esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results  
### Strain-Mode Rockburst Phenomenon  
The strainmode rockburst simulation experiment employed a GigaView high-speed camera to observe and document the rockburst process. Granite samples AG2 and AG3 were selected for the study to analyze rockburst behavior and to conclude the observed phenomena and experimental results. The observed strain-mode rockburst phenomena are illustrated in Figure 4. From Figure 4, it is clear that strain-mode rockbursts occur in distinct phases, with each phase becoming shorter and more intense as the process unfolds.  
(1) The stage of stress accumulation and elastic deformation (Stage I). In the initial stage, the surface of the granite sample is marked into zones, and at this point, the surface remains smooth and intact. At this point, the rock is under elastic deformation, with stress gradually accumulating. Microcracks are starting to appear in localized areas, but they have not grown enough to undermine the rock's overall stability.  
(2) The stage of crack initiation and stress concentration (Stage II). As the load increases, small particles begin to eject from the free surface. Rock fragments spall and eject from zones A through F at the top of the sample. At this stage, the rock gradually transitions into a plastic state, where macroscopic cracks increase in number and begin to propagate. The rock exhibits characteristics of nonlinear deformation.  
(3) The stage of critical instability (Stage III). Between 0.03 and 1.20 s later, large rock slabs of granite split and spall away from the free surface, covering zones A through  
Figure 4: The stage and progression of strain-mode rockburst. (**a**) The granite specimen AG2. (**b**) The granite specimen AG3. (I–V) represent the various stages of rockburst.  
L. At this stage, the applied stress approaches the ultimate strength of the rock. Cracks near the free surface rapidly propagate and coalesce, pushing the granite into a quasi-unstable condition.  
(4) The stage of rockburst occurrence (Stage IV). Then, in an instant (about 0.001 s), the detached rock slabs are violently flung outward at an extremely high speed, accompanied by small rock particles and clouds of rock dust. This sudden release is accompanied by intense explosion sounds, making the rockburst highly evident. At this stage, the elastic energy stored within the rock is suddenly released in the form of dynamic failure, resulting in the phenomenon known as a strain-mode rockburst.  
(5) The stage of residual stress adjustment (Stage V). Finally, in the subsequent frames, chunks of rock that had been split and sheared are shot outward, along with a large quantity of rock fragments and powders. The intensity of the rockburst is so extreme that it even dislodged the acoustic emission sensors that had been tightly fixed to the bottom of the granite sample. This stage marks the late phase of the rockburst, during which the stress within the rock undergoes readjustment and rebalance. While the overall residual stress diminishes, certain areas may develop new stress concentrations, which could even lead to secondary rockbursts.
## 2 Experiment Setup
### Data Collection Protocol  
During the experiments, subjects filled out voice and personality questionnaires and completed standard clinical acoustic and aerodynamic vocal function testing. Then, during study 1, the subjects received training by a certified speech-language pathologist to produce vowels with a pressed voice, as described in detail in [14]. In essence, the subjects were instructed to produce vowels while restricting airflow almost as if pushing out the sound. Performance was validated with airflow feedback using the Phonatory Aerodynamic System (Model 6600, KayPENTAX, Lincoln Park, NJ, USA). Then, a caliper (Lange Skinfeld Caliper, Beta Technology, Cambridge, MD, USA) was used to measure subjects' skin-fold thickness overlying the submental and infrahyoid muscle groups where electrodes were placed: three recordings per site were averaged. Figure 2 shows the electrode placement and the corresponding muscle groups.  
Figure 1: System setup for data collection showing the flow of signals through the equipment.  
Figure 2: Electrode placement used in this research: top pair of electrodes targeted the suprahyroid muscle group, and the bottom pair, the infrahyoid muscle group.  
Each subject produced three sets of syllables (uniform utterances); a series of normal and simulated pressed voice gestures (vowels); two sentences (for future relative fundamental frequency analysis similar to syllables, [16]); and two types of non-speech gestures (throat clear and cough). Finally, each subject was asked to press their chin on a dynamometer ([PERSON], Model LG-050 with curved compression fixture SPK-FMG-142, Ametek, Largo, FL, USA) to collect maximum voluntary contraction (100% MVC) and submaximal voluntary contraction data (50% MVC) with 1 min rest intervals between three trials per MVC condition. The tasks performed, which were ordered from least to most fatiguing, are detailed in Table 1. Subjects in study 2 did not complete the simulated pressed phonations and non-speech gestures. Each subject took approximately 1.5-2 h to complete the entire experiment, with the voice and speech production portions taking the longest (approximately 30-45 min) due to 55 repetitions.
