/by/4.0/. +  
0000-0001-6500-0001-6500][PERSON]  
1,2(c), [PERSON]  
1,3,*  
1 VALORIZA--Research Center for Endogenous Resource Valorization, Polytechnic Institute of Portalegre, 7300-110 Portalegre, Portugal; [EMAIL_ADDRESS] (L.A.C.); [EMAIL_ADDRESS] (S.D.)  
2  
Footnote 1: [[https://doi.org/10.3390/app15041979](https://doi.org/10.3390/app15041979)]([https://doi.org/10.3390/app15041979](https://doi.org/10.3390/app15041979))
## 3. Results and Discussion
### Strain Sensing Information from Numerical Results
#### 3.2.2 Cantilever Bending  
In this loading case, FOSs and adhesive layers are on the tensile side, as shown in Figure 10a. From Figure 10b, it can be seen that both axial strains (due to bending moment) and vertical shear strains contribute to the maximum principal strains of the FOS cores. At the fixed end, where the model bears the highest bending moment, the strain pattern is similar to that in the tension case. \(E_{11}\) dominates the peak areas, and its value is over four times the \(E_{22}\) in mid-line crossing areas. Gradually moving to the loading end, the dominance of \(E_{11}\) fades away, and the difference between them diminishes. At the loading end, the strain values read in the FOS vertical portions are even higher than those in the peak areas.  
Compared to strains in host materials, at the fixed end, observations are similar to those in the tension case: the peak-area strains of FOS cores, shown in Figure 10b, can represent the \(E_{11}\) component of the corresponding areas in epoxy adhesive; the strains measured in mid-line crossing areas correspond to the \(E_{22}\) component of epoxy adhesive. However, gradually moving to the loading end, the dominant effect from bending moment fades away, and the model is in more complex strain states. Thus, it becomes difficult to relate the strains in FOS cores to strain components in host materials.  
Similar to those in Section 3.2.1, the strain contours of FOS cores under cantilever bending (Figure 10b) can also illustrate the capabilities of dual-sinusoidal FOSs. In comparison, the resulting strain pattern from cantilever bending varies when gradually moving from the fixed end to the loading end, which distinguishes cantilever bending from tension. Although the strain pattern is consistent with what is expected, to more accurately interpret the strains measured along dual-sinusoidal FOSs under cantilever bending, especially in areas closer to the loading end, further correlations need to be established between numerical and experimental results.  
Figure 10: _Cont._
## 4 Experiments and Analysis  
We took the most common time series predicting weather forecast for example, to test the effects of different number of input atmosphere conditions on the rainfall forecasting accuracy of our case-based FCTF Reasoning System.  
Expert systems in meteorological area are expressed using rules in the following way:  
Given a rule base of \(r\) rules, e.g., \(n\)-input 1-output, the \(j^{\text{th}}\) control rule is described in the following form: \(R_{j}\): IF \(x_{1}\) is \(A_{j1}\) AND \(x_{2}\) is \(A_{j2}\), AND \(x_{n}\) is \(A_{jn}\), THEN \(y\) is \(B_{j}\), where \(j\) is a rule number, \(n\) is the total number of inputs. Rule-based expert systems can only deal with rules that is in the rule set.  
When we want to deduce another rule that is not in the rule set above (denoted as \(R_{a}\)): \"\(R_{a}\): IF \(x_{1}\) is \(A_{1}\) AND \(x_{2}\) is \(A_{2}\), AND \(x_{n}\) is \(A_{m}\), THEN \(y\) is \(?\)\".  
We can translate this rule-based expert system into a case-based reasoning system: one complete case is one complete rule; one incomplete case is one partial rule. Thus, a rule-based expert system can be transferred to a case-based reasoning system, which can deal with rules that are not in a rule set. This is somewhat like partial-rule matching algorithm [32].  
In our experiments, we take Euclidean distance and cosine similiarity to measure the similarities between the two instances. In our experiments, there is no difference between these two methods when we use them.
