esa-sceva/satcom-chunk-collection huggingface dataset:
# Privacy Protection Based on Special Identifiers of Intersection Base Computing Technology
## 3 Protocol Process and Proof of Security  
**Problem description**: The two-party PSU/PSI-CA problem is an extension of the two-party PSI problem, which requires the final calculation of \(|X\cup Y|/|X\cap Y|\) without revealing any other information (including any element information on oneself and the size of one's own set). This problem corresponds to many privacy computing scenarios in reality: for example, in social networks, two users can compare the proportion of their identical friends without disclosing their specific friend information to calculate social relationship overlap. In the field of health, customers holding private genetic data can confidently interact with public risk gene databases, thereby knowing their probability of contracting a certain disease. This agreement aims to discuss and solve the PSU-CA/PSI-CA problem.  
Scheme idea: Based on the above problem description, we know that the application of this protocol may face situations where the number of sets is not equal and the security requirements are very high. In such cases, we pay more attention to the non equilibrium of sets and the corresponding security. Following this idea, we propose using appropriate encoding protocols and semi-homomorphic encryption methods to solve our real-world problems. By using the appropriate data encoding protocols, complex data can be transformed into binary data that are convenient for computation using internationally recognized computer encoding protocols. The detailed operation of the protocol in this paper is shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Framework** & **N** & **t** & **Security** & **Protocols** & **License** \\ \hline ABY [16] & 2 & 1 & \(\Circarrow\) & A/B/Y & Lgpl-3.0 \\ PrivC [17] & 2 & 1 & \(\Circarrow\) & A/B & – \\ ABY3 [18] & 3 & 1 & \(\bullet\)**or**\(\Circarrow\) & A/B/Y & No license \\ Sharemind [19] & 3 & 1 & \(\bullet\)**or**\(\Circarrow\) & A/B & Payware3 \\ Trident [20] & 4 & 1 & \(\bullet\) & A/B/Y & \\ MP-SPDZ [21] & \(\geq\)2 & N \(-\)1 & \(\bullet\) or \(\Circarrow\) & A/B or Y & MIT-like \\ MOTION [22] & \(\geq\)2 & N \(-\)1 & \(\Circarrow\) & A/B/Y & MIT \\ \hline \end{tabular}
\end{table}
Table 1: Related mixed-protocol MPC frameworks with N parties, threshold t, and active (\(\bullet\)) or passive (\(\Circarrow\)) security.  
The PSI-CA Construction of This Article  
In this section, we will provide protocol construction and related proofs for solving the PSU-CA and PSI-CA problems. Firstly, we provide two protocol constructions (Section 3.1) to solve the PSU-CA and PSI-CA problems, and then provide their correctness proofs (Section 3.2).
# From Femtoseconds to Hours--Measuring Dynamics over 18 Orders of Magnitude with Coherent X-rays
## 4 Studies
### XPCS at Free-Electron Laser Sources
#### 4.2.2 Sub-Microsecond Dynamics of Non-Equilibrium Dynamics  
With the start of the European XFEL as the first MHz repetition rate hard X-ray FEL [148], sequential XPCS is possible down to the sub-\(\upmu\)s level. This has been demonstrated in an experiment at the SPB/SFX instrument [164] on a prototypical soft matter sample of silica nanoparticles dispersed in water [34]. The European XFEL offers a different pulse scheme than the normal-conducting FEL sources. Every 0.1 s pulse trains of up to 2700 pulses and repetition rates up to 4.5 MHz are generated. In the first XPCS experiment, the pulse trains consisted of 120 pulses with a repetition rate of 1.128 MHz. Diffraction patterns taken with the AGIPD are shown in Figure 10a. It was found, that already a single train of 120 pulses is sufficient to obtain the dynamics of the sample, as shown by the intermediate scattering functions at different \(q\) in Figure 10b. Different from the XPCS experiments at LCLS and SACLA as reported above, the temperature increase after one pulse does not relax before the next pulse impinges on the sample. Consequently, the sample heats up step-wise [156] resulting in a speeding-up of the dynamics during the pulse train. This was demonstrated by two-time correlation functions that enabled the determination of \(g_{2}\) starting from different pulse numbers within the train. Besides the water, the nanoparticles show a higher temperature increase, but with a faster relaxation. However, existing models of Brownian motion of such hot particles [165] overestimated the diffusion coefficients, instead the speeding-up could be modelled by non-linear and time-resolved temperature increase. Depending on the fluence of the X-ray pulses, effective temperatures above 450 K have been reported, indicating extremely overheated water states. The dynamics of the particles were still diffusive. While no futher radiation damage was observed, such XPCS experiments can be applied for measuring both equilibrium and non-equilibrium dynamics during gradual temperature increase.  
