esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Layer Fusion 3D Object Detection via Lidar Point Cloud and Camera Image
## 1 Introduction
Compared with the method of using only Lidar, this method can make use of the texture and color information of the RGB images to achieve more accurate object recognition. In addition, in order to prevent the loss of small object information, either adding an adaptive fusion module (AFM) to the feature extraction network or using multi-layer fusion methods such as D-fusion in the fusion network has produced positive results. The main contributions of this paper are as follows:  
1. A novel fusion method, D-fusion, is proposed, which can preserve the information of each layer of the fusion network to solve the problem of semantic loss and improve fusion performance. 2. We designed an adaptive fusion module (AFM) and applied it after using the feature extraction network, which effectively solves the problem of small-scale object loss in detection tasks. 3. An attention mechanism was introduced to optimize the efficiency of the feature extraction network. 4. We conducted comparative experiments on the challenging KITTI data set, and the results show that our network achieves satisfactory performance. In this paper, Section 2 introduces related 3D object detection works, Section 3 introduces the overall framework of the network, Section 4 describes the experimental requirements, ablation experiments and analysis of the results, and Section 5 summarizes the work and looks at future directions.
## 3 Results and Discussion
### Physical and Chemical Degradation
#### 3.2.1 Results of SEM Analysis
Different degradation signs of the wood samples (cellular deformation, tracheid wall fracturing, middle lamella deterioration and partial structural collapse in addition to microbiological attacks) are indicated by white arrows. In panel e, soil particulates are indicated at the point of energy-dispersive X-ray spectroscopy (EDX) analysis. (**f**): EDX analysis. Ca (as the indication of calcite and gypsum, the main compositions of the burial pit, Calcite Champer), Si (as the indication for SiO\({}_{2}\) from the sealing mortar of covering blocks), C and O were the main wood constituents. Small amounts of Al, Mg and Na were also detected as soil-derived components. Overall, the 3D SR-\(\upmu\)CT images and SEM micrographs revealed that the soil-induced wood decay was a complex, multifactorial process in which soil deposits played the role of catalysts for mechanical, chemical and biological breakdown. Indeed, soil particles are known to apply mechanical stresses to wood structures, inducing chemical changes and creating microenvironments that promote microbial decomposition. By infiltrating cellular spaces and simultaneously triggering multiple degradation mechanisms, these deposits significantly compromised the integrity of the wood, accelerating its natural decomposition [88]. EDX analysis showed the presence of specific elements that played distinct roles in the deterioration processes (Figure 4e,f). Calcium (Ca) indicates the deposition of calcite from the burial chamber, forming crystalline deposits within the wood cells that exerted mechanical stress through crystal growth. Gypsum, a degraded form of calcite, was confirmed by the presence of sulphur (S), which accelerated the breakdown of cellulose through mild acidification, forming hygroscopic compounds that attracted moisture and created physical stress through hydration/dehydration cycles. Silicon (Si), probably due to SiO\({}_{2}\) contamination of the sealing mortar, led to the formation of silicate complexes that penetrated the wood components, altering their physical properties. Overall, these mineral intrusions had a significant impact on the mechanical properties, chemical stability, porosity and long-term preservation potential of the wood, creating a complex degradation environment [89; 90]. The detection of carbon (C) and oxygen (O) corresponded to the wood's organic matrix, while trace elements including aluminium (Al), magnesium (Mg) and sodium (Na), are attributed to soil-derived minerals [12; 91; 92].
