esa-sceva/satcom-chunk-collection huggingface dataset:
# Physical and Chemical Methods to Assess Performance of TPO-Modified Asphalt Binder
## 1 Introduction
These parameters are measured by the dynamic shear rheometer (DSR) in standardized procedures. While the current methodology relies on standardized, bulk physical testing, testing for chemical changes could provide additional valuable information. Asphalt research has considered many modes of data acquisition outside the standardized methods. Fourier-transform infrared (FTIR) spectrometry can analyze the presence of functional groups in an analyte [19], and the quantitative determination of carbonyl and sulfoxide groups has been used to indicate aging [20; 21]. Nuclear magnetic resonance (NMR) has been used to identify chemical changes caused by rejuvenation through the analysis of \(T_{2}\) relaxation times [22]. Other methods of characterization include energy-dispersive X-ray spectroscopy [23], atomic force microscopy [24], differential scanning calorimetry, SARA fraction analysis, and gel permeation chromatography [25; 26]. Optical methods like scanning electron microscopy and other imaging techniques can also be used to see changes in the structure of asphalt [27; 28]. All in all, there are many chemical techniques, but no consensus can be reached regarding standardization. While some studies consider individual chemical compounds, few consider chemical environments that may affect the microstructure of the material. In this study, physical and chemical methods were used to indicate the blend quality and performance of TPO as an asphalt additive. DSR, FTIR, and NMR relaxometry were used to compare TPO-modified asphalt binders. DSR testing revealed bulk macrostructure properties (\(\text{G}^{\star}/\text{sin}\text{8}\)) and was compared with chemical testing that indicated changes in the asphalt binder microstructure. While FTIR testing displays the presence of chemical functional groups that make up the microstructure, NMR relaxometry directly probes into the microstructure itself.
## 4 System Design
### DeepSORT Multitarget Tracking Algorithm [19]  
The model that the SORT algorithm strives to achieve is an eight-dimensional model:  
\[X=\left[\mu\;v\;r\;h\;\dot{x}\;\dot{y}\;\dot{r}\;\dot{h}\;\right]^{T} \tag{9}\]  
The first three variables represent the current target's horizontal and vertical axis value and the size ratio and height of the BBox, respectively. The latter four variables represent the relative velocity of the predicted position in the next frame, including the horizontal and vertical coordinate and the BBox size.  
DeepSORT improves upon SORT by integrating appearance information to enhance SORT's performance. With this extension, the model can more effectively handle cases where the target is occluded for a long time, which reduces the ID switch indicators by 45%. DeepSORT adopts the same eight-dimensional features as mentioned above for target modeling, but it also adds the handling and management of target trajectories, which includes setting the states for trajectory addition, termination, and matching.  
The DeepSORT multitarget tracking algorithm, which is constructed on the Yolo v5s network, correlates data by tracking the targets and actual detection positions in the subsequent frame; it updates the bounding box by combining the results of the tracking and detection, and thereby it realizes multitarget tracking. The algorithm flow is shown in Figure 6.  
Figure 5: Yolo v5s structure diagram.  
Figure 6: DeepSORT algorithm flow block diagram.  
State Prediction:  
The tracks generated from the previous iteration undergo Kalman filtering prediction so that the mean and covariance for the current iteration can be calculated, and the state of the data (confirmed and unconfirmed) remains unchanged.  
First Matching:  
The tracks from Step 1 and the detections from the current target detector are fed into the cascade for matching purposes, which results in three states: unmatched tracks, unmatched detections, and matched tracks.  
Second Matching:  
However, some detections may be missed in Step 2. The detections are then combined with the undetermined tracks from Step 1 and are rematched using IOU (intersection over union) matching to obtain more reliable unmatched tracks, unmatched detections, and matched tracks.  
Handling of Expired Objects:  
The unmatched tracks that are still unconfirmed (unconfirmed) or confirmed (confirmed) but with an age exceeding the threshold are set to be deleted.  
Output Results and Prepare Data for the Next Iteration, where Tracks are Merged from the Following Three Sources:  
Merging of matched tracks from Steps 3 and 4, with a Kalman filtering update and an increment in age by 1, which results in output tracks.  
Creating new tracks from unmatched detections from Step 3.  
