esa-sceva/satcom-chunk-collection huggingface dataset:
# Industry 4.0 Roadmap: Implementation for Small and Medium-sized Enterprises
## 4 Discussion  
### Roadmap Comparison  
As described earlier in Section 2.2, the proposed roadmap aims to simplify access to Industry 4.0 technologies for SMEs. The main differences between the roadmap proposed within this paper and roadmaps presented in previous research works are its focus on SMEs and the practical viewpoint. To highlight this extended approach, the following three criteria will be used in a weighted decision matrix, as shown in Table 7:  
* Task-oriented: SMEs do not have the same structures as large enterprises and, therefore, the roadmap must be task-oriented rather than team-oriented. A roadmap that focuses on the completion of particular tasks makes it possible to create a roadmap with a single headcount, without involving several departments. This reduces the resources required to create the roadmap.
* Investment considerations: the roadmap will take into account the monetary limitations of SMEs for implementation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Step Number** & **Step Description** & **Application for This Solution** \\ \hline
0 & Identify bottlenecks & Work orders are often picked incorrectly, working processes are inefficient, the picking time is greatly increasingly and the increasing number of materials in the warehouse has visibility worsened. \\ \hline
1 & Develop a strategy & Based on the number and complexity of the work orders to be picked, 10 lights and a controller are needed to improve the picking process in the warehouse. \\ \hline
2 & Ideas and Prototypes & The most-picked work orders are selected. \\ \hline
3 & Connect/plug-in devices & Pick-to-lights were installed, connected to the controller, a dashboard was set, and the warehouseman was made ready to analyze the performance of the picking process based on the lights. Some dry runs were performed until the system ran stably. \\ \hline
4 & Analyze & The KPI was set to control the evolution of the picking time and analyze the results, asking the question ”Am I winning or am I losing”. \\ \hline
5 & Go Live & Once the final quantitative results are available, the tool is ready to go live and to be extended to other warehouse areas. \\ \hline \hline \end{tabular}
\end{table}
Table 6: Pick-to-light—implementation process.  
* Practical implementation: the roadmap should not conclude with a theoretical development of the strategy; instead, it should continue with its deployment in practice and the subsequent measurement of the results through KPIs.  
The fulfillment of the abovementioned criteria is represented in Table 7 (green circle: fulfilled, red circle: not fulfilled).
## 4 Channel Selection Algorithm Based on Multi-Armed-Bandit Algorithm
### Learning Rules of the Multi-Armed-Bandit Algorithms
#### 4.2.3 Tug-of-War Dynamics-Based Algorithm  
TOW dynamics is a reinforcement learning algorithm that rules reward estimates \(Q_{k}\) obtained by (5):  
\[Q_{k}(t)=N_{k}(t)-(1+\omega)L_{k}(t), \tag{5}\]  
where \(L_{k}(t)\) is the number of times channel \(k\) is selected and transmitted without receiving ACKs until time \(t\), and \(\omega\) is a weight parameter. Furthermore, (5) can be expressed as follows:  
\[Q_{k}(t)=Q_{k}(t-1)+\Delta Q_{k}(t), \tag{6}\]  
where \(\Delta Q_{k}\) follows the following rule.  
\[\Delta Q_{k}(t)=\begin{cases}+1&k=k^{*}\text{and if receiving ACK,}\\ -\omega&k=k^{*}\text{ and if not receiving ACK,}\\ 0&k\
eq k^{*}.\end{cases} \tag{7}\]  
The displacement \(X_{k}(t)\) at time \(t\), which is used for the decision of the TOW, is expressed using \(Q_{k}(t)\) in (5) and (6) as follows:  
\[X_{k}(t)=Q_{k}(t-1)-\frac{1}{K-1}\sum_{k^{\prime}\
eq k}^{K}Q_{k}(t) \tag{8}\]Reference [31] describes the way to obtain the optimal weight parameter \(\omega\). Let us consider the expected value of the reward estimate of machine \(k\) from Equation (9).  
