esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### In Vivo Treatment Evaluation  
For the in vivo evaluation of the DICOM-to-DICOM matching protocol, the accuracy assessment technique was applied, which consists of evaluating the matching of the planning dataset with the surgery results dataset. The patient received a second CBCT scan after the implant placement and delivery of the final prosthesis to perform an accuracy analysis (Figure 1D). The same CBCT device was used to obtain more qualitative data since the registration accuracy of implant planning software is significantly influenced by the preprocessing of imported data [31]. The area of interest (mandible) was segmented from the CBCT scan, which allowed for more qualitative matching. Data in DICOM format were inserted into the treatment evaluation tool of coDiagnostiX, and the matching of the preoperative and postoperative CBCT scans was performed (Figure 2A).  
The limitation of the DICOM-to-DICOM protocol might be the discrepancy between the CBCT scans. To minimize this influence, the same CBCT device was used, and matching was performed in multiple areas with clearly visible anatomical structures following the recommended protocol of the treatment evaluation tool. Each implant was aligned in the preoperative and postoperative CBCT, and accuracy values were automatically generated by the software (Figure 2B). The 3D, distal, vestibular, and apical deviations were recorded on the coronal (base) and apical (tip) points of the implants, as well as angular deviations. The results are reported in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Tooth Position** & **Angle** & \begin{tabular}{c} **3D Offset** \\ **(Base)** \\ \end{tabular} & \begin{tabular}{c} **Distal** \\ **(Base)** \\ \end{tabular} & \begin{tabular}{c} **Vestibular** \\ **(Base)** \\ \end{tabular} & \begin{tabular}{c} **Apical** \\ **(Base)** \\ \end{tabular} & \begin{tabular}{c} **3D Offset** \\ **(Tip)** \\ \end{tabular} & \begin{tabular}{c} **Distal** \\ **(Tip)** \\ \end{tabular} & \
\begin{tabular}{c} **Vestibular** \\ **(Tip)** \\ \end{tabular} &
\begin{tabular}{c} **Apical** \\ **(Tip)** \\ \end{tabular} \\ \hline
35 & 2.4 & 2.55 & \(-2.14\) & 0.93 & \(-1.03\) & 2.21 & \(-1.73\) & 0.93 & \(-1.02\) \\
32 & 5.3 & 2.61 & \(-1.86\) & 0.61 & \(-1.72\) & 3.47 & \(-2.98\) & 0.62 & \(-1.67\) \\
42 & 2.3 & 2.07 & 0.79 & \(1.11\) & \(-1.55\) & 1.77 & 0.5 & 0.72 & \(-1.54\) \\
45 & 2.8 & 1.32 & 0.49 & 1.23 & \(-0.02\) & 1.01 & 0.65 & 0.78 & \(-0.01\) \\ \hline \end{tabular}
\end{table}
Table 1: Accuracy analysis with postoperative CBCT.  
Figure 2: (**A**) Planning and postoperative CBCT matching; (**B**) Accuracy analysis with postoperative CBCT (blue colour—planned implant position, red colour—actual implant position).
## 4 Evaluation
### Discussion
#### 4.3.7 Framework Applicability to IIoT Environments  
The proposed blockchain-assisted FL framework offers a robust solution to critical challenges faced in IIoT environments, even though its evaluation was conducted using a general-purpose dataset. The integration of FL minimizes reliance on centralized data processing, significantly reducing latency while ensuring privacy. Blockchain technology strengthens the framework by securing the aggregation process and providing immutable validation of model updates, thereby addressing common bottlenecks in distributed IIoT systems. These features position the framework as an efficient and scalable solution, particularly suited for dynamic and resource-constrained IIoT infrastructures.  
Beyond its foundational evaluation, the framework's capabilities align closely with real-world IIoT requirements. For instance, the emphasis on XAI ensures interpretability in applications like predictive maintenance, where transparency in decision-making is crucial for operational safety and efficiency. Additionally, the framework's ability to handle heterogeneous data generated by various IIoT devices underscores its adaptability to complex industrial environments. By incorporating these design principles, the framework demonstrates clear potential for deployment in applications such as industrial wireless sensor networks (WSNs), where scalability, security, and adaptability are paramount.
