esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Adaptive Retrieval  
#### 3.2.1 Single-Step Strategy  
We adopt different strategies for different types after identifying the complexity of the query. For simple queries, this can be directly retrieved from an external knowledge base using a retriever. Specifically, let us assume the knowledge base is \(D\), the query is \(q\), and the retriever is \(Retriever()\). The retriever uses \(q\) as input to search the knowledge base and returns relevant documents \(d_{i}\), as shown in Figure 1.  
\[rd=Retriever(q;D)\]  
where \(rd\) is the set of relevant documents, \(rd\in\{d_{1},d_{2},\ldots,d_{i}\}\).  
The retriever can use any off-the-shelf retrieval model. However, for a fair comparison with other studies, we employed the BM25 retriever. During the retrieving process, documents are ranked according to their BM25 score. For a given document \(D\) and a query \(Q\), the BM25 score is calculated as follows:  
\[\text{BM25}(D,Q)=\sum_{t\in Q}\text{IDF}(t)\cdot\frac{f(t,D)\cdot(k_{1}+1)}{f( t,D)+k_{1}\cdot\left(1-b+b\cdot\frac{|D|}{\text{avgdl}}\right)}\]  
where:  
* \(t\): A term in the query \(Q\).
* \(f(t,D)\): The term frequency of \(t\) in document \(D\).
* \(|D|\): The length of the document \(D\) (in terms of number of words).
* avgdl: The average document length in the corpus.
* \(k_{1}\): A tunable parameter that controls the term frequency saturation.
* \(b\): A tunable parameter that controls the influence of document length normalization.
* \(\text{IDF}(t)\): The inverse document frequency of term \(t\).  
The inverse document frequency \(\text{IDF}(t)\) adjusts the weight of terms based on their rarity in the document corpus. It is calculated as follows:  
\[\text{IDF}(t)=\log\biggl{(}\frac{N-n(t)+0.5}{n(t)+0.5}+1\biggr{)}\]  
where:  
* \(N\): The total number of documents in the corpus.
* \(n(t)\): The number of documents containing the term \(t\).
## 5 Discussion
* Depending on the manufacturing process, certain lattice thicknesses or cell sizes may be impractical depending on material availability, production feasibility, and cost constraints. In those cases, the manufacturing capability needs to be assessed before fabricating a particular cell type model. By adjusting sensitivity levels, designers and engineers can tailor the TPMS structure to best fit specific operational requirements, ensuring an optimal balance between performance, manufacturability, and energy efficiency. Assigning weight percentages to the response parameters allows for the identification of the respective optimal by prioritizing each outcome according to their relative importance in the overall analysis. The use of TPMS structures to collect water from the air is a promising energy-saving and additive-free technology, especially suited for areas with water scarcity and high humidity. However, efficient materials may require advanced manufacturing processes and higher production costs. Additionally, there are seasonal and diurnal variations in collection efficiency, and the water quality may be affected by airborne pollutants. Additional filtration systems, renewable power supply, and suitable material selection are needed to tackle these limitations along with engineering enhancement.
## 4 Results and Discussion
effect of traffic management was greater in the case of adverse weather conditions with the same traffic demand. The relative difference varied depending on the density of traffic flow. Traffic management had the greatest effect in the case of medium density (about 12 veh/km), where the relative differences between weather conditions were small. We also calculated total delays as a function of the equivalent flow rate. In all weather conditions, traffic management could achieve lower delays; however, in the interval of flows from 1500 pcu/h to 2900 pcu/h, this difference was not as large as at higher flow rates. Owing to traffic management, delays were reduced by 2% to 3% in good weather conditions and by 5% to 7% in adverse weather conditions. However, at higher flow rates, delays could be reduced by up to 21%, regardless of weather conditions, but only up to a certain limit. When traffic flow rates exceeded 4500 pcu/h, the relative differences between delays under different conditions decreased. Similar to the case of relative delays, the results show that traffic management with truck overtaking bans at lower traffic demand was not as effective as for flow rates above 2300 pcu/h with a 10% share of heavy vehicles. Total delays may be equal to or even lower in the case of rain or a wet carriageway. We found that truck overtaking bans had a positive effect when considering the relative difference in total delays but only when the traffic flow rate of all vehicles and heavy vehicles on the carriageway separately exceeded a certain threshold value. As part of our research, we limited the proportion of HGVs in the total flow rate. If we were to vary this, we would achieve more results. With the research, we proved that sensing weather conditions is important in the case of decision-making for lane traffic control.
