esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Multimodal AI to Aid Scene Perception for the Visually Impaired
## 5 Conclusions  
The results show that multimodal models can determine the conditions under which a photo was taken, aiding in selecting parameters for 3D scene reconstruction. While real-time support is not yet feasible, they can assist with periodic detection and parameter control. However, forcing models to answer specific questions is challenging. They easily identified day or night and indoor or outdoor settings but struggled with details like pedestrian crossings or doors. Notably, when justifying answers, the model inferred doorssimply because a building was present and assumed pedestrian crossings existed just because the scene depicted a city.  
Despite these shortcomings, the use of AI models can significantly improve user experience, primarily by eliminating the need for manual parameter selection based on environmental conditions. Additionally, AI models can help diagnose system malfunctions by detecting issues such as dirt, obstructions, incorrect installation, or improper camera orientation relative to the ground.  
Implementing such solutions on portable devices with limited computing power is impractical due to response time constraints. However, using AI models in a Software as Service (SaaS) approach with Internet access is viable, as servers can return results in seconds, often faster than dedicated commercial models that queue tasks.  
Moreover, despite the models' less-than-perfect classification accuracy, selecting segmentation algorithm parameters based on AI-derived scene information is entirely feasible. By combining AI-generated inferences with simple, interpretable messages delivered to the user, such as specifying for which type of scene the parameters were chosen, these models can meaningfully enhance the usability and reliability of assistive systems.  
Future work will explore techniques such as model pruning to assess real-time feasibility. Additionally, the use of commercial models and background image analysis for parameter tuning will be considered.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15126442/s1](https://www.mdpi.com/article/10.3390/app15126442/s1)]([https://www.mdpi.com/article/10.3390/app15126442/s1](https://www.mdpi.com/article/10.3390/app15126442/s1)), Dataset S1: Annotated image set and classification results with evaluation metrics;  
This research received no external funding.  
Not applicable.  
The original dataset with results is available as Supplementary Material.  
The author declares no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ETA & Electronics Travel Aid \\ AI & Artificial Intelligence \\ RANSAC & Random Sample Consensus \\ SVD & Singular Value Decomposition \\ RAG & Retrieval-Augmented Generation \\ SaaS & Software as a Service \\ \end{tabular}
## 3 Review of Limbasiya et al.'s Protocol
### Formation Phase  
If a new vehicle requests registration with trusted authority \(TA\), \(TA\) computes and sends \(OBU_{i}\) and \(TPD_{i}\), which store the necessary values to the vehicle. Before registration, each vehicle computes parameters using unique identity \(RID_{i}\), password \(PWD_{TPD_{i}}\), and random number \(s_{i}\). The detailed equations are shown in Figure 2 and steps are as follows.  
**Step 1:** Vehicle \(v_{i}\) chooses unique identity \(RID_{i}\), password \(PWD_{TPD_{i}}\) and generates a random number \(s_{i}\). \(v_{i}\) computes \(X_{i}=(PWD_{TPD_{i}}||s_{i})\oplus RID_{i}\), and then sends \(RID_{i}\), \(X_{i}\) to \(TA\) through a secure channel.  
**Step 2:** After receiving \(RID_{i}\) and \(X_{i}\), \(TA\) calculates \(P_{pri_{i}}=s_{i}\oplus P\oplus RID_{i}\) and saves \(\{P\}\) in \(OBU_{i}\) and \(\{X_{i},P_{pri_{i}}\}\) in \(TPD_{i}\). Subsequently, \(TA\) sends \(OBU_{i}\) and \(TPD_{i}\) to \(v_{i}\) via a secure channel.
