esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method to Obtain the Transducers Impulse Response (TIR) in Photoacoustic Imaging
## 4 Tests and Discussion
### Comparison with Classical Wiener Filtering  
Finally, the method used in this paper is compared with the image before deblurring and Wiener filtering, and the reconstructed image as shown below is obtained. Figure 10a-c show the images reconstructed by the OPAS affected by TIR (reconstructed using time-reversal algorithm (TRA)), respectively, and the deconvolution algorithm in this paper obtains the reconstructed images after PAPS and the reconstructed images processed using Wiener filtering. As can be seen from Figure 10a, although the shape of the blood vessels can be seen in general, there are many interfering factors in the image. We can feel the effect of TIR on the image quality because there are many artefacts around the image, and it is difficult to see the details of the blood vessels in the image. As can be seen in Figure 10c, the Wiener filter image has eliminated some of the impulse responses, and the complete image of the vascular model can be seen, but the details in the image are still difficult to discern. In Figure 10b, the image obtained by the method used in this paper has a great improvement in sharpness compared to Wiener filtering. There is a significant reduction in artefacts around the image, with clearer details of the vessel model and sharper edges, indicating improved image resolution.  
Figure 10d shows the curve from the 22 nd to the 44 th pixel of the data projected by the No. 1 transducer for a more intuitive comparison. Through the comparison of the images, it can be seen that the pixel values of the method used in this paper are closer to the original images than those of the Wiener filter. In contrast, the pixel values of the Wiener filter are slightly deviated from the original image, resulting in blurring of the edges. This proves  
Figure 10: (**a**) The OPAS image is reconstructed using TRA. (**b**) A reconstructed image of the PAPS. (**c**) The image reconstructed by the Wiener filter algorithm. (**d**) Comparison of the pixel values of the two algorithms with the original image.  
that the problem of poor image quality due to TIR interference is significantly improved by the algorithm used in this paper. The algorithm can be used to solve the TIR of the transducer, thereby eliminating its influence on image clarity and improving image quality.
# Effects of the October 2024 Storm over the Global Ionosphere
## 2 Data and Methods
For improved spatial resolution and reduced noise, the data are gridded at 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) in geographic latitude and longitude and smoothed using a 5 \(\times\) 5 grid boxcar averaging method. This enhances the spatial continuity of the ionospheric features while preserving significant perturbation signatures associated with LSTIDs. Although the previously discussed global dTEC maps provide comprehensive coverage of ionospheric perturbations, their spatial resolution and data density are notably limited over the Southern Hemisphere, particularly in the Asian-Australian longitude sector. To address this gap and enable a more detailed investigation of ionospheric irregularities in this region, we utilized ROTI data from the Ina-CORS GNSS network, available through the BRIN Ionospheric Map Service ([[https://gattokaca.brin.go.id/petaionoser/ionosphericmap/roti_map/](https://gattokaca.brin.go.id/petaionoser/ionosphericmap/roti_map/)]([https://gattokaca.brin.go.id/petaionoser/ionosphericmap/roti_map/](https://gattokaca.brin.go.id/petaionoser/ionosphericmap/roti_map/)) accessed on 20 June 2025) for the period spanning 15:00 UT on 10 October to 20:00 UT on 11 October. The Ina-CORS network comprises approximately 300 GNSS receivers distributed across the Indonesian archipelago, covering a longitudinal range from 95\({}^{\circ}\)E to 140\({}^{\circ}\)E and a latitudinal extent from 5\({}^{\circ}\)N to 10\({}^{\circ}\)S. These stations record GNSS observations at a 30 s cadence, with data stored in the RINEX format. TEC values derived from these observations are used to compute the ROTI, which is defined as the standard deviation of the TEC rate of change over a 5 min sliding window. This metric is a well-established proxy for identifying ionospheric irregularities, particularly those associated with EPBs, which occur at spatial scales of a few kilometres. For visualization, ROTI values are mapped at an Ionospheric Pierce Point (IPP) altitude of 350 km, generating two-dimensional latitude-longitude grids at a resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\). To enhance spatial coherence and reduce noise, a 5 \(\times\) 5 boxcar averaging filter is applied across the grid. ROTI maps are generated at 10 min intervals between 09:00 and 23:50 UT, offering high temporal resolution snapshots of ionospheric activity over Indonesia. The ROTI map grid consists of a 241 \(\times\) 161 matrix, encompassing a geographic area bounded by longitudes 90\({}^{\circ}\)E to 150\({}^{\circ}\)E and latitudes \(-\)20\({}^{\circ}\)N to 20\({}^{\circ}\)N. Missing data points are denoted by \"NaN\" values.
