esa-sceva/satcom-chunk-collection huggingface dataset:
# Landslide Susceptibility Assessment Using the Geographical-Optimal-Similarity Model
## 5 Discussion
### Comparison with Other Assessment Methods  
The GOS model is based on the principle of geographic similarity for quantifying the similarity between unknown points and known points by calculating the similarity of their geographic configuration attributes, thus inferring the likelihood of landslide occurrence at the unknown points [24]. The optimality of GOS refers to its ability to enhance predictive performance by selecting a small but representative subset of the dataset for model training, rather than relying on the entire dataset. This approach ensures that predictions are based on the most relevant geographic similarities, thereby improving both accuracy and efficiency while minimising RMSE. Compared to data-driven machine learning methods (such as RF, LR, RBFC, among others), the GOS model has a nonlinear fitting ability and provides higher interpretability. Machine learning methods rely on their strong data processing capabilities, automatically extracting landslide features and assessing susceptibility. However, they require high data precision, and the model processes lack clear physical meaning, resulting in relatively weak interpretability. Meanwhile, machine learning models are easily influenced by sample data distribution and may exhibit significant variability in data imbalance or high noise cases.  
In contrast, the GOS model represents geographic configuration, eliminates highly collinear geographic attribute features, reduces computational load, and effectively avoids overfitting issues. In addition, the GOS model uses cross-validation to select a subset of representative observation points as training samples, establishing a nonlinear mapping relationship between geographic configuration attributes and landslide susceptibility. This trend effectively removes data noise and improves the model's computational efficiency and stability. The considerable improvement in AUC values compared to RF and other machine learning methods confirms the benefit of the GOS model in prediction accuracy, highlighting its remarkable ability to reduce the impact of historical data precision and enhance evaluation stability.  
Therefore, the GOS model integrates the strong fitting ability of data-driven analytical methods [36] with the interpretability of qualitative analysis while compensating for the shortcomings of physics-driven analytical methods' dependence on geographic data and critical parameters, offering stronger regional applicability and data adaptability. This phenomenon provides an efficient, stable, and interpretable innovative approach for landslide risk assessment in sophisticated geological environments.
## 4 Proposed Metrics  
In this section, we compare the performance of the MPH protocol with that of commonly-used protocols in sensor networks, such as AODV, DSR and the well-known algorithm, ZTR. Although AODV and DSR are reactive protocols and ZTR and MPH have a hierarchical routing configuration, we can measure their performance based on the specific metrics presented below and then compare the efficiency of each protocol regarding overall energy consumption. We use metrics, such as retransmissions, retries of the CSMA/CA algorithm when the channel is busy, overhead, discovered and valid routes and energy at the network. We analyze these metrics and observe their impact on problems, such as re-configuration, node damage, interference and energy consumption.  
Regarding the channel, the CSMA/CA (carrier sense multiple access with collision avoidance) protocol works such that a node listens to the channel, and, if it is free, the node transmits; if the channel is busy, the node recalculates a random delay for which it waits in order to listen again. This metric is what we call CSMA/CA retries. If these CSMA/CA retries happen many times, this means that the channel is busy most of the time; possibly much overhead will cause many collisions. Moreover, when the channel is constantly busy, there are several collisions and packet retransmissions. This action highly influences the energy demands, because nodes decrease their capacity if they are continually retransmitting the same packet. Therefore, if a node has to retransmit a packet many times, all of these retransmissions can degrade the network performance, increasing the number of collisions and the energy consumption.  
In the network layer, overhead is an important factor in energy consumption. The routing protocol efficiency can also be measured by the number of packets needed by the protocol to route information to the destination. A protocol with many control packets will contribute to packet collisions and a reduction of the throughput. Regarding discovered routes, in all considered protocols, nodes have the capacity to know their neighbors. Under AODV and DSR, nodes update the routes when they are required. In contrast, under ZTR and MPH, nodes update the routes on a periodic time basis. Knowing the neighbors helps nodes to have valid routes to the destination, to correctly forward packets and to decrease the number of retransmissions needed. Valid routes are routes that are not corrupted, and are available for immediate use. If under a routing protocol nodes can handle several valid routes, packets will have more chances to be forwarded, will not be continually retransmitted and energy resources will be saved.  
