esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### EBM/PBF Additive Manufacturing  
#### 3.3.1 Process Optimization  
Photographs of the EBM/PBF samples for each of the five optimization iterations are shown in Figure 13. The somewhat high observed sintering temperature of the alnico powders (\(\sim\)1100 \({}^{\circ}\)C) is relatively close to the melting temperature of the material (\(\sim\)1257 \({}^{\circ}\)C), leading to difficulty in maintaining a consistent heat flux through the build chamber without over-sintering the powders during preheating. Surface temperatures below \(\sim\)900 \({}^{\circ}\)C were observed to be susceptible to charge induced powder scattering events (i.e., \"smoking\" [29; 30; 31]), one cause of premature failure of these runs. On the other hand,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Br (kG)** & **H\(\boldsymbol{\text{G}}\) (Oe)** & **BHmax (MGOe)** & **Ms (kG)** \\ \hline Sintered (12 h) & 7.50 & 1660 & 4.30 & 10.40 \\ Cast & 7.50 & 2231 & 5.62 & 10.60 \\
1-1 & 7.06/* 7.81 & 2021/* 1833 & 3.72/* 3.80 & 11  
over-sintering would cause the entire layer to curl and separate from the powder bed, and the resulting vacuum gap breaks the conductive pathway and exacerbates the effect. For run #3, the 304 SS substrate was replaced with a Ti-6 Al-4V substrate with the idea that the lower thermal conductivity of Ti-6 Al-4V (\(\sim\)7 W/mK, vs. \(\sim\)15 W/mK for 304 SS) would help maintain a higher surface temperature in the build region. Run #3 eventually swelled from excessive heat input and was halted when the surface asperities interfered with the powder spreading. Run #4 utilized a higher surface temperature, lower beam currents and a higher base beam speed, and this resulted in a relatively stable build temperature and smooth, flat layering. These parameters were chosen as the basis for the larger sample in run #5, which was sized for magnetic property analysis.  
Figure 13: Photographs of **(1–4)** successive build trials and **(5)** the first successful PB-EBF build of alnico to a height of approximately 8 mm. Sample 1 was cut into sections for initial analysis not included here.  
During run #5, a second melting step using a defocused beam was added to each layer to improve the flatness of each layer and facilitate improved layering. Table 11 shows the chemistry of the sample produced in run #5 determined via XRF and EDS and at various points of processing (raw powder and after SOQ). Apparent from these results is that significant volatilization of aluminum has occurred during the EBM/PBF process, which is not unexpected given the high temperatures required to achieve process stability (e.g., proper sintering of the powder). Additionally, initial run parameters, especially run #1, indicated Cu segregation or deposition on the substrate during EBM/PBF processing.
## 2 Configurations and Design Principles
### Array Design: Sequential Rotation  
A good CP performance is essential for satellite communication, as the 3 dB AR bandwidth of the elements is comparatively a little narrow to meet the requirements of the application. The sequential rotation technique is used to increase the CP radiation performance and AR bandwidth of the phased array. The sequential rotation technique can disrupt the mutual coupling effect among array elements through rotation among them, thereby improving the antenna's cross-polarization performance and axial ratio performance. For a four-element subarray, four elements are respectively rotated around the center in an anticlockwise or clockwise direction; the phases of these elements are set to 0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\), respectively, and RHCP or LHCP will be obtained. With this method, the AR bandwidth of the array can be greatly increased.  
The basic principles of sequential rotation are shown in Figure 4. The C-band elements rotate in a clockwise direction to realize LHCP, while the S-band elements gradually increase the phase in an anticlockwise direction to realize RHCP within the same aperture.  
For brevity, the final layout of the proposed one-quarter subarray is shown in Figure 5. Due to the utilization of the as-proposed antenna element based on the ceramic dielectric substrate, it can be observed that the presence of air among the elements is also beneficial to the lightweight design of the antenna and offers more flexibility for antenna installation. Furthermore, the single-layer structure is not only conducive to the large-scale formation of phase arrays but also beneficial to the design of radio frequency feeding networks on satellite solar panels operating in space.  
