esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated Bone Age Assessment with Image Registration Using Hand X-ray Images
## 3 X-ray Image Dataset  
The dataset used to validate the automated bone age prediction system is obtained from the Radiological Society of North America (RSNA) Pediatric Bone Age Machine Learning Challenge [22]. The total number of hand X-ray images is 14,236, which has been arranged into three classes of training, validation, and testing sets with 12,611, 1425, and 200 images for each respective class. All of the X-ray images are stored in the Portable Network Graphic format, where the highest resolution is 2460 \(\times\) 2970 pixels, while the lowest resolution is 800 \(\times\) 1011 pixels. The dataset was collected from two children hospitals, which are Children's Hospital Colorado and Lucile Packard Children's Hospital in the United States of America. The age range for the dataset starts from one month old until 228 months old. The number of male samples is slightly higher as compared to the female samples where male samples make up around 54.13% out of the total samples. Gender information is annotated for each of the X-ray image samples, but we omit this information in our research, so that a more general regressor can be developed. Six medical practitioners have been tasked to annotate the ground truth age, where the weighted mean is used to calculate the final age values. This RSNA dataset poses various challenges for an automated system because of the heterogeneous contrast values and non-standard pose images, as shown in Figure 2. Furthermore, some sample images are also much smaller as compared to the others, as the size is not normalized beforehand.  
Because our proposed method utilizes image registration to standardize the X-ray images, the original validation dataset will be omitted as it is used to create the training set for our hand segmentation and four keypoints detector networks. Thus, the total number of X-ray images that are utilized in our experiment is reduced to 12,811 images, which is then divided into three classes of training, validation, and testing sets according to the ratio of 8:1:1, respectively. The original 200 testing images, as proposed by the RSNA challenge, are not ideal for performance comparison, as it is too small when compared to the total number of images in the training dataset.
## 5 Experiments and Analysis
### Performance Analysis
#### 5.4.2 Performance Variation of Algorithms Under Varying Transmission Bandwidths  
As depicted in Figure 7, the weighted delay and energy consumption of the Edge-only, MADDPG, and MATD3-TORA algorithms exhibited a decreasing trend as the transmission bandwidth increased from 1 MHz to 5 MHz. Notably, the Local-only algorithm is unaffected by changes in transmission bandwidth, since it does not involve task offloading via communication channels and was thus excluded from this analysis. The observed decline in the other three algorithms is attributed to the enhanced transmission rates resulting from increased bandwidth, which reduces the time required to transfer computational tasks to the UAVs, thereby lowering the weighted delay and energy consumption. This trend was particularly pronounced in the Edge-only algorithm, as it relies entirely on task offloading to the UAVs, making its performance more sensitive to variations in transmission rates. In contrast, the MATD3-TORA and MADDPG algorithms, through effective exploration, achieved optimal offloading ratios across different bandwidths. This adaptability is facilitated by the centralized training and decentralized execution mechanism of MADDPG, enabling these algorithms to operate efficiently in large-scale, high-dimensional, and dynamically changing environments. Consequently, MATD3-TORA and MADDPG were less susceptible to the significant performance fluctuations observed in the Edge-only algorithm when transmission rates varied. This analysis highlights the robustness of MATD3-TORA and MADDPG in optimizing task offloading under varying transmission bandwidths, demonstrating their superior adaptability and efficiency compared to the Edge-only approach.  
As seen in Figure 8, it can be found that when subjected to different transmission bandwidths, MATD3-TORA outperformed both MAPPO and QMIX in maintaining efficient task offloading and network stability. The algorithm's integrated channel-state awareness and distributed critic networks enable intelligent adaptation to bandwidth fluctuations, optimizing both communication and computation resources simultaneously. MAPPO's performance varies substantially due to its sensitivity to partial observability in dynamic channel conditions, while QMIX's centralized mixing network becomes a bottleneck under limited bandwidth. MATD3-TORA's novel federated learning-inspired update scheme  
Figure 6: The performance variation of algorithms (MATD3-TORA, MAPPO, and QMIX) under varying total task volumes.  
ensures robust decision making even in constrained bandwidth scenarios, preventing the performance collapse observed in baseline methods. This bandwidth-agnostic characteristic makes MATD3-TORA particularly suitable for UAV deployments where communication resources may vary unpredictably.  
