esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Energy Efficiency Fairness of Nodes in Wireless Powered Communication Networks
## 5 Numerical Simulations
### Effect of the Density of BSs  
For comparison, Figure 5 also shows the energy efficiency proportional fairness of the networks, in which the threshold is fixed as \(\theta=0.05\). As can be observed, the energy efficiency proportional fairness of the network with fixed threshold is substantially lower than those of the proposed optimal scheme. When the threshold is small, all of the nodes associated with the network can transmit information and consume the energy of the network. This scheme will not be beneficial to the energy efficiency of the networks. We can allocate some nodes to harvest energy by improving the threshold. The nodes can recharge their batteries by harvesting the RF energy via the WPT and exploiting the harvested energy for uplink transmission.  
In the current deployment of HetNet, we can obtain from Figure 5 that for improving the energy efficiency proportional fairness of the total network, when the density of BSs in tier 2 is high the bias factor of BSs in tier 2 should be small. This is because, if the number of low-power BSs increases, a lot of traffic will offload from the high-power BSs to low-power BSs. If we want to enhance the energy efficiency proportional fairness of the total network and balance the traffic in different tiers, the bias factor of BSs in tier 2 should be small.
## 8 Discussion
### Challenges
#### 8.1.2 Widespread Adoption of Machine Learning at the Network Edge  
Distributed machine learning algorithms, which may operate under severe time restrictions, are likely to play a vital role in a number of ways:  
(1) self-optimization of network resource allocation, with proactive solutions based on network learning and prediction being considered. (2) creation of smart mobile applications that learn from user behaviour and operate as a context-aware virtual intelligent assistant, running either directly on mobile devices or remotely via computation-offloading techniques. (3) creation of semantic inference algorithms and semantic communication strategies to integrate knowledge representation into communication. This will be very helpful in deploying holographic communications effectively.  
Figure 6: 5G and 6G Challenges.
# Optimisation of Shrinkage and Strength on Thick Plate Part Using Recycled LDPE Materials
## 2 Methods and Materials  
The thick plate part was designed as depicted in Figure 1. The meshes, feed system and cooling system are shown in Figure 2. The thick plate part chosen for this study hasa length of 150 mm, the width of 20 mm and thickness of 4 mm moulded using LDPE material. A 3D solid mesh in a tetrahedral form with four nodes covers the whole part and gating system, as shown in Figure 1. The meshed part and gating system consist of 345,914 tetrahedral elements, connected by 64,123 nodes. The ratio for the volume feed system to the volume of the part is 0.56, which is volumetrically adequate. Meanwhile, as shown in Figure 2, a cooling system was developed with a 6 mm diameter of the beam elements, as suggested by AMI software (Autodesk Moldflow Insight 2012, Moldflow, Melbourne, Australia). Eighty-eight beam elements were used to form the cooling system with a total of 90 nodes.  
Autodesk Moldflow provides information on material properties for more than 10,000 types of plastic [32]. The details of the material properties used are listed in Table 1. LDPE, with the commercial name Sumikathene 206P, manufactured by Prime Polymers Co. Ltd. (Tokyo, Japan), was selected from the AMI database for use in simulations study; the same grade was used in the experimental study. The database from AMI 2012 was available for only the virgin material, while there were no data available for the specific virgin to recycled ratios used in this study. The virgin to recycled ratio materials used in this study were 70:30, 60:40 and 50:50 respectively. The ratio of materials was denoted as R0 for virgin specimens, R30 for 70:30, R40 for 60:40 and R50 for 50:50 on virgin to material ratio specimens.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Plastic Material** & **LDPE** \\ \hline Trade name/Grade & Sumikathene 206P \\ Supplier & Prime Polymer Co. Ltd. \\ Melt density & 0.79516 g/cm\({}^{3}\) \\ Solid density & 0.92219 g/cm\({}^{3}\) \\ Specific Heat & 3095 J/kg \({}^{5}\)C \\ Thermal conductivity & 0.25 W/m \({}^{5}\)C \\ Maximum shear stress & 0.1 MPa \\ Mould temperature & 40–60 \({}^{\circ}\)C \\ Melt temperature & 160–180 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 1: Materials Properties of Low Density Polyethylene (LDPE) (Reproduced from [33], with the permission of AIP publishing).  
Figure 1: Thick plate part.  