Figure 4: Principles of sequential rotation technique.  
Figure 5: The final layout of the S-/C-band CP antenna subarray.
## 5 Threat Analysis and Risk Assessment (TARA)
### Attack Feasibility Rating  
The attack feasibility rating can be executed adhering to one of the following three approaches: attack-potential-based, Common Vulnerability Scoring System (CVSS)-based, and attack-vector-based. The latter two require in-depth knowledge of all the elements, technologies, and actors involved as well as an advanced system architecture design unavailable at the time of the realization of this research, the attack-vector-based approach has been pursued. According to it, the attack feasibility rating should be determined based on evaluating the predominant attack vector of the attack path. The scale used for the ratings takes into account the contextual framework that underlies the potential exploitation of attack paths. The rating of attack feasibility escalates as the attacker's remoteness increases in relation to the targeted attack path. This assumption stems from the idea that obtaining physical access to an asset requires more effort as well as being less desirable for a potential attacker than accessing it through the network. Thus, the scale used is composed of the following levels: high, medium, low, and very low, respectively mapped to the following context criteria: network, adjacent, local, and physical. More details can be found in the ISO/SAE 21434 document.
## 2 Experimental Studies
Therefore, both transmitter and receiver optics needed to be optimized further. In the test area, direct sunlight and background infrared (IR) radiation degraded the performance of the receiver. Therefore, in front of the receiver, to effectively filter out background light, a band-pass (FL905-10, Thorlabs) and long-pass filters (FEL850-10, Thorlabs) were mounted as close as to each other. Then, the total transmittance of the filters drops to 56%. In this work, for all experiments, the laser was fired at a repetition rate of 1 kHz with a 100 ns pulse width. As soon as the laser fired, we started acquiring data and the number of frames acquired can be controlled through the field programmable gate array (FPGA) board.
## 3 Acquisition Parameters and Evaluation Metrics
### Evaluation Metrics  
For spatial resolution, we use the criteria full width at half maximum (FWHM, \(-6\) dB beam width) [27] and point targets to analyze the performance. For contrast resolution, we use anechoic objects to analyze its contrast ratio (CR) [27] and generalized contrast-to-noise ratio (gCNR) [28].  
Spatial resolution could be measured through the response to individual scatters of the beamformed image. Because SCF mainly improves the lateral resolution given by Rayleigh criterion [18], we use the FWHM in the lateral direction to evaluate the performance of each beamformer. To be more specific, a lower FWHM means a narrower lobe and higher spatial resolution.  
To measure the contrast resolution, the criteria CR and contrast-to-noise ratio (CNR) are often used. They are measured using the following equation [27]:  
\[CR=20\,\log_{10}(\frac{\mu_{ROI}}{\mu_{B}}), \tag{20}\]  
\[CNR=20\log_{10}(\frac{\mu_{ROI}-\mu_{B}}{\sqrt{\sigma_{ROI}{}^{2}+\sigma_{B}{} ^{2}}}), \tag{21}\]  
where \(\mu\) is the added value of the beamformed envelope signal over a certain region and \(\sigma\) is the standard deviations of the signal. The subscript \(ROI\) and \(B\) represents the region of interest and background, respectively. However, [PERSON] et al. showed that the metric CNR has the opposite trend compared with the spatial resolution, which means it may not be an effective independent judgment criterion for contrast resolution [29]. [PERSON] et al. also showed CNR's characteristic which is susceptible to dynamic range [28]. Thus, we use gCNR to replace CNR. gCNR is resistant to dynamic range alterations [28], which could help us exclude the difference in dynamic range. It is given by the equation:  
\[\text{gCN}R=1\text{-}OVL, \tag{22}\]  
where \(OVL\) is the overlap area between the two probability distributions of the region of interest and background [28].
