esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 IETF 6 LoWPAN Working Group (IPv6)
### Key Protocols  
As mentioned in Section 4.1, the main objective of the 6 LoWPAN working group is coming up with extensions to IPv6 protocols so that IPv6 packets can be transferred in constrained networks such as IEEE 802.15.4. IPv6 forwarding routers, unlike IPv4 routers, do not support fragmenting outgoing packets. This means that the communicating hosts have to send packets with the right size (MTU) supported by the communication links of the router. To meet this requirement hosts may use Path MTU Discovery to find a suitable MTU along the path or just send packets that meet the minimum MTU requirement for all links, which is 1,280 bytes. However, this minimum value is still too big for IEEE 802.15.4 links that have an MTU of 127 bytes for the entire MPDU. To use IPv6 on top of IEEE 802.15.4 networks, a mechanism that allows transmitting IPv6 packets that are larger than the MTU of 127 bytes is required. The solution presented by the 6 LoWPAN working group is to use a layer between the network and the data link layers that supports packet fragmentation and reassembly. In addition, the 40 byte IPv6 fixed header takes a significant portion of the already small protocol data unit of the LLNs, leaving little room for IPv6 data payload. To solve this problem different sorts of header compression are proposed by the working group. Further, these fragments have to be routed between the LLN nodes. Layer 2 multi-hop data transmissions should also be addressed in relation to IPv6 adaptation. Accordingly, the 6 LoWPAN working group has introduced an IPv6 adaptation layer, named 6 LoWPAN Adaptation Layer that lies between the data link layer and the network layer of the protocol stack. The adaptation layer delivers three basic services: packet fragmentation and reassembly, header compression, and data link layer routing (for multi-hop connections).
## 3 Results and Discussion
### Strain Rate Effect  
The strain rate sensitivity of the NiAlCu alloy specimens can be calculated from the experimental stress-strain curves presented in Figure 1 in accordance with [29].  
\[\beta=(\hat{c}\sigma/\hat{c}\text{ln}\hat{e})=\frac{\sigma_{2}-\sigma_{1}}{\text {ln}(\hat{e}_{2}-\hat{e}_{1})} \tag{2}\]  
Figure 2a-c present the corresponding results obtained for the strain rate sensitivity of the NiAl alloys with Cu additions of 5 at%, 6 at% and 7 at%, respectively. It is observed that the strain rate sensitivity increases with increasing strain rate, strain and Cu content [5]. It is thought that the greater strain rate sensitivity is the result of a rapid multiplication of dislocations [30; 31; 32] in the deformed microstructure under an increased strain rate and the blocking of these dislocations by secondary phases precipitated under higher levels of Cu addition.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Alloy** & **Strain Rate (s\({}^{-1}\))** & \begin{tabular}{c} **Yielding Strength,** \\ \(A\) **(MPa)** \\ \end{tabular} & \
\begin{tabular}{c} **Material Constant,** \\ \(B\) **(MPa)** \\ \end{tabular} &
