esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Toxicology  
In vivo animal toxicity tests on delpazolid did not reveal specific toxicity profiles for six months in rats and for nine months in dogs. Furthermore, genetic toxicity tests, including the Ames test, in vitro chromosomal aberration test, and rat micronucleus test, as well as pharmacological safety tests including the hERG safety test, cardiovascular, respiratory and neurobehavioral tests, and reproductive toxicity tests were conducted, none of which revealed a specific toxicity profile (Table 3).  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**General Toxicity** & **Status** & **PO** & **IV** \\ \hline Single dose acute toxicity study in rats & Completed & MTD = 2000 mpk & MTD = 1000 mpk \\ Single dose acute toxicity study in dogs & Completed & MTD = 1000 mpk & MTD = 500 mpk \\
4- week toxicity study in rats with 4 -week & Completed & NOAEL = 60 mpk & NOAEL = 120 mpk \\ recovery & Completed & NOAEL (male = 20 mpk, \\
4- week toxicity study in dogs with 4 -week & Completed & female=10 mpk) & NOAEL = 15 mpk \\
26- week(6 months) toxicity study & & NOAEL & \\ in rats with 4 -week recovery & Completed & (male = 10 mpk, \\
39- week (9 months) toxicity study & & female=100\(\rightarrow\)75 mpk) & \\ in dogs with 4 -week recovery & Completed & NOAEL = 10 mpk & - \\ \hline
**Genetic Toxicity** & & & \\ \hline Ames test & Completed & Negative & \\ In vitro chromosomal aberration test & Completed & Negative & \\ Rat micronucleus test & Completed & Negative & \\ \hline
**Safety Pharmacology** & & & \\ \hline Assessment of blockage of hERG potassium & Completed & Negative(IC\({}_{50}\)\(>\) 100\(\mu\)M) \\ channels & Completed & Negative & \\ Cardiovascular telemetry study in beagle & Completed & Negative & \\ dogs & Respiratory (Pulmonary) study in rats & Completed & Negative \\ Neurobehavioral safety evaluation in rats & Completed & Negative & \\ \hline
**Reproductive Toxicity** & & **PO** & \\ \hline Fertility and Embryonic Development to & & NOAEL (male = 15 mpk, \\ Implantation toxicity in rat & Completed & female=60 mpk) & \\ Embryo-Fetal Development toxicity in rat & Completed & NOAEL = 15 mpk & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Toxicology summary (PO: per oral /IV: intravenous). The general toxicity of delpazolid in animals lasted up to six months in rats and nine months in dogs, and no unusual findings after long-term treatment \({}^{a}\).  
In a human bioavailability study, the bioavailability of the PO form was 99-100% (800 mg) of that of the IV form. Considering that the PK profiles between the IV and PO forms are similar, conversion would be relatively easy in the future. Because delpazolid is slightly polar, it exhibits low protein binding (37% in human), rapid clearance with no accumulation, and no food-related effects (Table 4).
## 5 Precoder and Combiner Design Based on Reinforcement Learning  
The optimization problem mentioned in Equation (26) is obviously a NP-hard problem, which cannot be solved via conventional optimization solvers. Hence, we proposed an RL-based approach to jointly solve the precoding matrix and the combining matrix. Several researchers of beamforming employed the deep Q network (DQN) to find optimal solutions. DQN is designed to solve tasks with discrete action space. To utilize DQN in continuous action space, we need to discretize the continuous action space, which will make the action space grow exponentially with the size of network [37]. While the DDPG algorithm can be used in continuous action space, the MADDPG can in addition reduce the action dimension of single agent. In the case of the joint design of the precoding matrix and combining matrix, MADDPG may be appropriate as it allows for decentralized decision-making while considering global state information. MADDPG enables each agent to interact with the environment and learn its own strategy based on local observations and rewards while also considering the joint actions and rewards of other data streams. The choice of MADDPG for jointly designing precoding and combining matrices may be based on its ability to handle multi-agent scenarios, its ability to handle continuous action spaces, and its use of target networks and experience replay for stability and efficiency. To employ an RL method, we model the process of generating the precoding and combining matrix into the Markov decision process (MDP). Then, we apply the MADDPG algorithm to explore the optimal policy of the MDP. The elements of MDP and the MADDPG algorithm will be presented in the following, respectively. In the context of optimizing precoding and combining matrices, MADDPG can be used to train a network of agents, where each agent represents a communication node equipped with multiple antennas. The agents collaborate to learn the optimal strategies for precoding and combining matrices, taking into account the interactions and dependencies among the nodes.  
