esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Resolution and Semantic-Aware Bidirectional Adapter for Multi-Scale Object Detection
## 4 Experiments
### Performance Comparison
#### 4.3.3 Comparison on Transformer-Based Method  
We further substantiate the generalizability of MSBA across transformer-based methods. As indicated in Table 8, we undertake relevant experiments encompassing both single-stage and two-stage detectors for both tasks. Our MSBA approach yields improvements of 1.2 and 0.9 points in the detection task when applied to pvt-tiny and swin-tiny methods, respectively. Moreover, even employing the same techniques, such as extended training schedules and multi-scale training, MSBA continues to demonstrate effectiveness and superiority when utilized with the more potent Swin-Small backbone, resulting in a 0.5-point enhancement over the baseline method. Due to the extensive multi-scale represen  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Backbone** & **MSBA** & \(\mathbf{AP^{b}}\) & \(\mathbf{AP^{b}_{5}}\) & \(\mathbf{AP^{m}}\) & \(\mathbf{AP^{m}_{L}}\) \\ \hline \multirow{4}{*}{Mask R-CNN} & \multirow{4}{*}{R50} & \multirow{4}{*}{\(\surd\)} & 38.2 & 21.9 & 34.7 & 47.2 \\  & & & 39.6 & 22.9 & 35.8 & 52.5 \\  & & & (**+1.4**) & (**+1.0**) & (**+1.1**) & (**+5.3**) \\ \cline{2-7}  & \multirow{4}{*}{R101} & \multirow{4}{*}{\(\surd\)} & 40.0 & 22.6 & 36.1 & 49.5 \\  & & & 41.7 & 24.2 & 37.3 & 54.5 \\  & & & (**+1.7**) & (**+1.6**) & (**+1.2**) & (**+5.0**) \\ \hline \multirow{4}{*}{Cascade Mask R-CNN} & \multirow{4}{*}{R50} & \multirow{4}{*}{\(\surd\)} & 41.2 & 23.9 & 35.9 & 49.3 \\  & & & 43.0 & 25.1 & 37.3 & 54.5 \\ \cline{1-1}  & & & (**+1.8**) & (**+1.2**) & (**+1.4**) & (**+5.2**) \\ \cline{1-1} \cline{2-7}  & \multirow{4}{*}{R101} & \multirow{4}{*}{\(\surd\)} & 42.9 & 24.4 & 37.3 & 51.5 \\  & & & 44.0 & 25.2 & 38.3 & 56.0 \\  & & & (**+1.1**) & (**+0.8**) & (**+1.0**) & (**+4.5**) \\ \hline \multirow{4}{*}{HTC} & \multirow{4}{*}{R50} & \multirow{4}{*}{\(\surd\)} & 42.3 & 23.7 & 37.4 & 51.7 \\  & & & 43.9 & 25.6 & 38.8 & 56.7 \\ \cline{1-1} \cline{2-7}  & & & (**+1.6**) & (**+1.9**) & (**+1.4**) & (**+5.0**) \\ \cline{1-1} \cline{2-7}  & \multirow{4}{*}{R101} & \multirow{4}{*}{\(\surd\)} & 44.8 & 25.7 & 39.6 & 55.0 \\  & & & 45.7 & 27.0 & 40.2 & 59.2 \\ \cline{1-1}  & & & (**+0.9**) & (**+1.3**) & (**+0.6**) & (**+4.2**) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Instance Segmentation: Performance comparisons with powerful instance segmentation methodologies. All baseline approaches incorporate FPN. The \(\dagger\) denotes the models trained with longer training schedules.  
Figure 5: Example pairs of instance segmentation results. (**Top row**) The results are from Mask R-CNN with FPN. (**Bottom row**) our MSBA method significantly enhances the instance classification performance and effectively mitigates duplicate bounding boxes within densely populated regions, as demonstrated qualitatively.  
tation facilitated by MSBA, the performance improvement for small objects in the detection task is particularly notable.
## 3 Problem Statement and Proposed Solution  
Although the studies do not point out explicitly, it is observed that the most significant difficulty encountered by digital forensics professionals, with regard to the acquisition of data from mobile devices, are the lock screens configured by the user. Such a restriction prevents the acquisition of data by the extensive list of forensic tools usually employed, with different models and manufacturers.  
