esa-sceva/satcom-chunk-collection huggingface dataset:
# Fused Gromov-Wasserstein Distance for Structured Objects
## 5 Examples and Applications
### Graph Barycenter and Compression
Also note that the sharp change at the center of the 8 class is conserved in the barycenters, which is a nice result when compared to other divergences that tend to smooth-out their barycenters (\(\ell_{2}\), for instance). Finally, note that, by selecting the number of nodes in the barycenter, one can compress the graph or estimate a \"high resolution\" representation from all the samples. To the best of our knowledge, no other method can compute such graph barycenters. Finally, note that \(FGW\) is interpretable, because the resulting OT matrix provides correspondence between the nodes from the samples and those from the barycenter. In the second experiment, we evaluate the ability of FGW to perform graph approximation and compression on a Stochastic Block Model graph [60; 61]. The question is to see whether estimating an approximated graph can recover the relation between the blocks and perform simultaneously a community clustering on the original graph (using the OT matrix). We generate two community graphs illustrated in the left column of Figure 14. We can see that the relation between the blocks is sparse and has a \"linear\" structure, the example in the first line has features that follow the blocks (noisy but similar in each block) whereas the example in the second line has two modes per block. The first graph approximation (top line) is done with four nodes and we can recover both the blocks in the graph and the average feature on each blocks (colors on the nodes). The second problem is more complex due to the two modes per block but one can see that when approximating the graph with eight nodes we recover both the structure between the blocks and the sub-clusters in each block, which illustrates the strength of \(FGW\) that encodes both features and structures. Figure 13: Illustration of \(FGW\) graph barycenter. The first column illustrates the original settings with the noiseless graphs, and columns 2 to 7 are noisy samples that constitute the datasets. Columns 8 and 9 show the barycenters for each setting, with different number of nodes. Blue nodes indicates a feature value close to \(-1\), yellow nodes close to 1.
## 6 Numerical Example
### The Effect of Parameters
#### 6.1.1 The Effect of \(\mathcal{N}\)  
In our numerical example, we can see the value of the parameter \(\mathcal{N}=1\). However, a change in the value \(\mathcal{N}\) may influence the ranking results. We observed that when we change the value of parameter \(\mathcal{N}\) in our proposed q-ROFAAWA and q-ROFAAWG operators, a significant variation occurs in the ranking sequence of alternatives. Such changes can be seen in Tables 6 and 7.  
Figure 1: The score values are represented geometrically. Where blue lines show the score values of WA while orange lines show the score value of WG aggregation.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Ordering** \\ \hline q-ROFAAWA & \(\mathfrak{a}_{4}>\mathfrak{a}_{5}>\mathfrak{a}_{2}>\mathfrak{a}_{3}>\mathfrak{a }_{1}\) \\ \hline q-ROFAAWG & \(\mathfrak{a}_{5}>\mathfrak{a}_{2}>\mathfrak{a}_{3}>\mathfrak{a}_{4}>\mathfrak{a }_{1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ranking of the score function.  
The aggregation findings of Table 5. are further represented in Figure 2. We can easily observe from the data there is no change in aggregation findings with the variations in \(\mathcal{N}\). It is also observed that there when we take \(\mathcal{N}\) as an even number, then there is no result identified.  
All rankings in Table 6. can be observed in Figure 2. It can be noticed that in the q-ROFAAWA operator, there is no change when we take \(\mathcal{N}\) as odd numbers. It is also observed that when we take \(\mathcal{N}\) as even, no result will be identified.  
Now, we also change the parameter \(\mathcal{N}\) for q-ROFAAWG operators, and all the findings obtained are presented in Table 6, as given below.  
The ranking of Table 7. can be seen geometrically in Figure 3, as given below. We noticed no change in a ranking order by the variation in \(\mathcal{N}\). It is also a highly significant factor; when we take \(\mathcal{N}\) as an even number, then no result is identified.  
