esa-sceva/satcom-chunk-collection huggingface dataset:
# A Practical Group Symbol and Group Name Approach (PSNA) according to EN ISO 14688-2:2018
###### Abstract  
Classification systems are crucial to the description and classification of soils, helping to facilitate soil identification, as well as the understanding, and interpretation of soil materials worldwide. In this study, EN ISO 14688-2:2018 and ASTM D2487-17e1 were employed to develop a practical soil identification approach called Practical Group Symbol and Group Name Approach (PSNA). The method presented herein is not a classification system or a standard but is rather an approach that makes EN ISO 14688-2:2018 usable in practice and enables soil particle identification in detail. The use of this practical method can eliminate any ambiguity in communicating soil characteristics. Practical flow charts were prepared for defining group names and symbols of soil particles. The method enables the classification of gravel, sand, silt, clay, and organic material. A user-friendly macro was also developed in Microsoft Excel, utilizing the integrated programming language Visual Basic for Applications (VBA) to facilitate the implementation of this approach. In the present study, an Excel-based triangular diagram was developed. In addition to the triangular diagram, two charts were considered. From these contributions, the group symbol and group name of soil mixtures can be easily and practically defined.  
soil naming; macro in Excel; group name; group symbol; triangular diagram +
Footnote †: journal: Applied Sciences
## 6 Conclusions  
Ensuring network QoS for real-time services in industrial and other domains is essential to meet the strict deadlines of real-time applications. Jitter or PDV is a crucial performance indicator for real-time applications in a communication network. The real-life performance of PDV depends on ever-changing background traffic due to the introduction of new devices, services, and applications to the communication network. We utilized network calculus toolboxes such as moment-generating functions and (min, +) algebra to study and investigate delay and PDV in a wired packet switched network. We derived an analytic expression for evaluating end-to-end delay and jitter bounds incurred by a traffic of interest by the presence of background traffic. The analysis based on simulations confirmed the scalability of the PDV expression with an increasing number of servers in a multi-hop network. The PDV analysis conducted with the help of the Markovian traffic model for background traffic showed that the background traffic parameters can significantly affect the end-to-end PDV bound. The analysis showed promising results to conceive of a traffic shaper for background traffic using the Markovian models. The end-to-end PDV can be controlled by changing the settings of the network model parameters. Thus, the gained knowledge can be utilized to shape background traffic to achieve the desired QoS performance required for real-time applications.  
PDV is significant in deciding the performance of many network-based applications. Bounding the PDV or jitter under a certain level can be beneficial for such applications. The next step within our work could be to assess the feasibility of employing the results of the PDV analysis in applications where the PDV needs to be bounded. Further, the possibility of designing a background network traffic filter can be investigated for such applications.  
Conceptualization, R.N.G., E.L., J.A. and M.B.; funding acquisition, J.A. and M.B.; investigation, R.N.G., E.L., J.A. and M.B.; methodology, M.B.; project administration, J.A.; supervision, E.L., J.A. and M.B.; writing--original draft, R.N.G.; writing--review and editing, R.N.G., E.L., J.A. and M B. All authors have read and agreed to the published version of the manuscript.  
This work was financed by the Future Industrial Networks (FIN) project, Grant Number 2018-02196, within the strategic innovation program for process industrial IT and automation, PiiA, and PiiA Research Etapp II, a joint program by Vinnova, Formas, and Energimyndigheten.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Effect of the Initial Cr(VI) Concentration and the pH Values on the AC@SCF Sorption Capacity  
The effect of solution pH value on Cr(VI) uptake capacity after 24 h as well as the effect of the initial Cr(VI) concentration on Cr(VI) sorption at pH = 3 are shown in Figure 4. At pH equal to and lower than 4, the Cr(VI) uptake was 100% for 7.44 ppm initial concentration, whereas this capacity was reduced to almost 17% at pH 6.5 (Figure 4a). At a lower pH, the carbon surface of AC@SCF was positively charged due to the presence of H\({}^{+}\) ions and attracted electrostatically the hexavalent chromate anions (HCrO\({}_{4}\)-, CrO\({}_{4}\)2-, and Cr\({}_{2}\)O\({}_{7}\)2-). At pH > 4.5, the carbon surface was more negatively charged (due to the presence of OH-) and the chromate anions (HCrO\({}_{4}\)-, CrO\({}_{4}\)2-) were electrostatically repulsed from the carbon surface. For this reason, the sorption efficiency gradually decreased. According to the literature [29], the predominant species of Cr(VI) in aqueous solutions are HCrO\({}_{4}\)- and Cr\({}_{2}\)O\({}_{7}\)2- for pH < 6 and Cr\({}_{2}\)O\({}_{7}\)2- and CrO\({}_{4}\)2- for pH \(\geq\) 6. The pH value also affects the charge of the AC surface  
Figure 3: Scanning electron microscope (SEM) (**a,b**) and transmission electron microscope (TEM) (**c,d**) images of the AC@SCF activated carbon.  
