esa-sceva/satcom-chunk-collection huggingface dataset:
# Foxn1 Control of Skin Function
## 1 Introduction  
Skin, as the outermost organ, creates a barrier that protects the body from the external environment while acting as a sensing and responding organ to internal and external stimuli [1, 2]. The skin is composed of three layers: epidermis, dermis, and subcutaneous tissue. The epidermis, the most superficial part of the skin, consists of several layers of keratinocytes, among which melanocytes (melanin-producing cells), Langerhans cells (part of the adaptive immune response), and Merkel cells (neuroendocrine cells with sensory function) are spread out. The basal layer of the epidermis, which is made up of proliferating, self-renewing keratinocytes, is attached to the basement membrane (BM) that separates the epidermis from the dermis. Keratinocytes in the basal layer undergo continuous asymmetric cell division, differentiate, and move continuously upwards, changing their phenotype to form stratum spinosum, followed by stratum granulosa to finally reach the outermost cornified layer (stratum comeum) [3].  
The dermis is composed of collagens, elastin, and glycosaminoglycans to form the extracellular matrix (ECM), which is produced by fibroblasts, the major cellular component of the dermis [4]. Two major populations of dermal fibroblasts (DFs) have been characterized according to their developmental origin, differences in gene expression, positioning/location in the dermis and function: (1) papillary dermis, which is located in proximity to the epidermis and (2) reticular dermis which is situated below [5, 6]. Recently, it has been recognized that cells with adipogenic potential form a third layer of the dermis, dermal white adipose tissue (dWAT), representing different fat depots from the subcutaneous layer [7, 8, 9, 10].  
The processes of keratinocyte proliferation, differentiation, and acquisition of progressively developed features are tightly controlled by transcription factors that regulate epidermal activities through downstream genes [3, 11, 12]. Increasing data suggest that epidermally expressed transcription factors act beyond host tissue, reaching dermal fibroblasts and dWAT in the dermal part of the skin [13, 14, 15]. AP-1, Ppar\(\beta\)/\(\delta\), HoxA3, HoxD3, Foxo1-3, Ovol1/2, and Wnt signalling, among others, have been shown to regulate intact and post-wounded skin [16, 17, 18, 19]. These transcription factors are expressed in the epidermis but have also been shown to be expressed in other locations. In this review, we focus on Foxn1, a transcription factor, whose expression is limited to epithelial cells of the thymus and the skin, and its function in skin physiology.
## 3 Research Methodology  
### Data Preprocessing  
Preprocessing of nighttime light data: The data were projected as equal-area conic projections and resampled to a resolution of 1 km. In order to ensure the accuracy and continuity of the data, the data were self-corrected by assigning the part of the luminance value less than 0 to 0, so that the highest luminance value of the data in the previous year was not higher than the highest luminance value of the following year, and the background noise was removed.  
POI data preprocessing: 5 km was selected as the search radius, and kernel density was processed for POI data.
