esa-sceva/satcom-chunk-collection huggingface dataset:
# Advances in Neural Network-Based Image, Thermal, Infrared, and X-Ray Technologies
## 2 Literature Review Methodology and State-of-the-Art
### Neural Network Algorithms  
Section 2.3 describes the current state of knowledge within various technologies related to neural networks. Therefore, it focuses mainly on technological aspects. For a complete picture, it is worth looking at how neural network algorithms relate to these technologies.  
Table 3 summarises the common algorithms used in neural networks. They are similar in each of the technologies discussed in the previous section. The main difference is in the way the algorithm is implemented and in the training data. For example, networks that analyse colour or greyscale data may have a different number of inputs in the first layer.  
In addition, the same objects in visible, infrared, and X-rays may look different. Therefore, each of these algorithms requires a different set of training data. However, this does not fundamentally change the idea of how the algorithm works.
While the phase-phase balance is a classical problem in power distribution networks, no previous scientific literature report exists that addresses this problem using integer-convex optimization. Mainly, the problem has been solved with metaheuristics due to its binary and non-linear nature [16]. Notwithstanding, we propose a mixed-integer quadratic programming (MI-QP) approximation, which has not been previously reported in the literature.  
Unbalance in a network, it is important to note, can also be reduced using power electronics technologies. For example, an active filter with a four-leg converter can control the neutral current, and unbalance is reduced [17]. However, these devices imply an investment cost, while the solution of the optimization model proposed here only requires a suitable rearrangement of the loads. Nevertheless, both solutions are complementary and may be used together.  
The problem of the optimal-phase balancing in electrical distribution networks and microgrids is complex for three main reasons, namely: (i) Power balance equations constitute a high-dimensional non-linear and non-convex set of constraints; (ii) the problem is combinatorial due to the binary variables, associated with the possible options for balancing loads and generators at each node [16, 18]; and (iii) the problem is stochastic due to the high variation of the load and generation of renewable sources [15]. These complications in the solution of the phase-balancing problem motivate the development of the current research. It is possible to obtain a convex relaxation of it by using a linear representation of the power flow problem in three-phase networks [19]. For this representation, a linearization is applied to the relation between voltages and powers, allowing the rewriting of them as a set of complex affine functions [20]. We recur to the integer convex optimization theory regarding the binary variables that model the phase-balancing problem. The optimal solution of an integer-convex optimization problem can be guaranteed via the branch and bound method [21]. For each binary combination of variables, the internal problem is convex, implying that each internal problem has a global solution [22].  
The remainder of the paper is organized as follows: Section 2 describes the main features of the problem; then, Section 3 presents the stationary state model of a microgrid and how the stochastic nature of the problem is managed. Section 4 shows the power flow linearization that convexifies the problem; after, Section 5 presents the Birkhoff polytope concepts, which allow an efficient geometric representation of the combinatorial part of the problem, with a reduced number of integer variables. Section 6 presents the numerical experiments performed and is followed by conclusions in Section 7 and the relevant references.
## 5 Conclusions  
We successfully developed a high-spatial-resolution TanDEM-X DEM with very sensitive height variation of the Petermann Glacier in Northwest Greenland. The small height of ambiguity with a large perpendicular baseline during the TanDEM-X Science Phase period enabled retrieval of very sensitive height variation on a low slope area such as a glacier surface. Moreover, the pursuit of monostatic TanDEM-X observations with only approximately 10 s of temporal baseline was among the very critical factors to maintain a coherent interferometric phase over a rapidly changing surface such as a glacier. High coherence greater than 0.8 was maintained over the glacier surface, which was sufficient to extract topographic information thanks to these configurations. We utilized the GIMP DEM by adding the differential interferometric phase to reduce the phase unwrapping error which can be caused by a very small height ambiguity.  