active sites and by extension the Cr(VI) removal capacity. This is also supported by Figure 4a, where the % uptake capacity versus pH is depicted. The 24-h adsorption capacity of AC@SCF increased by increasing Cr(VI) initial concentration and the experimental values for the initial concentration range (\(C_{init}\)) of 29-300 mg/g fell in the range of \(q_{max}\) = 29-109 mg\({}_{\text{Cr(VI)}}\)/g\({}_{\text{AC@SCF}}\). Figure 4b presents such values in details.
## 2 Results and Discussion
### Time-Weighted Average (TWA) Sampling Profiles of Benzene from Air Using Different Coatings
The ideal case pertains to Equation (1). Effect of the Diffusion Coefficient and Distribution Constant on Sampling of Analytes by 85-\(\mu m\) Carboxen/Polidimethylsiloxane Coating  
The Car/PDMS coating was used for simulating extraction of other common VOCs associated with a wide range of diffusion coefficients and distribution constants. During 100,000 s, 3.3, 3.9, 3.5 and 3.3 pmol of dichloromethane, acetone, toluene, and benzene, respectively, were extracted, which corresponds to 68, 65, 82 and 77% of the theoretical values predicted by Equation (1) (Figure 4). The lowest value was observed for acetone having a distribution constant close to dichloromethane, and the highest diffusion coefficient among studied compounds. Highest recovery was observed for toluene having the lowest diffusion coefficient and the highest distribution constant. Thus, both diffusion coefficient and distribution constant affect the recovery of sampled analytes. Highest recovery can be achieved at the lowest diffusion coefficient and highest distribution constant. At sampling times 1000 and 10,000 s, recoveries are greater (95-98 and 85-93%, respectively) and less affected by the analyte's properties. Figure 4: Effect of sampling time of TWA recoveries of analytes having different diffusion coefficients and distribution constants using 85-\(\mu\)m Car/PDMS fiber (\(T=298\) K, \(Z=10\) mm, 24 ga needle, \(p=1\) atm, \(C=0.641\) umol\(\cdot\)m\({}^{-3}\)). Figure 3: Concentrations of benzene in diffusion path air (**a**) and coating (**b**) of the retracted solid-phase microextraction (SPME) device after 100,000 s of time-weighted average (TWA) air sampling at \(Z=10\) mm.
###### Abstract  
Unmanned Surface Vehicle (USV) offers a cost-effective platform for high-resolution marine magnetic surveys using shipborne fluxgate magnetometers. However, platform-induced magnetic interference and electromagnetic interference (EMI) can degrade data quality, even with paramagnetic hulls. This study evaluates fluxgate magnetometer data acquired from a paramagnetic-hulled USV. Noise characterization identified EMI and maneuver-induced high-frequency noise, the latter of which was effectively reduced through low-pass filtering. We compared four different correction approaches addressing both vessel attitude and magnetization. The results demonstrate that the paramagnetic hull significantly reduces magnetic interference and shortens the duration of viscous magnetization (VM) effects caused by eddy currents in the platform, compared to conventional ferromagnetic vessels. Nonetheless, residual magnetization from onboard ferromagnetic components still requires correction. A method utilizing all nine components of the susceptibility tensor demonstrated improved accuracy and stability. Despite corrections, low-frequency VM-related noise during azimuth changes and a consistent absolute offset (~200 nT) remain when compared to towed scalar magnetometer data. These findings validate the use of paramagnetic USV for vector magnetic surveys, highlighting their benefit in VM mitigation while emphasizing the need for further development in VM correction and offset correction to achieve high-precision measurements.  
