esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring Decentralized Warehouse Management Using Large Language Models: A Proof of Concept
## 7 Discussion
### Limitations of the Proof of Concept  
As a proof of concept, our study has several important limitations that should be addressed in future work: Simplified simulation environment. We assume negligible transport times, uniform offer distributions, and a small fixed number of agents and slots. Real-world warehouses exhibit far more complex dynamics--variable transit delays, non-stationary demand, handling constraints, and stochastic failures--that are not captured here. Single LLM and prompting mode. We evaluated only one pre-trained model via stateless prompting. We did not explore alternative architectures, smaller on-premise models, stateful prompting, or systematic prompt tuning. Different choices could yield different trade-offs in accuracy, latency, and cost. Cost, latency, and scalability. While our API-based setup achieved sub-second decision times in simulation, larger-scale or higher-frequency deployments may incur prohibitive inference costs and delays. We did not benchmark throughput under concurrent load or consider hybrid architectures with rule-based fallbacks. Opaque decision logic. LLM outputs remain largely black box. We did not integrate explainability techniques, rule-based sanity checks, or human-in-the-loop validation to audit or justify agent decisions--features often required for operational use. Limited baseline comparison. We compared the LLM agent only against a randomized strategy. Without quantitative evaluation against rule-based heuristics, optimization solvers, or learned policies, it remains unclear how our LLM's performance gains compare to existing methods. No real-world data validation. All results were derived from synthetic game-based simulations. We have not yet tested the setup on real warehouse telemetry, historical bids, or live market data, so robustness and generalizability remain to be demonstrated. Addressing these limitations--by enriching the simulation, evaluating multiple models and prompting strategies, measuring real-world performance, adding explainability layers, and expanding baseline comparisons--will be the focus of our ongoing and future research.
# Wireless May Benefit Blockchain +
### State of the Art
#### 1.2.3 Blockchain over Wirelessly Connected Nodes  
This makes a compelling case where we urgently need a comprehensive framework evaluating the performance of blockchains with wirelessly connected nodes with respect to the blockchain trilemma. Many of the current consensus algorithms already consider the possibilities that nodes leave, and new nodes join. However, we claim that more is needed, as due to the uncertainty in wireless connections, a blockchain system established on a wireless network will draw a completely different environment than a one with ethernet-connected nodes [26].  
To elaborate, as a solution to practical limitations imposed by high node density and fluctuating wireless connectivity, a 6 G-empowered wireless network architecture was proposed [26], which in fact led to a significant improvement in several metrics, i.e., scalability, packet loss ratio, and latency. Moreover, there is a notion that signal ambiguity can also be a possible cause of a Byzantine fault. A recent proposal [27] mitigates the signal ambiguity by adopting the Second Order Blind Identification (SOBI) algorithm.  
Moreover, diverse avenues of recent research were sparked, exploring topics such as blockchain performance in the context of IoT [28], communication resource consumption [29], efficient consensus mechanisms among wireless nodes [30], connectivity-adaptive contract mechanisms [31], and Byzantine fault-tolerant (BFT) consensus among wireless nodes [32]. Specifically, the existing analytical framework (e.g., universal scalability law [33]) overlooked the impacts of wireless connectivity, which may cause serious imprecision these days with such a high proportion of wireless technologies in any given network.
## 3 Keep1-Nrf2-ARE Signaling System
### Corosolic acid (CA)  
Corosolic acid (**10**) is a triterpenoid and also known as 2\(\alpha\)-hydroxystolic acid. It is usually found in _Schisandra chinensis_, _Eriobotrya japonica_, _Lagerstroemia speciosa_ L., _Orthosibphon stannies_ and _Weigela subessilis_[49]. It has been reported to improve several cancers, including bladder, colon, liver, breast, lung cancer, etc., [228; 229; 230; 231; 232].  
