esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments
### Andro-Dumpsys Dataset  
We used the Andro-Dumpsys dataset obtained from [49], which has been widely used for malware detection. The original dataset consists of 906 malicious binary files from 13 malware families. As illustrated in Table 2, the number of malware variants and the total number of samples from different malware families varied. Almost half of the malware families had no more than 25 malware samples, while some only had 1 sample, as they were most likely the new malware detected lately (e.g., Blocal and Newbak). In addition to the original dataset, we also generated three additional synthetic malware variants, each of which was applied using the different control flow obfuscation techniques described earlier: function logic shuffling; junk code insertion; and function splitting, respectively. Two samples from each additional malware variant, a total of 6 additional samples for each malware family, were added to the original dataset.  
Figure 8 illustrates a snippet of how obfuscated malware is created by applying a junk code insertion. In this example, we created a dummy array that acts as a junk code, which we added in between two function calls from the original malware code. We applied a similar approach to the other two types of control flow obfuscation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Family** & **Number of Variants** & **Number of Samples** \\  & & **(+3 Synthetic)** & **(+6 Synthetic)** \\ \hline
1 & Agent & 39 (42) & 150 (156) \\
2 & Blocal & 1 (4) & 1 (7) \\
3 & Climap & 1 (4) & 5 (11) \\
4 & Fakeguard & 1 (4) & 10 (16) \\
5 & Fech & 1 (4) & 3 (9) \\
6 & Gepew & 4 (7) & 112 (118) \\
7 & Gidix & 6 (9) & 108 (114) \\
8 & Helir & 1 (4) & 15 (21) \\
9 & Newbak & 1 (4) & 1 (7) \\
10 & Recal & 2 (5) & 25 (31) \\
11 & SmFow & 23 (26) & 166 (172) \\
12 & Tebak & 10 (13) & 93 (99) \\
13 & Wroba & 23 (26) & 108 (114) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Andro-Dumpsys Dataset with synthesis samples.  
Figure 8: Example of inserting a junk code.  
We also increased the image sample size, so as to have at least 30 samples for every malware family, using a data augmentation technique (e.g., applying random transformations, such as image rotations, re-scaling, and clipping the images horizontally). The details of the augmentation parameters are shown in Table 3. In particular, ZCA whitening is an image preprocessing method that leads to transformation of the data, such that the covariance matrix is the identity matrix, leading to decorrelated features, while the fill_mode argument with \"wrap\" simply replaces the empty area, and follows the filling scheme.
# MAS-YOLO: A Lightweight Detection Algorithm for PCB Defect Detection Based on Improved YOLOv12
###### Abstract  
As the performance requirements for printed circuit boards (PCBs) in electronic devices continue to increase, reliable defect detection during PCB manufacturing is vital. However, due to the small size, complex categories, and subtle differences in defect features, traditional detection methods are limited in accuracy and robustness. To overcome these challenges, this paper proposes MAS-YOLO, a lightweight detection algorithm for PCB defect detection based on improved YOLOv12 architecture. In the Backbone, a Median-enhanced Channel and Spatial Attention Block (MECS) expands the receptive field through median enhancement and depthwise convolution to generate attention maps that effectively capture subtle defect features. In the Neck, an Adaptive Hierarchical Feature Integration Network (AHFIN) adaptively fuses multi-scale features through weighted integration, enhancing feature utilization and focus on defect regions. Moreover, the original YOLOv12 loss function is replaced with the Slide Alignment Loss (SAL) to improve bounding box localization and detect complex defect types. Experimental results demonstrate that MAS-YOLO significantly improves mean average precision (mAP) and frames per second (FPS) compared to the original YOLOv12, fulfilling real-time industrial detection requirements.  
defect detection; small target detection; YOLOv12; attention mechanism; loss function 2020 IEEEexample:BSTcontrolcontrol  
2021
# Telepresence Social Robotics towards Co-Presence: A Review
## 2 Co-Presence Taxonomy
### Immersive Qualities
#### 2.1.4 Haptic Feedback  
To improve the sense of reality, it is important to provide some type of physical feedback to the operator or bystander. Useful contributions include providing tactile cues to let the user recognize the surface texture and materials or support kinesthetic feedback to help the user experience the weight of a virtual object. These kinesthetic and tactile sensations enable haptic perception.  
