esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Efficient Dynamic Symmetric Key Based Protocol for Secure Traffic Exchanges in Smart Homes
## 2 Related Work
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Scheme** & **Pros** & **Cons** \\ \hline
[8] & Thwarts replay attacks & High computation costs \\ \hline
[11] & Provides user anonymity and secure mutual authentication & Susceptible to impersonation, MitM, and session key disclosure attacks \\ \hline
[14] & Upholds data confidentiality & Cannot provide authentication parameters privacy \\ \hline
[16] & Protects against malicious activities in distributed smart environments & Elongated execution time \\ \hline
[27] & Efficient & Its formal and informal security analyses are not carried out \\ \hline
[34] & Robust against cloning, impersonation, traceability, and physical attacks & High communication costs \\ \hline
[38] & Protects user privacy & High power consumption \\ \hline
[43] & Efficient & Cannot withstand de-synchronization attacks \\ \hline
[46] & Boosts efficiency and reliability & Cannot resist gateway spoofing, session key disclosure, and impersonation attacks. \\ \hline
[47] & Offers anonymity & Vulnerable to password guessing, stolen user device and impersonation attacks \\ \hline
[50; 51] & Privacy-preserving & Single point of failure \& scalability issues \\ \hline
[53; 54] & Faster response time & Not privacy-preserving \\ \hline
[57] & Upholds confidentiality, user, and device authenticity & Vulnerable to de-synchronization attacks \\ \hline
[62; 63] & Privacy-preserving & Heavy computation and storage overheads \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Pros and cons of current schemes. replay, and spoofing. On the other hand, the schemes in [8; 11; 14; 16] have been shown to have numerous security and performance issues. High communication and computation costs are the performance limitations of the majority of these schemes. On the other hand, lack of forward key secrecy and anonymity, coupled with susceptibility to impersonation, MitM, and DoS, are serious security and privacy issues in these protocols. In contrast, our protocol deploys transient parameters such as nonces, timing information, and secret values during the derivation of the session key to preserve forward key secrecy. In addition, shared secret keys are deployed to encrypt user and device identities to uphold their anonymity. This enciphering and re-computation of user identity using random nonces and exclusive OR operation with mobile device identity renders it hard for an attacker to eavesdrop on these identities for any possible impersonation attempt. To curb MitM attacks, the contents of the authentication verification beacons are concatenated before being hashed. This makes it computationally infeasible for the attacker to reverse the one-way hash to obtain these parameters for launching MitM attacks. Regarding the DoS attack, our scheme derives the verification token and sends it to the trusted authority. Here, this token is re-computed and compared with its received equivalent. If these parameters are not equivalent, the communication process is immediately terminated.
## 3 Material and Methods
### Anomaly Detection  
After removing invalid navigation messages in Section 3.1, abnormal navigation messages should be detected and rejected before the nominal SIS URE assessment in Section 3.3.  
* Anomaly detection with the URAI and space vehicle (SV) health flags contained in the broadcast message According to the BDS-2 Interface Control Document (ICD), two parameters are designed in the broadcast navigation message for satellite anomaly detection: The URAI and SV health. The URAI is an indicator for User Range Accuracy (URA), which is used to describe the satellite signal-in-space accuracy in the unit of meter [23]. The relationship between the URAI and URA can be found in the BDS-2 ICD. A URAI threshold of seven was selected for anomaly detection in this paper. SV health was selected as the satellite status indicator. The SV health indicator \"0\" means a healthy satellite, whereas \"1\" means unhealthy. These two indicators can be applicable for real-time anomaly detection.  
Figure 2: BDS broadcast navigation message analyzing flowchart.  
* Anomaly detection with external precise products references  
With precise orbit/clock products as the reference, we can calculate the broadcast orbit/clock errors. The comparison is carried out referring to time of clock (ToC) for every broadcast navigation message. Since both BDS broadcast orbit and precise orbit refer to the satellite antenna mass center [17], no phase center correction is necessary for the orbit comparison. As for the clock assessment, C01 was selected as the reference satellite to eliminate the clock datum difference between broadcast and precise products [24].  