It corroborates the statement that Cellebrite (Cellebrite DI is an Israeli company founded in 1999 and headquartered in Petah Tikva, which manufactures data extraction and data analysis devices from mobile devices, mass storage devices, and drones. The company is a subsidiary of Sun Corporation, Japan. Cellebrite DI has two subsidiaries, Cellebrite USA Corp. and Cellebrite GmbH, based in Parsippany, New Jersey, USA and Munich, Germany, respectively.) [50] already has 11 laboratories around the world, one of which is installed in Brazil, specialized in advanced unlocking and extraction services using UFED Ultimate [51]. The service offered by these laboratories is called Cellebrite Advanced Service (CAS), whose main objective is to unlock the device by breaking the password by brute force attack. Such a technique can be used in Apple devices with iOS, Samsung, Huawei, and LG devices [52] with an approximate cost of $ 2500.00 (two thousand and five hundred dollars) per unlock.  
Another company offering a similar solution is Gravshift, which has developed a product called GrayKey [53], intended exclusively for unlocking iPhones. In addition to the removal or bypass of the lock screen, it is also necessary to make the physical acquisition of the device data, to have access to the content deleted by the user and that of instant messengers, such as WhatsApp, without contaminating the evidence and allowing the analysis by tools specialized, commercial or not.  
In some cases, even with the device unlocked, it may not be possible to perform the physical acquisition, being necessary to enable the _root_ user on the device to do so. In some models of Android devices, during the procedure performed to _root_ the device, the internal memory is safely erased, which is not feasible from a forensic point of view.  
In considering these aspects, this section describes the problem addressed in this article and its proposed solution. This solution will result in bypassing the lock screen, rooting the device (if necessary) without the risk of losing or contaminating the user's data, subsequent data acquisition through a forensic tool for mobile devices, and analyzing the acquired data.
# PAR: Towards a Reference Architecture for Accessible Platforms in Respiratory Therapies
## 3 Results
### Usability Evaluation
The average of the usability test results is 72.56%, which falls within the \"Acceptable\" range according to the SUS scale; see Figure 10.  
5. Results Phase: Figure 11 displays the average analysis results for each question, along with their corresponding CSUQ and SUS scores obtained from the usability evaluation of the \"PAR\" platform. The data from both groups were analyzed, and it was concluded that most survey questions achieved over 70% acceptance with a standard deviation of 0.298, indicating consistency in responses between the two groups. The highest scores were recorded for \"CSUQ5--Easy to learn\", \"CSUQ11--Effective information\", and \"CSUQ2--Easy to Use\". The questions with the lowest acceptance rates were \"CSUQ7--Error messages\" and \"CSUQ8--Error recovery\", scoring 55.3% and 60.9% on average, respectively [32]. Figure 11: Usability evaluation results per question. Figure 10: Usability test results. Conclusions: The usability evaluation of both groups resulted in an average score of 72.56%, placing it within the \"Acceptable\" range on the SUS scale, as illustrated in Figure 10. The outcomes of the inquiries suggest that participants found the \"PAR\" educational platform to be a usable system. Incorporating web accessibility considerations into the usability evaluation aligns with the principles of the Sustainable Development Goals (SDGs). Specifically, SDG 3 prioritizes achieving healthy lives and well-being, representing a fundamental aspect of sustainable development. Correspondingly, SDG 4, focusing on quality education, underscores the importance of ensuring equal access to information and educational resources, irrespective of individual abilities. Furthermore, SDG 10 centers on reducing inequalities by promoting equal access to information and services for all. The integration of web accessibility, as emphasized in the usability evaluation, actively contributes to the overarching goal of sustainable development. By fostering inclusion and participation through accessible digital platforms, it plays a crucial role in creating a more sustainable, equitable, and interconnected global environment. The integration of the iPlus methodology is transparent, ensuring a user-centered design approach. Treatment access is facilitated through the platform's user-friendly interface, allowing individuals to navigate and engage with respiratory therapies more efficiently. This approach aims to overcome barriers related to complex interfaces, contributing to broader accessibility.
## 3 Results, Analysis, and Discussions
### Experimental Dam-Break Test of the IBS Frame
#### 3.2.3 IBS Frame Test 3 for 3 m Reservoir Water Level  
The third serial test of the 2D frame was conducted using 3 m impounded water depth, and the test lasted for about 17 s. In the first 7 s of the test, the frame structure deflected backward because of the high pressure from hydrodynamic forces which flowed against the  
Figure 17: Damages of the frame after the Test 2.  
