esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Convolutional Neural Network Applied to Fruit Image Processing
## 6 CNN-Based Approaches for Fruit Detection
They used a blob detector based on a fully convolutional network (FCN) to extract candidate regions in the images. A counting algorithm based on a second FCN estimates the number of fruit in each region. They used two different datasets composed of oranges in daylight and green apples at night, using human-generated labels as ground truth. Then, a linear regression model maps fruit count estimate to a final fruit count. The detection of fruits accurately, quickly, and reliably is essential for estimating fruit yield and automated harvesting in orchards. Based on that, [PERSON] et al. [36] adapted an object detector by using a Faster R-CNN (VGG-16) through transfer learning with images obtained from two modalities--color (RGB) and Near-Infrared (NIR). Additionally, they explored early and late fusion methods to combine the multi-modal information (RGB and NIR). The obtained multi-modal Faster R-CNN model achieved results of the F1 score from 0.807 to 0.838 for sweet pepper detection. [PERSON] et al. [96] presented a multi-sensor framework to identify, track, localize and map every fruit in a mango orchard. Data were collected using a vehicle equipped with color cameras and strokes, a global positioning inertial navigation system (GPS/INS), and a 3D LiDAR. The fruit was detected using a faster R-CNN detector (a modified VGG-16), and pair-wise correspondences were calculated between images using data of trajectory provided by a GPS. They automatically generated image masks for each canopy with a LiDAR component, linking each fruit with the corresponding tree. In the results we can observe that single, dual, and multi-view methods can achieve precise yield estimates. However, in the multi-view approach is not required a calibration and it achieves an error rate of only 1.36% for individual trees.
## 4 Discussion
### Daymet-Derived PARin and VPD  
Daymet-derived values are compared to those measured at the AmeriFlux sites in Figure 7. T\({}_{\text{ave}}\) values (Equation (1), using Daymet T\({}_{\text{min}}\) and T\({}_{\text{max}}\)) showed good agreement with AmeriFlux measurements (Figure 7a). Daily PARin values and VPD (Equation (2) using Daymet vapor pressure) were less consistent (R\({}^{2}\) of 0.48 and 0.54, respectively) (Figure 7b,c).  
the calibration is influenced by 17 irrigated field years at the Mead, NE sites (US-Ne1 and US-Ne2; 58% of the field years used to calibrate the LUEc). This may have biased the \(\sigma_{\rm WScalar}\) parameter to better reflect irrigated crop management practices while the other five fields are under rainfed crop management. The modeled VPD would likely be overestimated at the two Mead, NE sites due to VPD being anthropogenically lowered through the irrigation practice. While the rainfed sites have higher annual precipitation rates than the Mead, NE sites, the timing is irregular and can contribute to greater uncertainty; irrigation at US-Ne1 and US-Ne2 supplements rainfall and thus reduces uncertainty. Thus, irrigation bias could increase error in modeled GPP at the rainfed sites.  
Figure 7: Daymet-derived daily values as functions of AmeriFlux measured values for (**a**) average daily temperature, (**b**) PARin, and (**c**) vapor pressure deficit (VPD).
# A Closer Look at Few-Shot Classification with Many Novel Classes
## 5 Experiments
### Ablation Study  
This experiment's ablation study focuses on examining the impact of Z-score normalization and the role of pretraining strategies within the SHA-Pipeline. The study was conducted by excluding the Z-score normalization step from these methods and analyzing its impact on performance. Subsequently, the scenario where the backbone network is trained from scratch during the meta-training phase, omitting pretraining, was explored. Experimental results are displayed in Figure 10.  
**Impact of Z-Score Normalization**  
Notably, Figure 10 shows that the accuracy drops by approximately 0.9% when the Z-score normalization step is removed. This finding emphasizes the significance of Z-score normalization as a key element in SHA-Pipeline. The Z-score normalization step significantly contributes to addressing hubness-related issues, enhancing the quality of hierarchical clustering and subsequently improving performance.  
**Impact of Pretraining**  
Figure 10 reveals a substantial performance decline of over 10% when pretraining is omitted. In other words, the accuracy experiences a significant drop when meta-training is conducted without the benefits of pretraining. This result underscores the crucial role of pretraining in enhancing the initial state of the backbone network before starting meta-training. It highlights that the pretraining phase plays a key role in setting a solid foundation for subsequent meta-training operations, ultimately aiding in enhanc  
Figure 10: Ablation study of SHA-Pipeline (ViT-S, DINO, \(N=1000\), K = 1).  