The effect of CA on cellular transformation and upregulation of Nrf through epigenetic regulation in TRAMP-C1 prostate cancer cells was studied [233]. Here, CA induced mRNA and protein expression of Nrf2, and its target enzymes, such as HO-1 and NQO1. Moreover, CA abrogated the expressions of DNA methyltransferases (DNNTs) and histone deacetylases (HDACs), thereby hindering the processes of DNA hypermethylation and histone modification. This suppression of DNMTs and HDACs has extensive implications in the amelioration of prostate cancer [234; 235]. CA was found to be effective in abolishing tumor initiation and lung metastasis by abrogating the COX-2 and STAT3 expression in myeloid-derived suppressor cells [236]. Moreover, CA inhibited the proliferation of U373 and T98G glioblastoma cells by preventing the activation of STAT3 and NF-kB [237]. CA ameliorated cervical cancer in a dose-dependent manner by suppressing PI3K and AKT expressions [238]. A recent review on the anticancer effect of CA reported an accumulating body of evidence supporting CA's potential effectivity against gastric, hepatocellular, colorectal, ovarian, eye, bone, and lung cancer [239]. According to the review, the anti-gastric cancer effect of CA was mediated by the regulation of AMPK/mTOR, and the inhibition of NF-kB, HER2, and EGFR/neu oncogenic signaling. In addition, CA ameliorated colorectal, ovarian and lung cancer by suppressing Wnt/\(\beta\)-catenin and STAT3 signaling, respectively.  
Figure 7: Mechanism of Nrf-ARE activation by DC. The diagram was adapted and modified from [PERSON] et al., 2015 [224].  
Oxidative stress suppressing action of CA was reported in another study where CA-mediated Nrf2 nuclear translocation facilitated the upregulation of HO-1, NQO1 and GCLC [240]. This ameliorative action of CA was accompanied by inhibition of JNK and activation of AMPK signaling. CA also exhibited a synergistic effect with paclitaxel, cisplatin, doxorubicin, 5-FU and Adriamycin, and markedly enhanced the sensitivity to these chemotherapeutic agents [239]. It improved acute inflammation by suppressing interleukin receptor associated kinase-2 (IRAK-2) phosphorylation, which was independent of NF-kB [241].
# Firefly Optimized Resource Control and Routing Stability in MANET +
## 5 Simulation Results and Analysis
### Impact of Routing Stability  
The routing stability is defined using the stable energy nodes and bandwidth utilization rate through the packet transmission from the sender to the receiver. Figure 2 represents the routing stability of three different methods, such as the FFROR Technique, the EAER protocol [1], the MECOR protocol [4], the EESMR technique [25], and the OEERA scheme [26]. The figure shows that the proposed FFROR technique has achieved higher routing stability than the existing methods.  
Figure 2 shows the measurement of routing stability of three different methods via the FFROR technique, EAER protocol [1], MECOR protocol [4], EESMR technique [25], and OEERA scheme [26].In this figure, the x-axis denotes the percentage of routing stability and the y-axis denotes the different types of protocol. The proposed FFROR technique presents better performance results compared to the EAER Protocol [1], the MECOR Protocol [4], the EESMR technique [25], and the OEERA scheme [26]. The proposed FFROR technique aims to provide the minimum energy consumption nodes with the minimum bandwidth utilization nodes for sending the DP from sender to receiver. Therefore, the proposed FFROR technique improved the routing stability by 8% when compared to the EAER Protocol [1], 13% when compared to the MECOR Protocol [4], 5% when compared to the EESMR technique [25], and 14% when compared to the OEERA scheme [26].
# Place Recognition: An Overview of Vision Perspective
###### Abstract  
Place recognition is one of the most fundamental topics in the computer-vision and robotics communities, where the task is to accurately and efficiently recognize the location of a given query image. Despite years of knowledge accumulated in this field, place recognition still remains an open problem due to the various ways in which the appearance of real-world places may differ. This paper presents an overview of the place-recognition literature. Since condition-invariant and viewpoint-invariant features are essential factors to long-term robust visual place-recognition systems, we start with traditional image-description methodology developed in the past, which exploits techniques from the image-retrieval field. Recently, the rapid advances of related fields, such as object detection and image classification, have inspired a new technique to improve visual place-recognition systems, that is, convolutional neural networks (CNNs). Thus, we then introduce the recent progress of visual place-recognition systems based on CNNs to automatically learn better image representations for places. Finally, we close with discussions and mention of future work on place recognition.  