**Keywords:** marine magnetic survey; fluxgate magnetometer; viscous magnetization; magnetic correction; paramagnetic; Unmanned Surface Vehicle
## 2 IL-1 Family Cytokines, Receptors and Co-Receptors
### IL-36 Subfamily  
The IL-36 subfamily is a key regulator of the innate immune system and includes three agonists with pro-inflammatory activity (IL-36\(\alpha\), IL-36\(\beta\), and IL-36\(\gamma\)) and two antagonists (IL-36 RN or IL-36 Ra and IL-38) [32]. They are normally expressed in epithelial and immunecells; after binding to receptor complex IL-36R, agonists induce activation of nuclear factor-kB (NF-kB) and mitogen-activated protein kinases, leading to T-cell proliferation, expression of pro-inflammatory cytokines, chemokines, and co-stimulatory molecules by DCs and Th1 lymphocytes, as well as autocrine KCs signaling. The resulting pro-inflammatory milieu is composed of IL-1B, IL-12, IL-23, IL-6, TNF-\(\alpha\), CCL1, CXCL1, CXCL2, CXCL8, and GM-CSF, among others [32]. IL-36\(\alpha\) and IL-36\(\gamma\) are mainly produced by KCs but also by dermal fibroblasts, endothelial cells, macrophages, LCs, and DCs. As opposed to other IL-1 family cytokines, IL-36 cytokines are also produced as precursors but do not contain a caspase cleavage site. Following secretion, they are activated by neutrophil-derived proteases present at neutrophil extracellular traps (NETs)--such as elastase, cathepsin G, and proteinase 3--and by cathepsin S, produced by KCs and fibroblasts [33; 34; 35]. In addition, KCs secrete the protease inhibitors alpha-1-antitrypsin and alpha-1-antichymotrypsin (encoded by SERPINA1 and SERPINA3 genes), which inhibit processing of IL-36 cytokines by neutrophil proteases and thus regulate the inflammatory loop [36].
# 100 Picosecond/Sub-10\({}^{-17}\) Level GPS Differential Precise Time and Frequency Transfer
## 6 Discussion  
Compared with some existing results of GNSS pseudorange time and frequency transfer techniques, such as CV and AV, DPT indeed improves the time and frequency stability and thus enhances the performance of frequency comparison over averaging times of several hours or longer. In addition, a K-means clustering method was employed to estimate the SD receiver clock offset, which achieves accurate time transfer. The time stability can reach the picosecond level on the averaging time of 7 days, while the frequency stability can reach the sub-\(10^{-17}\) level. Among the existing GNSS time and frequency transfer methods, integer PPP (IPPP) has been proven to have the best clock comparison performance [32; 33], and the common clock IPPP approach has achieved a time transfer precision of 61 ps, which is comparatively less precise than DPT time transfer in the KOKB-KOKV baseline. In terms of frequency stability, the MDEV of the common clock IPPP experiment over a 30-day period is no less than 1 \(\times\)\(10^{-17}\), while the MDEV of the DPT frequency transfer for the KOKB-KOKV baseline can reach as low as 3 \(\times\)\(10^{-18}\) within 30 days. To our knowledge, these results represent the optimal proven time transfer precision and frequency stability performance of GNSS time and frequency transfer.  
As expected, for short baselines, single-frequency DPT provides higher precision than dual-frequency IF DPT, and the dual-frequency IF combination amplifies noise to some extent. Therefore, for short baselines, we can prioritize the use of single-frequency DPT for time and frequency comparison. The use of the DPT method is complementary to the PPP time transfer method and can achieve high-precision regional time transfer. It only uses broadcast ephemeris to achieve high-precision time transfer and is easier to implement in real-time dynamic applications. In addition, the excellent frequency stability of DPT frequency transfer helps to evaluate the frequency comparison performance in the region of the latest-generation optical lattice clock frequency standard. It should be noted that this work did not discuss longer baselines. For longer baselines, we will consider incorporating precise ephemeris in future research to improve time transfer accuracy.
# A Statistical Model Updating Method of Beam Structures with Random Parameters under Static Load
## 2 New Statistical Model Updating Method  
### Initial Equilibrium Equations  
Consider one \(N\) degrees of freedom of beam structure under the external static load. The static equilibrium equation of the beam structure at initial simulation state is written as:  
\[\mathbf{K}_{a}\mathbf{x}_{a}=\mathbf{F} \tag{1}\]  
where \(\mathbf{K}_{a}\) is an \(N\times N\) dimensional stiffness matrix, \(\mathbf{x}_{a}\) is an \(N\times 1\) dimensional displacement response vector of the structure under \(N\times 1\) dimensional static load vector \(\mathbf{F}\).  