Figure 18: Graph of 2 m reservoir water level test for the frame.  
structure. Figure 19a shows the front side of the structure during the test, while Figure 19b presents the back at the time of deflection. The severely damaged walls are presented in Figure 19c; the walls were damaged because of the hydrodynamic force that impacted the frame. The height of the water that impacted the structure was about 70 cm. The total volume of the water that caused the damage to the structure was around 5.52 m\({}^{3}\).  
Figure 19: Dam-break test 3 for 3 m reservoir water level: (**a**) front of the frame during the test; (**b**) back of the frame during the test; (**c**) damage to the frame after test 3; (**d**) sketch of the damages.  
Eight pressure meters (PMs) were attached to the frame's front surface to measure the applied hydrodynamic force on the structure. One pressure meter (PM9) was attached to the column's bolt to measure the structure's deflection. PM9 recorded the data of the through-bolt vibration when the structure deflected during the test. After completing the test, PM9 measured the highest value among the PMs because of the high deflections of the structure. The highest value (62.34 kN) was recorded by PM9 after 7 s, followed by PM4, with a value of 34.06 kN after 7 s. Figure 20 illustrates the graph of the results.
# A Replica-Selection Algorithm Based on Transmission Completion Time Estimation in ICN
###### Abstract  
As the Internet communication model changes from host-centric to content-centric, information-centric networking (ICN) as a new network architecture has received increasing attention. There are often multiple replicas of content in ICN, and how to reasonably utilize the characteristics of multiple replicas to further improve user experience is an important issue. In this paper, we propose a replica-selection algorithm, called the transmission completion time estimation (TCTE) algorithm. TCTE maintains the state of replica nodes in the domain with passive measurements in a limited domain of an enhanced name resolution system (ENRS), then estimates the transmission completion time of different replica nodes and selects the smallest one. When no replica is found in the ENRS domain, the nearest-replica algorithm will be used, so TCTE will not increase the traffic in the core network. Experiments show that TCTE not only effectively improves the user's download rate and edge node throughput, reduces download rate fluctuations, reduces user download delay, and improves fairness, but also has universal applicability.  
information-centric networking; replica selection; name resolution +
Footnote †: journal:  
0023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Human Appropriation of Net Primary Production: From a Planet to a Pixel
## 2 Background and Literature Review  
In the 1990s, [PERSON] and [PERSON] developed the ecological footprint concept as the area of biologically productive land and ocean that is required to continuously provide the resources consumed by a group of people (such as a country), and to process their wastes [5]. According to their analysis, the average human used 6 to 7 acres; since the Earth contains 5 \"productive\" acres per capita, a figure that declines with population growth, humans were overshooting planetary limits. They identified substantial variation due to consumption differences, with, for example, the average person in India using 2 acres, China 3, Germany 13, Canada 19, and the U.S. 25. For countries such as the U.S. that were exceeding domestic carrying capacity, the difference had to be made up through imports from lands abroad, or by utilizing domestic natural capital unsustainably. This ecological footprint framework also enabled the analysis of temporal trends, and the partitioning of ecological footprints into their components, such as diet, raw materials, energy needs (including carbon absorption), land development, and land contaminated by nuclear weapons and power development.  
As an important innovation in its time, these comparisons yielded insights and illustrated relevant temporal and geographical trends; they were fraught, however, with conceptual weaknesses and scientific imprecision. A generic, geographically unidentified acre of \"biologically productive land\" was the currency through which the ecological footprint was measured, yet the biological productivity of ecosystems varies enormously. Resource consumption that was not inherently land-based had to be converted to acreage using questionable stoichiometries. For example, how many acres are committed when a ton of coal is surface mined, burned in a power plant, and emits carbon dioxide that must either be sequestered or allowed to change the climate?
