esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Adsorption Equilibrium Study  
The adsorption isotherms were determined using tetracycline hydrochloride solutions with concentrations ranging from 25 to 500 mg\(\cdot\)L\({}^{-1}\). For each experiment, 0.3 g of natural or acid-treated bentonite was added to 25 mL of the solution in 250 mL Erlenmeyer flasks. The mixtures were agitated at 150 rpm for 30 min to ensure interaction between the adsorbent and the adsorbate. After the adsorption process, the solid and liquid phases were separated via centrifugation at 3000 rpm for 20 min.  
The experimental data were analyzed using non-linear forms of the Langmuir, Freundlich, Sips, and Temkin isotherm models, represented by Equations (5)-(8), respectively. These models were employed to describe the equilibrium behavior of tetracycline adsorption and evaluate the adsorption capacity and the nature of the interactions between the adsorbent and the adsorbate.  
\[q_{e}=\frac{q_{m}*K_{L}*C_{e}}{1+K_{L}*C_{e}} \tag{5}\]  
\[q_{e}=K_{F}*C_{e}^{\frac{1}{2}} \tag{6}\]  
\[q_{e}=\frac{q_{m}*K_{s}*C_{e}^{\frac{1}{2}}}{1+K_{s}*C_{e}^{\frac{1}{2}}} \tag{7}\]  
\[q_{e}=B*ln~{}K_{T}+B*ln~{}C_{e} \tag{8}\]  
where \(Ce\) is the concentration (mg\(\cdot\)L\({}^{-1}\)); \(q_{e}\) is the amount of adsorbed drug (mg\(\cdot\)g\({}^{-1}\)); \(q_{m}\) is the maximum adsorption capacity; \(K_{L}\), \(K_{F}\), \(K_{s}\) and \(K_{t}\) are the Langmuir, Freundlich, Sips, and Temkin constants, respectively; \(n\) is the Sips coefficient; \(K_{T}\) is the binding constant at equilibrium, and \(B\) is the constant related to the heat of sorption (\(B=\frac{K_{t}}{b_{t}}\)).
# A Review of the Influence of Steel Furnace Slag Type on the Properties of Cementitious Composites
## 1 Introduction  
As a by-product of the various processes of smelting metallic ores, slags are a potentially useful commodity in civil infrastructure applications. Specifically, this review focuses on the use of slags produced from various steelmaking processes. During the production of steel, the initial refinement occurs at a blast furnace, where iron ore is processed to make pig iron. The slag from this process--blast furnace slag--can be used as an aggregate in the form of air-cooled blast furnace slag (ACBFS), but is commonly used as a supplementary cementitious material (SCM) in the form of ground granulated blast furnace slag (GGBFS) [1; 2; 3; 4; 5]. However, blast furnace slags will not be covered in this review.  
After the blast furnace, the pig iron is refined to produce crude steel. Other crude steels are produced by refining recycled and scrap steel. There are a number of different processes that can be used, and the slag from these processes is broadly termed steel furnace slag (SFS). An estimated 1.8 billion tonnes of crude steel was produced worldwide in 2018 [6], with an estimated worldwide production of 169 to 254 million tonnes of SFS in 2017 [7]. The worldwide effective utilization of SFS is around 80% [8]. The scope of this review is to examine the various types of SFS and how they influence the properties of cementitious composites when used as an aggregate and/or as an SCM. The objective is to reveal that not all SFSs are the same and instead should be classified or qualified according to the intended purpose or use, especially when the SFS is being considered for a bound application, such as concrete, mortar, alkali-activated material, or soil stabilization.  
The motivation for this article stems from a previous finding that the type of SFS aggregate can drastically affect the cementitious composite properties when the mixture volumetrics are constant [9; 10]. When compared with other recycled aggregates, this finding for SFS has not been clearly explained; for instance, nearly all (if not all) studies of concrete with reclaimed asphalt pavement aggregates have shown a reduction in composite strength and modulus, which has been linked to the development and properties of the concrete microstructure [11; 12]. Similarly, concrete with recycled concrete aggregates (RCAs) will typically experience reductions in strength relative to virgin aggregates, but the degree of strength reduction is highly variable, which has been linked to microstructure development, adhered mortar, moisture absorption and content, RCA heterogeneity, quality, and so forth (e.g., [13; 14; 15; 16; 17]). In comparison, for SFS aggregates, even for the same SFS type, studies have found that concrete strength can increase, decrease, or be similar to concrete with virgin aggregates. Therefore, this review seeks to collate the existing literature to observe any general trends, to consider sources of variability, and to assess test method(s) to critically analyze SFS for use in cementitious composites.
