esa-sceva/satcom-chunk-collection huggingface dataset:
# AI, Optimization, and Human Values: Mapping the Intellectual Landscape of Industry 4.0 to 5.0
## 4 Theoretical Perspectives
### Understanding Industry 4.0  
Industry 4.0, commonly referred to as the fourth Industrial Revolution, marks the convergence of digital, physical, and biological technologies. Introduced during the 2011 Hannover Fair as part of a German government initiative, it aimed to enhance manufacturing competitiveness through advanced digitalization. However, its conceptual roots trace back to late 20 th-century developments in embedded systems and networked communication.  
[PERSON] and [PERSON] [3] (p. 307) described Industry 4.0 as \"an umbrella term used to describe a group of connected technological advances that provide a foundation for increased digitization of the business environment\". Similarly, [PERSON] et al. [10] (p. 145) emphasized its integration of machines, sensors, and software to enhance prediction, planning, and control.  
* Key distinctions from previous revolutions comprehend [11]:
* Real-time responsiveness;
* Human-machine interaction;
* Cyber-physical integration;
* Data-driven decision-making.  
These advances enabled intelligent, adaptive production systems capable of self-regulation, paving the way for autonomous and interconnected manufacturing ecosystems.  
Key Components  
Several foundational technologies enable the functionality of Industry 4.0:  
* Cyber-Physical Systems (CPSs): As the structural backbone of Industry 4.0, CPSs tightly integrate computation, networking, and physical processes through sensors and actuators [12]. They enable adaptive control, real-time feedback, and self-optimization [3].
* Internet of Things (IoT): IoT connects devices, machines, and systems across industrial environments, enabling continuous data exchange [2]. When coupled with analytics and AI, it supports predictive maintenance and dynamic process optimization [13].
* Internet of Services (IoS): IoS promotes a shift toward service-based models like product-as-a-service. It leverages Service-Oriented Architecture (SOA) and cloud computing to deliver manufacturing functions on demand [14].
* Smart Factory: Smart factories embody Industry 4.0 principles through flexible, interconnected systems capable of autonomous operation [15]. They integrate CPSs, IoT, and AI to create self-organizing production environments, enhanced by tools like digital twins and real-time scheduling algorithms [3; 16; 17].
## 3 Results
### Ionospheric Storms--Morphology and Background Mechanisms
* Electric fields: in this case, the height increase is caused by an ExB drift that is perpendicular to the inclined geomagnetic field, so it also leads to an uplifting of the ionosphere. Two major disturbance electric fields are: a. Disturbance dynamo electric fields: the disturbed thermospheric winds drive meridional winds to generate electric fields opposed to their quiet-time patterns. b. Penetration of electric fields originated from the solar wind/magnetosphere: high-latitude convection electric fields penetrate into the low-latitude ionosphere (a phenomenon known as prompt penetration electric fields, PPEFs) to produce changes known as the super-fountain effect [29]. iii. Advection of high-density plasma in combination to plasma uplift: nightside plasma is carried across the sunset terminator to draw out plumes of storm-enhanced density (SED) that stretches from the dusk sector to the noontime cusp ionosphere [30]. iv. Downward plasma flux from the plasmasphere. Most probably, all these mechanisms may be considered important, with relevant contributions to the occurrence of positive ionospheric storms. However, vertical plasma drifts can also explain the observed increase in layer height that usually accompanies positive effects, and in this respect, they are claimed to be the dominant ones [21]. Among them, it has been suggested that thermodynamics may be more important for disturbances at the peak electron density height, while electrodynamics may more efficiently control the disturbances above [23] with the penetration fields, to play a significant role in the development of positive storms during the initial phase of the storm, in the afternoon sector, and at middle-to-low and low latitudes [22]. In any case, the relative importance of equatorward winds and electric fields in the generation of ionization increases, especially in the middle latitudes, remains among present challenges, and is not yet fully understood.
## 2 Inverse Design of Silicon Photonics with Iterative Optimization Algorithms
### Optimization of QR-Code like Structures  
A QR-code like structure, where regular shapes like circles or rectangles are selectively etched on a periodic 2-D surface, is also an important approach for manipulating light propagation in waveguides. Different from empirical structure-based design, QR-code like  
Figure 2: Schematic diagram of GA for the design of PBS based on directional coupler. (**a**) Operating principle of GA. (**b**) Initial structure of PBS and abstracted parameters. (**c**) Final structure of optimized PBS.  
structure with hundreds of DOF is a more general approach for different problems. In a QR-code like structure, designers only have to define the pitch and size of etched holes as well as the footprint of design area. For each hole, to be etched or not is determined automatically to achieve better FOM. Using a compact QR-code like structure, many different functional components like power splitter [41; 45], polarization rotator [33; 42; 44], PBS [40], WDM [42], MDM [34; 39], crossing [66], GC [67] and photonic crystal [68] have been designed.  
