esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The study comprehensively evaluates the overall delivery needs in both disrupted and normal areas, utilizing vulnerability indicators associated with various road nodes. Key factors such as the operational costs of the regional delivery system, total delivery time, and fulfillment of customer needs in affected areas are optimized. Furthermore, the layout of dynamic take-off and landing points, along with the task configuration for the vehicle-UAV collaborative delivery, is strategically planned to derive the most effective air-ground collaborative delivery solution. This study addresses a complex problem involving multiple optimization objectives, wherein a vehicle and an unmanned aerial vehicle (UAV) converge at a designated location following the completion of their respective delivery tasks. A notable challenge arises when the vehicle that arrives first must wait for the later-arriving UAV. Consequently, minimizing waiting time is a critical aspect of the optimization process, which adds a layer of complexity to the problem. This paper proposes a multi-objective optimization algorithm that leverages adaptive resource allocation and objective space decomposition. An enhanced evolutionary algorithm is employed to facilitate the collaborative delivery operations of trucks and UAVs. The analysis comprehensively accounts for the impact of failed nodes on road vulnerability, while simultaneously optimizing delivery cost, delivery time, and demand coverage to derive an effective collaborative delivery scheme for trucks and UAVs. The efficacy of the proposed algorithm is validated through numerical examples generated in accordance with real-world conditions, and a thorough analysis of key parameters is conducted. The findings indicate that, following disturbances to the road network, the system based on the air-ground collaborative delivery strategy not only reduces delivery costs, but also enhances the delivery completion rate and customer satisfaction, thereby effectively mitigating the adverse effects of road network obstructions on delivery operations.
## 4 Channel Estimation Based on Deep Learning for RIS-Aided Communication Systems
### Application of Deep Learning in Channel Estimation for RIS-Aided Massive MIMO Systems  
Massive MIMO systems use a lot of antenna elements to achieve a high level of spatial multiplexing and beamforming, which improves the throughput and spectral efficiency of wireless communications. These advantages make massive MIMO an integral part of 5G and subsequent technologies. However, it poses challenges such as low coverage, high power consumption, and high cost. To mitigate these issues, RIS has been introduced as a  
Figure 18: RIS-aided communication system.  
promising solution. Nonetheless, the employment of RIS makes channel estimation more difficult. Against this backdrop, the intervention of deep learning offers a novel approach to addressing the complex issue of channel estimation.  
In 2022, [PERSON] et al. [84] utilized the cascaded channel's quasi-sparse structure to transform the channel estimation for RIS-aided massive MIMO systems into a CS problem. Their scheme targeted the uplink, introducing a residual structure-based OMP (RS-OMP) architecture. This architecture is designed to address the impacts of noise and grid mismatch on the traditional OMP algorithm. Within this architecture, the ResNet uses a rectangular convolutional kernel, which makes it more capable of learning the row or column sparsity. Through experimental comparison, the proposed RS-OMP outperforms the DrCNN and the traditional OMP algorithm in terms of NMSE.  
In [85], [PERSON] et al. investigated an estimation method based on deep compressive sensing (DCS) for RIS-aided massive MIMO channels, with the aim of reducing the pilot overhead. Specifically, they developed a ResU-Net by combining U-Net with the DCS framework while introducing residual learning. The received low-dimensional pilot signals are first transformed into high-dimensional signals by matrix multiplication. Then, the ResU-Net with skip connections is utilized to compress the input data and recover the high-dimensional cascaded channel. Experiments are conducted using varying pilot lengths and quantization bits, and the results verify the ResU-Net's excellent generalization ability and robustness.  
In 2023, [PERSON] et al. [86] proposed a skip-connection attention (SC-attention) network for accurate CSI acquisition in double-RIS-aided massive MIMO systems. In this scheme, they modelled channel estimation as an image restoration-like denoising problem. Moreover, they used self-attention layers to improve the model capacity and receptive field size of the network, which allows for recovering the CSI matrix more accurately. Meanwhile, by employing the skip-connection architecture, the estimation accuracy can be enhanced by 16% while reducing the computational complexity. Through experimental comparison, the SC-attention network exhibits a superior NMSE performance over the CNN and the conventional algorithms (LS and MMSE).
