esa-sceva/satcom-chunk-collection huggingface dataset:
# Novel Techniques for Microbiological Safety in Meat and Fish Industries
## 7 Conclusions  
Numerous food preservation techniques are being employed in meat and fish industries to ensure their safety and shelf-life extension. The main focus of this manuscript is to highlight the impact of novel techniques such as ohmic heating, irradiation, pulsed-electric filing, cold plasma, ultrasound and high-pressure processing on meat safety with compromising nutritional attributes. Non-thermal techniques also permit great maintenance of nutrition and organoleptic attributes of meat and meat-based products and maintain freshness and quality characteristics. Reviews have concluded that these novel non-thermal techniques also limit cooking time, juice loss reduction, and satisfactory color and surface. In meat and fish products, color and texture are essential physical qualities with DBD plasma with increased plasma treatment exposure time, resulting in a reduction in redness. Cold plasma treatment is a successful food processing intervention for improved product protection and extended shelf life by controlling spoilage microorganisms without leaving any known chemical residue. In certain sites of a fungal cell, cold plasma species interact and result in a loss of function and structure and, eventually, cell death. Similarly, ultrasound use is currently common in the meat industry to improve meat tenderization, bulk emulsification, marination, frostbite, homogenization, crystallization, drying and inactivation of all microorganisms, such as _E. coli_ and _Salmonella_. PEF also ensures the quality parameters of meat, such as texture, color and water holding capacity, which improve the overall characteristics in common preservation methods of curing and brining. Similarly, HPP is also effective in the reduction in processing times, minimal heat penetration to promote freshness, flavor, and color retention. Limitations in employing these techniques are their complexity and cost due to expensive systems. Meat technology focuses on meat protection, tenderization, super cooling, and rapid brining for preservation of optimal meat color by pre-slaughter antioxidant feeding, antioxidant addition, packaging technique, and appropriate temperature for long-term use by optimizing novel non-thermal systems at the industrial level.  
Conceptualization, M.F.J.C., S.T. and M.R.; writing--original draft preparation, T.M., A.K., J.M.L. and S.T.; writing--review and editing A.D., A.S., M.K., S.B., M.A.S. and M.T.; supervision, M.R.; project administration, M.R. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministry of Science and Education of the Russian Federation (Grant Agreement 075-15-2020-775).  
Not applicable.  
The data may be available upon request.  
The authors are highly thankful to the Department of Food Science and Technology for the contribution and all authors agreed to publish this final version of the manuscript.  
The authors declare that they have no conflicts of interest.
# Applying Wearable Technology and a Deep Learning Model to Predict Occupational Physical Activities
## 2 Materials and Methods
### Wearable Track Device with Inertial Measuring Units  
A lightweight (<0.7 kg) prototype wearable vest and arm cuffs that housed 8 IMUs (SwiftMotion, Berkeley, CA, USA) was used to quantify the kinematics while performing OPAs (Figure 3). The vest was designed with a shoulder harness, belt, upper arm straps and upper leg straps made by nylon mesh fabric, which was used to fix the positions of the IMUs. The vest was available in three different sizes (small, medium, and large), and the straps were length-adjustable to allow exact positioning of the sensors, independent of the body type of the subject.  
The specific positions of IMUs were as follows: (1) two were placed on the spine facing posterior, one between the 3 rd and 4 th thoracic spinous process (T3-T4) and the other between the 5 th lumbar and 1 st sacral spinous process (L5-S1); (2) two were placed on the medial segment of each upper arm facing lateral; (3) two were placed on the distal segment of each forearm just proximal to the hand facing posterior; (4) the last two were placed on the medial segment of each thigh facing posterior. With these 8 sensors and their corresponding anatomical locations it was possible to track the orientation of the trunk, upper arms, forearms, and thighs. The sensors were not placed directly on muscle bellies, as their orientation could then change during the activation of those muscles.  
Figure 2: Manual material handling tasks: bottle packing, carpet laying, and drilling.  
The small IMUs (50 \(\times\) 50 \(\times\) 20 mm) developed by the researchers included three-axis accelerometers, three-axis gyroscopes, and three-axis magnetometers, which measured accelerations, velocities, and position (orientation). Data from the IMUs were recorded at a sample frequency of 10 Hz.