Besides the dynamics study, the speckle contrast was measured as well for the different pulse schemes of the European XFEL. With the given setup, single pulse speckle contrasts were measured between 0.2 and 0.3 in SAXS geometry. This perfectly matches the calculated values taking beam size, wavelength and the AGIPD geometry into account. Most importantly, the same values were obtained from \(g_{2}\) function measured from static samples. Consequently, there is no significant contribution from SASE fluctuations during the pulse trains. Train-train correlations were about a factor of 3 lower, reproducing the results from LCLS and SACLA. These results indicate that a superconducting accelerator such as the European XFEL provides a superior stability that perfectly matches the requirements of sequential XPCS experiments.
# Estimating the Performance of MIMO SC-FDE Systems Using SISO Measurements
###### Abstract  
The demand for ubiquitous telecommunications services forces operators to have a special concern about signal quality and the coverage area they offer to their customers. This was usually checked by using suitable propagation models for Single Input Single Output (SISO) systems, which are no longer the case for new and future mobile generations, such as 5G and beyond. To guarantee good signal quality coverage, operators started to replace these models with Multiple Input Multiple Output (MIMO) ones. To achieve the best results, these models are usually calibrated with Drive Test (DT) measures; however, the DTs available for MIMO propagation models are sparse, in contrast to SISO ones. The main contribution presented in this paper is a methodology to extend the propagation models of SISO systems so they can be applied in MIMO systems with Single-Carrier and Frequency-Domain Equalization (SC-FDE), while still using DTs acquired for SISO systems. This paper presents the impact on Bit Error Rate (BER) performance and its coverage area resulting from the application of our proposed method. We consider a MIMO SC-FDE system with an Iterative Block Decision Feedback Equalization (IB-DFE) receiver and we present the improvement expressions for the BER that we illustrate with some simulations.  
Keywords:propagation model; SISO; MIMO; IB-DFE; BER performance +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Characteristics of the Seismic Signals of the Slope Failure (Baseline Case)
This complex interplay of wave forces shows that slope stability is not just a matter of static geological conditions but is significantly influenced by the dynamic forces exerted by waves. These forces cyclically and progressively weaken the slope, leading to various types of failure over time. Monitoring the seismic signals and analyzing the time-frequency spectrum and spectral magnitude profile provide valuable insights into the dynamic wave actions and slope failure processes.
# Floating Photovoltaic Plant Monitoring: A Review of Requirements and Feasible Technologies
###### Abstract
Floating photovoltaics have a relatively recent history, the first systems having been built less than 20 years ago [8], but it is receiving rapid and growing interest, as will be better discussed in paragraph 2. In addition to the issue of the indirect saving of land areas mentioned above, the reasons for this growth can be attributed to specific advantages of this technology:  
1. Better average PV system efficiency due to the mitigating thermal effect resulting from the thermal capacity of the water [9];
2. Indirect effect of water evaporation minimization [10];
3. Shorter installation times compared to ground-based systems [11];
4. Higher power density as compared to ground-based systems [12]. As mentioned above, paragraph 2 of this work is devoted to a review of floating PV technologies, mainly focused on inner water basins; paragraph 3 discusses more specifically issues related to monitoring technology related both to the part of an FPV plant above the surface of the water, and the one correlated to the quality of the water; paragraph 4 is a review of the sensors that can be used, in particular for monitoring water quality; in paragraph 5, the problems related to the autonomous monitoring of water quality are discussed; in paragraph 6, possible autonomous systems for monitoring water basins for photovoltaic use are proposed and analyzed; discussions are reported in paragraph 7, while the conclusions will be drawn in paragraph 8.