## 5 Methodology
### LSTM Encoder-GRU Decoder Architecture with Teacher Forcing
#### 5.1.1 LSTM Encoder  
The LSTM encoder, a crucial component of sequence-to-sequence models, analyzes input sequences to capture their temporal dependencies. For an input sequence \(X=\{x_{1},x_{2}\dots x_{T}\}\) of length \(T\), the encoder produces a hidden state sequence \(H^{\text{enc}}=\{h^{\text{e}}_{1},h^{\text{e}}_{2}\dots h^{\text{e}}_{T}\}\).  
Figure 2: The figure illustrates trajectory segmentation for the imputation process. The blue segments indicate the portions of the trajectory provided as input to the model, while the pink segments represent the randomly masked portions of the trajectory, which the model is tasked with imputing. This segmentation highlights the distinction between observed and unobserved data in the trajectory.  
The LSTM encoder updates its hidden states using the following equations:  
\[i_{t} =\sigma(W_{ii}x_{t}+b_{ii}+W_{hi}h_{t-1}^{e}+b_{hi}) \tag{2}\] \[f_{t} =\sigma(W_{gf}x_{t}+b_{gf}+W_{hf}h_{t-1}^{e}+b_{bf})\] (3) \[g_{t} =\tanh(W_{ig}x_{t}+b_{ig}+W_{hg}h_{t-1}^{e}+b_{hg})\] (4) \[o_{t} =\sigma(W_{io}x_{t}+b_{io}+W_{ho}h_{t-1}^{e}+b_{ho})\] (5) \[c_{t} =f_{t}\odot c_{t-1}+i_{t}\odot g_{t}\] (6) \[h_{t}^{e} =o_{t}\odot\tanh(c_{t}) \tag{7}\]  
where \(\sigma\) denotes the logistic sigmoid function; \(\odot\) represents element-wise multiplication; \(x_{t}\) stands for the input at time step \(t\); \(h_{t}^{e}\) represents the hidden state at time step \(t\); \(i_{t}\), \(f_{t}\), \(g_{t}\), and \(o_{t}\) denote the input, forget, cell, and output gates at time step \(t\), respectively; and \(W\) and \(b\) represent the weight and bias matrices for different gates. \(c_{t}\) represents the cell state at time step \(t\). The LSTM's unusual architecture, comprising input, forget, cell, and output gates, allow it to successfully capture long-range relationships in sequential data, making it well suited for jobs requiring memory retention over longer periods. The LSTM encoder is renowned for its ability to alleviate the vanishing gradient problem commonly encountered in traditional RNNs. This is achieved through its sophisticated gating mechanisms, which regulate the flow of information across time steps. The input gate \(i_{t}\) controls the influx of fresh information into the cell state \(c_{t}\), while the forget gate \(f_{t}\) regulates the confinement of former cell state information. The output gate \(o_{t}\) governs the flow of information from the cell state to the hidden state, ensuring that relevant information is propagated forward while irrelevant information is suppressed.
# Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme for Heterogenous Vehicular Network +
## 3 Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme
### Simple Adaptive Weight (SAW) Technique
#### 3.4.2 Weighting Factor  
Once each metric has been normalized to a comparable scale, the user's weighting factor must be specified to determine the priority of each metric's importance. The IV uses Equation (11) to calculate the weight of each NV and then ranks each NV according to its computed weight. The MGW is selected as the NV with the highest weight.  
\[W_{i}=\sum_{i=1}^{4}(P_{i}\left[F\right]*\ X_{i}) \tag{11}\]  
where \(F\) is the IV's priority factor, and one of the higher weights is chosen as the gateway. Every metric in this study is given the same priority factor as given in [15; 22].
# Fresher Experience Plays a More Important Role in Prioritized Experience Replay
## 5 Experiment
### Continuous Control  
Continuous control tasks refer to tasks with continuous action spaces i.e., the number of actions is not defined, but their range can be evaluated. In this paper, two MuJoCo [38] tasks based on OpenAI Gym are used to evaluate the performance of FPER in continuous control tasks: HalfCheetah-v1 and Ant-v1, shown in Figure 5.  
