esa-sceva/satcom-chunk-collection huggingface dataset:
# A Secure and Lightweight Group Mobility Authentication Scheme for 6 LoWPAN Networks
## 1 Introduction
### Motivation  
PMIPv6 faces critical challenges in securing group mobility. Frequent handovers of mobile nodes in 6 LoWPAN networks introduce vulnerabilities such as replay attacks, impersonation, and denial-of-service (DoS) attacks, which can exploit insecure handovers. Additionally, traditional authentication mechanisms such as ECC are too computationally expensive for 6 LoWPAN nodes, and existing solutions often prioritize individual handovers, failing to address group mobility scenarios where multiple nodes transition adequately simultaneously [6; 9]. Furthermore, many current protocols neglect essential cryptographic guarantees, including Perfect Forward Secrecy (PFS), resistance to key leakage, and protection against tracking. These gaps underscore the urgent need for an advanced solution that addresses these challenges while remaining compatible with the strict resource constraints of 6 LoWPAN devices. As IoT ecosystems expand, the demand for scalable and secure group authentication protocols has grown significantly. Current proposals for group mobility authentication schemes provide partial security for PMIPv6 networks but often lack critical features [10; 11; 12; 13]. They fail to ensure Perfect Forward Secrecy (PFS), leaving past communications vulnerable if session keys are compromised. Additionally, most existing protocols do not adequately safeguard against adversaries tracking the movement of devices across domains or protect against key leakage and DoS attacks. These limitations highlight the necessity of a secure and lightweight group authentication scheme that can cater to the needs of 6 LoWPAN networks while effectively addressing advanced security requirements.
# Analysis of the Current State of Automation of Hazard Detection Processes in BIM in Slovakia
## 3 Results
### Questions Related to BIM Hazard Detection  
For the questions in Figure 3b-d, the respondents said that they did not use BIM for risk and hazard detection during the design stage (Figure 3b--88.89%), but only during construction preparation (Figure 3c--70.00%) and construction (Figure 3d--90.00%).  
The most common hazards were manually detected by members of the pre-construction team when they worked with 3D BIM during pre-construction planning. The most common reasons for not detecting dangers in individual phases included personnel, time and financial difficulties or no access to BIM. The results of Figure 3b,c also showed the low implementation of the PtD approach and that the general contractor was involved in preparations mostly just before the construction began.  
Figure 3: Responses to the questions: (**a**) “Do you use BIM for health and safety processes in your company?” (**b**) “Do you use BIM for hazard detection during the design processes?” (**c**) “Do you use BIM for hazard detection during construction preparation?” (**d**) “Do you use BIM for hazard detection during construction?”  
Figure 2: (**a**) Map of Slovakia with the number of respondents and name of the cities where the headquarters of the companies are located. (**b**) Map of Europe highlighting Slovakia.  
However, the answers to the question in Figure 4 were the opposite, in which up to 53.33% of all respondents said that hazard detection should be performed in all phases: design, pre-construction and construction. Another 33.33% answered that it should start in the pre-construction stage.
# Arithmetic Optimization AOMDV Routing Protocol for FANETs
## 4 Performance Evaluation  
### Performance Metrics  
We utilize three performance metrics--the packet delivery rate (PDR), average end-to-end delay (E2E), and network lifetime--to demonstrate the effectiveness of the proposed routing protocol. The evaluation is conducted considering variations in the UAV node density and velocity.  
1. PDR: The number of data packets successfully received by the destination node divided by the number of data packets sent by the source node, excluding control packet traffic.
2. Average E2E: The total simulation time divided by the total number of data packets sent by the source node.
3. Network Lifetime: The time taken for the simulation until the first node's death, with a longer network lifetime indicating more robust routing.
