esa-sceva/satcom-chunk-collection huggingface dataset:
# Tracking Extended Targets: Novel TPMB Filter Driven by Model and Data Collaboration
## 2 Background
### State and Trajectory Variables  
At time step \(k\), the single-target state \(x_{k}\in\mathscr{X}^{d_{x}}\) includes its kinematic state and any potentially identified target extension states. At time step \(k\), the set of target states \(x_{k}\) is a random finite set, and \(\mathbf{x}_{k}=\left\{x_{k}^{1},\ldots,x_{k}^{n_{k}}\right\}\in\mathscr{F}( \mathscr{X}^{d_{x}})\). The measurements \(\mathbf{z}_{k}=\left\{z_{k}^{1},\ldots,z_{k}^{m_{k}}\right\}\) collected by sensors at time step \(k\) consist of the \(j^{\text{th}}\) single-target measurement \(z_{k}^{j}\in\mathscr{X}^{d_{\text{z}}}\) produced at time step \(k\) and \(j\in\{1,\ldots,m_{k}\}\). The sequence from the initial moment to the current time \(k\) is denoted as \(\mathbf{z}_{k}\).  
A target's motion trajectory is its finite sequence of states over continuous time steps, represented as \(X=(t,x^{1:v})\). Here, \(t\) is the initial time step of the trajectory, \(v\) is the duration of the trajectory steps, and \(x^{1:v}=(x^{1},\ldots,x^{v})\) is the finite sequence representing the target state trajectory. The parameter variable \((t,v)\) belongs to the set \(I_{(k)}=\left\{(t,v):0\leq t\leq k,1\leq v\leq k-t+1\right\}\). Thus, the single-target trajectory reaching a finite time step at moment \(k\) belongs to space \(T_{(k)}=\psi_{(t,v)\in I_{(k)}}\{t\}\times\mathcal{X}^{\mathrm{nd}_{x}}\), with \(\Downarrow\) representing the union of disjoint sets. The trajectory set up to the current moment \(k\) can be represented as \(\mathbf{X}_{k}\in\mathcal{F}(T_{(k)})\). The single trajectory function can be decomposed into \(p(X)=p(x^{1:v}|t,v)p(t,v)\), where \(p(t,v)\) is defined on \(I_{(k)}\) and \(X\in T_{(k)}\).
## 4 Experimental Results
### Evaluation with Raw Data  
Table 6 summarizes the classification results with raw data. According to these results, tree-based classification methods yielded the most successful results. RF had the highest metrics with 47.65% ACC and 85.93% AUC, EXT had 47.07% ACC and 85.46% AUC rates. DT demonstrated that tree-based algorithms were successful with 42.72% ACC. With the MLP method, 44.77% ACC and 85.57% AUC rates were obtained. The best rates with tree-based algorithms were obtained using MLP. For future studies, MLP can serve as a viable alternative method to tree-based algorithms. LR, SVM, and NB algorithms yielded less successful results compared to the other methods.  
After the tests with the raw data, studies were carried out to obtain models with better performance by processing the data.
## 6 Error Analysis and Model Reliability
### Summary of Error Analysis and Model Reliability  
In this section, different methods of unreliable prediction identification that are incorporated in the MSB framework are introduced. When evaluated separately, all these methods show satisfactory results on various sites, since the data identified as unreliable indeed has significantly higher error in general. These methods approach the same problem from different perspectives, so their results are not exclusive from each other. One single prediction can be identified as unreliable by more than one method. Note that the methods applied here are not exhaustive, and alternative methods and software packages exist in the literature. The methods and the empirical parameters are selected here based on their effectiveness and simplicity, so that the unreliability identification process does not significantly influence the efficiency of the framework.  
Figure 15 shows the accuracy of unreliable predictions identified by all methods combined and its comparison to global accuracy. The results show that the overall performance of the methods is satisfactory, with the RMSE of unreliable samples 1.1-1.5 times as high as site average. The contributions of each separate method have been demonstrated in previous sections.  