\begin{tabular}{c} **Work Hardening** \\ **Coefficient, _n_** \\ \end{tabular} \\ \hline \multirow{8}{*}{Ni\({}_{63}\)Al\({}_{32}\)Cu\({}_{5}\)} & \(10^{-3}\) & 449.3 & 3056.6 & 0.62 \\  & \(10^{-2}\) & 475.9 & 3410.9 & 0.64 \\  & \(10^{-1}\) & 520.67 & 3647.8 & 0.65 \\  & \(3\times 10^{3}\) & 925.8 & 2257.9 & 0.68 \\  & \(4\times 10^{3}\) & 1205.86 & 2626.8 & 0.72 \\  & \(5\times 10^{3}\) & 1562.66 & 2918.2 & 0.77 \\ \hline \multirow{8}{*}{Ni\({}_{63}\)Al\({}_{32}\)Cu\({}_{6}\)} & \(10^{-3}\) & 571.3 & 2072.8 & 0.63 \\  & \(10^{-2}\) & 656.57 & 2203.3 & 0.66 \\ \cline{1-1}  & \(10^{-1}\) & 771.74 & 2162.3 & 0.67 \\ \cline{1-1}  & \(3\times 10^{3}\) & 1073.07 & 1802.4 & 0.7 \\ \cline{1-1}  & \(4\times 10^{3}\) & 1236.94 & 1884.8 & 0.73 \\ \cline{1-1}  & \(5\times 10^{3}\) & 1331.2 & 2171.2 & 0.78 \\ \hline \multirow{8}{*}{Ni\({}_{63}\)Al\({}_{32}\)Cu\({}_{7}\)} & \(10^{-3}\) & 695.5 & 1628.0 & 0.65 \\  & \(10^{-2}\) & 750.7 & 1697.4 & 0.68 \\ \cline{1-1}  & \(10^{-1}\) & 809.0 & 1744.0 & 0.70 \\ \cline{1-1}  & \(3\times 10^{3}\) & 1048.6 & 1563.0 & 0.71 \\ \cline{1-1}  & \(4\times 10^{3}\) & 1289.6 & 1661.6 & 0.75 \\ \cline{1-1}  & \(5\times 10^{3}\) & 1676.5 & 2190.0 & 0.80 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical properties of NiAlCu intermetallic alloys deformed at room temperature (25 \({}^{\circ}\)C) under strain rates of \(10^{-3}\)-\(5\times 10^{3}\) s\({}^{-1}\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Alloy** & **Average Hardness Value** \\ \hline Ni\({}_{63}\)Al\({}_{32}\)-Cu\({}_{5}\) & 301.16(Hv) \\ Ni\({}_{63}\)Al\({}_{31}\)-Cu\({}_{6}\) & 325.63(Hv) \\ Ni\({}_{63}\)Al\({}_{30}\)-Cu\({}_{7}\) & 353.9(Hv) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hardness values of NiAlCu intermetallic alloys.
## 4 Experimental Result
### Discussion  
By implementing NLP methods, both ML models and neural network models have an excellent performance, with greater than 85% accuracy for all models. The neural network models outperform traditional ML models, with all neural network models reaching a greater than 90% accuracy. The best accuracy was achieved by the CNN with Global Maxpool model. There is an approximately 6% higher accuracy in neural network models than in ML models.
# Logistics Service Provider Lifecycle Model in Industry 4.0: A Review
## 1 Introduction
These technologies have been adopted in various applications, including logistics activities, advancing traditional logistics to become \"Smart Logistics (Logistics 4.0)\" [32; 33; 34; 35; 36; 37]. A literature review is essential to systematically analyze a new field of research and subsequently develop a framework for action, for example [15; 38; 39]. The Systematic Literature Review (SLR) is a systematic tool to avoid bias that focuses on identifying the scope of focus by analyzing the trend areas of research [28; 40]. This paper aims to integrate the concepts of lifecycle management and Logistics 4.0 into LSPs to generate innovation or re-design logistics services for encouraging customer needs with many levels of logistics operation by minimizing investment and resources in a company. The authors formulated the research questions as follows:  
1. How has the literature developed on the subjects of logistics service providers, logistics operations, smart logistics operations, and the concept of lifecycle management? 2. How many stages and criteria to assess LSPs exist in the current literature? 3. What kind of innovation in LSPs is discussed in the scientific papers? To answer the research questions, this paper provides a comprehensive review of Industry 4.0, Logistics 4.0, 3 Party Logistics (3 PL), Logistics Operations, and Lifecycle Management to summarize the gaps in the research field and outline further directions for this topic. This paper is structured as follows. Section 2 describes the systematic literature review to develop the LSP Lifecycle Model (LSLM) and the results. Section 3 presents a detailed review of the criteria and sub-criteria of the LSLM model. Finally, Section 4 presents the conclusion of the review and research limitations.