# ViTFER: Facial Emotion Recognition with Vision Transformers
## 5 Models
### Results of Experiments and Evaluation
#### 5.3.4 Confusion Matrices in AVFER Testing  
According to Figure 13, we can see that performance is similar even for the ResNet configuration. Furthermore, the lack of contempt for original samples can affect performance in the correct classification of this class.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No. of Epochs** & **Accuracy/Loss** & **ResNet-18** & **ViT-B/16/S** & **ViT-B/16/SG** & **ViT-B/16/SAM** \\ \hline \multirow{4}{*}{5} & Training accuracy & 61.38 & 73.99 & 74.41 & 83.88 \\  & Validation Accuracy & 50.43 & 55.42 & 55.81 & 55.14 \\  & Training loss & 10.50 & 7.09 & 7.12 & 7.15 \\  & Validation loss & 14.70 & 12.61 & 13.35 & 12.82 \\ \hline \multirow{4}{*}{10} & Training accuracy & 66.57 & 81.42 & 84.01 & 87.72 \\  & Validation Accuracy & 52.16 & 55.71 & 56.30 & 55.24 \\  & Training loss & 8.90 & 5.06 & 4.67 & 6.24 \\  & Validation loss & 14.90 & 14.33 & 15.05 & 13.39 \\ \hline \multirow{4}{*}{15} & Training accuracy & 68.01 & 79.07 & 87.79 & 87.88 \\  & Validation Accuracy & 52.26 & 56.01 & 54.94 & 55.29 \\  & Training loss & 8.49 & 4.12 & 3.63 & 6.27 \\  & Validation loss & 14.80 & 19.47 & 17.58 & 13.16 \\ \hline \multirow{4}{*}{20} & Training accuracy & 70.01 & 81.14 & 88.37 & 88.18 \\  & Validation Accuracy & 53.85 & 54.28 & 54.65 & 55.45 \\  & Training loss & 8.35 & 5.14 & 3.41 & 6.20 \\  & Validation loss & 14.20 & 15.03 & 18.76 & 13.08 \\ \hline \multirow{4}{*}{25} & Training accuracy & 70.02 & 84.28 & 88.66 & 88.64 \\  & Validation Accuracy & 53.89 & 53.98 & 55.52 & 56.22 \\ \cline{1-1}  & Training loss & 8.27 & 3.97 & 3.63 & 6.05 \\ \cline{1-1}  & Validation loss & 1.39 & 19.93 & 17.87 & 12.93 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy and loss of various models on different configured parameters.  
Figure 10: Training and validation accuracy for ResNet-18 vs. ViT-B/16 models.  
Figure 11: Training and validation loss for ResNet-18 vs. ViT-B/16 models.  
Figure 12: Tables of precision, recall, and F1-score for each class.
## 2 System Models  
Figure 1 presents a block diagram of the transceiver model used in the proposed system. On the transmitting side, we parallelize the data symbols \(S[k]\) by the serial-to-parallel (S/P) conversion and implement the OFDM modulation by an inverse fast Fourier transform (IFFT) with \(N_{\text{FFT}}\)-point. The output of the IFFT block can be expressed as:  
\[s[n]=\frac{1}{\sqrt{N_{\text{FFT}}}}\sum_{k=0}^{N_{\text{FFT}}-1}S[k]e^{j2\pi kn /N}\,\text{FFT}, \tag{1}\]  
where \(N_{\text{FFT}}\) denotes the IFFT length, which is equal to the number of all data and null subcarriers, and \(n=0,1,\ldots,(N_{\text{FFT}}-1)\). Note that we assumed that the outputs of the IFFT are real numbers, which implies that we arrange the data subcarriers in Hermitian symmetric form, in the considered baseband NB-PLC systems. The data symbols \(S[k]\) are produced by encoding the information bits with a forward error correction (FEC) channel encoder and then grouping the coded bits into symbols by a mapper. For the subcarriers do not intend to transmit data are called virtual carriers. Before transmitting the data symbol over a powerline, we serialize the output of the IFFT block by the parallel-to-serial (P/S) block and then prepend the CP to alleviate the multipath effects incurred by the powerline \(h[n]\). On the receiving side, the received signal \(r[n]\) is comprised of the output of the powerline \(y[n]\), the dominant CSIN \(\eta[n]\), and the background additive white Gaussian noise (AWGN) \(v[n]\). We propose using a FRESH-filter based signal processing not only to remove CSIN but also to enhance the OFDM signal. Following removal of the CP from the denoised signal \(\widehat{r}[n]\) and conversion to parallel form, the signal is transferred back to the frequency domain and then equalized by a frequency-domain equalization (FEQ). The equalized signals go through P/S, demapper, and FEC decoder blocks to estimate information bit stream sent from the transmitting side.  