Including confirmed tracks from Step 4 that have not exceeded the age threshold. These tracks from the three sources are merged as the output for the current iteration, and they also serve as the input for the next iteration to continue with Step (1).
## 2 Materials and Methods
### Data Analysis  
Spatiotemporal, kinematic, and CoP (center of pressure) variables were assessed for each stair ascent and descent trial. Spatiotemporal parameters included stride time, step time, stride length, step length, and walking speed. Kinematic outcomes comprised hip, knee, and ankle joint angles in the frontal and sagittal planes. The gait cycle was defined as the period from the initial foot contact on the step with the embedded platform to the subsequent foot contact on the third step.  
CoP measures were evaluated during the single-stance phase of the first step in stair descent. CoP trajectory was recorded using a force platform embedded in the second step, generating a time series of CoP positions in the AP and ML dimensions. CoP-related data were analyzed for the initial descent phase, from foot contact to toe-off on the force plate. Range and sway excursion of CoP displacement were calculated in both AP and ML directions during the single-stance phase. Mean CoP velocities in the AP and ML dimensions were determined using the first central difference method. CoP sway variability was assessed using root mean square (RMS) quantification in both dimensions.  
Knee pain was assessed pre- and post-intervention using a VAS, represented by a 10 cm scale ranging from 0 to 100 mm. Participants were instructed to mark their perceived level of knee joint pain experienced during daily stair ambulation.
# Design and Validation of a U-Net-Based Algorithm for Star Sensor Image Segmentation
## 4 Discussion
### PHOEBE Board  
PHOEBE Board [47] (Figure 17) was used by School of Aerospace Engineering, (Sapienza University of Rome) for the STECCO (Space Travelling Egg-Controlled Catadioptric Object) mission. PHOEBE Board Features include the following:  
* Microcontroller to handle data and tasks;
* FPGA for fast image data processing;
* Low power consumption: up to 0.3 W;
* Small Satellites Protocol: CAN, \(I^{2}C\) and RS485;
* Low Mass: 54 g;
* Dimensions: 99 \(\times\) 19 \(\times\) 29 mm\({}^{3}\).  
The algorithm will be implemented on the PHOEBE system on Chip/Field Programmable Gate Array (SoC/FPGA) hardware for taking advantage of the parallel computation capabilities of the FPGA which would boost the image processing time and make the proposed algorithm suitable for real-time applications. Indeed, as already demonstrated in another work [48], the SoC/FPGA hardware is able to reduce the processing time by a factor of 70 when compared with a personal computer based solution of the algorithm itself. At the moment, a personal computer processing time of this algorithm requires no more than 5 seconds per image. A materialization on the cited hardware would mean a realistic processing time, not higher than 100 ms, and thus a possibility of working with real 10 Hz star trackers and onboard cameras.  
Figure 16: Sagitta Star Tracker, (Credits to ARCSEC SPACE).  
Figure 17: PHOEBE board, (Credits to School of Aerospace Engineering).
## 5 Discussion
The importance factor has not been explicitly mentioned in the performed analysis, but it is correlated with the earthquake return period and it can be applied to existing results. The decision on required interventions for enhancing the seismic performance of a structure should be based on the performed assessment and extent of the damage. Furthermore, one of the most important criterions is the functionality of a structure that is needed during the earthquake and prevention of structural and non-structural damage. The decision on the type and scope of the retrofit should also account for social-economic aspects, such as retrofit costs, the importance of the building and the disruption of the use or occupancy during the intervention. When retrofitting structures of strategic importance, decisions should be in accordance with a cost-benefit analysis and an examination of different solutions. According to the analyses, target retrofit measures can be proposed to prevent, at least, local mechanisms. As acceptance criteria for this building have not been established, retrofit strategies of minimal invasiveness will be recommended in order to enhance the structural safety and load-bearing capacity. It must be pointed out that the level of the structural load-bearing capacity demanded by modern seismic regulations for new hospital buildings would not be easily achieved, because it would be very expensive. However, these measures could reduce some structural deficiencies identified by calculations. In addition to the increase in the structural capacity of critical elements, the proposed interventions could improve the ductility of brittle elements. Target strengthening of structural elements in critical building zones is recommended; this primarily refers to perimetric columns and beams which radially extend into the interior of the plan. It is recommended to extend existing elements or to construct new elements (columns) with the goal of preventing identified local failures. Frame structure elements can be also strengthened with steel or RC jackets, wrapping by fibre reinforced polymer (frp), etc. The ductility capacity of brittle walls may be increased with an application of high strength materials, such as frp, to walls and columns, where frp may be glued to the surface or wrapped around an element. In addition to primary seismic elements (structural), secondary elements (non-structural) are also important. Partial or full collapses of these elements in these states should be avoided by increasing their capacity and appropriate anchoring to prevent fallouts. An important conclusion based on the analysis is that horizontal and vertical corridors in the case of earthquake, especially stairs, are safe. In the observed building, the relatively safe spots include areas around central staircase and the elevator core. As the staircase is constructed monolithically, it was assumed that it will keep its load-bearing capacity and ductility until the collapse scenario.
