esa-sceva/satcom-chunk-collection huggingface dataset:
# Enabling Horizontal Collaboration in Logistics Through Secure Multi-Party Computation
###### Abstract  
The road transport sector is currently facing significant challenges, due in part to CO\({}_{2}\) emissions, high fuel prices, and a shortage of staff. These issues are partially caused by more than 40% of truck journeys being \"empty runs\" in some member states of the European Union and heavy under-utilization of deck space for non-empty runs. In order to overcome said inefficiency, this paper proposes a decentralized platform to facilitate collaborative transport networks (CTNs), i.e., to enable horizontal collaboration to increase load factors and reduce costs and CO\({}_{2}\) emissions. Our solution leverages secure multi-party computation (MPC) to guarantee that no sensitive business information is leaked to competing hauliers. The system optimizes truck assignments by modeling logistics as a weighted graph that considers orders and truck capacities while maintaining strict confidentiality. Our approach addresses key barriers to CTN adoption, such as lack of trust and data privacy. Implemented using MPyC without extensive optimizations, we demonstrate the efficiency and effectiveness in increasing the average load factor, while achieving acceptable running times (in the order of hours) for arguably meaningful instance sizes (up to 1000 orders). After leveraging a rather simplistic modeling inspired by previous work, we finally give an outlook of possible extensions toward more realistic models and estimate their impact on efficiency.  
collaborative transport networks; secure multi-party computation; distributed optimization  
1  
[PERSON], [PERSON]: 1 AIT Austrian Institute of Technology, 1210 Vienna, Austria; [EMAIL_ADDRESS]  
2 Fraunhofer Austria, 902 Klagenfurt, Austria; erich.teppan@fraunhoferat (E.T.); christin.petschnigg@fraunhoferat (C.P.); elena.wiegelmann@fraunhoferat (C.W.)  
3 Department of Artificial Intelligence and Cybersecurity, University of Klagenfurt, 9020 Klagenfurt, Austria  
1
## 4 Results and Discussion
### Refuse Clute Diameter Calculation  
Refuse chutes are generally required to be produced in accordance with British Standards (BS1703:2005) [45], American Standards (NFPA 82) [46], or the relevant national building regulations of the country. Some limitations need to be known in calculating the refuse chute diameter. For example, BS5906:2005 [47], states that it is not mandatory for a user to transport their waste beyond a distance of 30 m, while BS1703:2005 mentions that it is desirable for more than six residences not to share a chute duct. In this article, a waste chute design for a three-story building that met the above conditions was developed. The weekly waste amounts per floor used as the reference in the calculations [20; 42] are shown in Table 2.  
In this design, simultaneous waste disposal from each floor was prevented with the PLC control unit. When one door is open, the other door will not open. However, to eliminate the possibility of clogging the refuse chute with waste from the previous disposal, the waste calculation was conducted for all floors. The total amount of waste collected weekly can be found by numerically multiplying the weekly waste volume [20; 42], referenced in Table 2 by the number of floors in the building (\(f\)) and the safety factor (\(S\)). The daily waste amount \(V_{d}\) is calculated by dividing the total weekly waste amount by 7, which is the number of days in a week, and is given in Equation (2).  
\[V_{d}=V_{w}\cdot f\frac{S}{7} \tag{2}\]  
Here, \(V_{w}=0.3\,\mathrm{m}^{2},f=3\), and \(S=1.5\), and it was found that \(V_{d}=0.192\,\mathrm{m}^{2}\).  
Equation (3) provides the circular area, denoted as \(C_{A}\), of the refuse chute, where \(D\) is the diameter of the refuse chute.  
\[V_{d}=C_{A}=\pi\frac{D^{2}}{4} \tag{3}\]  
Here, with \(C_{A}=0.192\,\mathrm{m}^{2}\), the value of \(D\) was calculated as 0.494 m. For practical purposes, the refuse chute diameter \(D\) was approximated as 500 mm.
## 3 Experiments
### Dataset Acquisition  
#### 3.2.1 Manhole Cover Positioning Data Set Acquisition  
In this experiment, the DJI UAV M300 RTK equipped with the Zenmuse P1 camera (Manufacturer is DJI, company location is Shenzhen, China) was used for aerial photography at a university in Shanghai. The flight height was set to 50 m, with a heading overlap rate of 90% and a side phase overlap rate of 25%. To compare the experimental results under different light and weather conditions, the experiment was conducted on both sunny and cloudy days. Once the flight route was designed, the aerial photography was automatically performed. A total of 1276 photos were collected, with each picture having dimensions of 8192 \(\times\) 5460 pixels, as shown in Figure 12. Among these, 999 pictures were taken on sunny days, and 277 pictures were taken on cloudy days. The resulting manhole cover positioning dataset consisted of 5122 images. Among these, 3420 pictures required classification, while 1702 pictures did not require positioning. The dataset was divided into training, testing, and validation sets in an 8:1:1 ratio.  
