esa-sceva/satcom-chunk-collection huggingface dataset:
# Elliptic-Curve Crypto Processor for RFID Applications
### Our Contributions  
To address the current challenge, we have provided a PM architecture--specifically for RFID applications over \(GF(2^{163})\)--with a focus on low-latency (high-performance) and low-area parameters. Moreover, we have only used an 8-bit interface to load ECC parameters as well as the coordinates of an initial point externally. The building blocks of our architecture (shift buffers/registers, arithmetic unit and register array) are \(m\)-bit length, where \(m\) is the key size, i.e., 163. The additional contributions of this work to achieve low-latency and area reduction are given as follows:  
* To achieve low-latency, we have reduced the clock cycles with the use of:
* three shift buffers/registers to keep ECC parameters and a constant during the PM computation;
* same size of the input/output interfaces in all blocks/units of the proposed architecture.
* Towards the area reduction, we have used (for the first time) the Dimitrov-Jarvinen inversion algorithm with shared hardware resources of employed squarer and multiplier blocks for RFID applications.
* Finally, an efficient controller based on a finite-state-machine (FSM) that is used to control the inferred logic inside the proposed architecture.
## 3 Case Study Calculation
### Road Asset Value Calculation  
The road asset value, shown in Equation (11), represents the total value of the road asset, accounting for both its current category and structural pavement condition value:  
\[\begin{split} RAV=& RC_{CC}+ACNP*\frac{RSL}{DSL}\end{split} \tag{11}\]where \(RAV\) is the road asset value in EUR, \(RC_{cc}\) denotes the acquisition cost of the road asset in its current category in EUR, \(ACNP\) denotes the acquisition cost of pavement based on present prices in EUR, \(RSL\) is the structural life expectancy of the current pavement in years, and \(DSL\) is the design life of the desired pavement in years.  
The capital investment required to achieve a desired road asset value depends on the rehabilitation or improvement costs needed to bring the road asset to its target performance and serviceability standards. When the existing road asset has salvage value, this amount can be deducted from the required investment. For example, if the road pavement retains sufficient bearing capacity, the following process applies: First, calculate its structural life expectancy; then design either an overlay or mill-and-replace rehabilitation to meet the desired service life requirements for new pavement. The overlay thickness design utilizes the structural life expectancy of both the road structure and its materials, enabling road authorities to optimize capital costs while achieving the target asset value.
###### Abstract  
The dynamic and heterogeneous nature of marine environments, combined with severely constrained communication and energy resources, presents distinct challenges in constructing underwater Communication, Positioning, Navigation, and Timing (CPNT) systems compared to terrestrial Positioning, Navigation, and Timing (PNT) architecture. To address the inherent limitations of conventional decoupled CPNT systems - including high costs and low efficiency in communication and energy utilization - this study aims to propose a unified underwater CPNT (\(U^{2}CPNT\)) system that coordinates multi-modal data and resource allocation, thereby optimizing CPNT service performance in harsh underwater conditions. In this study, Cramer-Rao Lower Bound (CRLB) formalization is applied to theoretically analyze the feasibility of \(U^{2}CPNT\) system, and the design of \(U^{2}CPNT\) system is presented to realize the integrated design of CPNT. To validate the system performance, a real \(U^{2}CPNT\) system was built and sea trials were conducted. With \(U^{2}CPNT\) architecture, the integrated CPNT service can be provided, the positioning error is lower, the positioning continuity has improved by 7.68%, the velocity estimation error is less than 1 m/s, making \(U^{2}CPNT\) a potential solution for underwater CPNT service.  
acoustic communication; underwater positioning; navigation; timing; \(U^{2}CPNT\)
# MALDI-TOF/MS Analysis of Extracellular Vesicles Released by Cancer Cells
## 2 Applications  
### Mesothelioma  
Malignant mesothelioma [19] is a rare and aggressive malignancy with a poor prognosis, whose development is related to asbestos fiber exposure and genetic predisposition. The gold standard for its diagnosis is invasive pleural biopsy, and since no recommended screening tests are available for this cancer, the identification of novel biomarkers for its detection is vital. In what was the first application of MALDI-TOF as a tool for the analysis of EVs secreted by tumor cells, [PERSON] et al. [20] performed the characterization of the protein composition of exosomes released by malignant mesothelioma cell lines. Exosome preparations were subjected to one-dimensional electrophoresis (1-DE) and excised protein spots were then trypsin-digested and characterized using MALDI-TOF. The identified proteins were involved in antigen presentation, signal transduction, migration, and adhesion. Some years later the same authors reported a procedure [21] for the isolation of exosomes from cell lines in vitro followed by 1-DE coupled to MALDI analysis to characterize their protein composition. It was highlighted that this technology can provide useful information on the biological functions of exosomes, as demonstrated in the case of the exosomes derived from mesothelioma that may promote tumor growth.
