esa-sceva/satcom-chunk-collection huggingface dataset:
# Latency Analysis of Blockchain-Based SSI Applications +
## 6 Conclusions  
In this paper, we proposed a deployment architecture for SSI applications, and developed a Python application with containerized components for its realization. We evaluated our proposed architecture in terms of latency by deploying its components in the Google Cloud Platform, in different regions with individual VMs. We executed several test scenarios to obtain average latency measures for different system settings, namely different network sizes and different TX arrival rates. For arrival rates between 1 and 250, we found that the write response latency of an Indy-based BC containing four and eight nodes ranges between 1 and 16 s, while the read response latency with similar settings ranges between 0.01 and 5 s. We also compared the results with a local ledger in a Docker environment in our private cloud. In the future, we plan to enhance the scalability of our application by dynamic, automated BC node addition and removal. We have also started to design a solution for some of the mentioned performance issues in a global accreditation and credential verification application, and we have proposed a privacy-aware Fog-enhanced blockchain-based online credential solution in [30].  
All our implemented application components are containerized using Docker, which makes them easier to deploy for present, and future works. Our implemented application is publicly available at Github. Available online: [[https://github.com/sed-szeged/IndyPerf](https://github.com/sed-szeged/IndyPerf)]([https://github.com/sed-szeged/IndyPerf](https://github.com/sed-szeged/IndyPerf)) (accessed on 26 September 2022).  
Software, T.P.; Writing--review & editing, T.P., H.B. and A.K. All authors have read and agreed to the published version of the manuscript.  
The research leading to these results has received funding from the national project TKP2021-NVA-09 implemented with the support provided by the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, and from the UNKP-21-4 New National Excellence Program of the Ministry for Innovation and Technology from the source of the National Research, Development and Innovation Fund.  
Not applicable.  
The experiments presented in this paper are based upon work supported by Google Cloud.  
The authors declare no conflict of interest.
# Simulation Model for Correction and Modeling of Probe Head Errors in Five-Axis Coordinate Systems
## 2 Developed Simulative Model and Steps to Its Implementation
### Implementation Measurements  
In order to gather the input data set for model, the reference ring was measured with different angular orientations of the probe head. The reference rings used during experiments were attached to a solid block which was installed in a swivel and tilting vise. A ring with a 20 mm diameter was chosen for the measurements, while other rings were used during datum definition and in further experiments (Section 3.3). In each ring arrangement, the standard was measured 15 times in 64 evenly distributed measured points. For the datum system definition, the CMM works in standard three-axis mode while all reference ring measurements were done using only rotational moves of the probe head. Therefore, the possible influences of machine kinematics are minimized. The positions were selected in a manner that allows covering the majority of the probe head working range. The orientation of the reference ring was changed together with the angular orientation of the probe. The utilized vise allows us to rotate the installed object around two perpendicular axes of revolution, and thus it is possible to set the ring so its axis will be oriented parallel to the stylus in \(A\), \(B\) orientation. The authors decided to base the model on measurements carried out in 24 positions defined using \(B\) and \(A\) probe angles. Angle \(B\) changes at 60\({}^{\circ}\) in the range between \(-120^{\circ}\) and 180\({}^{\circ}\), while angle \(A\) changes at 30\({}^{\circ}\) in the range between 0\({}^{\circ}\) and 90\({}^{\circ}\). Measurements for one of the described positions are presented in Figure 3.