To investigate the quality of the generated TanDEM-X DEM, we utilized pre-existing DEMs such as the GIMP and global TanDEM-X DEMs and altimetric observations, such as the CryoSat-2 SARIn mode radar altimeter and IceBridge ATM laser altimeter for reference data. The validation results using these reference data showed a very good correlation coefficient (>0.9). The different levels of offsets were estimated as \(-12.5\) to \(-3.1\) m. The most advantageous aspect of the TanDEM-X Science Phase observations was that more detailed topographic and geomorphological characteristics can be extracted by comparing it to other conventional InSAR observations or other DEM sources. Thus, routine observations like TanDEM-X Science Phase mode or other constellation missions with a large perpendicular baseline can be a promising tool to better understand the dynamics of glacial movements and topographic variation of low slope areas.  
[PERSON]-[PERSON] collected TerraSAR-X, TanDEM-X, and global DEM data and completed the InSAR processing. [PERSON] and [PERSON] reviewed the results and organized the paper. [PERSON], [PERSON], and [PERSON]-[PERSON] contributed to the discussion of the results. All authors contributed in writing the article and the interpretation of the results and agreed on the conclusion.  
This research was funded by the Korea government (MSIT) and supported by the National Research Foundation of Korea (NRF) under the Space Core Technology Development Program (project id:2017M1A3A3A02016234). This study was also supported by the Korea Polar Research Institute (KOPRI) grant PE18120.  
**Acknowledgments:** We would like to thank the German Aerospace Center for access to the TerraSAR-X, TanDEM-X, and global DEM data through the DLR projects (No. XTI_GLAC6649 and DEM_GLAC1184). The CryoSat-2 data provided by the European Space Agency and the ATM data provided by the National Aeronautics and Space Administration are appreciated. This work was supported by Global Surveillance Research Center (GSRC) program funded by the Defense Acquisition Program Administration (DAPA) and Agency for Defense Development (ADD).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Mobile Anchor Node Assisted RSSI Localization Scheme in Underwater Wireless Sensor Networks
## 4 Simulation
### Simulation Results and Discussion
#### 4.2.3 The Error of the Perpendicular Distance from the Node to the Trajectory  
In Figure 14, the blue curve with circles is the measured curve after data processing, and the red curve with triangles is the selected reference curve after data processing. After the normalization operation, the range of the RSSI curve is reduced, which narrows the weight difference between the comparison results of each feature point in all comparison results. At the same time, the shape of the two curves has been greatly changed from that of Figure 12 after normalization. The total length of the X-axis of the two curves is shortened from 300 to 60, indicating that only a part of the curve is compared. The marks on the two curves are the selected feature points. As shown in Figure 14, the measured curve is very similar to the selected reference curve, so the perpendicular distance corresponding to the reference curve can be determined as the perpendicular distance from the node to the trajectory.  
As shown in Figure 15, the mean error of the perpendicular distance does not become larger as the broadcast point interval becomes larger, and it is greater than the mean error of the projection.  
Figure 14: Comparison of curves after data processing.  
Figure 13: Mean error of the projection of the node on the trajectory determined by different schemes.
# DeepGOMIMO: Deep Learning-Aided Generalized Optical MIMO with CSI-Free Detection
## 3 Detection Schemes for GOMIMO Systems
### ZF-ML Detection  
In order to avoid the high computational complexity of joint ML detection, a low-complexity ZF-ML detection scheme can be applied in GOMIMO systems, which is basically a three-step detection scheme [15; 30]. In the first step, ZF equalization is performed for MIMO demultiplexing. The estimate of the transmitted signal vector \(\mathbf{x}\) after ZF equalization can be obtained by  
\[\mathbf{\hat{x}}_{\text{ZF}}=\mathbf{H}^{\dagger}\mathbf{y}=\mathbf{x}+ \mathbf{H}^{\dagger}\mathbf{n}, \tag{8}\]  
where \(\mathbf{H}^{\dagger}\) denotes the pseudoinverse of \(\mathbf{H}\)[10].  