Figure 8: The performance variation of algorithms (MATD3-TORA, MAPPO, and QMIX) under varying transmission bandwidths.  
Figure 7: The performance variation of algorithms (MATD3-TORA, Edge-only, and MADDPG) under varying transmission bandwidths.
## 3 Multipath Multimedia Dynamic Source Routing (MMDSR)
### Basics of the General Framework  
The multipath routing scheme of MMDSR is based on the DSR (Dynamic Source Routing) protocol [12] to find available paths from source to destination. Video is distributed using RTP/RTCP (Real-time Transport Protocol/Real-time Control Protocol) [29] over UDP as transport protocols. Our system uses a layered MPEG-2 VBR coding of the video flow, which is formed by sets of frames, usually 4 to 20 frames, called GoP (Groups of Pictures), see Figure 1. A GoP has three types of frames: I, P and B, and has a unique frame-pattern in a video repeated in each GoP. I (Intra) frames encode spatial redundancy, they form the base layer, provide a basic video quality and carry the most important information for the decoding process at the receiving side. The whole GoP would be lost if the corresponding I frame were not available at decoding time. P (Predicted) and B (Bi-directional) frames carry differential information from preceding (P) or preceding and posterior (B) frames, respectively. Considering these characteristics, we assign different priorities to the video frames according to their importance within the video flow. Therefore, I frames should have the highest priority, P frames the medium priority and B frames the lowest one.  
In the MAC (Media Access Control) layer, we use the IEEE 802.11e [28] standard, which provides QoS support. It consists of four different Access Categories (AC), each with their own configuration parameters, _i.e._ contention window minimum (\(CW_{min}\)) and maximum (\(CW_{max}\)), arbitration interframe space (AIFS) and transmission opportunity (TxOp). Each packet from the higher layers arrives at the MAC layer with a specific priority value and is mapped into the proper AC. We defined the mapping of the different packets into each one of the four \(AC_{s}\) as follows:  
* AC0: signaling.
* AC1: high priority packets (I frames).
* AC2: medium priority packets (P frames).
* AC3: low priority packets (B frames + other best effort traffic).
## 8 Comparison between Centralized MPC and the Genetic Algorithm Approach
In the previously presented results, in the MPC algorithm the future weather conditions are assumed to be known in order to obtain reliable estimation of the future output and the control variable. Therefore, in order to obtain a fair comparison between the MPC and the GA approach, the GA algorithm has been applied again to the system but, in this case, the future weather conditions are assumed to be known. The test is carried out by considering, again, December 10. The results related to the rooms 5 and 7 are presented in Figures 17 and 18. The energy consumption of each subsystem and of the total building is shown in Table 10. As it is possible to see the performance of the GA approach are comparable to the MPC. If both of them take into account the future weather conditions in the optimization process, the energy saving is less than the 1%. Figure 17: One day simulation, 10 December, for room 5 (ARMAX model). **Top**: user defined set point \(\hat{T}_{i}\) (red line), optimized set-point ramp signal \(\hat{T}_{i}\) (blue line), temperature evolution \(T_{i}\) in case of genetic algorithm set-point signal (green line), temperature evolution \(T_{i}\) in case of MPC (light blue line). **Bottom**: resulting inlet water flow in case of ramp set-point signal (blue line) and in case of Model Predictive Control (MPC) (green line). \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Room**} & **GA Performance Index** & **MPC Performance Index** \\  & **Value (4) [kg/h]** & **Value (4) [kg/h]** \\ \hline
1 & 2837 & 2954 \\
2 & 2111 & 2047 \\
3 & 1898 & 1979 \\
4 & 2441 & 2328 \\
5 & 2249 & 2020 \\
6 & 10,821 & 10,641 \\
7 & 6760 & 6937 \\ Total (0) & 29,117 & 28,905 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance index value (4) for each room when both the genetic-based optimization and Model Predictive Control (MPC) are applied to the system. In this case, the genetic algorithms (GA) optimization is based on the weather conditions of the day after (10 December), as the MPC. Figure 18: One day simulation, 10 December, for room 7 (ARMAX model). **Top**: user defined set point \(\hat{T}_{i}\) (red line), optimized set-point ramp signal \(\hat{T}_{i}\) (blue line), temperature evolution \(T_{i}\) in case of genetic algorithm set-point signal (green line), temperature evolution \(T_{i}\) in case of MPC (light blue line). **Bottom**: resulting inlet water flow in case of ramp set-point signal (blue line) and in case of MPC (green line).