## 1 Introduction  
Among the different optical platforms, plasmonic ones are characterized by a quick response, simple operation mode, and high sensitivity. Surface plasmon resonance (SPR) or localized SPR (LSPR) represents a gold standard in the optical characterization of biomolecular interaction, and it is a widely used transduction method in biosensing. In this context, SPR/LSPR systems based on optical fibers represent an emerging field also due to the incoming of plastic fibers (POFs). Recent advances in POP-based devices open the way to the development of a new class of sensors [1, 2, 3], also taking advantage of different geometries, such as U-bend, D-shaped, side-polished, and tapered shapes of POFs [1, 2, 3, 4]. SPR sensors can be realized by exploiting multimode optical fibers via POFs, where the convolution of different resonance wavelengths is obtained for a specific resonance condition defined by a given angle-wavelength couple [2, 3]. In the multimode waveguides, the SPR sensitivity is intrinsically better with respect to the single-mode waveguide configurations, even if they present the worst value of the Full Width at Half Maximum (FWHM) in the SPR spectra. Moreover, their excellent properties, such as flexibility, great numerical aperture, and large diameter, make POFs extremely interesting platforms with an increased surface area available for functionalization and, consequently, target capture.  
More specifically, we focused on particular POP-SPR biosensors making use of aptamers as molecular recognition elements (MRE). In some application fields, this emergingclass of molecules has overcome antibodies, the gold standard in biorecognition mechanism, paving the way to develop a new class of biosensors. In the following paragraph, these two elements (aptamers and SPR plastic optical fiber probes) are reported, mainly focusing on their characteristics, and highlighting the high potentiality of their coupling in a single optical biosensor, as reported, e.g., in [4] for the SARS-CoV-2 detection. More specifically, POF-based platforms combined with aptamers are described in more detail.
# WorkloadGPT: A Large Language Model Approach to Real-Time Detection of Pilot Workload
###### Abstract  
The occurrence of flight risks and accidents is closely related to pilot workload. Effective detection of pilot workload has been a key research area in the aviation industry. However, traditional methods for detecting pilot workload have several shortcomings: firstly, the collection of metrics via contact-based devices can interfere with pilots; secondly, real-time detection of pilot workload is challenging, making it difficult to capture sudden increases in workload; thirdly, the detection accuracy of these models is limited; fourthly, the models lack cross-pilot generalization. To address these challenges, this study proposes a large language model, WorkloadGPT, which utilizes low-interference indicators: eye movement and seat pressure. Specifically, features are extracted in 10 s time windows and input into WorkloadGPT for classification into low, medium, and high workload categories. Additionally, this article presents the design of an appropriate text template to serialize the tabular feature dataset into natural language, incorporating individual difference prompts during instance construction to enhance cross-pilot generalization. Finally, the LoRA algorithm was used to fine-tune the pre-trained large language model ChatGLM3-6B, resulting in WorkloadGPT. During the training process of WorkloadGPT, the GAN-Ensemble algorithm was employed to augment the experimental raw data, constructing a realistic and robust extended dataset for model training. The results show that WorkloadGPT achieved a classification accuracy of 87.3%, with a cross-pilot standard deviation of only 2.1% and a response time of just 1.76 s, overall outperforming existing studies in terms of accuracy, real-time performance, and cross-pilot generalization capability, thereby providing a solid foundation for enhancing flight safety.  
pilot workload; large language model; low-interference device; real-time detection; cross-pilot generalization +
Footnote †: journal: Applied Sciences
# Research on Agricultural Product Price Prediction Based on Improved PSO-GA
## Appendix A
### Autoregressive Integrated Moving Average (ARIMA)  
Autoregressive Integrated Moving Average is a forecasting model that combines autoregressive (AR), moving average (MA), and integrated (I) components to analyze and forecast time-series data. It is a common method used in statistics and econometrics for modeling and forecasting time-series data. The calculation process is as follows:  
Step 1: Smoothness test: The time series is tested for smoothness by examining the statistical properties of the time series. If it is not smooth, then differentiate the series to make it smooth.  
Step 2: Identify the ARIMA parameters: Estimate the ARIMA parameters (p, d, q) using methods such as maximum likelihood estimation.  
Step 3: Fit the model: Fit the ARIMA model to the smooth time series.  
Step 4: Forecasting: Use the fitted model to forecast the future values of the time series.
# A Review on Recent Advancements in FOREX Currency Prediction
## 6 Brief Review of FOREX Price Prediction Techniques
### Regression Methods
The reviewed literature suggests that the system based on a regression model performed better than ARIMA and ARFIMA models [48], and the model performance may increase to a great extent [41] when a regression model is combined with other techniques. However, when working with a large number of currency pairs, it may become difficult with regression techniques, as most of the currency pairs return a higher MSE [49].
