esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Content Recommendation and Delivery in Mobile Wireless Networks with Outage Management
## 7 Performance Evaluation with Realistic Models
### Realistic Models  
In reality, the content file size and user interest do not follow simple uniform distributions. Measurements showed that the file size is generally subject to a power law distribution [45] or a lognormal distribution [46; 47; 48; 49; 50]. In this paper, we assume that the file size follows a lognormal distribution. As for the distribution of aggregated/sum user interests on different contents, existing literature suggest that it generally follows a power law distribution [51] or Zipf distribution [52]. We adapt the widely-accepted Zipf distribution in this paper. Moreover, multiple users have different interest in a particular piece of content. The interest distribution among multiple users can be modeled by a normal distribution [53], U-shaped (or J-shaped) distribution [54; 55], Beta distribution [56] or Levy alpha-stable distribution [57]. In this paper, the normal distribution is adopted as the multiuser interest model by default. Parameter values adopted for our simulation in this section are summarized in Table 2.  
Figure 4: Performance comparison of different algorithms and theoretical performance bounds (simple model). Opt, optimal; Heu, heuristic.
## 5 Conclusions  
By adjusting the weight of the cost function in MPC, the path tracking error can be reduced prominently. However, the weight of the cost function in a traditional MPC needs to be manually adjusted which is time-consuming and labor-intensive, and it is difficult to adjust to a satisfactory value. Therefore, in order to adaptively adjust the weight, MPC based on ANFIS is proposed. The ANFIS toolbox is used for data set training, and the training error is 0.14. In SIMULINK, MPC based on ANFIS and a traditional MPC are compared to verify the effectiveness and superiority. Experiments show that when the path is circular and \(\infty\), compared with a traditional MPC, the maximum fluctuation difference effect of \(\Delta I\) is reduced by around 50%, the maximum fluctuation difference effect of \(\Delta\varphi\) is reduced by around 70% (all the \(\Delta\varphi\) approach 0 when the path is \(\infty\)), and the stability is increased by around 60%. The error curve is much flatter and tends to stabilize faster compared with a traditional MPC. All the experiments show the effectiveness and superiority of the MPC based on ANFIS.  
[PERSON], M.L. and [PERSON] developed the methodology and drafted the manuscript; [PERSON] performed the numerical simulations and experiments; [PERSON] and [PERSON] revised the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Jiangsu Key R&D Program (Grant Nos. BE2018004-1, BE2018004) and the Fundamental Research Funds for the Central Universities (Grant No. B20020221).  
Not applicable.  
This paper is no data.  
The authors declare no conflict of interest.
# Electrochemical Control of Biofilm Formation and Approaches to Biofilm Removal
## 4 Electrochemical Biofilm Control
### Electrochemical Control of Bacterial Adhesion
It can be assumed that these ions probably serve as the ion bridges between the negatively charged bacterial cells and the surface. However, not every divalent metal ion has a similar positive effect, where Zn\({}^{2+}\) represents a typical example. Even at low concentrations of 500 umol L\({}^{-1}\) Zn\({}^{2+}\), the growth of _Streptococcus pyogenes_ and _Escherichia coli_ biofilms was inhibited up to 1.5 and 4.6 times, respectively, in comparison with the positive control [151]. Similar results were found for biofilm formation by bacteria of the genus _Bacillus_, namely for _Bacillus cereus_ and _Bacillus subtilis_[152]. A minimal or insignificant inhibitory effect on the biofilm formation of yeast _Candida parapsilosis_[153] and bacteria _Escherichia coli_ was observed for divalent ions of copper, cobalt, nickel, and manganese [154]. The above-mentioned studies have indicated the fact that electrochemical methods could be used in the continuous monitoring of concentration levels of the metal ions in an aqueous solution [155] during biofilm formation. Figure 3: Principle of indirect voltammetric control of bacterial adhesion.
## 3 Intelligent Tuning Based On DDPG
### Generalize the Learned Policy  
To learn how to tune a filter with RL is only a first and basic step. Since RL training always takes a long time to converge, and every product individual is different, how to adapt the individual differences is a challenging issue. If the trained policy cannot be generalized, RL loses its significance and will have no more advantage than supervised learning. In this work, we present a framework to tackle the generalization problem after RL training. Specifically, our aim is to tune a new detuned cavity filter to its target within as few steps as possible, after RL training on a benchmark product of the same type.  
The framework contains two phases: learning and transferring. In the learning phase, we randomly explore the new filter product, collect enough data pairs, and build a data-driven mapping model between the new filter product and the old one. To achieve this, an inverse estimation model needs to be first built, i.e., a mapping from the S-parameters to the screw positions of the old filter product. The inverse estimation model could be built with data examples collected during the RL training process. Once we have a new product individual, we first randomly place the screws and record their corresponding S-parameters curves. Then, the inverse estimation model is used to predict the screw positions on the old filter product (which has already been trained). Finally, the two sets of screw data are used for constructing the data-driven mapping model. The learning phase is concluded in Figure 5a.  
