esa-sceva/satcom-chunk-collection huggingface dataset:
# A Self-Recovery Fragile Image Watermarking with Variable Watermark Capacity
## 4 Experimental Results and Comparison Analysis
### Imperceptibility Analysis  
It is known that the quality of watermarked image determines the performance of watermark imperceptibility. The better the quality of watermarked image is, the better the watermark invisibility will be. Accordingly, the watermark information hidden in the watermarked image is more difficult to be perceived by human eyes. In this paper, the peak signal-to-noise ratio (PSNR) is applied to assess the watermarked image's quality, which is defined as:  
\[\text{PSNR}=10\log_{10}\frac{255^{2}}{\text{MSE}}(\text{dB}), \tag{10}\]  
\[\text{MSE}=\frac{1}{M\times M}\sum_{i=1}^{M}\sum_{j=1}^{M}[\textbf{I}(i,j)- \textbf{I}_{\text{w}}(i,j)]^{2}, \tag{11}\]  
where MSE is the mean square error between test image **I** and its watermarked version \(\textbf{I}_{\text{w}}\), and \(M\times M\) denotes the image size.  
From the watermark embedding process, we can see that different blocks contain different length of watermark information. The watermark capacity is 2-3 bpp, and the absolute difference between the pixel values before and after watermark embedding is 0-7. Supposing that the probability of the change for each LSB is the same, we calculate the expectation value of PSNR from two cases:  
1. For the blocks whose 2 LSBs are embedded by 8 bits watermark information, the expectation value of MSE is 1.5\({}^{2}\), then the PSNR's expectation can be computed by: \[\text{PSNR}=10\log_{10}(\frac{255^{2}}{1.5^{2}})=44.61\ (\text{dB}),\] (12)
2. For the blocks whose 3 LSBs are embedded by 12 bits watermark information, the expectation value of MSE is 3.5\({}^{2}\), then the PSNR's expectation is calculated by: \[\text{PSNR}=10\log_{10}(\frac{255^{2}}{3.5^{2}})=37.25\ (\text{dB}),\] (13)  
Figure 5: Test images: (**a**) Car1; (**b**) Car2; (**c**) Clock; (**d**) Airplane; (**e**) Cameraman; (**f**) Lena; (**g**) Barbara; (**h**) Venice; (**i**) Boat; (**j**) Goldhill.  
In practical application, if the PSNR value of a processed image is larger than 35 dB, people cannot tell the difference between the original image and its processed version.  
Table 2 gives the analysis of watermark imperceptibility for the test images shown in Figure 5. From the table, it can be observed that the more the texture blocks in host image are, the greater the watermark capacity will be. Accordingly, the PSNR values of the watermarked images will be decreased. However, from the final column of the table, we can see that the PSNR values of the ten watermarked images are all larger than 38 dB, which proves that the proposed variable capacity fragile watermarking achieves good watermark imperceptibility.
## 4 Data Diversity of UAV
### Methods Applied to the UAV Dataset  
Various methods are applied to UAV datasets to extract valuable insights and solve specific problems. These methods include machine learning, computer vision techniques, and advanced data processing algorithms. In Tables 3 and 4, an overview of the methods used and the analysis of results are given to gain a better understanding.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Dataset Name** & **Experimental Methods in Base Dataset Publication** & **Analysis of Results** \\ \hline RescueNet [6] & PSPNet, DeepLabv3+, Attention UNet, Segmenter [51] & Attention UNet achieved the best performance among all evaluated models. PSPNet showed better performance compared to DeepLabv3+ by using pyramid pooling. DeepLabv3+ provided moderate results, improving on the loss of boundary information. Segmentre showed varying results depending on the backbone (ViT-Tiny vs. ViT-Small), with heavier backbones achieving better results. \\ \hline UAV-Human [7] & Guided Transformer I3D Network, Video Transformers, Full Model (Author’s novel method) & Night-vision and IR videos outperformed previous findings in low-light conditions, achieving 28.72\% and 26.56\% accuracy, respectively. However, depth sequences face noise issues, and fisheye distortion impacts performance. In ablation studies, using KL Divergence Constraint resulted in 21.68\% accuracy, while employing guidance loss and Video Transformers yielded 21.49\% accuracy without RGB stream guidance. Overall, the full model had the highest accuracy among fisheye-based methods. \\ \hline AIDER [8] & Novel networks (ERNet, SCFCNet, SCNet, baseNet), VGG16, ResNet50, MobileNet & The VGG16 model had the highest accuracy at 91.9\% but a low frame rate of 2, while consuming 59.3 MB of memory. MobileNet had a high frame rate of 20 but lower accuracy at 88.5\%. Custom networks like ERNet and SCFCNet had good accuracy at 90.1\% and 87.7\% with high frame rates of 53 and 76, making them suitable for real-time UAV applications. \\ \hline AU-AIR [9] & YOLOv3-tiny, MobileNetv2-SSD & YOLOv3-tiny achieved higher mAP (38.2\%) and better FPS (22) compared to MobileNetv2-SSDLite (32.8\% mAP and 19 FPS), highlighting its better performance for real-time object detection tasks using UAVs. \\ \hline ERA [10] & VGG-16, DenseNet-121, NASNet-L, C3D [52] & DenseNet-121 achieved the highest overall accuracy (62.3\%) among the models, followed by NASNet-L (60.2\%) and VGG-16 (51.9\%). The C3D models had the lowest accuracy (around 30\%). \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of experimented methods and results on different datasets.