# Pilot-Assisted OFDM for Underwater Acoustic Communication
## 4 Shallow Underwater Acoustic Channel Model
### Random Channel  
Various experimental studies suggest that fading in a shallow UWA channel is more accurately represented using a Rician fading model [36]. Researchers in [37; 38] studied sea trial data from several experiments where the Rician shadowed distribution was found to  
Figure 2: Shallow underwater acoustic channel model.  
have the closest fit. In this work, the parameters used were \(m=0.4\) and \(k=2.0\)[38]. The signal \(c_{2}(t)\) was computed as:  
\[c_{2}(t)=h_{r}(t)\ \otimes\ c_{1}(t)+n(t) \tag{19}\]  
where \(n(t)\) and \(h_{r}(t)\) are the ambient noise and the Rician fading impulse response [39; 40] modelled together with the Doppler shift using the Rician object in MathWorks MATLAB R2019. Figure 3 represents the sample delay profile and path gains.
# Future Industrial Networks in Process Automation: Goals, Challenges, and Future Directions
## 4 Evolution Challenges
### Security Challenges  
The transition to more open network architectures, combined with Big Data and Cloud computing, will bring profound opportunities to smart manufacturing systems. At the same time, new security challenges are presented with billions of smart devices interconnected in the world of Industrial IoT. The biggest security concern comes from connecting IoT devices, including sensors, actuators, and edge-computing units, with existing controllers and end devices in automation and manufacturing information networks. These challenges are, of course, on top of applied secure protocols like OPC UA, or the necessity of securing other protocols like Modbus TCP, which are separate essential topics to address.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Types** & **Periodicity** & **Period** & **Synch.** & **Data Size** & **Criticality** \\ \hline
**Isochronous** & Periodic & \textless{}2 ms & Yes & 30–100 B (f) & High \\
**Cyclic** & Periodic & 2–20 ms & No & 50–1000 B (f) & High \\
**Events** & Sporadic & n.a. & No & 100–1500 B (v) & High \\
**Network Control** & Periodic & 50 ms–1 s & No & 50–500 B (v) & High \\
**Config. and Diag.** & Sporadic & n.a. & No & 500–1500 B (v) & Medium \\
**Best Effort** & Sporadic & n.a. & No & 30–1500 B (v) & Low \\
**Video** & Periodic & Frame Rate & No & 1000–1500 B (v) & Low \\
**Audio and Voice** & Periodic & Sampling Rate & No & 1000–1500 B (v) & Low \\ \hline \hline \end{tabular}
\end{table}
Table 2: Traffic types [13] (f: fixed, v: variable).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Types** & **802.10** & **TC** & **802.10** & **802.145** & **CT** & **802.10** & **802.10** & **802.10** & **802.10** & **85** \\ \hline
**Isochronous** & M & 6 & M & M & \(\circ\) & \(\circ\  
The existing OT and IT security approaches and policies [14; 15] will need to be adapted to embrace these new IoT security challenges. One important direction is the authorization management that assigns the different access levels to only access the necessary data from the OT domain. From the device perspective, smart industrial devices have much smaller footprints of computing power and operating systems. The convention in the traditional automation network assumes that no software or patches are needed once installed, which leaves them to be an important attack surface that is vulnerable to new types of malware or denial of service attacks.
# Robust DOA Estimation Using Multi-Scale Fusion Network with Attention Mask
## 2 Related Work
In addition, CVNN has shown faster convergence and better generalization than RVNN due to the fact that complex-valued representations capture richer information about the underlying data distribution, leading to more efficient learning and improved generalization to unseen data [44]. Therefore, wave information can be fully extracted when using CVNN compared to RVNN. In the field of speech processing, CVNN has shown excellent potential in various tasks [24; 25]. CVNN has great potential to be adapted to DOA estimation in challenging acoustic environments. At present, there is a paucity of consideration of CVNN network models for sound source DOA in the spherical harmonic domain. The objective of this study is to design a more suitable CVNN-based DOA framework for speech localization.
# Numerical Simulation of Non-Isothermal Mixing Flow Characteristics with ELES Method
## 4 Results and Discussion  
### The Phenomena of Backflow  
As a comparison to the LES results conducted by [PERSON] et al. [5] shown in Figure 10a, mean axial and vertical velocities were used to depict the time-averaged streamlines in the y-z plane in the W-ref case, and the characteristics of the jet bending and eddies' formation obtained with ELES are shown in Figure 10b, which show good agreement. On the other hand, due to the prediction ability near the wall of ELES, it was found that backflow happened near the wall around the tee region, which had not been mentioned in the LES results conducted by [PERSON] et al. [5]. It was noteworthy that these phenomena were not special cases at certain moments as they were mean results. It can be seen as a vortex generated by backflow, which drives the mixing of hot and cold flows.  