# Joint Image Compression and Encryption Using IWT with SPIHT, Kd-Tree and Chaotic Maps
## 2 Related Works
### Joint Image Compression and Encryption  
Several joint image compression and encryption algorithms have been proposed in the literature that achieves robust security in the compressed domain. [PERSON] et al. [33] proposed a secure JPEG algorithm for simultaneous compression and encryption. During JPEG compression, encryption is introduced by scrambling the DCT coefficients in the quantization step. [PERSON] et al. [34] proposed a hybrid compression and encryption algorithm that is based on the generalized knight's tour, DCT and the Chen chaotic map. In this method, the nested generalized knight's tour was used to scramble the plain image, and DCT and quantization coding were utilized to compress the image with a high compression ratio. Diffusion was performed by encrypting some of the DCT coefficients by using the Chen chaotic system. [PERSON] et al. [35] proposed joint compression and encryption using integer wavelet transform, SPIHT and multiple chaotic maps. First, integer wavelet coefficients are scrambled with the Lorenz map and the Henon map and compressed using SPIHT. At the end, the SPIHT bit stream is encrypted with a logistic map sequence. [PERSON] et al. [36] proposed a hybrid technique by combining compression using SPIHT and a chaos-based cryptosystem to yield a crypto-compression scheme that is based on a modified logistic map. In [37], joint compression and encryption of image data through cross-coupled chaotic maps and Non-uniform Discrete Cosine Transform was proposed. The compression of data is performed on blocks and permutation of these blocks and diffusion are carried out simultaneously. In [14], a joint lossless image encryption and compression scheme that is based on integer wavelet transform (IWT), set partitioning in hierarchical trees (SPIHT) with a hyper-chaotic system, a nonlinear inverse operation, and Secure Hash Algorithm-256 (SHA-256) was proposed. A JPEG based compression encryption algorithm is presented in [38]. This scheme is known as content adaptive because of the encryption key for altering the discrete cosine transform (DCT) orthogonal transformation and the encryption of the AC and DC coefficients performed through the key generated via BLAKE2 hashing algorithm applying to the plain image.
## 3 Two Methods for BDS-3 Orbit Determination
### BDS-3 Orbit Determination Based on SS  
The BDS-3 experimental satellite orbit determination based on NES is an effective method to avoid the observation inconsistency of the different tracking networks caused by different tracking signals. Similarly, the fixed parameters, such as station coordinates, tropospheric delays, and clock offsets of the iGMAS stations, can be introduced into the BDS-3 orbit determination by precise point positioning (PPP). Therefore, the SS method is also adopted in this study. Figure 6 depicts the flowchart of BDS-3 experimental satellite orbit determination based on iGMAS and MGEX networks by SS.  
Two methods for improving the accuracy of BDS-3 experimental satellites orbit estimations were provided by using different tracking networks. The NES contains more information on observations with the same data sources as the SS, which is more precise (in Section 4, as the perfect modeling of the stochastics, the orbit results estimated by the NES are more accurate and precise than the SS) in the  
Figure 5: Flowchart of BDS-3 orbit determination based on NES.  
Figure 6: Flowchart of BDS-3 orbit determination based on SS.  
parameter estimation. Based on the improved cycle-slip detection and repair algorithm and the two methods of orbit determination, data quality, and availability considerably improved given the limited number of observations. The precise orbit determination and the corresponding accuracy analysis are discussed in the next section.