Haptic feedback is a challenge. However, it may improve the degree of presence considerably. Considerable progress has been made in the field of visual and auditory displays, but haptic feedback is in its early stages, gaining much attention nowadays [64; 65; 66]. Touch contact plays an important role in human interactions. From an early age, babies explore their surroundings with their hands and feel physical contact with parents holding them, and at older ages, handshakes, kisses, and embraces trigger emotions and strengthen relationships. Nevertheless, the physical contact of a robot with a person raises safety issues, and that may justify why haptic feedback is not so prevalent.  
Touching a robot's part (e.g., hand or body) or sensing that component pulling our hand, operated by a remote party, can improve co-presence. Remote hand-shaking has been explored [67], and examples include the Nao robot hand-shaking the bystander while the robot's operator uses a low-cost haptic device (WiMote) to feel it [68]. In [69], a robot hand was attached under a videoconferencing terminal's display, and their evaluation demonstrated that mutual touch enhances the feeling of being close. However, the partner's action should not appear in the video. [PERSON] et al. [70] developed a tactile telepresence system prototype that enables a remote visitor to convey touching patterns on the forehead of an isolated patient through a tablet touch video interface. Regarding human-robot first encounters [5] and greetings, the authors of [71] used [PERSON]'s model [72] to develop an interaction that included six phases (initiation of approach, distance salutation, head dip, approach, final approach, and close salutation). Human tracked gestures were the inputs for a decision module (based on the hidden Markov model and behavior tree [73]) that initiated a specific phase at the right moment.  
Telexistence surrogate anthropomorphic robot (TELESAR) VI can mimic the user's movements and gestures from a mechanically unconstrained full-body master cockpit and provide haptic feedback to the operator [74]. The 10 fingers of the teleoperated robot are equipped with vibration, force, and temperature sensors that can realistically deliver these components of haptic information. Operators can shake the hand of another person through the robot and feel it.
# Two-Layer-Graph Clustering for Real-Time 3D LiDAR Point Cloud Segmentation
## 3 Proposed Method
### Storage Structures
#### 3.1.1 Range Graph  
For the entire point cloud scene, we use the range graph structure for storage. It can meet the point cloud-level traversal requirements and achieve high-speed access to any point data. The structure of the range graph is consistent in Figure 2. The length and width represent the horizontal angle and vertical angle of the point cloud, for each point cloud with a single point structure, located at the node position of the channel number and the horizontal number. Nevertheless, some node of the range graph does not have a valid point, so a valid attribute is designed in the single point storage structure.  
The single point structure in Table 1 not only stores standard point cloud information, such as point cloud x, y, z three-dimensional information, distance information d, serial numbers in the horizontal and vertical directions, but also contains several vital attributes, such as whether it is a valid point, the category number after the first segmentation \(\alpha\), and the final category \(\beta\). These attributes do not come from initial data but the attribute information that needs to be processed. The design of each attribute's information can help improve real-time performance.  
Figure 2: The range graph structure is similar to an unfolded cylindrical shape. After it is expanded, each node represents a single point. The nodes include valid nodes and invalid nodes. In the figure, orange represents valid nodes and blue represents invalid nodes.  
Range graph can well represent the neighbor relationship of point clouds; we can find the neighbor point fast by this structure. In TLG, the range graph is the first-level graph structure. Moreover, in this graph, we do the first segmentation of the point cloud in each channel.
## 3 Results and Discussion
### The Fourier Transform Infrared (FTIR) Test  
Table 3 shows the functional groups of the binders and the peak position obtained from the FTIR test for modified bitumen before and after ageing. The investigated range, which was mid-infrared spectrum (4000-600 cm\({}^{-1}\)), showed many groups before and after ageing. The FTIR test demonstrated that there are no new bands created after adding nano-clay particles for unaged 0 NCMB, 2 NCMB and 4 NCMB, as can be seen in Figure 3a-c. This means that there is no chemical activity between nano-clay and bitumen. Mixing did not create any new groups for unaged modified bitumen.  
