esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Improving the East Model
### Label Generation  
As shown in Figure 2, the labeled region of the EAST model is first shrunk by 0.3 on the long side and then by 0.3 on the short side, and the resulting region is used for extracting features, which can reduce interference factors introduced by labeling errors. One feature to be learned from this region is the location of the smallest outer rectangle of the bounding box, which is measured by the distance from each pixel location to the four edges. Another feature to be learned from this region is the rotation angle between this minimized outer rectangle and the horizontal plane.  
The EAST model has moderate success by shrinking the long and short edges by 0.3 to reduce labeling error; however, the following problems remain. For a small text area, shrinking the short edge by 0.3 will lead to a large change in the features of Chinese characters and some English letters in upper- and lower-case formats, which affects feature extraction from the text area and eventually leads to an inaccurate prediction bounding box for the text area. As shown in Figure 3, for letters such as \(p\), \(g\), and i, shrinking the short edge by 0.3 changes the meaning of letters in the region, leading the model to output inaccurate prediction.  
Figure 1: Overall flow of the EAST model.  
Figure 2: Label geometry of EAST.  
prediction boxes. Therefore, this paper proposes improving the shrinkage algorithm of the bounding box by retaining the shrinkage ratio of 0.3 for long edges and reducing the shrinkage ratio to 0.1 for short edges. The final shrinkage strategy algorithm is as follows: input the vertices coordinates of the text region; determine which pair of edges is the long edge and which pair is the short edge by calculating the Euclidean distance between the vertices of adjacent corners; shrink the long edge by 0.3 and the short edge by 0.1; and finally, output the new region's shrunken coordinates of the vertices.
# Security and Privacy in Physical-Digital Environments: Trends and Opportunities
## 3 Security, Privacy, and Confidentiality
### Digital Twins Security  
The advent of the Metaverse has brought a proliferation of digital services that collect, transmit, process, govern, and store user data. However, this process also poses significant risks to user privacy, particularly with the inclusion of digital twins. The latter may compromise sensitive user information such as location, habits, and living styles throughout the lifecycle of these digital services.  
To facilitate immersive interactions with digital twins in the Metaverse, it is imperative to conduct comprehensive data collection procedures. These procedures encompass acquiring personal information, behavioral data, and user preferences. It is of utmost importance to ensure the privacy of these data to prevent unauthorized access or misuse by third parties.  
Also, the effective creation and rendering of avatars and virtual environments in Metaverse services necessitate the aggregation and processing of voluminous data collected from the human body and its surrounding environment. However, this undertaking carries the potential for sensitive information to be exposed. Specifically, centralizing the private data of different users for training personalized avatar appearance models challenges user privacy and contravenes extant regulations such as the General Data Protection Regulation (GDPR). Besides that, due to the ability of avatars to reflect the behavior patterns, preferences, habits, and activities of their real-life counterparts, attackers can exploit this similarity to collect the digital footprints of avatars and create accurate user profiles that enable them to carry out illicit activities, posing a significant threat to the security and privacy of users. As such, it is imperative to safeguard users' data privacy and security in the Metaverse [28].  
Digital twins in MR represent real-world objects or systems within the digital space, enabling interactions that blend the physical and virtual realms. In MR applications, digital twins are often used to visualize complex data or provide interactive simulations, such as in manufacturing or medical training. Security concerns arise as these digital twins can access and mirror sensitive data from physical assets. Unauthorized access to digital twins in MR environments could lead to serious data breaches or manipulations, compromising physical assets. To address this, MR security models must ensure that data exchanged between digital twins and their real-world counterparts are securely encrypted [29].