Figure 2: Thick plate part with cooling system.
# Toward an Advanced Human Monitoring System Based on a Smart Body Area Network for Industry Use
## 3 System Model  
### Use Case  
This research supposes the following use case. The worker wearing the SmartBAN is monitored by an artificial intelligence (AI)-based operator in a factory as shown in Figure 3. The type of sensor node and the target data rate are listed in Table 4. Those target data rates were referred from [28; 29; 30; 31]. It was assumed that the hub and the AI-based operator performed bidirectional communication by a wireless communication system with a sufficient channel capacity, such as the fourth generation (4G) and fifth generation (5G) mobile communications systems, Wi-Fi 6 and so on. The AI-based operator monitored vital sign data, video and audio from the worker. The codecs for video and audio were assumed to be H.264 and Advanced Audio Coding (AAC), respectively. Therefore, a discrete cosine transform (DCT) was applied to those data. In addition, the AI-based operator transmitted instruction information to the worker's smart glasses. The relationship between the data rate and the video resolution was as follows: 500 kbps, 700 kbps, 1100 kbps and 2500 kbps corresponding to 426 \(\times\) 240, 640 \(\times\) 360, 854 \(\times\) 480 (standard definition (SD)) and 1280 \(\times\) 720 (high-definition (HD)), respectively [31].
# TRAL: A Tag-Aware Recommendation Algorithm Based on Attention Learning
## 5 Experiments
### Dataset Description and Evaluation Metrics  
To measure the proposed tag-aware model, we conduct a series of experiments on the following two public datasets: Delicious and LastFM, which are both published on the website of HetRec [33].  
* Delicious is a dataset obtained from the Delicious social bookmarking system, which allows users to annotate web bookmarks with various tags. In this dataset, bookmarks are regarded as items to be recommended.
* LastFM is a dataset collected from Last.fm online music system, where users are encouraged to tag music artists they have listened. In this dataset, artists are treated as items to be recommended.  
Note that user-item interactions are established by the tagging behaviors. Following the same assumption as in [5; 24], we consider that an item is liked by those users which have tagged this item. In addition, those infrequent tags used less than 5 times in Last.Fm and 15 times in Delicious are eliminated to alleviate sparsity of tagging information [5]. Specific statistics of the two processed datasets are summarized in Table 1. The task of the proposed model is to provide recommendations based on user-item interactions and tagging information.  
To measure the results of the recommendation, we perform the common leave-one-out evaluation [11; 34]. More specifically, for each user, we take the last interacted item as the positive test instance and leave the remaining interactions for training. Moreover, we sample 99 items as negative instances for each user randomly from the item set that are not interacted by this user. Adding the positive instance, a test set of 100 items is obtained. Instead of using all the non-interactive items as negative instances, the random sampling strategy can dramatically reduce the amount of calculation [35; 36]. After predicting the relevant scores of each item in the test set, the recommendation model will provide a top-n ranked list for each user. The performance of the ranked list is finally estimated by Hit Ratio (HR) and Normalized Discounted Cumulative Gain (NDCG) [37]. HR considers whether the positive item appears in the top-n list, while NDCG measures the quality of ranking by computing the position of the positive item in the list. The higher score of HR and NDCG indicate better recommendation results.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Dataset** & **\#Users** & **\#Items** & **\#Tags** & **\#Assignments** \\ \hline Delicious & 1843 & 65,877 & 3508 & 339,744 \\ LastFM & 1808 & 12,212 & 2305 & 175,641 \\ \hline \end{tabular}
\end{table}
Table 1: Statistics of the two datasets.
## 2 Theoretical Background  
### Operational Principle of the Solenoid Valve  
The ultra-high-pressure hydrogen solenoid valves discussed in this study employ a pilot-operated mechanism. This design is crucial for high-pressure applications as it allows for the control of large flow rates with a relatively small solenoid actuation force. Directly actuating a large main plunger against high inlet pressures would require an impractically large and power-consuming solenoid. The pilot stage effectively reduces the actuation force needed for the main stage. The operational sequence, as illustrated in Figure 1, consists of several distinct stages [16]:  
* (a) Initial state: As shown in Figure 1a, the main plunger is firmly pressed against the valve seat by a spring force, effectively blocking any fluid flow. In this closed state, the high inlet pressure acts on the unders  
Figure 1: Operation stages of a pilot-operated ultra-high-pressure hydrogen solenoid valve: (**a**) initial state; (**b**) pilot stage opening; (**c**) pressure equalization; (**d**) main stage opening.  
