esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Data Analysis by Response Surface Methodology
Finally, one can conclude that this experimental design is valid, because the error value is lesser than 5%. Figure 9 illustrates the 3-D response surface plots of the removal efficiency of MB as a function of two independent variables: (a) The initial concentration C\({}_{0}\) and adsorbent dose m, (b) the initial concentration C\({}_{0}\) and pH, and (c) the adsorbent dose m and pH. On the other hand, the optimum values of the variables X\({}_{1}\), X\({}_{2}\) and X\({}_{3}\) were obtained by solving the model Equation (15) with Minitab\({}^{\circledR}\)[17], to establish the values of variables maximizing the removal yield. Figure 10 shows the optimization diagram obtained with the maximum desirability function (i.e., D = 1) for MB yield removal: In separate columns, the response is related to each of the three factors: Red vertical lines indicate the current factor settings, while the horizontal dotted lines show the response for the selected factor levels. In our case, the simulation was adjusted to maximize the adsorption percentage. The statistical sweet spot was determined for a maximal MB removal yield of 99.09% and a desirability of d = 1.0, leading to: C\({}_{0}\) = 8 mg/L, m = 0.06 g, and pH = 9. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Factor** & **DF** & **Sum of Squares** & **Mean Square** & **\(P\)-Value** & **\(p\)-Value** \\ \hline Model & 9 & 5719.41 & 635.49 & 395.15 & \(<\)0.0001 \\ X\({}_{1}\)- Initial concentration & 1 & 3845.21 & 3845.21 & 2390.96 & \(<\)0.0001 \\ X\({}_{2}\)-m & 1 & 1732.54 & 1732.54 & 1077.30 & \(<\)0.0001 \\ X\({}_{3}\)-pH & 1 & 12.55 & 12.55 & 7.80 & 0.038 \\ X\({}_{1}^{2}\) & 1 & 7.47 & 7.47 & 4.64 & 0.084 \\ X\({}_{2}^{2}\) & 1 & 2.35 & 2.35 & 1.46 & 0.281 \\ X\({}_{3}\)\({}^{2}\) & 1 & 45.48 & 45.48 & 28.28 & 0.003 \\ X\({}_{1}\)-x\({}_{2}\) & 1 & 49.91 & 49.91 & 31.04 & 0.003 \\ X\({}_{1}\)-X\({}_{3}\) & 1 & 20.88 & 20.88 & 12.99 & 0.015 \\ X\({}_{2}\)-X\({}_{3}\) & 1 & 2.96 & 2.96 & 1.84 & 0.233 \\ \hline Residual & 5 & 8.04 & 1.61 & & \\ \hline Lack-of-fit & 3 & 6.83 & 2.28 & 3.77 & 0.217 \\ \hline Pure Error & 2 & 1.21 & 0.60 & & \\ \hline \hline \end{tabular} Standard variation error S = 1.268; R\({}^{2}\) = 99.86%; R\({}^{2}\)adjusted = 99.61%; R\({}^{2}\)predicted = 98.04%.  
\end{table} 
Table 5: ANOVA for the fit of the experimental results to response surface model.
# 8-W 2-Stage GaN Doherty Power Amplifier Module on 7 \(\times\) 7 QFN for the 5G N78 Band
## 2 Design of 2-Stage DPAM
### Interstage Matching Network  
Figure 2 depicts a schematic of the interstage matching network utilizing IPD technology to support a single-driver structure. It comprises input matching circuits for the carrier and peaking amplifiers of the main stage, the output matching circuit of the driver stage, a Wilkinson power divider, and the phase offset control circuit. Figure 3 illustrates the impedance transformation within the interstage matching network, featuring a 1 dB power contour for the driver amplifier. The input matching circuits for the carrier and peaking amplifier of the main stage utilize an L-section low-pass structure, inclusive of bond wires, which transforms the input impedances of \(Z_{c,in}\) and \(Z_{p,in}\), both distributed around 1 \(\Omega\), to \(Z^{\prime}{}_{c,in}\) and \(Z^{\prime}{}_{p,in}\) for the carrier and peaking amplifiers, respectively. \(Z^{\prime}{}_{c,in}\) and \(Z^{\prime}{}_{p,in}\) aim for approximately 25 \(\Omega\) and feature expansive distribution on the Smith chart. The phase offset control circuit consists of a 25 \(\Omega\) quarter-wave transmission line (TL), realized through a high-pass L-C-L network, which rotates \(Z^{\prime}{}_{c,in}\) to \(Z^{\prime}{}_{offset}\) around the central value of 25 \(\Omega\). For power division, the [PERSON] power divider is composed of 50 \(\Omega\) quarter-wave TLs, designed with lumped elements, to equally split the input power between the carrier and peaking amplifiers of the main stage. The input node impedance of the [PERSON] power divider, \(Z_{w,in}\), is carefully matched to remain close to 50 \(\Omega\). The output matching circuit of the driver amplifier also utilizes an L-section low-pass structure and includes the output capacitance of the transistor and the inductance of bond wires. The output matching network transforms the input node impedance of the [PERSON] power divider, \(Z_{w,in}\) to \(Z_{d,out}\) for the driver amplifiers, which consistently remains within the 1 dB power contour centered at 100 \(\Omega\) as demonstrated. Additionally, several shunt components located at the same node have been consolidated into single equivalent components to reduce circuit complexity and improve manufacturability.  
