esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
A disadvantage of analysing wear debris in the lubricant is that it needs advanced laboratory equipment and it is highly time consuming. In addition, this method also needs the robot to be completely stationary [18]. According to the author's knowledge, there is only one article that suggests using torque data of industrial robot joints to assess their health status. [PERSON] et al. studied in [12] the feasibility of using torque data for industrial robot and repetitive machinery condition monitoring. However, they did not measure the torque with sensors. Instead, they calculated the torque by estimating it from the electric current. The electric current in the motors is directly proportional to the torque required by each joint. The higher the torque, the more electric current each joint will require. In their study, [PERSON] et al. used kernel density estimates and the Kullback-Leibler distance to detect deviations in the repetitivity of an industrial robot joint's torque [12]. They considered both real data from accelerated wear tests and simulated data. Industrial robots are presently manufactured with a torque sensor installed in each joint and therefore, no additional torque sensor needs to be installed. Moreover, torque data is also useful to monitor and control the energy consumption of the robots. A deteriorated robot joint should require higher torque to accomplish a specific task compared to a healthy robot joint. However, the feasibility of torque sensors to detect medium and long-term mechanical deterioration in industrial robot joints has not been proven and it remains unclear. The novelty of the paper resides in three main contributions. First, we show the effectiveness of joint torque monitoring to detect motor brake failures inside robot joints. We perform an in depth mechanical inspection to find the root cause of a frequent failure of high payload industrial robots. Second, we emphasize the influence of the standby pose of industrial robots in the reduction of their RUL by analysing a dataset with more than 600 robots. Last but not least, we present and validate a methodology for industrial robot health assessment using torque sensors. The proposed methodology is based on the conclusions extracted from the experiments and the mechanical inspection carried out. The methodology proposed in this paper is applicable to any kind of 6 Degree-of-freedom (DOF) robot with any kind of load. The data is acquired in a non-intrusive way, as torque sensors are located inside the structure of the manipulator. The manuscript is structured as follows: Section 2 describes the experiment carried out, as well as the obtained results. In Section 3 we perform an in depth root cause analysis of the mechanical fault detected in the experiment. Section 4 analyses a possible cause of the reduction of the remaining useful life of the robots and suggests preventive measures to enlarge their RUL. In Section 5 we propose a methodology to monitor the health status of industrial robots based on the experiments explained in previous sections, and finally Section 6 sums up the main conclusions and future lines.
## 3 Results and Discussion
### Analysis of Coating Performance Test  
#### 3.2.1 Coating Densification Analysis  
The densification test results of the ceramic coatings are shown in Table 3. From the data, different heating rates have a significant effect on the densification of the coating: the ceramic coating with a heating rate of 5 \({}^{\circ}\)C/min started to diffuse water traces at 8 min; the ceramic coating with a heating rate of 2 \({}^{\circ}\)C/min delayed the diffusion phenomenon until 20 min.  
Combined with Figure 4b, the surface of the ceramic coating prepared by 2 \({}^{\circ}\)C/min heating up is more flat and has better densification, and no obvious defects such as pores are observed. This shows that the slower heating rate can help to improve the structural uniformity and densification of the coating.  
In addition, the nano-TiO\({}_{2}\) in the formulation plays a key role in improving the densification of the coating. Except for the nano-TiO\({}_{2}\), the rest of the components were mainly composed of a small number of micron-sized particles. Since the size of nanoparticles is much smaller than that of micron particles, they can effectively fill the pores between larger particles, thus reducing the porosity of the coating surface and improving the densification of the coating [54,55].
# An Efficient MAC Protocol for Blockchain-Enabled Patient Monitoring in a Vehicular Network
## 2 Related Work
### Blockchain Utility in Vehicular Networks  
Trust management in transmitting data by a legitimate node is one of the prime requirements in vehicular networks. That is why the use of blockchain technology in provisioning security, privacy and trust in different prospects of the vehicular network is in high research demand.  
