esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Discussion
However, the UGR'16 dataset contains more background traffic than any other kind and, therefore, the most probable event is that the portions on which the algorithm is trained only contain background traffic, without attacks. Therefore, the model will generate false negatives. Thus, we can conclude that this kind of architecture is not ready for training MLP networks by use of large datasets, because, despite using very powerful machines with multiple CPUs, GPUs, and large amounts of memory, the cost of training remains very high, as shown in Table 4.  
In contrast to this first approach, the same tests were attempted on a simpler algorithm such as the decision tree. For this method, a comparison was made of all the algorithms used to calculate the decision tree. The results obtained led to several conclusions. By using this kind of algorithm, it was possible to obtain results with machines with more limited resources and less time. In the worst case, the construction of the entire decision tree using the preprocessed dataset took less than 3500 s. Analyzing the decision tree created from the training, we observed how the IP addresses, both origin and destination, were taken as a key variable, something that led to unacceptable results. This was due to IP addresses being easily changeable data for most attacks. Therefore, we decided not to consider those data during the training. By adapting the training in this way, we obtained results that show that decision trees achieved a good overall precision. More specifically, the results of the model were successful in obtaining a classification network for denial of service attacks, with a success rate of 99.97%. Similar results were achieved when detecting network scans and spam, with 98.94% and 94.91%, respectively. The accuracy of the results was reduced when predicting botnet attacks (36.62%) and especially when predicting blacklist traffic (1%). In general, the results are faster and more reliable compared with neural networks. As stated in Section 2, there are different approaches for classification for IDSs based on anomaly detection. Table 10 compares the model proposed in this paper with some of those included in the literature, based on different aspects such as multi-class classification, binary classification (BC), training time, model proposed, scalability of the software, and quantity of data analyzed. Despite all the efforts made in this area of research, most of the existing approaches do not analyze truly large-scale datasets as proposed in this paper. Our solution demonstrated that it is able to train up to 14 GB of data (compressed) detecting new and reliable modern attacks with high accuracy using the benchmark dataset UGR'16. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **CPU** & **GPU** & **Memory** & **Time** & **Result** \\ \hline
**MLP** & 32 cores & None & 200 GB & 400,000 h per epoch & None \\
**DT** & 8 cores & None & 50 GB & 3500 seconds & 99.61\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison between MLP and decision trees.
# Development of Digital Device Using ZigBee for Environmental Monitoring in Underground Mines
## 3 Methods
### ZigBee Module for Communication  
ZigBee communication technology transmits measurement data even in underground mines, where it is difficult to connect to an external communication network. It is a low-power wireless communication protocol used for local-area wireless networking. As Bluetooth is a standardized communication method, ZigBee is also a standard communication method. Therefore, if the standard is followed, various products from different companies can communicate with each other using the ZigBee protocol. ZigBee communication has three major characteristics: First, it has a routing function that allows packet data to be passed through another module while it travels to its final destination. Second, an ad hoc network function can automatically instantly create an entire wireless network.  
Figure 1: Taeyoung EMC Samdo Mine. (**a**) Panoramic view, (**b**) mineral processing facility, and (**c**) 3D map of underground mine.  
Third, it has its mesh recovery capability that can automatically detect and reconfigure the network if one or more modules are missing [23].  
Based on these characteristics, each module in the ZigBee network can be assigned three roles: coordinator, router, and end device. Only one coordinator can exist in one network, performing administrative tasks, such as network formation, address assignment, security setting, and network maintenance. A router accesses the network to exchange information and performs routing tasks between distant devices. We must maintain power at all times because it acts as a channel for the network. The end device can access the network and send information; however, because it does not have a routing role, energy can be saved through the power-saving mode [24]. Depending on the purpose, different network methods can be applied between these modules, and they can be classified into star, tree, and mesh networks. The star network is a method in which the end device is directly connected to the coordinator, and there is no router; therefore, the network cannot be expanded widely. The tree network is branch-shaped, the routers are connected by a single line, and the end device is connected to the nearest router to transmit data to the coordinator. Finally, the mesh network allows multiple connections between the routers. This study performed ZigBee protocol-based wireless communication using Digi International's XBee 3 Pro product (Figure 2a). The XBee 3 Pro product has a communication range of approximately 3200 m outdoors and 90 m indoors.  