# Enhancing Far-Field Speech Recognition with Mixer: A Novel Data Augmentation Approach
## 4 Experimental Setup  
### Datasets  
The DiPCo [30] dataset is an open speech dataset, designed to simulate a family dinner party scenario, addressing the challenges of speech separation and recognition in multi-speaker environments. The dataset was recorded by Amazon employee volunteers engaged in natural conversations within everyday household settings. Specifically, it includes distant speech captured by 5 far-field devices, each equipped with a 7-mic circular array, totaling 35 microphones. As the DiPCo dataset is one of the benchmark datasets in the CHiME-challenge series, it i used to evaluate far-field speech recognition performance across multiple scenarios [11, 31].  
The dataset was initially preprocessed using the Weighted Prediction Error (WPE) [32] method to enhance the signals for each utterance. After WPE processing, the multi-channel audio from each array was transformed into single-channel audio using the array-based BeamformIt [33] algorithm. Consequently, the multi-channel audio from multiple array devices was consolidated into a single multi-channel audio stream, with the number of channels corresponding to the number of far-field devices.  
We utilized the development set for training and evaluation, and the test set for testing purposes. Subsequently, we filtered the audio and label lengths, excluding any audio segments shorter than 3 s or longer than 30 s. For the text content, we filtered out transcripts with fewer than 3 words or more than 100 words.
## 2 Related Work
### Spatiotemporal Meteorological Forecasting
(2018) proposed a multi-adversarial spatiotemporal graph neural network (main GNN), which jointly models air quality and weather forecasts, and uses heterogeneous recursive graph neural networks to capture the complex spatiotemporal dependencies between multiple types of monitoring sites to achieve multi-task collaborative optimization. [PERSON] et al. [PERSON] et al. (2018) analyzed the current state of graph neural networks in the field of short- to medium-term weather forecasting and categorized the methods based on different types of datasets. Deep learning methods such as graph neural networks have greatly enhanced the modeling of spatial and temporal dependencies in meteorological data. However, the conventional pairwise graph structure often struggles to fully capture the intricate and high-order interactions among multiple meteorological stations. This limitation becomes particularly evident in the forecasting of short-term, high-impact weather events where complex spatial correlations are critical. Therefore, there remains an urgent need for more expressive and flexible frameworks that can better represent and propagate these multi-station dependencies, ultimately improving the robustness and accuracy of meteorological forecasts.
# Aerospace
## 4 Single Small Satellite to Mars Orbit as Secondary Payload
### Small Satellite Approach Trajectory and Atmospheric Entry State  
The hyperbolic approach trajectory of the aerocapture vehicle until the atmospheric entry interface is computed using the analytic method developed by [PERSON] (1993). To target a near-equatorial orbit, the angle \(\psi\) is chosen to be \(3\pi/2\), which results in a 1.65 deg. inclination orbit. The choice of \(r_{p}\) determines the entry flight-path angle (EFPA) and must be selected so that the EFPA falls within the aero capture entry corridor. An approach trajectory must be first computed to obtain the atmospheric entry state vector, which is then used to compute the aerocapture entry corridor. This iterative process is repeated until an acceptable approach trajectory that delivers the vehicle to the entry interface within the aero capture corridor is found. An approach trajectory with \(r_{p}=(3389.5+52)\) km results in a planet-relative EFPA = \(-9.25\) deg. at the atmospheric interface (\(h\) = 120 km) and will be shown to fall within the aero capture entry corridor in Section 4.5. Figure 6 shows the approach trajectory of the vehicle until the atmospheric entry interface. Table 1 lists the nominal atmospheric entry conditions for the aerocapture vehicle at Mars.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Entry altitude, km & 120 \\ Entry longitude 1, deg. & \(-89.76\) \\ Entry latitude, deg. & \(-0.71\) \\ Atm. relative entry speed, km/s & 5.36 \\ Atm. relative heading angle 2, deg. & \(9.38\) \\ Atm. relative EFPA, deg. & \(-9.25\) \\ \hline \hline \end{tabular} \({}^{\dagger}\) Body-inertial frame following [PERSON] (1993). 2 Following the definition by [PERSON] (1993).  
\end{table}
Table 1: Mars aero capture vehicle atmospheric entry state.  