The energy consumption in a network is directly related to the complexity in managing routing or neighbor tables. As the sensors carry out many routing algorithms and processes, for example, to choose the best route among many available in the routing tables, they will increase the energy consumption.
# Using an Uptake Enhancer to Mitigate Nitrogen Leaching While Enhancing Uptake Efficiency
## 3 Results
### Effect of Uptake Enhancer Application on Vegetative Growth of Oats
#### 3.2.1 Morphological Parameters  
No significant differences were found when comparing the treatments within each harvesting time except for week eight, which showed that the control (treatment C) differed significantly from the CF and F treatments. As expected, oats showed an increasing trend in plant height with time, with the control treatment being constantly the shortest in height. In all four sampling times, CFA treatments consistently were the tallest in plant height compared to other treatments. Week 8 showed the most significant impact of the uptake enhancer with a 15% increase compared to treatment C, followed by week 16, which also increased by another 5%.  
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline
**Sampling** & \multicolumn{4}{l}{**4 Weeks**} & \multicolumn{4}{l}{**8 Weeks**} & \multicolumn{4}{l}{**12 Weeks**} & \multicolumn{4}{l}{**16 Weeks**} \\ \hline
**Treatment** & **C** & **CF** & **CFA** & **C** & **CF** & **CFA** & **C** & **CF** & **CFA** & **C** & **CF**  
Surprisingly, week 8 showed a significant decline in above-ground FM and DM in the CFA treatment. However, by weeks 12 and 16, an increase in plant available N and a subsequent absorption led to an increasing trend in the above-ground FM and DM of the oats in the CFA treatment when compared to the C treatment, although not statistically significant. At week 12, the FM in the CFA treatment exhibited an increase of 23% compared to the CNTRL treatment. By week 16, this increasing trend rose to 35% in dry mass compared to the control. Notably, the CF treatment had the highest DM, which is slightly higher than the CFA treatment by 4%.  
Root Measurements  
The root volume of oats exhibited consistent and statistically significant increases each week where N was applied, particularly in the CFA treatment, except for the first 8 weeks, which did not show any statistical significance. The uptake enhancer's impact on the root volume of oats was highest at week 12, with a remarkable 108% increase compared to treatment C. This significant effect persisted in week 16, with a substantial 101% increase. The root mass of oats also showed a noticeable increase, particularly from the 8 th week, even though week 8 was not statistically significant. The DM of oats increased significantly by 144% in the CFA treatment at 12 weeks when compared to all other sampling times.  
As expected, there was a declining trend in the leaf area of oats with time, starting from the 8 th week; although, none of the sampling times were statistically significant. The leaf area showed a discernible increase when N was applied with treatment CF and CFA, except for week 8, which showed an opposite trend, with C having the highest increase in leaf area.
## 3 IoV Enpowered IVI in 3G and 4G
### 3G: The Dawn of IoV-Based IVI  
Initially, the IoV-based IVI system was launched in the era of 3G. In 2002, Toyota introduced the G-Book system supported by KDDI CDMA 2000 for services of weather, music, news, games, e-mail, and e-commerce [63]. In 2003, Mercedes-Benz launched its first S-Class model with built-in 3G-UMTS module in Europe [64]. Apart from the basic communication and safety services, it also rendered the additional services of video conference, Video on Demand (VoD), and music downloading via WCDMA network. Since the commercialization of 3G network in China, supported by China Telecom CDMA 2000 network [65], the OnStar IVI system not only covered the functionalities of driving safety services (e.g., auto post-collision assistance, emergency rescue, and on-demand condition detection), but also audio control navigation and hand-free phone call [66]. Unlike OnStar, the inkaNet system released by SAIC and China Unicom in 2010 further satisfied the driver's needs of IVI and socializing by accessing Internet (e.g., online streaming media, audiobook, short messaging service, and navigation, etc.).  