place recognition; Convolutional Neural Network; feature extraction; bag-of-visual words (BoW); vector of locally aggregated descriptors (VLAD) +
Footnote †: journal: Applied Sciences
## 3 Results
### Biocompatibility Evaluation  
The cell viability was evaluated over 4, 24, 48, and 72 h by obtaining a medium containing HaCaT cells. The proliferation rate of cells over time showed a similar tendency (Figure 8). With respect to cell proliferation up to 72 h, it was confirmed that the cell proliferation rate was slightly increased in the group containing dextran, and the results showed the most effective biocompatibility in the Alg-CMC ratio (5:5) with the dextran group (Figure 8b). The higher the content of CMC, greater is the potential for the material to have characteristics of non-toxicity, biocompatibility, biodegradability, wide non-specific surface, and rich reactant group [17].
# On Developing Techniques for Sharing Satellite Spectrum with Indoor Small Cells in 5G +
## 3 Mathematical Analysis  
### Estimation for a Single Building  
#### 3.1.1 Macrocell User Equipment (UE) Capacity  
Let \(M_{\text{aTMS}}\) denote the number of resource blocks (RBs) in aTMS spectrum bandwidth where an RB is equal to 180 kHz. Using (A3) in Appendix A, the aggregate capacity of aTMS for \(M_{\text{aTMS}}\) RBs and \(Q\) TTIs is given by  
\[\sigma_{\text{aTMS}}=\sum\
olimits_{t=1}^{Q}\sum\
olimits_{i=1}^{M_{\text{aTMS }}}\sigma_{t,i}\Big{(}\rho_{t,i}\Big{)} \tag{1}\]  
where \(\sigma\) and \(\rho\) are responses over \(M_{\text{aTMS}}\) RBs of all macro UEs in \(t\)\(\in\)T. The average system-level spectral efficiency of aTMS in bps/Hz is given by  
\[\sigma_{\text{aTMS}}^{\text{SE}}=\sigma_{\text{aTMS}}/(M_{\text{aTMS}}\times Q) \tag{2}\]  
Let \(S_{\text{F}}\), \(S_{\text{W}}\), \(S_{\text{P}}\), and \(S_{\text{M}}\) denote the number of small cells per building, the number of CGC stations per building, the number of picocells per macrocell, and the number of macrocells, respectively. Additionally, let \(P_{\text{FC,SPS}}\), \(P_{\text{PC}}\), and \(P_{\text{MC}}\) denote the transmit power of a small cell, a picocell, and a microcell, respectively. Energy efficiency is defined as the amount of energy required to transmit a bit. Hence, the average energy efficiency of aTMS in joules/bit is given by  
\[\sigma_{\text{aTMS}}^{\text{EE}}=((S_{\text{P}}\times P_{\text{PC}})+(S_{ \text{M}}\times P_{\text{MC}}))/(\sigma_{\text{aTMS}}/Q) \tag{3}\]  
Figure 3: Interference management based on the ABS based eICIC technique for collocated SBSs with CGC stations in the same building [22].
## 2 Background  
As 6G networks continue to evolve, the Open Radio Access Network (O-RAN) architecture has emerged as a critical enabler of flexibility, intelligence, and interoperability. Designed to foster innovation through disaggregation and openness, O-RAN introduces new opportunities for efficiency and automation. However, this paradigm shift also brings forth new challenges, particularly in terms of security and performance, which must be carefully addressed to meet the demanding requirements of next-generation communication systems. This section provides an overview of the O-RAN architecture, highlights key security concerns, and outlines essential 6G requirements such as energy efficiency, latency, and trustworthiness.
