esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### The Result of the Study  
The dataset of this study is from [[https://homepages.dcc.ufmg.br/~rfsilva/tsptw/#literatur](https://homepages.dcc.ufmg.br/~rfsilva/tsptw/#literatur)]([https://homepages.dcc.ufmg.br/~rfsilva/tsptw/#literatur](https://homepages.dcc.ufmg.br/~rfsilva/tsptw/#literatur)) (accessed on 12 May 2022). Only the request time window and unit were applied. Both the weight (sourced from the table of random numbers, shown in Appendix A) and related costs (sourced from the price table of Shun-Feng, Wuhan City, China) were processed using one unmanned vehicle. We consider the starting coordinates of the delivery center to be (0, 0), the unmanned vehicle's maximum capacity to be 150, the total flying time to be 400 min, the speed to be 1.5 km/min, the operating time to be inversely proportional to the load weight carried, the inverse ratio to be 0.5, the early arrival penalty coefficient to be RMB 0.1/min, the late arrival penalty coefficient to be RMB 0.2/min, the operating unit cost to be RMB 0.5/km, and the operating fixed cost to be RMB 50. Since the study considers crush request conditions in the model, no similar problems were found in past studies. However, we structured the mathematical model as a linear model in this study. To confirm the accuracy of the algorithm process steps, we used LINGO software to process small-scale testing to compare the experiment results of the heuristic algorithm and mathematical model computed from LINGO in order to inspect the accuracy of the designed algorithm. Under the premise of only selecting 10 requests, the solution computed from LINGO is the same as from the heuristic algorithm; the greater the number of solutions there are to be computed, the more accurate the solution. Therefore, the study considers the result of the heuristic algorithm to be convergent, and that the accuracy of the algorithm can be guaranteed.  
Next, we choose one case to verify the result (the related data is shown in Appendix B). In the match state, information of the request is known, and it is calculated by the traditional TSP solution, and the UAVs will execute this solution. The sequence of the route is items 16, 20, 18, 19, and 6 in TSP1 case, and the profit of routing is 102.24. The sequence of routing is (16, 3), (28, 16), (31, 15), (36, 14), and (25, 5). When the new request occurs in real-time, the decision-making platform will add it into the experimental result of the match state, and the algorithm will determine that the new requests will be accepted if the new request can increase revenue more than the scheduling of the match state; otherwise, the request is rejected. Simultaneously, the decision-making platform determines new scheduling in real-time. The simulation result of this study is shown in Figure 5. We find that the new sequence of routing is (5, 4), (28, 16), (31, 15), (36, 14), (25, 5), and (16, 3). Comparing the two results between the match and real-time state, the decision-making platform would select the request near the service center.  
Figure 5: Difference result between match state and real-time state.
# Protecting Data at Risk of Unintentional Electromagnetic Emanation: TEMPEST Profiling
## 4 From van Eck Phreaking to TEMPEST Profiling
### Masking and Overlaying  
Masking and noise addition are countermeasure techniques used to add random noise or meaningless signals to the original emanation. Masking is a successful, cost-effective countermeasure, although the masking signal and additional noise must be carefully selected, as the signal may affect the proper functioning of other devices or nearby systems. Spatial noise is one of the active protection techniques usually used when passive methods are not feasible for various reasons. EM noise, including EMI, radio noise, radio interference, and active interference masking, refers to a dynamic EM phenomenon that has no informational content and can overlap or merge with a valuable signal. Spatial EM noise generators aim to prevent informative signal interception for subsequent analysis and retrieval of original information [96]. A study described in [97] demonstrates the use of an EM noise generator as a technique for obfuscating side-channel attack analysis that complicates the sensitive data extraction from EM emanations. The software defense mechanism is explored through a case study involving the Raspberry Pi. The authors investigated the sensitivity of devices using the AES algorithm. The AES implementation was found to be prone to side-channel attacks. In [98], the authors introduce a new cognitive approach to the mitigation of side-channel attacks without relying on architecture or operating system. The proposed method, unlike current protection strategies relying on randomization, is based on cognitive perturbations that appear legitimate but are designed to deceive attackers and lead them to wrong conclusions. By strategically inserting perturbations at key points in time to introduce an additional operation, the attacker is misled, and any extracted data are rendered useless. The proposed methodologies are listed in Table 8.  