# Multimodal Diarization Systems by Training Enrollment Models as Identity Representations +
## 1 Introduction
The assignment process can be seen as a binary task that consists of comparing each face or speaker present in the audiovisual file with all the enrollment identities and determining whether it belongs to one of them or not. A simple approach employed is a cosine similarity by averaging the representations of all the enrollment files for each identity to obtain the verification scores and decide the identity. Nevertheless, these representations are extracted from DNN systems which are not trained with this objective, so instead of using only cosine similarity, complex back-ends [11; 12] are often applied to improve the discriminative ability of these representations. The drawbacks of this kind of back-end are that it involves a more complex training process and, therefore, a high computational cost. Thus, to carry out the assignment process in the face subsystem of this work, a new approach based on [13] was applied to model the enrollment identities. This new approach was shown to be a promising technique to characterize each enrollment identity with a single learnable vector for the speaker verification task, but this is the first time that this technique has been applied in face verification. To train this back-end, the approximated detection cost function (aDCF) [14] was used as the objective loss. Hence, in this work, different parameter settings of this loss were explored and their effect on the errors produced by the system was studied. This paper is organized as follows. In Section 2, we provide a description of the challenge and the dataset employed. Section 3 describes the new approach based on training face enrollment models by network optimization and the loss function used as objective for the training. Section 4 details the face diarization subsystem. The speaker diarization employed is explained in Section 5. In Section 6, the performance metric employed is detailed. Finally, Section 7 presents and discusses results, and Section 8 concludes the paper.
## 3 Force Density Function of Variable Density Micro-Texture Ball-End Milling Cutter
### Test Formula for Cutter-Chip Contact Area  
In the process of cutting titanium alloy with a micro-textured ball-end milling cutter, the milling force is distributed unevenly along the length and width of the cutter-chip contact, and the cutter-chip contact area directly affects the stress density function of the cutter surface. With the change of cutting parameters and texture density in the cutter-chip compact contact area, the cutter-chip contact area also changes. Therefore, it is necessary to solve the experimental formula of the cutter-chip contact area to determine the force density function of the variable density micro-textured ball-end milling cutter when milling titanium alloy. The experimental data of tool-chip contact length \(l_{f}\) and width \(l_{w}\) were fitted and calculated by using MATLAB software. The relationships between \(l_{f}\) and feed per tooth \(f_{z}\), as well as \(l_{w}\) and cutting depth \(a_{p}\) were linear functions. Taking a micro-textured cutter with a texture density of 0.09-0.09-0.09 as an example, the obtained linear functions are  
\[\begin{array}{l}f_{z}=0.7808l_{f}-0.3824\\ a_{p}=0.6872l_{w}-0.2181\end{array}. \tag{10}\]  
Substituting Equation (10) into Equation (9), the milling forces for milling a titanium alloy using a micro-textured ball-end milling cutter with a texture density of 0.09-0.09-0.09 are  
\[\begin{array}{l}F_{x}=1129.5\cdot(0.6872l_{w}-0.2181)^{0.4685}\cdot\left(0. 7808l_{f}-0.3824\right)^{0.4301}\\ F_{y}=866.99\cdot\left(0.6872l_{w}-0.2181\right)^{0.6881}\cdot\left(0.7808l_{f} -0.3824\right)^{0.3336}\\ F_{z}=309.99\cdot\left(0.6872l_{w}-0.2181\right)^{0.6825}\cdot\left(0.7808l_{f} -0.3824\right)^{0.1807}\end{array}. \tag{11}\]
## 3 Results  
The following profiles are presented in UTC (4 h ahead of local time). The limitations on the weighting functions due to the sensing path geometry greatly reduce the reliability of the \(C_{n}^{2}\) predictions after roughly 350 m in each profile. A threshold was used during the pseudoinverse calculation to further reduce measurement noise. The threshold value determined the limit such that smaller singular values of \(M\) would not be inverted. This reduces the number of dropouts in the profile, where the noise causes the \(C_{n}^{2}\) estimates to be negative and therefore unphysical. Each of the following profiles used a pseudoinverse threshold of 35, which is 2.6% of the largest singular value of the weighting function matrix. The spatial resolution of the results is set by the profiling technique and is not constant along the path. Past the final notch in Figure 6a, there is no spatial resolution. The resolution along the path is shown in Figure 9 by the constant turbulence case calculated by the BLS. The imaginary ripples in the constant \(C_{n}^{2}\) case are noise due to the reconstruction technique and the limitations of the experimental setup (i.e., the number of available subapertures and their spacing).  