Satellites with significantly large orbit/clock errors are considered as abnormal in this paper. IGSO/MEO orbit error thresholds were set as 3/6/6 m in radial/along-track/cross-track directions, respectively. Radial/along-track/cross-track error thresholds of 3/45/6 m were selected for GEO orbits. The clock error threshold was set to 10 m for all satellites [19].
## 2 Materials and Methods
### Batch Adsorption  
The impacts of initial oil concentration (\(C_{0}\)), adsorbent dosage, adsorption time, and salinity on the adsorption capacity and removal efficiency were investigated via batch adsorption experiments. \(C_{0}\) varied from 25 to 200 ppm, and the rGO-Fe\({}_{2}\)O\({}_{3}\) dose varied between 1 and 20 mg, yielding a dissolved oil to the adsorbent ratio of 50 to 8000 mg/g. The emulsion salinity varied between 0 and 200,000 mg/L using NaCl.  
The adsorption capacity (\(q_{l}\), mg/g) and removal efficiency (\(RE\), %) were calculated using Equations (2) and (3):  
\[q_{l}=\frac{(C_{0}-C_{t})\times V}{\text{m}_{g}} \tag{2}\]  
\[RE=\frac{\left(C_{0}-C_{f}\right)}{C_{0}}\times 100 \tag{3}\]  
where \(C_{t}\) is the oil concentration (ppm) at time \(t\), and \(V\) and \(\text{m}_{g}\) are the emulsion volume (L) and the adsorbent mass (g).
# Food Processing with UHP Waterjets
## 1 Introduction  
A processed food is any food that has been altered in some way during preparation. Food processing can be as basic as freezing, canning, portioning, sterilizing, etc. What is relevant to this paper is the use of ultrahigh pressure (UHP) waterjet technology for food processing, although the use of UHP technology is not limited to cutting. The following is a list of current UHP technology applications in the food industry:  
* Cutting: In this case, high-velocity waterjets, generated by pressures up to 621 MPa (90 Ksi), are used for the following: \(\Box\)
* Portioning: This is to slice through food product to produce food portions.
* Excision: This is to cut undesirable food areas such as the fat off some meat cuts.
* Trimming: This is to trim off some areas of the food such as the crowns of carrots.
* Peeling: This is to remove the skin off some fruits or vegetables.
* Stripping: This is to separate food from each other such as deboning operations.  
This can be accomplished with different types of jets, not all of which have been commercially implemented.  
Pure liquid jets: In this case, a liquid jet is used which is commonly water but sometimes an alternative liquid may be used (Ex. coconut oil is used for cutting some chocolates). This is the most commercially used waterjet tool.  
Pure liquid fanjet: In this case, a fan jet is used either for peeling or stripping.  
Abrasive waterjet: In this case, solid particles are entrained into the waterjet to increase its cutting ability as may be needed to cut through bones.  
Air-Liquid Jets. Instead of abrasives, air is entrained into the waterjet to create a highly focused droplet jet.  
Other jets such are cryogenic jets (using liquid nitrogen, for example) are possible to use, as stated by [PERSON] and [PERSON] [1], but unlikely to be practical due to the cost and the special handling requirements.  
* Pressurization: Processing food products by static pressurization is used to sterilize the food by killing harmful microorganisms and thus extending the shelf life and safety of the food.
* Pumping: In this case, the food, mainly liquids, may be pumped through the nozzle for the purpose of homogenization or other effects where high shear rates are required.  
In this paper, we will focus on processing with UHP waterjets. Other papers to follow will address the areas of sterilization and pump-through processing.
## 3 Synthesis of Transition-Metal-Sulfide-Based Nanomaterials
### NiS\({}_{x}\)-Based Nanomaterials  
[PERSON] et al. investigated the electrochemical reduction of CO\({}_{2}\) to CO on NiS nanoparticles [177]. XAS experiments were used to investigate the behavior of the catalysts during the process, where a structural change in the NiS was observed [177]. The authors noted that the change in the Ni-S structure was similar to the that of single-atom-N-coordinated Ni. In addition, the results showed that the O-S bond formation was modulated at a current density of \(0.3\) mA/cm\({}^{2}\)[177]. However, the catalyst showed low stability due to the loss of S atoms. The faradaic efficiency observed for the NiS was approximately 85%.  