# Modeling and Assessment of GPS/BDS Combined Precise Point Positioning
## 2 GPS/BDS Combined PPP Models
### Simplified GPS/BDS Combined PPP Model  
Based on the scaled sensitivity matrix (SSM, [19; 29]) method, [PERSON] et al. [19] proves that there is no correlation between ISB and coordinate parameters in multi-GNSS combined PPP, and ISB parameter can be removed in multi-GNSS PPP. Based on this conclusion, we remove the ISB term in Equation (7) and rewrite observation equations as:  
\[\begin{array}{l}P^{\rm G}=\rho^{\rm G}+{\rm c}\cdot d\overline{t}^{\prime}+ mf_{\rm w}^{\rm G}\cdot ZWD+\zeta^{\rm C^{\prime}}\\ L^{\rm G}=\rho^{\rm G}+{\rm c}\cdot d\overline{t}^{\prime}+\overline{N}^{\rm G ^{\prime}}+mf_{\rm w}^{\rm G}\cdot ZWD+\epsilon^{\rm G}\\ P^{\rm C}=\rho^{\rm C}+{\rm c}\cdot d\overline{t}^{\prime}+mf_{\rm w}^{\rm C }\cdot ZWD+\zeta^{\rm C^{\prime}}\\ L^{\rm C}=\rho^{\rm C}+{\rm c}\cdot d\overline{t}^{\prime}+\overline{N}^{\rm C ^{\prime}}+mf_{\rm w}^{\rm C}\cdot ZWD+\epsilon^{\rm C}\end{array} \tag{8}\]  
where \(d\overline{t}^{\prime}\) is defined as new receiver clock, \(\overline{N}^{\rm G^{\prime}}\) and \(\overline{N}^{\rm C^{\prime}}\) are new ambiguity terms. Compared to Equation (7), the ISB term is absorbed in the new terms and pseudo-range residuals with:\[\begin{array}{l}d\overline{t}^{\prime}=d\overline{t}_{\rm G}+\lambda\cdot ISB\\ \overline{N}^{G^{\prime}}=\overline{N}^{\rm G}-\lambda\cdot ISB\\ \varsigma^{G^{\prime}}=\varsigma^{\rm G}-\lambda\cdot ISB\\ \varsigma^{\rm C^{\prime}}=\varsigma^{\rm C}+(1-\lambda)\cdot ISB\\ \overline{N}^{C^{\prime}}=\overline{N}^{\rm C}+(1-\lambda)\cdot ISB\\ \end{array} \tag{9}\]  
where \(\lambda\) is called the element of the SSM, which quantitatively defines the ratio of ISB parameter assimilated into the receiver clock.  
As described in [PERSON] et al. [19], the benefits of the new model are: (1) under some extreme circumstances, the traditional Multi-GNSS PPP model may final when there are only four satellites observed, while the new approach will still give coordinate solution; (2) the correlation between the clock and ambiguity parameters is reduced, thus making solution more stable using the new model; (3) Multi-GNSS PPP realization is simplified and unified. In this new model, observations of different GNSS systems are treated in a unified way as they were of the same satellite system. In this simplified model, the ISB is assimilated into a receiver clock and ambiguity parameter. Due to the much smaller weight assigned on pseudo-range observations, all pseudo-range observations will show big residuals. The GPS pseudo-range residuals are close to the amount of ISB value that assimilates into station clock parameter. The BDS pseudo-range residuals are close to the amount of ISB value that assimilates into BDS ambiguity.