DBS, a brute-force searching algorithm, initially proposed by [PERSON] et al. for the synthesis of digital holograms [69], has been proven to be efficient for the design of devices with QR-code structure. [PERSON] et al. introduced DBS to design a very compact integrated PBS on SOI platform [40], as shown in Figure 3. They defined the size of design area to be 2.4 \(\times\) 2.4 \(\upmu\)m\({}^{2}\) with each hole size to be 0.12 \(\times\) 0.12 \(\upmu\)m\({}^{2}\). There are 400 rectangular pixels to determine whether to etch or not. During each iteration, one pixel in the design area is reversed and the FOM (average transmission efficiency of TE and TM modes at the targeted output ports) is calculated and compared with the FOM for last iteration. If the new FOM is larger than the old one, the new geometry with reversed pixel is past to the next generation, otherwise, the old geometry is kept. The above procedure is repeated until the FOM is large enough to be acceptable. After about 140 h of optimization, the average transmission efficiency of optimized PBS is higher than 70% and its 1 dB bandwidth is 83 nm.  
In addition to DBS, heuristic algorithms like GA and PSO have also been applied for the design of integrated silicon photonic devices based on QR-code like structure. Different from DBS, which updates one pixel at each iteration, heuristic algorithms code all the pixels as an individual and update them together during each iteration, hence the total number of iterations could be reduced.
## 2 Materials and Methods
### Adversarial Attacks
#### 2.3.1 Jacobian-Based Saliency Map Attack (JSMA)  
Ref. [3] presented a targeted JSMA that utilizes the impact of forward derivatives to craft adversarial examples with minimally perturbed features. Their attack highlighted the benefit of directly computing the mapping between input and output. The number of perturbed features can drive extreme variations in the neural network's output. The neural network trains this forward derivative, called the Jacobian matrix of function \(F\). The Jacobian matrix used to construct the saliency map of the most important features should be included in the perturbation to evade the neural network. In this approach, no knowledge of training data is needed; only a known network architecture is required. The approach scenario consisted of three main steps.  
1. **Computation of the forward derivative \(\bigtriangledown F(x^{*})\) for the original sample \(x\)**, as given by: \[\bigtriangledown F(X)=\frac{\partial F(X)}{\partial X}=\left[\frac{\partial F _{j}(X)}{\partial x_{i}}\right]_{i\in 1\dots M,j\in 1\dots N}\] (4) where \(\frac{\partial F_{j}(X)}{\partial x_{i}}\) is obtained by: \[\frac{\partial F_{j}(X)}{\partial x_{i}}=\left(W_{n+1,j}\cdot\frac{\partial H _{n}}{\partial x_{i}}\right)\times\frac{\partial f_{n+1,j}}{\partial x_{i}}(W_ {n+1,j}\cdot H_{n}+b_{n+1,j})\] (5) where \(W\) is the weights vector, \(H\) is the output vector of the hidden layers, and \(b\) is the bias.
2. **Construction of the saliency map.** We used an increasing saliency map, that is, as the map increases, so does the probability of the target label (\(y^{*}\)), while the probabilities of other labels decrease.
3. **Perturbation of the most salient pixels/features with \(\theta\)**. The original paper used \(\theta=1\), whereas we set \(\theta=0.7\).  
These steps were repeated until we reached the maximum Y or successfully misled the model. The JSMA approach is described in Algorithm 1.  
```
1:Input:\(x,y^{*},F,\theta,\text{Y}x\gets x^{*}\)
2:\(\Gamma=1...|x|\)
3:while\(F(x^{*})\
eq y\) and \(||\delta_{x}\leq\text{Y}||\)do
4: Compute forward derivative \(\bigtriangledown F(x^{*})\)
5:\(S=\text{Saliency Map}(\bigtriangledown F(x^{*}),y^{*},\Gamma)\)
6: Modify \(x^{*}_{\text{max}}\) by \(\theta\)\(s.t\)\(i_{max}=argmax_{i}S(x,y^{*})[i]\)
7:\(\delta_{x}\gets x^{*}-x\)
8:endwhile
9:return\(x^{*}\)
```  
**Algorithm 1** Crafting the adversarial example for malware detection with the JSMA approach
## 3 Results and Discussion  
### Characterisation of the Paper Substrates  
Table 1 presents the results of smoothness and calliper measured on both paper substrates. One can see that UT paper has higher smoothness in comparison to CB paper.  