It is observed that when we take a variation of \(\mathcal{N}\) as odd, then the sequence of listed five companies comes out to be the same, but this is not always guaranteed. On the other hand, when we take the variation of \(\mathcal{N}\) even then, no result is identified.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Ranking of Score Values of q-ROFAAWA**} \\ \hline \(\mathcal{N}\) & **Ordering** & \(\mathcal{N}\) & **Ordering** \\ \hline \(1\) & \(\mathfrak{a}_{5}>\mathfrak{a}_{2}>\mathfrak{a}_{3}>\mathfrak{a}_{1}>\mathfrak{ a}_{4}\) & \(2\) & No result identified \\ \hline \(3\) & \(\mathfrak{a}_{5}>\mathfrak{a}_{2}>\mathfrak{a}_{3}>\mathfrak{a}_{1}>\mathfrak{ a}_{
# Signature-Based Security Analysis and Detection of IoT Threats in Advanced Message Queuing Protocol
## 5 Attack Description and Explanation
### Explanation of DoS Attack
#### 5.2.2 Traits and Signature of DoS Attacks  
The analysis revealed key characteristics of AMQP DoS attacks, summarized in Table 2. The methods and features used are as follows:  
* TCP Flag Analysis: The model evaluates TCP flags such as SYN, ACK, and RST, which are critical in identifying connection-based anomalies. For instance, a high frequency of SYN packets without corresponding ACK responses is indicative of SYN flood attacks.
* Packet Frequency Monitoring: By observing the rate of incoming packets over a specific time window, the model detects sudden spikes in traffic volume, characteristic of volumetric DoS attacks.
* Sequence Number Irregularities: The statistical model monitors the continuity of sequence numbers in packet flows. Disruptions or repeated numbers often indicate replay attacks or DoS activities attempting to disrupt normal communication.
* Payload Size Analysis: The model detects anomalous traffic patterns by examining payload sizes. Abrupt changes in payload size, especially a large number of packets with empty payloads, are often linked to malicious traffic.  
These anomalies form the basis for identifying DoS attack signatures.
# FETrack: Feature-Enhanced Transformer Network for Visual Object Tracking
## 3 Proposed Method
### Training and Inference  
**Training.** Similar to NLP modeling [8], our FETrack does not need to design various complex loss functions and only adopts a simple cross-entropy loss. The goal of training is to maximize the log-likelihood of the target tokens based on the previous sequence and the input video frame. Our objective function can be expressed as  
\[\text{maximize}\sum_{j=1}^{L}\log Q(\widehat{z}_{j}|s,it,dt,\widehat{z}<j) \tag{11}\]  
where \(Q(\cdot)\) represents the softmax probability, \(s\) is the search image, \(it\) is the initial template, \(dt\) is the dynamic template, \(\widehat{z}_{j}\) represents the target sequence, \(j\) represents the token position, and \(L\) denotes the length of the target sequence. \(\widehat{z}<j\) is used to predict the subsequence before the current token \(\widehat{z}_{j}\). The input sequence is the coordinates describing the template, and the target sequence is the coordinates of the search region predicted by the network. The entire training process aims to obtain the final predicted target sequence based on the input sequence under the constraints of the objective function.  
**Inference.** In the inference stage, the encoder first perceives and extracts the features of the template images (the initial dynamic template is the same as the initial template) and the search region image in the subsequent video frames. Then, given an initial input start of the decoder, this input is used to instruct the model to start generating predicted target sequences one by one. Each coordinate token value is sampled by the model from the vocabulary \(V\) according to the maximum likelihood result; that is, \(\widehat{z}_{j}=\arg\max_{z_{j}}Q(z_{j}|s,it,dt,\widehat{z}<j)\), where \(z_{j}\) is also a value in \(V\). After obtaining the predicted target sequence, it is scored. When the score is higher than the threshold, the dynamic template is updated. Then, according to the predicted bounding box, a new dynamic template is cropped from the original image and then input into the encoder for feature extraction of the next frame.
# Lightweight and Security-Enhanced Key Agreement Protocol Using PUF for IoD Environments
## 3 Preliminaries
### Physical Unclonable Function  
A PUF device employs unique randomness inherent in the manufacturing process to provide a distinct fingerprint to be used as a trust anchor for a physical entity [35; 36]. Although random variations are measurable, creating an identical physical copy is extremely difficult or impossible because completely controlling the fabrication variations at the micro and nano scales is infeasible. Moreover, PUFs are unclonable, cost-effective, and resistant to many physical attacks. These properties make them suitable for diverse applications, including authentication and key exchange. Specifically, PUF technology protects drones deployed in an IoD environment from cloning and tampering. A PUF can be mathematically represented as \(R=P(C)\), where \(P\) denotes the \(PUF\) function, \(R\) represents the output response, and \(C\) signifies the input challenge. The detailed characteristics of a PUF are listed below:  
* A PUF's output is inherently linked to the device's physical microstructure.