In the XCTU program (Figure 2b) provided by Digi International, various settings for the XBee 3 Pro network, such as the role of each module, network type, and power saving mode, are possible. The XBee module can be used in conjunction with a microcontroller, such as an Arduino or Raspberry, and because it has a total of 20 pins, it can be used independently in combination with various sensors.
## 2 Materials and Methods
### Interaction with DID-MB Blockchain  
Creating a DPP record for a physical product involves creating a DID document in the DID-MB blockchain and then creating and associating a passport data object in DPP-DB. The association between the two documents is conducted through DID ID.  
For the User Application to be effective in discovering a digital product passport and to interact with it, the associated DID document must contain a specific service endpoint with an identifier (e.g., \"#dpp-gateway\"), a service type \"LinkedDpp\", and an endpoint URL that directs to the digital product passport DPP-DB service gateway.  
To demonstrate the concept, using cheqd.io services, a DID document has been created in the testnet network resulting in a document with ID did:cheqd:testnet:a515028c-64 cc-47 db-8 fc2-e79d70713b42. This ID represents a unique product identifier, and it is used by the DPP-DB blockchain as the primary key for the DPP document associated with this product. Also, this ID is used to generate bar code or QR code which is to be associated with the product. For the User Application to interact with the DPP document, first it must lookup the DID document using the DID resolver based on the ID which was decoded from QR code, and then it must identify the service [[https://aut.utcluj.ro/dppgateway](https://aut.utcluj.ro/dppgateway)]([https://aut.utcluj.ro/dppgateway](https://aut.utcluj.ro/dppgateway)) (accessed on 20 May 2024) where the service gateway is deployed. Effective interaction (reading and updating DPP data object attributes) happens at the DPP-DB level and is intermediated by the service gateway. The created DID document can be accessed on Cheqd test network [[https://resolver.cheqd.net/1.0/identififiers/did:cheqd:testnet:a515028c-64](https://resolver.cheqd.net/1.0/identififiers/did:cheqd:testnet:a515028c-64) cc-47 db-8 fc2-e79d70713b42]([https://resolver.cheqd.net/1.0/identififiers/did:cheqd:testnet:a515028c-64](https://resolver.cheqd.net/1.0/identififiers/did:cheqd:testnet:a515028c-64) cc-47 db-8 fc2-e79d70713b42) (accessed on 20 May 2024):  
_\"did\"_; _\"did:cheqd:testneta515028c-64 cc-47 db-8 fc2-e79d70713b42\"_, _\"controllerKeyId\"_: _\"e0d9a07e2c4e9d701 fbfa414e0c545 ccdd5b714b9 af670039e338c8b71f7 bda\"_, _\"keys\"_: _[_\"kid\"_; _\"e0d9a07e2c4e9d701 fbfa414e0c545 ccdd5b714b9 af670039e338c8b71f7 bda\"_, _\"kms\"_: _\"postgres\"_, _\"type\"_: _\"Ed25519\"_, _\"publicKeyHex\"_: _\"e0d9a07e2c4e9d701 fbfa414e0c545 ccdd5b714b9 af670039e338c8b71f7 bda\"_,  
Figure 2: (a) Create DPP object flow. (b) Lookup existing DPP flow.  