Specifically, the second part of Figure 10 demonstrates the performance changes after the omission of the pretraining step. It is clear that the absence of pretraining leads to a significant decrease in accuracy. This further validates the importance of pretraining in few-shot learning, providing the model with better initial parameters, thus significantly enhancing the effectiveness of meta-training and the final classification performance.  
Through these ablation studies, we can better understand the critical roles of Z-score normalization and pretraining in SHA-Pipeline, offering valuable insights for further optimization of few-shot learning algorithms.
# The Non-Linear Impact of Industry 4.0 on Carbon Emissions in China's Logistics Sector
## 1 Introduction
### Research Questions  
This study addresses the following research questions:  
1. How do different Industry 4.0 technologies, such as IoT, AI, and automation, influence carbon emissions in the logistics sector?
2. What are the mediating factors, such as operational efficiency and business scale, that shape the impact of these technologies on emissions?
3. How do these relationships vary across different regions and stages of technological adoption in China? The answers to these questions are critical for several reasons. First, while Industry 4.0 has been widely promoted for its potential to revolutionize industrial processes, its environmental implications remain underexplored, particularly in developing economies. Second, understanding the specific mechanisms through which these technologies affect emissions can help policymakers design more effective regulations and incentives to promote sustainable technology use. Finally, by providing empirical evidence from China, this study contributes to the broader discourse on the role of digital transformation in achieving global sustainability goals.  
By answering these questions, this research aims to contribute to the growing body of knowledge on sustainable logistics and provide actionable insights for policymakers and industry leaders aiming to leverage Industry 4.0 for environmental benefits.
## 3 Results and Discussion
### Hardness Analysis  
Figure 6 shows the morphology of the 4.0 wt. % Mo coating after the microhardness test. The size of the rhombus is basically the same, which indicates that the hardness distribution in the coating is uniform. The coating has good ductility because there are no obvious cracks around the indentations [41].  
The microhardness test results of the coatings with different Mo contents are shown in Figure 7. The microhardness decreases with the increase of Mo content in the coatings. The microhardness of the 6.0 wt. % Mo coating is 288.0 HV, which is about half of the microhardness of the Mo-free coating. The XRD results show that the amount of M\({}_{23}\)(C, B)\({}_{6}\) increases and the contents of M\({}_{7}\)(C, B)\({}_{3}\) decrease with the addition of the Mo element. The microstructure analyses show that the martensite decreases and the ferrite increases gradually with the increase of Mo content in the coating. It is known from the literature that the microhardness of martensite is 525.4 HV and the microhardness of ferrite is 235.1 HV [42]. The microhardness of Cr\({}_{23}\)C\({}_{6}\) is 13.2 GPa and the microhardness of Cr\({}_{7}\)C\({}_{3}\) is 18.3 GPa [43]. Therefore, the microhardness of the coating decreases gradually with the increase of Mo content.  
Figure 5: The mapping scanning results of the 4 wt. % Mo coating: (**a**) surface scan diagram (**b**) Fe; (**c**) Cr; (**d**) Mo; (**e**) Si; (**f**) Ni; (**g**) C.  
Figure 6: Hardness indentation morphology of the coating (4.0 wt. % Mo).
# Estimation of the Block Adjustment Error in UAV Photogrammetric Flights in Flat Areas
## 2 Materials and Methods
### Image Acquisition  
Flights were planned with the DroneDeploy application for PC, which allows the importation of KML (Keyhole Markup Language) or KMZ (Keyhole Markup Language compressed). These formats are based on XML to store geographic data and related content, and are an official standard of the Open Geospatial Consortium (OGC) [45]. The flights were programmed prior to the data campaign, taking into account the autonomy of the equipment batteries and the study area; the blocks were imported to the DroneDeploy application in the KMZ format.  
Three flight missions were carried out with the Phantom 3 Pro following the scheme in Figure 5a, and one with the Inspire 2 (Figure 5b) to cover the four blocks. Another five flight missions were completed with the Inspire 2 (Figure 5c) for the cross strip at the height of 110 m (h\({}_{c}\), cross flight height). The images were acquired orthogonally and with a forward and side overlap of over 60% [46]. The flight configurations were the following:  
* Phantom 3 Pro: For Blocks 1,2, and 4, flight height (h\({}_{H}\)) 120 m (the maximum allowed by Spanish regulation), forward overlap 80%, side overlap 60%, and speed 9 m/s for an area of 19 ha, with a GSD of 5.1 cm. The flight duration was 14\({}^{\prime}\)8\({}^{\prime\prime}\), taking 326 images for Block 1, 296 for Block 2, and 310 for Block 4.