# Dense Small Object Detection Based on an Improved YOLOv7 Model
## 4 Experiments and Analysis of Results
### Comparative Experiments  
To evaluate the performance of our proposed method, we conducted a series of experiments on the VisDrone2019 verification set. We compared our method against several recent YOLO algorithms, including the YOLO family (YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOv9, and YOLOv10), and YOLO's improved algorithms Drone-YOLO [33] and MS-YOLO [34]. We chose the algorithms that had a similar number of parameters to our model and those that performed best. In particular, YOLOv7m is our benchmark model, and algorithm data improved by YOLO come from their paper.  
As can be seen from the ablation experiment results in Table 7, our method achieved the highest [EMAIL_ADDRESS]% of 55.1% and [EMAIL_ADDRESS]:0.95% of 32.5%, surpassing all other models, demonstrating its superior object-detection capabilities. Notably, it achieved this while maintaining a relatively low parameter count, comparable to lightweight models like YOLOv8s and YOLOv9s. These results demonstrate the effectiveness of our proposed method in terms of both accuracy and model complexity, making it a suitable choice for intensive small target detection applications.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **[EMAIL_ADDRESS]\%** & **[EMAIL_ADDRESS]\%** & **Parameters/M** \\ \hline YOLOv5s & 33.1 & 17.9 & 7.04 \\ YOLOv5x & 39.9 & 23.5 & 86.23 \\ \hline YOLOv6s & 37.1 & 22.0 & 16.30 \\ YOLOv6l & 41.0 & 25.0 & 51.98 \\ \hline YOLOv7m & 48.8 & 27.6 & 36.53 \\ YOLOv7x & 50.0 & 28.5 & 70.84 \\ \hline YOLOv8s & 39.5 & 23.5 & 11.13 \\ YOLOv8x & 45.4 & 27.9 & 68.13 \\ \hline YOLOv9s & 41.0 & 24.8 & 9.60 \\ YOLOv9e & 47.5 & 29.1 & 57.72 \\ \hline YOLOv10s & 39.0 & 23.3 & 8.07 \\ YOLOv10x & 45.7 & 28.2 & 31.67 \\ \hline Drone-YOLOs & 44.3 & 27.0 & 10.9 \\ Drone-YOLOI & 51.3 & 31.9 & 76.2 \\ \hline MS-YOLO & 53.1 & 31.3 & 79.7 \\ \hline Ours & 55.1 & 32.5 & 10.89 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparative experiment results.  
Figure 14: Comparison diagram of Loss between YOLOv7 and the proposed algorithm.
## 3 Materials and Methods
### Instruments and Equipment  
The morphologies of the samples were characterized using an S-4800 SEM (Hitachi, Tokyo, Japan) and an HT7700 TEM (Hitachi, Tokyo, Japan). The surface compositions and structural properties of the samples were characterized using an ESCALAB 250 Xi XPS (Thermo scientific, Waltham, MA, USA). FTIR spectra were recorded on an Excalibur 3100 FTIR spectrophotometer (Varian, CA, USA). The UV-Vis spectra and UV-Vis DR spectra were obtained with a Cary 7000 UV-Vis spectrophotometer (Varian, CA, USA). The average particle sizes and \(\zeta\) values of the samples were investigated by a zeta potential and nanoparticle sizer (ZSE, Malvernanalytical, Shanghai, China). The contact angle of the samples was recorded on a JC2000D3M contact angle measuring instrument (Databases, Munich, Germany). The TGA curves of the samples were tested by a NETZSCH STA 449 F5/F3 Jupiter thermal gravimetric analyzer (Netzsch, Bayern, Germany). CMYK water-based inkjet inks were prepared using a QM-3 SPI planetary ball mill (Nanjing university instrument factory, Nanjing, China). A Smart Tank 518 inkjet printer (HP, Palo Alto, CA, USA) was used for printing tests with water-based inkjet inks.