In [17], the sharing of secured data for a scalable network based on consortium blockchain is proposed. The digital signature technique is used for data integrity. For the transaction of data, a practical Bayzantine fault tolerance mechanism is used. In this work, the authors used smart contracts for pre-selected nodes to limit their triggering conditions. The authors claimed that their proposed scheme reduced data blocks about six times with an 83.33% increase in data transmission.  
Refs. [18; 19] introduced secure information sharing by introducing a regional blockchain environment for VANET. In [18], the authors proposed a local blockchain model by dividing the conventional blockchain into multiple geographic regions to ensure the scalability and timeliness of dissemination of message delivery trustworthiness of vehicular nodes. In [19],  
Figure 2: An architecture of vehicular networks (here, TCC: Traffic Command Centers).  
the authors proposed four types of government-administered blockchains such as certificate blockchain, revocation, message and trust blockchains to improve the trust management among vehicles in a vehicular network. In this scheme, the malicious messages are rated with low values, and a predefined threshold value is proposed. In case the message value is less than the threshold value, it will be discarded from the network. The authors further claimed that their proposed technique improved the packet delivery ratio with reduced latency in the presence and absence of malicious attacks.  
A blockchain-based secure mobility management scheme for the VANET environment is proposed in [20; 21]. The proposed scheme in [20] improves the VANET environment in terms of handover latency, signal overheads and computational and communication overheads by allowing rights to the central and revocation authority, whereas RSUs have only read rights and the OBUs of vehicles have no rights. The scheme proposed in [21] comprises registration, initialization and handover phases. Blockchain technology is used for group authentication and to avoid Sybil attacks and replay attacks by introducing secure key negotiation in building up each communication session.  
Most of the research regarding the use of blockchain in a vehicular network is in the light of providing security, and it focuses on malicious attacks and providing the validity of data delivery. However, according to our knowledge, there is no research on medium access techniques between mining and vehicular nodes in the blockchain environment for a vehicular network that is proposed.
# False Information Detection via Multimodal Feature Fusion and Multi-Classifier Hybrid Prediction
## 4 Experimental Results and Analysis
### Baseline Model
#### 4.3.2 Multimodal Model  
1. att-RNN [29]: att-RNN is to use RNN with attention mechanism to fuse text, image and social context features to achieve the task of rumor detection.
2. EANN [30]: Even adversarial neural network (EANN) has three main components: multimodal feature extractor, false information detector, event discriminator. In the model, the feature extractor will extract the textual and visual features of the event with the help of the event discriminator and then concatenate the obtained textual and visual features and finally use the false information detector to detect the authenticity of news posts.
3. MVAE [31]: Multimodal variational autoencoder (MVAE) trains three sub-networks to detect fake news. Here, a variational autoencoder is trained for better representation of textual and visual features. The shared latent features are further used for classification.
4. AMFB [23]: Attention-based multimodal factorized bilinear (AMFB). The network consists of three parts, a feature extraction module, a feature fusion module and a false information detection module. Attention-based multimodal factorization bilinear pooling to fuse text and image features.
5. OUR1: The false information detection model proposed in this paper consists of a feature extractor, an image feature extractor, an early fusion module and a late fusion module. The later fusion module of the model adopts the method of adding the probability distributions obtained by the three classifiers in proportion to obtain the final probability distribution and determine the result by the added probability distribution.
6. OUR2: This model adopts the same method as OUR1 in text feature extractor, image feature extractor and early fusion module, but it adopts a voting method in the later fusion module to obtain the result. Each classifier can cast one vote, and the one with the most votes is the final result.
7. OUR3: This model is the same as OUR2, only the late fusion module was changed. This model takes into account that the proportion of real information in real life is much larger than false information, so it adopts the 'one-vote veto' method in the later fusion module to obtain the results. The method first checks the classification results output by the three classifiers, and if one of them is true, the information is considered to be true.