In the second step, ML detection is executed to obtain the estimate of the spatial index vector according to \(\mathbf{\hat{x}}_{\text{ZF}}\). Finally, in the third step, the estimate of the constellation symbol vector can be obtained accordingly by using \(\mathbf{\hat{x}}_{\text{ZF}}\) and the estimate of the spatial index vector. For more details about the principle of ZF-ML detection for GOMIMO systems, please refer to our previous work [15].  
Compared with joint ML detection, the computational complexity of ZF-ML detection is significantly reduced. Nevertheless, the performance of ZF-ML detection is also largely degraded in comparison to that of joint ML detection, which can be explained as follows. On the one hand, ZF equalization inevitably causes severe noise amplification due to the high channel correlation in typical MIMO-OWC systems [8], which might greatly degrade the performance of GOMIMO systems. On the other hand, the detection error of spatial symbols might propagate to the estimation of the constellation symbols [31], which leads to further substantial performance degradation of GOMIMO systems.
## 3 Non-Thermal Technologies in Food Processing
### Ozonation
#### 3.3.3 Application of Ozone in Meat Products  
The microbial disinfection caused by the use of ozone in meat products is the main goal of this treatment, but sometimes, it also brings some negative effects. The oxidation of myoglobin and oxymyoglobin to methyloglobin is the main reason that ozone treatment in meat leads to discoloration [159]. Particular care should also be taken in the application of high-ozone concentrations, as they can cause the oxidation of lipids and proteins, which are also important quality indicators [183].  
Meat is one of the most vulnerable foods and substrates for the growth of a variety of microorganisms including Pseudomonas spp, lactic acid bacteria, Clostridium spp, Aspergillus, and Penicillium. Ozone is used for a wide variety of meats and meat products that are not only fresh but also processed. Among the various types of meat available on the market, chicken meat is of great interest, as it is widely consumed with a significant degree of vulnerability [184]. [PERSON] et al. evaluated the effects of gaseous ozone exposure on the bacterial counts and oxidative properties in chicken breast meat. Coliform and total aerobic and anaerobic bacteria were effectively inhibited by the application of ozone gas at 10\(\times\)10\({}^{-6}\) kg O\({}_{3}\)/m\({}^{3}\)/h [185].  
[PERSON] et al. studied the application of 5 ppm gaseous ozone on the microbial and quality attributes of pig carcasses. From the results of the study, two 4 h treatments with ozone gas proved to be effective not only against total aerobic mesophilic bacteria but also for maintaining its quality [183].
# On the Analysis of Coverage Feedback in a Fuzzing Proprietary System
## 7 Related Works
### Fuzzing Closed Systems  
As fuzzing became a popular and standard way for discovering memory errors in software, researchers started to adopt fuzzing to special systems such as closed-source embedded systems (e.g., UAV firmware, satellite firmware, IoT device firmware, etc.). Applying fuzzing to such systems raises significant challenges in measuring code coverage due to the impossibility of source-code instrumentation. Measuring code coverage for binary-only applications is feasible based on QEMU-AFL [23]; however, applying such fuzzers still requires a compatible operating system and CPU architecture (e.g., standard Linux and Intel/ARM architecture). Special systems such as satellite firmware are usually based on the SPARC Leon-3 [24] CPU architecture with the RTEMS [25] real-time operating system or VxWorks [26] systems. UAV firmwares such as the DJI drone firmware use proprietary systems with multiple SoC environments [20]. Moreover, such firmwares are often signed/encrypted to prevent any modification at the binary level [27]. Applying fuzzing techniques to such systems is feasible; however, supporting coverage feedback as in popular open-source-based fuzzers requires massive system porting and engineering. [PERSON] et al. [10] recently proposed a system for applying coverage feedback-based fuzzers to embedded firmwares and partially recovered code fragments. In the study, the authors defined a concept called _fuzzability_ to test if a fragment of binary could be fuzzed via conventional feedback-based fuzzers. However, their evaluation suggests that applying coverage feedback-based fuzzing to such systems/environments is highly costly (in terms of system engineering) and inefficient. This paper addresses such research problems and conducts an extensive fuzzing evaluation based on 92 real-world bugs found by OSS-Fuzz.