## 2 Equivalent Half-Vehicle Model
### Equivalent Half-Vehicle Model Description  
The active suspension of a 4-degrees-of-freedom vehicle can reflect the pitch and vertical movement of the vehicle. Therefore, this type of model is adopted as a base to establish a half-vehicle model that includes the influence of braking intensity. The front and rear wheel loads caused by the braking intensity are transferred to reflect the braking intensity in the half-vehicle model; the effect is equivalent to the front axle equivalent force \(F_{mrf}\) and the rear axle equivalent force \(F_{mrf}\) acting on the front and rear suspensions, respectively. The equivalent 4-degrees-of-freedom half-vehicle model, including the influence of braking intensity, is shown in Figure 1. This equivalent half-vehicle model means that the vehicle is running on an ideal road and there is no road input that affects the ride comfort of the vehicle. In fact, the road input will have certain undulations which will affect the ride comfort of the vehicle. In order to better understand the effect of braking intensity on ride comfort, an assumption that the vehicle is driving on an ideal road (i.e., road input is zero) is made.  
In Figure 1, \(v_{x}\) is the vehicle velocity; \(a\) and \(b\) are the distances from the mass center of the body to the front and rear axes, respectively; \(m_{b}\), \(I_{b}\), and \(z_{b}\) are half-body mass, half-body moment of inertia, and half-body mass center position, respectively; \(\theta_{b}\) is the pitch angle of the body; \(z_{2}\) and \(z_{4}\) are the vertical displacements of the front and rear bodies, respectively; \(z_{1}\) and \(z_{3}\) are the vertical displacements of the front and rear unsprung masses, respectively; \(C_{sf}\) and \(C_{sr}\) are the front and rear suspension damping, respectively; \(K_{sf}\) and \(K_{sr}\) are the front and rear suspension stiffnesses, respectively; \(F_{fc}\) and \(F_{rc}\) are the front and rear suspension control forces, respectively; \(K_{sf}\) and \(K_{tr}\) are the front and rear tires stiffnesses, respectively; and \(m_{uf}\) and \(m_{ur}\) are the front and rear unsprung masses, respectively. In addition, in this paper, the vertical motions of \(O_{f}\) and \(O_{r}\) are called the vertical motion of front and rear bodies, respectively.
# Urban Road Anomaly Monitoring Using Vision-Language Models for Enhanced Safety Management
## 2 Methods  
This study presents an innovative and fully automated framework, URA-VLMs designed to achieve lightweight operational efficiency and rapid response times. It selects a base model that aligns with the monitoring objectives. Utilizing the Chain of Thought strategy, we emphasize the model's lightweight capabilities and the accuracy of Urban Road Anomaly evaluations, further enhancing it through a systematic refinement and testing methodology.  
The research process is as follows (Figure 1): (1) data collection and annotation, (2) model selection, (3) training and optimization, and (4) performance evaluation.  
The proposed system employs a cloud-based framework (Figure 2) to facilitate the monitoring and analysis of roads in real time. Video streams captured by CCTV cameras are transmitted over a network to an online cloud service platform. Within this platform, the Urban Road Anomaly VLMs carry out functions such as classifying road debris and estimating the depth of floodwaters. The cloud infrastructure makes use of distributed computing capabilities to guarantee efficient and scalable data processing. Results from the analysis are transmitted instantly to mobile devices, including smartphones, laptops, and computers, through secure communication protocols. This architecture offers an effective, centralized, and accessible approach for monitoring road conditions in real time, rendering it suitable for both urban roadway networks.  
To closely simulate real-world scenarios, we conducted primarily tests on the entire process. First, we selected a lightweight, high-definition Wi-Fi camera (360 D1 PRO, Chengdu Panorama Intelligent Technology Co., Ltd., Chengdu, China) that can be powered  
Figure 1: The research process.  
Figure 2: The cloud-based system framework.  
by either a battery or an electrical source, offering a resolution of 1080P. This camera is positioned at the front of the car for monitoring purposes. The API interface facilitates the connection of the video to the VLMs. We employed both (1) cloud, as well as (2) edge and cloud methods to evaluate the response time under varying weather conditions, specifically sunny and rainy over two separate days in February 2025.