## 3 Coordination with Linear Programming
This is the constraint imposed by the operating time of the relays: \[t_{i,min}\leq t_{i,j}\leq t_{i,max}\] (3) where, \(t_{i,\text{min}}\) and \(t_{i,\text{max}}\) are the minimum and maximum operating times of relay \(i\) (at any point). * Time multiplier setting, TMS, limits--The operating time of a relay is directly proportional to the TMS. Therefore: \[\text{TMS}_{i,\text{min}}\leq\text{TMS}_{i,j}\leq\text{TMS}_{i,\text{max}},\] (4) where \(\text{TMS}_{i,\text{min}}\) is the minimum TMS value for relay \(i\) and, \(\text{TMS}_{i,\text{max}}\) is maximum value of TMS for relay \(i\). The TMS values usually taken are 0.025-1.2, respectively [32]. * Relay operating characteristics--To extend the work region of the overcurrent relay specified by the standard [33], the relay's parameters are optimized considering the maximum value of the relay and the minimum value of the relay, as expressed respectively. We consider all relays with the same characteristics: \[t_{op}=\frac{\alpha}{\text{PSM}^{\beta}-1}\text{TMS},\] (5) where \(t_{op}\) is the relay operating time and PSM is the plug setting multiplier (\(k_{ij}\) in Equation (1)). For inverse characteristic curves it is usually \(\alpha=0.02\) and \(\beta=0.14\)[32; 33]. The relay setting current, \(l_{ar}\), is determined from the system requirements. Thus, the above equation can be rewritten as:  
\[t_{op}=a\times\text{TMS}, \tag{6}\]  
where:  
\[a=\frac{\alpha}{\text{PMS}^{\beta}-1}. \tag{7}\]
# The Non-Linear Impact of Industry 4.0 on Carbon Emissions in China's Logistics Sector
## 5 Conclusions  
### Summary of Key Findings  
In this study, the complex, non-linear relationship that followed an inverted U-shaped curve between carbon emissions and China's logistics sector was analyzed in detail. Early phases of the transition to Industry 4.0 had the consequence of paradoxically increasing carbon emissions. This was due to the energy consumption requirements that come with the implementation of advanced technology and the integration process as well. Nevertheless, as these technologies advanced and were fully implemented into the logistics processes, the cost of integration was recovered where there was an evident reduction in emissions. Moreover, the mediation analysis noted a high production efficiency, business output scale, and integration of information systems as crucial regions, in which much emphasis was needed to lower emissions, thus affirming the significance of these dimensions in controlling the amount of emission in the logistics sector. This study also highlighted the existence of unequal regional impacts of Industry 4.0, with more developed areas receiving the most benefits because of having a better infrastructure and higher levels of technological synthesis.  
In conclusion, this study provides empirical evidence of the non-linear impact of Industry 4.0 technologies on carbon emissions in China's logistics sector. By integrating quantitative and qualitative analyses, this research offers a comprehensive understanding of how digital transformation can be managed to support sustainable development. The findings contribute to the literature by clarifying the conditions under which Industry 4.0 adoption leads to environmental benefits, thereby addressing a critical gap in current research. For policymakers, this study offers practical recommendations for promoting technology adoption in a manner that aligns with both economic and environmental objectives. Future research should further explore the long-term effects of Industry 4.0 and the potential of Industry 5.0 in advancing global sustainability goals.
###### Abstract
The authors of [26] developed a new hybrid fruit fly-based artificial bee colony (HFABC) algorithm to solve single- and multi-objective OPF problems using a fuzzy decision-based mechanism strategy to obtain the best compromise solution from the Pareto front. A Firefly and JAYA Algorithm based on the hybrid evolutionary technique (HFAJAYA) is suggested in [27] to solve the OPF problem by efficiently considering renewable energy sources. Various scenarios in the standard IEEE 30-bus test system network have been implemented to validate the simulation results of the HFAJAYA. The jellyfish Search optimizer (JS) algorithm is a modern meta-heuristic optimization technique recently developed by [PERSON] and [PERSON] [28] in 2020; it is inspired by the behavior of jellyfish when looking for food in the ocean. The JSO reaffirms its effectiveness by using a set of unimodal and multimodal benchmark functions and various engineering problems. In [29], the JS optimizer algorithm was applied to solve the economic load dispatch (ELD) in the electric power system. In order to obtain the best solution quality for the OPF problem, this paper proposes a new hybrid approach based on the JS and Moth Flame Optimization (MFO) algorithms. The MFO algorithm was developed by [PERSON] in 2015 [30] and is inspired by the flying behavior of moths with respect to the moon, as they can become trapped in spiral paths around artificial lights. Later, a spiral function was proposed to enhance the MFO algorithm's performance, as in [31]. The JS optimizer shows an adequate search in the exploration stage, but it suffers during the exploitation phase because it lacks a proficient exploitation operator of the best solutions in the search space. Inversely, the MFO algorithm, by imitating the flying spiral path performed by moths around flame lights, can be useful in the exploitation phase by exploiting the best solutions; nevertheless, it can become trapped in local optima. The main contribution issues can be summarized as follows:  
* Providing a hybrid JS-MFO algorithm to associate the advantages of both the JS optimizer in exploration and MFO in exploitation. * The effectiveness of the proposed hybrid approach has been investigated by solving various cases related to the OPF problem. * Applications were carried out on two test systems, the IEEE 30-bus and the Mauritanian RIM 27-bus electric power systems, to verify the JS-MFO's performance. This is an assessment study based on comparing our simulation results with those of other meta-heuristic methods reported in the literature. The rest of the structure of this work is ordered as follows: Section 2 presents the formulation of the OPF problem. The mathematical representation of the proposed optimization problem is given in Section 3. Section 4 provides the simulation results and discussions. Finally, a brief conclusion is given in Section 5.