Figure 11: K-value cross-validation dataset division.
## 1 Introduction
### Motivation and Contributions  
As discussed above, each of the three Grassmann clustering approaches has distinct characteristics. Additionally, the available clustering methods and (dis)similarity measures vary across these approaches, leading to various options. Notably, the extrinsic approach has been the most widely adopted [19; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43], while intrinsic methods have been explored in relatively few studies such as [41]. However, as discussed in Section 3.1 and Table 6, the intrinsic approach exploits the true manifold geometric characteristics, which may lead to a better clustering accuracy.  
Several studies have demonstrated that clustering performance depends on the chosen approach. For instance, Ref. [25] compared extrinsic and intrinsic approaches, Ref. [29] examined the differences between extrinsic and semi-intrinsic approaches, and Ref. [31] compared intrinsic and semi-intrinsic approaches. However, to the best of our knowledge, a systematic comparative study including all the three approaches has not yet been conducted in the literature. Given the differences in geometric characteristics and the lack of comprehensive comparisons, it is challenging to determine the most appropriate approach for specific applications.  
In this paper, we aim to fill this gap by systematically comparing the three approaches to provide a valuable reference for researchers. Our contributions are threefold:  
* We provide a detailed explanation of the three approaches, including a comparison of their distinct advantages and disadvantages.  
* We outline 11 Grassmann clustering algorithms, employing different clustering methods and (dis)similarity measures tailored to each approach.
* We conduct comprehensive experiments on the clustering performance of these 11 methods using a human motion MTS dataset.  
Integrating Grassmann clustering with deep learning techniques has also become prominent research topic, garnering significant attention [35; 36; 38; 42; 43; 44; 45]. However, this study focuses on analyzing and comparing the three approaches. Investigating deep learning-based approaches thus falls outside the scope of this work.
# Multi-Agent Reinforcement Learning: A Review of Challenges and Applications
## 3 The Limits of Multi-Agent Reinforcement Learning
### Partial Observability  
Most of the algorithms presented are based on the assumption of the full observability of the state by all agents. In most real-world applications, this condition is rarely present, as agents observe different instances of the state; for example, an agent may have vision of only its surroundings, making the observations correlated with its geographical position, or the agents might be provided with different sets of sensors. In the setting of DRL, the algorithms developed for the full state can only achieve desired performance if the observations are reflective of the hidden system state. In [34], an algorithm called the deep recurrent Q-network (DRQN) was proposed. The first layer of the DQN was replaced with recurrent long short-term memory (LSTM). This architecture was tested on flickering versions of Atari games in which the state (the pixels of the game screen) was sometimes presented to the agent and sometimes obscured. In this benchmark, DRQN achieved better performance than the traditional DQN. This idea was transferred to the multi-agent setting in [35], which integrated hysteretic learning [26] to deal with the non-stationarityof the environment and the capabilities of representing the hidden state space of deep recurrent Q-networks, proposing an algorithm called the decentralized hysteretic deep recurrent Q-network (Dec-HDRQN). In the work, a variation of the experience replay mechanism called concurrent experience replay trajectories (CERTs) was used. In CERT, each experience tuple \((o_{t}^{(i)},a_{t}^{(i)},r_{t},o_{t+1}^{(i)})\) containing the current observation, action, and reward, and the subsequent observation, is indexed by the agent number, the time-step of acquisition, and the episode. The samples given to the Q-network for training are then taken from this structure in a synchronized way.
However, HHP does not always reduce the activity of the polyphenol oxidase, necessitating other hurdles (such as thermal blanching) to stabilize grape pomace [29; 30; 31]. Several studies have examined the impact of grape pomace as an alternative ingredient to reduce the quantity of nitrite in different meat and meat products. Regarding dry fermented sausages, there are only a few studies that have used grape-seed extract in dry-cured sausage [12], in Spanish \"Chorizo\" [9; 32], in \"Cinta Senese\" or Italian dry fermented sausage [17; 33], and with grape-seed flour in Turkish dry fermented sausage or \"Sucuk\" [34]. These extracts were found to be effective inhibitors of oxidative reactions, with TBARS (Thiobarbituric acid-reactive substance) values under 1 mg MDA (malondialdehyde-hyde) kg\({}^{-1}\), which is considered as the threshold of sensory perception for lipid oxidation, thus not producing \"off-odours\" [9]. Moreover, extracts of grape pomace helped to avoid the growth of food pathogens (_Listeria monocytogenes, Salmonella spp., Staphylococcus aureus_,coliforms, and _E. coli_). Phenolic compounds exhibit antibacterial properties by depriving microbes of essential nutrients like iron and disrupting key microbial processes [15]. They specifically target the microbial cell membrane, causing structural damage and increasing permeability. This allows the phenolic compounds to penetrate the bacterial cell, leading to cell lysis, the release of intracellular ATP, and the loss of vital cellular contents. The antimicrobial and antioxidant properties of phenolic compounds make them viable alternatives to nitrates and nitrites in meat products. A study [17] showed that adding dry grape pomace extract to boiled sausages reduced the nitrite levels while maintaining both microbiological stability and sensory quality. This suggests that grape pomace can be used to lower the amount of curing salts with a toxic potential without affecting the taste or quality of the meat. Therefore, most of the reported studies deal with the use of polyphenol extracts from grape pomace, seed, or skin to promote antioxidant and antimicrobial activity in meat products. Nevertheless, the integral use of RGP provides the advantage of incorporating dietary fibers together with phenolic compounds. This is particularly beneficial since dietary fibers were identified as predominant compounds in red grape pomace [23]. To the best of our knowledge, no previous studies have investigated the impact of incorporating integral red grape pomace (RGP) stabilized with the HHP technique for preservation in dry-cured sausages. Therefore, the main objective of this study is to evaluate the incorporation of valorized RGP from Tempranillo var. into dry-cured sausages to improve their preservation and replace the use of synthetic additives like nitrifying salts.