# A Robust and Effective Two-Factor Authentication (2 FA) Protocol Based on ECC for Mobile Computing
## 4 Security Analysis
### Heuristic Analysis
#### 4.2.8 Replay Attack  
Suppose that the login request information \(\{M_{1},M_{2},K_{1}\}\) for the previous session has been acquired by \(\mathcal{A}\) over an open channel. When \(\mathcal{A}\) replays \(\{M_{1},M_{2},K_{1}\}\) to \(S_{j}\), \(S_{j}\) verifies \(M_{1}\). Since \(V_{i}\) is updated in every successive session, \(M_{1}\) is also changed to \(M_{1}^{new}\) each time. Therefore, \(S_{j}\) cannot check the previous \(M_{1}\) in the present session. In addition, if \(\mathcal{A}\) replays \(\{K_{3},M_{3}\}\) to \(U_{i}\), the previous message \(M_{3}\) cannot be checked by \(U_{i}\) in the present session. Accordingly, the proposed protocol is resistant against replay attacks.
###### Abstract  
This study investigates the efficiency of electrocoagulation (EC) in removing Tartrazine Yellow (TY) azo dye from synthetic wastewater using aluminium electrodes. The effects of current density, \(i\) (0.008-0.024 A cm\({}^{-2}\)), initial solution pH (3.0-7.0), and treatment time, \(t\) (10-50 min) on key process parameters, including pH, temperature (T), TY dye concentration (\(c\)) and removal efficiency (\(R\)), anode consumption, and sludge characterisation were studied. The experiments were conducted in a batch reactor according to the experimental plan developed in Design-Expert software, which was also used for the evaluation of the obtained results. As the EC process progresses, the removal efficiency of the TY dye increases, while the removal dynamics and the final value of \(R\) (ranging from about 28% to 99%) depend on the experimental conditions (\(i\), initial pH, and \(t\)). A high \(R\)-value is reached faster with the application of higher current densities and lower initial pH. This is associated with a higher proportion of carbon and sulphur in the sludge (from the TY dye) after the EC process. Additionally, a mathematical model was developed to predict the experimental data. A numerical optimisation method using response surface methodology (RSM) was applied to determine the optimal operating conditions for TY dye removal. This resulted in the following conditions: pH = 3.37, \(t\) = 18.74 min, and \(i\) = 0.016 A cm\({}^{-2}\), achieving a removal efficiency of \(\approx\)70%.  
electrocoagulation; aluminium electrodes; tartrazine; yellow dye; azo dye; SEM/EDS; response surface methodology (RSM)
# Magnetic Localization Method for Vehicles Based on Transformer
## 2 Related Work
### Learning-Based Methods
In addition, the spatial inconsistency of magnetic data caused by different speeds makes neural networks difficult to train; thus, the accuracy improvement achieved by the results is relatively limited. In summary, according to the magnetic localization algorithms mentioned above, deep learning-based methods greatly improve upon the positioning accuracy of traditional approaches by better extracting features and performing the matching process. However, better neural network models that incorporate the characteristics of vehicle positioning to achieve improved performance must be explored.
## 3 Results and Discussion
### Biomarker Analysis
#### 3.2.3 Malonedialdehyde Determination  
The ANOVA results indicate an increase in MDA concentration in the crabs from Ghannouch. There is a significant difference between the two localities for both gill and hepatopancreas tissues, respectively (F = 54.22 and F = 42.51; \(p<0.01\)) (Figure 6). Furthermore, according to our data for both sites, the concentration of MDA is higher in gills (Djerba: F = 51.46 and Ghannouch: F = 80.32; \(p<0.01\)) than in the hepatopancreas. This increase in MDA levels was observed in individuals from the site with the highest levels of metals in the gills. In the same context, the studies of [PERSON] et al. [78] confirm our results. They showed that following a 21-day exposure to Cd, MDA levels were elevated in the mussel _M. edulis_. This elevation was also demonstrated in _Pyganodon grandis_[79] and _Bathymodiolus azoricus_[80] captured from polluted environments and in crab species exposed to metals [73]. Therefore, we can suggest that an increase in MDA levels in individuals from Ghannouch is due to the bioavailability of heavy metals in the environment.  