* Inspire 2: Flight height 120 m (h\({}_{H}\)), forward overlap 80%, side overlap 60%, speed 10 m/s for an area of 23 ha, with a GSD of 2.1 cm. The flight duration was 14\({}^{\prime}\)38\({}^{\prime\prime}\), taking a total of 327 images in Block 3.  
Figure 4: Location of the ten GCPs at the corners of each block and nine ChPs distributed within the blocks; the reference system is UTM 30N ETRS89.  
For measuring 80 ha of the overall study area, it was divided into four areas (Figure 4) with its corresponding blocks (Figure 6), in a way such that a single set of UAV batteries was used for each block. Figure 6 displays the setup for the overall study area, Scenario C (next section).
# Understanding and Classifying Permanent Denial-of-Service Attacks
## 3 Case Studies of PDoS Attacks
### Remaiten  
Remaiten, identified in 2016, targets IoT devices, specifically Linux on embedded systems, primarily routers. It stands out for its strategy of exploiting IoT devices via remote access, primarily leveraging exposed telnet ports with weak authentication mechanisms.  
After infecting the devices, Remaiten is able to perform system-level actions, download more malware on a device, and even scan and remove competing bots on a system compromised by it. In addition, this malware has the capability to disable the networking of the routers, rendering devices unusable until factory reset.
###### Abstract  
In urban environments, radiation energy from the sun becomes a heat load on the human body. The radiation energy can be divided into visible, near-infrared, and far-infrared wavelengths. Far-infrared radiation is re-emitted from structures that have been warmed by the sun. On the other hand, visible and near-infrared light comes directly or reflected from structures. However, previous studies have measured environmental changes using only far-infrared cameras to study urban heat island (UHI) countermeasures. Therefore, it is important to measure the environmental radiation energy at each wavelength region to properly discuss the cause of the heat load. To conduct the measurements, we used three cameras operating at different wavelengths: visible, near-infrared, and far-infrared. In addition, the measurements were conducted in different urban locations, seasons, and weather conditions in the Tokyo area of Japan, an urban metropolis. The results for far-infrared wavelengths show that, in summer when the temperature is high, all structures except the sky have a positive heat load on the human body, while, in winter or cloudy weather when the temperature is low, the heat load on the human body is negative. The results of heat load measurements at visible and near-infrared wavelengths show that the heat load varies greatly depending on the proportion of sky, plants, and buildings, despite the proportion of ground area being the same. These results indicate that the differences in radiation wavelengths need to be considered when considering reducing the heat load on the human body in urban environments. Therefore, the proposed method could contribute to the development of more effective UHI countermeasures by measuring in a various environments and countries.  
radiation; heat load; urban heat island; wavelength; temperature; human body +
Footnote †: journal: Applied Sciences
## 2 Theory, Requirements and Synthesis
### Closed-Loop Control (CLC)
#### 2.3.2 The Process
At the drop port of the MRR (Figure 4c) we obtain a modified 3-color signal \(P_{\text{drop}}\), affected by the MRR's OTF \(H_{\text{MRR}}(\lambda-\lambda_{\text{res}})\) with multiple passbands spanned around the maximum transmission points at \(\lambda-\lambda_{\text{res}}\approx k\cdot FSR;k\in\mathbb{Z}\), where the observed resonance is a function of the MRR's temperature \(\lambda_{\text{res}}(\theta_{\text{MRR}})\). This temperature comes from environmental variations as well as from the self-induced heating of the system during normal operation, and is responsible for our system's instability. Hence, the need for a CLC. Finally, we capture the optical power spectrum at the low bandwidth PIN-FET module (Figure 4d), converting it to an electrical near direct current (DC) and voltage \(u_{\text{out}}(i_{\text{out}})\), while ignoring the other mixing products falling into the RF domain and beyond. Such a device basically functions as a power meter, measuring the sum of powers for all three frequency components, where the resulting voltage \(u_{\text{out}}\) is proportional to the incident optical power \(P\). Since the time constants of the other components are several orders of magnitude smaller than those of the TEC-driven DFB laser, we can conclude that the combined time constant of the system is approximately \(\tau_{\text{process}}\approx\tau_{\text{DFB}}\).