Another effective countermeasure is to superimpose an EM field stronger than the leaking emanation. The effectiveness of the eavesdropping barrier, however, is highly dependent on the interfering signal modulation pattern, so the interfering signal must be chosen very carefully [1]. This can be achieved by designing devices to reduce emissions at the component, system, circuit board, and device level [99].
## 5 Experiments
### The Impact of Interpolation Method  
The interpolation methods compared in this experiment are Linear, Cubic, Nearest, Previous, and Next. We conducted the experiment based on the model with the best performance in Table 2. Table 3 shows the average interpolation performance comparison for each method with MPJPE score. The Cubic method uses a polynomial to interpolate values. It is sensitive to outliers, meaning that the inclusion of even a single outlier has the potential to significantly decrease the overall performance. In our performance experiments, the interpolation performance was lower than those of the other methods. The other methods performed similarly, but Previous and Next simply copied values, which is not the purpose of this study, and Nearest performed well, but not as well as Linear Interpolation. The Linear Interpolation method is stable in the sense that outliers do not bring down the overall performance, and it has the highest interpolation performance.
## 3 Simulation and Experimental Results
Figure 10:1 (**a**) FFT output of the digital horn sound signal of the proposed AVAS device; (**b**) FFT output of the digital horn sound signal of the AVAS device used in one of the other suppliers. To date, there appears to be no more research in the literature specifically focused on the production of digital horn sounds. Furthermore, studies addressing digital horn sounds generated by AVAS devices are limited. In existing AVAS products featuring digital horn sounds, sound production is typically handled by sound engineers relying on traditional music theory and mathematical principles. This study introduces a novel approach, producing digital horn sounds using software equipped with oscillator and equalizer functions that adhere to traditional methods. This innovative methodology represents one of the key contributions of this study.
## 3 Numerical Evaluation
### Achievable Rate Performance
In addition, the obvious degradation in the fluctuation range of the achievable rate, up to more than 115.4 Mbps, could be identified for the case of the NSPW non-Lambertian optical beam configuration, with an intensification of the rate fluctuation of about 39.4 Mbps compared to the baseline Lambertian case.
## 2 Principles of Electrical Microgrid Management
### Demand Side Management  
Traditionally, the balance between energy generation and consumption in electrical grids has been accomplished applying Supply Side Management (SSM) techniques. Basically, SSM is based on the increase of the power generation in energy plants during the demand peak hours. Nevertheless, for sudden small variations in the energy demand, SSM is not effective because of the long time needed for energy plants to activate their electrical generators. Therefore, another possibility is to apply a Demand Side Management (DSM) strategy, a concept known and proposed for many years. There are different but near visions in the scientific community concerning the DSM procedures. For several authors [16], DSM can be categorized, according to the timing and the impact over the customer, into: Energy Efficiency, Time of Use, Demand Response (DR) and Spinning Reserve. Other authors make the distinction between DSM (energy audits, improvements of the operation and maintenance, replacement or retrofit, load-shaping strategies, and installation of control devices), DR and Distributed Energy Resources programs [17].  
Nowadays, in the frame of SGs, new techniques have been proposed [18; 19; 20]. In general, these solutions are based on economic incentives and intelligent management of generation and storage systems. Once again, the role of intelligent counters based on WSANs and communication techniques is crucial in order to achieve a correct DSM mechanism.
## 2 State of the Art  
### Die Bending  
Die bending is a bending process with a straight tool movement where the tool system consists of a punch and a die. Depending on the tool shape, a distinction is made between V and U die bending. It is often used industrially due to its increased robustness, compared to other bending processes. As long as the bending part has not established full contact with the punch and die, it is referred to as free bending. As soon as the workpiece establishes form closure in the tool, i.e., the bending angle is equal to the die angle, free bending is complete and the coining process begins. This is characterized by a sharp increase in the process force (\(F_{\text{P}}\)) which is required to press the workpiece into the die. This significantly increases the dimensional accuracy of the finished component and makes precision die bending the most accurate bending process. An exact calculation to quantify the punch force (\(F_{\text{P,req}}\)) required for coining is not known from the literature and can be almost arbitrarily large. However, it can be estimated approximately, e.g., according to [10] using Equation (1). The die angle is represented by \(\alpha\), \(r_{p}\) indicates the punch radius, \(b\) describes the sheet width and \(s_{0}\) the initial sheet thickness of the semi-finished product. Frictionalinfluences are taken into account by the friction coefficient \(\mu\), the tensile strength \(R_{\rm m}\), 0.2%, yield strength \(R_{\rm p0.2}\) and uniform elongation \(A_{g}\) represent material parameters.  
