esa-sceva/satcom-chunk-collection huggingface dataset:
# On-Edge Deployment of Vision Transformers for Medical Diagnostics Using the Kvasir-Capsule Dataset
## 2 Literature Review
### Kvasir-Capsule  
Created in 2020 and having been updated in 2022, the Kvasir-Capsule dataset remains one of the only comprehensive wireless capsule endoscopy (WCE) datasets available on an open-access basis [9]. This dataset builds on similar datasets available within the field, such as Kvasir [14] and HyperKvasir [15]. Most of the works using this dataset have been conducted between 2022 and now--the majority of which focus on image and video classification, with a few branching beyond into novel methods in deep learning. In [PERSON] and colleagues' work published in 2023, the authors explored the use of the Kvasir-Capsule WCE dataset for image classification and built further on it by adding an Explainable AI (XAI) aspect [16]. The authors began with an analysis of multiple transfer learning architectures, including InceptionV3, EfficientNet, VGG16, VGG19, Vision Transformer (ViT), ResNet152v2, and MobileNetV3 Large. Interestingly, MobileNetV3 Large appears to have performed exceptionally well with an average F1-score of \(0.95\pm 0.02\) across 10-fold validation. Given the robustness of the model and its intended uses being specifically for on-hand deployment, it is interesting that it performs so well compared to more expansive models such as the VGG-19 architecture (with 143.7 M parameters) [17].  
More recent studies on the Kvasir-Capsule dataset include the 2024 study conducted by [PERSON] and colleagues [18]. The authors here present a novel framework combining global and local features for learning. The authors first pre-processed the dataset and made it into a binary classification problem, with 3490 images in the abnormal class. The Foreign Body and Ampulla of Vater classes were excluded from the dataset, and the Normal class was down-sampled to 3500 images. This method first uses a ViT model for feature extraction and then combines that with a CNN that uses attention to retrieve the \"attention features\" of the image. Both these features are then fused and fed into an MLP classifier for classification. In comparison to other methods, the authors report a significantly higher F1-score than that of traditional CNN-based or ViT-based models, boasting an accuracy of 0.97 and an F1-score of 0.97 as well. It is important to note that the majority of recent developments for classification pertain to introducing novel architectures or frameworks within the scope of the medical field. For example, [PERSON] et al. [19] focused on addressing the issue of imbalanced medical datasets. In this work, the authors proposed cross-balanced pseudo-supervision for classification, which was then tested on the Kvasir-Capsule dataset (among others). Using only 20% of the labeled data (on the full dataset), the authors reported an F1-score of 0.9512 using their method. These results are summarized in Table 1.
## 5 Simulation Results
### Parametric Evaluation of the Proposed Method
#### 5.2.3 AGC Communication Delay  
A sensitivity analysis is conducted to evaluate the dependence of the proposed method on communication efficiency by varying the SCADA time delay, so far assumed equal to 1 s in all simulations (Table A2). To demonstrate the effectiveness of the proposed method for a wide range, system frequency and BESS active power are shown in Figure 13, for three different SCADA time delay values, namely 0.2 s (red/dash-dotted line), 1 s (blue/continuous line) and 5 s (green/dashed line). The frequency quality metrics in each case are presented in Figure 14. It is observed that the effect of delays in data transfer between the battery unit and the AGC are insignificant, with the performance of the proposed method remaining effectively the same.  
Figure 12: Response following a diesel unit outage at 150 s and synchronization of another at 900 s, for different SOC activation zones. (**a**) Frequency, (**b**) BESS active output power (positive: injection, negative: absorption), (**c**) SOC, (**d**) BESS aFRR-up participation factor.
