esa-sceva/satcom-chunk-collection huggingface dataset:
# Interpolating the Directional Room Impulse Response for Dynamic Spatial Audio Reproduction
## 4 Results and Analysis  
Using the databases described in Section 3, an experiment was conducted to evaluate the DRIR interpolation performance when choosing different listening positions and locations of the known DRIRs. Two representative approaches of DRIR interpolation are compared, including the linear interpolation proposed by [PERSON] [4] and the regularized least square algorithm by [PERSON] [16]. Firstly, a conventional linear approach similar to the proposed method is needed as a baseline in order to compare the performance of the proposed method; the conventional linear approach chosen is the one used in [4]. The only difference between the two algorithms is the signal decomposition and synthesis process based on the DOA and TOA information of the direct sound and first few specular reflections, which is crucial for guaranteeing the listeners' immersive perception. Secondly, a recent parametric method that shows high RIR interpolation accuracy is desired to be compared with to mainly evaluate the accuracy of the proposed method, and thus we chose [16] for this comparison. Moreover, the number of existing spatial audio reproduction approaches using B-format to record the soundfield for RIR interpolation is limited. The methodologies in [4,16] well match the proposed approach in general and are also the representative ones for comparisons in the research field. The performance was measured by finding the DOA estimation error and the TOA error for each of the extracted direct sound and specular reflections, which are dominant components of the overall FOA signal. The DOA estimation error is the absolute angular error between the azimuths and elevations derived for extracted specular reflections from the interpolated and known (ground truth) DRIRs at the chosen listening points. The TOA error is the absolute error between the time-points of the peaks of the direct sound and specular reflections extracted from the interpolated and known DRIRs at the chosen listening positions.  
Figure 4: In both real-world and simulation cases, illustrating the microphone positions that are selected to interpolate in: (**a**) the classroom horizontally at rows 3, 5, and 8 (orange, blue, and red circles), row 7 (black circles), and vertically at column 9 (green circles); (**b**) the great hall horizontally at row 4 (red, orange, and blue circles), row 9 (purple and black circles), and diagonally using rows 4, 7, and 9 (green circles).
## 5 Experiments
### Experimental Setup  
#### 5.1.1 Datasets  
We select the following five public datasets for the experiments.  
* Clinc150 [12] is a multi-domain dataset for intent detection. It contains a total of 22,500 sentences from 10 domains. Each domain contains 150 classes;
* Banking77 [25] is a single-domain dataset with 77 fine-grained categories. These categories all belong to the banking domain;
* Huffpost [26] contains news headlines published on HuffPost from the year 2012 to 2018. These headlines cover a wide range of news varieties;
* Hwu64 [27] is also a multi-domain dataset, which contains fine-grained intents from 21 domains;
* Liu57 [27] contains 54 imbalanced categories. It brings challenges for models to reduce overfitting on major categories. Details of the above datasets are shown in Table 3. In DG-FSTC, we use a multi-domain dataset, Clinc150, for meta-training. The other different types of datasets are used to evaluate model performance. For instance, Clinc\(\rightarrow\)Huffpost denotes meta-training on Clinc150 and meta-testing on Huffpost.
## 4 Systematic Review of the Research Pillars
### Digital Technologies and AI in Industry and Manufacturing
#### 4.2.3 IoT, AI, and Edge Computing in Manufacturing  
For the integration of IoT, AI, and Edge Computing in the manufacturing sector to be successful, several significant obstacles and constraints must be overcome. The potential advantages of these technologies may be limited by these difficulties, which include operational, technological, and human aspects such as privacy and data Security. Strong cybersecurity measures are necessary since sensitive manufacturing data can be subject to cyberattacks [73; 74]. Manufacturers are subject to data protection requirements, which might make implementation more difficult. Challenges with interoperability as the wide variety of platforms and devices used in production may cause problems with it. For efficient operation, it is essential to guarantee smooth communication between different IoT devices and AI systems [73; 75; 76]. These problems can be made harder by a lack of industry standards, which makes it more challenging to integrate new technology with current systems.  