# QESIF: A Lightweight Quantum-Enhanced IoT Security Framework for Smart Cities
## 4 Simulation Setup and Experimental Results  
To evaluate the proposed QESIF, a hybrid simulation environment was designed by integrating both classical IoT and quantum communication simulators. Specifically, QulSP was utilized to model QKD behavior, photon-level entropy variations, and QBER fluctuations under varying decoherence noise. This was integrated with OMNeT++ (using the INET and Castalia frameworks) to simulate IoT communication protocols such as MQTT and CoAP across smart city subsystems, including traffic control, energy monitoring, and healthcare telemetry. The layered simulation allowed dynamic key injection into IoT flows and real-time switching between quantum-secured and classical modes. For empirical validation, three comprehensive datasets were incorporated: TON_IoT [25] for multi-domain telemetry behaviors, Edge-IIoTset [26] for industrial IoT attack simulations, and Bot-IoT [27] for evaluating Q-IDS resilience under high-entropy attack scenarios. These datasets were fused with simulated QKD key logs to evaluate the framework's security, throughput, latency, and anomaly detection effectiveness. This simulation setup enabled the emulation of real-world QKD-enabled smart city infrastructures and provided a benchmark for assessing the QESIF's robustness under both adversarial and noisy conditions. For comparative analysis, the QESIF was evaluated against four recent quantum-secure approaches: QRHE [10], HyQuSec [21], QuIDS [22], and QLSN [23], all of which represent state-of-the-art advancements in quantum-enhanced security for the IoT and smart cities. A detailed summary of the simulation parameters is provided in Table 2.
###### Abstract
Interesting results obtained were that undesirable periodic regimes could be eliminated by coupling the NES with a properly tuned piecewise quadratic damping. [PERSON] and [PERSON] [28] studied the complex dynamical behaviors of coupled Dahl-type and nonsmooth systems at a fast time scale and a slow time scale. [PERSON] and [PERSON] [29] analyzed the periodic motion and frequency energy plots for a two-degrees-of-freedom system coupled with a piecewise smooth nonlinear energy sink. [PERSON] et al. [30] proposed a ground-limited NES with a piecewise spring. Based on the harmonic balance method, an analytical solution of NES was studied considering the approximate function of a piecewise smooth function. [PERSON] et al. [31] studied the vibration behaviors of a vibro-impact system with an elastic stop and analyzed its stability of the periodic solution using Floquet theory. There are few studies on analyzing the periodic solutions of the nonsmooth DVA model using the Melnikov method and it remains to be studied. Motivated by this, the stability and periodic motions of the two-degrees-of-freedom primary system coupled with an encapsulated nonsmooth DVA modeled by a six-dimensional piecewise smooth system are studied. The key contributions of this paper are as follows. 1. The fast-slow analysis method is introduced to study the stability of the equilibrium point of the nonsmooth dynamic vibration absorber model. The existence and stability of the admissible equilibrium points of an encapsulated DVA model with piecewise smooth properties are discussed in different parameter regions. The asymptotical stable and unstable regions of an admissible equilibrium point for a nonsmooth DVA model under different parameter conditions are obtained, which provides theoretical guidance for the design of a nonsmooth DVA structure. 2. Few studies have focused on the bifurcation and number of periodic solutions of a three-degrees-of-freedom nonsmooth DVA model. In this paper, the expression of a Melnikov vector function suitable for studying the bifurcation of periodic orbits of six-dimensional piecewise smooth systems is given, and it is applied to study the existence and number of periodic solutions of an encapsulated DVA model. 3. The relationship figure is introduced to provide guidance for estimating the number of periodic orbits. The existence and number of periodic solutions under different parameter conditions are given, and the geometric description is given. This paper is arranged as follows: In Section 2, the model and its governing equation are proposed. In Section 3, the existence and stability of admissible equilibrium points are discussed. In Section 4, the Melnikov function is calculated and the existence and number of periodic orbits are discussed. The phase configurations of periodic orbits are presented with a numerical simulation.
# Secure and Lightweight Cloud-Assisted Video Reporting Protocol over 5G-Enabled Vehicular Networks
## 3 Preliminary
### Attribute-Based Encryption Scheme
#### 3.1.1 ABE.Setup  
For the universe of attributes \(U=\{1,2,...,n\}\); let \(G_{1}\) be an additive group with a prime order \(q\) and \(P\in G_{1}\), where \(G_{1}\) is made of points on an elliptic curve and \(P\) is a generator of \(G_{1}\). ABE.Setup(0 sub-protocol works as follows:  
* On input of a random \(s\in Z_{q}^{*}\) as the attribute master secret key, output the corresponding public key \(PK=s\cdot P\).