[PERSON] et al. used FeS\({}_{2}\) on NiS to reduce CO\({}_{2}\) to methanol which was synthesized through a hydrothermal process [178]. The study showed that the reaction was initiated at an over-potential of 0.280 V; however, a faradic efficiency of 64% at a potential of \(-0.6\) V (reversible hydrogen electrode) was observed [178]. The catalyst showed stability for up to 4 h which was attributed to the ladder structure of the nanocomposite. In addition, the results indicated that the active sites were located between the interface of the FeS\({}_{2}\) and NiS. [PERSON] et al. investigated the reduction of CO\({}_{2}\) on nickel containing iron sulfides as a model for hydrothermal vents where CO\({}_{2}\) reduction was observed [179]. The authors synthesized FeNi\({}_{2}\)S\({}_{4}\) using hydrothermal synthesis as violarite in its mineral form. The results showed that the pristine greigite was ineffective at reducing CO\({}_{2}\); however, that of Ni-substituted greigite was improved by approximately 85%. In addition, methane was the preferred product with the Fe/Ni nanoparticles, while hydrogen was the preferred product with pure greigite. [PERSON] et al. studied the synthesis of Ni-S-C nanoparticles from S-doped Ni-triazolate starting material for the CO\({}_{2}\) reduction to CO [180]. The authors synthesized a pure Ni(II)-triazolate complex as a starting material followed by pyrolysis at three temperatures, 800, 900, and 1000 \({}^{\circ}\)C, which resulted in a Ni\({}_{3}\)S\({}_{2}\) material with carbon. The highest faradic efficiencies were observed at \(-1.5\) V (vs. Ag/AgCl). The results showed faradic efficiencies of approximately 67%, 45%, and 40% at \(-1.5\) V for the 800, 900, and 1000 \({}^{\circ}\)C synthesized products, respectively. The results reported on the use of nickel-sulfide-based nanomaterials in the CO\({}_{2}\)RR are of great interest. The hybrid nanomaterialsappear to show higher activities than the individual Ni\({}_{y}\)S\({}_{x}\) materials. The Fe doping of the Ni\({}_{y}\)S\({}_{x}\) and the Ni doping of the Fe\({}_{y}\)S\({}_{x}\) nanomaterials do enhance the reactivity of the Ni\({}_{y}\)S\({}_{x}\). There appears to be some type of synergistic effect from the Ni-Fe redox coupling within nanomaterials for the CO\({}_{2}\)RR. However, the Ni-sulfide-based nanomaterials appear less stable than other TMS in the CO\({}_{2}\)RR and thus show lower efficiencies.
###### Abstract  
In this article, we review the latest progress on data center interconnect (DCI). We then discuss different perspectives on the 400G pluggable module, including form factor, architecture, digital signal processing (DSP), and module power consumption, following 400G pluggable optics in DCI applications. Next, we experimentally investigate the capacity-reach matrix for high-baud-rate and high-order quadrature amplitude modulation (QAM) single-carrier signals in the unamplified single-mode optical fiber (SMF) link. We show that the 64 GBd 16-QAM, and 64-QAM signals can potentially enable 400 Gb/s and 600 Gb/s DCI application for 40 km and beyond of unamplified fiber link.  
Keywords:400 Gigabit Ethernet; coherent communications; data center interconnect; fiber optics links and subsystems; optical communications; QSFP-DD transceiver +
Footnote †: journal: Applied
## 5 Discussion
### Influence of the Tracked Joint Angle  
The logistic regression results indicated a small, but significant effect of the lower body variable. These impressions are supported when inspecting the boxplot of the angles in the ME (Figure 12). The boxplot shows a tendency of underestimating the upper body angles, overestimating the hip angles, and a difference in the mean between the knee and hip angles. To investigate this effect, we performed the ANOVA analysis on the ME. We shifted the ME to only include positive values and applied the logarithmic transformation similar to our proceedings of the MAE as described in Section 2.6. The observed angles show an influence on the result (\(\eta^{2}=0.26\), \(p=0.00\)). Post-hoc analysis using Games-Howell supports the suggestions that the differences lie between the upper body angles and lower body angles and between the hip and knee angles (Appendix C, Table A2).  
