esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
In 2019, according to the findings published in Lancet by the Global Research on Antimicrobial Resistance (GRAM) Project, bacterial infections were the second-leading cause of death globally, yielding 7.7 million deaths [1]. Due to the rise in antimicrobial bacteria, the annual global healthcare cost of bacterial infections has increased dramatically. Some pathogenic bacteria express fibers tipped with adhesins that bind to and penetrate the epithelial cell surface of the host tissues to reach the mucosa, the bloodstream, and finally to several organs, causing severe and chronic inflammation [2]. The invasion of bacteria is dependent upon the activation of protein tyrosine kinases (TKs) [3, 4, 5]. [PERSON] et al. have demonstrated that inhibition of the protein TKs can block this adhesin-mediated uptake into cells [6]. In another study by [PERSON] et al., it was shown that TK inhibitors prevent the uptake of pathogenic bacteria into rabbit corneal epithelial cells [2].  
In 2020-2021, several druggable compounds with significant binding affinity towards the Src homology-2 (SH2) domain of p56\({}^{\text{lck}}\) TKs were synthesized using the Mannich reaction and tested [7, 8]. Src protein TKs have demonstrated an important role in the regulation of inflammatory responses in tissue cells [9, 10, 11, 12]. Animal studies have demonstrated that Src protein TK inhibitors can reduce tissue injury and improve the health of patients suffering from several pathological conditions that cause inflammatory responses [9]. SH2 domainsare known to interact with phosphorylated tyrosine residues in proteins and mediate TK signal transduction [13; 14]. P56\({}^{\text{1 ck}}\) (LcK) is an Src-like, lymphocyte-specific TK which has an SH2 domain of \(\sim\)100 amino-acid residues [15]. In this study, we have used a combination of computer modeling techniques to identify novel small molecules that are predicted to bind strongly to the SH2 domain of P56\({}^{\text{1 ck}}\) protein TK and act as inhibitors for the treatment of bacterial infections. Utilizing the molecular features of the previously-reported highly potent compounds, we identified top hit compounds from virtual screening, modified the structures of the hit compounds, and studied their binding affinities to the p56\({}^{\text{1 ck}}\) protein TK. Using ligand-based e-pharmacophore modeling, virtual screening, ensemble docking, core hopping, and in silico pharmacokinetic properties prediction, we obtained hit molecules that showed high structural similarity to the known potent compounds and are expected to exhibit significant antibacterial activity. This molecule inhibitor design study can serve as a starting point for identifying potentially novel Src TK inhibitors that could be further subjected to in vitro studies for investigating their antibacterial activity.
# A Survey of Multi-Focus Image Fusion Methods
In this paper, the existing multi-focus image fusion methods are reviewed, sorted, and classified, and eight commonly used objective evaluation methods are summarized. Based on a large number of literature, the typical algorithm, fusion process, and key technologies of multi-focus image fusion are discussed, and the fusion results and fusion efficiency are compared and summarized. The image fusion can be divided into four categories: transform domain method, boundary segmentation method, deep learning method, and combination fusion method. The applicability of various methods is summarized. Finally, we analyzed and discussed the challenges faced by this field and proposed the solutions, and the future development of multi-focus image fusion technology has prospected. The first part of this paper is the introduction, which introduces the concepts of multi-focus image fusion, and summarizes the content of this paper; The second part is the fusion method and analysis, which analyzes and classifies a variety of multi-focus fusion methods; The third part is the evaluation indicators, which introduces the commonly used subjective evaluation and objective evaluation; The fourth part is the limitations, and gives the corresponding solutions according to the common fusion problems; The fifth part is the conclusion, which analyzes the application and development of multi-focus fusion.