# Research on Vehicle-to-Vehicle MIMO Wireless Channels in Various Tunnels
###### Abstract  
In this paper, we propose a geometry-based channel model for multiple-input multiple-output (MIMO) vehicle-to-vehicle (V2V) communications in practical tunnel scenarios. In the proposed channel model, the waves transmitted from the transmitter experience line-of-sight (LoS) and non-LoS (NLoS) before arriving at the receiver. The time-varying parameters of the propagation paths and angles are derived to characterize the channel non-stationarity. Furthermore, we derive the correlation property functions of the proposed V2V channel model; they are space cross-correlation functions (CCFs), temporal auto-correlation functions (ACFs), frequency CCFs, as well as the Doppler power spectrum distributions (PSDs). Numerous simulation results of the propagation characteristic for different physical properties of the tunnel, as well as the different motion properties of the transmitter and receiver, are studied and discussed; they demonstrate that the proposed channel model is suitable for describing the practical V2V tunnel communication scenarios.  
MIMO channel modeling; space CCFs; temporal ACFs; frequency CCFs; Doppler PSDs 202215 5222. [[https://doi.org/10.3390/en15145222](https://doi.org/10.3390/en15145222)]([https://doi.org/10.3390/en15145222](https://doi.org/10.3390/en15145222))
## 6 Experimental Results and Discussion
### ADE Loss  
Average Displacement Error (ADE) loss is a widely utilized statistic in trajectory prediction applications. This metric calculates the average Euclidean distance between anticipated points and their associated ground truth points throughout a series of frames. ADE loss is computed using the following formula:  
\[ADE=\frac{1}{N}\sum_{i=1}^{N}\lVert\hat{y}_{i}-y_{i}\rVert, \tag{21}\]  
where \(N\) is the total number of points in the sequence, \(\hat{y}_{i}\) represents the predicted position, and \(y_{i}\) is the ground truth position. Figure 9 shows the ADE loss over time.  
Figure 8: MSE loss variation across validation epochs: tracking the model’s performance.  
Figure 9: ADE test loss: evaluation metric for model performance on test dataset.
# Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey
## 3 Survey of State-Of-The-Art MAC Protocols
### Random Access Protocols
#### 3.1.1 MAC Protocol with Mobility Support  
The protocol in [66] is a variation of CSMA/CA that helps CR nodes adapt to the conditions of their surrounding vicinity to a Primary Exclusive Region (PER) by using the Radio Signal Strength Indicator (RSSI). The protocol also supports mobility. It utilizes a Common Control Channel (CCC) as a part of the request to trade the control frame, for example Ready-to-Send (RTS), Clear-to-Send (CTS) and Acknowledgment (ACK) frames. CM-MAC uses Transmitter Spectrum Sensing (TSS) and Receiver Spectrum Sensing (RSS). TSS is performed by the CR transmitter, while RSS by the receiver side. After performing the sensing, the spectrum sensing data are included in the CTS/RTS frames, where each CR node can obtain the spectrum accessibility information of one hop from broadcast RTS/CTS frames. The sensing information of TSS/RSS that is integrated into RTS/CTS/ACTS can be received by all of the neighboring CRs. Thus, this process is sufficient to ensure nodes have the necessary information for transmitting packets successfully to the next hop. After selecting the accessible channel set, to improve the throughput, CM-MAC transfers the split data payload simultaneously on multiple channels.
## 2 Experimental Design
### Sensor Layout Design  
The FBG sensor layout design is critical for damage detection. The sensors were bonded on the surface of the plate to sense the damage condition. According to the existing experimental data from  
Figure 1: (**a**) Schematic of the aluminum specimen and the FBG sensor layout. (**b**) Metallic homogeneous coupons bonded with the FBG sensors under the fatigue experiment test.  
our pre-research testing and the finite element analysis at different crack lengths [23], the crack usually initiates at the pre-crack position, grows on both sides of the hole, and then finally breaks the specimen. Thus, the central hole region was marked as the target area. Sixteen FBG sensors bonded by the liquid cyanoacrylate adhesive were uniformly placed on both sides of the hole. The adhesive Young's modulus of the FBG sensor was 1.7 MPa, and the length was 10.1 mm. Furthermore, the deformation spectrum introduced by the crack damage was caused by the axial strain profile. Considering that, the FBG sensors were placed at the terminal of the crack tip. The detail sensor placement layout is shown in Figure 1a, where red lines represent the FBG sensors near the target region.