In order to study the influence of temperature fluctuation caused by the backflow near the wall around the tee region, a y-z plane of W-ref case at \(X=0\) was taken to form the contour of temperature fluctuation intensity, the result of which is shown in Figure 11. Figure 11a shows the overview of temperature fluctuations around the right-angle structure upstream of the tee, and temperature fluctuations in the region approximately 1 mm away from the wall are shown in Figure 11b. It can be found from Figure 11 that the temperature fluctuation intensity is close to 0.3, which is almost the same downstream of the pipe shown in Figures 6-8. Obviously, the right-angle structure upstream where the welding exists is exposed to temperature fluctuations, which makes the thermal stress-sensitive area more prone to fatigue failure. Therefore, the phenomena of the backflow in the tee region are vital to the study of thermal fatigue and need more attention.  
Figure 11: Temperature fluctuation intensity distribution in the plane of \(X=0\) of the W-ref case.  
Figure 10: Time-averaged streamlines in y-z plane of W-ref case at \(X=0\). (**a**) LES results conducted by [PERSON] et al. [5]; (**b**) ELES results.  
In the following, more detailed quantitative analyses are further carried out to form a comprehensive understanding of the backflow in thermal mixing and the relationship between fatigue and backflow in the tee.
# Improved Anti-Collision Algorithm for the Application on Intelligent Warehouse
## 3 Group Mapping Query Tree Algorithm
### Example of GMQT  
It is assumed that there are six tags: A(00010010), B(00001000), C(00110011), D(1101111), E(00100000), F(01001001). The details of the identification procedure with GMQT are shown in Table 3. Their identification procedure can be described as follows:  
First slot: The reader broadcasts QUERY(\(\varepsilon\)) to all tags, and the tags transmit their IDs and group mapping data. The group mapping data the reader receives is '00??'; the reader can infer that there are three groups '00', '01', '11', and push them into GS.  
Second slot: The reader pops new group '00' out from GS and broadcasts QUERY(\(\varepsilon\),00); Tags A and B reply with their remaining IDs except prefix and the reader receives '00??0??'. There is no collision in the  
Figure 2: The flow chart of GMQT.  
top three bits. This means that there is only one possible combination '000' in group '00'. Thus, the reader sets the first collision bit to '0' and '1', respectively, and executes PUSH(PS,'0000') and PUSH(PS,'0001').  
Third slot: The reader pops new group '01' out from GS and broadcasts QUERY(\(\varepsilon\),01); Tags C, D and E reply and the reader receives '??????????'. In the decode data, every bit is a collision bit, and it means that there are two possible combinations '001','110' of tags. The reader executes PUSH(PS,'001') and PUSH(PS,'110').  
In the 4 th, 5 th, and 6 th slots, Tags F, B and A respond, respectively, and then these tags are identified.  
7 th slot: The reader broadcasts QUERY(001). Tags C and E reply with their remaining IDs except '001' and the reader receives '00???'. There is only one collision bit in the top three bits, so the reader sets the first collision bit to '0' and '1', respectively, and executes PUSH(PS,'001000') and PUSH(PS,'001100').  
In the 8 th, 9 th, and 10 th slots, Tags D, E and C respond, respectively, and then these tags are identified. The reader terminates the identification process when no prefix exists in the stack.
## 2 Materials and Methods
### Equations and Analytical Methods
#### Efficiency of the Prototype  
Different from the efficiency of the propeller system (refer to Equation (4)), the efficiency of the prototype is defined as the ratio of available power to the engine power. The available power can be found by knowing the thrust of the propeller system and the velocity of the incoming flow [36].  
\[\eta=\frac{P_{a}}{P_{engine}}=\frac{T\cdot\
u}{P_{engine}} \tag{6}\]  
where \(P_{a}\) is the available power, \(P_{engine}\) is the motor or engine power, \(T\) is the thrust, and \(\
u\) is the velocity of the incoming flow.
### Summary and Contribution  
Overall, the research gaps on LSDM assembly according to the above literature review can be summarized as follows:  
1. Previous research on LSDM assembly have focused on simulating the microgravity environment during the ground assembly process, and the assembly error model's significance for service performance improvement is ignored.
2. The assembly error modeling methods in the previous literature lack consideration of the different environmental factors between ground assembly and service in space.
3. The tolerance allocation in the previous literature is completed before the assembly task starts, which means each processes' tolerance remains unchanged during assembly. Its input is the as-designed data, and the considerable value of the as-built data collected from the actual assembly process is ignored.  
