esa-sceva/satcom-chunk-collection huggingface dataset:
# Soil and Rockfill Dams Safety Assessment for Henan Province: Monitoring, Analysis and Prediction
## 5 Discussion
### Stability Analysis of Soil and Rockfill Dam  
To further verify the monitoring results of PS-InSAR, the finite element analysis method is used to analyze the stress and seepage of Xiaolangdi soil and rockfill dam. The Xiaolangdi Reservoir Dam is divided into the main dam and an auxiliary dam. The main dam is located on the foundation of a deep overburden layer with an inner covering of a loam soil-inclined core rockfill dam. The maximum dam height is 160 m, the dam crest elevation is 281 m, the dam crest length is 1667 m, and the width is 15 m. The upstream slope ratio is 1:2.6 above the elevation of the main dam 185 m and 1:3.5 below. The downstream slope ratio is 1:1.75 above the elevation of 155 m and 1:2.5 below. The two-dimensional finite element analysis of the soil and rockfill dam is carried out from the static aspect by using the finite element analysis software. The values are taken according to the Duncan-Chang E-B model parameters and the material mechanics of Xiaolangdi soil and rockfill dam. The soil and rockfill dam model and dam material zoning are shown in Figure 18a, and the dam stress and seepage distribution are shown in Figure 18b,c.
# A Robust and Efficient Method for Effective Facial Keypoint Detection
## 4 Experimental Results
### Main Results
#### 4.2.7 Performance Analysis of Model Size, Computational Speed, and Running on Mobile Devices  
Model size: as shown in Table 5, our model, mobile-distill-prune-0.25x, is significantly smaller at 2.5 Mb, saving over 5 Mb compared to the mobile-distill-prune-1x. This reduction makes mobile-distill-prune-0.25x much more compact than most models, such as SDM (10.1 Mb), LAB (50.7 Mb), and SAN, which is around 800 Mb with its two VGG-based subnets measuring 270.5 Mb and 528 Mb, respectively.  
Processing speed: we measure the efficiency of each algorithm using an i7-6700K CPU(Intel Corporation, Santa Clara, CA, USA) (denoted as C) and an Nvidia GTX 1080 Ti GPU, Nvidia, Santa Clara, CA, USA (denoted as G), unless stated otherwise. Since only the CPU version of SDM [36] and the GPU version of SAN [30] are publicly available, their times are reported accordingly. For LAB [31], only the CPU version is available for download, but the authors mention in their paper [31] that their algorithm runs in approximately 60 ms on a TITAN X GPU (denoted as G*). Our results show that mobile-distill-prune-0.25x and mobile-distill-prune-1x outperform most algorithms in terms of speed on both CPU and GPU. Notably, LAB's CPU time is reported in seconds rather than milliseconds. Furthermore, mobile-distill-prune-1x required 2.25 times the CPU time and 1.89 times the GPU time compared to mobile-distill-prune-0.25x. Despite this, PFLD 1X remained much faster than the other models. Additionally, for PFLD 0.25X and PFLD 1X, we conducted tests on a Qualcomm ARM 845 processor (denoted as A). Here, mobile-distill-prune-0.25x processed a face in 12 ms (over 83 fps), while mobile-distill-prune-1x processed a face in 18.7 ms (over 53 fps).  
Mobile terminal evaluation: Table 6 shows the performance comparison of different models on three mobile devices (HUAWEI Mate9 Pro, HUAWEI Technologies Co., Ltd., Shenzhen, China; Xiaomi 9, Xiaomi Corporation, Beijing, China; and HUAWEI P30 Pro, HUAWEI Technologies Co., Ltd., Shenzhen, China). The comparison indicators include CPU specifications, RAM, and processing speed (in milliseconds).  
Figure 6: Experimental results under different pruning rates.  
Among all devices, the mobile-distill-prune-1x and mobile-distill-prune-0.25x models are significantly faster, with the mobile-distill-prune-0.25x model being the fastest, reaching a processing speed of 11 ms on the HUAWEI P30 Pro. While maintaining high accuracy, it greatly reduces the computational complexity and storage requirements of the model, allowing for inference in a very short time. This has important practical significance for real-time application scenarios such as facial recognition, augmented reality, and intelligent monitoring.