## 6 Performance Evaluation Results
### Comparison with a BER of 10\({}^{-5}\)  
Figure 7 presents a goodput comparison of the DTN different protocols with a BER of 10\({}^{-5}\). Similar to the transmissions at BERs of 0 and 10\({}^{-6}\), LTP LTP PRIM shows a significant advantage at all link delays. In comparison, the performance advantage is more significant than that at BERs of 0 and 10\({}^{-6}\). Also, different from the comparisons at BERs of 0 and 10\({}^{-6}\), the performance of UDP LTP PRIM, LTP UDP SEC and LTP TCP SEC binds at short link delays (<2000 ms). This is clearly seen from the statistical analysis in Figure 7b in which \(P\)-LTP TCP SEC vs. LTP UDP SEC, \(P\)-LTP TCP SEC vs. UDP LTP PRIM, and \(P\)-LTP UDP SEC vs. UDP LTP PRIM are either greater than 0.5 or greater than 0.05 at most link delays. This indicates that UDP LTP PRIM, LTP UDP SEC and LTP TCP SEC basically have no significant performance difference or have only implicative differences.  
However, the performance lines of the investigated protocols tend to separate each other and show difference with the increase in the delay (\(\geq\)2000 ms), as observed in Figure 7a. This is verified from Figure 7b, in which \(P\)-LTP TCP SEC vs. UDP LTP PRIM, \(P\)-LTP TCP SEC vs. UDP LTP PRIM, and \(P\)-LTP UDP SEC vs. UDP LTP PRIM are mostly less than 0.05 or even less than 0.01. This provides moderate or strong evidence to reject \(H_{0}\) and shows the reasonable or significant goodput difference between each pair.  
Different from the comparison at BERs of 0 and 10\({}^{-6}\) in which LTP TCP SEC shows a slightly higher goodput than the other two, UDP LTP PRIM has goodput advantage over both LTP UDP SEC and LTP TCP SEC at most delays with a BER = 10\({}^{-5}\). The advantage basically increases with the delay increase from 2000 ms to 5000 ms. This can be observed from a comparison of the absolute performance difference in Figure 7b, which shows that the goodput difference between LTP TCP SEC-UDP LTP PRIM and LTP UDP SEC-UDP LTP PRIM increases along with the increase in the delay from 2000 ms to 5000 ms. LTP UDP SEC show the poorest performance compared to any other protocols at all link delays. In comparison, LTP UDP SEC has higher goodput than UDP LTP PRIM at most link delays at BERs of 0 and 10\({}^{-6}\), especially for short delays \(\leq\) 3000 ms.  
Figure 7: Goodput (vs. link delay) comparison of three different protocols and their hybrid over the primary and secondary cislunar communication paths with a BER of 10\({}^{-5}\). (**a**) Goodput. (**b**) Goodput differences and \(p\)-values.  
Similar to BERs of 0 and \(10^{-6}\), UDP LTP PRIM shows consistent goodput performance at around 1500 bytes/s. In comparison, LTP UDP SEC and LTP TCP SEC show obvious performance degradation along with the increase in the delays at all three BERs.
## 2 Materials and Methods
### Methods
#### 2.2.8 Determination of Antioxidant Activity  
The antioxidant activity of honey was evaluated by the method of inhibiting the radical ABTS+ (2,2'-azinobis-[3-ethylbenzothiazol-6-sulfonic acid]. For ABTS determination a modified method of Wilczynska [11] was used. The ABTS+ was synthesized by the reaction of a 7 mM ABTS solution with a 2.4 mM potassium persulfate solution. The mixture was kept in the dark for 24 h. Afterward, the ABTS+ solution was diluted with methanol until the absorbance 0.7 was achieved at 734 nm (Shimadzu UV mini-1240). 2 g of honey sample was dissolved in 10 mL of distilled water, mixed, and filtered by a paper filter. Then 0.1 mL of the sample solution was mixed with 6 mL of ABTS+ solution, well mixed and after 15 min absorbance was measured at a wavelength of 734 nm. The control samples were prepared with distilled water in place of the honey. The scavenging capability of the ABTS+ radical (%AS) was calculated using the following equation:  
\[\%\text{AS}=\frac{100(\text{ABS control}-\text{ABS sample})}{\text{ABS control}}\]
## 4 Methodology
### Vision-Based Localization Information Acquisition  
In this section, we discuss visual localization technology constrained by step length. Existing vision localization techniques typically use data from cameras to generate localization trajectories through feature matching and tracking, local map construction, global map  
Figure 10: Diagram of the position estimation algorithm.  