Considering the translational and rotational degrees of freedom of the structure, Equation (1) can be rewritten as:  
\[\left[\begin{array}{cc}\mathbf{K}_{att}&\mathbf{K}_{at\theta}\\ \mathbf{K}_{a\theta t}&\mathbf{K}_{a\theta\theta}\end{array}\right]\left\{ \begin{array}{cc}\mathbf{x}_{at}\\ \mathbf{x}_{a\theta}\end{array}\right\}=\left\{\begin{array}{cc}\mathbf{F}_{ at}\\ \mathbf{0}\end{array}\right\} \tag{2}\]  
where \(\mathbf{K}_{att}\) and \(\mathbf{K}_{a\theta t}\) are respectively sub-matrices, which are related to the translational degrees of freedom of the initial stiffness matrix; \(\mathbf{K}_{a}\), \(\mathbf{K}_{at\theta}\) and \(\mathbf{K}_{a\theta\theta}\) are sub-matrices of \(\mathbf{K}_{a}\) with respect to the rotation degrees of freedom, which are not measured in this paper. \(\mathbf{x}_{at}\) and \(\mathbf{x}_{a\theta}\) are displacement vectors related to the translational and rotation degrees of freedom of the initial structure respectively. \(\mathbf{F}_{at}\) is the concentrated vertical force vector.  
According to the static condensation method, which is also used in literature [5] by [PERSON] et al., the rotational degrees of freedom of the stiffness matrix in the static equilibrium Equation (2) can be eliminated. In this way, Equation (2) can be condensed as:  
\[\mathbf{K}_{at}\mathbf{x}_{at}=\mathbf{F}_{at} \tag{3}\]  
where \(\mathbf{K}_{at}=\mathbf{K}_{att}-\mathbf{K}_{at\theta}\mathbf{K}_{a\theta \theta}^{-1}\mathbf{K}_{a\theta t}\).  
Of course, in the same manner, other unmeasured displacements can be removed.
## 4 Application layer
### IoT for Smart Cities  
IoT applications establish the functionality of smart cities, such as healthcare systems, parking space utilization, transport management, and food supply chains. The IoT can \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **LTE CAT-1** & **LTE CAT-4** & **NB-IoT** & **IEEE 802.11 ah** & **IEEE 802.11 ax** & **LoRaWAN** & **Sigfox** \\ \hline Frequency band & & Licensed band & & & & Unlicensed band & \\ \hline \multirow{2}{*}{Data rate} & DL: 10 Mbps & DL: 150 Mbps & \multirow{2}{*}{\(<\)150 kbps} & 433 to 6933 & 600 to 9608 & \multirow{2}{*}{100 b/s} \\  & UL: 5 Mbps & UL: 50 Mbps & & Mbps & & \\ \hline \multirow{2}{*}{Range} & Limited to & Limited to & Limited to & & & \\  & cellular cell & cellular cell & & Up to 1 km & 2.4/5/6 GHz & Up to 15 km & Up to 50 km \\ \hline Power consumption & & High to ultra-high & & Low & Low to ultra-low & \\ \hline Cost & & High & & & Low & \\ \hline Security & & High to ultra-high & & & Low to ultra-low & \\ \hline \multirow{2}{*}{Application} & Automotive & \begin{tabular}{c} Monitoring \\ \end{tabular} & \begin{tabular}{c} Smart grid \\ \end{tabular} & \begin{tabular}{c} Smart sensors \\ communication \\ \end{tabular} & \begin{tabular}{c} Smart sensors \\ and meters \\ \end{tabular} & \
\begin{tabular}{c} Smart building \\ (smart lighting) \\ \end{tabular} &
\begin{tabular}{c} Smart building \\ (electrical plugs) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Main characteristics of IoT long-range technologies [84; 85; 87]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**RFID** & **NFC** & **Zigbee** & **Bluetooth** & **Characteristics** \\ \hline ISO/IEC 13157 & 802.15.4 & 802.15.1 & IEEE 1451 & IEEE specification \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Varies \\ \end{tabular} } & 424 kbps & 250 kbps & 1 Mbps & Data rate \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Varies \\ \end{tabular} } & 13.56 MHz & 2.4 and 2.48 GHz & 2.4 GHz & Frequency band \\ \hline
1 m & 4 cm & 10–100 m & 10 m & Communication range \\ \hline \multirow{2}{*}{\begin{tabular}{c} Low \\ \end{tabular} } & Low & Low & Low & Cost \\ \hline \multirow{2}{*}{\begin{tabular}{c} No battery (passive tags) \\ \end{tabular} } & Intermediate & High & Ultra-high & \begin{tabular}{c} Power usage \\ (battery life) \\ \end{tabular} \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Tracking items, E-passport \\ \end{tabular} } &
\begin{tabular}{c} Smart tags for medical \\ applications \\ \end{tabular} & Smart metering & Smart home application & IoT applications \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Main characteristics of IoT short-range technologies [84; 85; 86]. Figure 9: Main interfaces for IoT networks. assist smart cities in various aspects; Table 11 summarizes the benefits of deploying the IoT for smart cities [22; 88; 89]. The IoT can be deployed for various smart city applications, achieving many benefits for each service. Table 12 introduces the main categories of smart city applications that can benefit from the IoT [88; 89; 90; 91].