Figure 2 is the framework of the MADDPG-based joint precoding and combing algorithm, which consists of two sets of the same number of agents and an interactive environment. The agents receive the states and rewards transmitted by the environment and output corresponding actions, which are recombined into a precoding/combing matrix, respectively, allowing the environment to compute and output new states and rewards.
# Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks
## 4 Supervised Learning for Estimating the Bit Error Rate (BER)
### Data Preprocessing  
During this stage, we undertake data preprocessing to optimize the usability of input and output parameters for the learning algorithm. Our preprocessing strategy entails removing superfluous details, notably the inclusion of the quantity of received bits and the corresponding percentage of received bits. While these details are related to the predicted variable (Bit Error Rate or BER), they are identified as non-essential for the learning system. By eliminating these extraneous details, we refine the dataset to facilitate more efficient analysis and model training.  
Moreover, we standardize our data using the Min-Max normalization technique. This approach enables us to perform a linear conversion on the original data within the range of 0 to +1, employing the following formula:  
\[X_{\text{norm}}=\frac{X-X_{\text{min}}}{X_{\text{max}}-X_{\text{min}}} \tag{1}\]  
where:  
* \(X\) is the original value;
* \(X_{\text{min}}\) is the minimum value in the dataset;
* \(X_{\text{max}}\) is the maximum value in the dataset;
* \(X_{\text{norm}}\) is the normalized value.
# Prediction of Freeway Traffic Breakdown Using Artificial Neural Networks
## 6 Conclusions  
Traffic breakdown disrupts the stable flow of traffic on the freeway. Merging traffic on the ramp tends to reduce traffic speeds on the mainline, potentially leading to traffic breakdown. Therefore, an ANN-based model is proposed to predict the occurrence of traffic breakdowns on freeways considering the impact of adjacent road segments and ramps.  
The ANN model uses spatial and temporal traffic data from mainline and on-ramps to predict the occurrence of traffic breakdowns. The Boruta feature selection method is used to select features to construct the ANN model. Specifically, the Boruta method recommends including on-ramp traffic volume, speed, and occupancy as input variables, as they are strongly associated with the occurrence of traffic breakdown on the mainline. When the training accuracy obtained from the grid search method is compared, the number of neurons in the hidden layer, the batch size, and the number of previous timestamps required for the model are determined and used in the final ANN model.  
The results show that the proposed ANN model has the ability to predict the occurrence of a traffic breakdown event with high accuracy. The ANN model can be applied by transportation agencies for ramp meter activation, as the results of feature selection indicate that traffic from the on-ramps has a significant impact on the occurrence of traffic breakdown. Based on ANN-based prediction of traffic breakdown events, transportation agencies can use real-time traffic data and regulate ramp metering rate to mitigate the impact of the breakdown or prevent it from occurring. Therefore, this mechanism provides a new approach to designing ramp metering systems. Another potential application for the proposed model is the travel information system. By predicting the occurrence of traffic breakdowns, the travel information and navigation system can alter the routes of drivers. Furthermore, if the model predicts that traffic breakdown is occurring, this information can be provided to the safety service patrol, allowing them to deploy proactively to the location where the breakdown is likely to occur.
