esa-sceva/satcom-chunk-collection huggingface dataset:
# Target Detection for Coloring and Ripening Potted Dwarf Apple Fruits Based on Improved YOLOv7-RSEs
## 2 Materials and Methods
### Dataset Production  
In this paper, we utilized the LabelImg software (1.8.6) for manual annotation of the images. We excluded apple images with over two-thirds occlusion to ensure annotation accuracy. This decision considers both apple appearance and shape and the picking robot end-effector's fault tolerance. After completing the annotation, an XML file was generated containing category and coordinate information. We divided the dataset into training, validation, and test sets using an 8:1:1 ratio. The test set comprises 150 images, including 50 showcasing apples in various complex scenes, and serves to evaluate the model's detection efficacy. Figure 2 presents sample images of apples in diverse complex scenarios. The sufficiency of image data is pivotal during the model training phase. Insufficient training data may result in overfitting. Presently, we possess 1325 original apple images, which partly capture apple characteristics. However, they may not fully cover the differences in light, weather, noise, clarity, and other factors found in natural environments. Hence, to enhance the performance and generalization capability of the target detection model, it is imperative to expand the apple image dataset. We took several steps to enhance our data, resulting in 7950 images, as shown in Figure 3. This expansion is crucial for enhancing the model's accuracy and generalization ability.  
\[I_{\text{new}}=aI_{old}+b \tag{1}\]  
\(I_{old}\) is the original image, \(I_{new}\) is the preprocessed image, and \(a\) and \(b\) are the adjustment parameters. The expanded image dataset will offer richer and more detailed target features for subsequent studies, thereby enhancing the model's capability to discern fruit shapes and features.  
Figure 2: Images of apples in different complex scenes. (**a**) Fairing; (**b**) backlight; (**c**) coloring period; (**d**) maturity; (**e**) close proximity; (**f**) long distance; (**g**) blade occlusion; (**h**) branch occlusion.
# Narrowband Internet of Things (NB-IoT):
## 5 Open Research Questions and Discussion
### Semi-Persistent vs. Dynamic Scheduling  
Most of the NB-IoT literature addresses dynamic uplink and downlink scheduling by studying the scheduling of logical channels and signals. There are still very few NB-IoT studies about the effectiveness of Semi-Persistent Scheduling schemes (SPS) even though SPS helps to reduce the NPDCCH overhead as compared to dynamic scheduling. It provides the NB-IoT UEs with longer allocated resources (more than one subframe) so that the NB-IoT device will not need the frequent downlink assignment as well as an uplink grant which is delivered by NPDDCH for each subframe. However, for applications that involve mobility or fast varying channel conditions, how is this scheduling scheme going to be effective knowing that NB-IoT has poor channel estimation capabilities as compared to LTE?
## 4 Discussion and Conclusions
Future research should investigate the validation of these models under various environmental conditions and geographical regions, thereby expanding their applicability and improving the prediction accuracy under various climatic conditions. The results reveal that temperature, pressure, and humidity have significant impacts on PV energy production, requiring models capable of capturing their non-linear interactions. Policies supporting localized weather monitoring and adaptive forecasting systems can enhance the accuracy of renewable energy management and planning. Investigating the impact of these models on the management and strategic planning of renewable energy resources can further strengthen their practical benefits in the energy sector. Strengthening the algorithms and working with real-time data can lead to new working approaches that will improve energy efficiency and sustainability. This study not only addressed the gaps identified through the literature but also highlighted the critical role of advanced tree-based EL models in the future of PV energy production forecasting. Proving that bagged trees offer the optimal balance between accuracy and computing efficiency. Establishing a comprehensive framework for the incorporation of environmental data into prediction models. Continuing research in this area merits further efforts through the evaluation of accuracy, computational cost, and applicability of such models in a holistic manner for developing robust renewable energy management and planning tools. Conceptualization, F.D. and S.O. ; methodology, F.D. ; software, F.D. ; validation, A.A., S.O. and D.B.U. ; formal analysis, S.O., F.D. and M.V. ; investigation, S.O. ; resources, D.B.U. ; data curation, F.D. ; writing--original draft preparation, F.D. ; writing--review and editing, M.V. ; visualization, F.D. ; supervision, M.V. ; project administration, A.A.; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the European Union's Horizon Europe research and innovation program under grant number: 101172877. This study does not require an ethics statement. It does not contain any information involving humans or animals. Not applicable. The dataset used in this study was obtained from EPIAS (Energy Exchange Istanbul). The data can be accessed through their official website: [[https://www.epias.com.tr](https://www.epias.com.tr)]([https://www.epias.com.tr](https://www.epias.com.tr)), accessed on 6 December 2024. Please note that specific datasets may require proper authorization or permissions as per EPIAS's data access policies. The authors declare no conflict of interest.