Professional Training and a workforce competent in AI and IoT technologies are necessary for the transition to smart manufacturing. It can take a lot of resources to hire fresh talent or upskill current staff [73; 77]. Successful implementation may also be hampered by employee resistance to change. The infrastructure required to support sophisticated IoT and AI systems may be lacking in many manufacturing facilities. This covers network capabilities as well as physical infrastructure [75; 78]. Although cutting-edge computing is useful for local data processing, it necessitates compatible hardware and software, which aren't always easily accessible [74].  
The integration of AI and edge computing in industrial applications significantly enhances productivity by enabling real-time data analysis, predictive maintenance, and optimized decision-making directly at the manufacturing site. Specific AI algorithms, such as convolutional neural networks (CNNs) for visual quality inspection and reinforcement learning for adaptive process control, facilitate automated operations with high precision [54; 64]. Edge computing frameworks, like Kubernetes for edge orchestration or TensorFlow Lite for deploying lightweight AI models, ensure low-latency processing and reduce reliance on centralized cloud systems. This localized approach minimizes downtime by enabling predictive maintenance through anomaly detection algorithms and enhances operational efficiency by dynamically adjusting production parameters. Additionally, edge-enabled AI can ensure data privacy by processing sensitive information locally, aligning with stringent data protection regulations. These capabilities collectively foster a smarter, more agile manufacturing environment [19; 39].
# Authentication-Based Secure Data Dissemination Protocol and Framework for 5G-Enabled VANET
## 4 System Model for A-MAC
### Validation Level
#### 4.3.2 Message Validation
When receiving \(\{L_{\theta_{2}},Reply\}\), vehicle \(\varrho_{1}\) is first recorded of the data acceptance and the timestamps are represented by \(S_{tx}\). Subsequently, vehicle \(\varrho_{1}\) ensures safety check whether disparity \(S_{rx}-S_{rx}\geq\alpha S_{2}\) is satisfied or not. If not, vehicle \(\varrho_{2}\) avoids communicating with vehicle \(\varrho_{1}\). Once disparity \(S_{rx}-S_{rx}\geq\alpha S_{2}\) is satisfied and found to be secure, vehicle \(\varrho_{1}\) will get a reply message from EN-Reply. To get the reply, vehicle \(\varrho_{1}\) should compute \(F_{\theta_{2}}\) perfectly and decrypt it successfully. Correspondingly, vehicle \(\varrho_{1}\) computes \(F_{\theta_{2}}\) according to Equation (16). Let \(F_{\theta_{2}}\) and \(F_{\theta_{2}}\) compute vehicle \(\varrho_{1}\), which is givens as:  
\[\theta_{TA}=h(\zeta_{\varrho_{0}}\parallel\varphi_{\varrho_{0}})\oplus\tau_{ TA} \tag{5}\]2  
The factor \(F_{\theta_{2}}\) is used to decrypt the EN-Reply and to get the reply message successfully given as:  
\[\theta_{TA}=h(\zeta_{\varrho_{0}}\parallel\varphi_{\varrho_{0}})\oplus\tau_{ TA} \tag{5}\]3  
where \(DCP_{F_{\theta_{2}}^{{}^{\prime}}}\) is the decrypted function. If \(F_{\theta_{2}}^{{}^{\prime}}=F_{\theta_{2}}\) is validated, vehicle \(\varrho_{1}\) can decrypt \(EN-Reply\) and get the reply. When correctly decrypted, vehicle \(\varrho_{1}\) deems that vehicle \(\varrho_{2}\) is protected and vehicle \(\varrho_{1}\) should communicate with vehicle \(\varrho_{2}\).