## 3 Intelligent Method for Modeling and Simulation (IM4 MnS)
### Entity Modeling with Case-Based Reasoning
#### 3.2.3 Case Retrieval
\[Sim\Big{(}R^{P},R^{T}\Big{)}=\lambda_{1}^{P}Sim\Big{(}R^{P},R^{T}\Big{)}^{QL}+ \ \lambda_{2}^{P}Sim\Big{(}R^{P},R^{T}\Big{)}^{QN},\ \Big{(}0\leq\lambda_{1}^{P},\ \lambda_{2}^{P}\leq 1, \lambda_{1}^{P}+\lambda_{2}^{P}=1\Big{)} \tag{3}\]  
\[Sim\Big{(}R^{P},R^{T}\Big{)}^{QL}=\frac{n\Big{(}S^{P}\ \cap\ S^{T}\Big{)}}{n(S^{T})}, \Big{(}(sa,sv)\in S,\ sat^{P}=sa^{T},\ \Big{(}sv^{P},\ sv^{T}\Big{)}\in\ QL\ type\ \Big{)} \tag{4}\]  
\[Sim\Big{(}R^{P},R^{T}\Big{)}^{QN}=\frac{1}{Y}\sum_{y=1}^{Y}\left[1-\frac{\sqrt {\left(sv_{y}^{P}-sv_{y}^{T}\right)^{2}}}{\max\left(sv_{y}^{P},\ sv_{y}^{T} \right)}\right]\ \Big{(}sa^{P}=sa^{T},\ \Big{(}sv^{P},\ sv^{T}\Big{)}\in QN\ type\Big{)} \tag{5}\]  
To determine the similarity between the previous and target cases, each similarity score of the environmental conditions, as well as the configuring modules is considered simultaneously. Thus, the previous cases that are employed to provide the module configurations in which the required functions are supported in a certain surrounding can be selected using the similarity score. Equation (6) offers the similarity score for the previous and target cases. \[Sim\Big{(}Case^{P},Case^{T}\Big{)}=\lambda_{1}^{P}Sim\Big{(}EC^{P},EC^{T} \Big{)}+\lambda_{2}^{P}Sim\Big{(}R^{P},R^{T}\Big{)},\ \Big{(}0\leq\lambda_{1}^{P},\ \lambda_{2}^{P}\leq 1, \lambda_{1}^{P}+\lambda_{2}^{P}=1\Big{)} \tag{6}\]  
Retrieval Process Using Similarity  
The procedure to retrieve the cases similar to the target case from the previous cases using the similarity score is shown in Table 1. \begin{table}
\begin{tabular}{c l} \hline \hline \multirow{2}{*}{**Notations**} & \(p\) : the index assigned to each previous case \\  & \(NP\) : the number of previous cases \\  & \(C^{T}\): a target case, \(C^{T}\): a previous case \\  & \(CL^{P}\): the list of previous cases selected \\ \hline
**Step 1:** & **For integer \(p\) \(\leftarrow\) 1 to \(NP\)do** \\  & Determine all previous cases using the platform required \\
**Step 1.1:** & Append the results to \(CL^{P}\) \\  & \(CL^{P}\)**append** (the previous cases) \\
**Step 2:** & **For integer \(p^{\prime}\) \(\leftarrow\) 1 to \(NP^{\prime}\)do** \\  & Calculate the similarity score between \(C^{P}_{P}\) and \(C^{T}\) \\
**Step 2.1:** & Append the score to each previous case of \(CL^{P}\) \\
**Step 3:** & Descending the previous cases of \(CL^{P}\) using the similarity scores \\
**Step 4:** & Select top 3 cases from \(CL^{P}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Process to retrieve similar cases. It retrieves the cases by sharing the platform with the target case from the case base and calculates the similarity scores. The top three cases of the scores are provided as the case similar to the target case. One of the results can be reused as a solution or can be modified according to the requirements.
# Pas: Privacy-Preserving Authentication Scheme Based on SDN for VANETs
## 4 The Proposed Scheme
### Security Analysis  
1. Anonymity. In the vehicle registration phase, the TA conceals the vehicle's true identity \(ID_{v}\) into \(PS_{v}^{i}\), where \(PS_{v}^{i}=H_{2}(ID_{v}||r_{i})\), \(r_{i}\) is the random number selected by the TA. As a result, adversaries have to launch the second-preimage attack to find \(x\), where \(x\) meets \(H_{2}(ID_{v}||r_{i})==H_{2}(x)\). However, due to the feature of weak collision resistance for \(H_{2}\), the probability of finding \(x\) is quite low.
2. Authentication. In VANETs, authentication includes identity authentication and message authentication. In PAS, all legal public keys are stored into the SDN RSU controller and only vehicles with legal private keys can generate legal signatures to participate in authentication. Thus, any adversaries cannot generate a set of legal pseudonyms, public keys, and private keys to participate in the authentication. In addition, since each signed message contains a timestamp, PAS is able to effectively resist replay attacks. The adversaries cannot forward the legal signature to the target disguised as a legal vehicle for authentication, which satisfies identity authentication. For message authentication, vehicles are required to adopt a BLS signature to prove the legitimacy of their identity. All legal public keys are stored into the SDN RSU controller, and adversaries cannot generate a set of legal pseudonyms, public keys, and private keys to participate in the authentication. Meanwhile, Refs. [31,32] have proved the security of the signature mechanism, which guarantees that the proposed scheme is able to resist an MOV attack. Furthermore, adversaries cannot obtain a session key unless the DH problem is solved.