# Securing the Smart City Airspace: Drone Cyber Attack Detection through Machine Learning
## 5 Simulations and Analysis
### Analysis
The reason for this could be that the attacks, such as frame loss, occuring during a DoS attack may also occur due to various other non-adversarial causes, including the longer distance of the drone (in-flight) from the ground controller, terrain specifications and weather conditions. Hence, it is challenging to detect attack instances when drones are operated in difficult conditions, and to differentiate the same against naturally caused malfunctioning. To further compare the generalization performance of classifiers, we obtained flight logs from a second DJI Phantom 4 drone obtained, from the VTO labs repository [23] comprising eight usable log files. After following a similar labeling process as was undertaken for the first dataset, we compared the performance of the four classifiers on the combined dataset, with results illustrated in Table 7. A comparison of classifier accuracies for both a single drone as well as a combined drone dataset is presented in Figure 13. For the combined dataset, the results show that the accuracy of the RF classifier slightly drops and slightly increases for other classifiers. However, looking at the precision and recall values in Table 7, all classifiers yielded a degraded performance. This could be due to an increased number of normal data samples when compared to attack samples in the combined dataset. These results also indicate that the RF classifier still outperforms the other classifiers in terms of accuracy and precision rates, making it more suitable for detecting DoS and GPS jamming attacks. Finally, looking at the time required to build the models by various classifiers, Naive Bayes yielded the lowest training time and SVM yielded the highest with just 5000 data samples. This is an important aspect to be considered when developing models for UAV intrusion detection. With low computation capabilities on a typical drone, on-device rapid training on smaller datasets is thus more realizable. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Accuracy** & **Precision** & **Recall** & **Training Time** \\ \hline RF & 0.9784 & 0.9759 & 0.8631 & 0.2544 \\ \hline NB & 0.8595 & 0.4930 & 0.9958 & 0.0306 \\ \hline LR & 0.8595 & 0.4930 & 0.9958 & 0.4992 \\ \hline SVM (5000 samples) & 0.848 & 0.4773 & 0.9856 & 130.086 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of various machine learning models for detection rates. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Accuracy** & **Precision** & **Recall** & **Training Time** \\ \hline RF & 0.9686 & 0.8919 & 0.7905 & 1.3088 \\ \hline NB & 0.8837 & 0.4679 & 0.9639 & 0.0458 \\ \hline LR & 0.8837 & 0.4679 & 0.9639 & 1.0421 \\ \hline SVM (10,000 samples) & 0.877 & 0.4261 & 0.9781 & 155.509 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of various machine learning models for combined dataset from two drones.
# Wound Dressing Materials: Bridging Material Science and Clinical Practice
## 1 Introduction
### Acute and Chronic Wounds  
There are two main classes of wounds: acute and chronic. Acute wounds are injuries to the skin or underlying tissues that occur suddenly, typically as a result of trauma, surgery, or other external factors. These wounds progress through the normal stages of healing (coagulation, inflammation, proliferation, and remodeling) within a predictable timeframe, usually leading to complete closure and restoration of the skin's integrity [12]. Common examples of acute wounds include cuts, abrasions, burns, and surgical incisions [8].  