## 2 Message Replication in OppNets for Sensed Data Collection  
In this section, we study message replication in OppNets for sensed data collection. First, Section 2.1 presents the need for message replication techniques in OppNets. In Section 2.2, OppNet characteristics general characteristics as well as those specific to scenarios for collecting sensed data that need to be observed by message replication techniques are identified. Then, Section 2.3 provides an overview on the existing message replication techniques. The current state of message replication in OppNets with respect to sensed data collection is then investigated in Section 2.4. Finally, we conclude this section by suggesting message replication guidelines for collecting sensed data with OppNets in Section 2.5.
# Latency-Aware Accelerator of SIMECK Lightweight Block Cipher
## 1 Introduction
### Critical Findings and Significance of This Work  
The proposed architecture is modeled in Verilog HDL language utilizing the Vivado IDE tool and the results of implementation after the post-place-and-route level is given on different 7-series FPGA devices (Kintex, Virtex and Artix). More precisely, on Kintex-7, Virtex-7, and Artix-7 devices, the utilized FPGA Slices are 49, 51, and 50. Our architecture takes 16 clock cycles to perform one encryption and decryption over 32-bit data. Moreover, we obtain the highest clock frequency of 388 MHz on Artix-7 FPGA. Additionally, our design requires the minimum time (latency) of 0.041 \(\upmu\)s for one encryption and decryption computation on Kintex-7 and Artix-7 FPGA devices. The highest throughput of 24.39 is calculated for Kintex-7 and Artix-7 FPGA. The minimum power consumption of 172 mW is achieved for Kintex-7 FPGA while the highest power of 189 mW is obtained for Artix-7. Moreover, we (also) considered the same operating frequency of 50 MHz for running synthesis on Kintex-7, Virtex-7, and Artix-7 devices. Our SIMECK architecture utilizes the minimum hardware resources in Slices of 51 on the Kintex-7 device. The least consumed power of 13.203 mW is obtained for the Kintex-7 device. To show the proof of concept, we have implemented our SIMECK design on the NEXYS 4 FPGA with the Artix-7 device. For throughput and area parameters at the same time, our design outperforms the most relevant state-of-the-art chip accelerators of wireless sensor nodes such as [15; 28; 30; 31]. Consequently, the implementation of results and comparison with the state of the art shows that the proposed architecture is more convenient for the applications (such as RFID [3; 4], WSNs [5], smart cards [10], WBANs [11], and the IoTs [13]) that require low area and consumed power for cryptographic computations.  
This paper structure is formulated as: Section 2 provides the related background for SIMECK block cipher. The proposed architecture is extensively expressed in Section 3. The state-of-the-art compassion of designs and implementation of results are given in Section 4. Section 5 is concluding the article and provides some future directions.