\[E[Q_{k}(t)]=\{p_{k}(t)-\omega(1-p_{k}(t))\}N_{k}(t). \tag{9}\]  
This paper describes the highest and second highest reward probabilities of the machine at time \(t\) as \(p_{1\text{st}}(t)\) and \(p_{2\text{nd}}(t)\), respectively. To ensure that the machine with the highest reward probability is always selected, both of the following two expressions should be satisfied:  
\[p_{1\text{st}}(t)-\omega(1-p_{1\text{st}}(t))>0, \tag{10}\]  
\[p_{2\text{nd}}(t)-\omega(1-p_{2\text{nd}}(t))<0. \tag{11}\]  
These equations can be written as follows:  
\[p_{1\text{st}}(t)<\frac{\omega}{1+\omega}<p_{2\text{nd}}(t). \tag{12}\]  
It can be confirmed that (13) satisfies (12), and the optimal \(\omega\) is derived.  
\[\frac{\omega}{1+\omega}=\frac{p_{1\text{st}}(t)+p_{2\text{nd}}(t)}{2}, \tag{13}\]  
\[\omega=\frac{p_{1\text{st}}(t)+p_{2\text{nd}}(t)}{2-(p_{1\text{st}}(t)+p_{2 \text{nd}}(t))}. \tag{14}\]  
The channel to be selected at time \(t+1\) is the channel \(k^{*}\), corresponding to Equation (15).  
\[k^{*}=\text{arg max}_{k\in\mathbb{K}}X_{k}(t+1). \tag{15}\]  
This algorithm has a small calculation cost compared with the champion algorithm UCB, which uses a complicated calculation methodology, such as the square-root calculation.
## 3 Results and Discussion
### Weather Real-Time Satellite Data  
The sensors that were implemented in the weather IoT terminal are temperature, relative humidity and air quality index PM2.5 and PM10. The collected data were sent to DEWASAT-1, and then the satellite downloaded the data using ultra-high frequency (UHF). The real-time satellite data were plotted and illustrated in the below Figures 23 and 24. The figures prove that the terminal performed perfectly, and it was able to send data to the satellite using the CSS modulation. Additionally, they show that the data were gathered continuously and were successfully collected by the satellite. Moreover, there were some days where no data were downloaded from the satellite to the ground station, probably because of the low elevation angle between the antenna and the GS. Therefore, if the satellite was launched in a lower inclination orbit, more data would be collected on a daily basis.  
Figure 23: Weather satellite data: (**a**) temperature, (**b**) relative humidity.  
Figure 22: Airtime for different spreading factors.
## 2 Materials and Methods  
### Bacterial Strains and Media  
Based on the literature regarding EE2 biodegradation, we decided to encapsulate two bacterial cultures that exhibit two biodegradation mechanisms: Metabolic and co-metabolic. An EE2-degrading _Rhodococcus zopfii_ (ATCC 51349) strain was purchased from the ATCC culture bank, and the bacterial strain _Pseudomonas putida_ F1 (ATCC 700007) was kindly provided by Prof. [PERSON] from the Technion-Israel Institute of Technology. The experimental media were composed of minimal salt medium (MSM) as described in [PERSON] et al. (2011), MSM enriched with 2% lysogeny broth (LB) (Sigma, Israel) and secondary effluents sterilized by autoclave. Secondary effluents were taken from a DWWTP (Betaniya, Israel), filtrated (0.45 \(\upmu\)m membrane, MF Millipore, Merck) and refrigerated for three days until the experiment. Analytical grade EE2 was purchased from Sigma-Aldrich (Israel). An EE2 stock solution (1000 mg/L) was prepared in methanol and diluted directly into the experimental flasks containing the different experimental media for achieving the required EE2 concentration. The methanol concentration within the test medium was negligible.
# iCaps-Dfake: An Integrated Capsule-Based Model for Deepfake Image and Video Detection
## 3 Materials and Methods
### Data Preparation  
Though some researchers tend to use Multi-task Cascaded Convolutional Network (MTCNN) [63] to detect faces, as did the winners of the Facebook Deepfake Detection Challenge [55]. Experiments have shown that MTCNN produced many false positive examples with more than 90% probability of being a face thus requiring an extra step for cleaning the data before training the network which is not only time consuming but also performance degrading thus, YOLO v3 [64] is chosen for more quality enhancements. Figure 2 shows samples of the detections that MTCNN wrongfully considered faces with high confidence in crowded frames.  
The first stage of iCaps-Dfake is data preparation where faces are detected, for our network to train on, from the input video using YOLO v3. In order to down sample from nearly 2.2M training frames in the dataset, a sliding window scheme for keyframe selection is followed with a window width of one. As shown in Figure 3, the first frame \(F_{0}\) is selected as a start then the window is slid by N frames to take the next. In each selected frame, the largest face is detected.  