## 2 Background and Related Works
### Related Works
The three types of features they considered for each window--time-domain features, frequency-domain features, and Wavelet features--led to the conclusion that time-domain features offered the most effective recognition. Additionally, they solely used accelerometer data because the gyroscope data comparison did not show a significant improvement. They compared Dynamic Time Warping (DTW), DTW-feat, SVM, and RBF-SVM for classification purposes. The trials were performed utilizing a 5-fold cross-validation for each performance individually (PD) and leave-one-out for the entire dataset (PI). They discovered that SVMs constructed using the suggested dataset have more significant generalization potential than DTW. As a result, SVM can compete with DTW regarding processing speed and accuracy [37]. With this evidence, it emerged that besides the great interest in using modern enabling technologies in sports, only a few have preliminary attempted to apply them to fencing. We propose a combination of wearable sensors and ML algorithms to classify the fencer'scategory into elite or novice, thereby allowing feedback to the fencer and coach and an improved training strategy.
# Research on Sparse Representation Method of Acoustic Microimaging Signals
## 1 Introduction
Furthermore, most of the existing studies address the overlap of two echoes [8; 10; 12; 13], while there are few studies on the overlap of multiple echoes for electronic packages detection. In order to quantitatively discuss [14] the overlap of multiple echoes and effectively improve the longitudinal resolution, it is necessary not only to improve the matching between the atoms in the dictionary and the echoes before overlap [32], but also to improve the accuracy of the decomposition and reconstruction algorithm [33]. In this paper, the AMI signal model and sparse representation theory are reviewed, and a constraint dictionary-based sparse representation (CD-SR) method is proposed to achieve super-resolution and high robustness in Section 2. In Section 3, a simulation model of the electronic package is developed to quantitatively test and compare the performance of the traditional SR method and the CD-SR method. Experimental results for 20 MHz and 200 MHz are given in Section 4. We conclude in Section 5. Figure 1: Scanning acoustic microscopy of electronic packages: (**a**) common structure of electronic packages; (**b**) acoustic microimaging (AMI) signal.
# Phased Antenna-Array Synthesis Using Taylor-Series Expansion and Neural Networks
## 5 Neural Networks for Synthesis and Optimization of Antenna Arrays
### Taylor-Neural Network Architectures
ReLU activation was applied to all hidden layers, while the output layer used a linear activation function. The network inputs include initial Taylor-based excitation values and a steering angle, and the outputs correspond to optimized excitation weights. The model was trained using the Adam optimizer with a learning rate of \(10^{-3}\) and mean squared error loss. A dataset of 1000 samples was synthetically generated by varying array parameters and desired pattern characteristics. A validation set of 200 samples was used, and training was stopped after 74 epochs based on the convergence of validation loss. In the hybrid Taylor-neural network architecture, the neural network is integrated with the phase distribution generated via the Taylor method. The Taylor method provides an initial phase distribution, \(\Phi_{\text{Taylor}}(\theta)\), which is then refined and optimized by the neural network. Let the phase distribution generated via the Taylor method be denoted as \(\Phi_{\text{Taylor}}(\theta)\), which serves as an input to the neural network:  
\[\Phi_{\text{opt}}(\theta)=f(\Phi_{\text{Taylor}}(\theta);\mathbf{W},b) \tag{17}\]  
The neural network adjusts the phase distribution by fine-tuning the excitation parameters or array configurations to achieve the desired radiation pattern while satisfying multiple constraints. This could include minimizing sidelobes, improving directivity, or optimizing element positions. The training process involves optimizing the weights \(\mathbf{W}\) and bias \(b\) to minimize the loss function, typically defined as the squared error between the desired output \(\Phi_{\text{desired}}(\theta)\) and the network's output \(\Phi_{\text{opt}}(\theta)\):  
\[L(\mathbf{W},b)=\sum_{i=1}^{N}\left|\Phi_{\text{opt},i}(\theta)-\Phi_{\text{ desired},i}(\theta)\right|^{2} \tag{18}\]  
where \(N\) is the number of training samples.