# An Enhanced Distributed Data Aggregation Method in the Internet of Things
## 4 Performance Evaluation
### Network Warming Time Test  
In each RPL network graph, the root node transmits a start message, which is an rudimentary DIO message. Afterward, the network requires an opportunity to create a network graph. As shown in Figure 17, despite former favorable results of the proposed method, this method requires longer network warming time for first time. The warming time of the proposed method consists of the restriction of the number of children assigned to the parent node and amalgamate it into DIO message format, as well as the priority comparison among child nodes for selecting the best parent among all available parents. This procedure requires more incipient exchanges as compared to the base approaches like RPL, M-RPL, and A-RPL. The degree restriction of parent nodes increases the number of levels and effort steps passed by child nodes for the sake of parent node selection; therefore, the height of the network tree has been increased. This approach has been regarded as desirability cost and routing management of network nodes, and, following the inevitable existence of both privileges and deficiencies in each proposed IoT protocol, this can be alluded to the cost of the proposed method. Also, the effect of the successful transmission rate of a link on the warming time also indicates the efforts made by nodes, in order to create the network graph.  
Figure 16: The comparison of average upward delay toward the sink node, while the number of traversed hops varies between 2–5.  
According to Figure 17, in the proposed method of this paper, as the successful transmission rate of links decreases, the waiting time for the creation of a network graph is increased up to four times. However, in RPL, M-RPL, and A-RPL approaches, this increasing trend has been up to at most three times. This observation highlights the significance of the link quality in the proposed method and BD-RPL approach.
## 3 Results
### Inter-Satellite LEO-GEO Link  
Galaxy codes are used in LEO-GEO communications due to their ability to effectively correct errors occurring under difficult channel conditions. These codes, known for their large minimum distance between codewords, provide robust protection against errors, making them suitable for such a scenario [27]. In the results below, we have used the extended Golay code (24,12,8) due to the balance between coding efficiency and low latency requirements. This code converts 12 bits of information into a 24-bit word and can correct up to 3 binary errors, having a minimum distance of 8 between codewords. For this analysis, in Table 10, we highlighted the coordinates of the two satellites used for the communication link, and in Table 11 we highlighted the parameters of the LEO transmitter, GEO receiver, and the link, which are typical values that could be present in such a case.  
In Figure 11 we see that the BER analysis using the previous mentioned code reflect the gain coding due to performance optimization, compared with the theoretical analysis from Figure 4. For example, 16 DPSK modulations have an Eb/No value of 21.4 dB for a BER of \(1\times 10^{-6}\). After we encode the data, for the same BER value and the same type of modulation, we get an Eb/No of 18.5 dB, so a coding gain of 2.9 dB.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **LEO Satellite** & **GEO Satellite** \\ \hline Latitude [deg] & 35 & 39 \\ Longitude [deg] & \(-\)40 & \(-\)51 \\ Altitude [km] & 700 & 35,786 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The LEO and GEO satellite proprieties.  
Figure 11: BER Analysis for PSK, DPSK, QAM, and OFDM modulation using Golay code (24, 12, 8).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**LEO Satellite** & **Value** & **Link** & **Value** & **GEO Satellite** & **Value** \\ \hline Tx feeder loss [dB] & 1 & Frequency [GHz] & 10 & Interference loss [dB] & 2 \\ Other Tx losses [dB] & 2 & Bandwidth [MHz] & 15 & Rx G/T [dB/K] & 20 \\ Tx HPA power [dBW] & 15 & Bit rate [Mbps] & 8 & Rx feeder loss [dB] & 1 \\ Tx HPA OBO [dB] & 4 & Required Eb/No [dB] & 8 & Other Rx losses [dB] & 1 \\ Tx antenna gain [dBi] & 30 & Availability [\%] & 99.9 & \\  & & Polarization mismatch [deg] & 3 & \\  & & Implementation loss [dB] & 1 & \\  & & Antenna mispointing loss [dB] & 1.5 & \\  & & Radome loss [dB] & 0.5 & \\ \hline \hline \end{tabular}
\end{table}
Table 11: The satellite transmitter (LEO), receiver (GEO), and link proprieties.  
The results obtained in this situation are close in value. For lower modulation orders, DPSK modulation offers the best link margin, and as we increase the order of the modulation, OFDM becomes preferable, as can be seen in Figure 12.