* A PUF's output must be unclonable and unpredictable.
* The evaluation and implementation of a PUF should be simple.
## 2 Materials and Methods
### Measures and Instruments  
The present study included valid measures such as:  
1. The Timed Up and Go (TUG) test is a simple and quick assessment tool commonly used to evaluate fall risk in the elderly population. When administered, it records the time taken for a person to rise from a straight-backed chair, walk 3 m, turn around, return to the chair, and sit back down. This is a valid and reliable test, as according to [PERSON] & [PERSON] (2018) it showed good test-retest reliability (ICC = 0.99) and good inter-rater reliability (ICC = 0.99). The TUG was the motor task completed during the performance of both the single activity and the DT activities in the present study. To measure the necessary distance for the TUG test, a tape measure and a plastic cone were used, which was placed at 3 m from the chair. A digital stopwatch was then employed to record the time of test completion.
2. The 30-S Chair Stand Test (30-CST) assesses the functional muscle strength of the lower limbs ([PERSON] and S  
complete as many full raises and chair sits as possible within 30 s, while the examiner is counting the number of raises. Being able to perform more repetitions indicates higher lower limb strength. According to [PERSON] et al. (2018), the test showed excellent criterion validity: r = 0.77 (95% CI = 0.64-0.85) compared to weight-adjusted lower limb strength performance for all participants. Also, excellent test-retest reliability was recorded for the entire sample: r = 0.89 (95% CI = 0.79-0.93), and excellent inter-rater reliability in a pilot test among 15 participants: r = 0.95 (95% CI = 0.84-0.97). 3. The Four Square Step Test (FSST) assesses dynamic balance ([PERSON], 2018). The participant is asked to step over 4 bars in a cross (+) configuration on a flat surface without touching them and to move as fast as possible in each square with both feet forward, right, backward, and left (clockwise and counterclockwise). Two trials are conducted, and the time for each is recorded in seconds. The best time is then chosen for further analysis. According to [PERSON] & [PERSON] (2018), the FSST showed excellent concurrent validity with the Step Test (r = \(-\)0.83) and the Timed Up and Go (r = 0.88), and adequate concurrent validity with the Functional Reach Test (r = \(-\)0.47). Excellent test-retest reliability (ICC = 0.98) and excellent inter-rater reliability (ICC = 0.99) were also recorded. 4. The Activities-Specific Balance Confidence (ABC) scale is employed to evaluate older people's balance confidence. This is a 16-question activity scale where the individual is asked to rate their confidence in performing supposed daily activities without losing balance or feeling unstable. Each question is scored from 0 (no confidence) to 100 (full confidence), and the final score is obtained by summing all the scores and then dividing by the number of questions ([PERSON], 2018). The ABC scale has been translated and adapted for a Greek elderly population by [PERSON] et al. (2018) with high validity and reliability.
# The Firmware Design and Implementation Scheme for C Form-Factor Pluggable Optical Transceiver +
## 3 CFP Firmware Design Scheme  
### Overall Structure Design  
The CFP firmware is ported to the CFP hardware board for operation. Therefore, this section describes the hardware structure of the CFP used in the firmware design. Figure 4 is a block diagram of the entire hardware architecture.  
In the proposed firmware design technique, an FPGA is used for the MDIO interface of the CFP and a standard memory configuration is employed. Using the FPGA, the design space is minimized by implementing multiple sub-blocks on a single chip. We also used a low-cost, reliable MCU for CFP transceiver operation and management functions. The CFP firmware must be able to control 4-16 channels. Therefore, to implement the control and monitor signals for 16 channels, it is necessary to use an MCU with large numbers of analog-to-digital converters (ADCs), digital-to-analog converters (DACs), and input/output (IO) ports for control signals. The design architecture proposed in this paper consists of external ADCs and DACs, and the MCU controls them through a communication interface to ensure channel scalability and firmware stability. In addition, ADC and DAC ICs can be selected to meet CFP transceiver performance requirements. Moreover, the IO ports for the control signals are implemented using an FPGA to relieve the burden on the MCU and achieve high speed processing.  