The inflammatory phase of wound healing involves the migration of neutrophils, macrophages, and lymphocytes to the wound, producing inflammation symptoms that last for about two weeks [13]. As previously mentioned, wound healing occurs in several phases, and interruptions at any point can lead to complications. When inflammation continues for an extended period, lasting months or even years, the wound is considered chronic. Such wounds are often linked to pathological changes, including heightened protease activity and the risk of infection [14]. Following the inflammatory phase, the proliferative phase occurs, characterized by the formation of new tissue, granulation, and epithelial tissue (re-epithelialization), enabling the restoration of the vascular network. Keratinocytes play a key role in restoring the epidermal barrier, while fibroblasts and endothelial cells are essential for forming new blood vessels (angiogenesis) and synthesizing the extracellular matrix (ECM). The remodeling phase involves the reorganization and contraction of the newly formed matrix, which may take years to complete [8].  
Acute wounds typically heal within at least three weeks, whereas chronic wounds persist for a minimum of three months post-injury [15]. One of the challenges in chronic wound healing lies in the harmful effects of various pathological conditions on biochemical signaling mechanisms, ECM deposition, and cellular migration. Hyperglycemia in diabetics inhibits ECM formation due to increased matrix metalloproteinases, as well as elevated levels of tumor necrosis factor-alpha (TNF-\(\alpha\)) and interleukin-1\(\beta\) (IL-1\(\beta\)) [16]. Additionally, diabetic foot ulcers (DFUs) are associated with reduced keratinocyte migration and leukocyte function, leading to infection. Low inorganic phosphate levels in diabetic ulcers also result in decreased adenosine triphosphate (ATP) production, weakening the immune response [16]. Epidermal growth factor (EGF) signaling molecules normally stimulate keratinocyte migration and proliferation during wound healing. Aging, disease, and sun damage impair keratinocytes' ability to respond to EGF and other growth-promoting factors. All these mechanisms contribute to delayed healing in chronic wounds. As such, chronic wounds are the focus of emerging therapeutic strategies aimed at incorporating ECM and various signaling molecules into the wound environment to promote skin regeneration and repair [12].
# Logistics Space: A Literature Review from the Sustainability Perspective
## 3 Methods and Results
### Literature Overview
#### 3.4.3 Classification on Data Sources  
The data sources of 28 papers mainly divided into three categories: Statistical Yearbook, network resources and logistics company sample survey data. The statistical yearbook is generally subdivided into zip codes (ZIP-CODE) for analysis. Since zip codes are relatively large spatial units, there are certain limitations [10]. The sample survey data of logistics enterprises, including the address of logistics enterprises, the number of employees, the type of logistics activities, etc., is suitable for in-depth study of logistics space. However, it also causes some limitations that sample size is small and the year of statistics is discontinuous. The development of logistics is accompanied by the production of a large amount of logistics data. The network data resources are abundant and easy to obtain, which will become an effective means of mining data. Google Earth, Tencent Map, and Amap provide data support for logistics space research. For example, [PERSON] collected 4396 logistics points of interest (POI) in Beijing based on Tencent Online Maps Platform [44]. NACE (European industry classification system) and NAICS (North American Industry Classification System) are used to identify establishments with logistics facilities in these databases. There are also other scholars who use the national standards for the classification of logistics enterprises. For example, [PERSON] used the Spanish National Classification of Economic Activities (CNAE), and [PERSON] referred to China's \"Logistics Enterprise Classification and Evaluation Index\" [11; 44].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Regions**} & **Population** & \multirow{2}{*}{**Area (km2)**} & **Population** & **Change in Number** & \multirow{2}{*}{**References**} \\  & **(Million)** & & **Density** & & **of Logistics** & \\  & & & **(Inhabitants/km2)** & & **Facilities** & \\ \hline Gothenburg metropolitan area & 0.973 & 3695 & 263.3 & +56.8\% & [6] \\ Västra Götaland County & 1.615 & 22,752 & 71.0 & +49.4\% & [6] \\ Los Angeles metropolitan area & 1.790 & 12,561 & 142.5 & +29\% & [10] \\ Tokyo metropolitan Area & 3.700 & 15,950 & 231.9 & \(-\)24\% & [2; 5] \\ Beijing & 2.172 & 16,410 & 132.4 & / & [44] \\ Randstad & 8.500 & 8357 & 1017.1 & +1\% & [50] \\ Paris & 11.900 & 12,012 & 990.7 & +34\% & [50] \\ Greater Toronto Area & 6.000 & 5904 & 1016.3 & +38.2\% & [19] \\ Zurich & 0.380 & 92 & 4130.4 & \(-\)0.1\% & [7] \\ North Brabant & 0.800 & 4929 & 162.3 & +67\% & [38] \\ Atlanta & 5.000 & 11,204 & 446.3 & +203\% & [8] \\ \hline \hline \end{tabular}
\end{table}
Table 4: The basic situation of study regions.  