It amounts 11.94 s, and for CB paper it amounts 0.68 s. According to these results, one can see that CB paper has significantly more expressed surface irregularities. Calliper values show that UT paper has higher thickness (0.617 mm) than CB paper (0.293 mm).  
Figure 1 presents microscopic images of used substrates UT (a) and CB (b) papers, under magnification of 200\(\times\). One can see that papers have similar surface structure with intertwined fibres arranged in different directions forming a complex structure of the surface.  
Table 2 presents the results of measured roughness parameters \(R_{a}\), \(R_{z}\), and \(R_{max}\). It can be observed that CB paper has rougher surface in comparison to UT paper. All the measured parameters have more expressed surface deviations on CB paper.  
The \(R_{a}\) parameter, which describes arithmetic mean deviation of the profile, has three times the value (6.940 \(\upmu\)m) of the UT sample (2.392 \(\upmu\)m). \(R_{z}\) and \(R_{max}\) parameters have significantly higher values as well, indicating the more uneven and irregular structure of CB paper. \(R_{z}\) parameter, which defines the mean height of unevenness in ten points, equals 12.425 \(\upmu\)m on UT paper and 36.642 \(\upmu\)m on CB. \(R_{max}\) parameter, indicating the largest single roughness depth of the surface, amounts to 16.539 \(\upmu\)m on UT paper and 45.022 \(\upmu\)m on CB. Surface profiles, presented in Figure 2, indicate the difference in surface structures of the observed papers. The deviation of the surface structure from the baseline profile level is expressed more on the CB paper, it amounts to between \(-\)20 \(\upmu\)m and 20 \(\upmu\)m. The UT paper has a relative deviation from the baseline profile level between \(-\)10 \(\upmu\)m and 10 \(\upmu\)m. The results of measurements of the surface roughness parameters correspond to the smoothness results, presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Paper Property** & **Standard** & **Measuring Unit** & **UT** & **CB** \\ \hline Smoothness (Bekk method) & ISO 5627 & s & 11.94 & 0.68 \\ \hline Calliper & ISO 534:2011 & mm & 0.617 & 0.293 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Smoothness and calliper of paper substrates.  
Figure 1: Microscopic images of the substrates: (**a**) UT; (**b**) CB (mag. 200\(\times\)).
## 2 Green Routing Algorithms in WSNs
### Setting Special Node-Based Routing Algorithms
#### 2.1.1 Hierarchical Node-Based Routing Schemes  
In hierarchical node-based routing algorithms, high hierarchical nodes act as an intermediate layer between low hierarchical nodes and a sink node, which is responsible for receiving, fusing, and delivering data sent from low-level nodes to the sink. By increasing the communication task, high hierarchical nodes force low hierarchical nodes to reduce their energy consumption for forwarding data.  
LEACH [51] is a classic routing algorithm based on hierarchical nodes. The system model is shown in Figure 2. The nodes in LEACH are divided into cluster head (CHs) and cluster members according to the roles they play in WSNs. Cluster member nodes collect the data of their surrounding area and send them to the CH in the cluster. Then, the CH processes the data and transmits them to the sink.  
LEACH balances energy consumption in the cluster by adaptively electing appropriate CHs. However, direct communication between CHs and the sink leads to unbalanced energy consumption between clusters. The reason for this is that energy consumption increases dramatically when the communication distance increases. Hence, CHs far away  
Figure 1: The classification of traditional green routing algorithms in WSNs.  
Figure 2: The system model of LEACH [51].  
from the sink will consume more energy, which results in load imbalance among clusters and prevents the LEACH protocol from being applied to large-scale WSNs.  
To overcome this defect, an energy-efficient concentric clustering scheme (EECCS) protocol was proposed [52], which constructs a multi-hop path among CHs according to distance from the sink node. The path eases the imbalance of energy load among clusters. EECCS selects cluster heads based on node weight value \(w\):  
\[w=(E_{Remain}*E_{Average})/d^{2}, \tag{1}\]  
where \(E_{Remain}\) is the remaining energy of the sensor node, \(E_{Average}\) is the remaining total energy of the cluster, and \(d\) denotes distance.  