In the era of 3G, the IVI system has made a breakthrough to interconnect mobile terminals, which can alleviate the shortage of applications in the vehicle-mounted ecosystem. As shown in Table 3, the interconnected systems cover all mainstream mobile OS with an increasing number of supported models and built-in applications. The MirrorLink standard proposed by Car Connectivity Consortium [67] aims to remove the barriers between the IVI system and mobile terminals (e.g., smartphones and tablet computers), which enable the IVI system to interconnect with mobile phones. Independent ecosystems can be mixed tentatively to enrich the functionalities and applications of the IVI, which becomes a landmark in the development of the IoV-based IVI. Following this trend, Apple and Baidu released their own cellphone-IVI interconnection solutions i.e., CarPlay and Carlife respectively.
# Q-Learning Based Fair and Efficient Coexistence of LTE in Unlicensed Band
###### Abstract  
The increased demand for spectrum resources for multimedia communications and a limited licensed spectrum have led to widespread concern regarding the operation of long term evolution (LTE) in the unlicensed (LTE-U) band for internet of things (IoT) systems. Because Wi-Fi and LTE are diverse with dissimilar physical and link layer configurations, several solutions to achieve an efficient and fair coexistence have been proposed. Most of the proposed solutions facilitate a fair coexistence through a discontinuous transmission using a duty cycling or contention mechanism and an efficient coexistence through a clean channel selection. However, they are constrained only by fairness or efficient coexistence but not both. Herein, we propose joint adaptive duty cycling (ADC) and dynamic channel switch (DCS) mechanisms. The ADC mechanism supports a fair channel access opportunity by muting certain numbers of subframes for Wi-Fi users whereas the DCS mechanism offers more access opportunities for LTE-U and Wi-Fi users by preventing LTE-U users from occupying a crowded channel for a longer time. To support these mechanisms in a dynamic environment, LTE-U for IoT applications is enhanced using Q-learning techniques for an automatic selection of the appropriate combination of muting period and channel. Simulation results show the fair and efficient coexistence achieved from using the proposed mechanism.  
efficient; fair; IoT; multimedia traffic; LTE-U; spectrum; Q-learning; Wi-Fi +
Footnote †: journal: Sensors
## 2 Results and Discussion
### Optimization of QuEChERS  
In order to create a quick and effective extraction method, QuEChERS was selected as it has been previously used to extract _Alternaria_ toxins from pomergnate fruits and juices [3], cereal-based foodstuffs [6], tomato products [24], and citrus [27], but it has not been tested for jujube. Thus, the aim of this study was to investigate if QuEChERS is suitable for extracting _Alternaria_ toxins (AOH, AME, TeA, and TEN) from jujube. In what follows, the ratio of sample/water, type and volume of extraction solvent, amount of salts, and type of clean-up agent are optimized, whereas the other parameters including equilibration time (10 min), extraction time (5 min), and centrifugation time (5 min) were selected based on the literature [3] and our previous experience.
# Design of Supply Chain System Based on Blockchain Technology
## 4 System Construction
### Smart Contract Design  
#### 4.4.1 Trade Smart Contracts  
The trade smart contract has two functions: to verify the transaction information and to confirm the transaction results and complete the fund transfer.  
When a consumer initiates a transaction, the request is sent to the trade smart contract, which verifies the request content and consumer permissions. After verification, the transaction information is sent to the corresponding producer account. Upon accepting the transaction, the producer sends a confirmation message to the trade smart contract, which begins to follow up. The process to verify the legality of transaction information is shown as Algorithm 1.  
Input: Consumer con1 sends a transaction request: \([id_{con1},jur_{con1},com1\to pr01,tab_{con1}(w,v,a,dl)]\)  
Output: If the transaction request is legal, the trade smart contract sends the transaction information to producer pro1: \([T\to pr01,inf_{T}(tab_{C},st)]\); if the request does not meet the conditions, then the trade smart contract rejects the request.  