In this paper, assembly error modeling and tolerance allocation methods are proposed for LSDM assembly. The core contributions are shown as follows:  
1. An assembly error modeling method considering the differences in the environment between ground assembly and service in space is proposed, and the an LSDM assembly error model is constructed. This contribution compensates for the first two research gaps mentioned above. On the one hand, the assembly error model is used to further reduce gravity's influence on the service performance in space, on the premise of gravity compensation. On the other hand, the assembly error model considers the different environments of the ground assembly and service in space, regarding gravity variation as the main influencing factor and introducing the changes in the hinge clearance and truss rod length caused by gravity variations into the error model.
2. A tolerance dynamic allocation method based on the as-built data is proposed. This contribution aims to compensate for the last research gap mentioned above. The as-built data measured during the assembly process have considerable value for tolerance allocation. The proposed method can dynamically allocate tolerance for subsequent processes using the completed processes' as-built data, which can relax the tolerance while guaranteeing the assembly quality. Therefore, it can reduce the assembly difficulty and cost. Of course, if the as-built data exceed the tolerance, the dynamic tolerance allocation model can be used to evaluate whether the current process should be reworked or to tighten the subsequent processes' tolerances to ensure the final quality.
## 2 Literature Review
### Last-Mile Distribution Solution  
The complexity of last-mile logistics [8] has given rise to diverse solutions, including home delivery, crowdsourcing logistics, drone delivery, and parcel lockers (PLCs) [9]. Among these, home delivery is a relatively straightforward method, yet frequent delivery interruptions occur due to recipients' absence. Crowdsourcing logistics optimizes resource allocation through the sharing economy but relies heavily on social trust and exhibits significant service quality fluctuations [10]. Drone delivery is suitable for remote areas but faces regulatory constraints and payload limitations [11]. Compared to other last-mile delivery solutions, parcel lockers (PLCs) offer a \"contactless self-pickup\" model that balances cost and delivery convenience, demonstrating unique advantages particularly in high-density urban areas [6; 12; 13; 14]. However, when parcel lockers cannot yet autonomously move or deliver users to PLCs via autonomous driving, enhancing user convenience significantly improves satisfaction with this delivery method [15]. Existing PLC research predominantly focuses on static facility location selection while neglecting the dynamics of user behavior, resulting in a disconnect between theory and practice [16; 17].
## 5 Experiments  
In this section, experimental results and analyses are presented to demonstrate the accuracy, efficiency, and sensitivity of GeneDiff. In our experiments, we utilized the GCC compiler (version 5.4.0) to generate binaries that target different ISAs for the benchmarks. As training and test data, the benchmarks contained some open-source software packages commonly used by IoT devices. All the experiments were performed on a computer running an Ubuntu 16.04 operating system and equipped with a 64-bit Intel(R) Xeon(R) E5-2650 2.00 GHz CPU, 64 GB of RAM, and no GPUs. The experimental results answered the following questions:  
1. Can the similarity of the instruction vectors generated by GeneDiff indicate the semantic similarity of assembly instructions?
2. What is the true positive rate and area under the curve (AUC) of the receiver operating characteristic (ROC) of GeneDiff in detecting the similarity of functions from different architectures?
3. How efficient is GeneDiff at detecting function similarity?
4. How do the parameters impact GeneDiff's performance?  
Section 5.1 answers Q. 1; it indicates that the similarity of the instruction vectors generated by GeneDiff can represent the relevance of two instructions from different ISAs. Section 5.2 answers Q. 2, therein proving that GeneDiff can detect similarity with high AUC-ROC with different compiler optimization options and targeted different architectures, being higher than the approach using symbolic execution. Section 5.3 answers Q. 3. This section verifies that GeneDiff is more efficient than the approach using symbolic execution. Section 5.4 answers Q. 4, revealing the impacts of different parameters on GeneDiff and confirming the reasonable parameter values for GeneDiff.
## 10 Social Impact and Clinical Applications of Robotic Prostheses with 3D Printing
### Social Impact on Labor and Social Inclusion  
Three-dimensional-printed robotic prostheses have proven to have a significant impact on labor inclusion and social participation for individuals with disabilities. By providing prosthetics that combine functionality with aesthetic appeal, 3D printing enables individuals with physical disabilities to re-enter competitive job markets, reducing stigma associated with limb loss. This improvement is especially relevant in fields requiring manual skills where precise movement is essential [5].  
Example and Impact: In addition to enhancing employability, the availability of affordable prosthetics has encouraged social engagement by allowing users to participate in recreational, sports, and cultural activities previously limited by their disability.  
3D Printing Advantage: Through reduced production costs and high customization capabilities, 3D printing not only improves functional capabilities for work and social activities but also promotes social inclusion by breaking down physical and psychological barriers associated with traditional prostheses.