## 4 Proposed Deep Filtration Learning Model
### Problem Definition  
In the suggested deep filtering-learning model scenario, the UWSN consists of numerous clusters governed by a fusion centre. The fusion centre collects all data obtained from network devices and transmits it to the back-end system. It connected the intended underwater wireless sensor network to positioning systems via IoT within various ponds in fish farms. Farming sensors were designed to meet the International Resource Identifiers System (IRIS) criteria and were powered by a communication M64a transceiver manufactured by Water Linked AS (Trondheim, Norway). An ESP8266-01 Wi-Fi module manufactured by Espressif Systems (Shanghai, China). And the Underwater GPS G2 positioning system manufactured by Topcon Positioning Systems (Livermore, CA, USA). In addition, these sensors are distributed in nine clusters throughout one pond. Within a WSN, the cluster consists of one head node and five sensor nodes, which are spread randomly. Furthermore, each sensor node in the cluster has an initial power source, a processor unit, a memory, an RF transceiver, and sensor devices. The sensor nodes' primary transmission mode is one-to-many broadcast. The transmission range of each sensor node is fixed. The sensor node saves the sensed data locally in its memory before sending it to its neighbours and the cluster head.  
In the proposed method, all cluster nodes have the same sensing radius and are aware of the distances between them and the fusion centre. The proposed method is based on the genetic algorithm (GA) in the clustering method, which is used to establish the optimal network structure that minimizes energy exhaustion after each transmission round. During the optimization phase, each GA chromosome serves as a cluster head identification map. A gene in a chromosome determines whether the relevant node serves as a cluster head. Given a cluster head, node clusters are generated using the closest neighbour criterion, and the fitness of a chromosomal WSN structure is calculated by evaluating all clusters. This method greatly extends the network lifetime by balancing the energy consumption among all sensor nodes in the WSN. In general, the UWSN experiences substantial communication problems as a result of its short lifespan, which is caused by a variety of issues such as insufficient communication overhead and sensor malfunctions during deployment operations due to harsh environments. Defects in data dissemination, caused by missing and noisy data, result in network misbehaviour and incredibility. In this paper, a predictive learning model is developed to address the deployment issues and energy challenges in the designated network. The suggested methodology considerably reduces the pace of data transmission for the UWSN by forecasting future data and missing measurements and then reducing the noise associated with the sent signals, improving energy efficiency. The following subsection demonstrates a description of the employed network architecture.