## 3 Databases and Results
### Databases
#### 3.1.1 Bonn Database  
Bonn database built by [PERSON] et al. at Bonn University, Germany, contains artifact-free data collected from five healthy subjects and five epilepsy patients. The artifacts due to muscle activity and eye movements were pre-removed through visual examination. Two classification tasks were used to evaluate the effectiveness of our method: (1) normal EEG (set A) versus ictal EEG (set E); (2) interictal EEG (set C) versus ictal EEG (set E). Each set contained 100 single-channel EEG segments of 23.6 s duration. EEG data were acquired at a 173.61 Hz sampling rate using 12-bit resolution. For more details, please refer to [34].  
In the preprocessing procedure, the single-channel EEG signal was normalized by Z-score. The normalized signals with zero-mean and one-standard deviation were prepared for subsequent feature extraction process.  
In feature extraction procedure, due to the relatively low segment duration and sampling rate, the approximation and detail coefficient sets at the 6 th decomposition level (\(A_{T1L6}\), \(D_{T1L6}\), \(A_{T2L6}\) and \(D_{T2L6}\)) were of extremely short length (64 coefficients), which cannot support a reliable complexity analysis. MMSE features extracted from these four coefficient sets were excluded from the feature vector. Accordingly, a 50-dimensional MMSE feature vector was constructed (the remaining 10 coefficient sets \(\times\) 5 entropy scales \(\times\) 1 EEG channel). As for energy features, the variance and mean absolute value were extracted from each wavelet coefficient set, constructing a 28-dimensional energy feature vector (14 coefficient sets \(\times\) 2 indicators \(\times\) 1 EEG channels). In summary, a 78-dimensional feature vector was constructed for each EEG segment.  
In the feature selection and classification procedure, we employed the \"leave-one-out\" cross-validation strategy. Because EEG data from all patients are mixed together in Bonn database, no patient label is available. Most studies in the literature also employed the cross-validation strategy on all data [6; 35; 36], which is consistent with our study. Considering that the dimensionality of original feature space is relatively low, all features with positive feature scores were retained while discarding those with zero scores (according to the last step of AGRM optimization procedure, the feature scores are non-negative values). We first held out one EEG segment as test data. All remaining data were allocated to training set, used for both AGRM-based feature score calculation and SVM model training. A label of \"normal\", \"interical\" or \"ictal\" for the held-out test data could be given by the trained SVM. We applied the same procedure to all EEG data so that all EEG segments were used to test algorithm performance. We employed sensitivity, specificity, precision, accuracy and F1 score as classification evaluation metrics.
## 2 Advanced Persistent Threat
### APT Attack Process  
Different approaches can characterise an APT; each APT campaign acts differently, and attacks are customised for specific victim or organisation. Generally, the first step is to create a point to gain access to the network. Then, personalised malware creates a communication network to maintain access, that allows attackers to inject malicious code multiple times. This malware moves sideways through the system (in a stealthy way), detecting vulnerabilities it can exploit and infecting other hosts on the network. It also makes copies of itself to maintain persistence within the system. The APT malware can establish other outgoing connections as they gain access to the system and obtain as much data as possible.  