# Influence of Some Additives on the Properties of OPC Solidified Sandy Silt
## 3 Results  
### Unconfined Compression Test  
Figure 4 and Table 4 shows the unconfined compressive strength test results with different additive replacement ratios. The rapid improvement in strength was observed in all 145 specimens at the age of 28 days and became stable after 60 days. For gypsum, the unconfined compressive strength reached its maximum level when the content was 2%, and the strength slightly decreased with the increase of the gypsum. For example, the reduction for SS3 and SS4 was 1.2 and 4.4% compared to SS2 at the age of 90 days. For the clay particles, it is noted that with the increase of content, the unconfined compressive strength increased at all ages. However, the effect of the clay particles on the solidified sandy silt is not ideal for long periods, which may attribute to the free water discharge [26]. It is noted that the addition of lime reduced the unconfined compressive strength. Specimens with 4% lime reached a minimum value of 0.79 MPa at the age of 90 days, decreasing by 77.0% compared to the control group. [PERSON] et al. [27] also reported similar conclusions.  
Figure 3: Microstructure test instruments: (**a**) FESEM and (**b**) XRD.  
Figure 2: Test set-up and failure details of the specimens: (**a**) unconfined compression strength, (**b**) tensile strength, (**c**) flexural strength, and (**d**) compressive resilient modulus tests.  
However, they found that the OPC experiences more retardation with heavy metals than with lime in the soil. For fly ash, the strength of the specimens shows an increasing trend, which is similar to that of gypsum. When the fly ash replacement ratio increased to 4%, the strength increased by 22.2 and 15.1% at the ages of 28 and 90 days. Figure 5 also illustrates unconfined compressive strength with the optimal content of each additive to the comparison. Overall, the unconfined compressive strength of the solidified sandy silt was increased in the order of 2% lime \(<\) 4% clay particle \(<\) 2% gypsum \(<\) 4% fly ash for 90 days.
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 3 Possible Attack Types on Physical and MAC Layer
### Smart Jamming
The mapping depends on time density and frequency density parameters. The effectiveness of a downlink PT-RS jamming attack is uncertain without knowing how often PT-RS are enabled and the density default set by base station vendors [56]. * The Downlink and Uplink User Data Physical Downlink Shared Channel (PDSCH) and the Physical Uplink Shared Channel (PUSCH) represent the main part of the frame. It is feasible to selectively interfere with these channels. The jammer could equally interfere with the whole UL and DL; hence, the jamming of PDSCH and PUSCH is one of the least important threats to consider. * The Physical Random Access Channel (PRACH), similarly to LTE, is used by the UE after the synchronization of SSB and the decoding of PBCH to transmit a preamble that is in the form of a Zadoff-Chu sequence that embeds a value used for the temporary identification of the UE. The gNB broadcasts the candidate locations of the PRACH in the time-frequency grid in case the UE attempt to connect [57]. From the jammer point of view, the many configurations of PRACH and real-time data decoding makes the jamming of PRACH unreliable. * Another published attack exploits the beam configuration of a cell to proceed a SSB-RA fingerprinting localization attack. The attacker creates a map detailing the exact locations of the BS and all beams within a cell. By monitoring the random access channel, the attacker can infer the beam selected by the UE from the random access occasion. The attacker also acquires the Timing Advance (TA) value from the RA response sent by the BS. These values are then used to calculate the UE's azimuth and distance from the BS, providing an estimated location of the UE. With a combination of PDCCH Order (PO), the attacker can also target already connected users. To quantify the complexity and power efficiency of jamming-type attacks against the 5G PHY layer, the Jammer to Signal (\(J/S\)) ratio was introduced in [19; 75]. According to Table 3, considering the complexity of attacks on PHY channels, the most effective jamming or spoofing attacks are those in DL against PSS, PBCH, SSS, and DM-RS of PBCH, respectively. The less effective attacks in DL are against PDCCH, and in the UL against PRACH and PUCCH.