# Bayesian Compressive Sensing Based Optimized Node Selection Scheme in Underwater Sensor Networks
## 3 Optimized Node Selection Scheme
### Scheme for Minimizing MSE of Estimation  
Let \(\widetilde{\mathbf{\Phi}}=\mathbf{\Phi}^{T}\mathbf{\Phi}\) be a diagonal matrix and \(\widetilde{\mathbf{\Phi}}\in\mathbb{R}^{N\times N}\) denote the sensor nodes' selection matrix. If the \(i\)th diagonal element is 1, it means that the \(i\)th sensor node is selected. If the \(i\)th diagonal element is 0, it means that the \(i\)th sensor node is not selected. It is well known that the MSE of Bayesian estimation is the trace of error covariance matrix. By setting the sensor nodes selection matrix \(\widetilde{\mathbf{\Phi}}\) as a variable and minimizing the MSE, the following optimization problem can be obtained:  
\[\begin{split}\min_{\widetilde{\mathbf{\Phi}}}& Trace( \sigma^{-2}\mathbf{\Psi}^{T}\widetilde{\mathbf{\Phi}}\mathbf{\Psi}+\mathbf{\Lambda})^{-1},\\ s.t.&\widetilde{\Phi}_{ii}\in\{0,1\},i=1,2,\cdots,N,\\ Trace(\widetilde{\mathbf{\Phi}})=M.\end{split} \tag{19}\]  
The first constraint condition decides whether the node is selected or not. The second constraint condition means that the number of selected nodes is \(M\). Notice that the proposed optimization problem is non-convex due to the non-convexity of the first constraint condition, and it is difficult to obtain the optimal solution of it. To overcome this difficulty, the constraint condition is relaxed:  
\[\begin{split}\min_{\widetilde{\mathbf{\Phi}}}& Trace( \sigma^{-2}\mathbf{\Psi}^{T}\widetilde{\mathbf{\Phi}}\mathbf{\Psi}+\mathbf{\Lambda})^{-1},\\ s.t.& 0\leq\widetilde{\Phi}_{ii}\leq 1,i=1,2,\cdots,N,\\ Trace(\widetilde{\mathbf{\Phi}})=M.\end{split} \tag{20}\]  
By introducing a slack variable \(\mathbf{Q}\) and applying the [PERSON]'s complement, the optimization problem can be rewritten as follows:  
\[\begin{split}\min_{\widetilde{\mathbf{\Phi}},\mathbf{Q}}& Trace (\mathbf{Q}),\\ s.t.&\left(\begin{array}{cc}\sigma^{-2}\mathbf{\Psi}^{T} \widetilde{\mathbf{\Phi}}\mathbf{\Psi}+\mathbf{\Lambda}&\mathbf{I}_{N}\\ \mathbf{I}_{N}&\mathbf{Q}\end{array}\right)\succeq 0,\\ &0\leq\widetilde{\Phi}_{ii}\leq 1,i=1,2,\cdots,N,\\ Trace(\widetilde{\mathbf{\Phi}})=M.\end{split} \tag{21}\]  
According to convex optimization theory, the optimization problem above is a semidefinite programming problem. A CVX tool cabinet in Matlab (R2017a, The MathWorks Inc., Natick, MA, USA) can be used to solve it effectively. There may be some other methods to solve it more efficiently, but it is beyond the scope of this paper.
# Evaluating the Efficacy of a Passive Exoskeleton for Enhancing Ergonomics in Manufacturing
## 2 Methods
### Survey Components and Analysis
#### 2.3.1 Borg Scale  
We used the Borg scale of 0-10 in this study, with \"0\" representing no perceived physical exertion (PPE), and \"10\" representing maximum PPE by the participant. The Borg CR10 scale is designed to rate muscle exertion and pain, which is particularly relevant in studies focusing on muscle fatigue and its effects on postural control. It provides a clear and direct measure of muscle exertion, which is central to understanding the impact of muscle fatigue on postural stability [38]. The Borg CR10 scale is validated for assessing perceived fatigue during work-related material handling tasks and can effectively discriminate between different levels of task difficulty. The applicability across various types of work and exertion levels has made it a broadly useful tool in occupational health studies to evaluate and understand work-related fatigue [39] and in studies with a specific focus on evaluating the ergonomic efficacy of exoskeletons in various occupational settings [8; 9; 29; 39; 40; 41].  
During the experimental trials, the participants evaluated their perceived discomfort and physical exertion using the Borg CR10 scale, where a score of 0 signifies no physical exertion, and a score of 10 represents the highest level of physical exertion experienced by the participants while performing the tasks [42]. The participants rated their discomfort in each body part, including neck, shoulders, upper body, chest, wrists, lower body, knees, ankles, feet, and elbows. The Borg scale is a reliable method for assessing user perception in experiments involving various tasks. This subjective evaluation provides direct insights into individual interactions with the exoskeleton and the intensity of physical effort. Lower ratings on the Borg scale after using the exoskeleton indicated its effectiveness in reducing strain and workload.
## 2 Materials and Methods  
### Monte Carlo Simulation  
Monte Carlo simulations are computational techniques that utilize random numbers to perform a large number of iterations, mimicking the behavior of complex systems [18]. These simulations are employed to estimate and manipulate mathematical calculations that would be infeasible to solve by hand. By generating random inputs, the Monte Carlo method provides a stochastic approach to tackle complex problems.  