## 3 Attacks on 5G NSA
### Active Attacks
#### 3.1.3 Message Attacks
Consequently, once the victim UE initiates the Attach Request and receives the authentication challenge from the core network, it fails the sanity check for sequence numbers and is discarded by the UE. The UE would then have to re-synchronize its sequence number by sending an authentication failure message to the EPC. In a _Service Disruption using Authentication Request Attack_, the adversary exploits a specification flaw related to the handling of sequence numbers of the Authentication Request message [27]. In order to perform the attack, the adversary first sends an Attach Request to the MME using malicious UE (using the victim's IMSI) and captures the Authentication Request message. Later, when the adversary plans to perform the attack, it replays the old Authentication Request message toward the UE, which forces the UE to regenerate all session keys, causing the UE to discard packets directed toward it. By conducting a _Linkability using Authentication Response Attack_, the adversary exploits different responses of the Authentication Request message to track the presence of a user in a particular cell area [27]. In order to perform the attack, the adversary needs to send an Attach Request to the MME using a malicious UE (using the victim's IMSI) and capture the Authentication Request message. Later, the adversary would have to operate a fake BS, connect to all nearby UEs, and then replay the captured Authentication Request message to all the devices. The victim UE would accept this message and reply with an Authentication Response message, while all other UEs in the cell respond with a Message Authentication Code (MAC) failure. In the _SON poisoning_ attack, the adversary exploits an implementation flaw of not verifying measurement reports sent by the UE. This attack allows the adversary to poison the SON feature of the network by injecting fake measurement reports using legitimate mobile devices as a covert channel. The attack could lead to a DoS attack against the network and the UE. It could also be used to waste network resources by generating ample X2 signaling. The adversary can perform this form of attack by either (a) setting up rogue UE to send maliciously crafted reports or (b) setting up rogue base station(s) to create a false radio environment around uncompromised UEs, causing them to send attacker-wanted reports to the legitimate base station. Notably, networks with robust SON implementations can mitigate such attacks [18], while those with poor SON implementation remain vulnerable. Although this attack can be performed using either a fake base station or rogue UE, we have classified it under Fake UE to simplify the classification.
# An Approach for Modelling Harnesses in the Extreme near Field for Low Frequencies
## 4 Conclusions  
In this work, we evaluate the accuracy of low-frequency electric field calculation, in cases where the length of the harness is comparable to the observation distance. For the accurate calculation of the field extremely close to the source, we propose the segmentation of the long harness. Furthermore, 20 segments are sufficient, since increasing their number up to 50 does not improve the field calculation more than 0.1%. Moreover, when the ratio of the measurement distance to the harness length is greater than 4, the harness can be approximated by a Hertzian dipole, since the field produced with and without segmentation differs less than 2%. The proposed methodology was applied in a typical complex cable geometry straightening, as per the authors' claims. This work has specific merit on space cleanliness applications, where unintentional harness emissions can cause interference to sensitive measuring sensors.  
Figure 8: Deviation percentage between the two modeling approaches.  
Figure 7: Comparison of the magnitude of the electric field (\({E_{x}}^{2}+{E_{y}}^{2}+{E_{z}}^{2}\))\({}^{1/2}\) versus the _ratio_ of the measurement distance to the cable length, for the Single Dipole Case (dashed line) and the Segmented. Cable Case with \(N=50\) segments (solid line).  
Author Contributions:Conceptualization, A.T.B. and C.D.N.; methodology, A.T.B. and C.D.N.; software, A.T.B., T.N.K. and C.D.N.; validation, A.T.B., T.N.K. and C.D.N.; formal analysis, A.T.B., I.O.V. and C.D.N.; investigation, A.T.B., I.O.V. and C.D.N.; resources, A.T.B., I.O.V. and C.D.N.; data curation, A.T.B., T.N.K. and C.D.N.; writing--original draft preparation, A.T.B. and C.D.N.; writing--review and editing, A.T.B., I.O.V. and C.D.N.; visualization, A.T.B., I.O.V. and C.D.N.; supervision, C.D.N. and C.N.C.; project administration, C.D.N. and C.N.C. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Data Availability Statement: The data used in this study are available on request from the corresponding author. The data can be easily reproduced from the theoretical analysis described in the study. Conflicts of Interest: The authors declare no conflict of interest.