Figure 1: iCaps-Dfake Proposed Model Diagram.  
Figure 2: Examples of multi-task Cascaded Convolutional Network (MTCNN) false face detections in crowded frames.  
In order to detect the face, YOLO v3 performs a single forward propagation pass through which a CNN is applied once to the selected frame to deduce face scores. This is achieved by splitting the frame into a grid of size M \(\times\) M such that the confidence score is calculated for B bounding boxes contained in each cell thus reflecting the certainty of having a face at each's center. Each box is represented by five measurements (x, y, w, h and confidence) modelling the center of the face as (x, y) coordinates along with the width, height and confidence showing the probability of a box to include a face.
## 8 The Immune Response and Its Interaction with the Gut Microbiota
### Gut Microbiota and Systemic Immune Regulation  
The influence of the gut microbiome extends beyond local mucosal immunity, affecting systemic immune responses mediated by both innate and adaptive immune cells. One mechanism involves the secretion of microbial-derived soluble factors into the circulation, which subsequently influences immune cell activation in peripheral tissues [83]. Additionally, resident immune cells in extraintestinal organs possess the ability to recognize and respond to microbial metabolites. The absence of these microbiota-derived signaling molecules leads to alterations in systemic immune function, increasing the host's vulnerability to infections [83].  
One of the most well-characterized mechanisms through which the gut microbiota modulates systemic immunity is its impact on the differentiation of adaptive immune cells, particularly T lymphocytes. Studies have demonstrated that the gut microbiota influences the balance among T-helper (Th) subsets, including Th1, Th2, and Th17 cells, as well as the induction of regulatory T cells [84; 85; 86]. SCFAs, particularly butyrate, facilitate the differentiation of peripherally induced Treg cells, thereby suppressing systemic inflammatory responses [87]. Additionally, pentanoate, another microbial metabolite, has been shown to regulate immune cell metabolism by promoting regulatory B cell differentiation while inhibiting Th17 cell expansion. This mechanism is particularly relevant in the pathogenesis of autoimmune and inflammatory disorders [88].  
Other microbial-derived metabolites further shape adaptive immune responses. For instance, ATP produced by the gut microbiota stimulates Th17 cell proliferation, whereas tryptophan metabolites enhance intraepithelial CD4+CD8\(\alpha\alpha\)+ T cell populations. Additionally, bacterial polysaccharides have been identified as key activators of regulatory T cells, thereby dampening excessive inflammatory responses [89; 90].  
The ability of the gut microbiome to regulate both local and systemic immune responses has profound implications for host defense, immune homeostasis, and disease susceptibility. As research continues to uncover the intricate interactions between the microbiota and the immune system, a more comprehensive understanding of these mechanisms will inform therapeutic strategies aimed at modulating the gut microbiome to enhance immune resilience and mitigate inflammatory diseases. Interestingly, microbiota have the potential to bolster the immune response in patients with chronic HCV by stimulating an increase in the counts of CD56+ natural killer (NK) cells and CD3+ T cells. The augmentation of the cytotoxic function of NK cells may play a crucial role in suppressing HCV replication [91].  
Figures 1 and 2 provide an overview of the primary microbiota-immune system interactions.
## 3 Preliminaries
### Evaluation Standards  
In this section, we introduce the coverage efficiency of the event set as well as the network coverage in order to measure the performance of the proposed method.  
**Definition 2**.: _Coverage entropy of the event set [19]. This measures the degree of coverage uniformity, and can be calculated as_  
\[H_{A}\left(E\right)=\sum\limits_{e_{i}\in E}{D^{\prime}}_{A}\left(e_{i}\right) \lg\frac{1}{{D^{\prime}}_{A}\left(e_{i}\right)}, \tag{5}\]  
_where the normalized coverage degree \({D^{\prime}}_{A}\left(e_{i}\right)\) is_  
\[{D^{\prime}}_{A}\left(e_{i}\right)=\frac{D_{A}\left(e_{i}\right)}{\sum \limits_{e_{j}\in E}D_{A}\left(e_{j}\right)}. \tag{6}\]  
It is well known that the coverage entropy of event set \(H_{A}\left(E\right)\) reaches its maximum value \(\lg m\) only when \({D^{\prime}}_{A}\left(e_{i}\right)=\frac{1}{m}\) (for \(i=1,...,m\)) has equal probability.  