One example life cycle approach has been described in the FireEye research about APT1; In the APT1 analysis, Mandiant (actually FireEye) has presented a report with an overview of the life cycle model of an ATP attack, consisting of eight stages: (1) Initial Recon, (2) Initial Compromise, (3) Establish Foothold, (4) Escalate Privileges, (5) Internal Recon, (6) Move Laterally, (7) Maintain Presence, and (8) Complete Mission. The stages between \"Establish Foothold\" and \"Complete Mission\" do not have to occur in this order every time [14]. This report is best known for identifying and understanding these types of threats.  
As some APT campaigns are discovered, it is observed that its anatomy is diverse and changes according to the specific objective for which it has been designed. The diversification of the attack vectors makes the detection of these threats a complicated task. Consequently, in Section 5, the different existing approaches of APT attack process are described. Then, a new model to improve early detection is proposed, based on the life cycle of an APT attack and applying machine learning techniques.
## 3 Results
### Total Sum of Forces on the X, Y, and Z Axes  
The total forces (Fx, Fy, Fz) applied to the canine teeth were measured in the four experimental groups. The Fx, Fy, and Fz vectors were calculated for each group to compare the magnitude and direction of the forces on the canine in three dimensions. The magnitude of the forces was significantly greater in Group 1, but did not differ significantly among Groups 2, 3, and 4. The sum of the vectors was 30.25 cN in Group 1, decreasing in magnitude through Groups 4 and 3, and finally reaching 12.82 cN in Group 2. The direction of force showed buccal, distal, and intrusive movements in all groups. Although Group 1 was subjected to the greatest intrusive (\(-22.53\) cN) and buccal force (20.09 cN), it experienced a mild distal force (191 cN), which resulted in the tooth being intruded and tending to move bucucally. Group 2 was subjected to a relatively small intrusive force (\(-10.65\) cN), a  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & & \multicolumn{2}{c}{**Groups**} & \\  & **1** & **2** & **3** & **4** & \(p\)**-Value (ANOVA)** \\ \hline Measurements & & & & & \\ Fx (cN) & \(20.09\)\(\pm\) 3.88 a & 6.38 \(\pm\) 2.33 b & 7.28 \(\pm\) 3.05 c & 4.42 \(\pm\) 2.32 b & \(<\)0.001 *** \\ Fy & \(1.94\)\(\pm\) 0.60 a & 3.43 \(\pm\) 0.53 b & \(1.20\)\(\pm\) 0.90 c & 5.67 \(\pm\) 1.19 d & \(<\)0.001 *** \\ Fz & \(-22.53\)\(\pm\) 3.11 a & \(-10.65\)\(\pm\) 4.27 b & \(-11.30\)\(\pm\) 5.25 b & \(-14.13\)\(\pm\) 3.01 c & \(<\)0.001 *** \\ Vector: & 30.25 a & 12.82 b & 13.50 b & 15.85 b & \(<\)0.001 *** \\ Sum of Forces & & & & & \\ Mx (Nmm) & \(-7.66\)\(\pm\) 1.64 a & \(-0.22\)\(\pm\) 1.19 b & \(-2.16\)\(\pm\) 1.38 c & \(-5.57\)\(\pm\) 0.81 d & \(<\)0.001 *** \\ My & \(-17.41\)\(\pm\) 1.88 a & \(-4.78\)\(\pm\) 2.14 b & \(-12.91\)\(\pm\) 8.13 c & \(-14.61\)\(\pm\) 1.57 ac & \(<\)0.001 *** \\ Mz & \(-13.73\)\(\pm\) 2.13 a & \(-5.72\)\(\pm\) 1.06 b & \(-5.14\)\(\pm\) 2.95 b & \(-2.72\)\(\pm\) 0.85 c & \(<\)0.001 *** \\ \hline \hline \end{tabular} Superscript letters indicate a significant difference (\(p<0.05\)). *** \(p<0.001\). One-way ANOVA with Tukey LSD post hoc. Abbreviations: ANOVA, analysis of variance; LSD, least significant difference; Fx, buccal force; Fy, distal force; Fz, intrusive force; Mx, mesiodistal moment; My, buccolingual moment; Mx, mesial rotation moment.  