# Heterologous Production of Flavour and Aroma Compounds in _Saccharomyces cerevisiae_
## 3 Heterologous Compounds Derived from Intermediates of the Mevalonate Pathway
### Isoprenoids
#### Norisoprenoids  
Norisoprenoids are a group of C-13-carotenoid-derived aromatic compounds, produced by many fruit and flowers, such as grapes, peaches, blackberries, and which are greatly valued as flavouring additives [81]. In plants, norisoprenoids are produced by the enzymatic activity of carotenoid cleavage oxygenases (CCO), which cleave carotenoids at positions C9 through C13 and thereby yielding different classes of these compounds [82].  
\(\beta\)**-ionone** is one of the most common of this group of aromatic compounds, displaying floral, violet-like characteristics and exhibiting an extremally low odour threshold of 0.007 ppb (in water) [81]. Due to its desirable properties \(\beta\)-ionone has been used as a food and beverage additive to enhance the flavour of ice cream, candy, baked goods, gelatines, puddings, chewing gum, and non-alcoholic beverages [83].  
While \(\beta\)-ionone can be extracted from plants, where it is naturally produced by the enzymatic cleavage of \(\beta\)-carotene by carotenoid cleavage dioxygenase (CCD1), the overall process is tedious and expensive, and highly depends on availability of agricultural resources. Thus, microbial heterologous production of this valuable compound is an interesting alternative. De novo biosynthesis of \(\beta\)-carotene in S. cerevisiae, which is a precursor for \(\beta\)-ionone production, were successfully achieved by expression of three heterologous genes from the ascomycete _Xanthophyllomyces dendorhous_; phytoGene desaturase(crtl), GGPP synthase (crtE), and phytoene synthase/lycopene cyclase (crtYB) [84] (Figure 1). Later, [PERSON] and colleagues [85] used the same enzymes, along with expression of carotenoid-cleavage dioxygenase from raspberry (RiCCD1), demonstrating the possibility of heterologous production of \(\beta\)-ionone in _S. cerevisiae_. Relatively low amounts (0.22 mg/L) of this compound were produced by the recombinant yeast, which was presumably associated with the low efficiency of translation of the single polycistronic episomal construct carrying _crtYB/crtl/crtE_ genes. Recently, [PERSON] et al. [86] reported engineering a more efficient heterologous strain of _S. cerevisiae_ that was able to produce more than 5 mg/L of \(\beta\)-ionone. It was achieved by genetically modifying previously reported, FPP overproducing strain (SCGIS22) [87], via overexpression of _tHMG1_ and GGPP synthase gene _BTS1_, along with expression of heterologous crtl and crtYB from X. dendrorhous, and CCD1 from _Petunia hybrida (PhCCD1)_ (Figure 1)
## 4 System Description
### Benefits of the Approach  
We developed a Continuous Displacement Algorithm (CDA) which computes the number of observations surpassing the physical activity threshold, inciting automatic update of the temperature threshold. The accelerometer inside the wearable device provided the acceleration value. The extremes were calculated based on the acceleration variation as the input of the CDA model. The average human body temperature ranges from 36.8 to 37.0 \({}^{\circ}\)C since each person's body temperature is different with negligible fluctuations. However, during physical exercise, the temperature hovers around 37.5 \({}^{\circ}\)C [35,36]. Additionally, ambient temperature and humidity are critical to impede the virus diffusion. The ambient temperature (AmT) should be around 20-24 \({}^{\circ}\)C, and the Ambient Humidity (AmH) approximately 20-60%. In this project, a wearable device based on Arduino nano 33 BLE sense's embedded ambient sensors, two other temperature sensors were added to this device to obtain reliable readings, namely, an MLX90614 infrared thermometer and an MCP9808 high-accuracy contact thermometer [37].  