3. Accountability and credential revocation. VANETs require the whole network to own the ability to record the misbehaviors of vehicles in time and exclude these vehicles from the network. PAS supports removing illegal vehicles from VANETs. Once the illegal vehicle is identified, vehicle revocation protocol is triggered to remove the public keys of illegal vehicles. Thus, the corresponding real identities are exposed in time. Furthermore, accountability denotes non-repudiation, which means that all messages transmitted cannot be denied by the senders. In PAS, all signatures need to be verified by the public keys stored at the SDN RSU controller. Since the mapping between the public keys and the real identities of the vehicles are stored in the TA, the vehicles cannot deny their signature. Meanwhile, these signatures also imply the real identities of the vehicles.
4. Restricted credential usage. In the proposed scheme, all legal public keys stored in the RSU SDN controller must be used within the validity period. Once the public key expires, the vehicle has to communicate with the TA again to obtain a new pseudonym, public key, and private key. Furthermore, each signature contains a timestamp and challenge value to resist a replay attack and Sybil attack.
# On Transform Domain Communication Systems under Spectrum Sensing Mismatch: A Deterministic Analysis
###### Abstract  
Towards the era of mobile Internet and the Internet of Things (IoT), numerous sensors and devices are being introduced and interconnected. To support such an amount of data traffic, traditional wireless communication technologies are facing challenges both in terms of the increasing shortage of spectrum resources and massive multiple access. The transform-domain communication system (TDCS) is considered as an alternative multiple access system, where 5G and mobile IoT are mainly focused. However, previous studies about TDCS are under the assumption that the transceiver has the global spectrum information, without the consideration of spectrum sensing mismatch (SSM). In this paper, we present the deterministic analysis of TDCS systems under arbitrary given spectrum sensing scenarios, especially the influence of the SSM pattern to the signal to noise ratio (SNR) performance. Simulation results show that arbitrary SSM pattern can lead to inferior bit error rate (BER) performance.  
Keywords:transform domain communication systems; spectrum sensing mismatch; cyclic code shift keying; internet of things +
Footnote †: journal: Sensors
## 3 Proposed Framework  
Figure 2 shows the proposed framework for the prediction of the quality of the image. The proposed architecture includes a visual saliency module that extracts the important region of the image and allows the neural network model to pay attention to the important region only. This allows removing the maximum outliers from the learned feature space. The distorted images are affected by noise, and the noise mainly affects the high-frequency regions. The high-frequency regions are extracted well by the static visual saliency module. The visual saliency map is thresholded to find the binary mask, which is multiplied by the input distorted image in order to obtain the masked distorted image, as illustrated in Figure 2. The extracted masked and distorted image is passed by Inception-ResNet-V2 in order to predict the quality of the image.  
There are various existing models using saliency detection for IQA. MMMNet, SGDNet and VSI are a few among these. In order to increase the generalization of the IQA task through multi-task training, MMMNet adds an auxiliary saliency task [23]. SGDNet [24] is another architecture which uses a saliency prediction subnetwork for quality prediction of an image. The VSI model [25] has used multiple visual saliency techniques to compute the quality of an image. However, VSI does not explore the neural network features to perform a quality assessment. Regardless of these networks, a network is required which performs saliency mapping as an individual task regardless of the type of distortion. That is where the proposed network can be used to solve such an issue.  
Below are the details regarding the static visual saliency module and Inception-ResNet-V2 used in the proposed framework.