Figure 16a,b show the time series of IMPROVER and MSB predictions for temperature and dew point in January 2023 and their comparison with observed values. Identified unreliable predictions are highlighted as red dots along the MSB curve. Some predictions with large prediction errors have been successfully identified, such as temperature predictions at 5 January and 19 January, and dew point predictions at 5 January and 15 January. The latter are due to sudden, real changes in dew point that are difficult for the model to capture, and inaccurate input data from IMPROVER, respectively. The identification of unreliable points agrees with intuition, since predictions on 5 January are visually unstable and oscillate with time. These low-cost identification methods do not aim to accurately capture all high-error predictions, so there are misses (e.g., temperature on 31 January)and false alarms (e.g., temperature on 12 January). In addition, the identification gives no information on whether the unreliable predictions are less accurate than IMPROVER input. For example, the minimum temperature prediction on 15 January in Figure 16a is marked as unreliable, but it is still closer to the observed value than IMPROVER.  
Figure 16: Cont.  
Figure 15: Summary of RMSE and critical error rate of all predictions and unreliable predictions due to all possible error types. **(a)** Temperature; **(b)** dew point temperature. Lead day: 0.
# Three-Dimensional Imaging via Time-Correlated Single-Photon Counting
## 3 Experimental Setup and Results
### 3D Imaging Experiment Based on Photometric Stereo Algorithm  
The photometric stereo algorithm is a method to reconstruct the 3D model of an object from multiple images. It uses a set of cameras to take pictures of objects from different directions and then obtains the 3D information by calculating the reflectivity and surface normal vector [37]. We define the direction of the light source is \(\vec{L}\), the normal unit vector of a point on the surface of the object is \(\vec{N}\), the reflectivity is \(\rho\) and the intensity of the pixel on the corresponding image is \(I\). Then we can get the Lambert model  
\[\vec{I}=\rho\vec{N}\cdot\vec{L}, \tag{11}\]  
where  
\[\vec{N}=\left(N_{x},N_{y},N_{z}\right)^{\mathrm{T}}=\left(-\frac{p}{\sqrt{p^{2 }+q^{2}+1}},-\frac{q}{\sqrt{p^{2}+q^{2}+1}},\frac{1}{\sqrt{p^{2}+q^{2}+1}} \right)^{\mathrm{T}}, \tag{12}\]  
T represents the matrix transpose operator, \(p\) and \(q\) respectively represent the two gradient directions on the surface of the object. Assuming that there are three known non-coplanar light directions, then Equation (11) can be obtained. Because \(\vec{L}\) is a full rank matrix, the normal vector can be expressed as \(\vec{N}=\frac{1}{\rho}I\cdot\vec{L}^{-1}\). Then we can get the depth information by integrating the normal vector of the object surface,  
\[\left\{\begin{array}{l}I_{1}=\rho\left(L_{11}N_{x}+L_{12}N_{y}+L_{13}N_{z} \right)\\ I_{2}=\rho\left(L_{21}N_{x}+L_{22}N_{y}+L_{23}N_{z}\right)\\ I_{3}=\rho\left(L_{31}N_{x}+L_{32}N_{y}+L_{33}N_{z}\right)\end{array}\right. \tag{13}\]  
Figure 7 shows the experimental setup. Three SPDs are used in this experiment to detect the 3D object (a rabbit model with a size of 4 cm \(\times\) 3 cm \(\times\) 7 cm) from different directions, which need to be calibrated in advance. The experimental data is processed by the photometric stereo algorithm to obtain a 3D image of the object. As shown in Figure 8, the method can reconstruct the 3D model of the object well.  
Figure 7: The schematic of the experimental setup for 3D imaging based on photometric stereo algorithm. Three SPDs are used in this experiment, and the object is a rabbit model with a size of 4 cm \(\times\) 3 cm \(\times\) 7 cm. The distance between the detectors and the object is 35 cm.
## 3 Critical Analysis of Image Tampering Detection Techniques  
A recent survey covers all of the state-of-the-art image tampering detection techniques [12]. Figure 3 showcases the current practices of image tampering. Due to the free availability of a plethora of photo editing applications and software, image tampering has become easy, but dangerous when in the wrong hands. Cyber criminals have been enjoying the full privileges of such image tampering techniques to commit false image injection attacks after compromising imaging repositories. The survey states that there is a very high probability of not being able to differentiate between tampered images and original images leading to confusion.  
Figure 4 reflects a generic framework for detecting tampered images. In the context of false image injection attacks, it is imperative to identify the tampered images in real time. It is practically infeasible for healthcare personnel or physicians to apply tampering identification techniques before analyzing medical images every time. Therefore, the existing image tampering detection techniques are inadequate to secure the privacy of sensitive medical images.  