# Deep Neural Network for Automatic Image Recognition of Engineering Diagrams
## 1 Introduction
However, R-CNN has inherent limitations; it is expensive and slow. All processes in R-CNNs, from the RPN to the convolutional network, render the model inefficient and slow to detect objects in an image in real-time. Advanced models such as Fast R-CNN [26] and Faster R-CNN [27] improve network performance and speed by including the RPN in the neural network. In fast R-CNN, by employing a selective search algorithm as the RPN and implementing it into the neural network, it becomes possible to combine the different procedures into one end-to-end network. In Faster R-CNN, to reduce time consumption in the selective search algorithm, the algorithm was replaced by a combined neural network, which made the detection much faster. Many other state-of-the-art algorithms are being proposed as next-generation object identification strategies [28; 29]. However, these have focused on problems such as the improvement of model accuracies for colored images, improvement of detection speeds, and image segmentation (i.e., the process of partitioning the image into a set of pixels with multiple segments). However, engineering diagrams have a characteristic difference from colored images; they are an almost-binary component matrix with a specific size and shape for each symbol. This makes it difficult for a model to classify symbols using only limited information. As an advanced application of object detection in EDs, we propose an R-CNN architecture containing clustering. For the RPN, we implement a sequential image-processing method that is modified for two types of target symbols: valves and instruments. By modifying the image-processing method for region detection, we propose candidate symbol regions using size-based detection. After the RPN, we get the symbols, but we also get the meaningless regions inevitably, such as truncated line, curve, noise, and so on, we call these 'dummy'. The detected dummy images are decomposed by unsupervised learning methods, and negative classes are assigned to them for image classification. For images containing positive classes, which are our target symbols, the dataset is augmented with padding-block. Through a simple convolutional network, the multi-class classification model is trained and applied to new diagrams for the model test. In this research, we propose a model based on an R-CNN architecture that features dummy image clustering. A sequential image-processing method is used for the RPN, instead of a selective search algorithm. After the RPN, the dataset is constructed by positive classes, and dummy clustering is applied to treat the unwelcomed detections as negative classes; thereby improving the classification performance of the convolutional network. The remainder of this paper is organized as follows. Section 2 provides the methodology for the extraction of target symbols from P&IDs. Based on our proposed method, the region proposal and symbol-recognition results are discussed in Section 3. Finally, we conclude the paper in Section 4.
# AODs-CLYOLO: An Object Detection Method Integrating Fog Removal and Detection in Haze Environments
## 4. Results
### Performance of Claylo
As illustrated in Figure 8, the eight example images highlight issues caused by haze that lead to misdetection, missed detection, and lower detection metrics in the original model. In images 1-4, the original model shows misdetection problems, such as misclassifying a small dog as a human in image 2. In images 5-7, the original model misses detections, failing to detect humans, bicycles, and cars in each respective image, errors not present in our AODs-CLYOLO model. Furthermore, the last image shows the original model detecting the doctor with a confidence score of only 0.39, while our AODs-CLYOLO model improves this to 0.63. Similar improvements in confidence scores can be observed in other images, demonstrating our model's enhanced predictive confidence. In summary, compared to the baseline YOLOv5s model, our model significantly improves detection performance in hazy conditions, effectively reducing missed and misdetection issues faced by the original model. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Parameters** & **FLOPs** & **Memory Usage** \\  & **(Millon)** & **(Billion)** & **(MB)** \\ \hline Faster-RCNN & 66.0 & 90.0 & 113.5 \\ SSD & 26.0 & 30.0 & 97.1 \\ YOLOv3 & 61.9 & 35.0 & 89.40 \\ YOLOv4 & 64.2 & 65.0 & 256.6 \\ YOLOv5n & 17.7 & 4.2 & 3.7 \\ YOLOv7-tiny & 6.03 & 13.2 & 12.3 \\ YOLOv5s & 7.02 & 15.8 & 14.4 \\ Ours * & 8.6 & 17.7 & 17.7 \\ \hline \hline \end{tabular}  
* Ours’ refers to the object detection model CLYOLO. Since the impact of AODs on the parameters and other metrics of AODs-CLYOLO is minimal, it has been disregarded here.  