# Electronic Voting System Using an Enterprise Blockchain
## 4 System Proposal
### Phases of the Proposed Protocol
#### 4.4.2 Voting Phase  
The only ones on the SuffragNet authorized to send voting requests (transactions) and interact with the ledger are the SUFFRAGE.VOTER organization. It is valid to clarify that each transaction issued will be executed and endorsed transaction signed by a peer by each network peer (see Section 3.2) before being committed in the ledger.  
1. The voter interacts with the application Figure 5 using one of the known methods, for example, email/password, one-time code, email confirmation, OAuth2, among others.
2. The IdP validates the voter as a valid user.
3. The SUFFRAGE CA registers and enrolls the anonymous identity in the SoftHSM.
4. Voter selects candidate on ballot.  
5. The voter casts the vote: 1. The voter signs the result, this operation returns a signed-result; 2. The result-signed is encrypted with the public keys of the member organizations.
## 3 UAV Power Supply and Charging Mechanisms
### Battery Powered UAVs  
The battery power supply is one of the main power supply techniques used to meet the energy requirements of UAVs. Batteries are mostly preferred in relatively smaller UAVs because classical batteries, such as lithium polymer (LiPo), can power a UAV for a maximum of 90 min [44; 45]. This severely limits the commercial and industrial applications of battery-operated UAVs. Despite significant advancements in battery technology, the limitations associated with the use of batteries to power UAVs are still far from over. Since batteries are the most predominant source of energy for miniature UAVs, and the energy stored in the battery is limited and can be easily depleted during the UAV operation, to increase the flight time and operation of UAVs, batteries must be frequently charged in order to replenish the depleted energy stored in them. To address such limitations, several battery charging techniques have been developed. Hence, in the following, we consider different battery charging mechanisms devised to recharge the UAV battery and ensure the longevity of UAV operation.
# Intent to Adopt Location Sharing for Logging Safety Applications
## 2 Materials and Methods  
### Survey Population and Administration  
This study and associated materials were reviewed and approved by the University of Idaho Institutional Review Board (project 18-037) on 28 February 2018. It was certified as exempt under category 2 at 45 CFR 46.1010(b)(2).  
Through a paper survey, we assessed logger intent to adopt specific LS safety practices. LS safety applications are still an emerging concept in forest operations, so previous exposure to this technology was expected to be minimal or nonexistent in the general population of Idaho loggers. To ensure quality responses, we required participants to have appropriate and relevant background information for answering specific questions on LS devices, capabilities, and applications. Thus, we defined our population as all professionally certified loggers in the Idaho Logger Education to Advance Professionalism (LEAP) program and surveyed a convenience sample that included all certified loggers attending the \"Location Sharing for Logging Safety\" presentation at the 2018 LEAP update meetings [40; 41]. The LEAP update, administered by University of Idaho Extension, is an annual, one-day workshop offering in-depth coverage of forestry concepts for loggers and logging contractors. Participation in the update meeting satisfies training requirements necessary to maintain Pro-Logger accreditation in the state [42]. In 2018, the update was offered at three Idaho locations: Lewiston (March 20), Coeur d'Alene (March 21), and Sandpoint (March 22). Pro-Logger accreditation is a voluntary program administered by Idaho's Associated Logging Contractors (ALC) for professional timber harvesters, including logging contractors, loggers, forest owners, and forest products companies [43]. It indicates the continuing completion of advanced professional and safety training.  
At the beginning of each LEAP presentation on LS, we distributed the survey to all attendees, along with a consent letter, LS informational handout, and raffle ticket. We explained the survey's purpose and procedure and informed attendees of the opportunity to win a scale model log truck through a raffle drawing at the conclusion of the presentation. Completion of the survey was integrated into the presentation, such that the presenter indicated when to answer specific questions and provided relevant content and explanation. Immediately following the LS presentation, we announced the winner of the log truck and then collected all questionnaires, including both blank and completed copies.  
In designing the LEAP presentation, the authors carefully considered the possibility of social desirability bias in survey response and thus attempted to present material in a factual, neutral manner. Due to potential negative biases arising from unease or hostility toward mandatory, safety regulation [26; 44; 45], we avoided language in the LEAP presentation and survey that would imply necessity of LS use. This allowed us to assess self-predicted adoption as a voluntary action.