Figure 1: Schematic of the proposed 2-stage DPAM.
## 2 Comprehensive Weight Calculation Model Based on AHP and Orthogonal Test
### Influence Weight Calculation Based on AHP
#### 2.2.1 Weight Vector of Test Index Layer  
The AHP method, proposed by [PERSON] [28], is a decision-making technique used to evaluate complex problems. Firstly, the two-level hierarchical structure including a target layer and an index layer with \(n\) test indices is constructed. Secondly, a comparison judgment matrix \(A\) is established as shown in Table 2, which is formed by a pairwise comparisons of the relative importance of the indices.  
Then, the eigenvector equation can be constructed and solved by:  
\[A\cdot W=\lambda\cdot W \tag{1}\]  
where \(W\) is the eigenvector for comparison judgment matrix \(A\) and \(\lambda\) is the eigenvalue.  
The normalized eigenvector corresponding to the maximum eigenvalue \(\lambda_{\text{max}}\), which can give the relative importance of each index, can be expressed by:  
\[W^{T}=[w_{1},w_{2},\cdots,w_{n}]\;(\sum_{i=1}^{n}w_{i}=1) \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Test No.**} & \multicolumn{4}{c}{**Factor**} & \multirow{2}{*}{**Index Value**} \\ \cline{2-2} \cline{4-5}  & \(\mathbf{B_{1}}\) & \(\mathbf{B_{2}}\) & \(\mathbf{B_{3}}\) & \\ \hline
1 & 1 & 1 & 1 & \(y_{1}\) \\
2 & 1 & 2 & 2 & \(y_{2}\) \\
3 & 2 & 1 & 2 & \(y_{3}\) \\
4 & 2 & 2 & 1 & \(y_{4}\) \\ \hline \multirow{2}{*}{\(K_{jl}\)} & \(K_{11}=y_{1}+y_{2}\) & \(K_{21}=y_{1}+y_{3}\) & \(K_{31}=y_{1}+y_{4}\) & - \\  & \(K_{12}=y_{3}+y_{4}\) & \(K_{22}=y_{2}+y_{4}\) & \(K_{32}=y_{2}+y_{3}\) & - \\ \hline \(m_{j}\) & 2 & 2 & 2 & - \\ \hline \multirow{2}{*}{\(k_{jl}\)} & \(k_{11}=K_{11}/m_{1}\) & \(k_{21}=K_{21}/m_{2}\) & \(k_{31}=K_{31}/m_{3}\) & - \\  & \(k_{12}=K_{12}/m_{1}\) & \(k_{21}=K_{22}/m_{2}\) & \(k_{31}=K_{32}/m_{3}\) & - \\ \hline \(R_{j}\) & \(R_{1}\) & \(R_{2 where \(w_{i}\) represents the relative importance for index \(i\) by comparing with other indices.  
In order to evaluate the consistency of the comparison judgment matrix \(A\) for determining the reliability of making the solved eigenvector \(W\) the weight vector, a consistency index (\(CI\)) and consistency ratio (\(CR\)) are defined as:  
\[CI=\frac{\lambda_{\max}-n}{n-1} \tag{3}\]  
\[CR=CI/RI \tag{4}\]  
where \(n\) is the number of test indices, and \(RI\) is defined as a random consistency index.  
The value of random consistency index (\(RI\)) is listed in Table 3.  
When \(CR<0.1\), the extent of inconsistency for a comparison judgment matrix is permitted, and the eigenvector \(W\) can be regarded as the weight vector.
