esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Cargo-Carrying Analysis System for Mountain Orchard Transporters Based on RGB-D Data
## 3 Model Construction and Volume Measurement Strategy and Deployment  
### YOLOv7-tiny Object Detection Algorithm  
We needed to optimize an efficient detection model that was suitable for edge-computing devices to quickly and accurately detect the cargo carried by the monorail transporter. In 2022, the real-time object detection algorithm YOLOv7 was proposed. The YOLOv7 family includes state-of-the-art models, such as YOLOv7x, YOLOv7d6, and YOLOv7-tiny [17]. In this study, we optimized the YOLOv7-tiny model to classify and obtain the 2D coordinate information of the detected objects, including \"basket\", \"orange\", and \"fullbasket\", because it has fewer parameters and is highly efficient.  
The YOLOv7-tiny network is divided into three parts: the backbone, the head network, and prediction [18], as shown in Figure 5. In the backbone, we modified the image frames to 480 \(\times\) 480 pixels and extracted features using two CBL modules and four E-ELAN modules. E-ELAN enabled the framework to learn better without destroying the original gradient path, considering the tradeoff between speed and accuracy. In the head network, the path aggregation network (PAN) [19] structure fused the spatial and semantic information of the backbone and the head network to enhance the learning ability of the model. YOLOv7-tiny predicts boxes at three different scales, so the detection is more accurate than that of YOLOv3-tiny [20] and YOLOv4-tiny. In this study, we predicted three bounding boxes at each scale, so the 3D tensor was \(N\times N\times[3\times(4+1+3)]\) for the four bounding box offsets, one objectness prediction, and three class predictions.
## 6 Nanofertilizers
### Iron Oxide Nanoparticles and Their Role in Plant Nutrient Fortification  
Iron is among the Earth's abundant elements and it majorly occurs as oxides of magnetite (Fe\({}_{3}\)O\({}_{4}\)), maghemite (g-Fe\({}_{2}\)O\({}_{3}\)), and hematite (\(\alpha\)-Fe\({}_{2}\)O\({}_{3}\)), among others. It is used in many applications due to its inexpensive nature, yet it has not been extensively exploited in the field of agriculture. Its low toxicity, availability, and superparamagnetic properties have positioned iron in recent times as one of the transition metal nanoparticles of current interest for improved agricultural production. However, the shortcomings of iron, being pyrophoric and extremely reactive, hinder its application [82].  
Most foods are deficient in iron, which arises from poor iron content of the soil unavailable for the plants to absorb. In calcareous soils, the applied iron fertilizers are transformed and may not be available for plant absorption. To solve the problem, iron-chelated fertilizers are used. Iron fertilization of crops in form of \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\) nanoparticles is gradually gaining attention, as it holds prospects towards elimination of iron deficiency in soils and crops. These nanoparticles increase seed germination, help the root to grow and also enhance water content in the chlorophyll. Due to the dynamic nature of nanoparticles, iron is released from \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)NPs when applied to the soil and they migrate from the roots to other parts tissues to supply the nutritional contents. [PERSON] et al. reported an increase in the iron concentration of _C. maxima_ shoots when the plants were exposed to both \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)NPs and Fe\({}^{3+}\) treatment compared to controls and Fe(II)-EDTA treated plants [83]. Moreover, when the roots were assessed, they observed that there were no significant changes in the Fe levels among all Fe treated and control plants, which indicated that the iron had been transported to other parts. Most researchers are of the opinion that nutrients get to other parts of the plant through the root, making it a major pathway for nutrient absorption; however, it has also been reported that nutrients can be translocated from the leaves to various parts of the plant tissues. [PERSON] et al. observed that carbon-coated iron NPs sprinkled on the leaves migrated from the leaves to other parts of the plant [83; 84].