# Comparison of Modern Deep Learning Models for Speaker Verification
## 1 Introduction
Advanced characteristics of speaker embeddings are achieved through training models on extensive voice datasets, allowing the capture of unique voice features crucial for reliable speaker recognition and verification. Modern models vary in architecture, methods of calculating embeddings, length of embeddings and numerous parameters [1; 12]. A key area of interest is comparing the performance of these models in real-world scenarios, including their adaptability and portability to other languages. This study presents a comparison of state-of-the-art models for speaker verification tasks, focusing on models that utilize speaker embeddings. These models offer notable advantages, such as scalability [13], allowing the addition of new users without the need for retraining or fine-tuning. This feature is particularly valuable in applications with constantly growing and changing user bases, where the ability to seamlessly integrate new users without the computational overhead of updating the model is crucial. Moreover, this scalability ensures that the model's performance remains consistent and reliable, making these models ideal for large-scale application [14]. The models' performance was compared using a custom dataset comprising 50 speakers. The experiment's source code and reproducibility details can be found in the following repository: [15]. The primary contributions of the paper include (1) an extensive comparison of state-of-the-art models such as WavLM, TitaNet, Ecapa and Pyannote, with an emphasis on their performance in speaker verification tasks; (2) the utilization of a newly compiled dataset, featuring non-English speech samples of Ukrainian politicians, for evaluating these models; and (3) a thorough analysis of the embeddings produced by each model, providing insights into their strengths, weaknesses and potential applications in real-world scenarios. The organization of the paper is as follows: Section 1 introduces the context and significance of speaker verification, Section 2 reviews related works and establishes the background for the research, Section 3 outlines the aims of the research and the rationale behind the selection of the models and dataset, Section 4 provides a detailed overview of the models under study, Section 5 describes the experimental setup and methodology, and, finally, Section 6 presents the findings and conclusions.
# Contrasting Dual Transformer Architectures for Multi-Modal Remote Sensing Image Retrieval
## 3 Experimental Results and Discussion
### Experimental Protocol and Evaluation Metrics  
To quantitatively evaluate the performance of our cross-modal retrieval model, we carried out several experiments mainly based on the first configuration, which uses language and vision encoders pre-trained on matching general image-text pairs. In particular, we analyzed the batch size effect, the generalization ability in cross-dataset settings, followed by a comparison against the second and third configurations. Then, we contrasted our results to the recent solutions proposed in the context of RS imagery.  
We trained the models for 100 iterations using a mini-batch size equal to 120 (larger mini-batch size given the memory constraints of our station). We employed the Stochastic Gradient Descent (SGD) with Nesterov momentum as an optimizer. We chose 0.1 for the initial learning rate and 0.9 for the default value of momentum. After 40 iterations of training, we reduced the learning rate to 0.01 and for the final 20 iterations, to 0.001. We discovered that applying gradient clipping with the gradients' maximum norm set to 0.1 is helpful for maintaining numerical stability. Each input image was reduced to 224 224 pixels, and we added the usual data augmentation techniques (such as random cropping, 50%-probability horizontal and vertical flips, and Color jitter). We chose 80% of the image-text pairs for training and 20% for testing across all datasets. Five sentences are used to describe each image; therefore, we choose one at random to use as learning material.  
We show the results in term of the Recall@K (R@K) metric with several values of (K = 1, 5, and 10), which are donated as R@1, R@5, and R@10. The following definition applies to these measures, which are frequently used to assess text-to-image and image-to-text retrieval techniques:  
\[\text{R@K}=\frac{TP@K}{TP@K+FN@K} \tag{6}\]  
where \(TP\) is the true positive and \(FN\) is the false negative. In addition, we provided the mean recall (mR) of all recalls (R@1, R@5, and R@10). Since each image is described by five sentences, we presented the R@1, R@5, and R@10 in terms of mean and standard deviation.  
We used PyTorch to implement our models, and HP Omen Station to conduct all of the tests according to the following guidelines: NVIDIA GeForce GTX 1080 Ti graphics processing unit (GPU), 32 GB of RAM, and Intel Core (TM) i9-7920 central processing unit (CPU) at 2.9 GHz (with 11 GB GDDR5X memory).