# Preparation and Characterization of Gold Nanorods Coated with Gellan Gum and Lipoic Acid
## 4 Conclusions  
Multimodal devices able to combine therapy and diagnostic are crucial to achieve efficient patient-tailored therapy. Although gold nanorods are potentially capable of fulfilling this aim, current research is still focusing on new biocompatible materials capable to allow proper in vivo use and to confer additional beneficial attributes. The query of this work was to test the natural materials lipoic acid and gellan gum as coating agents of gold nanorods, investigating how different coating strategies could influence the product properties and suitability in PTT. Briefly summarizing data collected, AuNRs were successfully coated with LA and GG leading to the formation of the anisotropic nanosystems AuNRs/LA, AuNRs/LA,GG, and AuNRs/GG-LA. AuNRs coated with the ester derivative of GG and LA (AuNRs/GG-LA) denoted some tendency to aggregate, displaying reduced stability in aqueous medium. More importantly, AuNRs/GG-LA, despite capable to reach temperatures suitable for hyperthermia, shown a far lower efficacy in in vitro PTT studies. On the other hand, AuNRs/LA and AuNRs/LA,GG dispersions demonstrated optimal stability and prominent photothermal properties that can suggest a use for in vivo PTT of cancer. Nevertheless, the presence of GG allows the possibility to store AuNRs/LA,GG as lyophilized powder and, in addition, GG shell could be employed to load bioactive molecules for drug delivery purposes. For these reasons, AuNRs/LA,GG represent the most promising choice for further use in in vivo photothermal therapy of cancer.  
All the authors contributed individually to the study and manuscript production with the following roles: M.L., conceptualization, data curation, supervision and writing; P.V.: preparation, characterization of nanosystems and writing; L.T.: thermal imaging analysis; G.C.: manuscript revision. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 9: LIVE/DEAD assay fluorescence microscopy images on laser-treated HCT116 cell culture (810 nm, \(10\times 10^{-3}\) W/mm\({}^{3}\), and 100 s). Here, 5x magnifications of: control + laser (**a**), AuNRs/GG-LA (**b**), AuNRs/LA (**c**), and AuNRs/LA,GG (**d**) are used. Acquisition channels: brightfield (1), fluorescein isothiocyanate (FITC) (2), Texas Red (3), and merge (4); scale bar: 100 μm.  
**Acknowledgments:** Authors thank the ATeN Center of University of Palermo--Laboratory of Preparazione e Analisi di Biomateriali, for SEM and XPS analysis of nanorods.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Water and Sediment Composition  
#### 3.3.1 Water Composition and Quality  
The main physiochemical characteristics of the water in the four lakes are shown in Table 2. The water in the Louro and Vixan lakes is characterized by alkaline pH (pH 8-9) and an elevated ionic concentration (EC mean values: 10-23 dS m\({}^{-1}\)). These results demonstrate the marine influence in both lake systems, which coincides with the definition of Coastal lagoons (habitat 1150): \"Lagoons are expanses of shallow coastal salt water, of varying salinity and water volume, wholly or partially separated from the sea by sand banks or shingle, or, less frequently, by rocks. Salinity may vary from brackish water to hyper salinity depending on rainfall, evaporation and through the addition of fresh seawater from storms, temporary flooding of the sea in winter or tidal exchange... \" [9]. By contrast, the water in the Xurno and Bodeira lakes was characterized by slightly acidic to neutral pH (6.6-7.2) and a low EC (>1 dS m\({}^{-1}\)), which indicate the strictly continental origin of these waters. The ionic concentration only increases during the winter periods as consequence of the deposition of marine spray [34]. Consequently, these lakes do not have the basic conditions established in the definition of coastal lagoons, and they should be assigned to the category Natural eutrophic lakes with _Magnopotam_ or _Hydrocharition_ (habitat 3150). In both types of coastal lakes, the water volume varied widely, with particularly large reductions at the end of summer or start of autumn, coinciding with the periods of highest drought (see below). The reduction in the volume of water in the lakes leads to a reduction in quality, due to increased concentrations of nitrogenated forms (nitrate and ammonium), phosphates, and DBO5 (Table 2), and thus to an increase in fish mortality events (e.g., Vixan lake, [34]). The toxic metal contents were very low in all the lakes under study (data not shown) [34].