\end{table}
Table 2: Comparative analysis of the forces and moments generated by different clear aligner groups for 0.25 mm distal bodily movement and various substitutions in extraction spaces.  
moderate buccal force (6.38 cN), and a distal force (3.43 cN), which caused the teeth to be both intrusively and bucally displaced, but more distal than in Group 1. In Group 3, the teeth had a tendency to intrude and move bucally, exhibiting the least distal movement among the four groups. Group 4 also showed intrusive and buccal forces but had the largest distal force.
# mIoT Slice for 5G Systems: Design and Performance Evaluation
## 3 Gap Analysis for 4G System
### The 4G EPS Bearer and Always-ON Connectivity  
4G LTE/EPC packet communication system, properly denoted as Evolved Packet System (EPS), relies on a connectivity model based on the \"EPS bearer\" and the \"Always-ON\" concepts.  
The EPS is designed to provide IP connectivity between a UE and a PLMN (Public Land Mobile Network) external Packet Data Network (PDN). This service supports the transport of traffic flow aggregate(s), consisting of one or more Service Data Flows (SDFs). The EPS bearer is the minimum level of granularity at which QoS, mobility and security are provided within EPS, i.e., SDFs mapped to the same EPS bearer are treated with the same QoS, mobility and security policy. When a UE attaches to a PDN, after authentication, it is allocated an IP address and an EPS bearer is established. The EPS bearer remains established throughout the lifetime of the PDN connection to provide the UE with Always-ON IP connectivity. The bearer established at the attachment is referred to as the default bearer that provides default QoS, determined by the network upon UE subscriber data. Any additional EPS bearer that is established for the attached UE to the same PDN is referred to as a dedicated bearer, established when the UE issues a Service Request or when the network triggers a Service Request, for which a dedicated QoS treatment needs to be provided.  
The EPS bearers are established by LTE/EPC CP, and they are E2E concepts: they provide connectivity from UE to the PLMN external PDN, hence traversing UP elements of RAN and CN. In particular, the EPS bearer is composed by the concatenation of the Radio bearer (between the UE and the access node), the S1 bearer (between the access node and the Serving GateWay, SGW) and S5/S8 bearer (between SGW and Packet Data Gateway, PGW). The 4G system architecture is depicted in Figure 1.
# Vegetation Loss Measurements for Single Alley Trees in Millimeter-Wave Bands
## 5 Discussion
### Wider Application of the Proposed Vegetation Loss Model  
Let us emphasize that the main focus of this work was to concentrate on fixed wireless access links in the vicinity of urban buildings. In such a case, the straight-line propagation through trees is one of the possible situations where the findings discussed in this paper can be applied directly. However, it is worth considering the wider application context of the presented measurements. In particular, we suggest that the measured vegetation loss could be considered as an additional component in the overall calculation of the total link loss (as part of a link budget calculation). For example, the vegetation loss model proposed in this manuscript could be directly included in simplified models such as the Free Space  
Figure 23: RTE vs. measurements—comparison.  
Figure 24: RTE vs. measurements—difference (percent error).  
PL model or similar, which reflect mainly the change of the path loss as a function of distance or frequency only; to some extent, the vegetation loss can be included as the additive components in more detailed models as well; however, more careful analysis would be beneficial to not overestimate the impact of vegetation loss on signal propagation. In the context of the FWA link, vegetation loss can be applied straightforwardly; while designing the point-to-point link, appropriate budget calculations have to be performed. When there is no natural obstacle (such as a tree), well-known models can be applied that do not consider the presence of vegetation loss. Similar conclusions can be drawn for the winter period, where the presence of trees is not that critical in path-loss modeling as there is no foliage. However, when one is aware of the presence of trees (and in particular knows the type of tree, their number, etc.), the measured vegetation loss can be included in the calculated link budget as an additive factor (in logarithmic scale).
## 2 Research Method  
### Base Model  
Prior to the implementation of numerical analysis, a model of a motor with an outer-rotor structure was investigated and utilized as a reference for the development of the model. Figure 3 illustrates the inner frame functioning as a stator, while the outer frame, which is secured to the rim where the tire is mounted, acts as an external PMSM rotor due to the hulpless in-wheel motor structure. To maintain the air gap between the stator and rotor, the rotor is radially supported by sixteen urethane bearings (eight on each side of the wheel motor). The average vertical load on each bearing is approximately 13.4 kg, based on a rear wheel load of 80 kg.  