# Developing and Field Testing Path Planning for Robotic Aquaculture Water Quality Monitoring
## 2 Overview of the Three Path Planning Algorithms
### Google Linear Optimization Package  
GLOP is a linear optimization Python package that computes the optimal or nearly optimal solution for a set of node locations given certain linear relationships [33]. Linear programming is a method used to optimize a linear objective function, subject to a set of linear inequality or equality constraints. In the context of the vehicle routing problem, GLOP can be used to minimize the maximum route length for a given set of node coordinates, which are converted to a distance matrix. It does this by using a Primal Simplex algorithm, which works by starting with a feasible solution to the problem, called the primal solution. The algorithm then iteratively improves the primal solution until an optimal solution is found. At each iteration, the algorithm chooses a direction to move in, called a pivot, that improves the objective function. The pivot is chosen based on the values of the objective function and the constraints at the current iteration. In this case, the objective is to obtain the minimum longest route. One potential disadvantage of using GLOP for the vehicle routing problem is that the complexity of the algorithm grows exponentially with the size of the problem, making it inefficient for large-scale problems.
# Sparse Plane Wave Approximation of Acoustic Modes to Address Basis Mismatch
## 3 Proposed Combined Two-Stage Method
### Stage 2: Estimation of PWAs and Directions Using Parametric SBL  
#### 3.2.1 Parameterized Dictionary  
To avoid dense discretized PW directions, we explicitly modeled the dictionary with unknown inclinations and azimuths. The high dimensionality of the known dictionary can be reduced at the expense of introducing unknown parameters; therefore, we can apply the low-dimensional dictionary \(\mathbf{H}\) here. Let \(\mathbf{h}(\mathbf{s}_{r})\) be the \(r\)th atom of the dictionary \(\mathbf{H}\) corresponding to the direction \(\mathbf{s}_{r}\). We assumed that the actual PW direction was \(\mathbf{s}_{l}\
otin\mathcal{S}^{\prime}\) for some \(l\in\{1,\dots,L\}\) and that \(\mathbf{s}_{\tau_{l}}\in\mathcal{S}^{\prime}\) was the direction nearest to \(\mathbf{s}_{l}\). In this case, we can approximate the unknown \(\mathbf{h}(\mathbf{s}_{l})\) using the multivariable Taylor expansion in the pre-discretized direction \(\mathbf{s}_{\tau_{l}}\)[30]:  
\[\mathbf{h}(\mathbf{s}_{l})\approx\mathbf{h}(\mathbf{s}_{\tau_{l}})+\mathbf{h}^{\prime}_{\theta_{ \tau_{l}}}(\mathbf{s}_{\tau_{l}})\times\vartheta_{l}+\mathbf{h}^{\prime}_{\phi_{\tau_ {l}}}(\mathbf{s}_{\tau_{l}})\times\varphi_{l}, \tag{6}\]  
where \(\mathbf{h}^{\prime}_{\theta_{\tau_{l}}}(\mathbf{s}_{\tau_{l}})=\partial\mathbf{h}(\mathbf{s}_ {\tau_{l}})/\partial\theta_{\tau_{l}}\), \(\mathbf{h}^{\prime}_{\phi_{\tau_{l}}}(\mathbf{s}_{\tau_{l}})=\partial\mathbf{h}(\mathbf{s}_{ \tau_{l}})/\partial\phi_{\tau_{l}}\), \(\vartheta_{l}=\theta_{l}-\theta_{\tau_{l}}\), and \(\varphi_{l}=\phi_{l}-\phi_{\tau_{l}}\). Extending Equation (6), we construct the parameterized dictionary as follows:  
\[\mathbf{H}(\mathbf{\vartheta},\mathbf{\varphi})=\mathbf{H}+\mathbf{H}_{\mathbf{\theta}}\text{diag}( \mathbf{\vartheta})+\mathbf{H}_{\mathbf{\phi}}\text{diag}(\mathbf{\varphi}), \tag{7}\]  
where  
\[\mathbf{H}_{\mathbf{\theta}}=[\mathbf{h}^{\prime}_{\theta_{1}}(\mathbf{s}_{1}), \dots,\mathbf{h}^{\prime}_{\theta_{\theta^{\prime}}}(\mathbf{s}_{R^{\prime}})],\] \[\mathbf{H}_{\mathbf{\phi}}=[\mathbf{h}^{\prime}_{\phi_{1}}(\mathbf{s}_{1}),\dots, \mathbf{h}^{\prime}_{\phi_{R^{\prime}}}(\mathbf{s}_{R^{\prime}})],\] \[\mathbf{\vartheta}=[\theta_{1},\dots,\vartheta_{R^{\prime}}]^{\mathsf{ T}},\] \[\mathbf{\varphi}=[\varphi_{1},\dots,\varphi_{R^{\prime}}]^{\mathsf{ T}}.\]  
The problem in Equation (2) is reformulated as  
\[\mathbf{\psi}=\mathbf{H}(\mathbf{\vartheta},\mathbf{\varphi})\mathbf{w}. \tag{8}\]  
The task is to estimate the PWAs \(\mathbf{w}\) and unknown parameters \(\mathbf{\vartheta}\) and \(\mathbf{\varphi}\). This problem can be solved using a sparse Bayesian framework in a probabilistic manner.