DDPG is used in continuous control evaluation. Adam [39] is adopted to train the neural network with a learning rate of \(3\times 10^{4}\) for the action-value network and actor  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Environment** & **LunarLander-v2** (\(Reward=-100\)) & **CartPole-v1** (\(Reward=300\)) \\ \hline FPER & \(0.75\times 10^{4}\) & \(3.5\times 10^{4}\) \\ \hline CER & \(2\times 10^{4}\) & \(4\times 10^{4}\) \\ \hline PER & \(3.5\times 10^{4}\) & \(3.7\times 10^{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Implemented time in discrete control tasks.  
Figure 4: Effect of different values of \(\mu\) on CartPole-v1.  
Figure 5: From left to right: HalfCheetah-v1, Ant-v1.  
network. The two networks show similar structure. In our work, there are two hidden layers in both networks and each layer contains 256 hidden units.The discounted factor is set as 0.99. The buffer size is set as \(10^{4}\). The mini-batch size is set as 32, and the updating rate of target network is set as 0.005. Moreover, the freshness discounted factor for HalfCheetah-v1 is set as 0.98 and set as 0.95 for another. A purely exploratory strategy is used for the first 10,000 steps. Gaussian noise \(\mathcal{N}(0,0.1)\) is added to each action afterwards.  
We compare our Algorithm 2 against DDPG with PER. The lifetime pointer is not combined with FPER in these tasks for reducing surplus computation. Other experimental settings are the same as those in Section 5.1.  
Figure 6 compares the plots of FPER and PER, which are applied in HalfCheetah-v1. The learning speed of FPER is obviously faster than PER. The reward of FPER almost increases 100%. Figure 6 depicts the curves of FPER and PER in Ant-v1. Although the improvement is not so obvious, FPER still outperforms PER. In a nutshell, the results mean that FPER performs better than PER in continuous control tasks.  
Table 2 lists the implemented time taken by the agent for different algorithms in different continuous control tasks. Notably, FPER can obtain the same reward in the half of the time steps that PER takes in different environment, demonstrating excellent learning capacity.  
Figure 6: Learning curves in continuous control tasks. The shaded region represents the standard deviation of the average evaluation over five trials, and the thick line represents the mean of the average evaluation over five trials. (**a**) HalfCheetah-v1. (**b**) Ant-v1.
# Retrieval of Chemical Oxygen Demand through Modified Capsule Network Based on Hyperspectral Data
## 3 Experiments and Results
### Evaluation Criteria and Result  
The data set was divided into training and testing sets. The coefficient of determination \(R^{2}\) was used to evaluate the performance of our model. To illustrate the effectiveness of our model, the convergence of the proposed network architecture was evaluated. In this model, it is important to note that the proposed network architecture makes use of several innovative layers that can estimate the probability of the corresponding instantiation parameters. For example, the potential transformations suffered by the corresponding constituent feature on the observable input data. As a result, the features can be intrinsically managed at a higher abstraction level throughout the network. In this context, the plot of the loss function was provided as well as the value of \(R^{2}\) in each epoch. The training procedure did not stop until it reached the predetermined number of iterations. Unless there is an early termination to the procedure, the predicted value of the COD can be obtained in each epoch at the same time the \(R^{2}\) is computed using Equation (4).  
\[R^{2}=1-\frac{\left(y_{\text{predict}}\,-\,y_{\text{true}}\,\right)^{2}}{\left( y_{\text{true}}\,-\,\overline{y}_{\text{true}}\,\right)^{2}} \tag{4}\]  
Clearly, the proposed capsule network only requires a reduced number of epochs to reach \(R^{2}=0.7\), which highlights remarkably the fast convergence and high accuracy of the proposed architecture compared with the conventional BP algorithm (Figures 6 and 7).  