* (b) Pilot stage opening: When electrical power activates the solenoid (Figure 1b), the electromagnetic force lifts the pilot plunger. This opens a small pilot passage, allowing a limited flow of high-pressure hydrogen from the inlet side to a chamber typically located above the main plunger or to the outlet side. The primary purpose of this initial pilot flow is to begin reducing the significant pressure differential across the main plunger.
* (c) Pressure equalization: As the pilot flow continues, the pressure in the chamber above the main plunger (or across the main plunger, depending on the specific design) starts to equalize with the downstream pressure or a reference pressure (Figure 1c). This equalization process significantly reduces the net hydraulic force holding the main plunger against the seat.
* (d) Main stage opening: Once the pressure differential across the main plunger is sufficiently reduced, the main plunger can be lifted with a much smaller force (either by the remaining pressure difference or assisted by the solenoid/spring assembly, depending on the design), as depicted in Figure 1d. This results in the full opening of the main valve orifice, allowing secured and high-volume fluid flow.
* Throughout this entire operational sequence, from fully closed to fully open, the integrity of the contact geometry between the main plunger and the seat is critical. It must ensure effective sealing, especially during the initial and pilot stages at varying pressure differentials, and maintain structural integrity under the extreme pressures experienced, particularly when fully pressurized. For clarity in subsequent discussions, the main plunger will be referred to simply as the \"plunger\". The design of this critical contact interface is the central focus of this study.
# Application of FBG Based Sensor in Pipeline Safety Monitoring
## 2 Pipeline Safety Monitoring Theory  
To implement pipeline leakage and corrosion simultaneous monitoring, a key parameter is required to reflect their respective characteristics. Thus, a hoop strain theory is proposed.
# Micro Activities Recognition in Uncontrolled Environments
## 7 Conclusions  
In this research, we proposed a model to control the assembly process of an ATM. Existing deep learning models to control the assembly process have been implemented on publicly available datasets. These datasets are either synthetic or generated in controlled environments. The dataset for this study was collected in an uncontrolled real-world environment. We implemented four different models to recognise the micro activities in the assembly process. The monitoring and recognition of micro activities in the ATM assembly process are complicated due to the tiny nature of components and uncontrolled working style of workers. Due to the nature of the data, we made modifications in existing deep learning models to fit for the task. The classification was challenging, having classes with very minor differences among them. The problem of the false positive was tackled with the addition of the rule layer between different classifiers. This modification improved the accuracy level from 91.40% to 98.34%. The results show that the assembly line sequence could be controlled using deep learning models. Our proposed model with the Inception-V3/LSTM will generate feedback in case of any discrepancy in micro activities by the worker. The core contributions of this paper are the recognition of micro activities during the assembly process in a real-world environment, the proposed modified deep learning model for the assembly process and normalization of the uncontrolled environment data.  
The proposed model is suitable for all those assembly processes which involve tiny components. Tools should be visible during the screwing of components. However, if there are assembly processes where tools are partially occluded, then this model will have limitations. As future work, we will use sensors to deal with this occlusion problem. We will also use an eye-tracking dataset because with the help of the eye-tracking dataset, we can localize the region of interest in a single frame which could make the model more efficient.  
**Author Contributions:** Conceptualization, A.A. and A.M.D.; methodology, A.A.; implementation, A.A.; data curation, M.H.; writing--original draft preparation, A.A. and A.M.D.; writing--review and editing, A.A. and A.M.D.; supervision, A.F. and M.H.; project administration, A.F. and M.H. All authors have read and agreed to the published version of the manuscript.  
Open Access Funding by the University of Linz. The authors would like to acknowledge support by FFG funded Pro2 Future under contract No. 854184.  
Data Availability Statement: Data cannot be published publicly.