# Lightweight Attention Pyramid Network for Object Detection and Instance Segmentation
## 4 Results and Discussion
### Experiment on the MS COCO Dataset

Table 3: The test results of the APN when the channel-wise addition fusion and convolutional fusion are used. Figure 6: Selected examples of the test results on the COCO test-dev images, using the proposed APN and running at 5 fps. As shown in Table 4, when the APN only contains the adaptive transformation module, the AP value of APN is 0.1 higher than that of the FPN using Resnet-50 as the backbone network. Thus, the adaptive transformation of feature maps from all stages marginally improves performance. However, from the channel-wise addition fusion strategy and the conv fusion on the last two rows of Table 4, we can see that the AP value of the APN on the last row of the table using the conv fusion strategy is 0.7 more than the APN of the fourth row of the table using the channel-wise addition fusion. Therefore, the conv fusion strategy gains greater benefits from fusing feature maps than the channel-wise addition fusion strategy. In addition, from the third and fourth rows in Table 4, we can see that, when the APN uses the feature attention block, the AP value is 2.3 more than when the APN only contains the adaptive transformation module. Rather than simply giving all the channels of the feature map the same weight as the FPN, our feature attention block learns to reweight different channels from all network stages. Such a reweight-strategy helps the network learn, through the training dataset, to take advantage of feature maps from different network stages to construct a better fusion feature map. The APN utilizes both the multiscale feature fusion and the channel-wise attention mechanism to generate new feature pyramids. It can be proven that the FPN is a special case of the APN. We look forward to seeing more application to replace our proposed APN with the FPN and yield better performance. In addition, we also carried out an experiment to analyze the inference time of the proposed module. The proposed APN ran in 4 ms on average during the inference time, which was ignorable compared with the inference time of the backbone ConvNet. The experiment was performed with the ResNet-50 model on a single P40 where the input size of the image was 512 \(\times\) 512.
# A CSI-Based Multi-Environment Human Activity Recognition Framework
### Background  
Currently, wireless technology is moving forward in adopting the concept of Multiple Input Multiple Output (MIMO) in communication systems that comprises multiple transmit-receive pairs of antennas. Each transmit-receive pair of antennas creates a communication channel that utilizes a modulation technique to transmit coherent information over the established channel. Among the different modulation techniques, the Orthogonal Frequency Division Multiplexing (OFDM) [37] has been widely used by several Wi-Fi standards such as 802.11a, 802.11n, and 802.11 ac to encode and transmit information from a transmitter to a receiver. OFDM creates several closely spaced communication channels to spread the data being transmitted over it. OFDM can be viewed as a digital-subcarrier modulation technique. Each of these subcarriers can be characterized by using the CSI.  
Assuming the existence of \(m\) transmitters and \(n\) receivers, each transmit-receive pair will create a communication channel that can be characterized by 30 CSI components as follows:  
\[H_{t,r}=[H_{t,r}^{1},H_{t,r}^{2},\ldots,H_{t,r}^{30}], \tag{1}\]  
where \(t\in\{1,\ldots,m\}\) and \(r\in\{1,\ldots,n\}\). Each of the 30 CSI components is defined by two main attributes, namely the amplitude and phase, as follows:  
\[H_{t,r}^{i}=A_{t,r}^{i}e^{\gamma i_{t,r}^{i_{t,r}}},\;i\in[1,\ldots,30], \tag{2}\]  
where \(A\) is the amplitude, \(\theta\) is the phase, \(t\) is the transmitter, \(r\) is the receiver, and \(i\) is the CSI subcarrier index of an OFDM channel between a pair of transmitter and a receiver.  