## 3 Results and Discussion
### Transmission Electron Microscopy (TEM)  
The TEM examination of the GNPs confirmed that the synthesized GNPs were clumpy agglomerates that were polydispersed (Figure 6). After careful observation of the TEM image, it was found that the GNPs were prepared with an amazing size range of 5-12 nm.
# GNSS Multipath Detection Using Continuous Time-Series C/N0
## 2 Features of Measured C/N\({}_{0}\) in Dense Urban Areas  
The methods to degrade GNSS positioning in dense urban areas are divided into three. First, when signals are completely blocked, they cannot be received and thus degrade the satellite geometry; such signals sometimes make positioning impossible. Second, when the direct signal is blocked (or attenuated) but the signal can be received via a reflected path, the process is called NLOS reception. NLOS signals sometimes exhibit very large errors reaches 100 m or more. The diffracted signal is also included in NLOS signals. Third, when both direct LOS and reflected signals are received, we observe that the errors fluctuate in both the positive and negative directions. This type of multipath error can be somehow mitigated by the signal and receiver designs. Figure 1 presents the three typical multipath types described here.
## 1 Introduction
[8] has filled the research gap pertaining to studying the key components of Industry 4.0 determining project success, while the work of [PERSON] et al. [7] aimed to develop a link between project management and digitalization, defining digitalization as the integration of various technologies into all aspects of a work environment. There is a scarcity of research that systematically breaks down digital infrastructure into enabling elements and analyzes these elements through a structured MCA. Therefore, this research aims to fill this gap by addressing the lack of a comprehensive evaluation and ranking analysis that identifies, categorizes, and ranks the enabling elements of digital infrastructure critical to project management success. To address the aim of this article, the following research objectives are identified:  
1. Defining the digital infrastructure in terms of three pillars and identifying specific enabling elements for each pillar;
2. Providing a methodologically rigorous evaluation and ranking of the enabling elements in terms of their effect on project management success, using advanced MCA techniques (FAHP and FTOPSIS), offering a clear prioritization that guides organizations on where to focus their digital efforts for maximum impact on project management success;
3. Testing the robustness of the MCA findings across various scenarios, which addresses the gap in understanding how changes in the weights of project management success criteria (indicators) might influence the importance of the digital infrastructure enabling elements, offering insights into the stability and generalizability of the findings. This article links project management success with the prerequisites of digitalization and implementation of Industry 4.0 enabling technologies. This linkage is done through the development of an MCA that aims to weigh and rank the enabling elements of the implementation of the prerequisites to achieve project management success. A hybrid approach of FAHP and FTOPSIS is used in the MCA in the methodology of this research. The Analytic Hierarchy Process (AHP) will also be integrated to exclude the criteria with low importance and to eventually reduce the number of criteria from 11 to 7. A scenario analysis is also conducted at the end of the study after adjusting the weights of the criteria used in the analysis. Hence, this article aims to answer the following questions:  
1. What are the enabling elements of each digital infrastructure that contribute to project management success? 2. How can these elements be ranked in terms of their impact? 3. How robust are the enabling elements of digital infrastructure when assessed under different scenarios, and do their rankings change when the weights of the project management success indicators (criteria) are adjusted?
# Integrated Components and Solutions for High-Speed Short-Reach Data Transmission
## 3 Receiver for Short-Reach Transmission Systems
### Single-End Photodetector under Kramers-Kronig Direct Detection
The bandwidth of the receiver component needs to be at least twice the signal bandwidth, while that of ADC also needs to meet the demands. Although the Kramers-Kronig direct detection scheme with simple receiver structure and high sensitivity is argued to be a potential solution for high-speed short-reach transmission systems, the receiver cost may be an important factor hindering its practical application.
# Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review
## 5 Findings
### Miscellaneous
Access to real-world data is key to building realistic models, and this research can support future research on practical considerations in automated systems. The innovation introduced by the paper is bridging the divide between academic insights and practical needs in order-picking planning. By identifying often-neglected practical factors and their implications, the study uniquely reorients the focus from purely theoretical models to more actionable insights for warehouse managers. While its strength lies in a comprehensive review and classification, the paper primarily uses existing literature and may lack new empirical findings. Future endeavours can prioritize empirical validations of these practical factors, develop real-world informed algorithms, and foster stronger academic-practitioner collaborations, especially as automated systems become more prevalent.
## 4 Numerical Study
### Explanations  
The superiority of our method can be explained by the properly designed main track priority and the sub-track jumping mechanism. The main track priority guarantees that the planned route utilizes the hierarchical structure of an HDS system and generates a shipping path along the main track first. The sub-track jumping mechanism pushes the search jump across the storage arrays though a nearby accessible sub-track once the current one is blocked and reaches the destination from the other side, if accessible. In this way, the obstacle can be bypassed, and the total number of traversed nodes can be reduced.  
In comparison, A-star (\(l_{1}\)) performs the second best. This method cannot plan a path to reach the destination directly due to the fixed traffic constraints on sub-tracks, and therefore it always returns an alternative path after traversing more nodes. The BFS algorithm searches the storage areas when moving along the main tracks by setting the optimal breadth direction weight \(b_{w}^{*}\) as 0.7. But, BFS (\(b_{w}^{*}=0.7\)) usually traverses more nodes than other methods and consumes almost the longest computation time. Although it can identify the destination, the generated path does not always comply with the traffic constraints and is thus infeasible, returning a lower completion rate. RRT-star (\(\eta\) = 3) is heavily influenced by optimal parent nodes' identification and rewiring. Although this method may find the optimal path with the smallest searching time, result stability is low, and it sometimes has to traverse the largest number of nodes, increasing the overall computational complexity in the end.  
In conclusion, through comparison to the properly modified benchmark algorithms of A-star, BFS, and RRT, per the requirements of the HDS system in both the scenario without obstacles and the scenario with an increased number of randomly settled obstacles, we demonstrate that our method is more feasible, optimal, scalable, and robust.  
Figure 10: ‘Completion Rate’ in different map scales. (**a**) Map size of 20 \(\times\) 20, (**b**) map size of 30 \(\times\) 30, (**c**) map size of 50 \(\times\) 50.
# Impact of Road Traffic Tendency in Europe on Fatigue Assessment of Bridges
## 2 Real Traffic Measurements  
Real-time measurements of lorry and axle weights are very topical issues in the management of bridges and, more generally, of road networks. These measurements are generally carried out by means of suitable WIM devices, located on the road or on the bridge.  
Since the heaviest lorries, especially overloaded ones, are among the major sources of degradation and damage of bridges and road pavements, the availability of WIM systems could be a very helpful tool to balance inspection, maintenance and repair costs.  
The main aim of a WIM device installation is twofold:  
* on the one hand, it gives information about actual traffic, so contributing to refine the assessment and to optimize the planning of the interventions;
* on the other hand, it allows early detection of overloaded vehicles, enabling prompt reaction, even direct enforcement [27; 28], to prevent potential damages.  
The improvements of WIM devices and of measuring techniques are currently the subject of several studies (see, for example, [4; 5; 6; 9; 12; 29; 30; 31; 32]).  
Of course, since traffic loads on bridges are governed only by commercial vehicles weighing more than 35 kN, the possible presence of cars and light vehicles can be generally ignored in the verifications.  
In the following, after a preliminary illustration of the main characters of the long-distance European traffic measurements taken into account to derive load models of Eurocode [1], the most important features of Moerdijk and Igualada traffics are summarized. Additional details can be found in [2; 6; 19; 33; 34].
## 2 Theory
### An Optimal Stopping Rule  
In this section, we propose a rule to guarantee matched contrast recovery for the exemplary object used to derive Equation (9), which can approximate an oncological lesion in an at least locally uniform background. It is also the same kind of object for which the noise reduction law was derived for PET systems (i.e., the center of a uniform phantom).  