Figure 1: Block diagram of the considered narrowband powerline communication (NB-PLC) transceivers.
# Industrial Internet of Things for a Wirelessly Controlled Water Distribution Network
###### Abstract
The (IEEE 802.11) wireless network design in our work adopts a star topology for both segments, with instruments at the wells (remote nodes) communicating directly with the water tank (Base Station). In the first section, the star network is further divided into three sectors, each collecting data from a number of wells to allow scalability and prevent a single point of failure. The second section, which only has three wells to supply the secondary tank, utilizes one omnidirectional antenna to connect all the wells in that section. The Trusted Wireless network enables the mesh topology and repeater functionality of its wireless modules. This allows the first section of the network to be designed as a cluster-tree topology, with certain wells acting as cluster heads that mediate between the remote nodes in their clusters and the base station (BS). Nodes can switch clusters and communicate with the base station through the CH with the best connection. The second section of the network adopts a star topology similar to the (IEEE 802.11) network. Both designs were then validated using Radio Mobile, which performs link budget calculations between every two points of connection. It draws a terrain map to calculate the path loss based on the coordinates of the water station, accompanied by the parameters of the wireless modules, such as TX power, gain, and receiver sensitivity. These parameters were systematically adjusted until each link met a specific Received Signal Strength Indicator (RSSI). This paper is organized as follows: Section 2 reviews related work in the area of network architectures and designs for deployment of industrial Internet of Things (IIoT) in real-life networks. Section 3 examines the water station infrastructure, followed by an exploration of possible topologies based on wireless technology features in Section 4. Section 5 proposes optimal network architectures and designs for the real-life water distribution network based on findings from the previous sections. Section 6 discusses mathematical models to validate the designs. Section 7 selects software Radio Mobile version 11.6.7 and wireless modules, with simulation results presented in Section 8. The discussion and conclusion are in Sections 9 and 10.
# Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
## 6 Recent Advancements in Clustering Optimization Algorithms  
Clustering is used in WSNs to meet performance requirements such as low energy usage. The design of clustering protocols does not only consider energy optimization, as there is also a need to ensure the quality of service (QoS) and a balance between numerous conflicting issues, such as service lifetime, coverage, and throughput [69]. Numerous bio-inspired, meta-heuristic, and artificial intelligence-based optimization techniques have been developed to address these concerns in the past decades. Energy efficiency is achieved in WSNs by combining optimization techniques with clustering protocols; this was accomplished by several researchers using different optimization techniques. In this study, most of the currently used optimization algorithms in the WSN clustering process are classified into meta-heuristic-based, fuzzy-based, hybrid meta-heuristic-based, and hybrid fuzzy-based techniques, as seen in Figure 10.  
The available research articles were categorized based on the optimization algorithms used, and a summary of each clustering protocol is presented to highlight the objectives and evaluation functions. This classification helps indicate the papers that discussed specific techniques, guiding beginners during research towards articles relevant for future studies. Detailed information concerning the selected articles is presented in Table 2.  
Figure 10: Classification of algorithms used in relevant articles. Figure 9: Data communication phase process. Comparison of the available techniques was based on qualitative metrics that portrayed the general features of the various protocols. The comparison of the clustering methods was based on the parameters for implementation, and these parameters portray the basic attributes of the related clustering methods, as discussed below. Figure 11 presents an overview of these implementation parameters.
## 4 Body-to-Body Communication and Connectivity
### Real Indoor Experiment Scenario: A Wireless Body-to-Body Connectivity Evaluation
This is due to many factors: (1) signal degradation caused by the fact of being out of range (and no closed node can relay the mobile's data); and (2) the unstable links between the nodes are caused by the interference effected by WiFi access points, wireless extenders and devices inside the office. Equally important, indoor obstacles raised major signal attenuation [40]. It is noteworthy that the delay in milli-seconds (ms) depicted in Figure 6 is reset to zero when a mobile node is disconnected (we consider that a delay higher than 1000 ms is an immediate disconnection). Hence, this leads us to investigate the accuracy of the delay and disconnection times. For that, we have set up a process to ping the distant CC node every millisecond. The resulted average round-trip time delay and the average end-to-end disconnections per hop count are illustrated in blue and red respectively in Figure 7.  