The primary protocols used to transmit the information are BLE and were used as peripheral, serial transmission to an OLED display attached to the wearable device [38]. The measurements were available to the user at any time. They could simply connect to a Bluetooth central application like LightBlue primarily used in this project for data decoding) or nRF Connect to access the available data [39, 40]. Altogether, the system is based on thermometer and accelerometer readings. The objectives include the smooth combination of contact--and non-contact--ambient thermometers to update the temperature and acquire accurate readings. The overall purpose of this device is to provide warnings if the body temperature is above the permissible limit, which the CDA detects.  
Besides the simplicity and accuracy, an additional benefit of this design is distant patient supervision. In future applications, it is highly upgradeable, and it can perform pulse oximetry which can assist in notifying supervising staff or patients of the help they need and acquire accurate information about their current condition. Furthermore, one of the most significant benefits is that the Arduino Nano BLE 33 Sense can implement machine learning models with TinyML or TensorFlow [41, 42].  
Overall, this paper is an alternative approach to the system described in [43], using similar principles for acquiring and handling the data. It offers excellent future upgradeability while maintaining power efficiency--one of the critical features of the BLE 5.0 protocol.
# Predictive Quantization and Symbolic Dynamics
## 3 Symbolization Dynamics
### Symbolization Algorithm  
It should be noted the the oracle structure found in the previous section contains \(T+1\) states \(Oracle(o_{0},o_{1},o_{2},\ldots,o_{T})\), with the time series observation located along the direct transitions between the states, which are also called internal forward links. Direct links are links to the immediate next state to the right (next step in time) of the oracle structure. For example, \(s_{1}\) is a label of the transition between \(o_{0}\) and \(o_{1}\), and \(s_{t}\) is between \(o_{t-1}\) and \(o_{t}\). Once the construction of the oracle structure is completed, the symbolization of the time series is described in Algorithm 1.  
Figure 6: A _VMO_ structure with symbolized signal \(\{a,b,b,c,a,b,c,d,a,b,c\}\). Upper (normal) arrows represent forward links with labels for each frame and lower (dashed) are suffix links. Values outside of each circle are the \(lrs\) value for each state.  
```
0:\(Oracle(o_{0},o_{1},o_{2},\ldots,o_{T})\)  Following the first direct link and all forward links from state \(o_{0}\), assign a unique symbolic label \(s_{i}\in\Sigma\) to each of the links. for\(n=1:T\)do Follow the suffix link from state \(o_{n}\). if forward external link exists from \(sfx(o_{n})\)then  label \(s_{n}\) by the symbol that was used to label that link, else  label \(s_{n}\) by the symbol of the direct internal link from \(sfx(o_{n})\). endif endfor
```  
**Algorithm 1** Symbolization  
To summarize, the VMO algorithm comprises a combination of the following innovations:  
* Extension of the FO algorithm to operate on time series with an approximate matching up to a given threshold;
* Symbolization of the time series according to forward links emerging from state zero. Forward links from state zero point to places in the time series where novel data observations were first encountered and these observations could not be included into continuations of existing suffixes since their distance from previously labeled observations exceeded a set threshold;
* Use of Compror to estimate the information rate at each threshold by considering the difference in coding length without and with Compror;
* Selection of the oracle with the highest information rate and using its symbolization as the best discrete representation of the time series.  