In the transferring phase, the learned data-driven mapping model is used to transfer the trained RL policy to the new product. When connecting the new product to the VNA, S-parameters could be measured. The trained RL policy network \(\mu(s)\) predicts the best action to take. However, since the policy network is based on the old filter product, the predicted action cannot be directly applied. The mapping model learned in the learning phase should be used to transfer screw positions from the old to the new. The action on the new product is executed. The process is shown in Figure 5b.  
It is worth noting that to build the inverse estimation model, data pairs including screw positions and S-parameters curves should be collected. Fortunately, the data could be collected at the same time with RL training. A way to correctly record screw positions is necessary.
## 3 The Circuit-Partitioning Algorithm Based on Partition Connectivity Clustering and Tabu Search  
The main goal of circuit partitioning is to divide a complex circuit system into smaller modules, to minimize the connections between modules, and maintain load balancing.  
The proposed circuit-partitioning method includes the circuit-modeling process and four core phases, which are illustrated in Figure 1:  
**Circuit Modeling**: The circuit is abstracted as an undirected graph, where each electronic component is represented by a vertex, and the number of direct connections between vertices serves as the edge weights.  
**Coarsening phase**: The circuit graph is coarsened based on Edge-Priority Matching. This coarsening process simplifies the circuit structure by merging vertices, reduces the graph's size and complexity, and improves the efficiency of subsequent processing.  
**Initial Partitioning phase**: The coarsened circuit graph is initially partitioned to minimize the number of cut edges while load balancing across subsets. An initial partitioning algorithm based on node-to-partition connectivity clustering is proposed to provide a good starting solution for the refinement phase.  
**Uncoarsening phase**: This is an inverse process of the coarsening phase, and provides some exact information for the next phase, the refinement phase.  
**Refinement phase**: The improved tabu search algorithm is used to reduce the number of cut edges generated during the initial partitioning phase. The algorithm enhances the search process by optimizing the neighborhood search rules and the candidate solution-selection mechanism based on node-exchange frequency, while the tabu list prevents redundant searches. Additionally, a perturbation method is introduced to increase the diversity of the solution space, improve global search capability, and avoid local optima. These improvements enable the improved tabu search algorithm to strike a better balance between local optimization and global exploration, ensuring load balancing and reducing cut edges, thereby optimizing the circuit-partitioning results.
## 2 Data Sources and Methodology  
The data source was Scopus, with the abstract and indexing database with full-text links produced by Elsevier. Scopus claims to index over 21,500 titles from more than 5000 international publishers, stating that it is the \"largest abstract and citation database of peer-reviewed literature\" [39]. An independent and expert advisory board selects the list of titles indexed using strict criteria, based on user demand and market research.  
It contains 38 million abstracts with references back to 1996. In addition to American journals, 60% of the covered titles are from countries other than the Unites States, highlighting European and Asian literature in English and other languages [40]. Therefore, Scopus covers a broad range of subjects and disciplines by the inclusion of thousands of journals in the life, health, physical, and social sciences. In order to improve search recall, in addition to keywords contributed by authors, extra index terms are manually added for most of the titles in the database, derived from thesauri that Elsevier owns or licenses.  
For the bibliometric analysis, the online search within Scopus was carried out in April 2017 by the selection of \"pavement management\" as keywords in the article title, abstract, keywords fields of the search engine, in order to compile a complete bibliography with all the articles related to research on this topic, published during the period from 2000 to 2013. Different steps or evolution periods about the concept of pavement management have been identified. It is said that the term was born in the mid-1960s as an answer to the several unpredicted pavement failures in North America [41]. Some initiatives in the 1960s and the 1970s showed the necessity to measure pavement behavior in intermediate steps [41]. In the late 1970s, the pavement management systems were able to predict the maintenance needs for the entire highway network of the road agency [42]. In the 1980s, the World Bank introduced the vehicle operating costs in the lifecycle cost analysis by means of HDM-III [42]. In the 1990s, the introduction of personal computers completely changed the toolsused in pavement management. The following step in pavement management evolution is aimed to be identified. With this aim, the final number of articles that were found in the period 2000-2013 was 727.  
Research related to pavement management during the period 2000-2013 was analyzed, in detail, to provide a firm basis for a better understanding of the global research production, allowing identification of long-term strategies for this field. Therefore, the analyzed aspects covered not only the quantitative description of publications, including annual outputs, mainstream journals, language, Scopus categories, and leading countries and institutions, but also, the research tendencies obtained from author keywords in that period. This can give a global panorama of the research in the field of the pavement management during the first years of the XXI century.