Figure 9: Histogram of errors during model training, validation, and testing.  
construction, and loop closure detection. In this paper, the improvements in the existing visual localization mainly focus on integrating step length into the tracking module and enhancing the feature match and keyframe decision of the module.  
The primary task of the tracking module is to estimate the camera position in each frame and determine when to insert new keyframes. To fully utilize the step length of the rescuer and optimize the accuracy of camera position estimation, step length estimation is used to improve the accuracy and efficiency of feature matching, and refine the selection of keyframes. This method not only improves the accuracy of visual tracking for each step but also effectively prevents localization failure of the system due to large visual tracking errors occasionally. When the adaptive step length estimation model detects the completion of a step cycle, the visual localization system estimates the position of the camera within the current step cycle and applies step length to constrain the position.  
In this process, the selection of keyframes is not only constrained by factors such as the number and distribution of feature matches, but also step length, aiming to improve the quality of keyframe extraction and the accuracy of feature matching. Figure 11 illustrates the principle of vision-based position tracking with step length constrain. This constraint mechanism of step length determines the selection of keyframes and image features, thereby improving the accuracy and stability of vision-based position tracking in complex environments.  
By capitalizing on step length information, keyframe processing is conducted through three aspects: keyframe insertion mechanism, translation component constraint, and step-length-based feature matching search region constraint. These collectively guarantee the visual tracking system's precision and robustness amidst complex scenarios.
# Vehicle Speed Estimation and Forecasting Methods Based on Cellular Floating Vehicle Data
## 4 Experimental Results and Analyses
### The Evaluation of Traffic Information Estimation Methods  
The proposed analytic models were evaluated for the evaluation of traffic flow estimation based on the amount of HOs and NLUs and the evaluation of traffic density estimation based on the amount of CAs and PLUs in Sections 4.2.1 and 4.2.2. Finally, the vehicle speed estimation methods based on the estimated traffic flow and the estimated traffic density were evaluated in Section 4.2.3.
# SAES: An Introduction to Self-Adapting Exploratory Structures
## 3 Dynamic Taxonomies (Faceted Search)
### Benefits of Dynamic Taxonomies  
Dynamic taxonomies are SAES, and additionally, they are taxonomically organized, are multidimensional, self-sufficient, no-zero result, and complete from the perspective of iteration and reach.  
The advantages of dynamic taxonomies over traditional methods are dramatic in terms of an extremely fast convergence of exploratory patterns and in terms of human factors. Three zoom operations on terminal concepts are sufficient to reduce a 10,000,000-item information base described by a compact taxonomy with 1000 concepts to an average 10 items [9]. Dynamic taxonomies only require a very light theoretical background: namely, the concept of a taxonomic organization and the zoom operation, which seems to be very quickly understood by end-users.  
Dynamic taxonomies cleanly separate the process of classifying documents from the use of the classification information in the browsing system and considerably simplify the design of the conceptual taxonomy. First, the extensional inference rule actually performs concept association mining: concept associations, which are often quite dynamic in time, need not be forecasted and accounted for in schema design. In addition, the user is presented with associations the schema designer might not even be aware of.  