## 3 Results and Discussion
### Flow Properties at Different Strain Rates and Temperatures
Figure 7: Grain boundary misorientation angles of samples (**a**) C, (**b**) D, and (**c**) E, and distribution of misorientation angles in (**d**) with a legend of the colour scale related to misorientation angles also shown in (**e**). Grain boundaries, or high-angle grain boundaries (HAGBs), are characterised by high misorientation angles \(>15\). Misorientation angles less than 15 are commonly used to describe sub-grain boundaries within a given grain and are usually referred to as low-angle grain boundaries (LAGBs). The misorientation of laths that influence the inclination of interfaces between adjacent laths further enhances the strength of parts by hindering the extension of slip across the material. The percentage of sub-grains, or LAGBs, was relatively low in the three forms of the LPBF Ti6 Al4V(ELI) alloy, accounting for \(>\)0.1%, 2.5%, and 3.7% in samples C, D, and E, respectively. The higher micro-hardness and dislocation density in samples E than in samples D obtained in references [42], as reported in the preceding paragraph, can be attributed to the higher LAGBs in samples E than in samples D. Generally, LAGBs are dislocations in nature, and their increase could possibly lead to an increase in micro-hardness as they offer increased resistance to localized plastic deformation induced by mechanical indentation. The forgoing discussion and the results obtained in Figure 5 suggest that the Hall-Petch effects on the strengthening of a material outweigh those of LAGBs. A relatively large fraction of the HAGBs had misorientation angles of between 55\({}^{\circ}\) and 70\({}^{\circ}\) in the three cases of samples, as seen in Figure 7. This is not surprising, as eight of the twelve possible \(\alpha\)-variants had misorientation between these angles. A large percentage of grains in samples C had a misorientation angle of 65\({}^{\circ}\), while misorientation frequencies in samples D and E peaked at an angle of 60\({}^{\circ}\). This suggests an ease in slip transfer in samples D and E in comparison to samples C, which further confirms the observation of yield stress and flow stress in Figure 5.  
Even though the highest peak frequency of misorientation angles for samples E was 53% compared to a value of 37% for samples D, the flow stress curves of samples D at all test conditions were higher than those of samples E. This suggests that the Hall-Petch strengthening mechanism dominates the influence of grain boundary misorientation. The difference in the highest peak frequency of misorientation in samples C, D, and E suggests that microstructural growth and transformation in Ti6 Al4V (ELI) are accompanied by the rotation of grains.
### Motivation  
According to [PERSON] et al. [9], further research should be conducted that includes more than one healthcare institution since, in their study, the interviewees were recruited from a single medical institution (i.e., more research is called for in this field). This encouraged us to conduct this research in order to expand on the related studies and enrich the health management industry with a practical solution to increase patient satisfaction in line with industry benefits. According to a survey conducted by [PERSON] and [PERSON] [10], almost two-thirds of sixty-one studies proposed general models to illustrate how blockchain could be a promising solution for health records without offering a prototype or implementation. Moreover, [PERSON] et al. [11] call for more studies based on design knowledge to explore and accelerate further solutions for health records based on blockchain. According to [12], due to cybersecurity concerns there is a need to provide more details regarding how users' data are managed in a blockchain. This research extended our previous proposal [13] twofold. The first was through a qualitative methodology, where we conducted interviews with various stakeholders from different healthcare industries. Second, we employed design science after the requirements were collected and proposed a solution based on Hyperledger Fabric, a blockchain framework, to illustrate how these could be applied. Our PHR solution is focused on achieving three objectives:  
* Data ownership: to ensure patients own and control their data;
* Access control: to allow patients to grant, revoke, or deny access to their data to other healthcare service providers or network researchers;
* Auditability: to allow patients to rigorously know how their data are being used, giving them authority to revoke access to anyone who has been found to be in violation. Thus, the main contributions of this research are as follows:  
1. It proposes a solution for personal health records based on blockchain to give patients the power to own and benefit from their data.
2. It introduces Hyperledger Fabric to achieve access control; govern identities for peers and users; and provide user authentication, credential validation, signature generation, and verification.
3. It designs and develops a working prototype based on the defined requirements.
4. It engages stakeholders from the industry to understand the real needs and to identify the requirements for PHRs.
5. It evaluates the functionality of the system's performance using the Hyperledger Caliper tool.  
This paper is organized as follows. Section 2 presents an overview of blockchain technology. Section 3 provides a literature review. Section 4 is the methodology, followed by the results, discussion in Section 5, and limitations in Section 6. Finally, the conclusions and future works are presented in Section 7.
# Predicting Dissolution Kinetics of Tricalcium Silicate Using Deep Learning and Analytical Models
###### Abstract  
The dissolution kinetics of Portland cement is a critical factor in controlling the hydration reaction and improving the performance of concrete. Tricalcium silicate (C\({}_{3}\)S), the primary phase in Portland cement, is known to have complex dissolution mechanisms that involve multiple reactions and changes to particle surfaces. As a result, current analytical models are unable to accurately predict the dissolution kinetics of C\({}_{3}\)S in various solvents when it is undersaturated with respect to the solvent. This paper employs the deep forest (DF) model to predict the dissolution rate of C\({}_{3}\)S in the undersaturated solvent. The DF model takes into account several variables, including the measurement method (i.e., _reactor connected to inductive coupled plasma spectrometer_ and _flow chamber with vertical scanning interferometry_), temperature, and physicochemical properties of solvents. Next, the DF model evaluates the influence of each variable on the dissolution rate of C\({}_{3}\)S, and this information is used to develop a closed-form analytical model that can predict the dissolution rate of C\({}_{3}\)S. The coefficients and constant of the analytical model are optimized in two scenarios: _generic_ and _allalline_ solvents. The results show that both the DF and analytical models are able to produce reliable predictions of the dissolution rate of C\({}_{3}\)S when it is undersaturated and far from equilibrium.  