## 4 Description of the Proposed Topology Construction
### Communication Abstraction of the Proposed Topology
#### 4.6.2 Communication between Chains  
Different lower-level chains communicate with each other using the higher-level chain. The lower-level leaders accumulate data sent by the member nodes of the chains, and transfer them to the higher-level leader. The higher-level leader then sends the data to the BS.  
If the BS or the higher-level leader wants to send some information or control messages to the chain members, the communication path remains the same, except the direction is opposite. In this case, the communication pattern is similar to hub-and-spoke topology. Figure 20 shows a situation where the BS sends control messages to the member nodes of a chain.
## 6 Model Result
### Single-Hop and Multi-Hop LoRaWAN  
This section presents a comparison between the two topologies in terms of network capacity, throughput, and energy consumption. The experiments were conducted considering 10 km of the pipeline (similar to the GW coverage), 1 GW, and 50 devices. We assumed that each sensor sends 1 packet per hour with a payload of 20, 30, or 51 bytes and set the following parameter values: BW = 125 MHz, TP = 2 dBm, and CR = 1. The parameters are listed in Table 2.  
In the single-hop scenario, the number of sensors was determined using Equation (1), taking into account the star LoRaWAN limitation (the model mentioned in [40]). Sensors are distributed in different zones; as we move away from the GW, the number of sensors decreases. To determine the actual number of sensors, divide the estimated number by 2e. This leads to varying transmission ranges. As a result, different SF values were used to transmit data (7, 8, 9, 11). Using different settings for SF results in different ToA and energy consumption values.  
Since all devices were assumed to have the same transmitted range in the multi-hop scenario, there was no limitation in the single-hop scenario, so SF = 7 was used (refer to Section 4). Data packets were assumed to be transmitted hop-by-hop to the GW. Various payloads were assessed to determine the optimal selection.
## 4 DRCE-IS Combined with D-Vine Copula and SNMM to Assess the Power System Reliability
### Pre-Simulation Stage of DRCE-IS
#### 4.2.2 IS PMFs of Homogeneous 2- and 3-State Units
\(x_{f,w}\), \(x_{d,w}\) and \(x_{o,w}\) are the number of WTGs in the failure, derated and operation states in the \(w\)-th group, respectively, which meet:  
\[\eta_{o,w}=1-\omega_{f,w}-\omega_{d,w},x_{o,w}=n_{w}-x_{f,w}-x_{d,w}. \tag{33}\]  
Similarly with (24), when we update \(\omega_{f,w}\) and \(\omega_{d,w}\), the objective function is shown as:  
\[\max_{\omega_{f,w}=\omega_{d,w}}\sum\limits_{s=1}^{n_{s}}I(x_{ss,s})Q(x_{ss,s })\ln p(x_{w,s})+C. \tag{34}\]  
Similarly with (26), the solutions of (34) are the updating formulae for \(\omega_{f,w}\) and \(\omega_{d,w}\), which are shown as:  
\[\omega_{f,w,t}=\frac{(1-\omega_{d,w,t-1})\sum\limits_{s=1}^{n_{s}}I(x_{ss,s}) Q(x_{ss,s})x_{f,w,s}}{\sum\limits_{s=1}^{n_{s}}I(x_{ss,s})Q(x_{ss,s})(n_{w}-x_{d,w,s})}, \tag{35}\]  
\[\omega_{d,w,t}=\frac{(1-\omega_{f,w,t-1})\sum\limits_{s=1}^{n_{s}}I(x_{ss,s}) Q(x_{ss,s})x_{d,w,s}}{\sum\limits_{s=1}^{n_{s}}I(x_{ss,s})Q(x_{ss,s})(n_{w}-x_{f,w,s})}. \tag{36}\]  
Equations (35) and (36) is valid for any \(w=1,\,\ldots,\,n_{wg}\). \(n_{wg}\) is the number of WTG groups. The set \(\{x_{w}\,|\,w=1,\,\ldots,\,n_{wg}\}\) forms \(\mathbf{x^{WT}}\) in (22). Then the LRF of WTGs \(Q(\mathbf{x^{WT}})\) is expressed as:  
\[Q(x^{WT})=\frac{\prod\limits_{w=1}^{n_{wg}}\left(\theta_{f,w}\right)^{x_{f,w }}\left(\theta_{d,w}\right)^{x_{d,w}}\left(\theta_{o,w}\right)^{x_{o,w}}}{ \prod\limits_{w=1}^{n_{wg}}\left(\omega_{f,w}\right)^{x_{f,w}}\left(\omega_{d, w}\right)^{x_{d,w}}\left(\omega_{o,w}\right)^{x_{o,w}}}, \tag{37}\]  
where \(\theta_{f,w}\), \(\theta_{d,w}\) and \(\theta_{o,w}\) are the original state probabilities of the \(w\)-th WTG group. We only deal with 2- and 3-state units in this paper. However, the proposed method is not limited to 2- and 3-state units. The method can be expanded to the case of multi-state units by replacing \(p(x_{c})\) and \(p(x_{w})\) with the PMF of the multinomial distribution, so it is widely adaptive.