Indeed, MDA is a biomarker generated secondarily after lipid peroxidation caused by an alteration of the plasma membrane through the attack of polyunsaturated fatty acids. This membrane lipoperoxidation seems to depend on the organ that accumulates more metals [81]. Lipid peroxidation is followed by structural changes in biological membranes [80] or other elements containing lipids [82]. This leads to a loss of permeability and the inactivation of receptors and membrane enzymes [83]. These functional disturbances can lead to cell death, and lipid peroxidation is an endogenous source of DNA damage [63].  
Figure 6: Comparison of MDA levels (nmol/mg Protein) in digestive glands and gills of _P. segnsis_ captured from the studied sites. Means that do not share the same alphabetic sign are significantly different.  
A previous study by [PERSON] et al. [84] revealed an increase in MDA levels in _Aulacomyaatra_ mussels after exposure to iron, aluminum, and cadmium. The same is true for the work of [PERSON] et al. [85], who revealed the induction of increased MDA levels in the hemolymph and soft tissues of snails treated with zinc. [PERSON] et al. indicated in their study [86] that there is a positive correlation between heavy metals and lipid peroxidation due to the presence of metal ions in the digestive gland of snails that catalyze the Fenton reaction and increase the risk of cell damage.
## 3 Results and Discussion: Qualitative and Quantitative Tool Comparison
### Qualitative Analysis  
Table 1 shows the qualitative analysis carried out. The first element analyzed was the hardware requirements. In this regard, DJI Terra required the most RAM (32 GB), followed by Agisoft Metashape. Other software applications such as Pix4 dMapper, Meshroom, and 3 DF Zephyr recommended 32 GB of RAM for proper operation, although they could work with 16 GB.  
Figure 2: Visual comparison of colored point clouds with different tools.  
The rest of the tools could work properly if they had between 8 and 16 GB of RAM. On a interesting note, ODM was the tool requiring the least RAM, since it could work with 4 GB. The next aspect to analyze was the type of license required. In this case, we had four open-source licensed tools, which were ODM, Meshroom, Regard3D, and 3 DF Zephyr, while the other four tools had a paid license with various types of plans, some of them costing more than EUR 3000.  
Another interesting aspect is the availability of an API; this allows the possibility to develop new functionalities in an easier way. In this case, the tools that had this feature were Agisoft Metashape, Pix4 dMapper, ODM, and Reality Capture. All the tools analyzed except Regard3D and RealityCapture had the ability to process multispectral images, a very interesting aspect in the field of digital agriculture since there are many indicators (normalized reflectance index or NRI, normalized difference vegetation index or NDVI, etc.) that are obtained from these types of images.  
The georeferencing of the models, generated by each of the analyzed applications, is a very important feature when we are talking about digital agriculture. This quality allows us to locate the digital twins on a map in an unequivocal way. All the applications, except Meshroom and Regard3D, allowed this feature. Linked to this, we have elevation models. Elevation models are a digital representation of the land surface. These can help farmers understand the topography of their land, which can be useful for planning water management, fertilizer application, and other aspects of farming. Meshroom and Regard3D were the only ones that did not have this feature. In addition, we included in this analysis the option of distance measurements on the generated 3D model. This aspect was supported by most software applications, except for Meshroom and Regard3D.  
Finally, the last feature analyzed was the possibility of distributed processing; in this case, ODM and Agisoft Metashape allowed this option. This can be an important aspect if you want to speed up processes and generate digital twins automatically after each flight.