# UAV-Enabled Mobile Edge-Computing for IoT Based on AI: A Comprehensive Review
## 3 UAV-Enabled and Assisted MEC State of the Art  
The rapid spread of smart devices has led to intensive computation in many applications, such as virtual reality, face and object recognition, and automatic driving. This poses a severe challenge when intelligent devices with limited computing and energy  
Figure 4: UAV-enabled architecture. resources need to perform the required tasks locally [75]. CC can be considered as an expensive solution to overcome these impairments. In addition, the CC solution suffers from high communication expenses and high transmission delays due to the core network's involvement. To this end, some prominent alternatives have been proposed to explore the latency delay, including recalling the computation resources from the cloud to the edge of the network. As an extension of the edge concept, recently, MEC was intensively investigated. MEC can bring information technology (IT) services and cloud-computing capabilities to a mobile network, guaranteeing ultra-low delays and offering high bandwidth to the user [76]. UAVs can be embedded with lightweight CPUs (central processing units) or GPUs (graphics processing units) to offer computation services. Therefore, UAVs fly MEC servers in the sky, offering controllable mobility and high-quality communication links, supplying the existing MEC servers on the ground. In this regard, we summarize the state-of-the-art situation regarding UAVs' possibilities as MEC enablers or as part of a MEC system in an IoT environment where end-nodes and actuators cannot deal with the generated data onboard. The research on UAV-enabled MEC or UAV-assisted MEC is in its early phases, including using the fog and edge computing concepts instead of cloud computing to enhance latency and computation performances in IoT users. A summary of related research activities regarding UAV opportunities as an MEC enabler or as part of a MEC system in the IoT is listed and highlighted in Table 4.
## 4 Conclusions  
In summary, we proposed a method to identify and eliminate the measurement configurations with redundant information based on dependence analysis in optical scatterometry. By assuming that the dimensions of profile parameters under investigation have some deviation from their nominal values, the differences between the actual and nominal dimensions were calculated by the forward optical model under each measurement point. A formulation was derived to identify the measurement configurations with redundant information through performance of a dependence analysis followed by an eigen-analysis. By eliminating redundant information from the measured data in a repetitive manner, a few optimal points remained and were used for the reconstruction process.  
Experiments performed on a 2D Si grating and a 3D photoresist grating revealed that the reconstructed grating profiles from the optimally selected subset of measurement wavelengths according to the proposed method have a higher accuracy than those from the randomly selected counterparts with a higher efficiency than the analysis making use of the whole spectrum. This suggests that the proposed dependence-analysis-based data-refinement method can be a powerful tool to enhance the reconstruction speed of nanostructure metrology using scatterometry without sacrificing the accuracies, especially for the nanostructures whose forward model is very complex and time-consuming.  
Conceptualization, X.C. and Z.D.; methodology and formal analysis, Z.D. and Y.S.; writing--original draft preparation, Z.D.; writing--review and editing, X.C., Z.D., X.W., Y.S., H.J., and S.L.; supervision, Z.D., Y.S., and X.C.; project administration, Y.S. and X.C.; funding acquisition, Z.D., Y.S., X.C., and S.L.  
This research was funded by the National Natural Science Foundation of China (Grant Nos. 51775217, 51727809, and 51525502), the Natural Science Foundation of Hubei Province of China (Grant Nos. 2018 CFB290 and 2018 CFB559), the China Postdoctoral Science Foundation (Grant Nos. 2016M602269 and 2019M652633), the National key research and development program of China (Grant No. 2017 YFF0204705), and the National Science and Technology Major Project of China (Grant No. 2017 ZX02101006-004).  
The authors would like to thank [PERSON] from the School of Optical and Electronic Information of Huazhong University of Science and Technology, and Shanghai Micro Electronics Equipment Co., Ltd. (Shanghai, China) for preparing the samples.  
The authors declare no conflict of interest.
## 4 Experiment Analysis
### Experiments on Real Datasets  
In this subsection, the validity of IKDC on the eight real datasets in Table 3 will be shown. For ACC, NMI, and ARI, IKDC is compared with DPC, DBSCAN, KNN-DPC, SNN-DPC, and FCM. Tables 6 and 7 show the comparison results of the six algorithms.  
As shown in Tables 6 and 7, IKDC has markedly more clustering effects for the Ionosphere, Wine, Waveform, Waveform (noise), Libras, WDBC, Spectrometer, and Seeds datasets. On Ionosphere and WDBC datasets, IKDC has improved values in ACC, NMI, and ARI compared to SNN-DPC and FCM. Overall, the clustering performance of IKDC outperforms the other five algorithms in high-dimensional space. On the Libras dataset, although the clustering effect of IKDC is the same as that of DPC, IKDC introduces three-way clustering, which is more consistent with human cognition, and IKDC outperforms the other five algorithms. On the other datasets, IKDC significantly outperforms the other five algorithms.  
To sum up, by comprehensively considering the performance of the six algorithms on the three indicators, it is demonstrated that the IKDC outperforms the other five clustering algorithms on the eight real datasets, which verifies that the clustering effect of IKDC is more excellent.  