In the field of medical physics, Monte Carlo simulations play a crucial role in tracking the transport of radiation. These simulations aid medical physicists in understanding and predicting dose calculations by considering the probabilities of interactions between radiation, tissue, and other molecules within the biological system [19]. The trajectory of a radiation particle within the system is referred to as a \"history\". The accuracy of the simulation increases with the number of histories simulated. However, due to computational limitations, a commonly employed practice is to use approximately 10 million histories in Monte Carlo simulations [20]. This balance between computational resources and accuracy ensures that meaningful insights can be obtained from the simulations.
## 2 Materials and Methods
### Statement of the Problem
#### 2.1.1 Data Exchange and Software Integration  
One such complexity involves establishing seamless data exchange between existing project management software. Construction projects typically involve a multitude of stakeholders and software platforms, each with its own data formats and workflows. Facilitating efficient data exchange and integration among these tools is crucial for leveraging existing project data and enabling streamlined workflows.  
In this study, the initial project plan was obtained from the Oracle Primavera P6 Enterprise Project Portfolio Management (EPPM) cloud solution in an extensible markup language (XML) format, which is suitable for automated data parsing. Subsequently, the analysis of spatial conflicts commenced with the generation of a 3D BIM representing the digital construction plan. The model elements were then spatially mapped to their corresponding activities within the Primavera P6 schedule. This integrated data allowed Synchro 4D to simulate and visualize the construction sequence over time, enabling a four-dimensional (4D) representation. This 4D simulation facilitated the identification of potential spatial conflicts, defined as situations where two or more BIM elements occupy the same physical space concurrently.  
These spatial incompatibilities need to be resolved to generate a new, conflict-free schedule. However, merging the identified spatial conflicts with the updated Primavera schedule is not a straightforward task. The integration of the spatial conflict information with the revised scheduling approach presents a significant challenge that must be overcome to ensure the successful and efficient execution of the construction project.
# Identifying Mis-Configured Author Profiles on Google Scholar Using Deep Learning
## 4 Comparison between Reliable Author Profiles and Mis-Configured Author Profiles
### Features of an Author Profile
#### 4.1.3 Features of Research Topics  
For a certain scholar, their research topics can be extracted from the title and publication venue (for argument's sake, we call the concatenation of paper title and publicationvenue as paper entry in this subsection) of each paper in the paper list. We first utilize Google Cloud Translation API [15] to translate all the paper entries into English. Then, we leverage Latent Dirichlet Allocation (LDA) [16], a generative statistical model that can learn the topic-specific word distribution, to get the probability distribution of each paper entry on different topics. LDA allocates each word in the paper entries with a latent variable choosing an underlying topic. Latent topic variables \(t\) has a probability distribution \(\theta_{d}=p(t|d)\) over paper entry \(d\), and word \(w\) is associated with a distribution \(\phi_{t}=p(w|t)\) over topic variable \(t\). Finally, we obtain the probability distribution of each paper entry on 30 topics. Limited by the time and interest, a scholar can only focus on a few related topics. We calculate some metrics representing the diversity of a scholar's paper list, which indicates the broadness of the scholar's research fields. We choose the symmetric cross-entropy divergence to represent the difference between two paper entries. For example, the difference between paper entry \(i\) and paper entry \(j\) is calculated as follows, where \(t\) means the topics obtained from LDA and \(p(t_{i})\) represents the probability of paper entry \(i\) to be related to topic \(t\):  
\[symCrossEntropy_{i,j}=\sum_{t}p(t_{i})log\frac{1}{p(t_{j})}+\sum_{t}p(t_{j}) log\frac{1}{p(t_{i})} \tag{3}\]  
We calculate the maximum, average, and standard deviation values of symmetric cross-entropy between each two paper entries of a scholar to characterize the diversity. \[maxCrossEntropy_{a}=\max_{i,j\in\mathbb{M}^{a}}symCrossEntropy_{i,j} \tag{4}\]  
\[meanCrossEntropy_{a}=\prod_{i,j\in\mathbb{M}^{a}}(symCrossEntropy_{i,j}) \tag{5}\]  
\[crossEntropyStd_{a}=\sigma_{i,j\in\mathbb{M}^{a}}(symCrossEntropy_{i,j}) \tag{6}\]  
Basically, papers of a single scholar can be divided into several clusters corresponding to different research projects. Therefore, we use non-negative matrix factorization (NMF) [17] to cluster the paper entries in the paper list according to their probability distribution on 30 topics and use silhouette score [18] to measure the distance between different clusters. We use the maximum silhouette score as the feature. Finally, the features we obtain from the scholars' author profiles are listed in Table 1, in which the meaning of the last two columns will be explained in detail in the next subsection. \(p\)-value is calculated by a two-sided \(t\)-test on two kinds of author profiles, and information gain (IG) is computed for feature selection.