# Recent Progress in Hybrid Additive Manufacturing of Metallic Materials
## 2 Hybrid Techniques Based on the Use of Other Manufacturing Techniques with AM
### Electron Beam Melting + Computer Numerically Controlled Rapid Prototyping  
Additive System Integrated Subtractive (AIMS) systems are the combination of Electron Beam Melting (EBM) and Computer Numerically Controlled Rapid Prototyping (CNC-RP) (Figure 2). The AIMS method is used for metallic components, such as the Ti-6 Al-4V alloy, in which the tolerances and surface finishes are improved by digital machining post-EBM-AM. The comparative studies between the CNC-RP technique and the AIMS method revealed that the AIMS method is faster, with an estimated buy-to-fly ratio of less than 3. On the other hand, the CNC-RP process has a buy-to-fly ratio greater than 13. The buy-to-fly ratio is the measure of the efficiency of raw material use. It gives the weight ratio of the raw material to the final product [27].  
The AIMS process has minimized tool wear and has better material utilization than the CNC-RP technique. However, the authors noted that the subtractive techniques require custom-made tools and fixtures, which can be a limitation for expanding this process [25]. The work conducted identified fixturing limitations when combining the two processes, necessitating the need for customized and more fixture designs tailored to different M-AM techniques.
## 2 Pump Model and Numerical Setting
### Grid Independence Examination
#### 2.3.2 Grid Generation and Grid Independence Examination  
The computational domain shown in Figure 2 was spatially discretized with grids through the commercial grid-generation code ANSYS ICEM CFD. In view of the complex geometry of the impeller blades, unstructured tetrahedral grids characterized by high body-fitting ability were used. In near-wall regions, grids were refined to capture local flow phenomena with small spatial scales. An investigation was performed to examine the dependence of the numerical result on the total grid number. Seven grid schemes with total grid numbers ranging from about 2.2 million to 9.3 million were designed. At a volumetric flow rate of water of 8.03 m\({}^{3}\)/min, a feed rate of sand of 2.10 m\({}^{3}\)/min, and a rotational speed of the impeller of 950 rpm, numerical simulations were performed with those grid schemes. The pump head obtained is shown in Figure 3.  
Figure 3: Pump heads obtained with different grid schemes.  
Figure 2: Three-dimensional geometric model of the computational domain.  
Initially, with increasing total grid number, the pump head decreases continuously, and the relative deviation of the pump head between neighboring schemes is even higher than 6%, which is reasonable since the effect of solid particles cannot be fully recognized with sparse grids. When the total grid number exceeds 6.0 million, the relative deviation between neighboring pump heads is minimized to lower than 2%. In this case, the effect of the total grid number on pump performance is insignificant. In consideration of both numerical accuracy and economy, the grid scheme with a total grid number of approximately 7.8 million was eventually selected. For such a gird scheme, the mesh-aspect ratio is approximately 0.86, the quality skew is approximately 0.41, and the values of \(y+\) are range from 12 to 89. A local view of the grids corresponding to this grid scheme is shown in Figure 3 as well.