Figure 5: (a) Map of the experiment scenario; (b) Data collecting from the on-body Shimmer sensors to the android mobile application and then to the IoT platform through the ORACE-Net tactical deployed network. OMD: ORACE-Net mobile device; OTD: ORACE-Net tactical device; FG: front gate of the office; BG: back gate. Figure 6: Hop count, instant delay and end-to-end link quality estimation variation during one hour of experimentation for WBAN node in an indoor scenario. What is important to know is that the average percentage of end-to-end disconnections and average round trip time delay increase accordingly with the hop count. With regards to the mobile smartphones used in the experiment (Samsung Galaxy S3 I9300-Battery: 2100 mAh-WiFi IEEE 802.11n), the experimental range is around 100 m. The experiment shows that the best performance is recorded within 1 hop (from the mobile node to the CC node) where average disconnection is around 12% and average round-trip time delay is equal to 21 ms. However, a connection within 4 hops (approximate distance between two nodes is 45 m) makes the average end-to-end disconnections exceed 43% as illustrated in Figure 7. The average round trip delay increases also to reach 72 ms. It is perceived that for more than 4 hops, average disconnection is expected to exceed 50%. As has been noted, these results were achieved with intermediate tactical static nodes, hence, results might be worse if all the nodes of the network are mobile. Therefore, based on the best performance recorded (12% among 1 hop), the standard WiFi IEEE 802.11n remains efficient within short distances only. Interestingly, we learned from our experiments that the standard WiFi IEEE 802.11n is only appropriate for body-to-body communications in the indoor scenario for an overall distance of 25 to 30 m (from one node to another). Our results and findings are consistent with the results provided by [PERSON] et al. in [41]. WiFi IEEE 802.11n remains a candidate for BBN communications for indoor scenarios, but with consideration of the above-cited limitation.
# Multifunction RF Systems for Naval Platforms
## 2 Requirements for Naval RF Functions
### Radar Function Requirements
The duty cycle requirements listed in Table 1 for the HS and VS subfunctions arise from a number of considerations, such as desired unambiguous range, size of range blind zone, and average waveform power, the latter of which is seen in the radar range equation to affect target detection. These duty cycle values are considerably less than the values listed for the TI subfunction and the other RF functions. In the case of the TI function, the illumination waveform is essentially narrowband continuous wave, which may have a transmission duty cycle of up to 100%, depending upon the capability of the semi-active missile seeker. Some semi-active missiles, such as the Evolved Sea Sparrow Missile [7], have seekers that support interrupted continuous wave illumination for terminal guidance, which allows the radar TI function to transmit with a duty cycle of less than 100%. As indicated in Table 1, all radar functions typically use linear vertical polarization, primarily because there are distinct advantages to doing so for HS and no disadvantage to using vertical polarization for the other naval radar subfunctions. The advantages of using vertical polarization in HS mode are twofold. First, it has been observed that at radar grazing angles less than a few degrees (which is typically the case for the HS function) and higher sea states, sea clutter returns for vertical polarization are less than those for horizontal polarization [8]. Secondly, forward specular reflections off the ocean surface at low grazing angles are less for vertical polarization than for horizontal polarization due to the Brewster angle effect [9]. With lower specular reflections, the formation of multipath nulls in the radar propagation factor that adversely impact target detection at small target elevation angles is somewhat mitigated, although still remaining an issue.
## 4 Experiments
### Semantic Segmentation  
#### 4.1.1 Training Details  
We train our single-frame semantic segmentation model according to the proposed MixSkip procedure. Note that we initialize our backbone with ImageNet [30] weights. We train our model for 500 epochs, with batch size 14 and cross-entropy loss. We set the learning rate to \(4\times 10^{-4}\) and decay it with cosine annealing to the minimal value of \(10^{-6}\). Our data augmentation involves random scaling, horizontal flipping, and cropping with size set to \(768\times 768\) pixels.  