## 4 Experiment Results
### The Performance of Object Localization and Tracking
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Target** \\ **Coordinate** \\ \end{tabular} } & \multirow{2}{*}{**Axis**} & \multirow{2}{*}{**LRL**} & \multirow{2}{*}{**LRL**} & \multirow{2}{*}{**LEF**} \\ \cline{2-2} \cline{5-5}  & & & & \\ \hline \multirow{2}{*}{\begin{tabular}{c} **Target** \\ **Coordinate** \\ \end{tabular} } & \multirow{2}{*}{**Axis**} & \multirow{2}{*}{**LRL**} & \multirow{2}{*}{**LRL**} & \multirow{2}{*}{**LRL**} \\ \cline{2-2} \cline{5-5}  & & & & \\ \hline \multirow{2}{*}{\begin{tabular}{c} **(14, 37)** \\ \end{tabular} } & \multirow{2}{*}{**y**} & 3.514 & 0.498 & 0.143 \\ \cline{2-2} \cline{5-5}  & & & 0.995 & 0.968 & 0.216 \\ \cline{2-2} \cline{5-5}  & & & 0.521 & 0.430 & 0.222 \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} **(14, 41)** \\ \end{tabular} } & \multirow{2}{*}{**x**} & 1.734 & 1.036 & 0.574 \\ \cline{2-2} \cline{5-5}  & & & 0.5203 & 0.448 & 0.292 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} **(18, 41)** \\ \end{tabular} } & \multirow{2}{*}{**x**} & 1.449 & 1.178 & 0.718 \\ \cline{2-2} \cline{5-5}  & & & 0.4146 & 0.530 & 0.246 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Case 2—Standard deviations of target coordinates (\(X\), \(Y\)) for human index finger (unit: cm). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Target** \\ **Coordinate** \\ \end{tabular} } & \multirow{2}{*}{**Axis**} & \multirow{2}{*}{**LRL**} & \multirow{2}{*}{**LRL**} & \multirow{2}{*}{**LRL**} \\ \cline{2-2} \cline{5-5}  & & & & \\ \hline \multirow{2}{*}{\begin{tabular}{c} **(14, 37)** \\ \end{tabular} } & \multirow{2}{*}{**y**} & 2.028 & 0.264 & 0.085 \\ \cline{2-2} \cline{5-5}  & & & 0.584 & 0.500 & 0.187 \\ \cline{2-2} \cline{5-5}  & & & 3.130 & 0.334 & 0.154 \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} **(14, 41)** \\ \end{tabular} } & \multirow{2}{*}{**y**} & 0.313 & 0.259 & 0.103 \\ \cline{2-2} \cline{5-5}  & & & 1.282 & 0.158 & 0.063 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} **(18, 41)** \\ \end{tabular} } & \multirow{2}{*}{**x**} & 0.567 & 0.903 & 0.206 \\ \cline{2-2} \cline{5-5}  & & & 1.405 & 0.301 & 0.049 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Case 1—Standard deviations of target coordinates (\(X\), \(Y\)) for the marker pen (unit: cm). standard deviations of the EKF method are very small. The smaller the standard deviation, the better the stability of the position estimation. Therefore, although there is a difference between the real trajectories and the estimated trajectories, the difference can be easily compensated by offset calibration because of the good stability of position estimation. Figure 12: Case 3-1: Target moves from (8, 40) to (20, 40), the velocity in the \(x\)-direction is about 1 cm/s. (Blue dots: the estimated coordinates of moving target; red line: the reference target trajectory.) Figure 13: Case 3-2: Target moves from (8, 36) to (20, 48), the velocity of the target is about \(\sqrt{2}\) cm/s. (Blue dots: the estimated coordinates of moving target; red line: the reference target trajectory.)
# The Performance Study of a Novel Vertical Variable Stiffness Disc Spring Seismic Isolation Bearing
## 5 Numerical Simulation of VVSDSI Bearing
### Boundary Conditions and Loading Configuration  
The lower connecting plate is fixed as the boundary condition, while the upper connecting plate is loaded using a displacement-controlled method. To prevent lateral movement of the disc spring during the loading process, horizontal constraints are applied at two symmetrical points on its lower edge.
# Spatiotemporal Mechanical Effects of Framework-Slope Systems Under Frost Heave Conditions
## 5 Freeze-Thaw Effects on Frames and Slopes
### Analysis of Soil Arch Effect  
During the reinforcement of the slope using a frame-anchor system, horizontal and vertical beams are orthogonally arranged along the slope surface, forming a series of quadrilateral frame units. Within these structures, horizontal soil arching develops between adjacent vertical beams, while vertical soil arching forms between adjacent horizontal  
Figure 11: Slope monitoring points. Variations in lateral frost heave pressure and ice content: (**a**) variation in lateral frost heave pressure at slope monitoring points; (**b**) ice content–stress relationship.  
beams. This section focuses on the evolution of vertical soil arching under frost heave conditions. A conceptual model of the soil arching mechanism is shown in Figure 12.  