Figure 5: Mars drag-modulation aerocapture feasibility chart with \(\beta_{1}\) = 20 kg/m\({}^{2}\), \(R_{N}\) = 0.235 m, target apoapsis = 2000 km. The star indicates the selected baseline \(\beta_{2}/\beta_{1}\) = 7.5 and \(V_{\infty}\) = 2.65 km/s.
## 4 Data-Driven Prediction Model
### Deep Learning
#### 4.3.2 Time-Series
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Authors(s)** & **Objective** & **Dataset** & **Feature** & **Methods** & \begin{tabular}{c} **Relevant Find-** \\ **ings/Performances** \\ \end{tabular} & \begin{tabular}{c} **Factor Category \({}^{*}\)** \\ \end{tabular} \\ \hline \multirow{4}{*}{[141]} & Wheat yield prediction & Sentinel-2 image data & \begin{tabular}{c} Minimum and maximum \\ temperature, integrated solar \\ radiation, cumulative \\ precipitation, soil texture, soil \\ chemical parameters, \\ hydrological properties \\ \end{tabular} & LSTM & \begin{tabular}{c} MRE (Mean relative \\ error) = 9.70\% \\ \end{tabular} & / \\ \hline \multirow{4}{*}{[99]} & CFO price prediction & \begin{tabular}{c} One hundred \\ thirty-six units of \\ data monthly \\ prices \\ \end{tabular} & Temperature, rainfall, radiation & \begin{tabular}{c} LSTM (Long \\ short-term memory) \\ \end{tabular} & RMSE = 280.463 & / \\ \hline \multirow{4}{*}{[142]} & CFO price prediction & Eighty-one complete sets of monthly observations & Monthly prices, total imports, and exports & \begin{tabular}{c} ARIMA \\ (Autoregressive \\ Integrated Moving \\ Average) \\ \end{tabular} & RMSE = 293,016.94 & \multirow{4}{*}{/} \\  & & & & \begin{tabular}{c} ARAR \\ (Autoregressive \\ Autoregressive) \\ \end{tabular} & RMSE = 19,161.84 & \multirow{4}{*}{/} \\  & & & & \begin{tabular}{c} ARIMA \\ (Autoregressive \\ Fractional \\ Moving Average) \\ \end{tabular} & RMSE = 43,333.98 & \multirow{4}{*}{/} \\ \hline \multirow{4}{*}{[143]} & Adverse side-effects & GPS device of plot & Temperature, wind, drought, and soil erosion & Time series & 95\% confidence interval & / \\  & prediction & & & & \\ \hline \multirow{4}{*}{[144]} & Age and biophysical & \
\begin{tabular}{c} MODIS (Moderate \\ Resolution Imaging \\ Spectroamiodimeter) \\ \end{tabular} & Vegetative and yield &
\begin{tabular}{c} Time-series-CART \\ (Classification And \\ Regression Tree) \\ \end{tabular} & RMSE = 4.7 & / \\ \cline{1-1}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Time-series prediction of crops and palm oil.
# Communication-Traffic-Assisted Mining and Exploitation of Buffer Overflow Vulnerabilities in ADASs
## 4 Practical Results and Physical World Threats  
This section applied the communication-traffic-assisted BOF vulnerability mining and exploitation method to over ten commercial ADAS devices. As a result, we successfully mined BOF vulnerabilities in two of them. Taking one of the commercial devices connected to a minibus as a case study, we elaborated on the details from firmware extraction to vulnerability exploitation based on the communication-traffic-assisted BOF vulnerability mining and exploitation method. Moreover, we designed an advanced exploit leveraging the communication traffic of the CAN protocol, demonstrating the disruptions in regular operations of the vehicle by exploiting the BOF vulnerability in ADAS devices and thereby confirming the effectiveness of our method.
## 4 Results and Discussion
### Flexural Tests of IR- Samples  
Data from the three-point bending tests on the IR- samples are shown in Figure 10a,b. Contrary to expectations, the flexural strengths showed no extensive distinction among all staple inserting methods. All samples failed into two parts immediately after reaching the highest load, with a photograph of a crack shown in Figure 11a, and the stress curves dropped when reaching the maximum value, as shown in Figure 10b.  