## 3 Results and Discussion
### Characteristics of Liquid Digestate and Slurry When Used for Ma-B Flocs Growth
#### 3.2.3 Ma-B Flocs Growth and Biomass Productivity on Liquid Digestate and Slurry
In our results, it is observed that as the total NH\({}_{4}\)-N concentration decreased (14.4 mg\(\cdot\)L\({}^{-1}\)), the algal biomass reached its growth optimum with 2.98 \(\pm\) 0.04 mg Chl (a)\(\cdot\)g\({}^{-1}\) VSS on day 14, in the case of the 10% liquid slurry. Thus, the maximum growth rate (0.028 days\({}^{-1}\)) and maximum Chl (a) concentration (2.98 mg\(\cdot\)g\({}^{-1}\) VSS) were observed with highly diluted substrate S\({}_{\text{L10\%}}\). The trophic status of the microalgal-bacterial community is explained by the autotrophic index (AI). In our batch experiments, the decrease in AI confirms the autotrophic abundance of microalgae in the Ma-B flocs inoculum. The lowest values were obtained after 14 days for the effluents S\({}_{\text{L0}}\), D\({}_{\text{L0}}\), D\({}_{\text{L30\%}}\), S\({}_{\text{t,10\%}}\), and synthetic wastewater. The AI decreased from 711.8 to 374.2 after 14 days of culture in D\({}_{\text{L30\%}}\) and to 532.6 for D\({}_{\text{L0}}\) (Figure 3). Figure 3: Ma-B flocs biomass productivity evaluated in terms of mg Chl (a)\(\cdot\)g\({}^{-1}\) VSS (mean value \(\pm\) SD) and autotrophic indices after 7 and 14 days of batch culture for different substrates: (**a**) DL, liquid digestate; (**b**) SL, liquid slurry (with and without dilution) and synthetic wastewater (WWS) a control. The slurry effluent S\({}_{\text{L10\%}}\) had the lowest activity index of 335.3, consistent with strong autotrophic dominance, in comparison to the other effluents. In this case, the slurry medium further promoted algal growth in Ma-B flocs and also increased ammonium and COD removal efficiencies.
# On End-to-End Intelligent Automation of 6G Networks
## 2 6G: Preliminaries and Enabling Technologies  
### Vision and Performance Targets  
#### 2.1.1 Vision and Goals  
Firstly, 6G represents the next evolution in the digital revolution. It will be the basis for billions of humans, things, and connected triphibian (communicating across land, air, and water) devices that will generate zettabytes of data [17]. Therefore, 6G is expected to be a \"self-contained ecosystem of artificial intelligence\" that will continually evolve by adopting a human- and machine-centric approach [17]. As such, 6G's aims go beyond just providing faster mobile internet access. Rather, its goals are [17]:  
1. **Converging the digital, physical, and human world** by supporting digital twinning (tight synchronization between domains to achieve digital twins), immersive communication (extending human senses by providing high-resolution visual/spatial, tactile/haptic, and other sensory data to offer an immersive experience), cognition (being aware of humans' intentions, desires, and moods), and connected intelligence (providing trusted AI functions that can act in and on the network).
2. **Providing network flexibility and programability**. This includes being able to efficiently internetwork between IoT devices and the communication network, support distributed edge computing solutions, and design protocols suitable for cloud support.
3. **Supporting deterministic end-to-end services**. This is particularly important given the need for 6G networks to support new services and applications such as remote-control and tactile internet.
4. **Emphasizing sustainability** by assisting in reducing energy and emissions' footprint. This can be done by suppressing the energy consumption increase while maintaining the data traffic increase.  
5. **Providing integrated sensing and communication** (for high-accuracy localization and high-resolution sensing services). This will allow for new ways to harvest and interpret the \"communication context\", particularly for applications and services such as e-health, autonomous vehicles/drones, and advanced cross reality.
6. **Providing trustworthy infrastructure** (the basis of future societies). This includes keeping terminals' locations private and ensuring that all the network entities (network functions, operating systems, hardware platforms, etc.) are continuously formally verified. As a result, this will build a trusted 6G network.
7. **Being scalable and affordable** to ensure global coverage across the world. This can be done by ensuring that the system is global (utilized worldwide), the number of interfaces is reduced, and the cost of mobile devices (with basic functionalities) can be lowered.  
Figure 1 summarizes 6G's vision and its corresponding goals.