## 3 Experimental and Numerical Simulation Data Analysis
### Finite Element Modeling  
The nonlinear dynamic finite element analysis program LS-DYNA was used to analyze the dynamic response of the shell along the thickness and length directions. The calculated explosive density was 1.65 g/cm\({}^{3}\), the shape and equivalent of explosion were consistent with that in the actual experiment, and the explosive was detonated at the middle of the cylindrical shell. The Arbitrary Lagrangian Eulado matter algorithm was applied for the explosive and air units in LS-DYNA [26], and the Lagrangian algorithm was used for the cylindrical shell units. The ALE and Lagrange grids overlapped and crossed each other during modeling, and the fluid\(-\)structure coupling simulation of the structural mechanical loading by explosive products in the explosion process was realized by the coupling of Lagrange and ALE. According to previous experimental measurements, the detonation velocity of TNT with a density of 1.65 g/cm\({}^{3}\) was about 7.8 km/s, while the detonation pressure was 176 GPa [27]. The steel cylinder was made of ideal elastic-plastic material, with a yield strength of 1000 Mpa, while the strain rate and strain strengthening effect were not considered. Figure 5 shows the numerical model diagram for simulating the cylinder deformation under an explosive load.
# Recent Developments in Low-Power AI Accelerators: A Survey
## 4 Results
### Summary of Company Accelerators
NPU uses the latest technology (5 nm) and has the highest power efficiency of all reviewed company affiliated accelerators. Gyrfalcon Technology's Lightspeeur 5801S [59] is an ultra low-power AI accelerator, targeting only CNN models. The accelerator is used in LG Electronics's smartphone as its AI chip. The accelerator's AI core is Gyrfalcon's patented APiMTM (Al Processing in Memory) technology, allowing for on-chip parallelism. Lightspeeur 5801S has the lowest power of all reviewed company affiliated accelerators. ARM's ETHOS-U55 [55] is a micro neural processing unit (NPU) for area-constrained embedded and IoT devices. It has a re-configurable MAC array, and can compute the core kernels used in many neural network tasks, such as convolutions, LSTM, RNN, pooling, activation functions, and primitive element wise functions, while other kernels run on the host CPU. ETHOS-U55 has the smallest area of all reviewed company affiliated accelerators. Quadric's first generation AI accelerator, q16 [76], uses 256 Vortex cores (4 kB per core memory) with 8 MB on-chip memory. The accelerator supports multiple precision formats for its MAC operations and comes embedded into an M.2 form factor. q16 has the highest throughput of all reviewed company affiliated accelerators. One observation that we made when going through the company accelerators was that none of them use fixed point arithmetic, which several research accelerators do. An explanation could be that when a company designs and implements an accelerator, both the development and the life time of the product are long. Thus, by using more flexible formats, such as floating point arithmetic, companies are more prepared for changes in future workloads.
# Research Progress of Self-Powered Gait Monitoring Sensor Based on Triboelectric Nanogenerator
## 1 Introduction  
With the advent of the technological era, intelligent information technology has been widely applied in multiple fields [1, 2, 3, 4, 5, 6, 7]. Real-time and accurate gait monitoring shows enormous application potential, such as medical diagnosis, rehabilitation assessment and treatment, and health management [8, 9, 10, 11, 12, 13]. Existing traditional gait monitoring methods include visual observation and measurement. The analysis results of visual observation are somewhat subjective, and are related to the experience and skills of doctors and physical therapists. The measurement method can objectively provide the kinematic parameters, kinetic parameters, and electromyographic activity parameters of the gait, but it cannot display data in real time. With the advancement of science and technology, high-precision and objective gait monitoring technologies have enabled real-time data collection and analysis, and have been applied in the fields of healthcare, rehabilitation, and sports training [14, 15, 16, 17, 18]. However, they still have shortcomings, such as reliance on external power sources, strict operating conditions, and environmental pollution issues and economic burdens brought about by frequent battery replacements. To address these deficiencies, many researchers have begun to pay attention to triboelectric nanogenerators (TENGs). TENGs harvest energy from the surrounding environment through the coupling effect of triboelectricity and electrostatic induction and convert it into electrical energy, such aswind energy, mechanical energy, and droplet energy [19; 20; 21; 22; 23; 24; 25; 26]. Consequently, they serve various fields, including health monitoring [27; 28; 29; 30], motion monitoring [31; 32; 33], energy harvesting [34; 35; 36], and self-powered sensors [37; 38; 39]. TENGs are regarded as promising technologies due to their low cost, simple manufacturing process, wide range of material choices, strong portability, and high energy conversion efficiency [40; 41; 42]. Many studies have successfully integrated TENGs into practical scenarios, such as inos, clothing and running tracks for real-time health and motion monitoring, and signal analysis [43; 44; 45; 46; 47; 48; 49; 50; 51; 52]. They have achieved outstanding research results and practical application value.  
Here, the progress in the field of TENGs for self-powered gait monitoring is examined. First, a comprehensive overview of the four common working modes of TENG technology is provided, including detailed explanations of two predominant modes: vertical contact-separation (C-S) mode and single-electrode (S-E) mode. Second, the research progress of self-powered gait monitoring sensors driven by TENGs in the fields of basic gait information monitoring, abnormal gait detection and fall monitoring, and rehabilitation assessment and assisted training, as well as identity recognition, is systematically summarized (Figure 1).