# Optimized Parameter Search Approach for Weight Modification Attack Targeting Deep Learning Models
## 6 Conclusions and Future Work  
This work presents a new version of the weight modification attack. Moreover, it was proven that it successfully attacks the clusters formed by the model's classes. Both classes of images are misclassified, reducing their accuracy considerably. Notably, cluster one, which began with 100% accuracy, ended with 0% accuracy, while cluster zero maintained 100% accuracy. In the same way, attacking cluster zero, initial accuracy was successfully reduced to 2.7% while maintaining the other cluster's accuracy at 100%. Therefore, this investigation presents a successful new version of the fault injection attack, showing a possible threat that a deep learning model can contain.  
This paper presents the proof-of-concept in a model with two classes with a specific dataset. However, the presented attack could be implemented for a model with more classes or with other datasets. In the same way, it may be good to misclassify smaller subclusters to increase the focus of the attack, but this must be demonstrated in future work. These cluster-detection methods can continue to be investigated and analyzed to determine if the identified clusters can specialize the attack. Moreover, other strategies to define clusters can be presented and studied. For example, another method to detect those clusters could be via the model's behavior, focusing on activation of the neurons.  
The experiments in this paper only analyzed the sensitivity of the parameter \(k\). Future work to be considered includes analyzing the effects of modifying the rest of the attack configuration parameters. Furthermore, in the presented version, the attacker manually selects the layer used for the attack without further information about the vulnerability of the parameters in that layer. An initial cross-layer analysis could be formulated to optimize the success of the attack and guide the attacker to optimize layer selection.  
[PERSON] and [PERSON] designed and implemented the experimental testbed and algorithm. [PERSON] and [PERSON] supervised the experimental design and managed the project. [PERSON] and [PERSON] reviewed the new approach of this research. [PERSON] performed the experimental phase. All authors contributed to the writing and reviewing of the present manuscript. All authors read and agreed to the published version of the manuscript.  
This research has been partially funded by European Union's Horizon 2020 research and innovation programme project SPARTA and by the Basque Government under ELKARTEK project (LANTEGIA.0 KK-2020/00072).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 9: The projection of the feature vectors generated by the PCA algorithm: (**a**) represents the elements from \(c_{0}\) and (**b**) represents the elements from \(c_{1}\).
## 3 Results and Discussion  
### Description Analysis for Both Fuel Options  
The experiment was conducted on a practical ship while transoceanic in the Pacific Ocean. These ships in the ocean sealed back and forth for 18 months, from Asia to C.A. The investigation measures the two fuel options, HFOWD and VLSFO, to evaluate the FO consumption and the CO\({}_{2}\) emissions. As shown in Table 3, the main engine output power was recorded on board. The specific gravity of fuel after heating was calibrated by Equation (3). The fuel oil suppliers provided lower heating values (LHV). The main engine fuel consumptions were calculated according to Equation (2), and the CO\({}_{2}\) emissions were calculated from Equation (1).  
Though the two voyages (HFOWD and VLSFO) are the same type of container ship and on the same transpacific routing, one may still question the comparability. Therefore, it is essential to demonstrate the issue. From Equations (1) and (2), CO\({}_{2}\) emission was calculated from fuel consumption corresponding to the main engine output power. Thus, if the main engine output power of the two voyages is similar (no significant difference), the two voyages can be compared.  