\[F_{\rm P,req}=\frac{b_{\rm 50}}{2}\left\{\frac{R_{m}-R_{p0.2}}{3A_{g}}\bigg{(} \frac{s_{0}}{r_{\rm P}+\frac{s_{0}}{2}}\bigg{)}^{2}+R_{p0.2}\bigg{(}\frac{s_{0} }{r_{\rm P}+\frac{s_{0}}{2}}\bigg{)}\right\}\frac{\cos\frac{\alpha}{2}+\mu\sin \frac{\alpha}{2}}{\frac{\alpha\pi}{2\cdot 180}} \tag{1}\]  
Although die bending results in higher dimensional accuracy compared to free bending processes, it usually requires a corresponding tool for each bending angle. In order to reach high accuracies, the process parameters usually have to be determined empirically. If process conditions fluctuate, such as varying materials or sheet thicknesses, this process often has to be repeated individually. A large number of tools, lengthy process setup and high forces can quickly result in high process costs. Moreover, it is often not possible to react appropriately to the uncertainty in forming processes, induced by the semi-finished product, tool, machine and environment, due to the rigidity of the process.
# Soft X-ray Microscopy Techniques for Medical and Biological Imaging at TwinMic--[PERSON]
## 1 Introduction  
X-ray microscopy gathers a cohort of techniques that allows the acquisition of two and/or three-dimensional views of an object at micrometric and sub-micrometric spatial resolutions by the use of X-ray radiation [1, 2]. Soft X-ray microscopy, which is a subset of it, employing soft X-ray photons (usually in the 200-2000 eV energy range) as the illumination source, is nowadays a standard technique in synchrotron radiation facilities [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]. Even though X-ray microscopy cannot compete with the nanometer or sub-nanometer spatial resolution of electron microscopes, it allows for a more relaxed sample preparation, especially for biological and organic specimens, and for higher penetration depths, thus permitting thicker samples to be analyzed. Moreover, the high sensitivity of X-ray-spectro-microscopy, such as X-ray fluorescence (XRF) microscopy, makes it an essential tool for tracking chemical changes at the micro- and nanoscale.  
The TwinMic beamline end-station at Elettra [7] is a soft X-ray microscope originally designed to provide a collection of imaging techniques in a single instrument [14], specifically with an easy switch between scanning transmission X-ray microscopy (STXM) and full-field modes. Afterward, STXM has become the more dominant operation mode, especially due to the development of the first worldwide low-energy X-ray fluorescence (LEXRF) system [15, 16, 17].  
TwinMic capabilities have attracted the interest of scientists from different research fields, from the material sciences [18], especially from the energy field [19, 20, 21, 22], to environmental sciences [23, 24, 25, 26, 27, 28, 29, 30, 31], the life sciences [32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46], and cultural heritage [47].  
In the present manuscript, we describe the TwinMic imaging and spectroscopic techniques made available especially to the biological and medical user community. We highlight the TwinMic potentialities and the recent achievements in the medical and biological field through suitable research cases, demonstrating its contribution and complementarity with other microscopy approaches.  
Finally, we introduce new ongoing developments regarding advanced smart data acquisition solutions and novel data analysis methods.
# Real-Time Anomaly Detection in Cold Chain Transportation Using IoT Technology
## 5 Discussion
Furthermore, the relationship between the core product temperature and the ambient temperature recorded by the monitoring system in this case is unknown. Therefore, additional work needs to be undertaken, trying different sensor locations and monitoring how the ambient temperature recorded changes. The warning system currently deployed, which offers alerts via SMS or email, can also be a limitation. It is sometimes the case that logistics staff working in warehouses do not have GSM cell coverage, rendering alerts sent on the SMS system undeliverable. While the email system provides an alternative alerting mechanism to ensure that while out of cell coverage but within WiFi range the end-user still receives alerts, many end-users do not have push notifications enabled for email due to the quantity they receive on a daily basis. Future work will focus on expanding the alerting system to a smartphone application such as that of [70] so that push notifications can be delivered while in the range of WiFi. Additionally, technologies such as the real-time anomaly detection system presented in this paper could be deployed at each stage of the cold chain to allow for the uninterrupted monitoring of perishable goods. This would result in anomalies being detected not only during transportation but also during storage, at the retailer, etc. Each of these efforts could further reduce potential food wastage in agri-food businesses.