* For each \(i\in U\), choose an attribute secret \(l_{i}\in Z_{q}^{*}\) to generate the attribute public key \(P_{i}=l_{i}\cdot P\).
* Set \(ABEmk=\{s,l_{1},...,l_{|U|}\}\) and \(ABE.params=\{PK,P_{1},...,P_{|U|}\}\).
* Returns \(\langle ABEmk,ABE.params\rangle\).
# Evaluation of the Effectiveness of Driver Training in the Use of Advanced Driver Assistance Systems
## 4 Results
### Model Validation Based on Behaviour Assessment in a Single Research Scenario  
The initial model aggregated inputs like number_of_attempts across all test scenarios. This revealed a significant flaw: a poor performance in the first scenario could disproportionately penalize the overall score, masking subsequent improvement. In contrast, trainer evaluations often considered the participant's progress and final skill level, as evidenced by comments such as 'The participant showed significant improvement after the first attempt'. The aggregated model could not account for this learning dynamic. Therefore, to enable a more valid comparison and to quantify skill acquisition, a second version of the model was developed to assess performance on a per-scenario basis. This revised model used inputs from the first and third research scenarios to evaluate performance at different stages of the test drive, allowing for the analysis of skill development. To enable accurate assessment, modifications to the model were necessary. In the original version, the input variable number_of_samples considered the total from all research scenarios. In the revised model version, this parameter was adapted to reflect individual scenario data.  
Figure 9 presents the increase in the average scores obtained for each type of training. The figure indicates that practical training yielded the highest mean results across both examined research scenarios, as well as in the trainer evaluations. The lowest scores were recorded for the brief manual instruction. The figure also reveals an increase in the average behaviour assessment scores across all types of training.  
The analysis of the model results for the individual test scenarios revealed an increase in scores for the majority of participants in subsequent scenarios. The scores of 83% of participants were either higher or the same in the third scenario compared with the first,  
Figure 9: Comparison of the mean results for the individual research scenarios, based on the revised per-scenario model.  
indicating that over time participants gained experience and improved their skills in operating the system. Among those whose score in the third scenario was lower than in the first, the difference was greater than 0.5 in only four cases.  
Additionally, the score improvement was assessed by calculating the ratio of the scores obtained in the third to the first scenarios. The average improvement for the entire group was 1.74, with a maximum of 2.9, demonstrating the high effectiveness of the training provided.
## 2 Materials and Methods
### Estimation of Aboveground Biomass of Corn  
Research has demonstrated that the implementation of NPP in green plants over a specific period primarily signifies a link between organic matter production via photosynthesis in the vegetation and the carbon fixation process. As plant stems and leaves mature, their photosynthetic and carbon fixation capabilities are enhanced [40]. According to this theoretical framework, researchers have proposed a biomass inversion model driven by NPP. This model establishes a distinct linear correlation between the biomass of green plants and cumulative NPP. Additionally, this relationship is affected by the ratio of roots to crowns and the carbon content of plants [41].  
\[B=\sum{NPP}\times\alpha/\beta \tag{5}\]  
where \(B\) represents the amount of aboveground biomass in the plant; \(\sum{NPP}\) is the total accumulated net primary productivity during a specific time period; \(\alpha\) is the root-to-crown ratio; and \(\beta\) is the carbon content of the plant. This equation reveals a straightforward linear relationship between plant biomass and accumulated NPP, allowing for the translation of NPP into plant biomass once the parameters for the plant root-crown ratio and carbon content were determined [13]. Table 1 illustrates the variations in the carbon content ratio, root-crown ratio, and other relevant metrics between winter wheat and summer corn crops, attributed to their different crop types and growth attributes. When converting NPP to biomass, it is essential to consider these factors. In this study, aboveground biomass was calculated specifically for corn, thus utilizing data pertaining to corn.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Crop Type**} & **Total Carbon** & **Dry Matter** & **Root-to-Crown** & **Moisture** \\  & **Content Ratio** & **Ratio** & **Ratio** & **Content** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline wheat & 44 & 85 & 11 & 12.5 \\ corn & 43 & 78 & 9 & 13.5 \\ rice & 40 & 85 & 10 & 14.0 \\ millet & 43 & 85 & 11 & - \\ sorghum & 45 & 91 & 9 & - \\ rapeseed & 44 & 90 & 6 & 13.5 \\ cotton & 40 & 90 & 6 & 8.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Carbon percentage, dry matter percentage, root-crown ratio, and water content of each crop type.