Figure 8: Example of how a turbulence profile calculated by Tikhonov regularization is affected by ridge parameter selection. (**a**) Under regularized profile with \(\alpha=15\); (**b**) Optimally regularized profile with \(\alpha=30\); (**c**) Over regularized profile with \(\alpha=60\).  
Figure 9: 25 July 22:00 UTC turbulence profile with \(\alpha=30\). At this time, the BLS measured a \(C_{n}^{2}\) of \(4.0\times 10^{-14}\,\mathrm{m}^{-2/3}\), which is multiplied by the influence function to represent a profile under constant turbulence.
# IoT-Portrait: Automatically Identifying IoT Devices via Transformer with Incremental Learning
## 5 Experimental Results and Analysis
### Device Identification of Lab Environment  
We built a small-scale smart home scene of six IoT devices: three smart cameras, two central controller devices, and a smart light. These devices are all connected to a central router via WiFi. We used the information collection module to label these six devices automatically. After collecting, the Os information and available services of 5 devices are obtained. Furthermore, in a further step, the type and vendor information of 2 devices are obtained after using ARE.  
We manually labeled the missing device information and collected traffic data for each device. All the devices operate normally during the traffic collection phase, and none perform frequent shutdown or startup operations. To complete the collection, all devices run for three consecutive days. In the model training phase, we selected 31 bytes for each packet of 20 packets as the initial setup. The training and verification data are selected from the previous two days, with a ratio of 6:4. The test data are selected from traffic on the third day, and the prediction accuracy is 99.5%. Compared with the recognition rate of information search, when the network behavior among devices is sufficiently distinguishable, the passive solution using traffic behavior-based identification can address the limitation of less openness of IoT devices compared with the active scanning method.
# Undrained Behavior of Microbially Induced Calcite Precipitated Sand with Polyvinyl Alcohol Fiber
## 5 Conclusions  
Clean Ottawa sand was treated with CCs of 0%, 2%, and 4% and fiber contents of 0%, 0.2%, 0.4%, and 0.6%. Its undrained behavior was investigated in terms of cohesion and friction angle, and its hydraulic conductivity was also examined. The test results are summarized as follows:  
1. As the CC increased from 0% to 4%, the friction angle and cohesion increased from 35.3\({}^{\circ}\) to 39.6\({}^{\circ}\) and from 0 to 93 kPa, respectively. This is mainly attributed to the precipitated calcite, which caused the cementation of the sand particles and made the particles more angular.
2. For a specimen with a CC of 4%, as the fiber content increased from 0% to 0.6%, the friction angle and cohesion increased from 39.6\({}^{\circ}\) to 42.8\({}^{\circ}\) and from 93 to 139 kPa, respectively. This can be explained by the fiber bridging of each sand particle.
3. The hydraulic conductivity of clean Ottawa sand decreased by a factor of more than 100 as the CC increased from 0% to 4%. This is because the calcite that precipitated within the sand particles  
Figure 8: SEM images of the sample with 0.6% fiber under different magnifications: (**a**) 150 times magnification; (**b**) 1500 times magnification.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multicolumn{5}{c}{**Fiber Content (\%)**} \\ \cline{2-6}  & **Clean Sand** & **0** & **0.2** & **0.4** & **0.6** \\ \hline Cohesion (kPa) & 0 & 93 & 127 & 135 & 139 \\ Friction angle (\({}^{\circ}\)) & 35.3 & 39.6 & 41.5 & 42 & 42.8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Strength properties of 4% CC samples with different fiber contents.  
reduced the void volume. On the other hand, the amount of fiber inclusion only had a minor effect on the hydraulic conductivity of 4% calcite precipitated sand because it already had a dense structure.  
Conceptualization, S.-G.C. and T.H.; Methodology, S.-G.C. and S.-S.-P.; Formal Analysis, S.-G.C. and T.H.; Writing--Original Draft Preparation, S.-G.C.; Writing--Review and Editing, S.-S.P.; Supervision, S.-S.P.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (No. NRF-2018R1A5A1025137).  
The authors declare no conflict of interest.