Table 1 compares the regular semantic segmentation training with the proposed MixSkip procedure. We evaluate two models based on ResNet-18 and DenseNet-121 backbones in two regimes: with and without skip connections. The results show that both procedures achieve comparable results when evaluating the models with skip connections turned on. However, models trained with the regular procedure reveal serious performance deterioration when evaluated without the skip connections. In particular, the model based on ResNet-18 loses 31 mIoU points if trained with the regular procedure and 2.3 mIoU points if trained with MixSkip. The difference is even bigger with the model based on DenseNet-121. Regular training leads to almost 60 mIoU points lost, while MixSkip reduces that drop to 2.8 mIoU points. We also notice that the stronger DenseNet-121 backbone improves by 2.4 points over the weaker ResNet-18 backbone. These results suggest that the models trained with the MixSkip procedure are more noise resistant and more appropriate for the forecasting application.
## 1 Introduction
These cables carrying data are exposed to environmental conditions [13] (snow, rain, humidity, temperature, etc.). In contrast, wireless monitoring solutions offer less environmental exposure compared to wired ones. Wireless solutions have faster decision-making, low maintenance costs, and higher response times, and they present increased mobility and network security methods [9]. A control system aims to act immediately after an unplanned event. The verification of a photovoltaic system can be done [11] physically by using cables or wirelessly by using Bluetooth or Wi-Fi connections. The monitoring and control of a system play a crucial role in ensuring the proper functioning of the photovoltaic system. In the research [14], the control part was not detailed enough as the paper primarily focused on the system's tracking approaches. In paper [11], the Internet was used to transfer the collected and controlled data using LabVIEW Web Server functions. To maintain the safety of PV systems, the applications that monitor and remotely control photovoltaic systems should adhere to the three main cybersecurity principles: confidentiality, integrity, and availability of data. According to paper [15], photovoltaic systems are vulnerable to cyber-attacks. According to [PERSON] et al. [16], researchers tried various types of cyber-attacks against a photovoltaic system using a simulated microgrid. The aim of this research is to review remote-control solutions for PV systems, AI techniques applied in PV systems, and cybersecurity techniques. Our research contributions are as follows:  
* Analyzing different implementations of PV systems. * Reviewing the literature on the remote control of PV systems. * Reviewing the literature on AI techniques and methods applied in photovoltaics. * Reviewing the literature on cybersecurity threats and vulnerabilities in smart PV systems and cyber-attacks on power systems. * Discussing the architecture of PV systems and how they can be implemented easily, efficiently, and securely using remote-control, AI techniques, and cybersecurity methods. This research paper consists of the following sections: The methodology used for the research is presented in Section 2, the configurations of PV systems are presented in Section 3, the implementation and remote-control methods are discussed in Section 4, Section 5 presents AI methods used in PV systems, Section 6 presents the vulnerabilitiesand threats to which PV systems are exposed, discussions about the architecture of the PV systems are in Sections 7 and 8 presents the conclusion.
## 3 Proposed System
### Edge-Server Architecture-Based Pedestrian Abnormal Behavior Detection System
In addition, the server can provide more accurate analysis using high-performance deep learning algorithms, providing real-time processing speed and high analysis accuracy. The proposed system adopts a dual-stage approach where initial analysis is performed on the edge devices, and only critical events are deeply analyzed on the GPU server. This structure allows each edge device to process initial data independently and only send the information to the GPU server when abnormal behavior is detected, significantly reducing the throughput and improving the response speed of the entire system. In particular, the integration control server (ICS) coordinates these two stages of processing and effectively manages the load on the server. The installation of the analysis module is based on docker, which is easy to install. The connection between the GPU server where the analysis module is installed and the ICS is simple, with only IP and port, which makes it easy to expand the entire system. This approach allows data from large-scale CCTV systems to be processed in real-time and ensures scalability and flexibility, enabling application in various environments.