As illustrated in Figure 13a, during the initial phase of frost heave, vertical soil arches begin to form. With decreasing temperature, ice crystals nucleate near the surface and gradually grow, causing localized volume expansion. Ice lens development initiates, and frost heave forces are transmitted through the soil arch to the surrounding frame structure. As the freezing front progresses deeper, ice lenses enlarge and intensify the frost heave pressure, resulting in increased arch height and the development of a more complete stress arch structure (Figure 13b). Once the soil becomes fully frozen, the formation of new ice lenses ceases and frost heave forces stabilize. At this stage, the soil arch reaches its maximum height and adopts a steady-state configuration (Figure 13c).  
Figure 12: Conceptual model of horizontal and vertical soil arching under a frame-anchor support system.  
Figure 13: Evolution of soil arching morphology during frost heave (unit: cm): (**a**) initial frost heave phase; (**b**) mid-period frost heave; (**c**) fully frozen stage.
## 4 Results and Discussion  
### Summary Statistics and Correlation Matrix  
The results of the summary statistics and correlation matrix are interpreted in Table 2. The results show that all variables are normally distributed, which is indicated by the Jarque-Bera statistics and probability values. The correlation analysis indicates that there is an existing positive correlation between CO\({}_{2}\) emission and cropped area, energy usage, fertilizer offtake, GDP per capita, improved seed distribution and total food grains.  
None of the variables got integration in the order of I(2) by indicating the results of the Augmented Dickey-Fuller and Phillips-Perron unit root tests; therefore an ARDL model was employed.
# Channel-Hopping Using Reinforcement Learning for Rendezvous in Asymmetric Cognitive Radio Networks
## 3 Methodology  
The reinforcement learning framework is designed to reflect the system model described in the previous section. Initially, each SU is assumed to have access to \(M\) channels. Channels currently occupied by PUs are considered unavailable, and the remaining channels are partially shared among different SUs. Within this reinforcement learning framework, each SU selects a channel, which is interpreted as an action by the environment. For the purpose of this study, the environment is modeled with only two SUs. The channels (actions) selected by each SU are then compared, and a corresponding reward is generated based on this comparison. In Figure 3, the previously explained reinforcement learning framework is illustrated simply. Each SU has its own agent, and whether a rendezvous with the other SU has occurred can be determined through the environment's reward. The state and reward obtained from the environment will be discussed later.
# A Lightweight and Practical Anonymous Authentication Protocol Based on Bit-Self-Test PUF
## 4 Formal Security Analysis
### Probable Security Threats and Prospective Attacks  
The insecure communication channels between the device and the server make the PUF-based authentication protocol that applies to RFID systems vulnerable to the following threats and attacks on security.  
_Tracking Attack:_ The attacker can trace the device by obtaining its ID through an insecure radio frequency channel and thus know the position and operation of the user. To secure against tracking attacks, the device must be anonymous throughout the authentication process.  
_Forward Security Threats:_ If an attacker is somehow able to obtain secret information about the device, then the attacker will be able to trace all communications between the device and the server. Therefore, the PUF used in the protocols should not only have good uniqueness, reliability, and randomness but should also have good resistance to physical attacks and machine learning, to ensure that the attacker cannot obtain the secret information generated in real time by the PUF in the device at the time of authentication, thus ensuring the backward untraceability of the protocol.  
_DoS and Desynchronization Attacks:_ By blocking the information between the device and the server, the attacker could cause desynchronization problems. In many RFID-based authentication protocols, both the server and the device need to update their secret information to ensure forward secrecy. Therefore, when the response message from the server or device side is blocked, the other side cannot receive the message to update the authentication information. This situation will result in one side updating the key and the other side failing to update the key, which leads to desynchronization.  
_Impersonation Attacks_: The attacker intercepts information between the device and the server and reuses this information to impersonate a legitimate device or server to create separate connections with each end of the communication and exchange the data it receives. In this way, the attacker can make both ends of the communication believe they are talking directly to each other over a private connection, when in fact the entire session is fully controlled by the attacker.  