Figure 5: Schematic showing the back-propagation algorithm architecture used for comparison in this study. It consists of one input layer, one output layer, two hidden layers and one dropout layer.  
In Figure 7, the final \(R^{2}\) value was 0.78 for the capsule network, while it was only 0.42 for the BP algorithm. For the PLSR algorithm, the \(R^{2}\) was only 0.29 (not shown). All the \(R^{2}\) and mean square error values (MSE) are given in Table 4. Moreover, both predicted and label values are given in Figure 8.
# Transmission Removal from a Single Glass Scene and Its Application in Photographer Identification
## 3 Proposed Method
### Application to Photographer Identification  
As one of the important applications of transmission layer removal, the photographer identification based on the recovered reflection layer image is of great importance in the investigation of shooting information. Figure 5 shows a schematic diagram of an application scenario for photographer identification, where unknown or suspicious photographers can be identified by the proposed transmission removal network as well as by several existing portrait recognition algorithms.  
As mentioned in Section 1, we have analyzed that when the face regions are present in a scene, they will provide more helpful information than in an ordinary environment scene. So we can only focus on the face region on the estimated images with a portrait. As mentioned in [20], the reflection component after reflection separation can be enhanced by the corresponding ground truth reflection scene image. Since the reflection layer is filled with brightness and color using the fixed reflection layer's environment, the final effect was satisfied with the laboratory environment. However, we cannot always obtain the corresponding ground truth reflection scene image. Each image should reveal more facial information for easier analysis of images with portraits. Therefore, we can change the expression of the predicted image directly by changing the style image. Figure 6 shows an example of how to implement image transfer on the estimated image by using the style image. To distinguish transmission removal from the general scenarios, we reconsidered the differences in content and style. In detail, we use grayscaled \(f(\mathbf{R})\) as the content image to retain content information and use color images with the corresponding semantic information as the style image to describe the distribution of colors. Note that when \(f(\mathbf{R})\) is originally a grayscale or dim image, we need to colorize it before setting it as a style image. These colorized images will better serve the application of photographer identification.  
Figure 5: Schematic diagram of an application scenario for photographer identification.  
Figure 6: Content image and style image settings in a scene containing a portrait.
## 3 Analysis of Program and Erase Window
### Program Window Effect
However, in Figure 5d, the difference in the programmed Vth decreases at higher program voltages. The reversal of this trend at higher voltages can be interpreted as changes in the fringing field of the read voltage applied to the selected WL depending on the programmed Vth value. Figure 5f shows the electron density of the channel when the read voltage corresponding to Vth is applied to each selected WL, with a high programmed Vth at 4.4 V and a low one at 0 V after the program operation. The Vth is defined as the read voltage when 50 nA flows in the poly-Si channel. As a result, when the Vth is high, a high read voltage is applied to the selected WL, causing a large fringing field applied to the bottom of the poly-Si channel, resulting in a small effective gate length. Conversely, when the Vth is low, 0 V is applied to the selected WL, leading to a small  
Figure 4: Electric field and e-trapped charge distribution in CTL during program operation in (**a**) perfect right-angle structure; (**b**) structure with 2 nm rounded edges. (**c**) Comparison of e-trapped charge in CTL by cutting a-a’ area in (**a,b**). fringing field and a large effective gate length. This means that in the high programmed Vth region, the influence of the electron concentration spread from the gate edge to the space region on the read operation is significantly reduced. Therefore, in Figure 5d, it can be interpreted that when Ls is reduced, the higher the program voltage of the selected WL, the less sensitive the reduction effect of the programmed Vth becomes, and the smaller the Vth gap with the reference device. Additionally, the red graph in Figure 5d represents the programmed Vth minus the effect of the initial read operation observed in Figure 2a. Looking at the results, it can be seen that in areas where the program voltage is high, the Vth difference in the Ls scaling case forms a higher value. This reversal phenomenon can be interpreted as a result of the increased concentration of e-trapped charges becoming more dense in the lower part of the selected WL when scaling Ls compared to the reference.