Figure 3: The countries to which 28 literatures researching logistics space belongs.
## 4 Performance Analysis and Evaluation  
For analyzing signaling costs, we use a two-dimensional (2D) random walk model [21-23] based on the properties of regular, absorbing Markov chains. Random walk mobility models are designed for dynamic location areas and are suitable for modeling user movement when mobility is generally confined to a limited geographical area. Such scenarios include homes, vehicles, hospitals, and department stores.
## 2 Materials and Methods  
### Task Equipment and Participants  
The GlassUp F4 smartglasses (F4 SG), produced by an Italian company (GlassUp, Modena, Italy), were used in this study. The F4 SG were selected for their features, specifically, since they were certified IP31 (International Protection for solid particles and liquids ingress), they came with protection lens and an elastic band to ensure that the glasses remain in position. Thus, this device was particularly suitable to perform, both indoor and outdoor, agricultural on-farm activities--also demonstrated by the results of a recent study, where the F4 SG where tested in laboratory and field environments [40]. The F4 SG are combined with an external joypad named \"box\". It provides energy and allows control of the glasses via navigation buttons (enter, clear, arrows up, down, etc.) and five function keys that can be set with various tasks, such as front lighting, photo capturing, video recording and scan-code function (Figure 1).  
Only the latter function was utilized in this study to scan Quick Response (QR) codes containing information linked to individual sheep. These particular smartglasses allow storing files (image, text, video, audio) on the flash memory. Each file was associated to a unique QR code that once scanned allow to see the augmented file on the SG display. This process could be performed also without internet connection. The scan-code function was coupled to the right-side key button on the F4 SG to render task flow more efficient.  
The evaluation trials were carried out at the Agricultural Research Agency of Sardinia (Italy) at the Bonassai Animals Research Station (AGRIS). The task consisted of identifying and selecting animals during milking that are well-suited for genetic improvement programs based on the information provided by the QR code of each ewe. The work was realized in the milking parlor with 16 participants, as found in a similar study [35]. The age range, mean and standard deviation (SD) of the participants were 29-62 and 48.4 (8.3) years, respectively. The education level of the participants was: middle school title (31%); high school diploma (31%); bachelor's degree for (38%). All the participants were expert milkers working with the milking machine. All participants received a training session the day preceding the trials that explained the underlying concept of AR and demonstrated the functions and operation of the F4 SG. The trials were conducted in the DeLaval MidiLine milking system composed of 24 groups for 48 stalls with an automatic cluster removal and a pit height of 85 cm.
## 4 Experimental Results
### Performance Metrics  
The key performance metrics used in our experiment include accuracy (A), precision (P), recall (R), F1-score (F1), and root mean squared error (RMSE) [41]. All these metrics are generally used to evaluate the brain tumor models in state-of-the-art (SOTA) methods. RMSE is the square root of the mean squared error. It is more sensitive to outliers than the mean absolute error (MAE) and is often used in regression analysis. It is calculated as the following equation, where \(L_{c}\) is the actual label, \(\hat{L}_{c}\) is the predicted label, and \(K\) denotes the total number of samples in the dataset:  
\[\text{RMSE}=\sqrt{\frac{1}{K}\sum_{c=0}^{C}\bigl{(}L_{c}-\hat{L}_{c}\bigr{)}^{ 2}} \tag{10}\]
# Clustering-Based Data Dissemination Protocol Using the Path Similarity for Autonomous Vehicles
## 4 Results
### Data Dissemination Rate Evaluation  
This section is about an evaluation for the data dissemination rate of the protocols. As previously stated, clustering data dissemination protocols should properly exclude messages from nodes other than their clusters to prevent flooding. If a protocol has been properly prioritized, it will have a higher data dissemination rate across the network compared to other protocols.  