In non-TOF PET, two objects with equal contrast \(\beta\) and dimension \(d\), positioned in two different backgrounds of dimensions \(D\) and \(D^{\prime}\), achieve the same convergence level (i.e., the same contrast recovery) when \(\left(\alpha_{j}^{\prime}\right)^{k^{\prime}}=\left(\alpha_{j}\right)^{k}\); therefore, the MLEM iteration numbers \(k\) and \(k^{\prime}\) are related by  
\[k^{\prime}=k\,\frac{\log\alpha_{j}}{\log\alpha_{j}^{\prime}} \tag{12}\]  
Particular attention must be paid to small objects with low contrast where, as previously shown, convergence is the slowest, therefore \(\alpha\to 1\). In these conditions \(\log\left(\alpha\right)\approx\alpha-1\), so  
\[k^{\prime}=k\frac{\frac{-d\beta}{D+\left(\beta-1\right)d}}{\frac{-d\beta}{D^{ \prime}+\left(\beta-1\right)d}}\approx k\frac{D^{\prime}}{D} \tag{13}\]  
where the last step assumes that \(D\gg(\beta-1)d\), which is always the case when \(\alpha\to 1\). This equation shows that contrast recovery for small and low contrasted objects depends very strongly on the background, i.e., a fixed number of iterations cannot provide consistent contrasts for these targets, if positioned in different patients/body districts/phantoms. For larger and more contrasted objects Equation (12) still holds, but the two approximations are no longer valid and the convergence speed is faster. Therefore, the rule proposed in Equation (13) provides higher contrast recovery than desired.  
Rule (13) is easily extendable to TOF-PET. Given two TOF-PET scanners with effective diameters \(D_{eff}\) and \(D^{\prime}_{eff}\), similar contrast recoveries for similar small and low contrast objects are achieved when  
\[k^{\prime}=k\frac{D^{\prime}_{eff}}{D_{eff}} \tag{14}\]  
or, in terms of CTR, when  
\[k^{\prime}=k\frac{\Delta t^{\prime}}{\Delta t} \tag{15}\]  
This simple stopping rule in TOF no longer depends on background characteristics. Therefore, it can be used to obtain similar contrast recovery levels for similar objects on TOF scanners with different CTRs. It is also possible to apply this rule to the non-TOF case, using Equation (13), but this would imply a different number of iterations for each patient and bed position, according to the \"diameter\" of that body region.  
Due to considerations in Section 2.2, rule (14) completely removes the dependence on object background only when the fraction of scatter and random counts is constant. With good CTR, the impact of randoms is expected to be negligible. The scatter fraction might instead partially change between different subjects/objects. Therefore, we expect that when the rule is applied on real data, an approximate matching is observed.
# FPGA-QNN: Quantized Neural Network Hardware Acceleration on FPGAs
## 1 Introduction
The developed models are trained with the Pytorch framework and Brevitas library, which is transformed into an Open Neural Network Exchange (ONNX) format. It is well-known that the ONNX format presents a lightweight environment to interchange models among DL frameworks. This new format is then transferred to the FINN framework, a powerful tool, to synthesize and implement the trained model on FPGAs. A detailed comparative analysis is provided to underline the superior performance of the proposed design over existing accelerators in terms of throughput, power efficiency, and scalability, addressing gaps in previous work that often focus on isolated metrics. A demonstration of the proposed system is entirely depicted in Figure 1. The proposed architecture makes the following summarized contributions:* The major contribution of this work relies on real-time embedded applications to develop a hardware accelerator on FPGAs using quantized neural network (QNN) models, which is called FPGA-QNN. * The proposed quantized models are based on the Pytorch framework and Xilinx Brevitas library for multi-layer perceptron (MLP) and LeNet CNN models. * To construct fast and efficient FPGA accelerators, the FINN framework is employed to synthesize the trained model, thus allowing for multi-bit quantized networks. * A performance demonstration to test the efficiency is carried out with MNIST, FashionMNIST, and CIFAR-10 datasets, in comparison with several existing approaches. The remainder of the article can be summarized as follows. Section 2 aims to provide a literature review along with key features of recent relevant works. The required background for the models used in all parts of the FPGA-QNN is provided in Section 3. The system models are defined with details of their implementation on FPGA in connection with the features of the FINN framework in the fourth section of the paper. Then, the performance efficacy of the proposed model is evaluated, using performance comparisons with existing studies. The paper is concluded in Section 6.