# Characterization of Sorghum Processed through Dry Heat Treatment and Milling
## 3 Results and Discussion
### Characterization of the Optimal Samples
#### 3.2.2 Color and Molecular Characteristics
that could lead to the increase in unsaturated carbonyl groups, the degradation of amino acids to aldehydes and their condensation with carbohydrate fractions, furfurals, and other species to set up chromophores and off-flavors [14]. For M and S fractions, no significant changes between optimal and control samples were observed regarding peak occurrence (Figure 5b,c). Peak intensities exhibited differences among fractions, the smallest values being obtained for L fraction. The band  
Figure 5: FT-IR spectra of control and optimal sorghum flour with: (a) L, (b) M, (c) S fraction. appeared at around 3328-3342 cm\({}^{-1}\) suggesting the O-H bond stretching from starch and protein-starch interaction and also from water molecules, alcohol, phenolic compounds, or carboxylic acid, while the modification in peak intensity indicates the degree of formation of hydrogen bonds (inter- and intramolecular) [54]. The lower intensities of peaks at 3328-3342 cm\({}^{-1}\) for all the optimal samples compared to the corresponding controls could lead to a rise in dehydroxylation reactions upon dry heat treatment [14]. The modification of this band position towards a lower peak intensity suggests an enhancement in the bond strength caused by the interaction within starch molecules and starch with other compounds [55]. The bands observed at about 1156, 1081, and 865 cm\({}^{-1}\) for all the studied samples suggest the presence of alcohol (C-O), anhydrides (C=O), and alkane (C-H) groups, respectively [54]. The modification in the intensities of these bands in the optimal samples could be related to the deformation of angular C-H bond, the vibration of--1-4 glycosidic bonds (C-O-C) and C-O linkages in the esters (developed between the -COOH and O-H group of protein and starch, respectively), and could have led to the appearance of new functional groups [14; 54]. The amide I structure study revealed that \(\alpha\)-helix/\(\beta\) sheet ratio was higher in Optimal S than Control S, while an opposite trend was observed for L and M fractions (Table 7). All the studied samples showed a predominant \(\alpha\)-helix protein conformation that increased with fraction size decrease and was reduced after heat treatment of sorghum grains. This means that heat treatment promoted the rise in \(\beta\)-sheet formation, which could indicate protein aggregation [56]. Regarding starch structure, the crystalline areas were more abundant compared to the amorphous ones in all samples and increased after dry heat treatment. Higher crystalline/amorphous ratios were obtained for treated samples than controls, except for L fraction (Table 7), indicating the increase in crystalline molecular structures, a similar trend being reported by [PERSON] et al. [57] for wheat starch and flour treated at temperatures up to 150 \({}^{\circ}\)C. The loss of water molecules during treatment seems to have played an essential role in the reorganization and recrystallization of starch chains.
## 4 Experiments  
### Datasets  
We trained our proposed CriSALAD model on the GSV-Cities dataset [43], which is a large-scale VPR dataset containing images taken with Google Street View Time Machine. With the images collected from 40 major cities across the world, the dataset covers about 67,000 places spanning 2000 km\({}^{2}\) geographically and 14 years in time. In addition, it provides a wide variety of viewpoints, illumination changes, weather conditions, etc. For evaluation, we followed the recent literature [19, 23, 34] and adopted several widely used benchmark datasets for visual place recognition, including Pitts30k [44], Tokyo24/7 [45], Nordland [46], and Mapillary Street-Level Sequences (MSLS) [47].  
Pitts30k is a smaller subset of Pitts250k [44], which is a large-scale dataset collected in Pittsburgh, encompassing urban environments with significant viewpoint variations. It contains approximately 30,000 database images. Moreover, Tokyo24/7 [45], which is also an urban environment dataset containing thousands of day-time and night-time images taken in the city of Tokyo, was used to evaluate the model's robustness against illumination changes. In contrast, Nordland [46] was designed for long-term VPR evaluation. It comprises approximately 1 million images captured during a single train journey across northern Norway, spanning all four seasons, to assess place recognition under extreme seasonal variations. In particular, images from the winter sequence are designated as the query set, with the summer counterparts constituting the reference database. Finally, MSLS is one of the largest and most diverse street-level datasets for VPR tasks. It contains millions of images taken from over 30 cities across the world, covering different viewpoints, cities, times of day, and weather conditions.
## 3 Optimal Service Provisioning Framework
### Customized Mist-Edge-Cloud Framework
#### 3.1.1 Perception Layer  
The lowest layer, consisting of medical sensors enabled by IoT, such as heart rate monitors, pulse oximeters, ECG patches, and ambient condition sensors, is responsible for data acquisition. These sensors operate on patient bedsides, ICUs or remote home care settings, collecting vital signs and contextual information in real time. The captured data are forwarded to the mist layer for low-latency processing.  
Figure 2: Architecture of the ML-RASPF framework for optimal service provisioning.