# Effect of Topography Truncation on Experimental Simulation of Flow over Complex Terrain
## 1 Introduction
Moreover, the slope of the ramp transition section should be small, otherwise flow separation and baffling flow will still occur. Accordingly, the ramp transition section is not applicable for highly undulating topography. In this case, [PERSON] et al. [23] proposed a theoretical curve based on the potential flow theory around the cylinder and then conducted a performance comparison between the theoretical curve and ramp transition. For application convenience, [PERSON] et al. [24] simplified the expression of the theoretical curve based on the original. [PERSON] et al. [17] proposed a comprehensive method to evaluate the performance of several curve transitions including the [PERSON] et al. [24] improved theoretical curve. [PERSON] et al. [25] considered different weights of speed-up factors, wind attack angle, and turbulence intensity by combining a weighted solution based on the correlation degree to ameliorate the evaluation method for determining the optimal form of the transition section. Although transition sections are widely implemented in experiments and numerical simulations, the feasibility is still difficult to explain, because neither wind tunnel nor numerical simulations can avoid \"artificial cliff\". Additionally, comprehensively detailed field measurement data of flow over complex terrain, which is time-consuming and highly expensive, are also difficult to obtain. Hence, analyzing the effect of the transition section on flow characteristics is not possible. Nevertheless, based on previous studies on wind tunnel simulations of intricate topographic flows, the details of terrain models play an important role [10; 26; 27]. In the study of [PERSON] et al. [10], the variation in the escarpment leading edge, which had round or sharp geometry, generated completely different flow behavior on the rear. The difference in annual energy production estimated based on the two scenarios differed from 20% to 51%. Furthermore, the structural dynamic response and aerodynamic characteristics are highly sensitive to changes in the flow characteristics [28; 29; 30; 31]. Accordingly, a systematic study is needed to evaluate the effect of topographic truncation on flow over complex terrain. Based on the above reasons, a wind tunnel simulation of flow over intricate terrain was conducted to obtain wind characteristics and analyze the effect of topographic truncation. The terrain model was scaled from an isolated island, which is the optimum simulation object to avoid the \"artificial cliff\" because the transition between island topography and sea level is smooth. Flow over complex terrain was investigated by assessing how changes to the forms of transition sections, including the original topography, affect flow behavior at different measurement locations. The flow parameters investigated included mean velocity, inclination angle, turbulence intensity, and velocity spectra.
## 2 Numerical Simulation and Tests
### Experiment Validations on External Characteristics  
Experimental measurement and CFD numerical calculation methods were used to study and analyze the external characteristics of the pump-turbine with splitter blades. The flow rate and head are normalized as flow rate coefficient \(\varphi\) and the head coefficient by the following equations:  
\[\psi = \frac{2 gH}{u_{2}^{2}} \tag{1}\]  
\[\varphi = \frac{Q}{u_{2}R_{2}^{2}} \tag{2}\]  
where \(H\) is the head, \(u_{2}\) is the outlet circumferential velocity of the runner, \(g\) is the acceleration of gravity, \(Q\) is the flow rate, and \(R_{2}\) is the radius of the high pressure side of the runner.  