# A Collocation Method for the Numerical Solution of Nonlinear Fractional Dynamical Systems
## 2 Materials and Methods
### The Fractional Derivative of the Cardinal B-Splines  
The Caputo fractional derivative of the interior functions can be easily evaluated by the differentiation rule (cf. [8; 21])  
\[{}_{C}D_{t}^{\beta}\,B_{n}(t)=\frac{\Delta^{n+1}\,t_{+}^{n-\beta}}{\Gamma(n- \beta+1)}\,,\qquad t\geq 0\,,\qquad 0<\beta<n-1\,. \tag{7}\]The Caputo fractional derivative of the left-edge boundary functions can be obtained by applying the fractional differentiation operator to their analytical expression [20]:  
\[{}_{C}D_{t}^{\beta}N_{kn}(t)=\frac{(k+1)}{|P_{kn}|}\sum_{r=1}^{k+1}(-1)^{r+k+1}{} _{C}D_{t}^{\beta}(r-t)_{+}^{n}\,|T_{kn}^{r}|\,,\qquad 0\leq k\leq n-1\,, \tag{8}\]  
where  
\[T_{kn}^{r}=\left(\begin{array}{cccc}1&1&\ldots&1\\ 2^{n-k+1}&2^{n-k+2}&\ldots&2^{n}\\ \ldots&\ldots&\ldots&\ldots\\ (r-1)^{n-k+1}&(r-1)^{n-k+2}&\ldots&(r-1)^{n}\\ (r+1)^{n-k+1}&(r+1)^{n-k+2}&\ldots&(r+1)^{n}\\ \ldots&\ldots&\ldots&\ldots\\ (k+1)^{n-k+1}&(k+1)^{n-k+2}&\ldots&(k+1)^{n}\end{array}\right)\,.\]  
Equation (8) shows that \({}_{C}D_{t}^{\beta}N_{kn}\) is a linear combination of the fractional derivative of the translates of the truncated power function whose expression is given by  
\[{}_{C}D_{t}^{\beta}(r-t)_{+}^{n} = \frac{1}{\Gamma(k+1-\beta)}\frac{(-1)^{k}n!}{(n-k)!}\frac{r^{n-k }}{t^{\beta-k}}\,\sum_{\ell=0}^{n-k}(-1)^{\ell}\binom{n-k}{\ell}\frac{(1)_{ \ell}}{(k+1-\beta)_{\ell}}\left(\frac{t}{r}\right)^{\ell}\] \[\qquad+\frac{(-1)^{n+k}n!}{\Gamma(n+1-\beta)}(t-r)_{+}^{n-\beta}, \qquad\qquad k-1<\beta<k,\]  
where \((p)_{\ell}\) denotes the rising Pochhammer symbol [20].
# Exploiting Blockchain Technology for Enhancing Digital Twins' Security and Transparency
## 4 Experimental Results  
In this section, we present the experimental evaluation of the implemented system, using the practical application described in Section 5 as a testbed. The experimental results were derived from transaction logs collected from the blockchain network's nodes and Docker container consoles, providing a comprehensive view of the system's operational dynamics. The evaluation focused on two primary aspects: the security and administrative functionalities of the system's components and the performance of the underlying blockchain framework. Initially, we conducted an in-depth analysis of the data flow during communication with the digital twin system, examining both _sending_ and _receiving_ operations to ensure reliability and consistency. Additionally, security tests simulated scenarios involving unauthorized data submissions and directionality mismatches to validate the system's resilience and the effectiveness of its smart-contract mechanisms.  
Moreover, to assess the framework's performance and robustness, we employed Hyperledger Caliper, a blockchain benchmarking tool, to conduct a series of experiments. These experiments measured critical metrics such as transaction throughput, latency, and network scalability under varying conditions.
## 1 Introduction
A specific South American case study in Quito, Ecuador, is investigated to gauge changes in air quality during the national strike of 2019 and up to 12 days later. Due to the location of the central government in Quito, protests, mostly indigenous groups, traveled hundreds of kilometers by foot to Quito from all over the country. Throughout the protests, there was a voluntary (private bus companies) and forced (city transportation lines and private vehicles) lockdown of urban transport circulation. Otherwise, circulating vehicles were often attacked and forced to stop by protesters. These unusual conditions allowed for estimating the impact of the motorized fleet on trace gas levels in this high elevation Andean capital. In this study, to estimate the protest impact on urban air quality in the South American city, first, the strike impact on concentrations of NO\({}_{2}\), CO, O\({}_{3}\) and SO\({}_{2}\) was evaluated using an inter-period observational data comparison, considering 12 days before, 12 days during and 12 days after the strike. Then, the evolution of trace gas levels was assessed using machine learning (ML) gradient boosting machine (GBM) modeled business-as-usual (BAU) conditions. The comparison between the two approaches was investigated. Finally, the two model performance evaluations conclude the study.