## 2 Data Source and Surveying Methodology
### Annual Trends in Scientific Publication  
The global annual number of YIG publications from 1961 to 2022 is depicted in Figure 1. The patterns were collected using the \"thin film\" and \"magnetic properties\" keyword selection method. As the graph demonstrates, the annual number of articles and annual growth rate fluctuated over time. According to the Scopus archives in 1961,an article was written by [PERSON] et al. [56] and published in the _Journal of Applied Physics_. Four authors were from the United States. This research reported the fabrication and properties of thin ferrite films using vacuum evaporation. These ferrites films attained high magnetizations of up to 2200 gauss, which enables optical transmission investigations of the material and should permit the observation of spin-wave resonances to determine magnetic exchange constants. From 1961 to 2000, a total of 233 papers were published; 239 papers were between 2001 and 2011, and 941 papers were between 2012 and 2022. Until the year 2000, the growth rate of papers was approximately 1%, which is fairly modest. Since then, the number of published papers has increased by approximately 1.6% per year until 2011. The highest growth rate was then observed in 2020, with a 9.66% increase in the total percentage of articles represented. During the selected period, the number of publications in YIG research quickly expanded, from a low of 233 papers from 1961 to 2000 (29 years) to a high of 941 papers from 2012 to 2022 (10 years). During recent years, there has been discernible growth. The highest number of papers was recorded in 2020, when 138 were published, marking a 9.66% growth rate in publications.  
YIG materials are employed in phase shifters, isolators, and circulators [57]. This is due to YIG having small magnetic damping. Currently, YIG materials are popular for spintronic and magnon device applications. YIG-based devices may provide faster domain wall motion, magnetization switching with lower currents, and more energy-efficient information transfer than metal-based devices [58]. This paper discusses YIG film development for future research in the area.  
Figure 1: Global annual trend of YIG from 1961 to 2022.
# Improved PEG-Based Construction of Analog Fountain Codes
## 3 Improved PEG-Based Construction
### EXIT Charts of Different Weight Coefficients  
Different weight coefficients contribute differently in the decoding process at CNs. To illustrate that, EXIT charts for various weight coefficients are presented below.  
Let \(\mathbf{b}=\mathbf{b}_{0}\cup\mathbf{b}_{1}\cup\cdots\cup\mathbf{b}_{d-1}\) denote the information bits. Without loss of generality, for each CN \(c_{j}\) connected to \(d\) VNs \(b_{i_{0}},b_{i_{1}},\cdots,b_{i_{d-1}}\), let \(g_{i_{s},j}=w_{s}\), where \(s\in\{0,1,\cdots,d-1\}\). Then \(b_{i_{s}}\in\mathbf{b}_{s}\). In this way, information bits are divided into \(d\) parts, where bits in \(\mathbf{b}_{s}\) are connected to CN through an edge allocated with \(w_{s}\).  