The pump-turbine model tests were carried out at the Hydraulic Machinery Laboratory of Ecole Polytechnique Federale de Lausanne (EPFL). The EPFL Hydraulic Machinery Laboratory is equipped with three closed circulation type universal hydraulic machinery test stands. In accordance with the requirements of IEC60193, a high-precision test instrument was installed on the test bench. The test bench can carry out research and development and test vertical or horizontal hydraulic machinery such as pumps, turbines, and pump-turbines. On the basis of this experimental bench, the head and efficiency of the pump-turbine were measured and recorded.  
Figure 3 shows the comparison of performance between the simulation and the experiment. Both the simulation head and efficiency show good agreement with the experiment. The maximum relative error rate on head in each condition was less than 4.5%, and the maximum relative error on efficiency was less than 3.2%, indicating that the simulation calculation can accurately predict the performance of the pump-turbine model in working pump conditions and ensure the accuracy of further analysis.
# Signature-Based Security Analysis and Detection of IoT Threats in Advanced Message Queuing Protocol
## 3 Literature Review
### Protocol Overview  
* **AMQP (Advanced Message Queuing Protocol)**: A robust message-oriented middleware designed for high-throughput, reliable communication. AMQP's transactional guarantees and brokered architecture make it ideal for enterprise-grade IoT applications but introduce specific security concerns [10; 15].
* **MQTT (Message Queuing Telemetry Transport)**: A lightweight publish-subscribe protocol optimized for constrained environments with low bandwidth, high latency, and limited resources [16].
* **CoAP (Constrained Application Protocol)**: A REST-based protocol designed for resource-constrained devices and networks, enabling efficient web-like interactions [17; 18].
## 3 Results and Discussion
### Stress Distribution  
The maximum von Mises stress obtained in the simulation presented localized values as a result of mesh interpolation with the formation of sharp edges, which generated a stress peak at the specific point analyzed. This phenomenon is characteristic of mesh configurations where abrupt transitions between elements cause elevated stress values in a localized manner.  
The modeling of the system was configured considering adequate mesh refinement and appropriate application of boundary conditions to enhance the accuracy of the stress distribution analysis. The results obtained indicated the following maximum stress values at the three main critical points:  
* Point 1--Lateral fixation region of the prosthesis: 12.276 MPa;
* Point 2--Metatarsophalangeal joint region of finger 2: 10.755 MPa;
* Point 3--Interphalangeal joint region (proximal/medial phalanx) of finger 3: 11.016 MPa.  
The results demonstrate that the stress concentrations remain within acceptable limits, indicating adequate accuracy in the analysis performed.  
The areas highlighted in blue represent regions with minimal or nonexistent forces, indicating areas of lower structural risk. Meanwhile, the areas in green and yellow tones show the points of highest stress concentration, especially at the joints and the fingertips, where direct contact with external surfaces occurs.  
Figure 23 presents the color map corresponding to the stresses observed in the simulation.  
The analysis suggests that the prosthesis structure is adequate to withstand typical gripping loads, but it may be necessary to reinforce the joints to enhance the device's  
Figure 23: Color map corresponding to the stresses observed in the simulation.  
Figure 22: Generated Type 3 prosthesis.  
durability. Figure 24 presents the color map corresponding to the stresses observed in the simulation.