Table 3 compares the navigation data acquired when the ships sailed from Asia to C.A. The mean values of the main engine output power are 59.37% and 58.54% of the \(p\) valve at 0.715, respectively. The statistic shows no significant difference in the output power between the two FO options. Similarly, while sailing from C.A. back to Asia, the mean values of the main engine output power pairs show the \(p\)-value at 0.776 (>0.05), indicating no significant difference. Therefore, we can assume that the two voyages are comparable in  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Voyage Route** & **Measurement** & **M (HFOWD)** & **M (VLSFO)** & \(t\) & \(p\) \\ \hline \multirow{8}{*}{Asia to C.A.} & Main engine output power (\%) & 59.37 & 58.54 & 0.368 & 0.715 \\  & Specific gravity of fuel after heating (mt/m\({}^{3}\)) & 0.94 & 0.933 & 4.795 & 0.000 \\  & Lower heating value (LHV) (kcal/kg) & 10,210 & 10,294 & \(-\)4.807 & 0.000 \\  & Main engine fuel consumption (ton/day) & 131.63 & 142.32 & \(-\)1.837 & 0.074 \\  & Main engine CO\({}_{2}\) emission (ton/day) & 435.18 & 474.8 & \(-\)2.084 & 0.044 \\ \hline \multirow{8}{*}{C.A. to Asia} & Main engine output power (\%) & 53.7 & 54.3 & \(-\)0.287 & 0.776 \\  & Specific gravity of fuel after heating (mt/m\({}^{3}\)) & 0.943 & 0.933 & 6.438 & 0.000 \\ \cline{1-1}  & Lower heating value (LHV) (kcal/kg) & 10,180 & 10,287 & \(-\)6.418 & 0.000 \\ \cline{1-1}  & Main engine fuel consumption (ton/day) & 128.05 & 140.09 & \(-\)2.223 & 0.031 \\ \cline{1-1}  & Main engine CO\({}_{2}\) emission (ton/day) & 421.98 & 467.019 & \(-\)2.564 & 0.014 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the parameters for HFOWD and VLSFO.  
discussing the CO\({}_{2}\) emission difference. The raw data the voyages provided are shown in Table S2.
# Multi-Feature Behavior Relationship for Multi-Behavior Recommendation
## 5 Experimental Results And Evaluation
### Overall Comparison (RQ1)  
Table 2 reports the analysis of the performance comparison results for MFBR, while the results for other approaches are shown in Table 2. Based on these results, we can reach the following conclusions.  
First, in terms of HR@10 and NDCG@10, MFBR outperformed all baseline methods on both datasets. For example, on the Taobao dataset, compared with MB-GMN, the performance indicators HR@10 and NDCG@10 were improved by 4.52% and 6%, respectively, for MFBR. On the ML-10M dataset, compared with MBGCN, the MFBR performance indicators HR@10 and NDCG@10 improved by 3.37% and 2.71%, respectively. This performance improvement could be attributed to the sequence encoder and the graph encoder simultaneously capturing user preferences from two aspects and modeling the dependencies between different behaviors. GNMR is based on graph neural networks that capture implicit behavioral dependencies automatically. At the same time, MFBR combines data features from both sequence and graph aspects to jointly capture multi-behavioral interaction patterns.  
The performance gap with the state-of-the-art models proves that incorporating multi-interaction features into the recommendation model can adequately model user interest representations.
###### Abstract  
A textile multi-band antenna for LTE and 5G communication services, composed by a rectangular microstrip patch, two concentric annular slots and a U-Shaped slot, is considered in this paper. In the ground plane, three sleeved meanders have been introduced to modify the surface current distribution, leading to a bandwidth improvement. The U-Shaped slot, the dual circular slots, and the meanders shape have been optimized by means of the Coral Reefs Optimization with Substrate Layer algorithm (CRO-SL). This population-based meta-heuristic approach is a kind of ensemble algorithm for optimization (multi-method), in which different search operators are considered within the algorithm. We show that the CRO-SL is able to obtain a robust multi-band textile antenna, including LTE and 5G frequency bands. For the optimization process, the CRO-SL is guided by means of a fitness function obtained after the antenna simulation by a specific simulation software for electromagnetic analysis in the high frequency range.  