t pacs: **Keywords:** tricalcium silicate; analytical model; ion activity; dissolution kinetics; deep forest +
Footnote †: journal:
# Detection and Attribution of Greening and Land Degradation of Dryland Areas in China and America
## 2 Materials and Methods
### Dominant Factor Distribution  
To investigate the regional dominant driving factor that affects the vegetation growth, we apply the ternary mapping of three main factors (CO\({}_{2}\), LU, CC). The three limit conditions are linearly stretched in the range from 0 to 255, then assigned to red (CO\({}_{2}\)), green (LU) and blue (CC). The color map can show the results of which driving factor is the dominant factor and the synergistic effects of three factors [33]. The ternary maps are plotted by the package of python-ternary.
# Comparative Study of Sound Transmission Losses of Sandwich Composite Double Panel Walls
## 4 Methodologies and Models for Obtaining Sound Transmission Loss of Double Panel Walls  
### Models and Methods for Sound Transmission Loss Calculation  
Over the years, quite a number of numerical and mathematical models, theories and methods have been developed by researchers to describe vibro-acoustic response and calculation of the STL of sandwich and composite DPS. For example, the DPS with corrugated cores investigated by [PERSON] et al. [93] was described by using the Timoshenko beam theory for the sandwich panels while the coupling between the core and the faceplate was described using the classical lamination theory. Some selected theories and models are herewith discussed.
# TAEO-A Thermal Aware & Energy Optimized Routing Protocol for Wireless Body Area Networks
### Problem Statement and Contribution  
In WBAN, a node communicates with its neighbors or sink using radio frequency (RF) signals. A transceiver in a sensor device is responsible for the communication of data through electromagnetic waves. This radio frequency transmission dissipates heat, which is absorbed by tissues, and consequently raises the temperature. The second factor causing temperature rise is the internal circuitry of a sensor node that generates heat while processing the collected data. The amount of heat that is generated by a node circuitry is dependent on its architecture. There is a need to control a node from dissipating excessive heat. Therefore, heat that is generated by the sensor nodes needs to be controlled for effective implementation of WBAN. An increase in the thermal dissipation of a sensor node due to the transmission of packets is one core safety issue in WBAN [4]. This is the major theme of the proposed protocol i.e. to keep the heated node away from the transmission process until its temperature revamped in order to provide a safe WBAN system for the health monitoring of patients. As discussed earlier, heat dissipation mainly depends on electromagnetic communication and the internal circuitry of a node. Therefore, this issue needs to be intelligently addressed in a practical  
Figure 1: Wireless Body Area Networks (WBAN) basic architecture depicting deployment of sensors for gathering patient’s data and submitting it to a remote server via the internet.  
implementation of WBAN technology. Otherwise, an increase in the temperature of a sensor node to a menacing level results in the damage of human tissues or DNA. In WBAN, this is also known as hot-spot node problem.  
In this work, a novel routing protocol for WBAN is proposed, which provides a solution to the following two problems:  
* effective detection and avoidance of a hot-spot node based on Specific Absorption Rate (SAR) and
* enhancement in stability period and network lifetime.  
The proposed routing protocol has out classed two state-of-the-art routing protocols in terms of stability period, throughput and residual energy. Moreover, an analysis of the ratio of heated and normal nodes is also conducted.  
Rest of the paper is organized, as follows: Section 2 discusses the related work. Section 3 presents the system model. The proposed methodology is described in Section 4. Section 5 presents the results and discussion. Finally, Section 6 concludes the paper.
## 2 Preliminaries
### Generate-and-Solve  
The G&S framework was introduced in [7; 8; 9; 10] to cope with hard combinatorial optimisation problems and it is based on problem instance reduction [24]. The general idea resides on the identification of a reduced sub-instance of a given problem instance, such that the sub-instance contains high-quality solutions to the original problem instance. This way, one could apply an ILP solver to the reduced sub-instance in order to obtain a high-quality solution to the original problem instance [25]. As a matter of fact, ILP solvers are highly effective for small to medium sized instances of hard problems and, in those cases in which a problem instance can be sufficiently reduced, it might be very efficient in solving the reduced problem instance.  