Consumer con1 calls the trade smart contract and sends \([id_{con1},jur_{con1},con1\to pr01,tab_{con1}(w,v,a,dl)]\) to the trade smart contract;  
The public key address of consumer con1 is obtained, consumer = msg. sender;  
If con1!= legal user []  
return;  
else  
If the target product w = = p of con1 []  
then  
If commodity w matches agreement a  
then  
The trade smart contract sends \([T\to pr01,inf_{T}(tab_{C},st)]\) to producer pro1;  
else return;  
end If  
Producer pro1 verifies the information in \(\text{Inf}_{T}\) (tab\({}_{\text{con1}}\),st);  
end If  
If the transaction request meets the requirements  
then  
Confirmation information is returned to the trade smart contract;  
else  
Reject this transaction;  
end IfWhen the transaction is over, the trade smart contract determines the transaction completion and sends funds to the producer. If the transaction is completed on time, then the trade smart contract will normally transfer the funds to the producer pro1 account; if the transaction is overdue, then the trade smart contract will pay part of the funds to the producer according to the agreement, and the remaining funds will be returned to the consumer. The identification of the transaction result can be implemented by Algorithm 2.  
Input: transaction completion status (transaction completion time \(ct\));  
Output: Confirmation of the transaction result, and funds are sent to the producer according to the transaction result.  
If ct \(\leq\) dl  
then  
transfer (address _pro1, v);  
Send [\(T\)\(\rightarrow\)\(P\), \(tab_{T}(v,ct,N)\)] to the producer account;  
else  
\(v^{\prime}\) = v - m;  
transfer (address _pro1, \(v^{\prime}\));  
Send [\(T\)\(\rightarrow\)\(P\), \(tab_{T}(v^{\prime},ct,\omega)\)] to the producer account;  
end If  
**Algorithm 2**. Transaction result identification
## 2 Methods and Materials
### Data Processing  
A total of 396 pull strength trials and 594 CR-10 scores were measured. Descriptive statistics were performed to show the changes of pull strength and CR-10 score over time. Analyses of variance (ANOVA) were conducted to determine the effects of TT and number of breaks on the pull strength and CR-10 score. Regression analyses were performed to establish the PS models. The data were analyzed using the SAS\({}^{\circledR}\) 9.0 (Cary, NC, USA). A significance level of \(\alpha\) = 0.05 was adopted.
## 5 Evaluation
### Dataset Description  
To evaluate the mutation-based ICS time-series anomaly generation proposed in this study, the HAI [43] and SWaT [42] datasets were used. HAI is a testbed (and dataset) collected using a hardware-in-the-loop (HIL) simulator composed of turbines, boilers, and a water treatment system. In this testbed, normal and attack scenarios were repeatedly executed in an unmanned supervised control and data acquisition (SCADA) operating environment, and data were collected accordingly. This testbed has been continuously enhanced since 2017, and the dataset has been updated accordingly.  
For our experiments, we used HAI 22.04. This version of the dataset provides six training datasets (each file ranging from 45 MB to 136 MB) and four test datasets (each file ranging from 33 MB to 69 MB). Each dataset consists of 86 features. The training datasets comprise only normal data, while the test datasets include anomaly data along with label information.  
The SWaT dataset was collected from a six-stage water treatment process, where each stage is autonomously controlled by PLCs. This testbed is designed to closely mimic a real water treatment system, ensuring that the collected data can be applied to actual systems. In SWaT, communication among sensors, actuators, and PLCs is realized using a combination of wired and wireless channels, allowing for extensive experiments in realistic environments. The SWaT dataset has also been enhanced over time, with corresponding updates to the dataset. For this experiment, datasets collected in 2015 were used. Although the training and test datasets are not explicitly separated, the dataset provides two normal datasets (each file sized 127 MB) and one attack dataset (file sized 113 MB). Excluding the labels, this dataset consists of 51 features.  