## 2 Introduction to IPv6
### Icmpv6  
The Internet Control Message Protocol Version 6 (ICMPv6) is an important element of IPv6, and at least parts of ICMPv6 have to be used in every network based on IPv6. Similar to the ICMP of IPv4, it handles error messages and can help with network diagnoses through echo requests (ping) and other familiar features. The protocol itself is documented in RFC 4443 [34]. ICMPv6 is the foundation of  
Figure 1: Depletion of Internet Protocol (IPv4) from February 2012 to January 2013.  
some new protocols specially designed for the use with IPv6. These protocols are: Neighbor Discovery, Path MTU, Autoconfiguration and Multicast Listener Discovery (MLD).  
Neighbor Discovery (ND) is one of the most important new protocols based on ICMPv6 and serves many functions. It enables each node to find all other reachable nodes within its link and to learn their MAC and link addresses, which replaces the Address Resolution Protocol (ARP). Together with Stateless Address Autoconfiguration it enables devices to receive a network prefix and other configuration information from a router in order to automatically configure the IPv6 address; moreover, a Duplicate Address Detection (DAD) can be performed to ensure uniqueness within the link (or within the scope of the prefix if the link was given its own subnet). Other uses are to check which neighbors are still available and detect changes of link-layer addresses. ND is specified by the IETF in RFC 4861 [35].  
As mentioned above, IPv6 does not only feature _stateful_ autoconfiguration via DHCPv6 (Dynamic Host Configuration Protocol), but also _stateless_ autoconfiguration using ICMPv6 messages and DAD, specified in RFC 4862 [24]. When an interface becomes enabled, it automatically defines its link-local address by combining the fixed link-local prefix _fe80:0_ with a unique interface identifier, which could be an Extended Unique Identifier (EUI-64) address as in RFC 4291[36]. If the router of the link announces a global unicast prefix, nodes can also configure a global unicast address which is routable on the Internet [24].  
Path MTU (PMTU) describes the biggest possible packet size that can be sent via a particular path through a network. The smallest MTU of IPv6 is 1280 byte. This enables the link layer to perform an encapsulation without exceeding the 1500 byte limit of Ethernet. A recommendation is to implement PMTU discovery on every node. The reason for this is that with IPv6 only the source and destination are able to fragment and defragment the packet (exceptions apply if tunneling is used). Every time a packet reaches a node which cannot handle its size, the packet does not get fragmented further; instead an ICMPv6 message \"Packet to big\" is triggered and sent back to the source. By using PMTU discovery on every node, the PMTU can be established in advance and thus prevent unnecessary traffic. Fragmentation can furthermore be avoided to a large extent by only sending packets of the size of the PMTU if possible. A complete specification of the PMTU discovery can be found in RFC 1981 [37].
## 4 IoT Protocols
### Application Protocols  
* **Message Queue Telemetry Transport (MQTT):** This protocol was invented by IBM in 2000 for telemetry applications using low power data rates [47]. This protocol operates in a publish-subscribe model and is designed exclusively for lightweight applications. It requires a small code footprint and low bandwidth.
* **Extensible Messaging and Presence Protocol (XMPP):** This communication protocol is based on XML to realize the message-oriented middle-ware and applications [48; 49]. This protocol is also used in publish-subscribe systems, file transfer services, and some communication applications of embedded IoT.
* **Advance Message Queuing Protocol (AMQP):** This is a standard for asynchronous messaging by wire [50; 51]. Encrypted and interoperable messaging between organizations and applications will be done. This protocol is popular for IoT device management that uses client-server messaging.
* **Constrained Application Protocol (CoAP):** A web protocol for constrained devices that exchanges messages in asynchronous mode [52; 53]. Uniform resource identifier support is enabled with minimum complexity in this RESTful protocol.
* **Data Distribution Service (DDS):** This is a machine-to-machine communication protocol that enables data exchange through the publish-subscribe method [54; 55]. Unlike MQTT and CoAP, DDS uses a broker-less architecture. As this model has a data bus that directly connects producers and subscribers; it employs multi-casting techniques for data transmission and a high-quality service in small memory footprint devices.