## 3 Resonator Design
### Data Coil Tuning
This evokes an additional transient output signal \(y_{\text{c}}(t)\), which can be obtained from the inverse Laplace transform:  
\[y_{\text{c}}(t) =\mathcal{L}^{-1}\{Y_{\text{c}}(s)\}(t)=\mathcal{L}^{-1}\{G(s) \cdot X_{\text{c}}(s)\}(t), \tag{13}\] \[=\mathcal{L}^{-1}\left\{G(s)\cdot a_{\text{c}}\cdot\frac{\omega_ {0}\cos\phi_{\text{c}}+s\sin\phi_{\text{c}}}{s^{2}+\omega_{0}^{2}}\right\}(t),\] (14) \[=a_{\text{c}}\cdot\left(e^{-\alpha t}\left(A_{1}e^{j\beta t}+A_{ 2}e^{-j\beta t}\right)+A_{3}\cos(\omega_{0}t)+A_{4}\sin(\omega_{0}t)\right), \tag{15}\]  
Here, \(A_{i}\) are complex coefficients depending on the equivalent circuit parameters and the excitation frequency \(\omega_{0}\), while \(\alpha\) and \(\beta\) are analytically given as  
\[\mathrm{Re}\left(\frac{R_{\mathrm{L}}}{1+j\omega_{0}C_{\mathrm{P}}R_{\mathrm{L }}}\right)\overset{!}{=}R_{\mathrm{c}}\;\;\mathrm{and}\;\;\mathrm{Im}\left( \frac{R_{\mathrm{L}}}{1+j\omega_{0}C_{\mathrm{P}}R_{\mathrm{L}}}\right) \overset{!}{=}-\omega_{0}L_{\mathrm{c}}, \tag{8}\]0 \[\mathrm{Re}\left(\frac{R_{\mathrm{L}}}{1+j\omega_{0}C_{\mathrm{P}}R_{\mathrm{L }}}\right)\overset{!}{=}R_{\mathrm{c}}\;\;\mathrm{and}\;\;\mathrm{Im}\left( \frac{R_{\mathrm{L}}}{1+j\omega_{0}C_{\mathrm{P}}R_{\mathrm{L}}}\right) \overset{!}{=}-\omega_{0}L_{\mathrm{c}}, \tag{8}\]1  
Hence, the steady state solution is determined by the last two terms of (15) and the transient response by its first term, which is a decaying oscillation of angular frequency \(\beta\in\mathbb{R}\) and the decay time  
\[\mathrm{Re}\left(\frac{R_{\mathrm{L}}}{1+j\omega_{0}C_{\mathrm{P}}R_{\mathrm{L }}}\right)\overset{!}{=}R_{\mathrm{c}}\;\;\mathrm{and}\;\;\mathrm{Im}\left( \frac{R_{\mathrm{L}}}{1+j\omega_{0}C_{\mathrm{P}}R_{\mathrm{L}}}\right) \overset{!}{=}-\omega_{0}L_{\mathrm{c}}, \tag{8}\]2  
As the transient signal shall settle within a fraction \(\
u\) of a period \(T_{0}=2\pi/\omega_{0}\), i.e., \(\tau\overset{! }{=}\
u T_{0}\), we get from (18):  
\[\mathrm{Re}\left(\frac{R_{\mathrm{L}}}{1+j\omega_{0}C_{\mathrm{P}}R_{\mathrm{L }}}\right)\overset{!}{=}R_{\mathrm{c}}\;\;\mathrm{and}\;\;\mathrm{Im}\left( \frac{R_{\mathrm{L}}}{1+j\omega_{0}C_{\mathrm{P}}R_{\mathrm{L}}}\right) \overset{!}{=}-\omega_{0}L_{\mathrm{c}}, \tag{8}\]3  
Solving (19) for \(R_{\text{c}}\) and substituting the result into (10) yields the optimal series loss resistance and resistive circuit load to achieve both fast transient settling and simultaneous impedance matching:  
\[\mathrm{Re}\left(\frac{R_{\mathrm{L}}}{1+j\omega_{0}C_{\mathrm{P}}R_{\mathrm{L }}}\right)\overset{!}{=}R_{\mathrm{c}}\;\;\mathrm{and}\;\;\mathrm{Im}\left( \frac{R_{\mathrm{L}}}{1+j\omega_{0}C_{\mathrm{P}}R_{\mathrm{L}}}\right) \overset{!}{=}-\omega_{0}L_{\mathrm{c}}, \tag{8}\]4 Equivalent circuit of the receiver’s resonator consisting of the data coil \(L_{\text{c}}\), a series resistance \(R_{\text{c}}\), a parallel tuning capacitor \(C_{\text{P}}\), and the load resistance \(R_{\text{L}}\) representing the receiving amplifier with additional impedance matching circuits.
## 3 Hotline-Based Reliable WSN Topology
### Network Clusters  
Due to the availability of gateways, we can efficiently organize and manage the sensor nodes in a network. Each node would have to associate to a particular gateway making it the default gateway. All the nodes associated to one gateway form one cluster. Please note that gateway and cluster head are same name used interchangeably.  