\end{table} 
Table 3: Comparison of computational performance across various object detection algorithms.
###### Abstract  
Satellite reflectarray synthesis using the intersection approach by means of algorithms of the alternating generalized projection (AGP) method to avoid trap points with sequential stages of different illuminations is presented. In this scenario, we examine two distinct sets: the set of radiation patterns producible by the reflectarray and the set of radiation patterns which meet the mission's criteria. These sets are generally non-convex. Therefore, it is expected that conventional algorithms of the method of alternating projections (MAP) converge to trap points (i.e., local minima of the distance between the involved sets). Thus, the AGP method, which takes into account the trap points, has been considered. When large reflectarrays are considered for satellite applications, several trap points can appear, producing oscillatory behavior between several trap points. A mathematical formalism which supports the idea that a reduction in the edge illumination of an antenna involves fewer trap points is described. Thus, the oscillatory behavior can be avoided by using sequential stages in the synthesis process with different edge illumination levels in each stage. In this work, we demonstrate this synthesis technique with sequential stages, using the proposed algorithm to avoid trap points.  
alternating generalized projection method; method of alternating projections; trap points; phase-only synthesis; reflectarray antennas; satellite antennas Article
## 3 Results
### Reduction in Total System Time  
In addition to reducing travel distances, the EA improved the system's operational efficiency by decreasing the total manufacturing process time from 9284.09 min to 8538.33 min, representing a 13.2% improvement (Table 5).  
Figure 5 compares the average time in the system for each product across two scenarios: the initial layout and the optimized layout using the EA. The results show a significant reduction, particularly for Product 2, where the processing time decreased from 4096.53 min to 3219.8 min. This reduction is attributed to better workstation synchronization and reduced interference among operators, minimizing blockages and waiting times.  
These findings confirm that the EA is a viable solution for optimizing artisanal elaboration, enhancing workspace organization, and increasing productivity in multi-tasking environments.  
Figure 4: Impact of the EA on travel distance optimization. (**a**) Comparison between initial travel distance and optimized travel distance after applying the EA. (**b**) Total travel distance reduction across SLP, CRAFT, and EA optimization methods.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Product** & **Layout** & **TO** & **CQS** & **ATS (min)** & **ATM (min)** & **AWT (min)** & **AOT (min)** & **ABT (min)** \\ \hline \multirow{3}{*}{Product 1} & Initial & 1 & 0 & 2510.70 & 2.47 & 0.358 & 2199.68 & 308.19 \\  & Optimized & 1 & 0 & 2451.76 & 1.95 & 0.13 & 2070.51 & 379.16 \\ \multirow{3}{*}{Product 2} & Initial & 1 & 0 & 4096.53 & 7.42 & 330.77 & 3758.34 & 0.00 \\  & Optimized & 1 & 0 & 3219.8 & 3.62 & 317.51 & 2896.7 & 1.96 \\ \multirow{3}{*}{Product 3} & Initial & 1 & 0 & 2676.85 & 7.16 & 0.00 & 2589.42 & 80.27 \\  & Optimized & 1 & 0 & 2866.77 & 3.63 & 0.00 & 2797.06 & 66.07 \\ \hline \multirow{3}{*}{Total System Time} & Initial & — & — & 9284.09 & — & — & — & — \\  & Optimized & — & — & 8538.33 & — & — & — & — \\ \hline \end{tabular}  
* TO: Total outputs, CQS: Current quantity in the system, ATS: Average time in the system, ATM: Average time in movement, AWT: Average waiting time, AOT: Average operation time, ABT: Average blocked time.  
\end{table}
Table 5: Comparison of average processing time per entity in the initial and EA-optimized layouts.