Figure 6: Clustering effect of the FCM algorithm on Aggregation, Flame, and Jain.  
Figure 7: Clustering effect of the IKDC algorithm on Aggregation, Flame, and Jain.
## 3 Methods  
This study proposes a cell semantic segmentation method called CellGAN, based on Generative Adversarial Networks (GANs), to address the cell segmentation task in biological microscopy images. This method mainly includes the following two innovative aspects: the Feature Completion Mechanism in the generator and the Adversarial Error Fusion for Accuracy Rectification strategy.  
As shown in Figure 1, the original image undergoes feature extraction and upsampling through the improved U-Net generator network to generate a cell segmentation result image. Subsequently, the generated segmentation image and the Ground Truth annotation image are concatenated with the original image channels, forming a pair of real and fake images, which are input into the discriminator network. The discriminator then discriminates their authenticity and obtains the discrimination error. This discrimination error is backpropagated to optimize the parameters of the generator and discriminator. The generator and discriminator promote each other's generation and discrimination abilities through adversarial gameplay. After iterative training, this method ultimately generates high-resolution cell segmentation images with clear boundaries.  
Regarding the generator loss function, this study adopts cross-entropy loss as the evaluation criterion for generation loss. Specifically, this loss function calculates the loss value pixel-by-pixel for each input image. It constructs the generation loss for the entire image by weighted summation, as mathematically expressed in Equation (1).  
\[L_{Generator}=L_{Discriminator}+\alpha(-\frac{1}{m}\sum_{pixel}\ [c\log(p_{i})+(1-c)(1-p_{i})]) \tag{1}\]  
where \(m\) represents the total number of pixels, the symbol _pixel_ represents a single pixel, \(c\) represents the class to which the pixel belongs in the Ground Truth label image, \(p_{i}\) represents the prediction value output by the generator at that pixel, and \(\alpha\) is the balancing coefficient for generation loss and discrimination loss.  
The discriminator's loss function is based on the binary cross-entropy loss, used to discriminate whether the input is a real sample or a generated sample, mathematically expressed in Equation (2).  
\[L_{\text{Discriminator}}=-\frac{1}{n}\sum_{x}\left[y\ln a+(1-y)\ln(1-a)\right] \tag{2}\]  
where \(n\) is the total number of samples, \(x\) represents a single sample, \(y\) represents the actual label (real sample or generated sample) input to the discriminator, and \(a\) represents the probability of authenticity predicted by the discriminator for that sample.  
The loss function design is reasonable and can effectively guide the adversarial training of the generator and discriminator, promoting the generator to generate high-quality cell segmentation results and facilitating the adversarial game between the generator and discriminator.  
Figure 1: CellGAN Network Framework.
_J. Low Power Electron. Appl._**2013**, \(3\), 9-26; doi:10.3390/jlpea3010009  
Journal of  
_Low Power Electronics_  
_and Applications_  
**ISSN 2079-9268**  
www.mdpi.com/journal/jlpea  
_Article_  
**Fully Integrated Solar Energy Harvester and Sensor Interface Circuits for Energy-Efficient Wireless Sensing Applications**  
**[PERSON] *, [PERSON] and [PERSON]**  
Ecole Polytechnique Federale de Lausanne, CH-1015 Lausanne, Switzerland;  
E-Mails: [EMAIL_ADDRESS] (F.K.); [EMAIL_ADDRESS] (M.K.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +41-216-934-609; Fax: +41-216-933-640.  
_Received: 4 December 2012; in revised form: 11 January 2013 / Accepted: 6 February 2013 / Published: 28 February 2013_  
**Abstract:** This paper presents an energy-efficient solar energy harvesting and sensing microsystem that harvests solar energy from a micro-power photovoltaic module for autonomous operation of a gas sensor. A fully integrated solar energy harvester stores the harvested energy in a rechargeable NiMH microbattery. Hydrogen concentration and temperature are measured and converted to a digital value with 12-bit resolution using a fully integrated sensor interface circuit, and a wireless transceiver is used to transmit the measurement results to a base station. As the harvested solar energy varies considerably in different lighting conditions, in order to guarantee autonomous operation of the sensor, the proposed area- and energy-efficient circuit scales the power consumption and performance of the sensor. The power management circuit dynamically decreases the operating frequency of digital circuits and bias currents of analog circuits in the sensor interface circuit and increases the idle time of the transceiver under reduced light intensity. The proposed microsystem has been implemented in a 0.18 \(\upmu\)m complementary metal-oxide-semiconductor (CMOS) process and occupies a core area of only 0.25 mm\({}^{2}\). This circuit features a low power consumption of 2.1 \(\upmu\)W when operating at its highest performance. It operates with low power supply voltage in the 0.8V to 1.6 V range.  