## 3 Materials and Methods
### Sampling  
The experiments were conducted between 1 November 2023 and 14 January 2024. Participant recruitment was facilitated through various channels, including mailing lists, classroom announcements, and direct contact, leveraging university networks such as the SDM master's program at MIT. The participants were informed about the general setup of the experiment--remotely playing a game in a team--as part of the recruitment. Before participating, the participants needed to sign an informed consent document pointing out that their faces would be tracked, and the resulting videos would be used for analysis. Participation was voluntary and un-compensated. Participants were assigned to either the control or treatment groups using a randomized sequence based on their sign-up order. To maintain logistical efficiency, participants were not mixed between sites. Experiments were conducted on a rolling basis, allowing participants to choose time slots that suited their availability, despite the significant coordination efforts required to assemble groups of three for each session.  
The study successfully conducted 29 experiments, with 28 being analyzable. The experiments took place across three locations: 14 in Cambridge, USA; 13 in Monterrey, Mexico; and 3 in The Hague, Netherlands. Despite minor technical issues in some experiments, all were retained for analysis. Exclusion criteria included non-uniform team sizes, leading to the removal of one group that consisted of only two participants. Technical issues necessitated reassigning some groups between control and treatment conditions, but all adjustments were carefully documented to ensure the integrity of the experimental design.
## 2 Materials and Methods  
### Sample Preparation  
The murta fruits (_Ugni molinae_ Turcz) were obtained from 23-2-INIA ecotype (Instituto de Investigaciones Agropecuarias INIA Carillanca Murta Gene Bank). The ecotype used in this research was selected on agronomical and organoleptic characteristics [24]. The murta powder was supplied by the Food Science Laboratory of the Universidad de La Frontera, and its chemical composition analysis was carried out on SGS Chile Laboratory, according to the norms explained below. Briefly, for the preparation of murta powder, murta fruit was pulverized in a cold mill and passed through a 0.5 mm sieve. The chemical antioxidant \"Plus Color\", usually used in the frankfurter's preparation, was acquired by Prinal S.A. (Santiago, Chile). The chemical composition of both antioxidant sources is shown in Table 1.  
Three different frankfurter batches (5 kg each) were prepared. The control was produced without an antioxidant (T0). The other two batches were formulated with different antioxidant types at the same concentration (T1: with Plus Color, a chemical antioxidant, and T2: frankfurter formulated using murta powder as antioxidant). The final concentration of each antioxidant was the same quantity of chemical antioxidant usually used in frankfurter manufacturing (0.53% of formulae). Table 2 shown the frankfurters formulations.  
Briefly, the procedure to elaborate the different frankfurters treatments was as follows: lean raw pork and beef meat were mixed with salt, nitrites, antioxidants (murta powder of Plus Color), and species (pepper, garlic powder, and cumin). This mixture and bacon were grounded separately and were kept at 4 \({}^{\circ}\)C. Then, lean meat, and bacon were homogenized for 2 min in a meat cutter, to obtain a meat batter. Ice water was added, and the meat batter temperature was kept below 8 \({}^{\circ}\)C during all processes. Then, the meat batter was stuffed into a collagen casing (diameter 22 mm). The resulting frankfurters were cooked in a water bath at 75 \({}^{\circ}\)C until to obtain a frankfurter internal temperature of 72 \({}^{\circ}\)C. After cooking, the frankfurters were cooled in ice, drained for 30 min and stored at 4 \({}^{\circ}\)C for subsequent analysis. All manufacturing process was replicated twice, with the same ingredients and  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Parameter**} & **Murta** & \multirow{2}{*}{**Plus Color**} \\  & **(_Ugni molinae_ Turcz)** & \\ \hline Energy value (Kcal/100 g) & 328.3 & 181 \\ Ash (\%) & 3.0 & 0 \\ Crude fiber (\%) & 7.1 & 0 \\ Fat (\%) & 1.5 & 0 \\ Moisture (\%) & 9.7 & 0 \\ Protein (\%) & 3.3 & 0 \\ Sodium (mg/100 g) & 51.2 & 20.662 \\ Total carbohydrate (\%) & 75.4 & 38.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of antioxidants ingredients used in frankfurters formulation.  
processing conditions. Six samples of each treatment and manufacture replicate were used for the analysis.