## 3 Methodology
### Trajectory Determination
#### 3.2.2 Observation Models
Once an initial sparse set of 3D points and exterior orientation estimates are known for each of the key frames, the orientation of the non-key frames which are selected to create a 5 Hz dataset to match the GPS observation rate can be determined. A reduced set of points with a strong spatial distribution is selected from each non-key frame based on an initial estimate of that frames geometry. These points are matched to points within the key frames and a spatial resection is performed to determine the exterior orientation of the non-key frames. The initial alignment of the camera was determined based on ground control targets placed near the take-off and landing area. A minimum of three targets was used for this purpose, however, the use of four targets is preferable in order to provide redundancy and avoid gross errors. If the boresight angles \(e_{b}^{c}\) between the camera and body frames are known, the orientation component of each exterior orientation estimate can be transformed into the body frame giving \(q_{c}\). This provides the observation model used for each video frame. For consistency the orientation is calculated in quaternion space as follows:  
\[q_{c}=e_{\phi}\cdot(e_{b}^{c}\cdot e_{k}) \tag{15}\]  
where \(e_{\phi}\) is the estimate of camera orientation in the camera frame given by the SfM algorithm. \(e_{k}\) is the stochastic measurement noise term within the SfM estimates of orientation. This noise is weighted based on the output of the bundle adjustment and non-key frame observations are attributed a higher noise than the surrounding key frames.
# A Software Defined Radio Evaluation Platform for WBAN Systems
## 3 Proposed SDR Evaluation Platform for WBAN Systems  
### Functionality Description  
As mentioned in Section 2, WBAN supports three types of communications, namely NB, UWB, and HBC channel. Various methods of encoding, operation frequency, modulation, and other communication parameters shall be utilized in different communications in WBAN systems. Further, the security scheme could also vary based on the security level of the communications. Therefore, for the evaluation purpose of WBAN systems, all supported configurations including methods of encoding and decoding, operation frequency, methods of modulation, and specific security scheme need to be implemented in the evaluation platform. In this paper, an SDR evaluation platform for WBAN systems is proposed that supports all the communications specified in the IEEE 802.15.6 standard utilizing RF as a carrier. In addition, since HBC does not utilize RF as the communication carrier, it is not supported in the proposed evaluation platform. The detailed architecture and specifications are given in the subsections to follow.
## 4 Experiments
### Quantitative Comparisons
#### 4.2.2 Robustness Evaluation  
Spacial robustness evaluation (SRE) and temporal robustness evaluation (TRE) [2] are commonly utilized to evaluate the robustness of a tracker. SRE evaluates the robustness of a tracker with different bounding boxes by shifting or scaling the ground-truth in the initial frame, while TRE evaluates the robustness of a tracker with different initialization at a different start frame. The experimental results of robustness evaluation are shown in Figure 5. It can be seen from the figure that the CFSA tracker performs better than other trackers in terms of different evaluation metrics. In the precision plots, SAMF and KCF achieve the best performance among these state-of-the-art trackers, while CFSA tracker outperforms SAMF and KCF with better scores of 85.4% in TRE and 77.3% in SRE.  
Figure 4: Precision and success plots over OTB-100 benchmark dataset using OPE.  
Figure 3: Precision and success plots over OOTB benchmark dataset using one-pass evaluation.