# Autonomous Intersection Management by Using Reinforcement Learning
## 3 Methodology
### Deep Reinforcement Learning for Intersection Control
#### 3.4.2 Action Space  
Once the agent has observed the current state of the intersection, it needs to select an action from the action space \(A\). For constructing the action space, we have gone through three phases. In the beginning, the intuition for defining the action space is to let the leader vehicle in a specific lane move to the intersection. Assuming the total number of lanes at an intersection is \(n\), we have \(n\) actions to select at each time step. However, choosing only one vehicle to pass at each time step may make the system inefficient during rush hour. In addition, the change of state is tiny (only one vehicle passed), thus making the time needed for learning longer. In other words, the difference between state \(s_{t}\) and state \(s_{t+1}\) is small; thus it is difficult to measure the effectiveness of action \(a_{t}\). Secondly, we tried to enhance the first idea by making the change from letting only one leader vehicle at a specific lane pass the intersection to letting vehicles that have sent requests at a specific lane pass. This enhancement addresses the inefficiency issue and speeds up the learning process. The reason to add these two actions is that if the agent is only allowed to choose vehicles of a specific lane to pass at each time step without any other choices, it may cause a traffic jam and significantly reduce the throughput of traffic because of the continuous incoming vehicles that are allowed to pass the intersection at each time step. Two types of action are defined as follows: (1) allowing all cars to pass who have received requests, and (2) forbidding all vehicles from passing.
# Ephemeral Node Identifiers for Enhanced Flow Privacy
## 2 Background
### Defence of Identity Privacy
#### 2.4.2 Virtual Private Network (VPNs)  
A common defence is to use a Virtual Private Network (VPN): communication flows are tunnelled through a third party, and it is their address that is exposed to an attacker, rather than the client's. As a VPN connection (virtually) places a device on a different network, it will use a different routing prefix. This provides location privacy but not identity privacy, as an attacker can still correlate flows based on IID values from the virtual network addresses. So, VPNs still need identity privacy solutions.  
As with CGNAT, VPNs (and other solutions from external service providers), present additional challenges. The user must trust their VPN provider--a third party with a privileged view of the communication--to act with integrity, and be free from compromise. VPNs also require indirection (including non-optimal routing) and traffic encapsulation, incurring a performance cost. Finally, use of VPN communications are blocked or considered illegal in some countries, and vulnerable to legal intercept in others [21].  
Other privacy solutions that rely on indirection are vulnerable to the same classes of issue, e.g., Tor [22], which, in addition to non-optimal routing and legal barriers, has the added disadvantage of relying on an overlay network of application-specific gateways, so does not provide a general-purpose solution.
## 4 Initial Trajectory Generation
### Covariant Gradients for Trajectory Generation  
The significance of covariant gradients technique is that both \(J_{obs}(\Gamma)\) and \(J_{smooth}(\Gamma)\) depend solely on physical characteristic of the desired trajectory. In other words, the trajectory generation is invariant to its parameterization. If gradient descent is applied, it depends on the way trajectory is parameterized. The covariant gradients technique removes this dependency. Hence, covariant gradient technique depends solely on physical representation or dynamic quantities of the trajectory with respect to an operator, \(\Theta\):  
\[\|\Gamma\|_{\Theta}^{2}=\int\sum_{n=1}^{k}\xi(\Gamma(t)^{(n)})^{2}dt, \tag{26}\]  
where \(\xi\) is a constant and apices \({}^{(n)}\) determine the \(n\)th-order derivative. The correlation of derivatives between two trajectories: \(\Gamma_{1}\) and \(\Gamma_{2}\), which are defined by assuming inner product as given (27).  
\[<\Gamma_{1},\Gamma_{2}>=\int\sum_{n=1}^{k}\xi\Gamma_{1}(t)^{(n)}\Gamma_{2}(t)^{(n)}dt. \tag{27}\]  
The primary objective of \(\Theta\) is to distinguish the norm (26) and the inner product (27) from the L2 norm [50].
## 5 Discussions  
Based on the analysis of our results, we arrived at the following discussions:  
* The AR guidance system can reduce the search time for users; however, current AR technologies still have positioning issues that may lead to misjudgment and increase the search time. Thus, with this positioning method, it is currently not recommended for use in venues where targets are densely distributed and the precision requirement is high.
* In distance travelled, the results from the six books presented no significant differences between the two scenarios. We speculate that this is because the bookcases were spaced quite close to one another. With positioning inaccuracies, the distance travelled increased. If shorter distance travelled is a consideration, then navigation routes will be required to reduce unnecessary distance travelled.
* With the assistance of AR, users can clearly know where a book is, which enhances the directionality of their searches.
* Users perceived good usability in book finding with the AR guidance system and hold positive attitudes towards using the system to help with book finding in the future.  