# Novel Techniques for Microbiological Safety in Meat and Fish Industries
## 5 Novel Thermal Practices
### Radio Frequency (RF)  
High-frequency heating, which incorporates radiofrequency (RF) and microwave (MW) warming, has acquired expanded industrial interest and shown extraordinary potential to turn into the options in contrast to conventional techniques for heat processing [1]. Radio frequency is viewed as a volumetric type of heating which accomplishes speedier cooking times and can possibly prompt more uniform heating because of dielectric energy. Dielectric energy prompts molecular friction in water particles to create heat, which is incompletely reliant upon the moisture content of food. RF has assigned frequencies approved by the U.S. Federal Communication Commission (FCC) for modern heating, with 13.56, 27.12, and 40.68 MHz as the radio frequency range (Table 2).
# Resource Optimization for 3D Video SoftCast with Joint Texture/Depth Power Allocation
## 5 Simulation Results  
Several simulation experiments were conducted to assess the performance of the proposed uncoded 3D video wireless transmission.  
_Test Sequence_: For the multi-view plus depth video datasets, different standard reference 3D video sequences were considered: Kendo, Balloons [34], Newspaper provided by GIST, South Korea, and PoznanStreet, PoznanHall [35], Dancer, gtFly provided by Nokia, Finland, and Shark by NICT, Japan. The standard video sequences Dancer, gtFly, and Shark are computer-generated 3D scenes with ground-truth depth maps, whereas the content of the other 3D videos consist of captured real 3D scenes. The tested sequence configurations are listed in Table 1.  
_Parameter settings_: The GOP size was set to eight frames. Depending on the sequence resolution in Table 1, each frame was divided into 16 \(\times\) 16 = 256 chunks. As mentioned previously, virtual viewpoint synthesis requires transmitting the texture and depth frames of the two adjacent cameras near the selected virtual point. Hence, one GOP consists of \(2\times 2\times 8\times 256=8192\) chunks. The 3D-HEVC Test Model (HTM) v16.3 software [36] was used to synthesize the virtual viewpoints. MATLAB was used to conduct the simulation experiments, where the wireless transmission parameters were based on the 802.11 a/g standard. In addition, the wireless transmission experiments were investigated over AWGN-based channels.  
_Metric:_ For video quality assessment, although the proposed algorithms were designed based on the objective performance metric PSNR, the perceptual metric SSIM still revealed important performance evaluation aspects.
# SIoT: A New Strategy to Improve the Network Lifetime with an Efficient Search Process
## 4 Our Proposal
### Friends Sorting for Service Search  
To reach the providers rapidly and balance the available resources during the service search process in a social network, the ordering and reordering of friends is performed by the nodes.  
To choose the efficient friends, we calculate the sorting factor \(\varepsilon^{\rm(f)}\) based on four factors which are: capability of node c, capability of group c\({}^{\rm(g)}\), centrality of group r\({}^{\rm(g)}\), and popularity of group \(\eta^{\rm(g)}\).  
\(\varepsilon_{\rm ij}^{\rm(f)}\) is the factor value of node j at node i, and it is given by:  
\[\varepsilon_{\rm ij}^{\rm(f)}=\alpha\times\varsigma_{\rm j}+\beta\times \varsigma_{\rm j}^{\rm(g)}+\gamma\times\tau_{\rm j}^{\rm(g)}+\delta\times\eta_{ \rm i,k}^{\rm(g)} \tag{11}\]  
where \(\alpha\), \(\beta\), \(\gamma\) and \(\delta\) are the weights of \(\varsigma_{\rm j}\), \(\varsigma_{\rm j}^{\rm(g)}\), \(\tau_{\rm j}^{\rm(g)}\) and \(\eta_{\rm i,k}^{\rm(g)}\), respectively, \(0<\alpha,\beta,\gamma,\delta<1\), \(\alpha+\beta+\gamma+\delta=1\), and j belongs to group k.  
Using the sorting factor, the nodes list their friends; the node with the highest value of \(\varepsilon^{\rm(f)}\) will receive the future request. Furthermore, the sorting factor is recalculated after a period of time.  
Below some explanations and limitations of the proposed solution.