**Definition 3**.: _Network coverage \(C_{v}\) is_  
\[C_{v}=\frac{\tilde{f_{e}}}{t_{e}}, \tag{7}\]  
_where \(\tilde{f_{e}}\) is the number of the events covered by nodes and \(t_{e}\) is the total number of the events._  
**Definition 4**.: _The coverage efficiency of the event set is [19]_  
\[\eta\left(E\right)=\alpha\frac{H_{A}\left(E\right)}{\lg m}+\beta\frac{\tilde {n}}{n}, \tag{8}\]  
_where \(\alpha,\beta\in\left[0,1\right]\), \(\alpha+\beta=1\) and \(\tilde{n}\) is the number of events covered by nodes._  
From Definition 4, we can see that when all nodes cover events, that is, \(n=\tilde{n}\), and simultaneously the coverage entropy \(H_{A}\left(E\right)\) reaches a maximum value of \(\lg m\), \(\eta\left(E\right)\) will reach its maximum value of 1. Putting it simply, the main goal of underwater node deployment is to place nodes so as to achieve the maximum value of \(\eta\left(E\right)\).
###### Abstract
To capture the impact of static wayfinding instructions on user route choice behaviors, this study adopts the assumption that users choose paths according to the GNL principle and developed a novel space-saving guidance information storage structure integrated with the GNL-SUE mathematical program. This model has the following advantages over previous analyses of static wayfinding instructions: First, a small matrix is sufficient for storing static wayfinding instructions information guiding one destination, which enhances the model's applicability. Second, parameters in the GNL-SUE model considering static wayfinding instructions can be efficiently obtained through matrix calculations. Due to the complexity of the GNL-SUE program, heuristic algorithms are widely used as solution algorithms, with disaggregate simplicial decomposition (DSD) proving capable for large-scale real-world networks [19; 20; 21]. This study applied newly developed step size finding methods to enhance the convergence performance of the DSD algorithm. Unlike the traditional method of successive averages (MSA) and the predetermined method, the method of successive weighted averages (MSWA) and the self-regulated averaging (SRA) method have more flexible principles for determining step size and can converge in fewer iterations [22]. Different step size calculation methods are compared in this study to reveal varying convergence rates. Finally, changes in travel time costs on network links after considering static wayfinding instructions are presented, demonstrating the potential impact on traffic flow under different scenarios. The results can assist decision-makers in evaluating the benefits of placing static wayfinding instructions at specific locations and selecting efficient guidance information. The main contributions of this paper are as follows: First, this study introduces a novel and efficient storage structure for static wayfinding instructions, which facilitates the analysis of their impact on network flow. Second, a GNL-based SUE model for static wayfinding instructions is established by integrating the proposed storage structure with a mathematical programming framework. Third, numerical studies are conducted to validate the efficiency of step size determination methods and further reveal the potential effects of static wayfinding instructions at nodes in networks guiding certain destinations. The remainder of this paper is structured as follows: Section 2 provides a brief overview of related work. Section 3 outlines the theoretical framework of the GNL-basedSUE model, considering static wayfinding instructions. Section 4 details the solution algorithm employed to solve the proposed model. Section 5 presents numerical analyses and discusses the results of applying the proposed model to a real-world transportation network. Finally, Section 6 concludes with the main findings and outlines potential directions for future research.
## 4 Data Traffic Control Scheme for Periodic Data Collection
Our scheme incorporates multiple groups in a single group as shown in Figure 4. A central node transmits at most \(N_{all}\) queries every \(\Delta t_{s}\) according to a sequence of nodes in a QF. This polling behavior is as same as a monocycle polling scheduling. We consider that a central node controls transmitting timing of a polling query so that down stream traffic from a central node is less than pre-assigned downlink traffic of an IWSN protocol. Then, a scheduler provides a SF that is a multicycle polling schedule during the least common multiple of collection cycle for all groups as shown in Figure 5. In a SF, each QF may assign a different sequence of all nodes. For example, a sequence of all nodes in \(QF_{1}\) may be assigned in ascending order and one in \(QF_{2}\) may be assigned in descending order.