# Point Cloud Repair Method via Convex Set Theory
## 2 Methods
### Filtering of Spatial Subunits
#### 2.4.1 Coarse Screening  
We used the similarity feature of the normal point-cloud neighborhood vector to conduct coarse screening. First, we used the point set of the neighborhood of point \(\hat{x_{i}}\) (\(\hat{x_{i}}\) is the midpoint of \(x_{i}\) and \(x_{j}\)) to calculate normal vector \(\vec{n}\). Considering that 3D spatial unit \(S\) on different scales contains a different number of points, it is impossible to accurately calculate the normal vector of the surface composed of \(x_{i}\) and the set of neighborhood points. To solve this problem, we calculated normal vector \(\vec{n_{i}}\) at three scales (the number of points on the three scales was 7, 14, and 21) and mean value \(\hat{\vec{n}_{i}}\) as the contrast value. Then, the surface normal vector formed by the central point of each subunit and the nearest neighbor point of \(k\) (\(k\) was 7, 14, or 21), and mean value \(\sigma\) were calculated. Lastly, we used cosine similarity to calculate the difference between normal vector \(\vec{\sigma}\) and contrast value \(\hat{\vec{n}_{i}}\). The cosine similarity was calculated as follows:  
\[cos\left(\vec{\sigma},\vec{\hat{n}}\right)=\frac{\sum_{i=1}^{3} \left(\vec{\sigma}_{i}\times\vec{\hat{n}}_{i}\right)}{\sqrt{\sum_{i=1}^{3} \left(\vec{\sigma}_{i}\right)^{2}}\times\sqrt{\sum_{i=1}^{3}\left(\hat{\vec{n }}_{i}\right)^{2}}}. \tag{1}\]  
We needed to specify a rule to retain those spatial cells with high normal vector similarity. Therefore, we calculated the degree of difference of any two vectors in \(\vec{n_{i}}\), and found the maximal \(cos_{max}\) and took it as the threshold. if \(\cos(\vec{\sigma},\vec{n_{i}})\geqslant\theta*cos_{max}\), then we reserved the subunit. \(\theta\) is a controllable parameter. In this paper, \(0.50\leqslant\theta\leqslant 1.00\). Coarse screening eliminates some useless subunits, reducing the calculation amount for the subsequent fine screening.
# Data-Driven Network Latency Processing for Auxiliary Services in Virtual Power Plant
## 3 System Architecture
### The Network Latency Acquisition Mechanism  
In this paper, the backhaul network is regarded as a one-hop virtual link. The network latency is monitored through the cooperation between the VPP monitoring and scheduling platform and the gateway by a delay probe. Based on the analysis and processing of the historical network latency data, the VPP services supported by the active DERs can be evaluated to improve the monitoring and scheduling availability.  
To obtain network latency, we need to synchronize the time between the gateway and the VPP monitoring and scheduling platform. In our research, we regard the carrier network as a virtual link and use the VPP monitoring and scheduling platform as the clock source to synchronize the time of each accessing gateway. When constructing the VPP, an NTP time server is configured on the platform to run the ntpd service, which is a network time protocol service in a Linux system. The VPP monitoring and scheduling platform will actively calibrate the time with the public network. When the gateway is connected, configure the ntp client to periodically send time-matching requests to the platform.  
In order to obtain real network latency, we design a channel-associated delay probe based on the MQTT protocol, which also encapsulates the practice VPP service. The VPP platform initiates network latency measurement tasks and collects the measurement results of each link of the gateway. A detailed flowchart of the measurement task is shown in Figure 2. The VPP platform is responsible for assembling messages with time stamps \(t1\) and pushing them to the broker-agreed topic \(latency\_request\). Each gateway consumes messages from the agreed topic \(latency\_request\), records the message arrival time \(t2\), and calculates the downlink communication latency \(latency_{DL}=t2-t1\). Then, each gateway sends a response message to the topic \(latency\_response\) with \(latency_{DL}\) and sends time \(t3\). The VPP platform consumes upstream packets from the topic \(latency\_response\), and records the arrival time \(t4\). At the same time, the VPP platform parses packets to obtain \(latency_{DL}\) and \(t3\), and calculates the uplink latency \(latency_{UL}=t4-t3\). Finally, the master program writes the measured values of the uplink and downlink latency \(latency_{UL}\) and \(latency_{DL}\) to the database, and the network latency measurement task is completed. The measurement tasks will be performed according to a predetermined configuration, which may be event-triggered or periodic depending on the needs of the VPP services.  
Figure 2: Time probe procedures.