# The SF12 Well in LoRaWAN: Problem and End-Device-Based Solutions
## 4 The Problem
### Simulated Scenarios  
In the simulations, a LoRaWAN network comprises 100 EDs. A subset of the EDs, which are referred to as Confirmed mode EDs (CEDs), use confirmed transmission. The rest of the EDs, called Unconfirmed mode EDs (UEDs), perform unconfirmed transmissions. The number of LEDs ranges from 30 to 100. Physical layer frames have a length of 20 and 15 bytes for data (uplink payload) and ACK (downlink payload) frames, respectively. Foreach ED, the time between the two consecutive packets follows an exponential distribution. Different uplink traffic loads are considered, from 0.9 packets/h to 18 packets/h. These values correspond to a wide range of mean time between consecutive packet transmissions at the application layer, from 4000 s to 200 s, respectively. All the EDs use a transmit power of 14 dBm, and their initial spreading factor is SF7, with a bandwidth of 125 kHz, which leads to an initial physical bit rate of 5470 bit/s (see Table 1). The EDs follow a uniformly random spatial distribution and are deployed on a square-shaped area of 142 \(\times\) 142 m\({}^{2}\), with a gateway in the middle of this area (see Figure 3).  
All the EDs are within the range of the gateway using SF7, ensuring that losses are not due to low link quality. The ADR mechanism is disabled in both the ED and the NS sides. Duty cycles of 1% and 10% are enforced for the EDs and for the gateway, respectively. We assumed a duty cycle of 10% for the gateway as a favorable, less restricted downlink scenario, which also corresponds to an alternative use of RX1 and RX2 (as proposed in [9]). As will be shown, the SF12 Well problem occurs even in such favorable conditions.  
Each individual simulation is carried out for a simulated time of 86 days, which ensures that all the CEDs reach SF12 before the end of the simulation in all the considered scenarios.
## 3 Materials and Methods
### Step 4: MOOM-OEP Formulation
#### 3.5.2 Objective Functions  
The goal is to find solutions \(x\) (configurations of decision variables) that [5; 19]:\[1.\quad\text{Maximize Operational Efficiency ($f_{1}(x)$):}\] \[\max f_{1}(x)=\sum_{i=1}^{n}\sum_{j=1}^{m}\sum_{k=1}^{p}(w_{3}\cdot TP_{ijk}-w_{ 1}\cdot CT_{ijk}-w_{2}\cdot IT_{ijk}-w_{4}\cdot DR_{ijk}) \tag{7}\]
2. \[2.\quad\text{Minimize Environmental Impact ($g_{2}(x)$):}\] \[\min g_{2}(x)=\sum_{i=1}^{n}\sum_{j=1}^{m}\sum_{k=1}^{p}(w_{5}\cdot EC_{ijk}+ w_{6}\cdot CE_{ijk}+w_{7}\cdot WG_{ijk})\] (8)  
where \(n\) is the number of lines, \(m\) the number of machines, and \(p\) the number of activities. The performance metrics for the specific activity under configuration \(x\) are \(CT_{ijk}\), \(IT_{ijk}\), \(TP_{ijk}\), \(DR_{ijk}\), \(EC_{ijk}\), \(CE_{ijk}\), and \(WG_{ijk}\). The weighting factors \(w_{1},\ldots,w_{7}\) reflect the relative importance and were normalized to 1.0 for the main optimization run, while the TOPSIS sensitivity analysis explored different priority scenarios.
## 5 Challenges and Actionable Pathways for AI Adoption in SMEs
### Overcoming Financial Barriers to Enable AI Adoption  
This subsection addresses the financial resource constraints facing SMEs, categorized under the organizational dimension of the TOE framework. High initial investments, ongoing maintenance costs, and uncertainty about returns often hinder SMEs from adopting AI technologies. These limitations can delay strategic digital transformation and increase risk aversion commonly found in resource-constrained environments.  
As noted in [97], limited access to financial capital remains one of the main organizational obstacles in the successful implementation of AI in SMEs. Similarly, ref. [100] emphasize that in emerging economies, budgetary constraints significantly reduce AI readiness and limit experimentation with new technologies. Ref. [115] identify the upfront costs of infrastructure modernization and integration as deterrents, particularly in traditional sectors such as manufacturing. Moreover, ref. [112] argue that uncertainty about AI's return on investment (ROI) contributes to delayed decision-making, especially when SMEs lack internal strategic foresight or financial risk tolerance. Together, these findings reinforce the need for flexible financing models, government-backed incentives, and phased adoption strategies that reduce the perceived and real financial burden on SMEs.  