## Materials and Methods
### Electrochemical Characterization  
Electrochemical properties of HEOs were evaluated using CR2032-type coin cells, which were assembled in an Ar-filled glovebox. First, 70% active materials (HESO), 20% conductive carbon black (Super P, Timcal, Billerica, MA, USA) and 10% polyvinylidene fluoride (PVDF, Alfa Aesar, Karlsruhe, Germany) were homogeneously mixed with N-methyl-2-pyrrolidone (NMP, anhydrous 99.5%, Sigma-Aldrich, Steinheim, Germany). The resulting slurry was uniformly cast on Cu foil (Goodfellow, Hamburg, Germany) with a doctor blade apparatus and then dried in a vacuum oven at 60 \({}^{\circ}\)C for 1 h. After a cold-laminating step, electrodes with a diameter of 12 mm were punched out and dried overnight at 120 \({}^{\circ}\)C under vacuum using a Buchi glass oven. The mass loading of active material was \(\approx\) 1.5 mg cm\({}^{-2}\). Lithium metal foil was used as both counter and reference electrodes. 1 M LiPF6 (99.9% battery grade, ABCR, Karlsruhe, Germany) solution in a mixture of ethylene carbonate (EC, 99.9%, ABCR), diethyl carbonate (DEC, 99.9%, ABCR) and dimethyl carbonate (DMC, 99.9%, ABCR) with a 1:1:1 volume ratio was used as the electrolyte. A glass microfiber filter (Whatman, Steinheim, Germany) was used as a separator.  
Galvanostatic charge-discharge (GCD) cycling was carried out at room temperature using a CT2001 A battery testing system (Landt Instruments, Wuhan, China). Cyclic voltammograms (CVs) were measured on a Bio-Logic (Seyssinet-Pariset, France) VMP3 multichannel potentiostat/galvanostat with a built-in electrochemical impedance spectroscopy (EIS) analyzer. EIS was tested at open-circuit potential (OCP), in the frequency range of 100 kHz to 0.1 Hz with an amplitude of 10 mV.
# A Compact Sub-GHz Wide Tunable Antenna Design for IoT Applications
## 2 Design Details  
The major challenges for IoT antenna designs are the requirements for compact antenna structures and sub-GHz operations for better power management along with long-distance communication. In this section, the detailed antenna geometry of the proposed design is presented, equivalent circuit diagram and the physics behind the antenna's operation, followed by a step-by-step antenna design procedure.
Good-quality activated carbons are prepared by plant biomass using orthophosphoric acid H\({}_{3}\)PO\({}_{4}\) as a chemical activating agent [48]. The AC resulting from these treatments acquires an adsorbing [49] and catalyzing capacity [50], which is highly sought after in several fields [46]: the pharmaceutical, food, and automotive industries. AC is widely used in water purification. It allows for the removal of organic (e.g., pesticides) and inorganic (e.g., heavy metals such as Pb) materials [51]. During the last decade, the ability of agricultural by-products to give ACs with a high adsorption capacity and very advantageous physicochemical properties including, among others, a low ash content, has not ceased to attract the attention of researchers [46]. Numerous works have been undertaken on the plant material of various origins: corn straw [52], olive pits [53], sunflower seed shells [54], sugarcane bagasse [55], almond shells [56], peach pits [57], grape seeds [58], apricot kernels [56], cherry pits [58], peanut shells [59], walnut shells [60], rice hulls [61], corn hulls [62], and barley seeds [63]. In Morocco, the agricultural activity attached to the production of organ fruit (Argania Spinosa) for oil extraction is rapidly emerging because of developing interest regarding its usages for culinary and cosmetic purposes worldwide. So far, the increased popularity of organ oil has prompted an annual production up to 4000 tons by Morocco, which leaves behind about 80,000 tons of hard shells [64]. The latter is currently considered as an agriculture by-product without any significant economic value and is mainly used by the local population as a domestic combustible [65]. Even more interesting, organ shells are well known by their rich lignocellulosic content [66], with high potential for use as raw material to produce activated carbons. Indeed, we previously reported successful production of nanoporous activated carbon made from organ shells using optimal preparation conditions following an empirical approach [67]. The purpose of the present work was to initially obtain an activated carbon by chemical activation of organ fruit shells, then to investigate its capacity of adsorption on caffeine and diclofenac. This property is determined by the depollution of various industrial effluents.