One of the important benefits of the clustering approach is to facilitate efficient and reliable communication using gateway hotlines. For example, one set of nodes is sensing the environment to get the data and then that data is being sent to another set of nodes or a sink for further computation. The default gateways can provide a fast and reliable routing mechanism to communicate this data.  
Nodes become the part of a cluster depending upon their hop count from the gateway. The nodes would get associated to any gateway by using the information in the Router Advertisement (RA) message [6]. The routers would send router advertisement (RA) messages to help the nodes to associate to a particular gateway. When a sender node receives RA messages from several routers, it would differentiate the messages with help of current time to live (Cur TTL) field. It must be noted that Cur TTL has been modified. Every router sets the value of Cur TTL field to be 255 and with each hop the value is decreased by 1. So the nodes get associated to the router with a maximum Cur TTL, since a higher value of Cur TTL implies less hop count to that router. The RA message format is shown in the following Figure 2. Our field of interest here is Current Hop Limit Field which is a byte long.  
Figure 1: Hotline deployed WSN Topology.
## 2 Preliminaries and Related Works
### Methods Directly Learn from Noisy Labels
#### 2.1.1 Robust Loss Function  
The main idea of the robust loss function against noisy labels is that, when the data are generally clean, the traditional cross-entropy loss function learns a small number of negative samples, which can improve the robustness of the model. Conversely, when the proportion of noisy data is relatively large, the cross-entropy will be deviated by the noisy data, and the loss function needs to be modified to make the weight of each training sample equally important.  
The main purpose of these methods is to provide a robust loss function so that, even if there are noisy labels in the training data, it can also bring a small risk to the clean samples. [PERSON] et al. (2015) proved that the loss function based on the mean absolute error (MAE) is inherently robust to label noise; therefore, standard back propagation is enough to learn the true classifier even under label noise. However, MAE may perform poorly with DNNs and challenging datasets. In order to solve this problem, [PERSON] et al. (2016) presented a GCE loss, which can be seen as a generalization of MAE and categorical cross-entropy (CCE), and it achieved a good performance in a wide range of noisy label scenarios. In addition, inspired by the symmetric KL-divergence, [PERSON] et al. (2017) proposed a symmetric cross-entropy (RCE) approach, which can simultaneously address both the under-learning and overfitting problem of cross-entropy in the presence of noisy labels.
## 2 Materials and Methods
### Test Field Specification
#### 2.2.4 Coordinates Determined by GNSS  
The points were measured independently twice with a Leica GS18. The average standard deviations of the GNSS point determinations were calculated from the dual measurements. Standard deviations from the dual measurements of the GNSS coordinates of the points are presented in Table 3.
## 3 \(D^{2}\)-SPDM: Faster R-CNN-Based Defect Detection and Classification with Label Enhancement  
### System Structure  
This section offers an overview of the \(D^{2}\)-SPDM framework, designed for efficient automated detection of steel surface defects, with a comprehensive discussion of defect image classification and segmentation label generation. Figure 4 depicts the structure of the \(D^{2}\)-SPDM framework described in this paper. The framework is structured into two stages: the first involves data collection and defect detection, and the second, defect classification and label enhancement.  
Initially, in stage 1: the defect detection process, defect images from the NEU-DET dataset, together with defect-free images created through interpolation and image augmentation, are fed into the ResNet-50 model for detecting defects on steel surfaces. This simulates an instance where images randomly obtained from a real hot-rolling process are classified based on the presence or absence of defects. Images identified as defect-free by the ResNet-50 classifier are stored in a defect-free database, whereas those identified as defective are moved to Stage 2.  
In Stage 2: SPDM, the faster R-CNN model generates bounding boxes to capture the local information of defects, and these bounding boxes are input to the DeepLabv3+ model. These bounding boxes serve as initial mask data for the DeepLabv3+ model. To convert these bounding boxes into initial mask data, the work applies the GrabCut algorithm, which designates 80% of the bounding box's interior as foreground and the remainder as background. This method produces an initial segmentation mask that closely adheres to the object's boundaries. The model uses this generated mask as the ground truth for the first training round. Through iterative learning, the segmentation results from each round are used as input for the subsequent rounds, gradually enhancing the model's performance.  
The proposed framework integrates the SPDM process by detecting defects at an early stage during the hot-rolling stage of the steel manufacturing process and generating segmentation masks based on bounding boxes created for defect images. Providing such precise, pixel-level defect information allows process managers to analyze defect shapes and sizes more accurately, thereby facilitating more informed decisions regarding appropriate corrective actions.