Deep learning is considered the most popular algorithm in recent times in the field of artificial intelligence [13]. The mechanism of deep learning can efficiently capture the complex architecture of data. The algorithm has been welcomed and adopted by technology giants such as Google, Facebook etc. Unlike neural networks, deep learning has the capability to provide stability, scalability and the generalization to deal with big data [14]. It is becoming a popular algorithm for showing the highest predictive accuracy in a wide range of applications. In [15], a convolutional neural network (CNN)  
Figure 3: A taxonomy of image tampering techniques, adapted from [12].  
Figure 2: An example of a false image injection attack.  
is utilized to detect image forgery. However, the method requires labeled data to extract features for forgery identification. Considering the nature of false image injection attacks, deep learning based solutions are not suitable when prevention of the healthcare images are prioritized more than detection after the suspected compromise. The issue remains the same for all other supervised learning based image forgery or tampering identification. Although, the deep learning approaches can be customized for unsupervised learning and subsequent forgery detection, the performance might not be same as supervised learning. Next, the backbone of the proposed approach, blockchain technology, is discussed.
# Fooling in Membrane Distillation, Osmotic Distillation and Osmotic Membrane Distillation
###### Abstract
To control fouling phenomena, researchers have tried various strategies such as the consideration of feed pretreatment(s), increase of feed flow rate creating turbulent flow regime, application of periodic hydraulic and/or chemical cleanings, reduction of membrane surface roughness and/or change of its surface charge [16; 18]. It must be mentioned that MD, OD and OMD membrane technologies suffer from temperature and/or concentration polarization, or, equivalently, vapor pressure polarization. Various strategies have been adopted in order to reduce the vapor pressure polarization (i.e., the water vapor pressure at the membrane surface become closer to that of the bulk solution) including the increase of the flow rate of both the feed and permeate solutions, turbulent promoters, etc. It must be noted that concentration and temperature polarization can also have a major influence on fouling. This review deals with the observed fouling phenomena in MD, OD and OMD. It highlights different detected fouling types (organic fouling, inorganic fouling and biofouling), fouling characterization techniques as well as various methods of fouling reduction including pretreatment, membrane modification, membrane cleaning and antiscalants application. Updated research studies and interesting observations on fouling and pretreatments are summarized in tables for different MD configurations, OD and OMD. Not only the characterization techniques that have been used so far in MD are cited in the present review, but other useful techniques for fouling analysis and detection considered in other processes are included. These will improve the understanding of fouling in MD, OD and OMD, to prevent it properly.
# Selected Aspects of Designing Modular PEMFC Stacks as Power Sources for Unmanned Aerial Vehicles
## 2 Experimental Parts  
### Description of Designed and Assembled Modular PEMFC Stacks  
1.1 First Power Source: A 2000-W PEMFC Stack Constructed from Two 1-kW Modules Electrically Connected in Parallel  
The first power source (I) was a 2-kW PEMFC stack consisting of two fuel cell modules connected in parallel, each containing 44 single MEAs connected in series.  
Bipolar graphite plates were applied. All modular PEMFC stacks were cooled by air. In each stack, the end plates compressing individual MEAs were made of carbon-epoxy laminate. The MEAs consisted of Nafion 112 membranes, catalyst layers with a total Pt loading of 0.4 mg cm\({}^{2}\) at both anode and cathode sides and SGL carbon with a microporous layer as gas diffusion layers (GDL) were commercially supplied by Fuel Cell Store. The active area of each MEA is ~58.9 cm\({}^{2}\). The dimensions of the graphite bipolar plates used for assembling the PEMFC stack are 196 mm \(\times\) 30 mm \(\times\) 2.2 mm.  
These plates were compressed using threaded rods which ensured the required degree of tightness in each module. As shown in Figure 1a,b, the 2-kW PEMFC stack consisted of two fuel cell modules, (1) and (2), connected in parallel. High-current Schottky diodes were used to protect the individual stacks from reverse polarity caused by inconsistent operating conditions. The system was equipped with two independent microcontroller unit systems for management of the parallel-connected (1) and (2) modules of the PEMFC stack. The operation of the air cooling fans was controlled by means of a feedback loop based on the measurement of the temperature inside modules (1) and (2). The operating temperature of each module was set at 55 \({}^{\circ}\)C. Air fans (Sunon model PF9225), dimensions: 92 mm \(\times\) 92 mm \(\times\) 25 mm, each with electrical power of ~5 W and maximum air flow of 120 m\({}^{3}\)/h were selected for the construction of the cooling system. Four fans were installed in each module. Hydrogen was supplied first to a common pressure regulator, then independently to modules (1) and (2).