In this paper, attention was given to groups believed to help the detection of changes that occur during ageing of the samples. The selected groups, based on their compounds, were (1) oxygen-containing compounds, (2) aliphatic hydrocarbons, (3) aromatic compounds and (4) sulfur compounds.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{3}{c}{**Viscosity (Pa.s) at 135 \({}^{\circ}\)C**} & \multicolumn{2}{c}{**VAI**} \\ \hline
**NC \%** & **Unaged** & **STA** & **LTA** & **STA** & **LTA** \\ \hline
0\% & 606 & 820 & 1068 & 1.35 & 1.76 \\
2\% & 702 & 790 & 886 & 1.13 & 1.26 \\
4\% & 770 & 859 & 992 & 1.12 & 1.29 \\ \hline \end{tabular}
\end{table}
Table 2: Viscosity ageing index.  
\begin{table}
\begin{tabular}{c c} \hline
**Functional Group** & **Peak Position (cm\({}^{-1}\))** \\ \hline C–H asymmetric stretching & 2920 \\ C–H symmetric stretching & 2851 \\ C=O stretching & 1694 \\ C=C stretching & 1597 \\ CH\({}_{2}\) deformation & 1457 \\ CH\({}_{3}\) deformation & 1375 \\ S=O stretch & 1027 \\ CH out-of-plane deformation & 900–690 \\ \hline \end{tabular}
\end{table}
Table 3: Functional groups and their peak position.  
Figure 3: FTIR spectrum results for **(a)** 0 NCMB, **(b)** 2 NCMB and **(c)** 4 NCMB.
# A Transformer-Based Approach for Efficient Geometric Feature Extraction from Vector Shape Data
## 3 Experiments
### Experiments and Analysis
#### 3.3.4 Few-Shot Learning Capability  
In cartography and geographic information science, large-scale labeled data are limited and costly. Thus, the few-shot learning capability is crucial for evaluating the generalization of the vector shape features.  
In the few-shot scenario, each class used only 10 samples for the training dataset, whereas the remaining 4900 samples formed the test dataset. The training and prediction results of the two methods were compared.  
In Figures 15 and 16, gray represents the pre-trained model, and orange represents direct end-to-end training. With identical parameters and sample conditions, the loss of the PSRT model decreased to 1.798, and an accuracy of 87.13% was achieved within 2000 steps. In comparison, the loss without pre-training was reduced to 2.045 at 2000 steps and remained at 1.83 even after 50,000 steps. The model without pre-training achieved only 84.53% accuracy, showing a decline of approximately 8% compared with training with a normal sample size.  
The findings suggest that in few-shot situations, the performance of the PSRT model is approximately 8% lower than that of a normal sample size, far superior to non-pre-trained models, with a training efficiency improved by approximately 25 times.  
Figure 14: Validation accuracy evolution during training for PSRT and SRT (the higher and faster the climb, the better).  
Pre-trained models offer parameter initialization with meaningful features, which is more effective than random initialization, because these models have learned common features from large datasets. For downstream tasks, only fine-tuning is required to adapt the model and accelerate the training process. Pre-trained models leverage general features learned from extensive data, providing superior performance even with limited data in downstream tasks, thereby reducing the need for large-scale labeled data. The use of pre-trained models enables knowledge transfer from one task to another, thereby enhancing the generalization ability and performance of the model.  
Figure 16: Validation accuracy evolution for few-shot PSRT and SRT.  
Figure 15: Loss function evolution during training for few-shot PSRT and SRT.  
Employing pre-trained models is important for improving the training efficiency, resource conservation, and model performance enhancement.
# Responses of Forest Carbon Cycle to Drought and Elevated CO2
## 3 Elevated CO2 Effect on Plants and Ecosystem  
Increasing global CO2 emissions positively stimulate the terrestrial gross primary productivity. Previous research on plant response to elevated CO2 has been developed across a range of scales, from leaf-level physiological experiments to satellite observations and the modelling of ecosystem response. We discuss CO2 fertilization effects from the perspectives of plant physiology to ecosystem response below.
## 3 Results
### Influence of MPs Ingestion on Growth and Development of A. salina  
Total body length showed significant differences between the Group A and B (\(p\) = 0.028) (Figure 4). Indeed, analysis of variance highlighted significant variations also within both experimental groups. Group A showed significant variation between A0 and all others treatments (\(p\) < 0.001), except with A1 (\(p\) > 0.05). The highest differences of 258 \(\upmu\)m in total body length were detected at T168 between A0 (1366.824 \(\upmu\)m) and A5 (1108.746 \(\upmu\)m) and of 116 \(\upmu\)m between A0 (1366.824 \(\upmu\)m) and A4 (1249.606 \(\upmu\)m). At T96, a difference of 61 \(\upmu\)m was detected between A0 (1162.024 \(\upmu\)m) and A5 (1101.746 \(\upmu\)m).  