## 4 Conclusions  
The performance of double-sided DWT VGs was experimentally investigated and compared with that of the LS and plain tube VGs. The conclusions drawn are as follows. The presence of DWTs yields a higher heat-transfer rate, friction factor, and thermal performance factor than the presence of the LS and plain tube. Thermal performance increased with an increase in the \(R_{b}\) of the DWT. The DWT with an \(R_{b}\) of 0.42 outperformed the other DWTs with a lower \(R_{b}\) in terms of heat-transfer rate, friction factor, and thermal performance factor. DWTs performed the best thermo-hydraulic performance in  
Figure 11: Comparison between the experimental \(\eta\) with predicted \(\eta\). Figure 10: Comparison between the experimental \(f\) with predicted \(f\). low \(Re\), whereas its performance decreased with a higher \(Re\). There was high correlation between the experimental and predictive data with a relative deviation of \(\pm\)4.36% for the Nusselt number, \(\pm\)7.5% for friction, and \(\pm\)3.47% for thermal performance. **Acknowledgments:** The first author wishes to thank the Ministry of Research, Technology, and Higher Education (RISTEKDIKTI) and the Ministry of Finance, Indonesia Endowment Fund for Education (LPDP) of the Republic of Indonesia, for their support of this study within the framework of BUDI-LN. The authors wish to thank their former colleague, [PERSON], for his assistance during the experiments. **Author Contributions:** [PERSON], [PERSON], [PERSON] and [PERSON] conceived the main concept and contributed the analysis. [PERSON] performed the experiments and wrote the paper. All authors contributed in writing the final manuscript. **Conflicts of Interest:** The authors declare no conflict of interest. **Nomenclature**
## 6 Results and Discussions
### Network Performance Behaviour under DDoS Attacks with Different Idle_timeout  
In the next set of experiment, we change some important metrics of OpenFlow (Idle_timeout) and TCP operations (initial TCP windows) in order to investigate their effect. The default idle_timeout value is 10 s, while the transmission interval of TCP traffic is over 10 s, i.e., 60 s for periodical TCP traffic. Thus, an IoT-DAG has to enquire the controller every time a forwarding request is received, because the existing flow entry already expired before a new request comes in. As the impact on the traffic could be resulted from the switch, the controller, or the link between them, we increase idle_timeout value to 120 s (the biggest transmission interval is 120 s in this test) so that the switch has no need to contact the controller once a flow entry is made. Furthermore, we adjust the window size of TCP traffic to limit the bandwidth in each transmission. Here, we add \"-w 5000\" in the iPerf tool configuration to reduce the bandwidth of a single TCP traffic to around 16 Mbps. Thus, the switch is still capable of handling DDoS attacks if performance improves. Otherwise, the switch is the root cause of network deterioration.  
The result is depicted in Figure 8. We can see that successful transmission ratio of periodical TCP/UDP traffic has remarkably increased after the extension of timeout value, which proves that the switch is still working under DDoS attacks. However, from the output of random UDP and TCP traffic, it shows that (i) packet loss in UDP is still very high and (ii) the bandwidth of TCP is not the reason for inferior random TCP performance, because the network has adequate bandwidth resource to transit multiple TCP traffic simultaneously. Therefore, we conclude that metrics of OpenFlow and TCP connections can play a crucial part in keeping at least a minimum performance under a DDoS attack.  
In order to find out the importance of flow entries during a DDoS attack, we start attack after a first successful transmission of each type of traffic. Therefore, switches have the required flow entry already, they only need to send out traffic according to the entry. However, the result is even worse than the one in Figure 8, which means the performance of switches is unstable under DDoS attacks.
## 2 Materials and Methods
### Silver Quantification  
Quantitative determination of the percent silver content by mass for silver oxynitrate and the Ag\({}_{7}\)NO\({}_{11}\):SiO\({}_{2}\) was completed in triplicate by potentiometric titration against sodium chloride (NaCl). In brief, samples (0.25 g) of the higher oxidation state silver compounds were digested in approximately 25 mL of dilute nitric acid (1:4, HNO\({}_{3}\):H\({}_{2}\)O) overnight to liberate all silver into free Ag (I) ions in solution. This digested solution was then quantitatively transferred to a 250 mL volumetric flask and diluted with RO water. A 5.0 mL aliquot of this solution was quantitatively transferred to a 50 mL sample vial containing 10 mL of dilute nitric acid (1:4, HNO\({}_{3}\):H\({}_{2}\)O) and 20 mL water. The potential is measured across the analyte solution over the duration of titration with a 0.1 M NaCl titrant. The silver content of the sample volume is determined from the second derivative of the potential voltage plotted versus titrant volume, at an equimolar ratio of silver to chloride. In this manner, the mass percent silver content for silver oxynitrate and the grey Ag\({}_{7}\)NO\({}_{11}\):SiO\({}_{2}\) powders was determined.