The optic driver IC is an IC needed for the photoelectric and electronic conversion of high-speed data in the optical transceivers. The optic driver IC includes a laser driver IC that produces an optical transmission signal, a limiting amplifier IC that amplifies the signal from the optical receiver, and a clock and data recovery IC to recover the transmitted and received signals. The ICs produced by  
Figure 4: CFP Optical Transceiver Block Diagram.  
Figure 3: Fault, Alarm, Warning, and Status (FAWS) Signal Model for a Single Bit [11].  
most manufacturers are controlled by a communication interface and memory map, and several optic driver ICs are required depending on the number of optical channels needed. The state signals monitored by the optical driver IC are designed to be processed at high speed through the FPGA without going through the central processing unit, because they need to be updated to the CFP standard memory quickly.
# Cross-View Outdoor Localization in Augmented Reality by Fusing Map and Satellite Data
## 4 Results
### Ablation Studies
#### 4.4.3 Contribution Ablation  
Finally, to show the different steps taken to go from the model introduced by [PERSON] et al. to a model competitive with the recent successes seen in other papers, we trained different models to show the benefits of certain contributions, which are outlined in Table 5. The original LM model was trained with just 2 epochs, but we found the evaluation metrics steadily increased when training the model for more epochs. This increase in performance was consistent over the training, evaluation, and test sets. Increasing the U-Net model's size improved the metrics when looking at all positional variables together, but interestingly enough, this also resulted in a decrease in the lateral recall rate. Adding OpenStreetMap data mostly recovered the lateral recall rate, but so far, all the models struggle on the longitudinal metrics. Finally, by starting the model from the minimum longitudinal value, we ended up with a model with all-around good performance.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multirow{2}{*}{**Name**} & \multicolumn{8}{c}{**Cross-Area**} \\ \cline{2-10}  & \multicolumn{3}{c}{**Lateral**} & \multicolumn{3}{c}{**Longitudinal**} & \multicolumn{3}{c}{**Azimuth**} \\ \cline{2-10}  & **d = 1** & **d = 3** & **d = 5** & **d = 1** & **d = 3** & **d = 5** & **\(\theta\) = 1** & **\(\theta\) = 3** & **\(\theta\) = 5** \\ \hline original & **59.96** & **83.81** & 87.24 & **22.25** & **48.28** & **58.13** & **60.94** & **95.90** & **99.48** \\ switched & 59.39 & 83.78 & **87.32** & 19.50 & 45.83 & 57.52 & 57.49 & 94.93 & 99.35 \\ \hline \end{tabular}
\end{table}
Table 3: **Ablation using different ground-level images. The original model uses both satellite and OSM data and is trained for 26 epochs with the original ground-level image. The switched model uses another random ground-level image from the test dataset instead. The best results are shown in bold**.
## Active Debris Removal  
The orbital lifetime of many disused spacecraft, especially in the already densely populated orbits at 800 km altitude, is so high that many of these objects can serve as a potential collision partner for a long time. A catastrophic collision event can lead to a drastic increase of the population in these orbits, making these objects pose a high risk. Active Debris Removal (ADR) is, in addition to consequent mitigation, the only possibility to stabilize critical space debris environment regions in the long term. Therefore, the Institute also deals with the problem of actively removing such objects in important secondary areas. The major challenge of a debris removal mission is the non-cooperativity of the target, i.e., the absence of Global Navigation Satellite System (GNSS) data, attitude or orbit control, reflectors for vision based navigation, or a specific interface for docking. For this reason, two major areas are investigated:  
* Proximity operations: development and verification of guidance, navigation and control concepts to rendezvous with a non-cooperative target.
* Docking and capture technologies: development and verification of suitable mechanisms to dock or capture with a non-cooperative target.  
The overall objective is to identify innovative key technologies for ADR and prove their relevance by achieving high Technology Readiness Levels (TRL).
# Performance of Eco-Friendly Zero-Cement Particle Board under Harsh Environment
## 2 Materials and Methods
### Tests
#### 2.3.4 Thickness Swelling  
The thickness swelling assessment was conducted per the specifications outlined in the ASTM standard D1037-03 [29]. Initial measurements of the samples' thicknesses were precisely recorded utilizing a Vernier caliper. Subsequently, the samples underwent immersion in water for 2 and 24 h, after which their final thickness measurements were taken. The quantification of water absorption for each sample, expressed as a percentage, was calculated using Equation (3), thereby facilitating the analysis of water-induced dimensional changes.  
\[ThicknessSwelling\ (\%)=\frac{Tf-Ti}{Ti}\times 100 \tag{3}\]  
where \(T_{f}\) = the final thickness of the particle board; \(W_{i}\) = the initial thickness of the particle board.