* Our proposed approach considers several issues that can happen during the communication among nodes in SIoT by supporting self-organization and self-healing. Each node has different friends and the communication is decentralized using local information. For that, the failure that can happen at the level of a node is local. Thus, the failed nodes are avoided in the incoming requests according to the sorting factor proposed in subsection.
* The friendship selection in our proposed work is based on four factors: node capability, group capability, popularity, and centrality. Each node has different friends that are sorted periodically based on their capabilities (Section 4.6), such that the most capable nodes will receive the incoming request, they have higher residual energy than other nodes in a community. Using the proposed mechanism, the residual energies of all nodes will be semi-balanced, which increases the lifespan of nodes. In our work, we try to increase the lifespan of nodes to avoid their failure. In the case of the failure of the non-master node, it will be removed from the friends' list of all nodes.
* The master node is the most competent node in a group, which is selected based on its capability, it has more energy than others in the group. Moreover, the role of the master node is flexible, its importance is to make an agreement on a group capability between surrounding master nodes, where this task is realized once. After that, it is considered as a normal friend. Then, if the master node fails, it will be removed from the list of friends without affecting the communication in the group.
## 6 Opportunities and Future Research Directions
### Security  
LoRaWAN needs to have the best data security system to stand out from other LPWAN technology market competitors. With raising awareness of user data privacy, data security-related issues have become a challenging element in all communication protocols. Some companies have developed a specific encrypted IC for LoRa devices to decrease the threats of data being decrypted by a third party. Simultaneously, LoRaWAN service providers have also built considerably safer links using IPSec and MQTTS technologies for protecting data transmission from gateways to network servers. Therefore, and due to data privacy issues, users will not be motivated to join the enterprise's data flow ecosystem, and more data transmission providers will eschew large data servers like MQTT, CoAP, and LWM2M which appear in the market. The challenge will be in allowing such big companies to work individually under the LoRaWAN Alliance and maintaining data security and privacy. Activation by personal and over time air activation is a method of registering the end devices offered by LoRaWAN. ABP's security method is sharing keys beforehand without them being sent on the network. The attacker is not able to decode the payload under this activation method. While OTAA uses an AES-128 key to encrypt and decrypt in the pre-shared AppKey, if an assailant tries to break the network encryption, they might be successful. Moreover, an attacker who is familiar with the protocol might be able to change the packet's path to the desired destination due to unencrypted data. This issue needs more investigation and research into viable GITE solutions that can be produced. The gateway can collect routing from multiple nodes that could even be providing false data to disrupt the network. Thus, secure data transmission must be involved in LoRaWAN, analysis countermeasures, and different attacks. These topics need to be focused on in investigations.  
Additionally, 5G security services have been deployed, installed, and tested in a real-world 5G-LoRaWAN testbed, demonstrating their feasibility and security viability [119]. This study aimed to enable handover roaming in LoRaWAN. The integration of LoRaWAN and 5G has been thoroughly examined, analyzed, and contrasted using two alternative methodologies. LoRaWAN joining procedures can be extended to enable the piggybacking of 5G security material to allow for roaming in LoRaWAN. The current LoRaWAN network does not need to be covered by 5G, but significant changes to the LoRaWAN and 5G specifications are required. Using 5G authentication services, the second solution has been chosen for deployment and validation because it adheres to nearly all standard procedures with just minor alterations. The LoRaWAN joining service and the 5G AUSF have successfully implemented this strategy with the necessary modifications for the IoT device (including in the 5G SIM card component) as outlined in [119].