MPs concentration less influenced total body length within the Group B, showing at T96 a difference of 66 \(\upmu\)m comparing B0 (981.26 \(\upmu\)m) with B5 (915.164 \(\upmu\)m), but not statistically relevant.  
Qualitative anatomic features analysis performed from images of analyzed specimens showed significant differences between the two groups. Group B showed a significant delay in the development as compared to the Group A specimens. Indeed, the maximum reported development stage for the Group B (B0) ranged between instars II and III at T96 (before the total mortality due to starvation), where the naupliar eye was still a simple eye, and the antennae were the only active appendages because of the total absence of thoracopods. However, Group A (A0) reached at the same time point with the instar IV,  
Figure 4: _Artemia salina_ total body length at each concentration and experimental time.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{8}{c}{**Exposure Time**} \\ \hline
**Experimental Groups** & **T0** & **T6** & **T12** & **T24** & **T48** & **T96** & **T168** \\ \hline A0 & 0 & 0 & 0 & 0.55 & 0 & 0.55 & 1.11 \\ A1 & 0 & 0.55 & 0 & 0 & 0.55 & 1.11 & 1.66 \\ A2 & 0 & 0 & 0.55 & 0 & 0.55 & 1.11 & 1.66 \\ A3 & 0 & 0 & 0 & 0.55 & 1.11 & 2.22 & 2.22 \\ A4 & 0 & 0 & 0 & 0 & 1.11 & 3.33 & 1.66 \\ A5 & 0 & 0 & 0 & 0.55 & 0.55 & 2.22 & 2.77 \\ B0 & 0 & 0 & 0 & 0 & 1.11 & 6.66 & 92.22 \\ B1 & 0 & 0 & 0 & 0.55 & 2.22 & 8.33 & 88.88 \\ B2 & 0 & 0.55 & 0 & 0 & 3.33 & 11.11 & 85 \\ B3 & 0 & 0 & 0 & 0 & 2.22 & 12.22 & 85.55 \\ B4 & 0 & 0 & 0 & 0.55 & 3.88 & 14.44 & 81.11 \\ B5 & 0 & 0 & 1.11 & 0 & 3.33 & 13.33 & 82.22 \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Artemia salina_ mortality rate (%) calculated for the experimental Group A and B.  
where the nauplair eye was in differentiation towards the compound structures and an evident primordium of thoracopods were present. Moreover, at T168 in Group A replicas, the instar V was reached with the complete differentiation of the eye in median and compounds and an evident formation of thoracopods that become swimming appendages (Figure 5).  
No evident anatomical differences were detected between the different treatments of Group B (Figure 6).  
Within Group A, A0 at T168 reached instar V; A4 and A5 replicates reached instars IV and III respectively, showing different average developmental stages (Figure 5).
# Tag-to-Tag Interference Suppression Technique Based on Time Division for RFID
## 2 RFID Collision Problem
### Types of RFID Collision
#### 2.1.1 Reader-Reader Collision  
A reader-to-reader collision occurs when neighboring readers simultaneously interrogate a specific tag in the same frequency band. Furthermore, it may occur when the tag is within the readable range of one reader and the interference range of the other reader; the signal from the latter reader may interfere with the return signal from the tag. Reader-to-reader collision is discussed in more detail in Colorwave [9; 10; 11; 12].
# Cooperative Admission Control with Network Coding in 5G Underlying D2D-Satellite Communication
## 1 Introduction
### Contributions  
The major contribution of this paper towards literature are as follows.  
* Joint formulation of admission control with network coding supported by D2D-satellite multiple interfaces leads to the complex problem due to multiple constraints and not feasible to solve as one piece. So the objective function is divided into subproblems and then solved using heuristic algorithms (ATPSPN and MANC) to maximize the capacity and lower packet completion time. In the end, the matching algorithm is used for joint optimization, which leads to convergence and less complex solution overall.