# Resource Allocation in Downlink VLC-NOMA Systems for Factory Automation Scenario
###### Abstract  
Industry 4.0 requires high-speed data exchange that includes fast, reliable, low-latency, and cost-effective data transmissions. As visible light communication (VLC) can provide reliable, low-latency, and secure connections that do not penetrate walls and are immune to electromagnetic interference; it can be considered a solution for Industry 4.0. The non-orthogonal multiple access (NOMA) technique can achieve high spectral efficiency using the same frequency and time resources for multiple users. It means that smaller amounts of resources will be used compared with orthogonal multiple access (OMA). Therefore, handling multiple data transmissions with VLC-NOMA can be easier for factory automation than OMA. However, as the transmit power is split, the reliability is reduced. Therefore, this study proposed a deep neural network (DNN)-based power-allocation algorithm (DBPA) to improve the reliability of the system. Further, to schedule multiple nodes in VLC-NOMA system, a priority-based user-pairing (PBUP) scheme is proposed. The proposed techniques in VLC-NOMA system were evaluated in terms of the factory automation scenario and showed that it improves reliability and reduces missed deadlines.  
visible light communication (VLC); non-orthogonal multiple access (NOMA); reliability; power allocation; deep neural network (DNN); user-pairing scheme; factory automation 2021229407. [[https://doi.org/10.3390/22239407](https://doi.org/10.3390/22239407)]([https://doi.org/10.3390/22239407](https://doi.org/10.3390/22239407))  
27 October 2022  
30 November 2022  
2 December 2022
# Beyond REM: A New Approach to the Use of Image Classifiers for the Management of 6G Networks
###### Abstract  
The management of cellular networks, particularly within the environment rapidly advancing to 6G, presents considerable challenges due to the highly dynamic radio environment. Traditional tools such as Radio Environment Maps (REMs) have proven inadequate for real-time network changes, underlining the need for more sophisticated solutions. In response to these challenges, this work introduces a novel approach that harnesses the unprecedented power of state-of-the-art image classifiers for network management. This method involves the generation of Network Synthetic Images (NSIs), which are enriched heat maps that precisely reflect varying cellular network operating states. Created from user location traces linked with Key Performance Indicators (KPIs), NSIs are strategically designed to meet the intricate demands of 6G networks. This research delves deep into a comprehensive analysis of the diverse factors that could potentially impact the successful application of this methodology in the realm of 6G. The results from this investigation, coupled with a comparative assessment against traditional REM usage, emphasize the superior performance of this innovative method. Additionally, a case study involving an automatic network diagnosis scenario validates the effectiveness of this approach. The findings reveal that a generic Convolutional Neural Network (CNN), one of the most powerful tools in the arsenal of modern image classifiers, delivers enhanced performance, even with a reduced demand for positioning accuracy. This contributes significantly to the real-time, robust management of cellular networks as we transition into the era of 6G.  
5G; 6G; image classification; REM; location-aware +
Footnote †: journal: _Sensors_**2023**, 23, 7494. [[https://doi.org/10.3390/s23177494](https://doi.org/10.3390/s23177494)]([https://doi.org/10.3390/s23177494](https://doi.org/10.3390/s23177494))
## 2 Materials and Methods
### Pressure Drop and Filtration Efficiency Analysis  
The breathability of the material was measured in terms of pressure drop. Pressure drop readings were taken over a range of face velocities between 0 m/s to 0.39 m/s and at each humidity level. Readings were taken using the differential manometer (UEI Test Instruments) connected to the material holder that measures the pressure difference across the sample material. Three pressure drop measurements for each face velocity were taken to determine a standard deviation.  
To calculate FE, we took six measurements at each humidity and face velocities of 0.05 m/s, 0.10 m/s, and 0.19 m/s. Each reading was taken over ten seconds, alternating between filtered and reference streams. \(FE\) is calculated using Equation (1):  
\[FE=\frac{n_{ref}-n_{f}}{n_{ref}}*100\% \tag{1}\]  
where the \(n_{ref}\) and \(n_{f}\) are the size-resolved aerosol concentrations measured by the APS for each size bin in the reference and in the filtered stream. The data collected from the APS is recorded in particle mass concentrations with an assumed density of 1.03 g/cm\({}^{3}\) for NaCl and in number density for the smaller particle bin (d\({}_{\text{p}}\) = 0.3-0.52 \(\upmu\)m). Though the APS records the particle in the range d\({}_{\text{p}}\) = 0.3-20 \(\upmu\)m, the data did not show a significant concentration of particles > 2.5 \(\upmu\)m. To evaluate the FE as a function of particle size, we have binned the data into d\({}_{\text{p}}\) = 0.3-0.52 \(\upmu\)m, d\({}_{\text{p}}\) < 0.97 \(\upmu\)m (PM1), and d\({}_{\text{p}}\) < 2.46 \(\upmu\)m (PM2.5). Any calculations showing a negative FE due to the reference and filtered measurements variance are reported as \"zero.\" The three replicates are averaged for each condition providing the standard deviation shown as error bars in the plots in the result section.  