## 3 Materials and Method
### Proposed Method  
This study proposes a data obfuscation security method based on probability density and information entropy, aiming to ensure data security while maintaining high usability and analytical accuracy, as shown in Figure 2.  
The entire workflow is divided into five main modules: data input, probability density extraction, information entropy fusion, obfuscation strategy optimization, and final output. The following sections provide a detailed explanation of each module and their interactions. The processed data first enter the probability density extraction module, which is responsible for extracting the probability distribution characteristics of the raw data. This step provides essential information for the subsequent obfuscation strategy. After extracting the probability density features, the data flow to the information entropy fusion module, which serves as the core of the proposed method. The primary objective of this module is to dynamically adjust the obfuscation intensity using information entropy, thereby ensuring data privacy while minimizing the impact on task performance. Subsequently, the data processed by the information entropy fusion module are directed to the fusion loss function module for optimization. In this module, a fusion loss function is designed to simultaneously optimize data security and usability.
## 3 Control Strategy of Air Spring Height  
The Kenworth Airglide 200 longitudinal-connected air suspensions investigated in this paper are passive suspensions. In order to enhance their comprehensive driving performance preliminarily, the air spring height control strategy shall first be designed based on the driving conditions. The natural frequency of the air suspension is expressed as  
\[f=\frac{1}{2\pi}\sqrt{\frac{K_{y}}{m_{1}}} \tag{11}\]  
where \(f\), \(K_{y}\), and \(m_{1}\) indicate the natural frequency of air suspension, spring stiffness, and sprung mass, respectively. \(K_{y}\) is formulated as [33]  
\[K_{y}=k\cdot p_{s10}\cdot\frac{A_{s1}^{2}}{V_{s1}}+\frac{\text{d}A_{s1}}{\text {d}y}(p_{s10}-p_{0}) \tag{12}\]  
\(P_{s10}\) changes with the sprung mass (\(P_{s10}=m_{1}\text{g}/A_{s1}+p_{0}\)); by introducing Equations (6) and (7) into Equation (12), the air spring stiffness at different heights can be obtained.  
The design range for the natural frequency of rear suspension of the semi-trailers is 1.70\(\sim\)2.17 Hz [34]. Based on Equations (11) and (12), the proper ranges of spring heights can be derived with different sprung masses [35], and the optimal heights were determined as the medians of the ranges. Therefore, four control heights were set for the air suspension [33], specifically as follows:I. In time of load-free idling and driving (when the actual load is less than 1/2 of the full load, it is defined as no load), the height of the spring is \(H_{0}\) (0.16 m).  
II. In time of full load (when the actual load is equal to or more than 1/2 of the full load, it is defined as a full load), the spring height is set based on the vehicle speed and road condition as follows:  
(1) In the case that the vehicle is kept static or moving at low speed (speed \(<\) 50 km/h), when the road is in good condition, the spring height is set to \(H_{1}\) (0.13 m); when the road condition is relatively poor, the spring height is set to \(H_{2}\) (0.15 m). The number of times that the height signal from the suspension height sensor exceeds the calibrated height value within an observation period (0.5 s) is recorded as \(N\). If \(N\) is greater than 4, the road condition is considered as poor; otherwise, the road condition is good.  
(2) If the vehicle is moving at high speed (speed \(>\) 70 km/h), when the road is in good condition, the spring height is set to \(H_{3}\) (0.11 m).  
(3) 50-70 km/h is the hysteretic interval of the speed [33]. Hysteresis of 20 km/h is provided to prevent frequency occurrence of height level changes during travelling.
## 2 Algorithm Paradigms for Wireless Sensor Networks  
Sensor applications demand the communication of nodes to execute certain procedures or algorithms. In fact, three kinds of algorithms can be executed on wireless sensor networks [4]:  
* _Centralized Algorithms_: They are executed in a node that posses the knowledge of the whole network. These algorithms are quite rare because of the cost of transmitting the data to make the node know the status of the complete network.
* _Distributed Algorithms_: The communication is supported by message-passing.