## 3 Experimental Setup
### Case Studies  
#### 3.2.1 Microturbine with Counter-Rotating Runners-Bulk Version  
The first case study consists of a fully instrumented laboratory prototype of an axial microturbine with counter-rotating runners (see Figure 4) dedicated to recover the energy dissipated by release valves of water supply networks. Its multi-stage concept, similar to the one of centrifugal pumps, makes it appropriate for high head operating conditions, specific to the Pelton turbines. Indeed, each stage, composed by two counter-rotating axial runners, generally used for low head and large discharge conditions, recovers a fraction of the total head. The one-stage turbine of 2.65 kW is composed by two counter-rotating runners with 5 and 7 blades respectively. The outer diameter of the runners, as well as the pipe diameter, is \(\phi\)100 mm; the turbine inner diameter is \(\phi\)80 mm. The tested version includes a band, installed at the periphery of each runner, provided with a labyrinth in order to limit the leakage between the tip of the blades and the outer fixed wall. At the nominal operating point (a discharge of 37.5 m\({}^{3}\)/h and a head of 20 mWC), for a ratio \(\alpha=\rm{N_{B}/N_{A}}=1\) between the runners absolute rotational speed, its hydraulic efficiency, obtained by numerical simulation, reaches 85% ([PERSON] et al. [22], [PERSON] et al. [9]). The optimal operation of the turbine is ensured by the relative rotational speed between the two runners along with their absolute rotational speed. Further, the runners are driven by two independent electrical generators specially designed for this turbine, [PERSON] et al. [23], placed into the upstream and downstream bulbs respectively. Two frequency converters are used to drive the variable-speed electrical generators at the desired constant rotational speed value, whatever the sign of the mechanical torque. Finally, for each runner, an incremental encoder, used mainly for the rotational speed driving, along with a torque meter ensure the mechanical power measurements. A sealed magnetic coupling separates the wet and the dry regions of the machine to protect the embedded instruments.  
Figure 3: Hydraulic test rig (2016 version) of the HES-SO Valais//Wallis—Switzerland [12]. (Main characteristics: (1) Maximum head: 160 mWC; (2) Maximum discharge: \(\pm\)100 m\({}^{3}\)/h; (3) Generating power: 20 kW; (4) Pumping power: 2 \(\times\) 18.5 kW and 1 \(\times\) 5.5 kW; (5) Maximum pumps’ speed: 3500/3000 min\({}^{-1}\); (6) Total circuit volume: 4.5 m\({}^{3}\)).
# An Efficient Dummy-Based Location Privacy-Preserving Scheme for Internet of Things Services
## 7 Conclusions  
In this paper, we present an Enhanced-DLP scheme for IoT services and applications to protect the user's location privacy. In the Enhanced-DLP scheme, we adopted the enhanced greedy scheme instead of the original greedy scheme, making full use of the relationship between the data, so that the optimal dummy location can be found easily without traversing all the locations in the candidate set. The Enhanced-DLP scheme can guarantee the same entropy as the anonymous set generated by the existing scheme. In the verification of the effectiveness of the Enhanced-DLP scheme, we conduct the simulations on two scenarios. In the first scenario, the DLP and the Enhanced-DLP use almost the same scheme when generating the dummy location, so the computational cost of the two schemes are almost the same. In the second scenario, the dummy location generation time of the Enhanced-DLP scheme is 68.14% lower than that of the DLP scheme, and the probability of the Enhanced-DLP scheme against the ADLS scheme is 33.63% stronger than that of the DLP scheme. Our scheme only considers the query probability as the side information and the ADLS scheme as the attack scheme.However, but in such side information, the user's location information cannot be completely hidden. In subsequent research, we will consider more background knowledge and context in the process of generating dummy locations, and more attack schemes will be adopted.  