# Effect of Ti-B Grain Refiners on Wear and Corrosion of the A332 Alloy with Sr Modification
## 1 Introduction
In the mechanical properties, the alloy reinforced with graphene shows higher hardness values, followed by the condition with grain refiner and Sr. A similar effect was observed in the wear tests, highlighting the positive impact of combining grain refiner and Sr modifier. Although some studies analyze the effect of grain refining in the Al-Si alloys, few papers study the relationship between corrosion, wear, mechanical properties, and grain refinement. [PERSON] and [PERSON] [18] suggest that an increase or decrease in corrosion resistance of the alloy with grain refinement depends on the ability of the environment to make the alloy passivated. [PERSON] et al. [19] mentioned that the addition of a GR alloy did not reveal any marked improvements in the corrosion properties of Al-Mg-Si alloy. Revision and extensive literature review of the works of [PERSON] and [PERSON] [20] about grain refining, and by [PERSON] et al. [9] about grain refinement and corrosion in metals, shows several methods for grain refining and their results applied to cast aluminum alloys. Nevertheless, there is a lack of studies on the relationship among corrosion, wear, and mechanical alterations in the combination of specific AlTiB master alloys and Sr modifiers in the hypoeutectic Al-Si alloys. This research is aimed at studying the effect of individual addition of two different grain refiners, Al5 Ti1B and Al5 Ti2B, in an A332 hypoeutectic aluminum alloy modified with Al10 Sr in ball-on-disk wear, corrosion, mechanical properties, and microstructural changes.
## 4 Parallel Design and Implementation of DMAS Algorithm  
In this paper, PySpark is used for the parallel implementation of DMAS. PySpark is an interface that gives access to the Spark framework using the Python programming language. It employs the RDDs to increase the execution speed by performing in-memory computations.  
The iterative procedure of the FDMAS makes it suitable to be implemented using a big data framework such as Spark. The parallelism and dependency among different processes in the DMAS algorithm was first identified. There are multiple MapReduce operations for the parallel scheme of the DMAS algorithm. The entire input data are uploaded to the  
Figure 2: Working principle of the Spark framework. The input data are distributed on multiple machines (workers) and operated in parallel. The reduce operation retrieve the intermediate results and integrate it for the final results (output).  
shared memory accessible to the master and all worker nodes. The master node converts these data into RDDs, and each map and reduce operation executes the required operation on the data block. These MapReduce operations are associated with one computing unit. When the entire data set is split and executed simultaneously, the execution time is expected to reduce.  
The two most computationally intensive parts of the DMAS algorithm can be parallelised: delay and pixel value calculation. Since the delay values in the DMAS algorithm need to be calculated once, its parallel executions do not affect the overall performance [34]. The most computationally and memory-intensive part is the multiplication of signals in pairs and the summation of all antennas for each focal point in the resulting images. In this part, each process is independent and can be executed in parallel by caching intermediate results in memory across iterative computations.
## 5 Experiment
### Localization  
To confirm the positioning error, two localization cases are compared with 50 number of particles setup for executing the particle filter. Figure 7(a),b contains the localization results when using the learned trajectories as the prior for the particle filter. Figure 7(c),d contains the localization results without using the learned trajectories. Because the user is moving, it is unable to measure the ground truths of the user's path all the time. Instead, some waypoints are designated to alarm the user to record its location with time stamps. Based on these waypoints, the average error is calculated. Ten positioning experiments by ten different users for each case are implemented. The average error of the developed algorithm is 2.2 m. By contrast, the average error for the localization without the learned trajectories is 3.4 m. In the worst case, the proposed algorithm has 2.8 m error and the benchmark one has 5.1 m. In the best case, each has 1.3 m and 2.5 m error, respectively. Thus, the map learning improves the localization accuracy by 1.2 m on average. It is noted that in Figure 7(c) the positioning is remarkably inaccurate because of the indoor environment composing majority of open space, whereas the localization on the aisles are relatively more precise. On the other hand, as shown in Figure 7(a), the accurately learned map information supports to overcome this environmental restriction by enhancing the accuracy. The last remark of the experimental result analysis between Figure 7(b),d is the positioning regarding the south room. Because the true map information is not given in this paper, it is unable for the localization in Figure 7(d) to recognize the wall between the room and aisle. On the other hand, due to aid of the trajectory learning, the proposed method recognizes the isolation by the wall, in which Figure 7(b) shows the clear-separate position estimations between the room and the aisle.  
Figure 7: Experimental result of trajectory learning.