* _Local based Algorithms_: The nodes use restricted data acquired from a close area. With this local information, the algorithm is executed in one node.  
The algorithm paradigm is an important factor to take into account when deciding about the routing protocol to employ in the network. If localized algorithms are used, the routing protocol should reinforce and optimize the communication between neighbors. On the other hand, for centralized algorithms, combining the messages that simultaneously go the central node (even when they are generated by different sources) could be an advantage. The distributed algorithms should efficiently support the communication between any two pairs of nodes. Finally, local based algorithms depend on some solution that provides geographic coordinates, like GPS, making the solution more expensive.
## 2 Description of the Power Converter System Solution for a Cogenerator with an Induction Machine
### Topology of the Grid AC-DC Power Converter  
The most commonly used two-level four-leg topology was selected for the construction of the PC2 power network converter. The four-leg topology is required due to the unbalanced load and the need to regulate the voltage in each phase separately. Figure 3 shows the selected topology of a four-leg DC-AC converter with an LCL filter and two contacts, ST1 and ST2. ST1 is the main contactor and ST2 is the starting contactor. An important feature of the four-leg converter is that the voltage in the DC circuit should exceed the value of the phase-to-phase voltage amplitude of the grid. Since the grid voltage amplitude is \(\surd\) 400 V = 565 V, it is assumed that Vdc must be higher than 600 V.  
Figure 3: Scheme of a two-level, four-leg grid power converter. Source: author’s development.  
Figure 2: Supply system operating states. Source: author’s development.  
IGBT power transistors, in the form of MB150 BC120 IGBT half-bridges, were used in the prototype of the grid power converter. The transistor parameters were: maximum voltage 1200 V, maximum current 150 A.
# State of Industry 5.0--Analysis and Identification of Current Research Trends
## 3 Data Analysis and Discussion
### Term Frequency Analysis  
The terms identified from the overall data set enabled to gain understanding on what specific terms were more focused upon by the researchers addressing Industry 5.0 paradigm. A measure of frequency for the identified terms is used to plot on a line graph, the relative use of terms over the years to identify their usage trends by the researchers. Based on the terms extracted in Section 3.2, the following terms were identified for analysis (a) \"twin\"--to understand the trend on exploring the use of digital twins in enabling Industry 5.0, (b) \"data\"--for exploring the trend on use of big data i.e., set of diverse actionable data to empower Industry 5.0, (c) \"intelligence\"--to understand the trend on the use of artificial intelligence to aid Industry 5.0, (d) \"cloud\"--for understanding the trend in exploring the use of cloud based technologies and cloud computing as an enabler of Industry 5.0, (e) \"IoT\"--for exploring the trend on identifying IoT as an enabler for Industry 5.0, and (f) \"machine\"--for exploring the dialogue on the use of machine learning towards Industry 5.0 transformation. Figure 2 illustrates the trends on the use of the aforementioned terms over the past years. The use of the terms starting from year 2016 in the graph indicates to the limitation of the data that was gathered for analysis, starting from year 2016 where the first peer reviewed publication on Industry 5.0 was observed. Please see Table 1. The use of term \"twin\" in 2016 indicates to the initial attempt at exploring the use of digital twins to address Industry 5.0. Starting year 2018 the use of terms \"intelligence\" \"cloud\", and \"IoT\" are observed, indicating an initial interest in exploring artificial intelligence, IoT, and cloud computing technologies as enabler of Industry 5.0, with more interest in IoT. A peak in use of actionable data sets i.e., Big Data in the year 2021 followed by IoT and machine learning indicate more interest among the research communities to explore from an integration perspective toward a well-connected, distributed, intelligent, and actionable human centric systems. Please note that the abrupt drop in the term usage reflects to the limitation on the data gathered on publications until early 2022 considered for the analysis.