Conceptualization, G.C., X.Z. and Y.D.; methodology, G.C., T.L. and X.Z.; software, G.C.; validation, G.C. and X.Z.; formal analysis, G.C., X.Z. and T.L.; investigation, G.C.; resources, Y.D.; data curation, G.C.; writing--original draft preparation, G.C.; writing--review and editing, G.C., X.Z., Y.D., T.L. and J.J.; visualization, G.C. This research was funded by name of the NSFC project (grant no. 61762058, and no. 61861024), the Science and Technology project of Gansu Province (grant no. 1610 RJZA056), the youth Science Foundation of Lanzhou Jiaotong University (grant no. 2014026), Foundation of A Hundred Youth Talents Training Program of Lanzhou Jiaotong University and the National Key Research and Development Program of China(2017 YFB0802201). The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
IoT Internet of Things  
POI point of interest  
LBS Location-based services  
LSP Location-based services  
DLS Dummy location selection  
DLP dummy-based location privacy-preserving  
ADLS Attack algorithm for DLS  
Enhanced-DLP Enhanced dummy-based location privacy-preserving
## 5 BN-MAC Protocol Design  
BN-MAC is proposed with the aim of supporting multiple applications, particularly military applications, which require mobility-aware and static devices to be controlled from remote places. MAC design in WSNs is ant involved process because WSNs are based on mechanisms that are entirely different from the traditional networks. WSNs have limitations due to storage, computational capability, and energy resources. Therefore, the MAC protocols should be well organized to distribute the bandwidth fairly and be energy efficient, with appealing features that may stimulate the robust design of the communication media. One of the key factors for introducing BN-MAC is to reduce energy consumption while addressing idle listening, overhearing, mobility, and congestion concerns. BN-MAC also shortens the latency while guaranteeing the reliability of the WSN.  
BN-MAC improves the existing Z-MAC, A-MAC, Speck-MAC, ADC-SMAC, and LPRT-MAC protocols by adding new features. The mechanism of BN-MAC supports the hybrid topology that combines the features of TDMA and CSMA. The network is constructed as a flat single-hop topology. The features of TDMA are used to improve the contention, whereas CSMA works as a baseline. BN-MAC follows the concept of the owner slot. The node has complete access to its owner slot, similar to TDMA-based approaches. The remaining slots are accessed through the CSMA approach. The CSMA approach preserves energy and controls collisions. In addition, BN-MAC eliminates idle listening in each region to achieve a considerable energy saving. Bi-directional traffic inside each region of the WSN promotes smooth data exchange and efficient use of the bandwidth. Additionally, BN-MAC uses dynamic contention free slot exchange, which increases network scalability under even a heavy traffic load.  
BN-MAC consists of the following phases: finding the list of one-hop neighbors, intra-semi-synchronous transmission scheduling, inter-synchronous transmission scheduling, and selection of a BN. These operations are performed once during the setup process and are not performed again until the network topology is physically changed. In this approach, the initial costs for running these operations are balanced while achieving a better throughput and reduced energy consumption during intra- and inter-transmission.
## 2 Material and Methods
### Anti-Inflammatory Activity  
2.8.1. Cell Culture  
A549 cells were purchased from ATCC (ATCC, Manassas, VA, USA) and cultured in T75 flasks (Eppendorf, Hamburg, Germany) in Dulbecco's Modified Eagle Medium (DMEM, Sigma Aldrich, St. Louis, MO, USA) supplemented with 10% Fetal Bovine Serum (FBS, Sigma Aldrich, St. Louis, MO, USA) and 1% Antibiotic/Antimycotic (Thermo Fisher Scientific, Waltham, MA, USA) at 37\({}^{\circ}\)C and 5% CO\({}_{2}\). Cells were grown until reaching 80% confluence, after which they were washed with sterile phosphate-buffered saline (PBS). Cells were then detached by adding 1.5 mL of Trypsin-EDTA solution (Sigma Aldrich, St. Louis, MO, USA) to the flask, followed by 5 min incubation at 37 \({}^{\circ}\)C and 5% CO\({}_{2}\). Detached cells were collected, transferred to fresh cell culture medium, and seeded in 12-well plates (Eppendorf, Hamburg, Germany) in 1000 \(\upmu\)L/well of cell culture medium (100,000 cells/well). Plates were incubated for 24 h at 37 \({}^{\circ}\)C and 5% CO\({}_{2}\) prior to cell treatment.  