# Tutorial Review on Space Manipulators for Space Debris Mitigation
## 6 Freeflyer Manipulator Kinematics
To ensure that the robotic arm movements do not affect the base, the coupling momentum must be zero, so the manipulator joint rates are given by:  
\[\dot{\theta}=(I-D_{\zeta}^{+}D_{\zeta})\dot{\zeta} \tag{47}\]  
where \(D_{\zeta}\) = coupling inertia between base and robotic arm, \(\dot{\zeta}\) = null space vector of \(D_{\zeta}\) of dimension \(n-3\). The internal degrees of freedom allow the specification of zero reaction on the base. This is an extremely computationally intensive solution, even more complex than the generalised Jacobian. One means to reduce the computational complexity is to adopt a Jacobian transpose approximation of the form \(J^{T}(K_{p}e+K_{v}\dot{e})\) over model-based controllers [142]. For example, the Jacobian transpose can implement a version of the damped least squares inverse [143]:  
\[J_{*}^{T}=J_{*}^{T}\left(J_{*}J_{*}^{T}+\lambda^{2}I\right)^{-1} \tag{48}\]  
It trades off accuracy against feasibility through the weighting factor. The enhanced disturbance map (DM) is a path planning algorithm that selects manipulator paths that minimise base attitude disturbances but this approach was also highly complex [144]. A bidirectional approach exploited a Lyapunov function to regulate both manipulator joint angles and spacecraft attitude searching from start-to-goal and from goal-to-start configurations simultaneously, but again at the cost of high computational complexity [145]. It utilises the Moore-Penrose pseudo-inverse of the generalized Jacobian \(J_{*}^{+}=J_{*}^{T}\left(J_{*},J_{*}^{T}\right)^{-1}\) to overcome dynamic singularities using the redundant degrees of freedom. The path-independent workspace is the workspace in which there are no dynamic singularities, but it is smaller than the enclosing path-dependent workspace in which dynamic singularities can occur [146]. The large search spaces invoked by most of these path planning techniques introduce the possibility of using techniques such as genetic algorithms, but they are unsuited to real-time operation. Hence, the computational complexity of these methods renders them unsuitable for space manipulators. Many of the issues associated with free-floating manipulators vanish with free-flying manipulators with a stabilised spacecraft attitude. Indeed, dedicated attitude control system are employed on all spacecraft. This eases many of the computational complexity problems associated with freefloating systems. The reaction of the robot manipulator dynamics on the mounting spacecraft must be compensated for to maintain a stable attitude.
## 2 System Design
### Hardware System Design
#### 2.4.3 Control Modules Design  
The controller design is mainly based on the finite state machine (FSM). When the start signal is triggered, the FSM begins to calculate. The input of the first CONV layer, which is marked as CONV1, is from a random-access memory (marked as RAMX), while the inputs of other CONV layers are from DDR. The principle of the SYS_CTRL module is shown in Figure 7a. The system first runs the convolution step of CONV1, performs its ARMP operation, and executes CONV and ARMP layer by layer. When all convolution layers are handled thoroughly, the SYS_CTRL starts to calculate the FC layers. Finally, output the result when FC layers are finished.  
DDR_CTRL module controls 3 FIFOs: read address FIFO (RAF), read data FIFO (RDF), and write data FIFO (WDF). The state transition diagram is shown in Figure 7b. When memory access request signal mem_req is valid, DDR_CTRL will be transmitted into WR_RAF: Read addresses transferred by MEM_CTRL are accepted and stored. When all read addresses are transferred, DDR_CTRL starts to read the RAF and emit read request signal to Memory Interface Generator (MIG). When the RDF receives a certain length of the request, the data will be transferred to SYS_CTRL to feed the computation modules. The output of computation modules is written into WDF, and until it reaches a certain length, DDR_CTRL requests MIG to write it in burst mode.