## 3 The papers
### Problem area # 1 : sequence analysis
The rounding of LP-relaxation solutions is also used by the following papers to obtain an approximation algorithm (in the form of a Polynomial Time Approximation Scheme) for both the consensus problem and the farthest sequence problem:* [14] [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] (1999) [LP-APPR]
* [15] [PERSON], [PERSON] and [PERSON] (2002) [LP-APPR]
* [16] [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] (2003) [LP-APPR]  
Sequence assembly.Integer programming formulations have been used also in the context of _sequence assembly_, i.e., the process of reconstructing a long DNA sequence by the merging of short, overlapping, DNA fragments. * [17] [PERSON] and [PERSON] (2001) [ILP]  
focuses on the problem of correctly assembling the fragments when _repeats_ are present. Repeats are distinct DNA regions of identical content, and their presence baffles most assembly algorithms. In fact, fragments coming from distinct repeats, having perfect overlap, tend to be merged together into one single copy of the repeat. The approach followed here is to formulate the problem of correcting a wrong assembly, in the presence of repeats, as an integer linear programming problem, solved by Branch-and-Bound. * [18] [PERSON], [PERSON] and [PERSON] (2002) [ILP]  
also consider the problem of assembling DNA fragments to reconstruct an original target sequence. The focus in this paper is on the possibility that gaps are present, and so the reconstruction should not build only one sequence (called _contig_), but perhaps many. The objective is to reconstruct the smallest number of contigs such that each fragment is covered by one contig, and each contig is obtained by chaining some fragments, \(f_{1},\ldots,f_{n}\), such that the last \(l\) characters \(f_{i}\) (for some \(l\)) are identical to the first \(l\) characters of \(f_{i+1}\). This problem is formulated by integer programming, and solved by Branch-and-Cut. _Motif finding_ is still another type of sequence analysis problem. This problem consists in the study of a set of sequences, in order to identify recurring patterns (motifs) that are present -modulo small mutations-in all sequences. This problem is reduced to a graph problem, solved by integer programming, in the following works:  
* [19] [PERSON], [PERSON], and [PERSON] (2006) [RED,ILP]
* [20] [PERSON] and [PERSON] (2006) [RED,ILP]
## 3 Modeling of the VLC System and Its Components in the Indoor Environment  
The proposed indoor VLC system uses advanced techniques in signal optimization and reception to address the inherent challenges of NLoS scenarios. The system integrates an IRS composed of flat and convex mirrors strategically placed to enhance signal reflection, along with ADRs equipped with photodiodes arranged in hemispherical and pyramidal configurations. These ADRs incorporate innovative signal selection and combination mechanisms, including MRC and SBC, augmented by an RNN-based optimization algorithm to maximize the received SNR. The design and placement of the IRS, the geometric configuration of the ADRs, and the application of machine learning techniques are meticulously modeled to improve the overall system performance in terms of received power and the SNR.  
In this study, a single LED transmitter was used to simplify the analysis and focus on evaluating the impact of IRS placement and ADR configurations on received power and the SNR. However, in practical indoor environments, multiple LED transmitters are often deployed to provide uniform illumination and redundant communication links. The extension of this work to scenarios with multiple LEDs could offer additional insight into the performance of RNN-based IRS optimization. With multiple LEDs, the optimization problem becomes more complex, as the RNN must account for overlapping light distributions, interference, and increased spatial diversity [21; 22]. Future work will expand the framework to include multiple LED sources, leveraging the same RNN method ogy to dynamically optimize IRS positions and further enhance ADR performance in multi-transmitter VLC scenarios.  