* The problem formulated for joint admission control is mix integer nonlinear problem, Np hard in nature, and requires exhaustive search due to integer combinatorial nature. As its complexity increases exponentially, so using an exhaustive search method is not a feasible solution. The relaxation of the problem based on an outer approximation to linearization is proposed, ensuring convergence to a global optimum solution with less complexity. The formulated problem named Adaptive Terrestrial Power Sentient Network (ATPSPN) is then solved by NOMAD, Branch cut, and Branch bound solver.
* Novel multiple access network coding framework (MANC) is proposed for stream and batch techniques supported via the D2D-satellite network. The stream technique ensures the decodability of the data immediately after receiving the packets and essential for the crucial application. Batch uses random linear network coding (RLNC) and provides less packet completion time. The theoretical lower and upper bounds are mathematically derived for lossless second stage channel, which is then extended to the second stage lossy channel for practical results.
* A bi-directional matching algorithm is proposed for cooperative admission with network coding for joint optimization to provide better performance overall. The matching algorithm named as Joint Admission-Multiple Access Network Coding (JAMANC) provides the optimization in both directions, i.e., forward and backward, unlike the Gape Sharpley algorithm. The advantage is that this technique provides optimization concerning both sets, i.e., JPFL (Joint admission preference list) and MPFL (multiple access network preference list).
* The satellites' role in 5G communication with the advancement in Micro-Sat cannot be denied and part of 5G future vision. However, the link budget cost has caused slow progress in the integration of terrestrial-satellite communication. In our algorithm, the satellite is efficiently integrated to decrease overall the link budget cost.
## 5 Flow Gallery
### Transport of a Single Confined Fiber  
We used the photo-lithography technique to study the transport of individual confined fibers in a Hele-Shaw microchannel. We previously reported [12; 14] experimental results completed with numerical simulations, treating in particular the role of confinement and orientation on the fiber dynamics. We have shown that under these confined conditions, a transport anisotropy is observed: a fiber-oriented perpendicular to the flow direction is transported faster compared to a parallel fiber under similar flow conditions [14]. When such an isolated and confined fiber is flowing with an angle (different from zero or ninety degrees) to the main flow direction, it will thus drift with respect to the flow direction and will inevitably approach one of the lateral walls of the microchannel. Hydrodynamic interactions between the fiber and the wall align the fiber parallel to the latter. Any small perturbation will cause the fiber to rotate again and to drift away from the wall with a constant orientation until the interaction with the opposite wall will again lead to fiber alignment. This series of rotation and drift leads to a very regular oscillation of the fiber between the two walls, as illustrated by Figure 11 and described in more detail in [22]. This phenomenon could for example be used as an active mixing method.  
The dynamics becomes more complicated when the fiber is flexible enough to be deformed by the viscous flow (see Figure 12) when approaching the channel walls. Due to the deformation of the fiber, the symmetry between fiber approach and departure is broken. The precise effect of flexibility on the oscillation dynamics remains however still largely unexplored.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Photo-Lithography** & **Colloidal Assembly** \\ \hline
**Fiber** & & \\ \hline Geometry & Square or rectangular cross-section & Necklace \\ Length & Mask & Channel height \\  & _50-300 \(\mu m\)_ & _20-300 \(\mu m\)_ \\ Width & Mask & Colloids’ diameter \\  & _5-30 \(\mu m\)_ & \(\sim 1\)\(\mu m\) \\ Height & Channel height and inhibition layer & Colloids’ diameter \\  & _5-30 \(\mu m\)_ & \(\sim 1\)\(\mu m\) \\ \hline Elasticity & Cross-linked polymer & Polymer chains bridging the colloids \\  & _Young’s modulus: 50 kPa-10 MPa_ & _Bending elasticity: \(7.8\times 10^{-26}\) J:m_ \\ \hline Roughness & Smooth unless drawn on mask & Roughness from structure \\ \hline Initial position & User-defined & Random \\ \hline Orientation & User-defined & Controlled by external magnetic field \\ \hline
**Flow** & & \\ \hline Channel geometry & Flat & Close to square \\ \hline Flow profile & Hele-Shaw flow & Poiseuille flow \\ \hline Confinement & By top and bottom walls & By gravity \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the two micro-fiber fabrication methods. Typical dimensions are given in italics.