By using an exhaustive search over a range of threshold values, an oracle with the highest information rate is retained as the optimal representation.
## 1 Introduction
### Challenges of Flexible Fluid Bag Inspection  
Detecting foreign particles inside intravenous fluid bags presents a variety of challenges that are not encountered with more rigid containers such as glass bottles or vials, which are uniformly transparent and do not change the shape of objects. Unlike bottles, which are unwrinkled, straight, and uniform, fluid bags are flexible and can change shape depending on how they are held. This flexibility can cause irregular refraction and reflection in images captured by cameras, leading to difficulties in detection. The same shape of air bubbles or foreign particles can appear distorted depending on their position, and objects moving at a constant speed can seem to accelerate or decelerate due to these refractive distortions. The light around the fluid bags is reflected on the irregular surface of the bags, introducing various shapes of saturated regions in the image.  
Additionally, when we use a high-resolution camera to magnify the bags, making small particles appear bigger and clearer, the surface of a fluid bag is not as clear as glass but has fine lines, patterns, and variations in thickness, which can further distort the shapes of internal air bubbles and foreign particles, making it difficult to differentiate between them. Even when fluid bags are manufactured in cleanrooms and carefully handled, close inspection often reveals numerous dust particles, fiber fragments, and small specs on the bag's surface. These external contaminants are shown to be similar to internal contaminants, making it hard to distinguish between them based solely on appearance.  
Moreover, the flexibility of fluid bags means that, when the fluid inside sloshes around, the bag itself can move, causing particles or dust adhered to the surface to appear as moving objects, complicating detection. This leads to situations where not only internal contaminants but also numerous external dust particles are identified as potential contaminants, increasing the computational burden of subsequent processes and potentially leading to misidentification.  
The print on the surface of fluid bags also poses a challenge for detection, as it can obscure or be confused with foreign particles or air bubbles when viewed with backlighting. Small air bubbles inside the fluid bag can appear as tiny dots under backlighting, similar to small contaminants, making it difficult to distinguish between them. Large numbers of these tiny air bubbles can be generated when the fluid bag is vigorously shaken, posing a significant challenge for real-time detection systems due to the sheer volume of data to be processed. Thus, detecting foreign particles inside flexible fluid bags is particularly challenging due to the bag's irregular shape, its continuous deformation due to its flexibility, its difficulty in distinguishing between surface and internal contaminants, its interference from surface print, and the presence of numerous small air bubbles. Addressing these issues requires solving problems related to shape distortion, continuous deformation, differentiation of surface contaminants, and the increased number of air bubbles.
## 2 Literature Review
### Review of Classification Using ML  
Machine and deep learning methods are widely used for user traffic analyses, owing to advancements in artificial intelligence technology. Many relevant techniques have been studied, including decision tree, a traditional ML method [31; 32], support vector machine (SVM) [33; 34; 35], \(k\)-nearest neighbor (KNN) [36; 37], hidden Markov model [38; 39], and \(k\)-means [40; 41]. There have been recent studies on traffic analyses using deep learning, which has strengths in terms of accuracy [42; 43; 44]. Although there are some differences in the techniques and forms of the applications, they all tend to capture and analyze traffic based on features. Analysis targets range from captured packets to open datasets, but little consideration has been given to OTT data.  
[PERSON] et al. proposed a method of classifying users based on OTT usage data [11; 12; 45]. They used various ML methods to analyze OTT traffic and classified consumers into three consumption categories: high, medium, and low. Their study is highly significant in that it was the first to attempt to classify OTT users. Their dataset contained real-world data that were equally weighted based on the three consumption classes. However, their validation was performed in an environment different from a real one, and equal weighting was problematic, as we discuss herein. Our previous study was significant in that a deep learning method was applied alongside traditional ML methods [13]. In particular, we proposed a framework to overcome the temporal disadvantages of applying a deep learning method alone. Nevertheless, our study had a limitation in that a dataset detached from the real-world environment was used. In this study, we propose a method that overcomes these limitations.