To mitigate this challenge, SMEs should adopt a combination of financial strategies and risk-minimizing implementation models. These include the following:
# A Multi-Physics Modeling-Based Vibration Prediction Method for Switched Reluctance Motors
## 3 The Proposed Multi-Physics Modelling-Based Method
### The Controller and Drive Circuit Models  
As shown in Figure 3, the modeling of SRM controller is divided into four parts: speed controller, current controller, position and speed calculation module, and execution angle calculation module. The speed controller outputs the reference current \(I_{ref}\), while the current controller generates a gate signal \(G_{i}\) that controls the interruption of insulated gate bipolar translator (IGBT). In addition, the position and speed calculation module compute the rotor angle \(\theta_{est}\) and speed \(\omega_{est}\), while the execution angle calculation module obtains the turn on and turn off angles, i.e., \(\theta_{on}\) and \(\theta_{off}\). For SRMs, the modeling of the drive circuit mainly involves the modeling of the switching device IGBT. If the change of the capacitance parameter and the switching process characteristics are ignored, the saturation voltage \(V_{sat}\) and the current \(I_{sat}\) can be calculated as follows:  
\[\left\{\begin{array}{l}V_{sat}=A_{FET}\times(V_{GS}-V_{P})^{M_{FET}}\\ I_{sat}=\frac{k}{2}\times(V_{GS}-V_{P})^{N_{FET}}\end{array}\right., \tag{1}\]  
Figure 2: Schematic diagram of an electromagnetic field and electromagnetic force.  
where \(A_{FET}\) is the saturation factor, \(V_{GS}\) is the gate to source voltage, \(V_{p}\) is the pinch-off voltage, \(M_{FET}\) is the saturation exponent, \(N_{FET}\) is the transfer characteristic exponent, and \(k\) is the transfer constant. Meanwhile, the current \(I_{C}\) of the linear region is expressed as:  
\[I_{C}=B_{N}\times I_{sat}\times(1+KLM\times V_{DS})\times(2-\frac{V_{DS}}{V_{ sat}})\times\frac{V_{DS}}{V_{sat}}, \tag{2}\]  
where \(B_{N}\) is gain of current, the \(KLM\) is the channel length modulation factor, and \(V_{DS}\) is the drain to source voltage. The coefficient \(k\), the voltage \(V_{p}\), and the current gain \(B_{N}\) are parameters that vary with temperature and can be corrected based on the actual temperature value.
## 6 Dynamic Path Optimization with Charging Selection  
In this section, we consider dynamic path optimization with charging selection based on real-time network information, taking into account the minimum travel cost and charging cost.
## 4 Discussion
### User Experience  
The integration of brain-computer interfaces and virtual reality has opened up new avenues for enhancing user experiences in a wide range of applications. BCI technology allows users to control digital environments and interfaces using their brain activity, while VR provides an immersive, multisensory experience that can amplify the benefits of BCI-based interactions.  
One key aspect of the integration between brain-computer interfaces and virtual reality is the ability to create highly immersive and interactive virtual environments that can dynamically respond to the user's cognitive and motor abilities. These virtual worlds can adapt in real-time to the user's brain activity and physical movements, allowing for a more natural and intuitive interaction. This enhanced level of responsiveness and personalization can lead to increased engagement, motivation, and a deeper sense of presence and control within the virtual environment, ultimately enhancing the overall user experience [67].  
For example, users can use their brain activity to control the movement and interaction of virtual objects or to navigate through complex virtual scenes and scenarios.  