## 6 Other Applications  
Outside of the use of peanut skins for edible applications, peanut skins have been reported to be carbonized to prepare nanospheres [123]. The skins were subjected to pyrolysis at 700 \({}^{\circ}\)C in a nitrogen atmosphere and then oxidized with acid and dried. The creation of nanospheres was confirmed by microscopy and the particles characterized. In the quest to find low-cost applications for this available agricultural waste material, these types of studies are of interest. Use of peanut skins incorporated into nanoparticles to detect a synthetic dye [124]. After sterilization, the peanut skins were extracted with water. The extracts were combined with different precious metal salts under pressure to produce nanoparticles, which were then incorporated into glassy electrodes. These electrodes were able to sense Sudan IV, a synthetic dye that has been used as a food adulterant and is an industrial contaminant in the environment. Extracts of peanut skins prepared using hot water were used to create iron nanoparticles to be used for heavy metal removal from aqueous solutions [125]. A similar application used peanut skin extracts to dye fibers, which were then embedded with silver ions to create nanoparticles [126]. These fibers were used to produce fabrics with antimicrobial properties from a natural source. After evaluating several organic solvents, water was used as the extractant for the peanut skins as that gave the desired color and adequate antioxidant properties for the application. The advantage of these materials is that they are considered environmentally friendly to create and to use. Toxic reagents were not needed, and they were prepared from an agricultural waste product.  
To take advantage of the antioxidant properties of peanut skin extracts, they were incorporated into films made from the polysaccharides that make up an Asian mushroom [127]. The gelling properties of the white jelly mushroom (_Tremella ficiformis_) were used to create a biodegradable film. Peanut skins were extracted using 70% ethanol in water and then dried. The powder was incorporated into the gel upon casting. The films did show antioxidant capabilities with the addition of the peanut skin extracts but became more brittle and opaque as the amount of extract was increased over a range of 0 to 100 mg/100 mL of gel. Another study used chitosan as the source of the gel matrix to create a biodegradable packaging film [128]. Starch was chosen as the source of the polysaccharides with chitosan added to adjust the water solubility and flexibility. Extracts of peanut shells and skins were used to add a natural source of antioxidants. The peanut skins were extracted with 80% methanol to determine the antioxidant capacity using the DPPH assay and to characterize the polyphenols present using HPLC-MS. However, the peanut skins were ground to a fine powder and added to the gels to form the films. Extracts of the peanut skins were not used in this case.
## 2 Instrument Design  
### Instrument Geometry and Functionning  
MPA consists of an aperture, a magnetic chamber and two permanent magnets, as illustrated in Figure 1. Ions within a limited field of view enter the magnetic chamber through the aperture. Inside the gap between the two permanent magnets, ions are deflected by the magnetic field according to the Lorentz force:  
\[m\,\frac{d\mathbf{v}}{dt}=q\,\mathbf{v}\wedge\mathbf{B}, \tag{1}\]  
where \(m\) and \(q\) are the mass and charge of the ion, \(\mathbf{v}\) is its velocity vector and \(\mathbf{B}\) is the magnetic field inside the chamber. In a simplistic case in which \(\mathbf{B}=B_{0}\mathbf{e}_{y}\) and the incoming particles have an initial velocity \(\mathbf{v}_{0}=v_{0x}\mathbf{e}_{x}\) with an initial position \(\mathbf{r}_{0}=0\,\mathbf{e}_{x}+0\,\mathbf{e}_{y}+0\,\mathbf{e}_{z}\), the solution of Equation (1) is:  
\[\begin{cases}x(t)=\frac{v_{0x}}{\omega_{0}}\sin(\omega_{0}t)\\ z(t)=\frac{v_{0x}}{\omega_{0}}-\frac{v_{0x}}{\omega_{0}}\cos(\omega_{0}t) \end{cases}, \tag{2}\]  
where \(\omega_{0}=qB_{0}/m\) is the homogeneous gyro-frequency inside the magnetic chamber. Equation (2) describes a circular trajectory of radius  
\[R=\frac{mv_{0x}}{qB_{0}}. \tag{3}\]  
Figure 1b illustrates the trajectories of three ions inside the chamber. According to Equation (3), the curvature radius of the particles is directly proportional to their \(v_{0x}\). Assuming that all particles are protons and knowing the magnetic field strength \(B_{0}\), it is thus possible to measure the speed of the particles directly by measuring their curvature radius, represented by their hit distance in the sensor plane.