## 2 Preliminaries
This implies that \(G[X\cup\{v\}]\) is connected. The case of a connected dominating set is analogous. This immediately gives a polynomial-time algorithm that given a connected vertex cover \(X\) (resp., a connected dominating set) of \(G\), computes a minimal connected vertex cover (resp., a minimal connected dominating set) of \(G\) that are contained in \(X\). We also show that one can decide in polynomial time whether a given vertex set \(X\) is a capacitated vertex cover (and a capacitated dominating set) of \((G,c)\). Proposition 2: _There are polynomial-time algorithms for checking whether a given vertex set \(X\) is a capacitated vertex cover and is a capacitated dominating set of \((G,c)\), respectively._  
Proof: We first consider the case for capacitated vertex cover. We reduce this feasibility-check problem to the bipartite matching problem as follows. The bipartite graph \(H\) consists of two independent sets \(V_{E}\) and \(V\). The first set \(V_{E}\) is defined as \(V_{E}=\{w_{e}:e\in E(G)\}\), that is, \(V_{E}\) contains a vertex \(w_{e}\) for each edge \(e\) in \(G\). The second set \(V\) is defined as \(V=\{w_{v}^{j}:v\in V(G),1\leq i\leq c(v)\}\), that is, \(V\) contains \(c(v)\) vertices for each vertex \(v\) in \(V(G)\). For \(v\in V(G)\), \(1\leq i\leq c(v)\), and \(e\in E(G)\), we add an edge between \(w_{e}\) and \(w_{v}^{i}\) if \(e\) is incident to \(v\) in \(G\). The graph constructed in this way is indeed bipartite, which we denote by \(H\). It is easy to observe that \((G,c)\) has a capacitated vertex cover \((X,\alpha)\) if and only if \(H[V_{E}\cup\{w_{v}^{i}:v\in X,1\leq i\leq c(v)\}]\) has a matching \(M\) saturating \(V_{E}\), as the function \(\alpha\) is straightforwardly defined from the matching and vice versa. For capacitated dominating set, we modify the construction of \(H\) as follows: replace \(V_{E}\) with \(V^{\prime}=\{w_{v}:v\in V\}\) and add edges between \(w_{u}\in V^{\prime}\) and \(w_{v}^{i}\in V\) for all \(1\leq i\leq c(v)\) if and only if \(\{u,v\}\in E(G)\). Similarly, \((G,c)\) has a capacitated dominating set \((X,\beta)\) if and only if \(H[\{w_{v}:v\in V(G)\setminus X\}\cup\{w_{v}^{i}:v\in X,1\leq i\leq c(v)\}]\) has a matching saturating \(\{w_{v}:v\in V(G)\setminus X\}\). We can find a maximum cardinality bipartite matching in polynomial time, proving this proposition. For a non-negative integer \(k\), a vertex ordering \((v_{1},\ldots,v_{n})\) of \(G\) is called a \(k\)_-degenerate ordering_ of \(G\) if each vertex \(v_{i}\) has at most \(k\) neighbors in \(G[\{v_{i},\ldots,v_{n}\}]\). A graph is said to be \(k\)_-degenerate_ if it admits a \(k\)-degenerate ordering [31].
## 2 Materials and Methods
### Data Sources  
The used RS data includes 8-day synthetic products of MOD16A2.006 (ET), MOD17A2H.006 (GPP), and MOD15A2H.061 (LAI) from the GEE geodatabase ([[https://developers.google.com/earth-engine](https://developers.google.com/earth-engine)]([https://developers.google.com/earth-engine](https://developers.google.com/earth-engine)), accessed on 12 May 2022) with a spatial resolution of 500 m and the period of 2002-2021. In a single year, 46 values were acquired. The data was evaluated by several regional ground observations and is widely used in regional or global scale studies [5; 8; 11; 16]. ET, GPP, and LAI products contain data quality control layers (ET_QC, Psn_QC, and FparLai_QC), which record sensor information, cloud coverage, and quality reliability. In this study, we used GEE to accomplish data preprocessing.  
The climate and soil moisture data were gathered from the National Tibetan Plateau Data Center (TPDC) ([[http://data.tpdc.ac.cn/en/](http://data.tpdc.ac.cn/en/)]([http://data.tpdc.ac.cn/en/](http://data.tpdc.ac.cn/en/)), accessed on 15 August 2022). The climate data were extracted from the China meteorological forcing dataset (CMFD), which covers the period 1979-2018 with a spatial resolution of \(0.1^{\circ}\)[41; 42]. The soil moisture data were derived from the Soil Moisture in China dataset (SMC). The period span is 2002-2018, and the spatial resolution is \(0.05^{\circ}\)[43].  
The digital elevation model (DEM) data used were the SRTM DEM acquired by INSAR technology with two spatial resolutions of 30 m (SRTM1) and 90 m (SRTM3). We used SRTM3 (data revision V4) to calculate the slope, aspect, slope position, and topographic position index (TPI) [44]. Table 1 details the data used in this paper.  
Figure 2: Spatial location, vegetation types, and zones of the study area. (**A**–**G**) shows the photos taken on the spot.