## 3 Results and Discussion
### Formation Temperature of DES  
Table 3 displays the different formation temperatures for each DES composition analyzed. In general, the addition of water leads to a decrease in the formation temperature of the DES compared to the formation temperature of the base components. This could potentially translate into energy savings if this technique were to be implemented on a larger scale. This effect can be explained by water acting as an agent that partially disrupts the hydrogen bonds formed by the HBAs and the HBDs. This, in turn, decreases the viscosity of DES and lowers its melting temperature.  
Figure 1: FTIR spectra of choline chloride–ethylene glycol-based DES.  
Conversely, the data in Table 3 indicate that choline chloride-ethylene glycol-based DES are formed at lower temperatures than the rest of the DES, from 45 \({}^{\circ}\)C to 63 \({}^{\circ}\)C. This suggests that these DES phases form more readily at relatively mild temperatures. In contrast, choline chloride-fructose (CF)-based DES require higher formation temperatures. The minimum temperature observed for CF-based DES is 73 \({}^{\circ}\)C when the system contains a higher proportion of water. Conversely, choline chloride-fructose-based DES need an initial addition of water during their preparation. Without the inclusion of water, the melting point of this DES rises significantly, resulting in impractically high viscosities. Consequently, the composition of 2:1:0 could not be studied in terms of extraction capacity.
# Implementation of Robots Integration in Scaled Laboratory Environment for Factory Automation
## 2 State of the Art
### Industrial Robots and Tools  
Next to mobile robots, industrial robots are inevitable for accomplishing flexible factory automation. Combined together, industrial and mobile robots are able to perform various tasks, including warehouse management, pick and place, transportation, machine tending, etc. For an industrial robot to successfully handle/manipulate an object, a gripper is being imposed as a medium between the manipulator and the manipulated object. If a gripper is to perform a task of grasping an object, one should be able to securely and safely hold it. Depending on the given object in the task, a suitable gripper is designed for the job.  
Industrial mechanical grippers should be made as robust, rigid objects with very few moving parts, easy to attach and detach. Depending on the task, a gripper also could be required to handle various sized objects, with different shapes, materials and mass [19]. In the case of pneumatic grippers, they can also be made as rigid mechanical grippers, or as soft, muscle-like mechanisms [20]. For a soft pneumatic gripper, grasping is mainly conducted by a suction-like mechanism, which holds firmly to the attached item and conforms itself to the shape of the grasped object [21]. In addition, soft pneumatic grippers can be designed to resemble a biologically inspired method of grasping [22]. Pneumaticgrippers, whether they are soft or rigid in their design, can adapt to the various shapes of the grasped item. Because of these abilities, pneumatic grippers are convenient for their usage in the food industry.
## 3 Results and Analysis
### Co\({}_{2}\) Fluxes  
There was a significant difference (\(p<0.01\)) between the average CO\({}_{2}\) exchange flux and the DO concentration at H1 and H2, as shown in Figure 4. The CO\({}_{2}\) exchange flux at H1 ranged from \(-118.49\) to \(116.53\) mmol/(m\({}^{2}\cdot\)d), with fluctuations that caused it to decrease during the day and increase at night. Throughout the entire sampling period, the average CO\({}_{2}\) exchange flux at H1 was \(12.62\) mmol/(m\({}^{2}\cdot\)d), indicating that this section may be a major gas source. The trend of the change in CO\({}_{2}\) exchange flux at Q1 did not show significant fluctuations. The carbon flux calculated during most sampling time periods was positive, and the CO\({}_{2}\) exchange flux at H2 ranged from \(-490.83\) to \(228.34\) mmol/(m\({}^{2}\cdot\)d). The maximum flux appeared in the late afternoon, while the minimum flux was observed in the morning, which is exactly the opposite of the DO. Throughout the sampling period, the average CO\({}_{2}\) exchange flux at H2 was \(-0.68\) mmol/(m\({}^{2}\cdot\)d), indicating that the section may act as a sink for atmospheric CO\({}_{2}\).  
Figure 3: Diurnal variation of DIC components in water bodies (note: The gray portion represents the period of night during the sampling period in the study area, while the white portion represents the period of daytime).