This section details the mathematical models, algorithmic approaches, and simulation parameters that underpin the design and evaluation of the communication system in a 3D indoor environment. Figure 1 shows a basic schematic of a VLC system for indoor environments, assisted by an IRS composed of a mirror array. It can be seen that when the LoS component of the channel is blocked, the light wave is reflected by the IRS and reaches the receiver, which consists of an ADR with \(N\) PDs. Therefore, several components that would otherwise be NLoS are reflected in a controlled manner using the IRS. The IRS consists of an \(M\times N\) mirror array, which, for the purpose of this work, can be flat or convex.
# Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights
## 4 Future Research Prospects
### Suitable NB-IoT Industrial Propagation Model  
Since the industrial wireless environments are characterized by high reflectiveness, which causes multi-path effect and electromagnetic resonance, this phenomenon creates significant harshness on wireless communication in indoor industrial environments [116]. While every industry is unique in its setup, many works of literature have presented propagation models that attempt to adequately describe the characteristics of wireless propagation in industries [3; 107]. Understanding the wireless characteristics of an indoor industrial environment in terms of its stringent quality of service (QoS) requirements is necessary and makes it easy to plan and design wireless systems suitable for industrial applications. A comprehensive comparative analysis of channel models among low-powered and high-powered IoT (including NB-IoT) for industrial applications, and a generic propagation path-loss (PL) for 900 MHz application for the indoor industrial environment was presented as shown in Equation (1) [4].  
\[PL=61.65+24.9\log_{10}(d_{3D}/15),\ \sigma=7.35 \tag{1}\]  
Therefore, to the best of our knowledge in this survey, the wireless propagation of NB-IoT in an indoor industrial environment still requires further research that investigates the latency, reliability, and coverage characteristics of its propagation. Further research in this respect is already ongoing, and subsequent addressing of these challenges will give the industry confidence in the wireless connectivity of their devices and processes.  
The success of NB-IoT and the subsequent decentralization of industrial processes will allow rearrangement of the hierarchy of industrial flows, information and processes leading to a more productive environment. This is where we believe state-of-the-art distributed ledger technologies in harmony with edge computing will allow for rich analytical information that could unlock industrial processes that were never even considered before [10]. For instance, in addition to the further directions discussed above, blockchain-based offerings looking to disintermediate the business of outsourcing storage and computing will break the monopoly of expensive, wasteful and environmentally harmful dominance of corporate data-centers, bringing full control of processes to local industries. Research efforts in these areas that decentralize the data, analyze it using machine learning techniques and then use edge computing for real-time analytics will unlock several industrial applications.
# IoTDS: A One-Class Classification Approach to Detect Botnets in Internet of Things Devices
## 4 IoTDS: A Host-Based Botnet Detection System for IoT
### IoTDS Architecture
#### 4.3.1 IoTDS Agent  
The IoTDS Agent is installed in the IoT devices, and it is responsible for capturing the device's data, analysing it, and sending alerts to the Management Console when necessary. This element has the following modules: Data Collection Module, Analysis Module, Communication Module, and Countermeasure Module. The Data Collection Module collects the following descriptive data about the system behaviour: CPU and memory utilisation, number of running tasks, and CPU temperature. This module is used in both the Model Induction and Continuous Analysis phases.  
The data collected by this module is processed to be used by the Analysis Module or sent by the Communication Module to the IoTDS Management Console. How data is processed depends on the purpose of its collection. During the Model Induction Phase, the data is collected each second during an interval of \(y\) seconds, stored, and then sent to the Management Console, which will build the model. In the Continuous Analysis phase, this data is consumed by the Analysis Module to be classified.  
The Analysis Module uses a model sent by the IoTDS Management Console to analyse data collected in real time and determine whether an anomaly is occurring. The Communication Module is responsible for the communication between the Agent and the Management Console, sending collected data and alerts, as well as establishing a secure channel for this communication. Finally, the Countermeasure Module is responsible for rebooting the system when it receives the system's reboot command.