Second, since dynamic taxonomies synthesize compound concepts, these need usually not be represented explicitly, so that we avoid the exponential growth due to the description of all the possible concept combinations, and the resulting taxonomy is significantly more compact and easier to understand. [PERSON] [1; 7] developed a number of guidelines for taxonomies that are compact and easily understood by users. Some are superficially similar to the basic faceted classification scheme by [PERSON] [6]: the taxonomy is organized as a set of independent, \"orthogonal\" subtaxonomies (facets or perspectives). As an example, a compound concept, such as \"Musicians from Mobile, Alabama\" need not be explicitly accounted for, because it can be synthesized from its component concepts: _Arts\(>\)Musicians_ and _Location\(>\)USA\(>\)Alabama\(>\)Mobile_, where Arts and Location are facets.  
It is worth stressing that a faceted organization is just a design strategy for the intension (the taxonomy), in a way similar to Normal Forms for relational schemata, and it is not a substantial aspect of the model: the model does not require any specific taxonomy organization, apart from a multidimensional classification.
## 3 Results
* The accuracy of vital sign data collected by monitoring devices: To assess the accuracy of critical sign data collected by monitoring devices, values recorded by the devices were compared with values obtained by standard measurement methods. Laboratory tests and clinical validation were performed to determine the accuracy of the devices in measuring vital signs. In this case, the machines were accurate to about 0.95, indicating that the data collected was correct. * The data transmission success rate from devices to the cloud system: The data transmission success rate was determined by tracking data transmissions from the monitoring devices to the cloud system. Successful and failed transmission attempts were recorded, and the ratio of successful messages to total shots was calculated. In this case, the data transmission success rate was about 0.99, indicating high reliability in data transfer. * The response time of medical personnel when receiving alerts from the IoMT system (in minutes): To determine the response time of medical personnel, the times elapsed from the sending of a signal by the system until the medical personnel responded and took action were recorded as appropriate measures. These times were recorded, and an average was calculated to obtain the value of 2 min. * System-detected health issue resolution rate: The health issue resolution rate was determined by tracking health issues detected by the IoMT system and the actions taken by medical staff to resolve them. The cases where an appropriate measure was born and where a detected health problem was resolved were recorded. In this case, the system was found to have a health issue resolution rate of approximately 0.8, indicating that most of the health issues detected were successfully addressed and resolved.
# A PUF- and Biometric-Based Lightweight Hardware Solution to Increase Security at Sensor Nodes
## 4 Novel Biometric Sensor Nodes Using QFM-Based Fingerprint Features  
One of the most distinctive biometric traits is fingerprints. Fingerprint images are composed of ridges (depicted in dark in the fingerprint image from FVC 2002 DB1a shown in Figure 2) and valleys (depicted in white in Figure 2). Generally, fingerprint images are not compared directly at the verification step because there are variations between different captures. Instead, fingerprint images are processed to obtain distinctive features. According to the type of information extracted, there are three levels of fingerprint features: level 1 or global information such as orientation textures and singular points, level 2 or local information such as minutiae (ridge endings and ridge bifurcations), and level 3 or finer details (such as pores and scars). Distinctiveness is higher at higher levels of information, but also higher computational complexity is required. The use of texture-based features and singular points can offer better tradeoff between computational cost and recognition performance. Singular points are classified into delta points (situated where ridge lines intersect) and core points (located where ridge lines have maximum curvature), which in turn can be classified into convex and concave core points [15]. A convex core point is depicted with a red circle in the fingerprint image shown in Figure 2.  
Among the texture-based approaches, well-known features are FingerCodes, Histograms of Oriented Gradients (HoG), Local Binary Patterns (LBP), and Local Directional Patterns (LDP) [23; 24; 25; 26]. In order to develop novel biometric sensor nodes, this work proposes a novel texture-based feature named QFingerMap16 (QFM), which can be implemented with lower cost, as commented in the following. It is a refinement of the QFingerMap proposed in [27] to be more robust against noise so as to provide competitive results without complex image enhancement. The steps involved to work with  
Figure 2: Salted Data generated with a QFM feature from a fingerprint image and a SRAM PUF response.  
QFM (orientation computation and quality estimation, alignment, feature extraction, and obfuscation) are illustrated in Figures 2 and 3 and explained in the following.