\"meta\":[ \"algorithms\":[ \"EdDSA\", \"Ed25519\" ]]]],\" serices\":[ { \"id\":\"did:cheqd:testnet:a515028c-64 cc-47 db-8 fc2-e79d70713b42#dpp-gateway\", \"type\":\"LinkedDpp\", \"serviceEndpoint\":[ \"[[https://aut.utcluj.ro/dppgateway](https://aut.utcluj.ro/dppgateway)]([https://aut.utcluj.ro/dppgateway](https://aut.utcluj.ro/dppgateway)) (accessed on 2 June 2024)\" ]],\" provider\":\"did:cheqd:testnet\" } ```
Plant growth-promoting bacteria (PGPB) stimulate plant growth [10] with two different lifestyles: free-living soil bacteria associated with the root surface of many different plant species referred to as plant growth-promoting rhizobacteria (PGPR) [4,11,12] and those that live inside host plant tissues [3,4,13]. Stimulation of plant growth by PGPB would be provided either through indirect or direct mechanisms [10]. Indirect mechanisms include prevention of the deleterious effects of phytopathogenic organisms by induction of systemic resistance (ISR), the synthesis of antimicrobial compounds, and the release of volatile organic compounds [3,4]. Direct promotion of plant growth may occur by atmospheric nitrogen fixation and subsequent supply to plants, secretion of siderophores, synthesis of phytohormes such as indole acetic acid (IAA), phosphate solubilization, and synthesis of enzymes that can modulate plant ethylene levels [3,14]. _Arabidopsis thaliana_ is the model plant used for examining PGPB impacts on plants along with agronomically important crops e.g., wheat (_Triticum durum_). The use of both plants is highly relevant for the investigation of plant growth promotion mechanisms utilizing model crops and the proof of concept of applying non-tractable agronomically important crops to highlight their possible applications in the field. The present investigation aimed to recover bacterial isolates from halophytes, their rhizosphere and NaCl affected surrounding soil from three salt marshes. Recovered endophytes, PGPR, and marsh soil bacteria were evaluated for different stress and plant growth-promoting (PGP) traits, including temperature, pH, PEG, and high NaCl tolerance. The selected bacteria were then used as biofertilizers and tested for their ability to mitigate salt stress in _Arabidopsis thaliana_ and durum wheat (_Triticum durum_ cv. Waha).
## 4 Case Study
### Discussion  
#### 4.2.1 Analysis of SES Operation Results  
The optimal operation results of SES are shown in Figure 4, and the purchase and sale price of SES for VPPs is shown in Figure 5.  
Figure 4: State of charge–discharge power and electric quantity of SES.  
As Shown in Figure 4, the power transaction between the SES and outer power grid is concentrated in the time period from 12:00 to 21:00, and a small part of the power transactions are also used to maintain the normal operation of the whole system in the rest time periods. From 1:00 to 2:00 and 16:00 to 17:00, the SES presents a charging state, indicating that during this period, the VPP has sufficient power to meet the power demand of the lower PVP alliance and has surplus power to be sold to the SES operators, and it reaches the maximum energy storage capacity from 3:00 to 12:00.  
From 13:00 to 14:00 and from 18:00 to 20:00, the SES shows a state of discharge, indicating that the VPP does not have sufficient electrical energy to meet the power demand of the lower PVP alliance. And, at this time, due to the upper limit of the power interaction with the power grid, it is forced to buy electricity from the SES power station to maintain the power balance. The minimum energy storage capacity is between 14:00-15:00 and 20:00-24:00, the SES can only buy electricity from the grid to meet the electricity demand of VPP, so the VPP power purchase price is the highest at this time. For the rest of the time, the shared VPP does not carry out energy transactions with the SES but meets the demand for power balance through its own unit output or by buying electricity from the higher power grid.  
As shown in Figure 5, the purchasing and selling price is basically consistent with the power grid price trend, but the purchasing price will be slightly higher when VPP cannot meet its power balance.
## 2 Materials and Methods
### Experimental Equipment and Experimental Scheme
#### 2.2.2 Experimental Scheme  
After drying the collected natural coarse-grained saline soil, a coarse-grained saline soil sample with a moisture content of 6% was prepared onsite on the basis of the initial moisture content, which was then sealed for 24 h. The sample was loaded into an organic glass cylinder with a height of 85 cm and a diameter of 50 cm in 16 batches, and the compaction degree was controlled to 0.95 by unidirectional compaction. The thickness of the organic glass cylinder was customized to 5 cm to achieve visualization of the internal soil while minimizing heat exchange with the outside environment. The test soil sample was divided into 9 sections, with a total of 8 three-parameter sensors arranged every 9 cm to monitor the temperature, humidity, and conductivity of the saline soil in real time. After sample preparation, a layer of plastic film was placed on the top surface for sealing to prevent moisture evaporation during the subsequent freeze--thaw cycles. The temperature control strategy during the freeze--thaw cycles was based on the annual temperature data of the city of Kashgar. The upper guide plate exhibited a freeze--thaw cycle duration of 96 h, with 48 h of freezing and 48 h of thawing. The specific cycle temperatures were set to 15, 10, 5, 0, \(-\)5, \(-\)10, and \(-\)15 \({}^{\circ}\)C.