## 4 Results
### Local Waveform Variability
Figure 8: Waveform evolution and SAR imagery over a large ridge feature on 2016-03-15 with time separation less than five minutes. **(a)** SAR HV image with blue rectangles marking the altimeter footprints, (**b,c**) HH and HV mean backscatter with standard deviation interval in light blue and minimum and maximum values in grey, **(d)** waveforms, **(e)** mean inverse power and freeboard and **(f)** SSD and PP. [Contains Copernicus Sentinel data 2016]  
Figure 11 shows the SAR image and waveform spatial evolution over a small MYI floe that is split in two by a band of thinner ice with high SAR backscatter and smooth texture, i.e., brash ice or thin ice with a rough surface or frost flowers atop. The data was taken on 2018-02-24 with a time separation of about 10 min, i.e., for average drift speeds between 6 cm/s and 10 cm/s that means 36 m to 60 m. In the SAR image (Figure 11a) only the spatial context separates the MYI floe from the high backscatter FYI. A subtle difference between co- and cross-polarization channel mean backscatter can be observed. In the first one the backscatter around the feature is slightly increased, while for the latter one the backscatter is slightly decreased (Figure 11b,c around sample 20). The waveform peak power decreases with the start of the MYI floe but increases significantly over the thinner FYI area (Figure 11d)). That means that the FYI area must include areas of smooth ice, e.g., low density layer of frost flowers or open water between small flows that are responsible for the high power return. PP and SSD show only slight variations between the FYI before the floe and the MYI floe itself, while the difference in IMP is more pronounced (Figure 11e,f). The freeboard at the first part of the floe is mostly negative and only the smaller second part shows an increase of freeboard compared to the surrounding FYI. The three presented test cases show that altimetric waveforms respond well to changes of the sea ice surface in the order of a few along-track altimeter footprints. Retrieval of a robust freeboard estimation of floes, however, requires significant larger features of a few kilometers to overcome uncertainties in estimating the surface elevation. Figure 9: Evolution of waveforms over the ridge in vicinity of the large negative freeboard value. Figure 10: Waveform evolution and SAR imagery over a large MYI floe on 22 February 2018 with time separation of about 20 min. (**a**) SAR HH image with blue rectangles marking the altimeter footprints, (**b**,**c**) HH and HV mean backscatter with standard deviation interval in light blue and minimum and maximum values in grey, (**d**) waveforms, (**e**) mean inverse power and freeboard and (**f**) SSD and PP. [Contains Copernicus Sentinel data 2018]
# Medical Image Segmentation Network Based on Dual-Encoder Interactive Fusion
## 2 Related Work
### Combining CNN and Transformer for MIS  
In recent years, efforts have been made to fully leverage the strengths of CNNs and Transformer networks by integrating these two architectures for MIS. One notable example is TransUnet [20], which innovatively combines Transformer networks and CNNs in a sequential manner. This model uses the Transformer network to process feature maps produced by the CNN, enhancing segmentation performance through capturing global information via a self-attention mechanism. Similarly, [PERSON] et al. [36] introduced UTNet, which features an efficient self-attention module in its encoder to minimize the computational demands of capturing multi-scale long-range dependencies. Additionally, UTNet incorporates a unique self-attentive decoder designed to restore fine-grained details from skip connections. Contrasting with this serial integration, some recent studies have adopted a parallel approach to simultaneously extract both global and local features. For example, [PERSON] et al. [22] developed the TransFuse network, which uses separate encoders for CNNs and Transformers. These encoders independently extract image features, which are then fused and relayed to the decoder via a BiFusion module. In a similar vein, [PERSON] et al. [23] proposed a dual-encoder hybrid model combining CNN and Transformer technologies, where both encoders generate features that are subsequently fused and forwarded to the decoder for up-sampling through skip connections. Furthermore, [PERSON] et al. [24] introduced HiFormer, employing a combination of a Swin Transformer and a CNN-based encoder to create two distinct multi-scale feature representations. Additionally, [PERSON] et al. [37] developed BEFUnet, which enhances robustness and accuracy in edge segmentation through local cross-attention feature fusion and dual-encoder feature integration. Importantly, as depicted in Figure 1a, these parallel approaches typically fuse the features from both encoders in a straightforward manner and directly use them in the decoder, often lacking effective interaction between the different encoders.  
Figure 1: Two network architectures based on parallel combination of CNN and Transformer. (**a**) Fusion of only features from different encoders. (**b**) The architecture of our proposed approach.