The filter quality factor Q combines the material's FE and pressure drop describing its overall performance [11]. By combining the FE and the pressure drop, the desired functions of a mask, such as comfort and high filtration abilities, can be presented by a single value. The best filter is the one that has the highest FE with the lowest pressure drop. The \(Q\) is calculated using the following equation:  
\[Q=\frac{ln\bigg{(}\frac{1}{1-\frac{FE}{100}}\bigg{)}}{Pressure\;Drop} \tag{2}\]  
Figure 2: 3D render of the experimental setup. A custom-built aerosol chamber (**left**) contains two mixing fans, two nebulizers, and a hygrometer. Nebulizers generate polydisperse NaCl aerosols and control the humidity within the chamber. Attached to the chamber are two sample holders (**right**), one containing a material insert and one left empty to be used as a reference. The containers are connected to a mass flow rate controller and a manometer to measure the pressure drop. The flow passes through the material holders (one at a time) to the APS for data collection.
## 3 5G and TSN Integrated Networks for VPP
### Semi-Persistent Scheduling with Reserved Bandwidth Sharing and Pre-Emption
#### 4.2.2 Semi-Persistent Scheduling with Reserved Bandwidth Sharing and Pre-Emption Mechanism
Regardless of the actual bandwidth requirements for each type of traffic, the frequency of one RB is used in Figure 3 to represent the bandwidth of the traffic. Assume that the data size for each transmission of the ET flow is 1 RB, and the data size for each transmission of the TT flow is 4 RBs. Assuming \(T^{event}=T_{S}=50\) ms and \(T^{time}=1\) s, then \(T^{time}=20T^{event}\). As shown in case (a) of Figure 3, ET traffic may arrive at any time within \(T^{event}\). Since \(T^{event}\) represents the minimum interval between events, it is also possible to pass several \(T^{event}\) before the next ET flow arrives. The TT traffic arrives at a fixed time in \(T^{time}\). This is assumed every time the TT flow arrives at the second TTL. In one SPS period, if the ET flow arrives before the TT flow, the TT flow can directly use the idle reserved resources for transmission when it arrives, as shown in case (b) of Figure 3. If the ET flow and the TT flow arrive at the same time, the ET flow will be transmitted first, and the TT flow will be transmitted after the ET flow transmission is completed, as shown in case (c) of Figure 3. When the TT flow is delayed to the next \(T^{event}\), if another ET flow happens to arrive at this time, the TT flow will continue to be delayed, as shown in case (d) of Figure 3. If the event occurs after the transmission of the TT flow starts, when the ET flow arrives, the transmission of the TT flow frame will be interrupted and the resources  
Figure 3: Reserved bandwidth sharing and pre-emption diagram. will be pre-empted. The remaining frames of the TT flow will continue to be transmitted after the transmission of the ET flow is completed, as shown in case (e) of Figure 3.  
In the worst case, TT flows may require dynamic scheduling, as shown in case (f) of Figure 3. Assume that the data size of the ET flow is 3 RBs and the data size of the TT flow is 6 RBs. In this case, when events occur frequently, the possible idle reserved resources are not sufficient. The TT flow will be pre-empted multiple times and delayed for multiple \(T_{S}\). When the delay of a TT flows approaches its deadline after multiple delays, dynamic resources are directly allocated to the TT flows. On the other hand, pre-empting a TT flow multiple times may increase its packet error rate. Therefore, when the possible idle reserved resources are less than the threshold, resources will be dynamically allocated to the flow. Since the delay requirement of TT flows in the VPP system is much larger than \(T_{S}\), even if the TT flow is delayed to the next semi-persistent scheduling period, or dynamic scheduling is performed in the worst case, the target delay requirement of the TT flow can still be met.