## 3 Digital Modulation Classification  
Automatically recognizing the modulation technique of a detected signal, the midway step between signal detection and demodulation, is the main task of an intelligent receiver. Clearly, with no information of the transmitted data and many unknown parameters at the receiver, such as the signal power, carrier frequency, phase offsets, and timing information, etc., a blind identification of the modulation format is a challenging task. This becomes even more challenging in real-world situation [30].  
Recognition of the modulation type of an unknown signal offers valuable insight into its structure, source, and properties. If the modulation type of an intercepted signal is extracted, jamming can be carried out more accurately by focusing all resources into essential signal parameters.  
Modulation is the process of varying a periodic waveform, i.e., a tone, in order to use that signal to convey a message. The most essential digital modulation techniques are Amplitude Shift Keying (ASK), Frequency Shift Keying (FSK), Phase Shift Keying (PSK), and Quadrature-Amplitude Modulation (QAM). Modulation recognition is an intermediate step on the path to complete message recovery. Therefore, correct retrieval of the message per se is not an objective or even a requirement [31; 32].  
Early on it was known that modulation classification is, first and foremost, a classification problem well-suited to pattern recognition algorithms. A successful statistical classification needs the correct set of features extracted from the unidentified signal. This is where the use of classification algorithms such as AdaBoost arises. The proposed model in this paper is shown in Figure 2.  
The system is built up as follows:  
1. Generate random input data.
2. Modulate the input data using BPSK or QPSK (coherent and noncoherent).
3. Add AWGN to the modulated data.
4. Transmit the modulated data through a Rayleigh fading channel.
5. Demodulate the noisy received data using BPSK or QPSK (coherent and noncoherent).
6. Feed the demodulated noisy signal into the AdaBoost algorithm.
7. Calculate the BER.
8. Plot the results of the BER vs. signal-to-noise ratio (SNR).  
Figure 2: Flowchart of the proposed system. AWGN = additive white Gaussian noise; BER = bit error rate; SNR = signal-to-noise ratio.
## 3 Materials and Methods
### Methods Used for Interface Quality Testing of T1 DCoad Application
#### 3.3.2 Eye-Tracking Research  
Eye-tracking research allows determination of which elements attract users' attention, for how long and in what order. These tests are non-invasive and are carried out in conditions similar to the normal use of a computer or smartphone. Research conducted using this technique uses eye trackers, i.e., devices for recording, measuring and analyzing eye movements, attention, blinks and changes in pupil diameter over time. The main measures of attention are fixations and saccades. Fixations are moments of stopping (focusing) the eyes on a specific point lasting approximately 100-300 ms. During fixation, visual information is downloaded to the brain. In turn, saccades are rapid eye movements lasting from 20 to 80 ms, associated with shifting attention to another place. Three to five saccades occur in one second. When the eyes move, no visual information is transmitted to the brain.  
Fixation sequences (and their durations) create scan paths. Scan paths are visualized in the form of circles of various sizes connected by lines representing the route followed by a given user's attention while performing actions in the application interface. It has been observed that the course of scanning paths may be influenced by the tasks performed by the experiment participant and various individual factors, such as gender, knowledge or user experience [40; 41]. This type of visualization is sometimes used to check the effectiveness of user searches--longer scan paths may be interpreted as less effective information search [42].  
Due to the fact that the tasks performed are goal-oriented, various quantitative metrics are used in eye-tracking research. These are:  
* execution time of individual scenario tasks and the entire scenario,
* the average number and average duration of fixations and the average number of blinks while performing individual scenario tasks (indicating cognitive load);
* success rate of completing individual tasks of the scenario and the entire scenario (as %).