2.8.2. Gene Expression Analysis-qPCR  
Cell culture medium was removed from 12-well plates and fresh medium was added. WOLE-S and oleuropein suspensions were prepared in ultra-pure H\({}_{2}\)O and added to wells. Final concentrations were 0.15 mg/mL and 0.33 mg/mL for both WOLE-S and oleuropein. TNF\(\upkappa\) (Sigma Aldrich, St. Louis, MO, USA) was dissolved in ultra-pure H\({}_{2}\)O and added to wells to a final concentration of 1 \(\upmu\)g/mL. Plates were incubated for 120 min at 37 \({}^{\circ}\)C and 5% CO\({}_{2}\), after which cell culture medium was removed and total RNA from sampleswas extracted using Aurum Total RNA Mini Kit (BioRad Hercules, CA, USA) according to the manufacturer's instructions. The quantitative real-time PCR method (qPCR) was used to assess inflammatory response and induction of oxidative stress following cell treatment with WOLE-S and oleupopein. Extracted sample RNA was used as a template for cDNA synthesis (5 uL of sample RNA was used for each reaction) by iScript cDNA synthesis kit (BioRad, Hercules, CA, USA). cDNA synthesis was performed following kit instructions on Eppendorf ThermoMixer C (Eppendorf, Germany). PCR was performed with iTaq Universal SYBR Green PCR Supermix (BioRad, USA) on Applied Biosystems 7500 Real-Time PCR System (Applied Biosystems, Waltham, MA, USA). The following program was used for cDNA amplification: 95 \({}^{\circ}\)C for 10 min, followed by 40 cycles of 95 \({}^{\circ}\)C for 15 s and 60 \({}^{\circ}\)C for 60 s. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as a reference gene. Primers used to amplify targeted genes are listed in Table 1. Relative quantification of interleukin expression was calculated using the 2\({}^{-\Delta\Delta\text{Cq}}\) method [39].
## 5 Experiments and Results
### LLMs Algorithms  
As illustrated in Table 4, AraBERT achieved the best accuracy (89%) after eight epochs, followed by DarijaBERT with 85% accuracy and F1-measure after only three epochs. The fine-tuned DistilBERT model--originally trained on English text--overfitted the dataset, reaching 99% accuracy. The confusion matrices for the tested algorithms are illustrated in Figures 16-18, showing the strong performance of LLMs in text classification. AraBERT performs well overall, but it often confuses PASS and CIN, which may indicate a difficulty in distinguishing these two intentions. Figure 19 shows that AraBERT reached 89% accuracy at eight epochs, whereas DistilBERT overfitted with 99% accuracy at three epochs, as indicated in Figure 20, which is highly relevant following its creation on the basis of English corpora.  
Figure 19: Training and loss accuracy for AraBERT model.  
Figure 17: DarijaBERT model performances.  
Figure 18: DistilBERT model performances.  
Initial experiments on LLM algorithms were conducted on a workstation equipped with an NVIDIA Quadro K5000 GPU graphics card, but execution times exceeded 17 h. To optimize performance, models were tested on Google Colab with T4, reducing execution times to 5-10 min for the same algorithms, depending on the number of epochs. Table 5 compares the two graphic cards.
# A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model
## 5 Conclusions  
The bottleneck of mainstream low-light image enhancement algorithms is the color distortion. In order to overcome the mentioned problem, in this paper, at first, we consider human visual characteristics, transform the low-light image from RGB to HSI color space where the hue component \(H\) is kept constant. Then we use the segmentation exponential method to stretch the saturation component \(S\), make full use of the powerful learning ability of the deep convolution neural network extracting the intrinsic features from massive data and fitting complex functions to learn end-to-end mapping relationship between the intensity component of low-light and ideal-light image thus obtain an enhanced intensity component. Finally, we transform the enhanced HSI image to the RGB color space. The experimental results according to the synthetic and real low-light images in terms of both objective and subjective criteria show that the proposed algorithm not only improves the brightness and contrast, but also avoids the color distortion as well. In the future, we will continue to optimize the network model to further improve the brightness of the dark area.  
Conceptualization, S.M.; Methodology, S.M., H.M., Y.X. and S.L.; Software, H.M., S.L. and C.L.; Validation, S.M., H.M., S.L.; Writing--Original Draft Preparation, H.M.; Writing--Review & Editing, Y.X., S.M.; Experiments design and implementation, H.M., S.L. and S.M.; Analysis of experiment results, S.M., H.M., M.Z. and C.L.  
This research was supported by National Natural Science Foundation of China (Grant No. 61701524) and the Aeronautical Science Foundation of China (Grant No. 20175896022).  
The authors declare no conflict of interest.