**Keywords:** analog integrated circuits; solar energy harvesting; ultra-low power circuits; power management circuits; sensor interface circuits; wireless sensor networks
# SOBER-MCS: Sociability-Oriented and Battery Efficient Recruitment for Mobile Crowd-Sensing
## 3 Background
### Task Allocation
#### 4.4.2 Winner Selection  
As illustrated in Figure 3, the recruitment policy consists of two consecutive steps: (i) winner selection (i.e., Step 5 in the figure); (ii) payment determination (i.e., Step 6 in the figure). Figure 8 presents the winner selection procedure. Briefly, the winner selection relies on selecting the users whose marginal contribution are higher than their bids. To this end, in the assessment of the marginal contribution of a user, the sensing cost of users is estimated by two separate metrics: ordinary cost (\(B_{u}\)) and energy cost (\(E_{u}\)). Ordinary cost (\(B_{u}\)) is an indicator of user discomfort such as security, energy overheads and WiFi/mobile data plan. On the other hand, energy cost (\(E_{u}\)) represents the energy consumed by the mobile device to participate in an upcoming sensing campaign. The marginal value of user \(i\) is formulated as the difference between the value of the recruited population before and after recruiting that user as formulated in Equation (6). As shown in Figure 8, in every iteration, the platform aims at finding a user that maximizes the difference between their sensing cost (i.e., bid) and marginal value (formulated as \(V_{i}-(B_{i}+E_{i})\)). This difference denotes the marginal contribution from user \(i\) to the recruited crowd population. If the marginal contribution of user \(i\) is positive (\((B_{i}+E_{i})>V_{i}\)), the MCS platform calculates the energy factor (\(E_{i}^{f}\)) for the user before proceeding with appointing the user as a winner. \(E_{i}^{f}\) is used to balance the overall energy consumption between users with respect to the minimum residual energy, \(\Re\), that is set by the platform. Mobile devices with residual battery level (\(\Re_{i}\)) below \(\Re\) are discarded by the platform, whereas the others are added to the winners' set. Thus, the MCS platform continues to add smart devices until their marginal contribution and energy factor turn non-positive.  
\[V_{u}(W)=V(W\cup\{u\})-V(W)|u\,\epsilon\,U \tag{6}\]  
As seen in the figure, winner selection is followed by payment determination. Our payment determination procedure adopts the payment step in [39]. Thus, each winner \(i\) is temporarily removed from the participant list (\(U^{\prime}\Leftarrow U\setminus\{i\}\)), and over the rest of the participant list (\(U^{\prime}\)), the maximum hypothetical sensing value of another user (\(j\in U^{\prime},j\
eq i\)) is sought, which would still result in user \(i\) if the user were a member of \(U^{\prime}\).
## 3 Model
### Answer Decoder
#### 3.3.1 Discriminative Decoder  
We use the last hidden states of the forward LSTM to encode sentence representations of answer candidates, denoted as \(s^{a}=\{\overrightarrow{u}_{i,n_{a}}^{a}\}_{i=1}^{100}\in\mathbb{R}^{d_{k}\times 1 00}\). We rank them according to the dot products of the candidates \(s^{a}\) and multi-view aligned representation \(m^{enc}\) (i.e., output representation of the encoder), then apply the softmax function to obtain the probability distribution of the candidates, denoted as \(p=\text{softmax}((s^{a})^{\top}m^{enc})\). Note that dimension of each answer candidate representation is the same as that of the encoder output. We use cross entropy loss as the discriminative objective function, formulated as:  
\[\mathcal{L}_{Disc}=-\sum_{i=1}^{100}y_{i}\text{log}p_{i}, \tag{11}\]  
where \(y_{i}\) is a one-hot encoded vector of the ground truth answer.