# Development of Highly Sensitive Temperature Microsensors for Localized Measurements
## 3 Results and Discussion
### Calibration of Temperature Microsensors  
To evaluate the sensitivity and linearity of the sensors with 5, 8, 12, 16, 20 and 24 windings to monitor the temperature of the surrounding environment, preliminary tests were performed (using the experimental setup described in Section 2.4), measuring the electrical potential of those RTDs with the constant excitation current of 100 \(\upmu\)A. The RTDs were placed in a temperature-controlled hotplate, for calibration. The temperature was increased from 35 to 45 \({}^{\circ}\)C with increments of 0.1 \({}^{\circ}\)C. Figure 10 and Figure S3 in the Supplementary Information shows the RTDs response. As expected, it is evident there is a high linearity between temperature and electrical potential. Furthermore, when the number of windings is increased, the sensitivity also increases linearly (cf. Figure 11). The experimental sensitivity ranges of the sensors from 0.0719 to 0.3542 mV/\({}^{\circ}\)C for 5 to 24 windings, respectively, which are close to the simulated values (0.1022 to 0.3966 mV/\({}^{\circ}\)C), are shown in Table 2. However, the experimentally measured electric potential was slightly higher than those obtained in the simulations, which can be explained by the increase of the electrical resistance of lead wires used in the electronic circuit connections. However, the RTDs performance is in agreement with the simulated results. Since the higher sensitivity was obtained for the 24 windings sensor, \(\sim\)35 \(\upmu\)V for a step of 0.1 \({}^{\circ}\)C, this geometry was chosen for being integrated in the organ-on-a-chip. The calibration equation for the sensors with 24 windings is expressed as follows:  
\[V\left(\text{mV}\right)=0.3542\ T\left({}^{\circ}\text{C}\right)+150.89 \tag{7}\]  
Figure 9: Experimental characterization of the microsensor with 24 windings using different excitation currents.
# High-Pressure Sensors Based on Laser-Manufactured Sintered Silicon Carbide
## 1 Introduction
Adopting the same optoelectronic detection set-up, in this work thick cylindrical samples of S-SiC ceramics have been used to fabricate fiber-optic FP pressure sensors. A fiber laser apparatus was employed to ablate S-SiC creating a hole and leaving a flat diaphragm with defined thickness. The FP cavity is represented by the end-face of a single mode fiber (SMF) and the S-SiC diaphragm surface. Deforming the S-SiC membrane, the external pressure is measured evaluating the cavity length by means of the optoelectronic system coupled to the SMF and able to acquire the light interference signal. Sensor pressure sensitivity and full-scale range can be properly designed adjusting diaphragm diameter and thickness. The proposed structure would represent a valid alternative for the fabrication of pressure sensors able to work in the harsh environments the S-SiC ceramics sustains.
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 4 Advanced Theories and Methods for TD-NIRS
### Perturbation Theory
#### 4.5.2 First Order TD-Perturbation Using the TD-DE  
[PERSON] [28] reported the analytical formulations of the first order TD perturbation approach based on the TD-DE for homogeneous infinite media, semi-infinite media and infinite slabs. The paper describes the analytical formula of the kernels of the integrals in Equation (33) and (34), so called the absorption and diffusion kernels, respectively. The kernels in the inversion problems are essentially the Jacobians which have a physical meaning of probability density function, or sensitivity of a measurement by the perturbation, and the images of the TD Jacobians for various geometries are shown. [PERSON] and [PERSON] reported experimental results using a breast-like slab phantom to show reconstructed DOT images of inclusions in the phantom by use of the perturbation method in the preceding paper [28; 87]. The spatial resolution of scattering inclusions was found to be better than that of absorbing inclusions.  
[PERSON] et al. experimentally demonstrated the performance of a TD perturbation approach for detection of cylindrical inclusions embedded in a 20-mm thick scattering slab simulating compressed breasts measuring the TR transmittance in a collinear geometry of the source, inclusion, and detector [88]. They reported that the changes in the transmittance by absorbing perturbations were two-orders of magnitudes stronger than those by scattering perturbations.  
[PERSON] et al. [89] studied the accuracy of a TD perturbation model to predict the effects of absorbing and scattering inclusions on light propagation. They compared the results of the perturbation model with those of Monte Carlo simulations and phantom experiments for the TR-transmittance through a 40-mm thick scattering slab simulating breasts and verified that the changes in the TR transmittance caused by absorbing perturbations were independent of those by scattering perturbations.  