_Physical Attack and Cloning Attacks_: Attackers compromise the chip and access its memory through a variety of attacks. For example, they can reverse-engineer the chip by opening the chip package and using optical images to reconstruct the layout, or they can probe the signals on the chip bus using micro-probes without destroying the chip, or using the side-channel attack to probe the chip contents directly from the outside. Moreover, since the chip in most devices is not tamper-proof, an attacker can build a cloned chip used for authentication by eavesdropping on communication channel information and physical attacks, which causes the device to be authenticated by the server as a legitimate device.
## 2 Materials and Methods
### Characterization of the CTS-MBs, aCTS-MBs and bacCTS-MBs  
The particle sizes of all prepared CTS-MB materials were measured using a Zetasizer Nano-ZS (Malvern Instruments, Malvern, UK) with 100 randomly selected CTS-MBs from each preparation. The CTS-MB1, aCTS-MB1, bacCTS-MB1 and _P. putida_ cell samples were dried using vacuum freeze-drying procedures. These samples were mixed with KBr, pressed into tablets and subjected to FT-IR spectral analyses using an FT-IR spectrometer (Spectrum One; PerkinElmer, Waltham, MA, USA). All infrared spectra were recorded within the 480-4000 cm\({}^{-1}\) spectral range. For the SEM observations of aCTS-MB1, bacCTS-MB1 and _P. putida_ cell morphology, a small amount of each prepared sample was dehydrated using a series of gradient ethanol solutions (40%, 70%, 90% and 100%); subsequently, the samples were vacuum dried, gold coated using a sputter coater and then observed under a JSM-6390/LV SEM (NTC, Tokyo, Japan) following the manufacturer's instructions.
## 4 Discussion and Conclusions
This setting was fixed in accordance with the expected minimum size of the detectable lesion with the first prototype of Wavelia and in order to avoid extreme degradation of the overall specificity of the system. Detection of a single abnormality zone, which remained persistently contrasted against the surrounding breast tissues, was targeted with the applied MBI imaging algorithm and morphological lesion detector, at this preliminary stage of development, and in the context of the FiH feasibility study of Havelia. Patients with bilateral breast disease were excluded and the focus was on the detection of a single (the largest) cyst, in the case of patients with multiple cysts in their breasts. This constraint is planned to be loosened in future clinical investigations, while evolving towards more realistic and generalized subsequent phases of the clinical evaluation of the Wavelia QIF. The MBI scans were performed at least two weeks following the biopsy, in the case of Group-1 (invasive carcinoma) and Group-3 (benign biopsied lesion) patients. The two-week time lapse was considered sufficient to allow healing of the biopsy site in the breast. It is noteworthy though that, in most of these patients, a metallic biopsy clip was placed in their breast, as standard-of-care practice, to mark the tumor site. The size of the biopsy clip was small (\(\sim\)3 mm) compared to the targeted tumor sizes in this study (all palpable lumps), therefore its impact on the MBI images and the associated breast lesion detectability and characterization results were not considered to be significant. However, as the impact of the presence of a biopsy clip has not been characterized so far, future clinical investigations enabling the MBI examination to be performed prior to biopsy will be needed to investigate the impact of the biopsy clip. In this FiH study, there were only two Group-1 patients (P008, P043--both ILC's) and one Group-3 patient (P017--fibroadenoma) with no biopsy clip placed in their breast prior to the MBI examination and positive MBI findings nevertheless. Quantitative evaluation, by means of a computable confidence level, is also planned to be implemented in subsequent versions of the Wavelia QIF, such that the imaging system performance can be assessed both in terms of lesion detectability rate and detection confidence level, in the case of various lesion types (solid/liquid, mass-like/non-mass-like, malignant/benign) and different breast density levels. Other factors, such as the breast size, the location of the lesion in the breast (superficial/deep, distance to the chest wall), the size of the lesion, the patient's age and breast deformability in the scanner, will also be investigated in terms of achievable lesion detectability rate and detection confidence level, as well as malignant-to-benign lesion separability. The assessment of these factors will be only feasible at a pivotal clinical investigation stage, and after sufficient stabilization of the Wavelia MBI scanning system and the associated QIF.