This enhanced sense of presence and agency within the virtual world can foster a profound connection and immersion for the user, leading to increased engagement, motivation, and overall satisfaction. The ability to directly interact with and manipulate the virtual environment using one's own brain activity can instill a heightened feeling of control and empowerment, further amplifying the user's sense of embodiment and investment in the virtual experience. This symbiotic relationship between the user's cognitive inputs and the responsive, adaptive virtual world can cultivate a truly immersive and captivating experience where the user's mental and physical faculties become seamlessly integrated with the digital realm. The result is a heightened state of flow, where the user becomes deeply absorbed and intrinsically motivated to explore, interact, and achieve their goals within the virtual environment [49; 68].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Area** & **State of the Art** \\ \hline VR Control Signal & Discrete Control: Selecting objects or triggering events in the VR environment using specific brain patterns; \\  & Continuous Control: Navigating or manipulating objects in the VR environment using continuous brain activity modulation; \\  & Shared Control: Combining BCI control with traditional input methods or assistive technologies to enhance usability and performance. \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._
# Easy Fabrication of Ultrafiltration Membrane via Polyethersulfone-Fumed Silica
## 2 Materials and Methods
### Membrane Preparation  
Ultrafiltration membranes were prepared by non-solvent-induced phase separation (NIPS), a well-known process for creating various asymmetric membranes [21]. The casting solution was maintained at a constant 20 wt.% fraction of polyethersulfone. Initially, a solution containing PES and NMP was stirred at 70 \({}^{\circ}\)C until all polymers were completely dissolved. Subsequently, low concentrations of fumed silica particles were added as an additive. The PES solution was then mixed with 0.1 wt.% to 0.4 wt.% of fumed silica, heated, and stirred to create a uniform dope solution at 70 \({}^{\circ}\)C for 3 h. The dope solutions were then cooled down at room temperature for the further membrane-making process. After cooling, the dope solution was poured onto a glass plate and evenly casted using a film applicator (Elcometer, Manchester, UK) to achieve a uniform wet thickness of 150 microns. The glass plate containing the casted solution was then carefully transferred to a pure water bath to start the coagulation process. The membrane was soaked in pure water for 24 h before membrane testing. For clarity, the membranes were named based on their FS content: FS-0.0 (pure PES membrane), FS-0.1, FS-0.2, FS-0.3, and FS-0.4.
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Protocol Name** & **Sleep Scheduling** & **Sleep Scheduling** & **Routing** & **Application-Adapted** & **Independent** \\  & **Mechanism** & **Control Level** & **Method** & **Sleep Mechanism** & **Node Unit (IN)** \\ \hline
[9] & MH-LEACH & No & - &
\begin{tabular}{c} Multi-hop \\ (Static) \\ \end{tabular} & No & No \\ \hline
[10] & LEACH-TL & No & - &
\begin{tabular}{c} Multi-hop \\ (Static) \\ \end{tabular} & No & No \\ \hline
[11] & EDMHT & No & - &
\begin{tabular}{c} Multi-hop \\ (Dynamic) \\ \end{tabular} & No & Yes \\ \hline
[12] & K-means & Yes &
\begin{tabular}{c} CH-Level \\ \end{tabular} & Single-hop & No & No \\ \hline
[13] & EESAA & Yes &
\begin{tabular}{c} Pair-based Level \\ (Pair of Nodes) \\ \end{tabular} & Single-hop & No & No \\ \hline
[14] & SEED & Yes & Node-Level & Single-hop & No & No \\ \hline
[15] & T-LEACHSAS & Yes & CH-Level & Single-hop & No & No \\ \hline
[16] & IT2 FUS & Yes & Node-Level (Fuzzy) & Single-hop & No & No \\ \hline
[17] & ETAAR & Yes & CH-Level &
\begin{tabular}{c} Multi-hop \\ (Static) \\ \end{tabular} & No & No \\ \hline
[18] & DE-MAC & Yes & CH-Level & Single-hop & No & No \\ \hline
[19] & RMIS & Yes & CH-Level & Single-hop & No & No \\ \hline
[20] & RL-Coop Event & Yes & Cluster-Level & \begin{tabular}{c} Single-hop \\ (Intra-cluster) \\ \end{tabular} & No & No \\ \hline \hline - & EDMR & Yes & \begin{tabular}{c} BS-Level \\ (Centralized) \\ \end{tabular} & \
\begin{tabular}{c} Multi-hop \\ (Dynamic) \\ \end{tabular} &
\begin{tabular}{c} Yes \\ (Static/Dynamic) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative summary of EDMR and related protocols.