## 4 The Energy Balance Problem  
In order to save energy and keep the network functional for as long as possible, various approaches, including hop-by-hop transmission techniques [8; 25; 26], as well as clustering techniques [23] and alternating power-saving modes [44] have been proposed.  
All such techniques, in their basic form presented in sections 2 and 3, do not _explicitly_ take care of the possible overuse of certain sensors in the network. As an example, remark that in hop-by-hop transmissions towards the sink, the sensors lying closer to the sink tend to be utilized exhaustively (since all data passes through them). Thus, these sensors may die out very early, thus resulting to network collapse although there may be still significant amounts of energy in the other sensors of the network. Similarly, in clustering techniques the cluster-heads that are located far away with respect to the sink, tend to spend a lot of energy.  
In this paper, we present the EBP (Energy Balance) protocol, introduced in [16], that probabilistically chooses between either propagating data one hop towards the sink or sending directly to the sink. The first choice is more energy efficient, while the latter bypasses the critical (close to the sink) sectors. The appropriate probability for each choice in order to achieve energy balance is calculated in [16]. We also mention a related, extended protocol (VTRP: Variable Transmission Range Protocol), proposed in [3], that implicitly contributes to energy balance by appropriately adapting (increasing) the transmission range, thus bypassing critical sensors and avoiding possible obstacles.
# Dialog CIN: Contextual Inference Networks for Emotional Dialogue Generation
## 2 Related Work
### Emotional Dialog Generation  
In recent years, research highlighted the significance of incorporating emotional factors in the development of human-like conversation generation models [1; 2]. The existing approaches to emotional dialogue generation can be broadly classified into two categories. The first category involves controllable emotion-based dialogue systems that rely on user-input emotions. [PERSON] et al. [5] first proposed an Emotional Chatting Machine (ECM) based on the Sequence to Sequence (Seq2 Seq) model [23], which uses emotion category embeddings to simulate emotional expressions, encodes emotional vectors, and improves the decoder to generate emotional statements. [PERSON] et al. [24] proposed a new framework called Curriculum Dual Learning (CDL), which extends controllable emotion-based reply generation to a dual-task approach, alternating between generating emotional replies and emotional queries. [PERSON] et al. [25] explicitly or implicitly integrated specific emotions into coherent phrases and proposed a semi-supervised approach to creating an emotion lexicon to generate coherent and meaningful replies. These approaches aim to improve emotional expression in dialogue generation by leveraging specific emotion vectors and enhancing the overall emotional quality of the generated responses. However, they rely on user-input emotions or require additional emotion input, which may restrict their practical application. The other type of emotional dialogue generation involves models that autonomously learn emotional states from the dialogue in order to generate emotional responses. [PERSON] et al. [7] addressed this by considering both sentence-level and word-level granularity to capture subtle emotional differences more accurately. They introduced an adversarial learning framework that facilitates the generation of appropriate responses. [PERSON] et al. [8] proposed a heterogeneous graph-based framework that leverages multi-source knowledge, including dialogue history, emotion flow, facial expressions, and speaker personality. This framework constructs a heterogeneous graph based on the dialogue content and employs an encoder to learn the graph's representation. By doing so, it comprehensively understands the dialogue content, perceives emotions, predicts appropriate emotional states, and ultimately generates coherent emotional replies.