## 2 Theory and Method
### Correction of Geometric Parameters for Kinematic Models  
In order to facilitate the adjustment of the attitude, the pitch and roll mechanism of the pulley and the arc groove of the clearance fit the main mating surface for the pulley column surface and the arc groove of the lower arc surface. Notably, pulleys are comparatively easier to machine, resulting in higher geometric accuracy. However, the circular groove, being a curved surface profile, necessitates high precision and proves challenging to machine, thus leading to potential geometric errors [29].  
In industrial manufacturing, the term \"profile of a surface\" is often employed as a geometric tolerance to measure the accuracy of freeform surfaces. This indicates the variation between the actual and ideal profiles. For a surface profile with a design datum, its tolerance zone is the area between a series of spherical envelopes with a diameter of tolerance value \(\zeta\), whose centers are located on the theoretical correct geometric shape of the measured feature determined by the datum. This can comprehensively control the size, shape, orientation, and position of the measured feature [30]. Therefore, using a surface profile with a design datum to measure the radial allowable offset of the actual arc surface of a circular arc groove relative to the ideal arc surface, the actual offset \(\delta\) is defined as the contour error. For the internal arc surface that meets the surface profile tolerance requirements, there exists the relationship \(\zeta\geq 2\delta\).  
Figure 4: Mechanism principle of pitch layer.  
Considering the effect of the contour error \(\delta\), the positive kinematic model of the pitch layer can be modified as  
\[\dot{x}_{4}^{\prime}=\arccos\left(\frac{{{r_{4}^{\prime}}^{2}+m_{4}^{2}-s_{4}^{2}} }{2{r_{4}^{\prime}}m_{4}}\right)-\theta_{4\text{m}} \tag{3}\]  
where \({r_{4}}^{\prime}\) denotes the actual radius of rotation of the tilting mechanism, \(\delta_{4}\) denotes the face profile error of the circular groove, and the mathematical relation between them is \({r_{4}}^{\prime}=r_{4}+\delta_{4}\). Similarly, the positive kinematics model of the table's roll layer can be modified as  
\[\dot{y}_{5}^{\prime}=\arccos\left(\frac{{{r_{5}^{\prime}}^{2}+m_{5}^{2}-s_{5}^{ 2}}}{2{r_{5}^{\prime}}m_{5}}\right)-\theta_{5\text{m}} \tag{4}\]  
Since the motions between the pitch and roll layers are independent of each other, their inverse kinematic correction models can be expressed separately as  
\[s_{4}^{\prime}=\sqrt{m_{4}^{2}+{{r_{4}^{\prime}}^{2}-2{r_{4}^{\prime}}m_{4} \cos(\hat{x}_{4}+\theta_{4\text{m}})}} \tag{5}\]  
\[s_{5}^{\prime}=\sqrt{m_{5}^{2}+{{r_{5}^{\prime}}^{2}-2{r_{5}^{\prime}}m_{5} \cos(\hat{y}_{5}+\theta_{5\text{m}})}} \tag{6}\]
# Empirical Study on 5G NR Cochannel Coexistence
###### Abstract  
The 5G non-public network deployments for industrial applications are becoming highly interesting for industries and enterprises owing to dependable wireless performance characteristics. With an increasing trend of network deployments in local licensed and/or shared spectrum, coexistence issues naturally arise. In this article, we present our detailed empirical results on the performance impact of a 5G NR indoor non-public network from a 5G NR outdoor network operating in the same mid-band spectrum. We present experimental results on the uplink and downlink performance impact of a non-public indoor network deployed on an industrial shopfloor. Our results quantify the impact on the uplink and downlink performance characteristics based on realistic traffic loads in a non-public indoor network when using synchronized and unsynchronized Time Division Duplex (TDD) patterns, different UE deployment locations and interference levels. We also present results on mitigating interference effects through robust link adaptation techniques. We believe that this is the first article, which reports quantified 5G NR cochannel coexistence results based on a detailed and systematic study, and provides significant insights on the cochannel coexistence behavior in realistic deployment scenarios of an industrial shopfloor.  
5G NR; coexistence; empirical results; 5G performance; TDD; interference mitigation Article