EECCS assumes that the number of nodes in each cluster should be different, the farthest cluster from the sink should have the least nodes, and the nearest cluster should have the most nodes because the farthest cluster will consume the least energy.
## 5 Discussion
### Land Cover Changes Relative to Drought and Wildfire
#### 5.2.1 Detected Changes Compared to Previous Studies and Current Restoration Activities  
The significant decrease in grasslands and shrublands was mostly observed in northwestern, northeastern, and southeastern NM--consistent with [4] that indicated that degradation was mostly over grassland-savanna. Degradation of grasslands, in some cases, was directly linked to increased productivity of shrublands. In NM, increased productivity in shrublands was noticed southwestern deserts and plains. This increase was attributed to land cover conversion from black drama and other valuable grasses-dominated areas to bushes due to over grazing and drought [69]. The evident climate warming, expected increase interannual precipitation variability, and projected increase in aridity can further enhance the growth of shrubs (i.e., encroachments of woody plants) over grasses since the later are heavily depended on transient surface moisture [70; 71]. It was also argued that increased CO\({}_{2}\) concentration in the atmosphere can improve plant CO\({}_{2}\) uptake and reduce water loss through plant's stomata thus increasing the photosynthesis process and this mechanism actually favor woody vegetation over non-woody ones [72; 73].  
The general notion that was suggested in some studies was that encroachments of shrublands to grasslands can be considered as one stage of degradation that can threaten the integrity of the rangelands [74]. However, it was argued that, shrublands are not necessarily degraded, nor do they necessarily represent \"degradation\" due to their ability  
Figure 10: A summary of breakpoints with (**a**) decreasing and (**b**) increasing trends in productivity along with the weighted average of self-calibrated Palmer Severity Drought Index (sc-PSDI).  
to support valued ecosystem services [71; 75], and they also have a long-term mean annual above ground NPP--equivalent to that of grasslands [76]. In NM, there have been a number of restoration and brush management efforts undertaken to control invasive species, and retain favored shrub species [64]. This suggested that the attribution of drivers to changes in productivity in environmental studies remains challenging owing to the diversity of driving forces and limited sources of ground truth data for validation [77].  
The increased productivity in grasslands that was observed in western NM can partially be attributed to local scale successful restoration efforts by the Bureau of Land Management [64]. These efforts targeted the replacement of Creosote and mesquite by healthy grasslands, and reclamation of surfaces resulted from oil and gas extraction operations. According to [PERSON] [78], gradual improvements in range conditions (increased productivity) in southwestern NM was not only related to the results of better moisture, but also cumulative efforts on rangeland management, such as proper stocking, vastly improved grazing distribution, and brush management.
## 3 Outage Analysis of Blind-RIS-NOMA Uplink System  
In this section, uplink outage probability expressions are derived considering RIS as an SR and AP.
# Magnetic Field-Based Vehicle Positioning System in Long Tunnel Environment
## 4 Experimental Results and Discussion
### Test Scenarios Explanation  
We have conducted four scenarios to verify the proposed system performance in the test bed. The experimental conditions are listed in Table 1. Two smartphones--Samsung Galaxy 9+ and Galaxy 20 Ultra--were used in the experiments. Galaxy 9+ was also used for DB data acquisition. As depicted in Figure 10(a), the smartphone was fixed on the dash board and the experiment was conducted with the same smartphone pose in the first, third scenarios. Figure 10(b) shows another smartphone attitude in the rest of the scenarios. Figure 11(a) shows the smartphone attitude estimation results for each scenario. In the first and third scenarios, the pitch value is close to 90 degrees and the roll value is close to 0 degrees. We can also see that the pitch value in the other scenarios is approximately 60 degrees. This experiment demonstrated that a stable positioning accuracy can be obtained inside the tunnel through the proposed system, regardless of the attitude or brand model of the smartphone. Figure 12 shows the vehicle speed of all scenarios.