Furthermore, before presenting the analysis of the experimental results, it is worth noting that various attack methods can be included in actual attack scenarios against ICS. Consequently, the types of anomalies can also be diversified. However, the HAI and SWaT datasets used in this evaluation only categorize the data as either normal or attack. This study does not aim to present a model with high anomaly detection performance. Nonetheless, we conducted anomaly detection experiments to verify that synthetic anomalies help train detection models. Additionally, the primary purpose of these experiments is to distinguish attack data from normal data, rather than categorizing the types of attacks in detail.
# 5G-MEC Testbeds for V2X Applications
## 3 Existing Testbeds and Implementations
### 5G-MEC Testbeds
#### 3.1.1 5 Gcity  
The 5 GCity testbed involves different platforms and services and can support applications such as video streaming and smart cities. The 5 GCity testbed provides an MEC-neutral host platform that focuses on UltraHD video streaming [30]. The testbed was deployed in three European cities, Bristol, Lucca, and Barcelona. The project developed a 5 GCity scalable orchestrator using OpenStack as the VIM and an SDN RAN controller. The MCAO is deployed using Open Source MANO (OSM) [31] and implements the Mm1 interface in a proprietary way [32]. This allows for the creation and deletion of MEC applications and services. Furthermore, the functionalities of the MEPM are performed. The MEC platform is deployed with an Mp1 interface that interacts with the MEC applications. The 5 GCity Software Development Kit (SDK) supports the resource placement and network slice application using the slice manager element. The 5 GCity SDK includes a monitoring manager for the VNFs and MEC applications [33].
## 2 Materials and Methods
### Study Area and Sampling  
The North Ionian Sea is located in the transition zone that stretches between the Ionian and Adriatic Seas. The long and complex Ionian-Adriatic coastline leads to the creation of diverse sedimentary and hydrodynamic environments [15]. A multitude of anthropogenic activities can potentially introduce marine litter into the wider region of the North Ionian Sea, including poor waste management practices, fishing, aquaculture, shoreline tourism, recreational activities, and shipping [15; 46]. Also, river outflows that end up in the Ionian Sea (e.g., from the Aoos and Kalamas rivers) could possibly contribute further to the contamination of the marine environment.  
Mussels (_Mytilus galloprovincialis_) were hand-collected from a fish farm in Thesprotia (Epirus, Greece), while sea bream (_Sparus auruta_) and sea bass (_Dicentrarchus labrax_) samples were obtained from an aquaculture also located in the Northern Ionian Sea. A map of the sampling area was reported in a previously published study from our lab [47]. Before MP separation, mussel and fish samples were stored whole at \(-20\)\({}^{\circ}\)C.  
Searwater was sampled from an aquaculture facility near Igoumenitsa (Northwestern Ionian Sea) using a microplastic net sampler (Hydro-bios) from the surface layer. Afterwards, the net was rinsed with bi-distilled water into the final collection container and further sieved over 1 and 5 mm mesh-size metal sieves. Plastic particles (>1-5 mm) were collected from the sieve using metal tweezers, left to dry, and transferred into amber glass bottles, finally analyzed by ATR-FTIR. Water analysis was performed only indicatively to identify polymer types and their weathering state.
## 1 Introduction
In a real-world scenario, the following instrumentation and field measurement techniques could be used to obtain the necessary data: (a) Slope inclinometers and extensometers to monitor slope deformations  
Figure 1: Some structural collapse caused by the landslide on the expressway. and displacements at critical locations. (b) Piezometers to measure groundwater levels and pore water pressures within the slope. (c) Rainfall gauges to record precipitation data. (d) Topographic surveys using total stations or LiDAR to track changes in slope geometry over time. (e) Geotechnical laboratory testing of soil and rock samples to determine the physical and mechanical properties of the materials. The combination of field measurements, laboratory testing, and numerical modeling would provide a comprehensive understanding of the slope behavior and failure mechanisms, which is crucial for the analysis and mitigation of such landslide events.