Interestingly, the exercise and movement of the hip center were the influencing factors for the MAE in contrast to the results of the ME. In the MAE, the difference between the angles is not observable anymore. The upper body error is mapped to a similar MAE as the lower body joints by only considering the absolute error (Figure 12). The ME for the whole dataset is \(-0.83^{\circ}\), meaning that overestimating the lower body joints and underestimating the upper body joints could be subject to error cancellation when considering the entire body. This effect could explain the MAE's dependency on the selected exercise while no dependency on the angle was observed.  
The ANOVA results show an effect for the upper body variable and support the respective tendency of over-and underestimation. However, as explained in Section 2.6, the ME is prone to error cancellation effects. This unclear influence impacts the explanatory power, so we did not include these thoughts in the results and findings.
## 3 Results and Discussion
### BTA Release Studies from Carboxymethyl Cellulose Microspheres  
3.5.1. Encapsulation Efficiency (EE%) and Loading Capacity (LC%)  
The encapsulation efficiency (EE%) results, for the different experimental conditions, varied from 34% to 39%, and are shown in Table 4. Encapsulation efficiency is defined as the amount of corrosion inhibitor successfully encapsulated in regard to the initial amount before spray drying; similarly to the process yield, it depends on different factors, where spray drying parameters such as inlet temperature and spray flow rate--and consequently, outlet temperature--as well as carrier and encapsulated material properties (e.g glass-transition temperature) [49], are amongst the most relevant ones. Nevertheless, EE% values were not significantly influenced by the processing conditions. The loss of benzotriazole may have occurred in the drying step, where more volatile compounds can evaporate simultaneously with the solvent. Similar results regarding the use of spray drying technology as a microencapsulation process have been reported by [PERSON] et al. [40] for carboxymethyl cellulose microparticles with different drug:polymer ratios, with encapsulation efficiencies in the range of 41 to 60%. Moreover, [PERSON] et al. [52] encapsulated Mulberry leaf extracts in carboxymethyl cellulose and maltodextrin particles, achieving EE% values between 36 and 42%.  
The loading capacity reflects the amount of inhibitor loaded (encapsulated) per unit weight of the particles, indicating the percentage mass of the particle that is due to the inhibitor (in this case BTA). For particles prepared with a 440 L/h spray flow rate, it is possible to observe a slight decrease (from 16 to 15%) in the loading capacity of CMC-MS with increasing inlet temperature, contrary to the results obtained for higher spray flow rates, where no influence of temperature or spray flow rate is observed.
## 2 Related Work
### Routing in Multi-Layer Satellite Networks  
A multi-layered satellite routing algorithm (MLSR) was proposed for a multi-layered satellite IP network comprised of GEO, MEO, and LEO satellites to calculate routing tables efficiently using delay measurements [10]. A survivable routing protocol was presented to provide the ability to survive under LEO or MEO satellite failures and minimum delay routing using a topology control strategy in LEO/MEO satellite networks (LMSNs) [34]. A QoS-aware load-balancing was proposed to alleviate the delays and improve throughput in LEO satellite networks by congestion-prediction-based detouring of the traffic via MEOsatellites [35]. A traffic distribution from the LEO to the MEO layer to minimize packet delivery delay in multi-layered satellite networks was proposed by considering propagation and queuing latency [36]. An ant-colony-based inter-layer link handoff algorithm was proposed to reduce the number of inter-layer link handoffs by considering the inter-link layer distance, duration, and QoS metrics [19].
## 3 Results and Discussions
### Sample Extraction Techniques  
Figure 4 displays the comparison of different potential methods of extracting volatiles for analysis from animal hide. Based on the results of this study, improved results (_i.e._, richer chromatograms) were generally obtained when the HS-SPME extraction was performed directly on the residue of interest rather than on an extract from an item. However, due to the common practice of performing solvent extractions from items of interest in archaeological research, both approaches were tested. Further studies with sample replication may demonstrate the potential effects of different solvents or ultrasonication to optimize extraction of specific residues and provide additional comparison to performing direct analysis of residue.  