In this experiment, an arbitrary node is periodically selected every five seconds, and a dissemination message is sent from the corresponding node. When each message is disseminated and finally dropped and extinguished on the network, the number of nodes that existed in the area at the time of the first message transmission (i.e., the network size and the number of nodes that actually received the message) are measured.  
Figure 21 is the message dissemination rate calculated by accumulating the number of nodes that successfully received the message and those that needed to receive the message. We see that the dissolution rate converges gradually as the two algorithms are repeated. Depending on the context within the scenario, the nodes may be close to each other or scattered, but the proposed protocol has a higher propagation rate than CPB. The converged dissemination rate is approximately 22.63% for CPB and 53.03% for the proposed protocol.  
Figure 20: Total Transferred Header Size (Kb) per Node Density.
## 4 Evaluation Metrics
### Routing Protocols Metrics
#### 4.3.3 End-to-End Delay  
The End-to-End delay is the averaged delay over all of the data packets that make it from the source to the destination.
## 2 Materials and Methods  
In this study, an average digital compact camera (Nikon-D 3300) and a smartphone (Huawei Mate 10 lite) were utilized to capture the photogrammetric data, Figure 1. The specifications of both cameras are provided in Table 1.  
It was important to start the assessment on construction elements with regular geometric shapes for which their volumes and surface areas can be easily measured so that measurement errors are eliminated, thus providing an accurate assessment. For this purpose, two specimens of casted concrete were made using standard models of different shapes and sizes, i.e., a square beam (15.2 cm \(\times\) 15.2 cm \(\times\) 75.6 cm) and a cylinder (15 cm \(\times\) 30 cm), Figure 2. In addition, a small sand pile (5490 cm\({}^{3}\), Figure 2c) was formed to further assess the geometrical accuracy for irregular material.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Specification** & **Digital Camera** & **Smartphone** \\  & **(Nikon-D 3300)** & **(Huawei Mate 10 Lite)** \\ \hline Camera Model & D-3300 & RNE-L21 \\ Sensor Type & CMOS & BSI-CMOS \\ Sensor Size & APS-C (23.5 mm \(\times\) 15.6 mm) & \(\sim\)1/2.9\({}^{\circ}\) 4.8 \(\times\) 3.6 mm \\ Camera Resolution & 24 MP & Dual 16 MP + 2 MP \\  & 6000 \(\times\) 4000 (3:2) & 4608 \(\times\) 3456 (4:3) \\ Pixel Size & 4.04 μm & \(\sim\)1.06 μm \\ Focal Length & 18–55 mm & 27 mm \\ (35 mm equivalent) & 1.43 & 6.75 \\ Crop Factor & 100–12,800 & 50–3200 \\ \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \end{tabular}
\end{table}
Table 1: The specifications of the utilized cameras.  
Figure 1: The capturing devices used in this study: (**a**) the compact camera (Nikon-D 3300) with the utilized lens (18–55 mm); (**b**) the smartphone (Huawei Mate 10 lite).  
Figure 3 presents the workflow followed by this study to assess and compare the quality of the photogrammetric outputs along with the geometrical accuracy of the final 3D models for both cameras' data sets.  
Figure 2: The specimens used to conduct the study: (**a**) standard casted concrete square beam (15.2 cm \(\times\) 15.2 cm \(\times\) 75.6 cm); (**b**) standard casted concrete cylinder (15 cm \(\times\) 30 cm); (**c**) sandpile (5490 cm\({}^{3}\)).