[PERSON] et al. [90] employed Pade approximants to extend the application of the first order TD perturbation to large-volume inclusions using a non-linear contrast of the measured TR transmittance as a function of absorbing and scattering perturbations. Their perturbation model and experimental setup were similar to those of [PERSON] [89], and the employment of Pade approximants improved the accuracy of the reconstructed images for the large-volume inclusions.  
[PERSON] et al. [91] developed formulations of the TD perturbation model for two- and three-layered media with absorbing perturbations supposing human heads. The eigen-function solutions of the TD-DE for two- and three-layered media were used [96; 97], and results of their model agreed well with those of MC simulations, and TD-sensitivity maps were also shown.
## 1 Introduction
The external loop provides a reference position/velocity to the inner loop. Examples of inner/outer force control loops on rigid manipulators can be found in the works of [PERSON], [PERSON] et al. [18], [PERSON] and [PERSON] [19], [PERSON], [PERSON] et al. [20] and [PERSON] and [PERSON] [21]. An advantage of the inner/outer loop is that this control does not need to know the dynamic parameters of the robot [20]. The errors in the dynamic model can be modelled as force disturbances. Some researches apply the inner/outer algorithm directly to the robot motion control. This has the advantage of executing the task relatively straightforward [22; 23; 24], while others develop a macro-mini manipulator, where the robot is the macro manipulator, and a special end-effector is the mini manipulator or active device [25; 26; 27]. As collaborative robots are lighter and cheaper, the use of mini manipulators does not appear to be an available alternative. The mini manipulator requires the development of another and specific mini controller to each process. This method requires actuators, power amplifiers, active control devices, and algorithms. They are expensive and relatively difficult to implement. Besides, a specific device generates less stiffness and raise weight in the end effector [28]. According to the above, this article proposes the analysis and experimentation of a force control with an inner motion loop on collaborative robot arms. The methodology implemented allows us to demonstrate the possibility for cobots to perform a force control, although the internal parameters of the robot remain unknown. This work proves the application of these control in a polishing operation. Additionally, the analysis proposed considers the effect of the stiffness in the robot when it is used in force tasks with an inner/outer control loop. This article is structured as follows. Section 2 presents the dynamic model for robots with elastic joints. Section 3 describes the inner/outer control loops with their advantages and drawbacks. Then, Section 4 shows an analysis of the possible inner loops and how the inner loops can affect the response of the force control. Section 5 shows the methodology, experimental platform, techniques used, and task planning. Section 6 exposes the results and discussion of the experiments performed with a commercial collaborative robot. In this section, the inner position loop and the velocity loop are compared. Besides, different algorithms for outer loops are contrasted. The best inner/outer loop is applied in a polishing task. Finally, Section 7 collects the conclusions.
# Improving the Robustness of AI-Based Malware Detection Using Adversarial Machine Learning
## 2 Theoretical Background
### Malware Analysis  
Before initiating malware detection techniques, there is a need to analyze the different functionalities of a particular malware, how it executes these functionalities, and the purpose behind its development. Malware analysis techniques are classified into three main categories [27]:  
1. Statics Analysis--This includes examining a program's or software's source code without running it. The static information from the source code is extracted in this procedure to see if it contains any dangerous code. Debuggers, de-compilers, de-assemblers, code analyzers, and other tools are used for this type of analysis. File format extraction, string extraction, fingerprinting, AV scanning, and disassembly are some of the methods used to do perform static analysis with the help of these tools.
2. Dynamic Analysis--The functionality of the software or a program is examined using this method, which involves running the source code. In other words, a software's behavior is examined, and the software's intents or purpose are deduced from these observations. This is commonly done in a virtual environment with a sandbox, simulator, emulators, RegShot, a process explorer, and other tools. This strategy makes malware detection simple.
3. Hybrid Analysis--This is a hybrid of static and dynamic analysis techniques. The source code is first analyzed in this manner, and then it is run in a virtual environment to examine its actual behavior [28]. Table 1 shows the comparative analysis of static and dynamic methods.