# Automated Traumatic bleeding Detection in Whole-Body CT Using 3D Object Detection Model
###### Abstract  
Traumatic injury remains a major cause of death worldwide, with bleeding being one of its most critical and life-threatening consequences. Whole-body computed tomography (WBCT) has become a standard diagnostic method in trauma settings; however, timely interpretation remains challenging for acute care physicians. In this study, we propose a new automated method for detecting traumatic bleeding in CT images using a three-dimensional object detection model enhanced with an atrous spatial pyramid pooling (ASPP) module. Furthermore, we incorporate a false positive (FP) reduction approach based on multi-organ segmentation, as developed in our previous study. The proposed method was evaluated on a multi-institutional dataset of delayed-phase contrast-enhanced CT images using a six-fold cross-validation approach. It achieved a maximum sensitivity of 90.0% with 587.3 FPs per case and a sensitivity of 70.0% with 46.9 FPs per case, outperforming previous segmentation-based methods. In addition, the average processing time was reduced to 4.2 \(\pm\) 1.1 min. These results suggest that the proposed method enables rapid and accurate bleeding detection, demonstrating its potential for clinical application in emergency trauma care.  
bleeding detection; whole-body computed tomography; 3D object detection; atrous spatial pyramid pooling (ASPP) Article
# SROR: A Secure and Reliable Opportunistic Routing for VANETs
## 6 Simulation Results
### Comparison with Benchmarks  
To analyze the effectiveness of the proposed SROR, it is compared with three baseline algorithms in terms of PDR, EED, and Attack Success Ratio (ASR). ASR is equal to the ratio of number of packet dropped by the attacker to the number of packets that should have been forwarded. Three baseline algorithms are followed: (1) Strength Pareto Evolutionary Algorithm (SPEA) [9], (2) Q-learning-based Trustworthy Routing Technique (QL-TRT) [43], and (3) Probability-based Reliable Opportunistic Routing (PROR) [53].
## 4 Classification of Routing Protocols
### Temperature-Aware Routing Protocols
#### 4.2.2 Least Temperature Rise (Ltr)  
A thermal-aware routing protocol named least temperature rise is presented in [55], to reduce the amount of heat produced in bio-medical sensor networks, power consumption and end-to-end delay. The researchers assume that each node knows the temperature information of its neighbor nodes by observing their communication activities. This scheme selects that node as next-hop node which has the least temperature until the destination is reached. Unlike TARA, which buffers the data packet if the destination is a hotspot node, it directly forwards the packet to the destination node. A hop-count is associated with each packet and is incremented by one each time a node forwards it. A packet is dropped if it exceeds the threshold value (MAX_HOPS), which depends on the diameter of the network. In addition to the hop-count, each packet also keeps record of nodes through which it passed to avoid looping. Figure 10a, redrawn from [15] shows an example of how LTR works.
# Evaluation of Structural Behavior and Fatigue Performance of a KR-Type Rail Clip
## 3 Finite Element Analysis  
### Modeling Details  
In addition to the laboratory testing, FEA was performed to study the structural behavior of the rail clip, including the stress fields acting on it. Due to the complex shape of the clip, 3D scanning of the clip was performed, and the scanned shape of the clip was imported into the FEA software Abaqus [8]. The 3D solid elements (referred to as C3D8R in Abaqus) and the inelastic material properties were applied to the model. The trilinear input material model of the analysis (shown in Figure 8) was assumed based on the tensile coupon tests outlined in Section 2.1. A Poisson's ratio of 0.29 and an elastic modulus of 205 GPa were assigned. To account for the stiffness of the elastic pad underneath the rail, spring elements with a stiffness of 12,500 N/mm were added in the vertical direction at the clip-to-rail contact location. Total number of nodes, elements and degrees of freedom of the model were 15,867, 12,880 and 47,601, respectively. Minimum size of an element was 2.2 mm and aspect ratio was limited to 1:4. The size of the element was determined based on a mesh convergence test.  
Two loading steps were applied to simulate the installation of the rail clip and vertical displacement during train operation, as shown in Figure 9. In step 1, the top surface of the central part of the clip was tied to the center point, and a vertical displacement was applied to this center point, simulating the tightening of the screw spike. The level of displacement applied in step 1 was 11.4 mm, based on the laboratory measurements in Figure 5. In step 2, vertical displacements of 1 mm, 2 mm, and 3 mm were applied to the clip-to-rail contact  
Figure 8: Properties of the input material.  
Figure 7: Modal test setup.  
location to simulate the movement of the clip during train operation. In step 1, modal analysis was also performed to evaluate the natural frequencies and the corresponding mode shapes.