## 3 Results
### Effect of Tool Path on Part Residual Stress and Distortion  
#### 3.3.1 Unidirectional  
Figure 14 represents the predicted normal residual stress (=\(\sigma_{zz}\)) in the z-direction contours of the Ti wall and plate before and after the restraints were released from the plate. The residual stress contour for DB-1 is shown in Figure 14a,c and the contour for DB-2 is shown in Figure 14b,d. The residual stress was generally tensile over the wall. Notice that stress concentrated at the corner of the Ti-wall and -plate interface during cooling in Figure 14a,b, and then decreased when the bolts were released, as shown in Figure 15a,b. If the tensile stress is high enough in other parameter conditions, the corner would possibly be a visible cracking initiation spot (inset in Figure 14a). The stress distribution of DB-2 was similar to that of DB-1. However, the maximum value of the residual stress was approximately 30 MPa higher in DB-2 than that in DB-1. Moreover, higher contour values, in orange and red color, were observed in a wider area in DB-2. It can thus be concluded that shorter interlayer cooling time not only produced less distortion but also lowered residual stress.  
As seen in Figures 14 and 15, the residual stress distribution is spatially non-uniform over the part. To quantify the inhomogeneity, the distribution and magnitude of residual stresses at the bottom, middle and top region of the wall along the x-axis (=longitudinal direction) are plotted in Figure 16. As expected, a large residual stress was found at the bottom corners of the wall in the DB-1 full clamp condition, shown in Figure 16a, but not in the DB-5 center clamp condition, shown in Figure 16b. High tensile stresses were observed at the bottom of the wall corners, where cracking was observed in DB-1.  
Figure 12: Validation of plate distortion (blue = simulation and red = experiment). (**a**) DB-1 with 0 min and (**b**) DB-2 with 2 min interlayer cooling time. The magnitude of distortion became larger w with increasing interlayer time.  
Figure 13: Validation of plate distortion (blue = simulation and red = experiment). (**a**) DB-4 with 4 min and (**b**) DB-5 with 0-minute interlayer cooling time. The magnitude of distortion became larger with increasing interlayer time.  
Since DB-5 did not constrain the wall corners with bolts, there was no significant development in residual stress at the corners. In the middle region, the residual stress was relatively high at the left edge and maintains a constant level across the central region. Then, the stresses suddenly drop at the right edge region. This is because the tool path was unidirectional and ended at the right edge, such that heat accumulated, and stress relaxation occurred in that region.
## 3 Test Results and Analysis
### Acceleration Spectrum Analysis  
The variation of the vibration signal spectrum is related to the stiffness of the filling materials under compaction [42; 43]. Thus, the Fast Fourier Transform (FFT) was used to decompose the vibration  
Figure 5: Acceleration time-history curves.  
signal into the frequency domain in order to analyze the frequency information, as shown in Figure 8. It can be seen that the frequency and amplitude of the fundamental wave and multiple harmonic waves appear in the acceleration of each measuring point. The fundamental wave frequency of the vibration acceleration is basically stable near 21 Hz during the compaction process, and the fundamental wave frequency increases slightly from the vibration wheel to the filler surface, maintaining stability in each filling layer.  
Figure 9 shows the variation of fundamental and multiple harmonic amplitudes of each measuring point in the compaction process. When the vibration signal propagates from the vibration wheel to the deep layer, the ratio of fundamental amplitude to total amplitude decreases with the increase of buried depth. With the increase of buried depth, the amplitude of fundamental, primary, secondary, until fifth harmonics decreases exponentially, and the attenuation models are shown in Figure 9.  
Figure 8: Fourier spectrum.  
Figure 6: Peak acceleration of each measuring point.  
Figure 7: Peak attenuation rate of acceleration of each measuring point.  
The concrete functional relationship among different amplitudes of harmonics can be summarized as shown: y = Ae\({}^{-\text{BX}}\). In the formula, A and B represent the coefficient, X represents the order of harmonic and Y represents the amplitude of harmonic when its order is X.  
Figure 10 shows the relationship between the amplitude of harmonic and rolling compaction times, which shows that with the increase of compactions passes, the amplitudes of the fundamental wave and the multiple harmonic waves generally show a trend of \"increase \(\rightarrow\) decrease\(\rightarrow\) increase\", and the decrease of amplitude occurs in the sixth pass compaction process.