## 3 Results and Discussions
### Nutrient Removal  
Figure 2 shows the ammonium and phosphate removal by _Scenedesmus_ sp. (a) and _C. vulgaris_ (b) in the presence of EDCs for 7 days (LOM experiments). Figure 2a exhibits a  
Figure 1: (**a**) Growth curves during 7 days for Scenedesmus sp. in the presence (doped—solid green circle) or absence (control—open green circle) of EDCs and C. vulgaris in the presence (doped—solid orange square) or absence (control—open orange square) of EDCs. (**b**) pH curves over 7 days for doped _Scenedesmus_ sp. (circle, dashed green line) and doped _C. vulgaris_ (square, dashed orange line). Error bars represent \(\pm\) standard error of the mean (\(n=3\)).  
complete phosphate depletion within 4 days of _Scenedesmus_ sp. cultivation. However, only 32% of the ammonium was removed in 7 days. This low ammonium removal efficiency is associated with this microalgae's limited growth. Thus, the simultaneous complete removal of phosphate restricted ammonium consumption, which has also been reported in previous studies. For example, _Scenedesmus_ sp. LX1 will completely remove (100%) the total phosphorus (TP) regardless of the N/P ratio, while the removal of total nitrogen (TN) strongly depends on that nutrient ratio [73]. A similar trend was observed when _Scenedesmus_ sp. was cultivated under phosphorous-starved conditions. In this study, complete phosphorus depletion occurred within 11 days, but nitrogen removal remained negligible beyond this point [74].  
Figure 2b shows the complete ammonium and phosphate removal in 7 days by _C. vulgaris_. This result agrees well with other reports in the literature. In this sense, _C. sorokiniana_ completely depleted both ammonium and phosphorus in 7 days when the initial nitrogen concentration ranged from 20 to 40 mg N/L [75]. In addition, _C. vulgaris_ cultivated in different anaerobic digestion effluents exhibited a nearly 100% removal of ammonium and phosphates within 10 days with an initial ammonium concentration of 40 mg N/L [76]. This total nutrient removal achieved during the cultivation of _C. vulgaris_ in the presence of EDCs provides promising insights for the future development of WWTP biological treatments based on microalgae.
###### Abstract
[8] demonstrated the application of deep learning models for real-time safety monitoring, such as detecting safety helmet violations in engineering environments. The application of these AI technologies can significantly contribute to protecting workers' safety and reducing accident rates. Moreover, the application of human-centered technology is emphasized as an important factor in Safety 4.0 [5; 9]. [PERSON] et al. [5] proposed the concept of Operator 4.0, aiming for human-machine symbiosis by enhancing workers' capabilities and safety through advanced technologies. [PERSON] et al. [9] elaborated on solutions like wearable devices, Augmented Reality (AR), and collaborative robotics to empower and engage industrial workers, ultimately improving operational safety and productivity. However, several challenges exist in implementing Safety 4.0 [6]. [PERSON] et al. [6] discussed the applications and challenges of Safety 4.0, pointing out obstacles such as data security concerns, lack of standardization, and organizational cultural changes. Additionally, [PERSON] and [PERSON] [10] emphasized the importance of systematic reviews to synthesize fragmented research and provide comprehensive insights, particularly in fields like safety management, where interdisciplinary approaches are critical. This fragmentation leads to redundant studies and overlooks critical areas, hindering effective technology adoption. Therefore, to maximize the effectiveness of AI utilization in safety management during the Safety 4.0 era, a systematic analysis of existing studies is necessary. A systematic literature review is essential to identify current research trends, application areas, and technological limitations and to suggest future research directions and practical application strategies [4; 10]. Through this, we can prevent research duplication, promote efficient resource utilization, and proactively recognize and strategize responses to technical, organizational, and ethical issues that may arise when adopting AI technologies [7]. The purpose of this study is to systematically identify and analyze how Artificial Intelligence (AI) and smart technologies are currently utilized to enhance safety management factors across various industries within the Safety 4.0 paradigm. Through a comprehensive literature review focused on studies explicitly mentioning \"Smart\", \"AI\", or \"Artificial Intelligence\" in their titles, we aim to achieve the following:  
* [noitemsep,topsep=0 pt]
* Examine the specific applications of AI and smart technologies in safety management, including the technologies used, algorithms implemented, and the contexts in which they are applied. * Identify patterns, trends, and insights from the current body of research that can inform future developments and practical implementations in safety management. By conducting this in-depth analysis, we seek to provide a comprehensive understanding of the role of AI and smart technologies in transforming safety management practices.