# A Novel Indoor Localization Method Based on Image Retrieval and Dead Reckoning
###### Abstract  
Indoor pedestrian localization measurement is a hot topic and is widely used in indoor navigation and unmanned devices. PDR (Pedestrian Dead Reckoning) is a low-cost and independent indoor localization method, estimating position of pedestrians independently and continuously. PDR fuses the accelerometer, gyroscope and magnetometer to calculate relative distance from starting point, which is mainly composed of three modules: step detection, stride length estimation and heading calculation. However, PDR is affected by cumulative error and can only work in two-dimensional planes, which makes it limited in practical applications. In this paper, a novel localization method V-PDR is presented, which combines VPR (Visual Place Recognition) and PDR in a loosely coupled way. When there is error between the localization result of PDR and VPR, the algorithm will correct the localization of PDR, which significantly reduces the cumulative error. In addition, VPR recognizes scenes on different floors to correct floor localization due to vertical movement, which extends application scene of PDR from two-dimensional planes to three-dimensional spaces. Extensive experiments were conducted in our laboratory building to verify the performance of the proposed method. The results demonstrate that the proposed method outperforms general PDR method in accuracy and can work in three-dimensional space.  
Keywords: indoor localization; PDR; VPR; fusion navigation +
Footnote †: journal: Applied Sciences
# Performance Analysis of Dual-Hop Hybrid RF-UOWC NOMA Systems
## 4 Ergodic Capacity Analysis  
In this section, we derive an approximated closed-form expression for the ergodic capacity (EC) of the proposed system under the condition \(a_{i}=b_{i}\). The instantaneous channel capacities for the two messages, \(C_{x_{l}},C_{x_{2}}\), are given by [13; 22]  
\[\begin{split} C_{x_{1}}&=\frac{1}{2}\text{log}_{2}( 1+\min(\gamma_{R}^{1},\gamma_{D1}^{1},\gamma_{D2}^{1}))\\ C_{x_{2}}&=\frac{1}{2}\text{log}_{2}(1+\min( \gamma_{R}^{2},\gamma_{D2}^{2})).\end{split} \tag{15}\]  
The EC, defined as the expectation of the channel capacity, can be mathematically expressed as [21]  
\[EC_{x_{i}}=\frac{1}{2\,\ell n2}\int\limits_{\gamma=0}^{\infty}\frac{1}{1+ \gamma}\Big{[}1-F_{\gamma_{j}}(\gamma)\Big{]}d\gamma, \tag{16}\]  
where \(j\in\{a,b\}\). The ergodic sum capacity (ESC) can be expressed as  
\[ESC=EC_{x_{1}}+EC_{x_{2}}. \tag{17}\]  
In the following subsections, we derive the individual ECs.  
Figure 2: OPs versus SNR for thermally uniform UOWC links for both IM/DD as well as heterodyne detection.
## 2 Materials and Methods
### Machine Learning Algorithms Used in the Study
#### 2.5.5 Support Vector Regression  
Support vector machine (SVM) has found extensive application in diverse predictive scenarios, encompassing both classification and regression tasks. In the realm of regression, it takes on the name of support vector regression (SVR) [78]. In regression, the model undergoes a transformation of inputs into a higher-dimensional space, constituting the foundation of SVR's training process grounded in the principles of structural risk minimization (SRM) [79]. A pivotal role in this intricate transformation is assigned to a kernel function, responsible for mapping inputs to the higher-dimensional space. The key hyperparameters critical to SVR's performance are C, epsilon, and kernel. The parameter C influences the trade-off between a smooth decision boundary and accurate fitting of the training data. Epsilon defines the margin of tolerance, determining the sensitivity of the model to errors. The choice of kernel, whether linear, polynomial, or radial basis function (RBF), significantly affects the model's ability to capture complex relationships within the data. Each hyperparameter plays a crucial role in shaping the SVR model's accuracy and generalization capability, demanding careful consideration during the model tuning process.