This is the underlying idea of the G&S, which prescribes the integration of two distinct conceptual components: the Generator of Reduced Instances (GRI) and the Solver of Reduced Instances (SRI), as illustrated in Figure 1. An exact method (e.g., ILP solver) encapsulated in the SRI component is responsible for solving reduced instances (i.e., subproblems) of the original problem that still preserve its conceptual structure. Thus, a feasible solution to a given subproblem will also be a feasible solution to the original problem. At a higher level, the GRI (e.g., a metaheuristic [26]) works on the complementary optimisation problem of generating reduced instances, which, when submitted to the SRI, produce feasible solutions whose objective function values can be used as a figure of merit (fitness) of the associated subproblems, thus guiding the search process. The interaction between GRI and SRI continues until a given stopping condition is satisfied. The best solution obtained by the solver to any of the subproblems generated by the GRI is considered to be the solution to the original problem instance.  
In fact, advances in exact solution methods and hardware technology have encouraged a number of researchers to design heuristics that incorporate phases where ILP models are solved, the so-called matheuristics [27]. The relation between the original problem and the mathematical programming model incorporated in a matheuristic may vary significantly. For example, the aforementioned problem instance reduction idea is related to column generation-based matheuristics and set-covering approaches [28; 29], in which the exact method is modified to speed up the convergence, thus loosing the guarantee of optimality. A further advantage of column generation-based approaches is that they are flexible and easily adaptable to different problem characteristics.  
As an example, a set-covering-based formulation was used to obtain a general heuristic approach for bin packing problems [30]. The approach operates in two phases that are heuristically performed. In the first phase (column generation), a very large number of feasible item-sets (columns) is generated, while in the second phase (column optimization) a feasible solution of the problem is obtained by solving the associated set-covering instance.
# Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction
###### Abstract
Convolutional neural networks (CNNs) are useful for extracting visual features [27]; long short-term memory (LSTM) is a type of recurrent neural network (RNN), and it is used to label the long dependencies of sequences along the text [28]; and connectionist temporal classification tackles the issue of no matching between input- and output-sequence lengths [29]. After training, the text is extracted from the test set; however, the text may still need some correction. Since the output of the previous step may need correction, another class of techniques within deep learning can be utilized; this was the subject of some previous experiments. These continued to apply the concept of using datasets to train deep neural network models, but they also used parts of Transformer, as well as language models [30] trained on voluminous amounts of data and capable of generating texts as outputs. Examples include T5 [31], GPT-3.5 [32], part of which forms the basis of ChatGPT, and BERT and its various variations [33]. These models can be used to predict masked words. Lastly, the accuracy is calculated by comparing the character-error rate [34] and the word-error rate [35] between the correct ground-truth text and the predicted text. In summary, since the research on Arabic handwritten-text recognition still contains a gap due to the lack of works surveying this problem in its recent form, this paper aims to investigate and analyze the deep-learning techniques used to perform the OCR and text correction of Arabic handwritten text. The novelty of this work resides in the fact that no previous work has specifically tackled this problem of critically analyzing deep-learning-based Arabic handwritten-text recognition in recent years, from 2020-2023.
## 4 Experiments and Results
### Experiments
#### 4.3.1 Static Environment Experiments  
Four static environments are constructed to compare the four algorithms' performance. The four environments, namely ENV-S1, ENV-S2, ENV-M, and ENV-L, feature varying complexities, i.e., ENV-S1 and EVN-S2 are relatively simple, ENV-M features a medium-level complexity, and ENV-L is the most complex with three alternative targets. The path results and experimental data are shown in Figures 5 and 6, respectively. Notably, DDPG produces collisions in ENV-M and ENV-L, and KDDPG produces a collision in ENV-L, for which the corresponding curvature smoothness is set to the maximum and illustrated in shadows in Figure 6.  
As shown in Figure 6, regarding path planning efficiency, KDDPG outperforms DDPG with a 9.9% smaller NT and a 9.2% shorter PL. However, KDDPG performs several unnecessary actions and shows limited improvement in the complex ENV-L because it fails to utilize the environment's global information. In contrast, D*-KDDPG achieves an 11.5% less NT and a 13.6% shorter PL than KDDPG by consulting the global information, and the same model results in a 0.8% more NT and a 3.9% longer PL than D*-DWA, another model utilizing the global information. For path smoothness, KDDPG's performance is much better than DDPG but poorer than D*-KDDPG and D*-DWA, as the latter two produce equivalently low curvature smoothness. For robot safety, D*-KDDPG maintains a safety distance 67.3% larger than those of D*-DWA.  
Figure 5: Performance comparison of four algorithms in different static environments. The green dots represent target points. The black lines indicate the D*-DWA paths, the yellow lines indicate the DDPG paths, the red lines indicate the KDDPG paths, and the green lines indicate the D*-KDDPG paths.  
In summary, D*-KDDPG can plan shorter, smoother, and safer paths within reduced periods than DDPG and KDDPG, and it produces paths featuring significantly enhanced safety and approximately the same length and curvature smoothness as D*-DWA and D*-KDDPG without additional time costs.