# Whole-Body Motion Planning for a Six-Legged Robot Walking on Rugged Terrain
## 1 Introduction
decompose the planning problem of a quadrupedal robot into two main phases, namely an initial global planning phase, which searches for feasible footstep trajectories by the use of the R\({}^{*}\) search algorithm, and an execution phase, which dynamically generates complete joint trajectories according to the planned footstep trajectories [18]. All these attempts have demonstrated the effectiveness of appropriately generating whole-body motions for legged robots and thereby increased the robot mobility on unstructured terrain. In this paper, we focus on the issue of terrain geometry based whole-body motion planning for a six-legged robot walking on rugged terrain. In our method, leg support and swing are planned respectively. For leg support, maximizing the stability margin of support-swing transition is mainly considered, which is distinct from the existing method. In terms of leg swing planning, the problem was formulated as an optimal control procedure that satisfies a series of locomotion task terms while minimizing a biologically-based objective function. To better concentrate on the motion planning problem, it is assumed that the terrain has been already obtained in advance and described by a 2.5D grid-type digital elevation model (DEM). DEM provides a compact representation, allows for efficient processing, and avoids the complexity of using full three-dimensional maps. The remainder of the paper is organized as follows: Section 2 presents a whole-body motion planning method for six-legged robots, including support and swing planning. Then, the results of both the simulation and experiment are presented and analyzed in Section 3, followed by a necessary interpretation of the observed behaviors. Finally, Section 4 concludes with a brief summary of the paper.
## 3 Proposed Fault Detection and Classification Algorithm
### W-Index  
The current signal is measured at the sending terminal of the transmission link during a fault event. The Wigner distribution of the samples over a quarter cycle is calculated with a moving window over one sample with a frequency of sampling of 3.84 kHz. The SAVs of the quarter cycle are calculated, and the maximum of these values over the quarter cycle window are recorded. The Wigner index (W-index) is represented by the maximum values of the Wigner distribution over the quarter cycle, moving with one sample. The decomposition of the current signals by utilizing the WDF provides the W-index. To estimate the fault, the WDF depends on the energy density of the current signals, which is available in the time frequency frame. Due to the utilization of the time domain current signal \(f(t)\) twice, it is also presented as a bilinear analysis. The advantage of this analysis is that a high time-frequency resolution and energy concentration can be obtained [22]. The following relation presents the W-index for the current signals \(f(t)\)[23].  
\[WI=\int_{-\infty}^{\infty}f(t+\frac{\tau}{2})f^{*}(t+\frac{\tau}{2})e^{-j \omega\tau}d\tau \tag{2}\]  
where \(t\) represents the time variable (sliding), \(\omega\) represents the signal frequency (angular), and \(\tau\) represents the signal in the time domain.
## 4 Conclusions and Recommendation  
The current paper systematically explored virtual water flow among industries in Gansu province as well as the inter-provincial virtual water trade patterns. The results of this research indicate that the dynamic variations in virtual water patterns in Gansu province were mediated primarily by the primary and secondary industries from 2007 to 2012. During this period, virtual water for local use decreased, while virtual water exports to developed and water-abundant coastal areas, increased. In addition, it was found that a significant imbalance in virtual water distribution exists across primary, secondary, and tertiary industries, with the majority of virtual water flowing towards tertiary industries, and the smallest volumes flowing towards primary industries.  
In view of the imbalance between virtual water distribution across the three industry categories, and the mismatch of inter-provincial virtual water trade with their water resources storage, it is suggested that Gansu emphasizes the promotion of water-saving agricultural techniques, adjusting the structure of the industrial sector by encouraging water-saving and highly-profitable industries, and reducing virtual water export while broadening their import paths, especially from water-abundant areas such as the import of virtual water from adjacent water-abundant areas (Shanxi province and Inner Mongolia), through the establishment of long-term cooperative relationships using appropriate economic policies, to reduce the proportion of water-reliant productions in all industries.  
Conceptualization, J.C., W.W. and J.F.A.; Methodology, J.C.; Validation, J.C.; Formal Analysis, W.W.; Investigation, W.W., C.L., Y.L., Y.Z., X.W. and H.S.; Data Curation, W.W.; Writing--Original Draft Preparation, W.W.; Writing--Review and Editing, J.C. and J.F.A.; Visualization, W.W.; Funding Acquisition, J.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Major Program of the Natural Science Foundation of Gansu province, China (18)R4 RA002), the Key Laboratory of Ecohydrology of Inland River Basins, Chinese Academy of Science (KLERB-ZS-16-01), the Open Fund for the Key Laboratory of Land Surface Processes and Climate Change in the Cold and Arid Region of the Chinese Academy of Sciences (LPCC2018008), and the Innovation Improvement Project for colleges and universities in Gansu Province in 2019 (2019B-033).  
The authors declare no conflict of interest.