# Enhanced IoT-Based End-To-End Emergency and Disaster Relief System
## 5 Experimentation  
### Deployment Scenario and Experimental Setup  
In our experiments, we consider a disaster scenario in our office, Qatar Mobility Innovations Center (QMIC), in Qatar Science and Technology Park (QSTP). Our test-bed consists of four Raspberry Pi devices (Raspberry Pi Foundation, Cambridge, UK) and two rooted Samsung Galaxy S3-I930 smart phones (Samsung Electronics Co. Ltd., Suwon, South Korea) with the ORACE-Net routing protocol implemented. The office map is shown in Figure 5. The scenario is as follows: rescue teams access the office from the Back Gate (BG). First, they deploy the CC node in a trusted and safe location (near the entrance gate) where they are connected to the Internet through an Ethernet or WiFi access point (these links could be provided with military microwave or satellite connections). Upon their entrance inside the office, rescuers start deploying tactical devices (OTD) as base stations in order to have the maximum wireless network coverage in the operations area. Two to five OTDs are deployed (as shown in Figure 5). Mobile nodes (smart phones) carried by the rescuers are connected (to the CC node) through the tactical network. Shimmer sensors are connected to ORACE-Net mobile devices via Bluetooth. Since the experimentation area is limited, we reduced the Raspberry Pi's and smart phone's WiFi antenna transmission power to 0 dBm. Experimentation parameters and configuration settings are detailed in Table 2.
## 1 Introduction
The chemometric techniques used to obtain information from these data are identified as pattern recognition techniques and can be divided between unsupervised methods (principal component analysis and cluster analysis) and supervised methods that allow the creation of predictive models, using various techniques including the regression technique. Exploratory data analysis, such as PCA, is a fundamental step in the analysis of this type of data. It provides an overall view of the system under study, allowing the detection of possible similarities/dissimilarities among samples, the identification of clusters or systematic trends, the discovery of those variables that are relevant to describe the system and that can be discarded in principle, and the detection of possible outlying, anomalous, or at the very least suspicious samples [19]. The proper management of outliers is necessary to develop models that are effective [20]. Regression models can be used to predict and quantify selected maturity indices of products. Chemometric techniques are, therefore, fundamental for extracting useful information from optical data and for creating simplified models. In this case, a PLS (partial least square) regression model was used, which represents a particular type of multivariate analysis that is capable of modeling the relationship between two matrices, i.e., the relationship between the predictors, X, and the variables we want to predict, Y [21]. The experimentation tested a cost-effective and portable prototype to estimate the qualitative parameters of Chardonnay grapes directly in the field in order to support operators in rapid and objective decision making. The aim of this work is to obtain immediate results on grape ripeness to identify the optimal harvesting time. The optical prototype is equipped with vis/NIR bands and predictive models to promote a sustainable approach and viticulture 4.0.
## 2 Experimental Section
### Synthesis of **9**  
Using a similar procedure as described for the synthesis of **1**, reaction of _N_-Boc-\(\beta\)-alanine (8.5 g, 45.0 mmol) with _n_-octanol (5.85 g, 45.0 mmol), 1-[3-(dimethylamino)propyl]-3-ethylcarbodiimide hydrochloride (8.62 g, 45.0 mmol) and 4-(dimethylamino)pyridine (5.49 g, 45.0 mmol) in dichloromethane (350 mL) provided _n_-octyl-_N_-Boc-\(\beta\)-alanine (12.75 g, 42.3 mmol, 94%). Using a similar procedure as described for the synthesis of **2**, _n_-octyl-_N_-Boc-\(\beta\)-alanine (12.0 g, 39.87 mmol) was deprotected with trifluoroacetic acid to obtain _n_-octyl-\(\beta\)-alanine (6.8 g, 33.8 mmol, 85%). Reaction of _n_-octyl-\(\beta\)-alanine (0.573 g, 2.85 mmol) with 1\(H\),1\(H\),2_H_-perfluorooctyl isocyanate (1.1 g, 2.85 mmol) provided **9** as a pale yellow oil (1.95 g, 62%): \({}^{1}\)H NMR (CDCl\({}_{3}\)): \(\delta\) 4.06 (t, \(J\) = 7.2 Hz, 2H), 3.53 (t, \(J\) = 6.4 Hz, 2H), 3.26 (m, 2H), 2.30 (m, 2H), 1.55 (m, 2H), 1.20 (bs, 10H), 0.81 (t, distorted, 3H); \({}^{19}\)F NMR (CDCl\({}_{3}\)): \(\delta\) \(-\)81.2 (m, 3F), \(-\)114.4 (m, 2F), \(-\)122.2 (s, 2F), \(-\)123.2 (m, 2F), \(-\)124.0 (m, 2F), \(-\)126.5 (m, 2F).