## 3 Methodology
### The Geometric Model  
To optimize the nodal discretization mesh, the study domain was delineated using the Geometry module of the ANSYS Fluent 17 software. The geometric configuration of the model was limited to the fundamental components of the passive facade: the external glazed layer, consisting of a single glass pane with a thickness of 6 mm; the internal glazed assembly, composed of two glass panes with thicknesses of 6 mm and 5 mm, respectively, separated by a 16 mm air gap; and the internal cavity, characterized by a distance of 0.12 mbetween the glazed surfaces. This strategy simplifies the analytical process, reducing the computational model's complexity without compromising the fidelity of the obtained results. The assessment of the radiative balance was conditioned by the intensity of solar radiation (both direct and diffuse components), the specific orientation of the facade, the view factors characterizing the radiative interaction between the various building elements of the facade, as well as the radiative properties of the materials. For this investigation, a radiative heat flux value of 66.90 \(\mathrm{W/m^{2}}\) was adopted, representative of the Moldova region in January with southern exposure [52]. From this incident radiant energy, it was considered that 10% is reflected by the surface, and another 10% is absorbed by the material, under an external ambient temperature of 255.15 K [53].
# Advancing TinyML in IoT: A Holistic System-Level Perspective for Resource-Constrained AI
## 6 Future Directions
### Benchmarking and Standardization  
As the field grows, the lack of universally accepted benchmarks and metrics for AI on constrained devices presents hurdles to reproducibility and fair evaluation [84]. While standard image classification or object detection benchmarks exist for large-scale networks, they do not necessarily reflect the unique constraints of TinyML contexts [20]. Moreover, measuring energy consumption, memory usage, and inference latency in a consistent manner can be challenging, given the diversity of hardware platforms. Initiatives like MLPerf Tiny, which focus on embedded inference, are steps in the right direction [32]. Further standardization efforts would promote transparent comparisons of compression methods, frameworks, and hardware designs, ultimately accelerating the pace of innovation in the ecosystem [23].
## 4 Simulation and Test Verification
### Simulated Test
#### 4.1.3 Simulation of Interference Detection Performance for Anti-Jamming Algorithm  
The next step involves verifying the interference detection performance of the proposed algorithm. For this purpose, the Interference Detection Ratio (IDR), denoted as \(IDR=N_{d}/N_{j}\), was defined to evaluate the interference detection performance, where \(N_{d}\) represents the number of interference spectral lines detected and determined by the algorithm in the resampled data after secondary sampling when SAIOR is activated in the simulation setup, and \(N_{j}\) represents the total number of interference spectral lines of the set interference signals in the resampled data. Three experiments were designed to compare the interference detection performance under different conditions, with the specific simulation parameters shown in the Table 2.  
Figure 8 shows the interference spectral line detection rate under different JSR values. The horizontal axis represents the JSR, and the vertical axis represents the interference spectral line detection rate.  
Figure 8 demonstrates the following: (1) For the SAIOR algorithm, as the PFM interference modulation period increases, the probability of interference detection decreases. This is because, as the modulation period increases, the amount of resampled data obtained after optimization resampling decreases, and the errors introduced by interference parameter estimation have a greater impact on interference detection. (2) In different scenarios, the interference detection rate of the SAIOR algorithm is always higher than 96%, indicating the algorithm's excellent performance in suppressing PFM interference.
## 1 Introduction
### Planes  
Airborne sensors are widely used for land monitoring. As in the case of satellite sensors, this technology was originally developed for non-civil uses. Nowadays, planes can carry spectral sensors able to analyze geomorphology, land coverage, or vegetation features. Although airborne surveys are usually based on specific sensors, such as Light Detection and Ranging or Laser Imaging Detection and Ranging (LiDAR) or hyperspectral cameras [34; 35], tailored multispectral cameras, suitable for agricultural applications, are sometimes used [36; 37]. Images acquired by plane-mounted sensors require photogrammetric and mosaicization processes to stitch images together. The stitching process requires a partial image overlap (\(\sim\)80%) to provide suitable blended images. Plane earth observation requires specific setting of the flight plans and large areas for take-off and landing. This latter condition decreases the scalability of these techniques and increases the costs.