The results can explain that the short staples scattered without overlap and functioned separately by locking a narrow cement inside its pin bounds, where there were gaps around the pins in the composite. First, the staple was too short (a length of 11 mm) to reinforce a large amount of composite, while embedded steel cables improved flexural strength by 290%, inserted corrugated fibers improved the strength by 130% [42], and steel mesh achieved 170-290% improvement [43]. Second, in references [14; 16; 17; 18; 19; 20; 21; 44; 45], short fibers were mixed before the printing. These mixed fibers were distributed close, near, overlapped, and aligned [46], then functioned together to improve the toughness by 63% [47]. However, in this study, the staples were scattered in the composite and did not overlap much, and thus functioned separately. Third, there were gaps around the staple pins, as shown in Figure 11b, and the bonding was much lower than that of the fibers mixed in the mortar.  
Figure 11: Failure of the IR- samples: (**a**) photograph of the broken IR-3 and its location, and (**b**) photograph of the broken IR-7 and its location with gaps in red circles.  
Figure 10: Data of the three-point bending tests on the IR- series samples. (**a**) Flexural strengths and (**b**) flexural stresses of typical testing samples, with time axis offsets.  
Printed outputs with high staple density would require a long time interval between the fabrication of two adjacent layers, causing a decrease of composite interfacial bonding. Further discussion about the inserting equipment is given in Section 4.7.
## 1 Introduction  
LaFe\({}_{13-x}\)Si\({}_{x}\) compounds with a NaZn\({}_{13}\)-type crystal structure (denoted as 1:13 phase) showed first-order magnetic phase transitions at a Curie temperature (\(T_{C}\)), which induced a giant magnetocaloric effect (MCE) when LaFe\({}_{13-x}\)Si\({}_{x}\) compounds underwent a change from a ferromagnetic state to a paramagnetic state at \(T_{C}\)[1]. Additionally, the peak value of the magnetic entropy change (\(\Delta S_{M}\)) with temperature variation broadened to just above \(T_{C}\) for LaFe\({}_{13-x}\)Si\({}_{x}\) compounds because of the itinerant-electron magnetic transition [2]. These features make LaFe\({}_{13-x}\)Si\({}_{x}\) compounds ideal refrigerants for magnetic refrigeration technology [3; 4]. However, LaFe\({}_{13-x}\)Si\({}_{x}\) compounds display large magnetic and thermal hysteresis due to the first-order nature of their transitions. The magnetic hysteresis decreases the actual efficiency of magnetic refrigeration [5]. Thermal hysteresis would also make the adiabatic temperature change (\(\Delta T_{\rm ad}\)) unstable as the refrigeration cycle runs [6]. Thus, the developments of the refrigerants with a large magnetic entropy change and small hysteresis losses are still the key for practical applications [7; 8]. Partial substitution of Fe by Co in LaFe\({}_{13-x}\)Si\({}_{x}\) compounds could completely eliminate hysteresis and thermal hysteresis. However, the order of the magnetic phase transition changed from first to second-order type, and the magnetic entropy change decreased greatly near a Curie temperature by Co additions [9; 10]. Grinding the compounds into micron scale particles could also reduce the hysteresis [11], but too small particles were easily oxidized because the LaFe\({}_{13-x}\)Si\({}_{x}\) compounds consisted of the rare earth La. In addition, the addition of carbon in LaFe\({}_{13-x}\)Si\({}_{x}\) systems effectively led to a reduction in magnetic hysteresis loss [12].  
On the other hand, the influence of different element additions in LaFe\({}_{13-x}\)Si\({}_{x}\) compounds on phase structure and MCE have also been extensively studied to optimize the MCEs of LaFe\({}_{13-x}\)Si\({}_{x}\) compounds. Ni, Cr, and Nb additions had limited solubility in the 1:13 phase. Higher contents of the additions caused a large residual of impure phases after annealing, and a decrease of magnetic entropy change [13; 14]. The substitution of rare earth Ce and Pr for La resulted in the enhancement of the magnetic entropy change. Ce and Pr additions had higher solubility in the 1:13 phase compared with the additions of Ni, Cr, and Mn [15; 16]. Until now, little knowledge on the partial substitution of La by other rare earth elements with no orbital angular momentum contribution, like Y, in the LaFe\({}_{13-x}\)Si\({}_{x}\) compounds has been reported.  
In the present work, the effect of a partial substitution of Y for La in LaFe\({}_{11.5}\)Si\({}_{1.5}\) on the phase composition and magnetocaloric effect are investigated.