antenna design; constrained optimization problems; coral reefs optimization algorithm; meta-heuristics +
Footnote †: journal: Applied Sciences
# Advancing the Solar Radiation Pressure Model for BeiDou-3 IGSO Satellites
## 2 Materials and Methods
### Box-Wing Model
From Equations (4) and (6), the partial derivatives of the acceleration w.r.t the optical properties of the satellite bus and panel surfaces can be obtained as [16]:  
\[\frac{\partial a_{b}}{\partial(\alpha_{i}+\delta_{i})}=-\frac{A_{i}}{M}\frac{ S_{0}}{c}\cos\theta_{i}(\overset{\to}{\epsilon}_{D}+\frac{2}{3}\overset{ \to}{\epsilon}_{N,i}) \tag{7}\]  
\[\frac{\partial a_{b}}{\partial\rho_{i}}=-\frac{A_{i}}{M}\frac{S_{0}}{c}2\cos^{ 2}\theta_{i}\cdot\overset{\to}{\epsilon}_{N,i} \tag{8}\]\[\frac{\partial a_{sp}}{\partial(1+\rho+\frac{3}{2}\delta)_{sp}}=-\frac{A_{sp}}{M} \frac{S_{0}}{c}\overset{\rightarrow}{e}_{D} \tag{9}\]  
Two non-optical parameters, i.e., \(SB\) and \(Y_{0}\), are considered in the ABW model to compensate for the potential misalignments of the solar panels around the \(Y\)-axis and constant accelerations along the \(Y\)-axis, respectively. The \(Y_{0}\) parameter of the ABW model is the same as that of the ECOM-type models. The partial derivatives of the acceleration w.r.t parameter \(SB\) can be obtained as [16]:  
\[\frac{\partial a_{sp}}{\partial SB}=-\frac{A_{sp}}{M}\frac{S_{0}}{c}2(\frac{ \delta_{sp}}{3}+\rho_{sp})sign(\frac{\bullet}{c})\overset{\rightarrow}{e}_{B} \tag{10}\]  
where \(\overset{\rightarrow}{e}_{B}\) is the unit vector of the \(B\)-axis of Sun-oriented \(DYB\) frame [8], and \(\varepsilon\) is Sun-spacecraft-Earth angle, which can be expressed as:  
\[\cos\varepsilon=\cos\beta\cos U \tag{11}\]  
where \(\beta\) is the sun elevation angle above the satellite orbital plane, and \(U\) is a geocentric orbit angle between the satellite and the midnight point in the orbital plane.
## 3 Experimental Test Program and Results  
An extensive experimental test program was executed to gather AE data during the corrosion process of RC components, ranging from small mortar samples to RC beams. AE monitoring was performed with 2D and 3D sensor setups to validate the AE post-processing and source localization protocol. This section presents the results of the first two scales: a small mortar sample and two RC prisms. In the next section, the setup and protocol are upscaled towards RC beams.
# Spatially-Precise Video Editing with Reference Imitation: A Region-Aware Cross-Modal Framework
## 1 Introduction
Last but not least, through extensive experiments and validations, it is proved that this method is superior to the existing methods in terms of the quality of fine local video editing, temporal consistency, and user satisfaction, providing strong support and impetus for the development of video editing technology in the direction of fine local editing. In practical applications, our approach meets critical demands in industries such as film post-production and e-commerce video creation. For instance, as shown in EffiVED [24], there is a growing need for tools enabling efficient video editing that balances accuracy and computational efficiency--especially for real-time interactive platforms. Our method directly addresses this challenge by enabling precise control over structural integrity and appearance changes. It builds on the objectives of recent studies such as the 2024 arXiv preprint \"Video Diffusion Models: A Survey\" [25], which emphasize the need for scalable solutions in complex editing scenarios.
###### Abstract  
Applications of remote sensing data for archaeology rely heavily on repurposed data, which carry inherent limitations in their suitability to help address archaeological questions. Through a case study framed around archaeological imperatives in a Scottish context, this work investigates the potential for existing satellite systems to provide remote sensing data that meet defined specifications for archaeological prospection, considering both spatial and temporal resolution, concluding that the availability of commercial data is currently insufficient. Tasking a commercial constellation of 12 spacecraft to collect images of a 150 km\({}^{2}\) region in Scotland through the month of July 2020 provided 26 images with less than 50% cloud cover. Following an analysis of existing systems, this paper presents a high-level mission architecture for a bespoke satellite system designed from an archaeological specification. This study focuses on orbit design and the number of spacecraft needed to meet the spatial and temporal resolution requirements for archaeological site detection and monitoring in a case study of Scotland, using existing imaging technology. By exploring what an ideal scenario might look like from a satellite mission planning perspective, this paper presents a simulation analysis that foregrounds archaeological imperatives and specifies a satellite constellation design on that basis. High-level design suggests that a system of eight 100 kg spacecraft in a 581 km altitude orbit could provide coverage at a desired temporal and spatial resolution of two-weekly revisit and \(<\)1 m ground sampling distance, respectively. The potential for such a system to be more widely applied in regions of similar latitude and climate is discussed.  