# Remote Sensing for International Peace and Security: Its Role and Implications
## 5 Future Perspectives
### Conflict Management Perspective
#### 5.2.4 Peacebuilding  
Remote sensing will useful in post-conflict community rehabilitation like zoning, resource accessibility, and community planning. With the advancement in image processing algorithms, satellite imagery can be used in a better way than it was used a few decades ago for peacebuilding. Besides that, community project mapping by remote sensing should be considered to avoid project fatigue and duplication. In conflict zones, security aspects along with the political and social aspects influences the data collection. Here, remote sensing technology can be an enabler for exploring and assessing the risks to the ecosystem during uncertain and violent situations [115].
# Effect of Processing Methods on Amino Acid and Fatty Acid Composition of _Parkia biglobosa_ Seeds
###### Abstract  
_Parkia biglobosa_ (PB) is among the various underexploited nutritious plants. A comparative study was conducted on the effect processing PB has on the amino and fatty acid composition. Fermented (FPB), defatted (DPB), and protein isolates (PI) samples were prepared from _Parkia biglobosa_ seeds using various processing methods, and evaluated using standard analytical protocols. The PI showed the highest significant total non-essential amino acids, essential amino acids, and conditionally essential amino acids when compared with FPB and DPB. In addition, the PI showed the highest significant (\(p<0.05\)) total neutral amino acids, basic amino acids, total aromatic amino acids, and the lowest percentage cystine ratio of total sulphur amino acids and total amino acids in comparison with FPB and DPB. The PI also showed a better-predicted protein efficient ratio, essential amino acid index, biological value, protein content, and nutritional index than FPB and DPB. Furthermore, PI amino acid composition was compared favourably with the reference scores for a whole hen's egg, preschool child, and provisional scoring pattern. Arginine and histidine values of PI were higher than the recommended values. The FPB (42.29%) showed the highest polyunsaturated fatty acids compared with the DPB (41.94%) and PI (26.7%). The dominant saturated, monomunsaturated, and polyunsaturated fatty acids were stearic acid (15.1 DPB), linoleic acid (42.29 DPB), and oleic acid (14.05 PI). The DPB (1.84) showed a better polyunsaturated to saturated (P:S) ratio than FPB (1.74) and PI (0.91). The results revealed that the processing methods improved the relative amino acid composition, whereas the fatty acid composition improved in DPB. Therefore, PI could serve as an alternative in the formulation of complementary foods.  
amino acids; fatty acids; _Parkia biglobosa_; protein isolate 25 September 2024  
24 October 2024  
2024
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 4 Challenges and Open Research Issues
### QoS and QoE Requirements  
Many researchers have worked with QoS-aware resource management problems in C-RAN in a different manner. One of the potential methods to solve the problem can be the use of deep learning (DL)techniques. In [65], DL was applied to solve the QoS-aware power management problem because of its efficiency of mapping inputs to outputs. The fundamental concept was to train a deep neural network to obtain the optimal power management results by learning the potential relationship between the input variables automatically. The same concept can be implemented for a C-RAN by training neural networks to obtain optimal resource management.  
Whereas QoS is related to the network services, the consideration of parameters such as throughput, latency, jitter, and quality of experience (QoE) is another important subjective measurement that addresses the satisfaction level of a user while using a telecommunication service. Considering the forthcoming requirements of the 5G cellular network, it is also necessary to develop QoE-driven resource management schemes and identify appropriate KPIs for the network. The knowledge of the instantaneous and average QoE per user may aid the MNOs in modifying the network resource usage for each cell size, set different bandwidth limits dynamically, and identify the minimum resource required to achieve the desired QoE [66]. Developing efficient cache management can be a good approach to improving QoE by reducing the transmit delay in the network.