The CSI describes the link state information carried in each transmitted wireless packet. These packets are affected by the environment conditions as follows:  
\[R_{x}=H\times T_{x}+N, \tag{3}\]  
where \(R_{x}\) is the received signal vector where each entry of this vector represents a packet, \(T_{x}\) is the transmitted signal vector where each entry of this vector represents a packet, \(H\) is the channel state information matrix, and \(N\) is the additive noise. In this study, we refer to the CSI values recorded at a particular time index for all subcarrier frequencies and all pairs of transmit-receive antennas as packet.  
Assuming that there are \(m\) transmitters and \(n\) receivers in the MIMO system, the CSI components (\(H\)) of any of the received packets can be expressed as follows:  
\[H=\begin{bmatrix}H_{1,1}&H_{1,2}&\ldots&H_{1,n}\\ H_{2,1}&H_{2,2}&\ldots&H_{2,n}\\ \cdot&\cdot&\ldots&\cdot\\ \cdot&\cdot&H_{t,r}&\cdot\\ \cdot&\cdot&\cdots&\cdot\\ H_{m,1}&H_{m,2}&\ldots&H_{m,n}\end{bmatrix} \tag{4}\]  
A technical diagram illustrating the CSI values exchanged between a transmit-receive pair of devices through time is provided in Figure 2. More information about CSI can be found in [38,39].
## 2 Testing Methods  
### Theoretical Basis of Ultra-Sub-Arc-Level Angle Measurement  
For long-distance small-angle testing, we usually consider using optical non-contact measurement methods, such as the autocollimation method [15; 16], Moire fringe method [17; 18], internal reflection reflectance method [19], internal reflection interference phase method [20; 21], etc.  
The biplane reflection calculates the angle change value through the displacement change value of the light spot [22] as shown in Figure 4: two plane reflector mirrors are parallel to each other. One of them serves as the target plane mirror, rigidly fixed with the measured object, deflecting along with the measured object, while the other plane mirror serves as the reference reflector mirror. The laser and the spot position detector are separated on the left and right sides of the reference plane reflector mirror. The laser is incident at a small angle, forming \(n\) reflections between two biplane mirrors. The final output laser is received by the spot position detector, recording the initial position of the light spot. The distance between the light spot and the emitting end is:  
\[S_{0}=2 nH\tan\alpha \tag{1}\]  
\(S_{0}\) is the initial position of the laser spot, \(\alpha\) is the incident angle of the laser, \(H\) is the distance between two planar mirrors, and \(n\) is the number of times the laser reflects between two plane reflector mirrors, as shown in Figure 4.  
When the measured object is deflected along with the target plane reflector mirror \(\Delta\theta\), the light spot on the detector will generate a displacement \(\Delta S\), with a total displacement of \(S\):  
\[S\approx Htan\alpha+\sum_{i=1}^{n-1}2 Htan(\alpha+2i\Delta\theta)+Htan(\alpha+2 n\Delta\theta) \tag{2}\]  
Displacement of light spot \(\Delta S\) can be approximated as:  
\[\Delta S=S-S_{0}\approx 2n^{2}H\Delta\theta \tag{3}\]\[\Delta\theta =\arc\tan\biggl{(}\frac{\Delta S}{2n^{2}H}\biggr{)}\approx\frac{ \Delta S}{2n^{2}H} \tag{4}\]  
\[d\theta\approx\frac{ds}{2n^{2}H}<0.1^{\prime\prime} \tag{5}\]  
Among them, \(d\theta\) represents the angle testing accuracy and \(ds\) represents the displacement resolution of the spot position detector.  
We can see from Equation (5) that both increasing the number of reflection cycles \(n\) and increasing the distance \(H\) of the double-sided mirror can improve the sensitivity of angle measurement. As long as we select a detector with appropriate resolution and match the number of reflections n and the testing distance \(H\), we can theoretically obtain a testing accuracy of ultra-sub-arc. Moreover, this testing principle is not limited by the pitch angle and can be tested at any pitch angle.