## 2 Results
### Derivatives of 1018: Anti-Biofilm and Immunomodulatory Activity
cenocepacia_ \\  & & & **PA14** & **4813** \\ \hline
1018 & VRLIVAVRIWRR & Parent & 99\% & 52\% \\ HE1 & RRWIRVAVILRV & Reverse sequence & 93\% & 73\% \\ HE2 & VRLIRAVRAWRV & Break hydrophobicity; maintain charge & 42\% & 24\% \\ HE3 & VRWARVARILRV & Reverse of HE2 & 31\% & 21\% \\ HE4 & VRLIWAVRIWRR & Substitute in another W for V & 99\% & - b \\ HE10 & VRLI\textasciribar\textasciribar\textasciribar\textasciribar\textasciribar\textasciribar\textasciribar\textasciribar\textasciribar\textasciribar\textasciribar\textasciasciribar\textasciasciribar\textasciasciribar\textasciasciasciribar\textasciasciasciribar\textasciasciasciasciribar\textasciasciasciasciribar\textasciasciasciasciasciribar\textasciasciasciasciasciribarasciasciasciasciasciasciribarasciasciasciasciasciasciascirib  
We previously identified 1018 as a potent immunomodulatory peptide with excellent _in vivo_ protective and anti-inflammatory activity [28, 29, 30]. The identification here of 1018 derivatives enabled us to see if the two properties of anti-biofilm and immunomodulatory activity were conserved in the new 1018 derivatives. Therefore, we assessed the relative ability of HE1, HE4, HE10, and HE12 cf. 1018 to induce monocyte chemotactic protein-1 (MCP-1), a key chemokine that promotes recruitment and infiltration of monocytes and macrophages [17]. At 20 and 100 \(\upmu\)g/mL, peptides HE1, HE4, and HE10 significantly induced MCP-1 in human peripheral blood mononuclear cells (PBMCs) to the same (or greater) extent as 1018 _cf._ the untreated control (Figure 4A). In contrast, peptide HE12 was virtually inactive. None of these peptides showed substantial cytotoxicity as assessed by the release of cytosolic lactate dehydrogenase (LDH) (Figure 4C). As persistent lower airway infection contributes to the lung pathology exhibited in cystic fibrosis, the peptides were also assessed for their ability to reduce the production of tumor necrosis factor-alpha (TNF-\(\alpha\)) in response to pro-inflammatory bacterial lipopolysaccharideescharides (LPS). Interestingly all HE peptides substantially reduced--by more than 95%--the levels of LPS-induced TNF-\(\alpha\) (Figure 4B). These results indicate that peptides HE4 and HE10, like 1018, are not only potent anti-biofilm agents, but also possess notable immunomodulatory potential by selectively enhancing chemokine production while balancing the pro-inflammatory response. Figure 3: Biofilm inhibitory activity against methicillin resistant _Staphylococcus aureus_ (MRSA) in 96-well plates. Dilutions (1/100) of overnight cultures were incubated in BM2 biofilm-adjusted in polypropylene microtiter plates (Fisher Scientific, Waltham, MA, USA) in the presence of the different peptides for 24 h at 37 \({}^{\circ}\)C. Planktonic cells were removed, biofilm cells adhering to the side of the tubes were stained with crystal violet, and absorbance at 595 nm was measured using a microtiter plate reader (Bio-Tek Instruments Inc., Winooski, VT, USA).
# Energy Saving Strategy of UAV in MEC Based on Deep Reinforcement Learning
## 3 System Model  
This paper considers a wireless-powered MEC network system, which is composed of a multi-antenna AP integrated with an edge server and a set \(N=\{1,\ldots,N\}\) of single-antenna UAVs placed on the ship. The AP has a stable power supply and higher computing power than the UAV. The AP can not only receive data from the UAV for calculation, but also transmit power to the UAV through WPT. The computing tasks of the UAV can be executed locally or offloaded to the MEC for computing, following by the transmission of the returned results. At the same time, the wireless energy transmitted by the harvesting AP can be used for local computing or task offloading, and the residual energy can be used to charge the battery. The system model is shown in Figure 1.  
The UAV computing task adopts a binary offloading decision. In other words, all tasks are executed locally, as shown in UAV \(1\) in Figure 1, or all of them are offloaded to the AP for execution, as shown in Figure 1 for UAV \(2\) and UAV \(3\). The indicator variable is represented by \(u_{i}\in\{0,1\}\), where \(u_{i}=0\) indicates that the task is computed locally, and \(u_{i}=1\) indicates that the computing task of the \(i-\)th UAV is offloaded to the AP for execution. We use \(U=\{1,\ldots,N\}\) to represent the set of all UAVs, where \(N\) represents a total of \(N\) UAVs in the system. \(U_{0}\) denotes the set of UAVs whose  
Figure 1: System model.  
computing, and \(U_{1}\) denotes the set of UAVs whose computing mode is offloaded to the edge server, so it is mutually exclusive with the two sets and has \(U_{0}\cup U_{1}=U\).