archaecological remote sensing; satellite mission design; satellite archaeology; archaeological survey 28 August 2020; Accepted: 11 December 2020; Published: 15 December 2020
## 4 Simulations and Results  
A test system has been devised in order to simulate the proposed scheme. We consider a small number of prosumers in order to keep the simulations simple. However, the proposed scheme is scalable i.e., the ideas and methodology presented here are equally applicable to a large system. The details of the test system and simulation results are presented in the subsequent subsections.
## 2 Data Collection
### The First Data Collection Campaign  
In the first data collection campaign, the users were requested to carry the phone along with them as they normally would during three to four weeks. Our software would then ask the users to annotate their environment (e.g., outdoors, street), activity (e.g., walking) and the location of the phone (e.g., pocket) at set intervals. Once the annotation was input the phone sensors would then record data for two minutes. The default time interval between recordings was 20 min, but could be set by the user to be from a minimum of 10 min to a maximum of 60 min. The annotations were tagged to the recorded sensor data by a file name convention. The user interface used by the users to provide the annotations is depicted in Figure 1. There was a fixed set of predefined environments, activities, and phone locations available but the users could also add their own if they thought none of them matched their own situation.  
* _Accelerometer_: The sampling rate of the 3-axis accelerometer was approximately 100 Hz. Data was collected for one minute.
* _GPS_: Three minutes of GPS data was collected with a 1 Hz sampling rate. The recorded data included position, velocity, satellite elevation and azimuth angles and signal to noise ratio.
* _WLAN_: WLAN signal strengths and WLAN station identifiers (IDs) and Media Access Control (MAC) addresses were recorded. Samples were collected for one minute and the sampling interval was approximately 12 s. Thus, five scans were performed during one minute.
* _Bluetooth_: A single scan of the Bluetooth environment was performed, and the names and addresses of the visible Bluetooth devices were logged.
* _GSM/3G Cell information_: Global System for Mobile Communications (GSM) or 3 rd generation mobile communications (3G) location area codes (LAC) and cell IDs were recorded
* _Audio_: One minute of audio with a single mono channel with 16-bit resolution at 16 kHz sampling rate was recorded.
# A Non-Fungible Token Solution for the Track and Trace of Pharmaceutical Supply Chain
## 2 Literature Review
### Blockchain  
According to [58], \"blockchain is a decentralized, immutable shared ledger that can be applied to a variety of business settings involving transaction processes\". Advantages of blockchain are speed, security, traceability, immutability, transparency, and accessibility of data provenance. Blocks are connected by cryptography; changing one block would cause a rework from the earliest to the latest transaction in blocks, as illustrated by [76]. This is an advantage but also a limit, because in the event of mistakes, it can be quite difficult to correct them. Other limits are generated by the interoperability among different blockchain networks and the issues of data privacy (people might refuse to have their data stored permanently on the blockchain). On the other hand, blockchain technology is an appropriate solution for several critical application domains such as healthcare, transportation, agriculture, education, forecasting, etc.  
In the pharmaceutical industry supply chain, due to cryptography, traceability can improve and mitigate the issues given by counterfeit drugs [77]. This last matter is the objective of this paper and is discussed in detail in the next sections. In [58], a decentralized solution for the tracking and tracing of the pharmaceutical drugs along the supply chain is presented. This solution is based on the Ethereum blockchain, which is the most famous public permissionless blockchain. Due to this great feature, the Ethereum blockchain is used and abused (as it is at the basis of tons of applications), and this decreases its performance while increasing the transactions costs. For this reason, the Ethereum blockchain is not the most appropriate for a supply chain application and stakeholders should look for a different blockchain platform. As it will be shown in the next sections, the VeChain Thor blockchain, a public permissioned blockchain, will be examined.