Previous studies of archaeological residues have typically relied on extraction and derivitization [5; 9; 10]. However, the development of a non-destructive direct HS-SPME extraction from the residue is a substantial benefit, as further analyses can then be conducted on the item in its original state after it has been screened by VOC analysis. This requires no solvents or derivatization agents and less physical handling of potentially important items and residues. It is currently unknown what amount of material is necessary in order to detect the volatile signature directly from various samples by GC\(\times\)GC-TOFMS. For example, the volume of blood, mass of resin, and/or mass of sediment collected may be too low to produce a sufficiently concentrated headspace for analysis. In addition, residues may be a combination of several traces. For example, this could occur if a stone tool was used to cut both vegetal material and a piece of meat. At trace levels, it is unknown how these volatiles signatures may interact and whether this technique would be capable of differentiating such minimal traces.  
Figure 3: Total ion current (TIC) contour plot for a piece of hide sample analyzed on the Rtx-5 MS \(\times\) Rxi-17 Sil MS column set demonstrating “L”-shaped peaks for some carboxylic acid peaks (green). Several labeled peaks demonstrate the value of the GC\(\times\)GC selectivity and typical compounds obtained (red).
## 2 Materials and Methods
### Factor Analysis  
Similar to PCA, FA is a technique for dimensionality reduction. However, FA assumes that observed variables are influenced by underlying latent factors, and it aims to identify these factors among the eleven variables. The purpose of FA is to identify latent factors that underlie the observed correlations among variables. These latent factors are assumed to influence the observed variables, and FA aims to uncover them from the dataset. The objective of FA is to provide an explanatory model for the observed data by identifying and describing the underlying structure of the variables. Through dimensionality reduction and the identification of latent factors, FA helps in understanding the relationships and patterns present in the dataset, thus facilitating insights into the underlying mechanisms driving the observed correlations among variables [36]. FA posits that the observed variables are influenced by both latent factors and unique factors. The latent factors are not directly observed but are inferred from the correlations among the variables. The latent factors are interpreted as underlying constructs that contribute to the observed correlations among variables. These factors are often given meaningful labels based on the variables with which they are associated. Latent factors can be correlated, allowing for a more flexible representation of the underlying structure [50].  
FA and PCA share similarities in their approach to reducing dimensionality, but FA introduces the concept of latent factors [19,22]. The relationship between the two lies in their common goal of reducing the number of variables while retaining important information.
# Systematic Review of Discrete Event Simulation in Healthcare and Statistics Distributions
## 4 Discussion
### Application Areas
#### 4.2.5 Laboratory and Diagnostic Services  
Laboratory operations benefit significantly from DES-driven workflow optimization. [PERSON] et al. [41] identified applications in streamlining test processing, enhancing equipment utilization, improving staff scheduling, and optimizing result reporting. Their findings showed a 22% increase in throughput following process redesign guided by DES, demonstrating its capacity to enhance diagnostic efficiency and turnaround times.
# A Secure Communication in IoT Enabled Underwater and Wireless Sensor Network for Smart Cities
## 5 Simulation and Results
### Simulation Setting  
NS-2 with Aquasim package is used for the evaluation of TBRL. Taking the area of 1000 \(\times\) 1000 m\({}^{2}\), 500 mobile nodes were randomly deployed inside the water and multiple sink nodes are placed at the water surface. The sink nodes are equipped with radio and acoustic type of communication medium. Sink nodes considered to be static throughout the simulation environment, but remaining nodes can move from 1 to 4 m/s inside the water. The diagonal and vertical movement of nodes is ignored. Only horizontal movement of nodes is considered for simulation environment. Different set of nodes are tested against TBRL. Two nodes placed at the seabed and selected as source nodes. The data packet size is fixed at 1000 bytes. Each source node generates a constant bit rate traffic. The simulation results are taken from the average value of 50 runs for each node dataset. Table 4 also shows the simulation setting provided to TBRL.