## 3 Results (Study 1)
### Phase 1: Performance Differences Across the Four FOD--Advanced Combinations
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Significant Variables** & **ANOVA Result** & **Summary** \\ \hline \hline \(F\)(3,30) = 5.485, \(p\)\(\leq\) 0.001. The effect size (\(\eta^{2}\) = 0.111) indicates 11.1% of the variance in the target E accuracy was due to different combination selection. Post hoc: Combination 1 \(>\) 4. & The accuracy of responding to target E is in combination 1 is significantly greater than the harder workload in combination 4. No other statistical differences were observed. \\ \hline \hline \(F\)(3,30) = 171.775, \(p<0.001\). The effect size (\(\eta^{2}\) = 0.796) indicates 79.6% of the variance in the average reaction time to detect the target E was due to different combination selection. Post hoc: Combination 1 \(<\) 2, 3, 4; 2 \(<\) 3, 4; 3 \(<\) 4. & As the workload increased, the reaction time also increased significantly. Each of the four combinations is meaningfully different than one another. \\ \hline \hline \(F\)(3,30) = 2.946, \(p<0.05\). The effect size (\(\eta^{2}\) = 0.064) indicates only 6.4% of the variance in the extra target E pressed was due to different combination selection. The pairwise test incorporates the \(p\)-value adjust method for obtaining statistically significant value. The result after the pairwise adjustment method showed that the variation is not statistically significant. \\ \hline \hline \(F\)(3,30) = 13.737, \(p<0.001\). The effect size (\(\eta^{2}\) = 0.238) indicates that 23.8% of the variance in the distractor E presses was due to different combination selection. Post hoc: Combination 1 \(<\) 3, 4; 2 \(<\) 3, 4. & Regardless of the level of workload, there is no significant difference in the participants’ target E extra presses. \\ \hline \hline \(F\)(3,30) = 13.737, \(p<0.001\). The effect size (\(\eta^{2}\) = 0.238) indicates that 23.8% of the variance in the distractor E presses was due to different combination selection. Post hoc: Combination 1 \(<\) 3, 4; 2 \(<\) 3, 4. & In combination 1 and 2, the distractor E were not used. There is no meaningful difference when the distractor E is used in the combination (i.e., 3 and 4). \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of the statistically significant variables using ANOVA and post hoc pairwise comparisons.
## 4 Performance Evaluation of Millimeter-Wave V2V
### Performance of Millimeter-Wave V2V Communications to Support Safe Overtaking
Figure 21: Required data rate using a non-linear spacing LiDAR sensor and the 0.01% outage data rate realized by the 5, 30, and 60 GHz bands under \(d_{\text{be}}=10\) m.  
Figure 20: Required data rate and 0.01% outage data rate realized by the 5, 30 and 60 GHz bands under \(d_{\text{be}}=15\) m.  
For the final discussion, we compared these results with the current requirements for cooperative perception. In [5], one-thousand megabits per second and 10 ms of max end-to-end latency were required for a higher degree of automation to prevent an imminent collision by extended sensors or cooperative perception, which allowed the maximum sensor data rate at 100 Mbps. On the other hand, in [8], one gigabit and 3 ms of end-to-end latency were required for collective perception of environment or cooperative perception, which allowed the maximum sensor data rate at 3 Mbps. In our analysis, even if an automated vehicle drives at around 30 km/h, around a 1 Gbps sensor data rate is required for safe driving. From the above comparison, although we considered only LiDAR sensors for recognition, considering safety affected the requirements. These results show that millimeter-wave communication has a big potential to contribute to safe overtaking and smooth traffic. Although the actual data rate will be lower than these outage data rates, millimeter-wave communication especially at 60 GHz has a large margin. Therefore, we concluded that millimeter-wave communication has the ability to perform safe overtaking at a high velocity, and considering the actual data rate, sixty gigahertz would be a promising frequency.