The limitations of this study were as follows. Using AR for positioning may result in errors ranging from 0.1 m to 0.5 m, which makes it difficult to precisely position books in a library. The participants expected the target to be accurate, which led to some misjudgment and caused participants to go to the wrong bookcase. Furthermore, books may not always be in the same spot on library bookshelves, so if AR is incorporated into library collection search functions, the target should be set on the shelf in coordination with the call number, instead of directly on the book. We suggest that with current AR technology limitations, if this AR target function was used in venues where targets are less densely distributed, the slight positioning errors would be more acceptable to users.
## 2 PV Model Modeling and Problem Formulation  
PV modeling involves the development of mathematical models that accurately depict the behavior of PV systems across different scenarios. The utilization of models enables the examination of PV system performance, the enhancement of system operation, and the advancement of novel PV systems. PV models can be categorized into various classifications.  
* **SDM**: The I-V characteristics of a PV cell or module are commonly described by a widely employed and uncomplicated model. The proposed model includes a single diode to represent the current passage through the PV cell or module, joining other elements such as series and shunt resistances.
* **DDM**: The proposed expansion involves the integration of a second diode through the SDM system, which aims to reduce the negative impacts of recombination losses occurring in the PV cell or module. In various experimental conditions, the model has the capability to generate predictions of the I-V characteristic with enhanced accuracy.
* **Analytical models**: The behavior of PV systems is described by mathematical formulas in these models. These models can be utilized for the purpose of analyzing and enhancing the operational efficiency of PV systems.
* **Empirical models**: The models are derived from empirical data and can be utilized for approximating the performance of PV systems. In cases where there is an absence of comprehensive information related to the PV system, these models are commonly used.
* **Computational models**: Numerical approaches are employed to simulate the behavior of PV systems. These models have the capability of predicting and enhancing the efficiency of PV systems across a variety of situations.  
The process of PV modeling involves the identification of the model's parameters, which can be obtained through the analysis of experimental data. After its construction, the model can be utilized to analyze the efficiency of the PV system influenced by several factors, including temperature, irradiance, and load. Additionally, the models can be used to optimize the efficacy of PV systems. This involves determining the most effective tilt and configuration of solar cells to maximize energy production.  
The SDM and the DDM are two circuit models that are frequently employed in the academic field to illustrate the correlation between current (I) and voltage (V) in solar and PV cells. These models are considered congruent in their representation of these cells' electrical behavior. This section provides a concise overview of the SDM and the DDM in order to demonstrate the I-V characteristics of PV devices. The objective function utilized for determining the parameters of both models is also provided.
# User Trust Inference in Online Social Networks: A Message Passing Perspective
## 3 The Proposed Model
### Implementation  
Our implementation of the proposed model, including the model construction, inference, and relevant training and predicting procedures, was based on the framework introduced in our previous work [48]. For the sake of brevity, we only discuss some concerns on the model implementation in this section which may greatly affect the model's performance.  
**Numerical overflows and underflows.** These are very common in BP and other message passing algorithms. For example, some terms in the potential functions of unitary or pairwise structures (e.g., Equations (5) or (6)) may be overflowed due to the exponential calculations; in message passing (e.g., Equation (8)), if the messages passed via each edge are not constrained, then some messages will exhibit underflow after certain iterations of message updates; and calculating beliefs (e.g., Equation (7) and (9)) may cause overflow or underflow or both. The trick to tackling the overflow problem here is to shift every exponent in the calculated potential by subtracting the largest exponent. For the underflow issue during message passing, it is necessary to normalize messages frequently.  
**Parameter learning rate in model training.** According to Equation (19), if the learning rate \(\alpha_{m}\) for step \(m\) is too large, the new parameters will move too far in the direction of the gradient; if it is too small, the training procedure will be very slow to accomplish. Thus, it is essential to properly schedule learning rates in the whole model training process. A simple common approach is to let \(\alpha_{m}\) decrease slowly to 0 as step \(m\) grows, which is  
\[\alpha_{m}=\frac{1}{\sigma^{2}(\alpha_{0}+m-1)}, \tag{20}\]  
where \(\alpha_{0}\) is the initial learning rate and \(\sigma^{2}\) is the L2 regularization. The initial learning rate \(\alpha_{0}\) can be manually set or obtained by running a few passes of gradient descent over the graph [49].  
**Implementation of bi-directional message passing.** For bi-directionally passing messages via each edge, the implementation of a non-directional edge in the model is built as two mutually opposite directional edges. Consequently, the number of _real functioning edges_ in the implementation will be doubled and the number of calculations in the probabilistic inference procedure will greatly increase. In order for the model to perform LBP efficiently, parallelism could be deployed in synchronous message passing. It is also beneficial to offload the LBP algorithm to _graphics processing unit_ (GPU) devices to further accelerate the model inference and training.