Figure 2: Flowchart of the process employed to analyze the characteristics of a hulpless e-bike motor.  
The rotor and stator have large diameters; thus, a number of poles and slots were de-signed, as shown in Figure 4. The hubless in-wheel motor has no brush and requires less maintenance, has excellent speed-torque characteristics, and is designed with a PMSM structure without loss in a wide operating area. Table 1 shows the basic specifications of the hubless in-wheel motor used in the numerical analysis model in this study. The target tire wheel torque was 75 Nm, the target motor torque was 20 Nm, the number of slots was 72, the number of poles was 42, and the rectangular permanent magnet was arranged in the shape of an SPM. The winding was circular, with a diameter of 1.2 mm. The front and rear wheels of a typical e-bike are the same size, and the stator and rotor sizes of the motor are designed to range from 12 inches to 26 inches. The outermost diameter of the outer rotor was 403 mm and the inner diameter was 374 mm. The outermost and innermost diameters of the stator were 372 mm and 280 mm, respectively. The applied voltage and current were 36 V and 14 A, respectively. In this motor model, the side of the motor is attached to the rim, and the axial faces are exposed externally.  
Considering these structural characteristics and the advantageous heat dissipation of an outer-rotor motor, the design omits the need for additional housing and cooling systems, thereby enhancing manufacturability and design flexibility. The utilization of outer-rotation motors is advantageous in that they employ a greater number of permanent magnets and exhibit a more favorable power output than that of inner-rotation motors.  
Figure 3: Layout of the hubless in-wheel and motor. (**a**) Overall shape; (**b**) electric motor view.
## 7 On-Field Evaluation and Management
### Hospital Transfer Criteria and Further Care  
Suspicion of the spinal cord or other life-threatening injury warrants immediate imaging to assess the extent of the injury and, therefore, transfer to a hospital [10]. Symptoms of a stinger lasting more than 5 min and occurring for a second time also require imaging [10], mandating transfer to a facility with such capacity. Once the possibility of a serious injury is ruled out, the athlete should be evaluated with the appropriate physical examination, focusing on inspecting for asymmetry indicating dislocation or fracture, persistent cervical or neck tenderness, range of motion for neck and shoulder, and limb strength [5; 6; 34; 35]. Importantly, muscles innervated by the brachial plexus should be evaluated in detail beyond cursory grip strength testing alone. For the ideal management of stingers, [PERSON] [35] emphasizes the need for conducting at least a minimum test of the spinatii, deltoid, biceps, brachioradialis, triceps, serratus anterior, wrist flexors, extensors, and grip strength, with the unaffected side serving as a control. Pain due to nerve injury within the spinal foraminacan be reproduced with extension and lateral flexion ([PERSON]'s test), while brachial plexus injury lacks this feature [6].  
Other suspected injuries of vascular origin should be evaluated in a hospital setting to confirm their presence and characterize them for the appropriate management. CT angiography, MR angiography supplemented with digital subtraction angiography, and ultrasonography are all used to verify and plan interventions for vascular injuries [5; 42; 46].
# 3D Global Path Planning Optimization for Cellular-Connected UAVs under Link Reliability Constraint
## 2 Related Works
Based on the results of the reviewed literature, it can be seen that more research is still needed on 3D global path planning to enhance the safety of UAVs, especially for BVLOS operations. In addition, despite the popularity and capability of intelligent optimization algorithms in solving path planning problems, the algorithms still need to overcome some shortcomings, such as being trapped in local optima and early convergence, especially when the dimension and complexity of problems increase. Furthermore, there are only a few articles on the optimization of UAV path planning based on cellular communication objectives/constraints. Finally, there is a gap in proposing an optimization method for the 3D global path planning in the presence of obstacles and the realistic constraints of the terrestrial cellular networks in providing reliable aerial coverage. On the other hand, the intelligence optimization algorithms, such as particle swarm optimization (PSO) and genetic algorithm (GA), have been widely utilized to solve path planning problems. Although in 2D cases their performance is stable and converges to the global optima, when increasing the problem complexity, the algorithms suffer from their inherent shortcomings, such as being trapped in local minima and premature convergence. To address these issues, a combination of PSO and GA (here referred to as PSO-GA) is utilized in this study. The PSO-GA algorithm advantages the information flow of PSO as well as the crossover and mutation operations of GA.