# A Distributed RF Threat Sensing Architecture
## 3 Proposed Approach  
### Low-Cost RF Sensor Design Using a Logarithmic Amplifier  
In this paper, we propose the design and operation of a low-cost RF jamming detection sensor, incorporating an RF environment characterization algorithm, capable of operating in multiple RF bands for either RF jamming detection or interference monitoring, including potential drone detection for short distances. Monitoring and further processing is performed, centralized through a SOC, allowing also further centralized machine learning processing capabilities. The initial design comprises two modules for demonstration purposes, one for monitoring 2.4 GHz band (2340 MHz-2530 MHz) and the second for 5.8 GHz band (5490 MHz-5895 MHz), using a logarithmic amplifier with a high dynamic range. The logarithmic amplifier's sensitivity level is further enhanced through the use of specific RF band directional antennas, a low-noise amplifier, and RF band selective filtering, for the purpose of filtering the Signal band of Interest (SoI). In this way, lower-level RF signals are captured, aiming to provide more sensitive and longer distance interference monitoring.  
Both aforementioned sensors on the two RF bands were designed based on the AD8318, which is a 9-stage demodulating logarithmic amplifier that provides RF measurement and power amplifier control functions. AD8318 has an input frequency range that extends up to 8 GHz with a 60 dB dynamic range. The covered frequency and dynamic range characteristics can fulfill the frequency range monitoring criteria, covering several widely used wireless networks, as well as the typical frequencies used in drone operations [45]. Additional module sensors for other frequency bands of interest within the 8 GHz frequency range can be added to the same overall RF sensing architecture.  
Figure 1 provides a basic block diagram of the developed RF sensor system used during the environment RF profiling and field tests.  
Distributed communication and control of module sensors is achieved by a WT32-ETH01 micro-controller that supports Ethernet, making it well-suited for the networking functions of embedded devices, as per Figure 2.  
Figure 1: RF sensor connectivity outline.  
An algorithmic code base was implemented in the device side, providing functionality to parameterize and calibrate the sensor and perform real-time RF monitoring, alarm push, and recording in the cloud, through the use of the MQTT (Message Queuing Telemetry Transport) protocol. More specifically, the code provides the following functionality:  
* Connecting the device to the module manager.
* Accessing the network and configuring the module IP for SOC interconnection.
* Monitoring the module status.
* Configuring the module parameters (general information, configuring the system clock and the reporting server, and calibrating the modules of the RF sensor).
* Setting up active RF measurement and RF jamming monitoring.
## 3 Simulation Parameters and Scenarios
In other words, they serve as reliable models that accurately represent the behavior of the antennas in real-life scenarios. The antenna pattern of the MSS is based on Recommendation ITU-R S.672, which is stated in the ITU filings of the system [13]. The antenna patterns are presented in Figure 4. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Rural-eMBB-s** \\ \hline Orbit height & 550 km \\ Inclination angle & 53 deg \\ Number of planes & 72 \\ Number of satellites per plane & 22 \\ EIRP spectral density & \(-2.3\) dBW/Hz \\ Satellite max antenna gain & 38 dBi \\ Spectral power density & \(-40.3\) dBW/Hz \\ Max input power & 19.7 dBW/MHz \\ Out-of-band attenuation & 56 dB \\ Out-of-band power & \(-36.3\) dBW/MHz \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Simulation parameters of the Starlink system. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Orbit height & 650–1500 km \\ Inclination angle & 82–96 deg \\ Channel bandwidth & 25 KHz \\ User terminal antenna pattern & Omni \\ EIRP of user terminal & 5–10 dBW \\ Satellite antenna gain & 20 dBi \\ Gateway antenna gain & 48 dBi \\ Modulation & QPSK \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation parameters of the MSS system.
# Handling Skewed Data: A Comparison of Two Popular Methods
###### Abstract  
Scientists in biomedical and psychosocial research need to deal with skewed data all the time. In the case of comparing means from two groups, the log transformation is commonly used as a traditional technique to normalize skewed data before utilizing the two-group \(t\)-test. An alternative method that does not assume normality is the generalized linear model (GLM) combined with an appropriate link function. In this work, the two techniques are compared using Monte Carlo simulations; each consists of many iterations that simulate two groups of skewed data for three different sampling distributions: gamma, exponential, and beta. Afterward, both methods are compared regarding Type I error rates, power rates and the estimates of the mean differences. We conclude that the \(t\)-test with log transformation had superior performance over the GLM method for any data that are not normal and follow beta or gamma distributions. Alternatively, for exponentially distributed data, the GLM method had superior performance over the \(t\)-test with log transformation.  
biostatistics; GLM; skewed data; \(t\)-test; Type I error; power simulation; Monte Carlo +
Footnote †: journal: Applied  
_Article_  
Handling Skewed Data: A Comparison of Two Popular Methods