## 4 Conclusions  
In this paper, a novel, transparent, all-organic OFET structure has been presented. With respect to other devices reported in the literature, the low voltage operation and the sole employment of large-area fabrication techniques, namely inkjet printing and chemical vapor deposition, can be highlighted. Interestingly enough, the same fabrication process can be applied to different kinds of substrates, from flexible PET to ultra-thin, compliant Parylene C films. A statistical characterization of the devices' performance on both kinds of substrates demonstrates that these devices can be operated at voltages not exceeding 3 V (on PET) and 4 V (on Parylene C), with significant reproducible electrical parameters: the threshold voltage is close to 0 V, the charge carrier mobility exceeds 0.1 cm\({}^{2}\)V\({}^{-1}\)s\({}^{-1}\) and the subthreshold slope is in the range of 100 mVdec\({}^{-1}\). Ultra-thin devices, with a relative transmittance exceeding 80%, maintain their functionalities when transferred onto different surfaces, including human skin, where a significant conformability to its ridged structures has been demonstrated. Good electrical and electromechanical stability has been demonstrated, with restrained threshold voltage and mobility variations upon 1000 bias cycles and negligible mobility degradation at significant deformations. The unique characteristics of transparency and conformability reported here pave the way for the development of innovative devices for epidermal electronics and wearable sensor applications.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/10/19/6656/s1](http://www.mdpi.com/2076-3417/10/19/6656/s1)]([http://www.mdpi.com/2076-3417/10/19/6656/s1](http://www.mdpi.com/2076-3417/10/19/6656/s1)). Figure S1: pictures showing fabricated devices with highlighted gate, source and drain structures, and microphotographs obtained during source/drain printing process with the fiducial camera of the printer. Figure S2:  
Figure 6: Output (**a**) and transfer characteristic curve (**b**) of an ultra-conformable OFET transferred onto the skin.  
Pictures showing the successful transfer of ultra-conformable OFETs to human skin. Figure S3: Absorbance for bare PET (black), OFETs fabricated on PET without (red) and with (blue) TIPS pentacene, and for ultra-conformable devices on Parylene C films (magenta). These data correspond with those on transmittance reported in Figure 5 of main paper.  
**Author Contributions**: [PERSON] and G.C. designed and fabricated devices. [PERSON] and G.C. performed electrical, electromechanical and optoelectronic testing. P.C.R. performed optical characterization. [PERSON] and A.B. coordinated research activities. All authors equally contributed to the analysis of the results and writing of the paper. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest**: The authors declare no conflict of interest.
## 4 The Proposed Model
### Ensemble Method
#### 4.3.2 Recurrent Neural Networks  
A recurrent neural network (RNN) is a network of directed connection between each node. The main feature of this network is the hidden state (memory) that can capture the sequential dependence in data. So, we utilized LSTM networks in our work rather than gated recurrent unit (GRU) [38], which has a problem with remembering long sequences. As shown in Figure 6, we used the same architecture as CNN, but we replaced the convolution layer with the LSTM layer which contains three main gates as follows: Forget gate is responsible for controlling what information should throw away from memory, Input gate is responsible for controlling what new information should be added to hidden state from the current input, Output gate decides what information to output from the memory. Then the output of this layer is entered to fully connected dense layer to produce the output.
###### Abstract  
This paper addresses the critical security challenges in the internet of things (IoT) landscape by implementing an innovative solution that combines convolutional neural networks (CNNs) for feature extraction and the XGBoost model for intrusion detection. By customizing the reptile search algorithm for hyperparameter optimization, the methodology provides a resilient defense against emerging threats in IoT security. By applying the introduced algorithm to hyperparameter optimization, better-performing models are constructed capable of efficiently handling intrusion detection. Two experiments are carried out to evaluate the introduced technique. The first experiment tackles detection through binary classification. The second experiment handles the task by specifically identifying the type of intrusion through multi-class classification. A publicly accessible real-world dataset has been utilized for experimentation and several contemporary algorithms have been subjected to a comparative analysis. The introduced algorithm constructed models with the best performance in both cases. The outcomes have been meticulously statistically evaluated and the best-performing model has been analyzed using Shapley additive explanations to determine feature importance for model decisions.  
Keywords:inter of things; feature reduction; convolutional neural networks; XGBoost; reptile search algorithm +
Footnote †: journal: Applied Sciences