Let \(I_{\text{A,C}}\) and \(I_{\text{E}_{\text{s,C}}}\) denote the input mutual information and extrinsic mutual information of the \(s\)-th weight coefficient for CN, respectively. For each \(\mathbf{b}_{s}\), the mutual information between \(\mathbf{b}_{s}\) and its corresponding log-likelihood ratio (LLR) value \(L_{s}\) computed at the CNs is represented as  
\[I_{\text{E}_{\text{s,C}}}=\sum_{\mathbf{b}_{s}\in\{-1,1\}}\int_{-\infty}^{+ \infty}p_{E}(L_{s}|\mathbf{b}_{s})\text{log}_{2}\frac{p_{E}(L_{s}|\mathbf{b}_{ s})}{p_{E}(\mathbf{b}_{s})}dL_{s}. \tag{4}\]  
Here, \(p_{E}\) represents the conditional probability distribution function of \(L_{s}\). As can be seen in Equation (4), each calculation of \(I_{\text{E}_{\text{s,C}}}\) is based on bits \(\mathbf{b}_{s}\) and LLR \(L_{s}\). It means that after one decoding process at the CNs, CNs output extrinsic mutual information \(I_{\text{E}_{\text{s,C}}}\) to VNswhich are connected through weight coefficients \(w_{s}\). It is difficult to compute \(p_{E}\) directly because of the convolution operation. Thus, the Monte Carlo methods are used to estimate \(p_{E}\) for AFC [7]. Note that \(L\sim\mathcal{N}(0,\sigma_{L})\), where \(\sigma_{L}\) can be generated from \(I_{\text{A,C}}\) as  
\[\sigma_{L}=J^{-1}(I_{\text{A,C}}). \tag{5}\]  
The detailed definition of \(J\)-function can be reached in [15].  
Figure 2 shows the mutual information for four different weight coefficients of AFC, respectively. It can be seen that with the same \(I_{\text{A,C}}\), the mutual information is larger when weight coefficient is larger. These results indicate that larger weight coefficients contribute higher in the BP decoding.
## 3 Time-Frequency Representation
### Wigner-Ville Distribution  
The WVD is a basic non-linear analysis method, which was originally proposed by [PERSON] in quantum mechanics, and the Wigner-Ville distribution of signal \(x(t)\) is expressed as:  
\[\begin{array}{l}\mathrm{WVD}\ (t,\ f)=\int_{-\infty}^{\infty}z\big{(}t+\frac{ \tau}{2}\big{)}\overline{z}\big{(}t+\frac{\tau}{2}\big{)}e^{-j2\pi ft}d\tau\\ z(t)=x(t)+j\int_{-\infty}^{\infty}\frac{x(u)}{t-u}du=x(t)+jH[x(t)]\end{array} \tag{4}\]  
where \(z(t)\) is the analytical signal of \(x(t)\), \(H[x(t)]\) represents the Hilbert transform of signal \(x(t)\), and \(\overline{z}\) is the complex conjugate of \(z\)[30].  
If \(x\ (t)=x_{1}(t)+x_{2}(t)\), then:  
\[\mathrm{WVD}\ (t,\ f)=\mathrm{WVD}_{\mathrm{x1}}(t,\ f)+\mathrm{WVD}_{\mathrm{x2}} (t,\ f)+2\mathrm{Re}[\mathrm{WVD}_{\mathrm{x1}\mathrm{x2}}(t,\ f)] \tag{5}\]  
where \(\mathrm{WVD}_{\mathrm{x1}\mathrm{x2}}(t,\ f)\) is the cross term of the Wigner-Ville nonlinear distribution:  
\[\mathrm{WVD}_{\mathrm{x1}\mathrm{x2}}\ (t,\ f)=\int_{-\infty}^{\infty}x_{1} \Big{(}t+\frac{\tau}{2}\Big{)}\overline{x}_{2}\Big{(}t+\frac{\tau}{2}\Big{)} e^{-j2\pi ft}d\tau \tag{6}\]
## 2 Definition of System Requirements and Assessment of Fitting Communication Technologies
### Information Relevance  
If the PEEV-WS messages were broadcasted, it would be difficult to judge if the message content, often a request to take an action, is relevant for the message recipient. For example, an EV sends a request to a vehicle that is interfering with an EV's trajectory. If the message is broadcasted, then all the vehicles in the communication range receive the message, so the vehicles need additional information to evaluate if they interfere with the EV's trajectory. [PERSON] et al. [16] proposed to communicate a segment of the EV's route to other vehicles on the road. This can certainly solve the problem, but it introduces a risk that this sensitive data could potentially be abused. The solution adopted in this paper is to let an EV to determine who is interfering with its future trajectory and to select the specific and relevant communication targets only. Vehicles equipped with the VANET communication modules are exchanging cooperative awareness messages (CAMs) containing their instantaneous position, speed, heading, acceleration, and other parameters. This information can be used by an EV to identify a prospectively interfering vehicle. A routing protocol can then be used to find a communication route to the relevant vehicle.