## 2 Experimental Section
### Device Fabrication and Characterization  
The polymer solar cells (PSCs) were fabricated with the structure of ITO/PEDOT:PSS/polymer:PC\({}_{61}\)BM/Ca/Al from our previous work [20; 26]. The ITO glasses were cleaned by a sequential ultrasonic treatment in detergent, deionized water, acetone, and isopropanol for 20 min. Then PEDOT:PSS was filtered through a 0.2-\(\upmu\)m filter and spin-coated at 3500 rpm for 30 s on top of ITO electrode. Subsequently, the PEDOT:PSS film was baked at 140 \({}^{\circ}\)C for 10 min in the air, and then moved into a glovebox. The blend solution of PC\({}_{61}\)BM and synthesized polymers in a solvent was filtered with/without a 0.45-\(\upmu\)m filter and spin-coated at 800 rpm for 30 s on top of the PEDOT:PSS layer. These devices were thermally annealed at various temperatures for 10 min, followed by capping with Ca (\(\sim\)20 nm) and then Al (\(\sim\)60 nm) in a thermal evaporator at a base pressure of ca. 10\({}^{\pm 6}\) Pa. The active area of the devices is 0.06 cm\({}^{2}\). The current density-voltage (\(J\)-\(V\)) measurements of the devices were conducted on a computer-controlled Keithley 2400 Source Measure Unit under AM 1.5G simulated solar irradiation at 100 mW\(\cdot\)cm\({}^{-2}\). The light incident intensity was calibrated by a mono-Si reference cell with a KG5 filter (PV Measurements, Inc., Boulder, CO, USA), which was pre-calibrated by the National Renewable Energy Laboratory. The space charge limited currents (SCLC) measurements were carried out using a Keithley 2400 source meter (Keithley, Cleveland, OH, USA) under dark condition. The hole mobilities of were evaluated in a hole-only device configuration, ITO/PEDOT:PSS/(pristine polymer thin films or blend with PC\({}_{61}\)BM) (\(\sim\)200 nm)/Au (\(\sim\)80 nm); the electron mobilities of were evaluated in a device configuration, ITO/SAM-NH\({}_{2}\) (self-assembled monolayer, 4-aminobenzic acid)/(pristine polymer thin films or blend with PC\({}_{61}\)BM)/Ca (\(\sim\)20 nm)/Al (\(\sim\)60 nm). The IPCE spectra were recorded under illumination by a xenon lamp and a monochromator (TRIAX 180, JOBIN YVON, Edison, NJ, USA), and the light intensity was calibrated by using an OPHIR 2A-SH thermopile detector (OPHIR Photonics, North Logan, UT, USA).
## 4 Methods
### Explainable AI Methods  
When machine learning-based prediction models are used in practice, the prediction itself may not be enough. If the product emits an alarm for a high failure risk in predicted quality, the user needs information on the reasons behind the result as well. In general, a powerful but complex machine learning model remains as a black box for the user, but some transparency can be layered on top of it by using explainable AI methods (XAI). The interpretability of the results can be increased with an understandable view of the modeling analysis for the user. In this work, four XAI methods are used to increase the intelligibility. The methods are the Partial Dependence Plot (PDP), Accumulated Local Effects (ALE), Shapley Additive Explanation (SHAP), and parallel coordinates plot [22].  
GBM provides information about the strength of the importance of each variable in the model, and the effect on the response variable can be visualized with PDP [23] and ALE [24]. The benefits of ALE compared to PDP are that it can handle the correlated features better and it is less computationally expensive [24]. The interactions between variables are also important, and the strength of the interactions can be estimated in GBM. The methods that explain the averaged relationships in the model do not provide explanations for the prediction of a specific observation. One solution is to use simplified, easy-to-interpret surrogate models but, in real life applications, their predictive capabilities are often not adequate. The game-theory based SHAP method can be used to analyze the results of more complex machine learning models. The method describes the deviation of a single observation's prediction from the average prediction for each variable individually [25]. The SHAP method identifies the variables with the strongest contribution to the deviated prediction, when an observation is compared to its counterparts, but it lacks information about the actual direction in which the diverged values should be shifted in order to achieve better quality.  
Parallel coordinates enable the visual data mining of multidimensional data with 2D plots [26]. When a product with poor predicted quality is compared to similar products with good quality by using SHAP visualization, the user discovers only the divergent process variables, and the information regarding whether each particular variable improves or deteriorates the quality. With parallel coordinates, the user can immediately pinpoint not just the variables that most strongly contribute to the poor prediction but also the level of the variable; i.e., should the value be increased or decreased in order to save the product. The information can be derived into a recommendation for actions in an automated decision support system as well.