## 4 Experimental Setup  
### Hardware and Topology  
As the 5G core, we used the Amarisoft Callbox Classic base station [46]. The license allowed us to utilize up to 60 MHz of bandwidth. It is a cost-effective solution offering an all-in-one approach (an integrated 5G network core with a gNB) selected from among various available solutions, including carrier-grade telco core/gNBs, as the solution best suited for the implementation of private 5G networks. Similar testbeds were used in other works described in, e.g., [47; 48; 49]. The programmable attenuator was a Vaurix LDA-802Q Lab Brick [50], which enabled us to introduce attenuation in a controlled manner, simulating different radio-link conditions and allowing us to analyze its impact on network performance. The antenna used was Alpha Wireless AW3941-T0-F with gain of 8.5 dBi and az beamwidth of 360 degrees. The Remote Radio Head (RRH) was AW2S Panther, offering a power output of 33 dB per chain.  
On the sensor side, the 5G router was the MikroTik Chateau 5G ax [51]. On the opposite end, we used the Siemens SCALANCE SC646-2C. Both devices functioned as Virtual Tunnel Endpoints (VTEPs) for VxLAN traffic. The LiDAR sensor used was the Ouster OS-0-128, which allows configuration of the field of view, data resolution, and packet format. The sensor was mounted on an industrial unmanned ground vehicle (UGV), the Husarion Panther, which also served as its power source.  
The endpoint device was a computer equipped with an Intel Core i7-11800H processor, 32 GB of RAM, and running Ubuntu 20.04.6 LTS. This setup enabled comprehensive evaluation of latency, throughput, logical tunneling mechanisms (VxLAN), and the effects of signal degradation in a controlled 5G industrial communication environment.  
The experimental setup scheme is shown in Figure 4. The LiDAR sensor mounted on the UGV was directly connected to a 5G router that encapsulated the traffic using the Virtual Extensible LAN (VxLAN) protocol. It then sent the traffic to the 5G core. The radio link between the UE and the base station included a programmable attenuator. The core forwarded the traffic to the industrial switch, which decapsulated the VxLAN traffic--a commonly used approach to integrating 5G systems with Industry 4.0 equipment [52]. These data were delivered to the client. The client simultaneously generated traffic-simulating control messages for the UGV. Additionally, a direct link was established to the Mikrotik 5G router. Downlink traffic was mirrored to this link to facilitate the analysis of end-to-end latency between the client and the UE.  
When it comes to the types of media used to connect individual components and the underlying technologies, the 5G core utilized the eCPRI (evolved Common Public Radio Interface) protocol to connect to the Radio Unit (RU) via a fiber optic cable. The RU was connected to the Remote Radio Head (RRH), with a programmable attenuator placedbetween them. The RRH communicated wirelessly with a Mikrotik 5G device, which was connected to the LiDAR sensor via an Ethernet cable.
# H2O: Secure Interactions in IoT via Behavioral Fingerprinting
## 6 Security Model of H2O Network
### Security Analysis
#### 6.2.1 **Sp.1**--Resistance to Node Tampering Attacks  
This attack occurs when a malicious user has physical access to an object and can modify its whole software/hardware or just part of it. More specifically, an adversary physically compromises a node by inserting malicious code to the node that will give him illegal access to the system [70]. Moreover, this type of attack can include also the altering of some sensitive information, such as cryptographic keys or routing table [73].  
Having the control of the node, the attacker can compromise the strategy adopted by the node to gather information and allow for the computation of the fingerprint of its contacts. This would lead to issues during the training phase of the classifiers during the _safe starting phase_ and, hence, to wrong fingerprint rates estimation during the subsequent phase. The node, as breached, would provide wrong values once requested by its neighbor. It is worth noting that, in this case, the behavior of the node would not be altered, because the node will continue to work normally, but it will leverage wrong models to produce fingerprint rates. However, thanks to Assumption **A.3** such an attack could not happen, because during the _safe starting phase_ all the objects are considered safe.  
Always in the context of this kind of attack, instead of jeopardizing the training phase, an attacker could modify directly the response to a query of another object during the operational phase. In this case, our approach enforces two protection mechanisms. On one hand, the use of behavioral fingerprinting models would detect an anomalous behavior of the attacked object; indeed, related literature in this setting proved that an attacker controlling an object would alter some of the features, thus impacting the object's fingerprint [53]. On the other hand, this scenario does not invalidate the computation of the reliability for any node; indeed this score is obtained by a consensus of at least \(r\) nodes and for Assumption **A.1** colluding nodes are at most \(t<r\).