esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods  
### Dataset Description  
To test the correctness and flexibility of the proposed method and analyze its properties, we used 2 datasets. The solder images dataset (named as DS hereafter), as shown in Figure 1, was provided by an IC manufacturer. It consists of two types of defect images and two types of normal images (Figure 2). All images are scaled to 30 \(\times\) 120 pixels for processing. The training set consists of 1240 normal samples (558 from the first type, and 682 from the second type); the testing set consists of 2251 defective images and 1814 normal images. No defect samples were used during the training.  
Figure 1: (a) PCB with solder joints; (b) Extracted solar joints.  
We have used another dataset, containing bare PCBs, with different defects, while taking into account that there are no publicly available relevant datasets. This is a set of the PCB images, acquired by a Point Grey FlyCapture camera, setup on industrial manufacturing line. The resolution of each image is 1288 \(\times\) 964, and the dataset contains 78 samples of normal PCBs, and 155 samples of defective PCBs with the following defect types: scratch, broken edges, extra holes, missing holes, and missing washers. Figure 3 shows a sample of defect-free bare PCB from the PCB dataset. As a preprocessing step, all training and test images were aligned using the SIFT [32] algorithm, and the non-PCB areas of the images were removed, resulting in the images of 1100 \(\times\) 600 pixels. These PCB images were divided into 66 nonoverlapping patches, forming a grid of 11 \(\times\) 6 patches of 100 \(\times\) 100 pixels in each patch. A class number K (from 1 to 66) was assigned to each patch, which corresponds to its grid location on the PCB image. All the samples of the same class, \(K\), had the same layout, with minor variations, due to manufacturing process, and they were not the exact copies of each other. Each of the 66 patches were analyzed separately to verify the correctness of the whole PCB images.  
As our method is based on one class classification, the validation dataset will not be used for training process; there are no negative samples in the dataset.
# Remote Sensing and Bio-Geochemical Modeling of Forest Carbon Storage in Spain
## 3 Study Area and Data
### Data Used for the NPP Modeling
Detailed methods on its production can be found in [19]. Note that rooting depth used by Biome-BGC does not indicate depth to bedrock. Instead, it refers to an effective soil depth defined as the depth at which plants are able to grow roots [15]. A 30-m spatial resolution GSV map was obtained from the combination of around 50000 NFI3 plot-level GSV estimations and a multitemporal Landsat dataset (\(\sim\)8000 Landsat-5 TM and Landsat-7 ETM+ scenes covering the study area and the NFI3 period, 1997-2007). A total of 805 predictors were calculated from Landsat spectral reflectances including time metrics, texture metrics, and vegetation indices. The most important predictors were identified by means of a guided regularized random forests algorithm (e.g., [43, 44]) and used to generate the GSV map. Google Earth Engine [45] was used for data management. Detailed methods on the production of this map can be found in [28]. The 30-m spatial resolution GSV map was aggregated to 1-km spatial resolution (Figure 2c) for coherence with the other datasets used in the present study.
## 4 Impact of Climate Change on Oceans: Leveraging DL for Monitoring and Analysis
### Unveiling Changes in Different Vulnerable Marine Ecosystems Using AI
#### 4.2.3 DL for Coastal Wetland Mapping and Analysis
Although quantifying uncertainty is critical for practical applications of DL models in decision making, such as in SST forecasting or sea ice extent monitoring, only a few studies have incorporated uncertainty quantification [254,255]. Most DL studies only focus on enhancing predictive performance using metrics like accuracy, RMSE, and R\({}^{2}\) while neglecting to assess the uncertainty associated with their predictions. As a result, even DL models with high reported accuracy on the test datasets may not be reliable in real-world applications. For example, models trained on cloud-free, high-quality imagery may perform well on curated test data in sea ice classification tasks. However, when applied to the real world with varying atmospheric conditions or underrepresented ice types, their performance can degrade significantly, misinforming navigation and other safety-critical operations in polar areas. In addition, calibrating DL models, i.e., adjusting the predicted probabilities to align with actual observations, is another critical step in building reliable models for real-world decision making [256]. However, this step is often overlooked in existing DL-based oceanographic studies. Moreover, many DL studies in oceanography rely on multiple input variables to enhance model accuracy. For example, in the ocean surface pH estimation task, parameters such as SST, SSS, dissolved oxygen, chlorophyll concentration, and nutrients are commonly integrated. Understanding the relative importance of these inputs is essential, especially in scenarios where certain variables may have higher uncertainty. [PERSON] et al. [99] developed a neural network model for ocean pHestimation and conducted a sensitivity analysis by perturbing each environmental variable (e.g., temperature, dissolved oxygen, salinity, and nutrients) by 5%. The results indicated that the model was most sensitive to salinity, followed by dissolved oxygen and temperature, while showing low sensitivity to nutrient variations, providing insights into which parameters mainly influence the model's estimations. Although understanding the relative importance of these inputs is essential, especially in scenarios where certain variables may have higher uncertainty, most existing DL-based oceanographic studies do not explicitly evaluate how individual input parameters affect model performance [118, 139], posing risks for practical applications when DL models rely on some uncertain inputs. Methods such as permutation importance, feature ablation, and SHapley Additive exPlanations (SHAP) could be used to highlight critical features, improving model interpretability.
## 5 Experimental Investigation and Results  
The designed cross dipole antenna and plane resonator was fabricated by a LPKF-E33 circuit printer, as shown in Figure 9. The figure shows only a cross dipole antenna with a connected SMA port and plane resonator integrated cross dipole antenna with an SMA port, respectively. Experimental measurements were carried out by an Agilent PNA-L vector network analyzer as given in Figure 10a, and measured transmission values are plotted in Figure 10b. According to the measured transmission values, the resonator plane reduced transmission values between each antenna of cross dipole antenna, which led to a mutual coupling effect being decreased when using this resonator layer. At the frequency of 2.4 GHz, a cross dipole antenna has mutual coupling (S21) of -19.7 dB but it is -25.3 dB with the use of a plane resonator layer. As a result of this study, a trained artificial neural network provides an accurate solution for mutual coupling effect between each element of cross dipole antenna with an error rate of less than 2%, as given in Table 2.  
Figure 8: The mutual coupling effect between cross dipole antennas.  
Figure 9: (**a**) Fabricated cross dipole antenna and (**b**) resonator layer integrated cross dipole antenna.
## 3 Numerical Simulation Analysis
### Doppler Tolerance  
After analyzing the simulation results, it is evident that CCF-MSS significantly outperforms the other three traditional reception methods under time-varying channel conditions. Moreover, the coarse Doppler estimation steps for CCF-MSS, CDE-MSS, and FDE-MSS are entirely identical, while the subsequent decoding performance indicates that there are differences in tolerance to Doppler estimation errors among the three methods.
## 2 IA for Two-Unit Series System with Three States
### Numerical Solution and Error Analysis
Bring (25) into (24), then we can obtain  
\[A\left(t_{i}\right)=e^{-\left(\lambda_{1}+\lambda_{2}\right)t_{i}}+\frac{h}{2 }[A\left(t_{0}\right)\left(\tilde{Q}^{\prime}\left(t_{i}-t_{0}\right)+O\left( h^{4}\right)\right)+2\sum_{k=1}^{i-1}A\left(t_{k}\right)\left(\tilde{Q}^{ \prime}\left(t_{i}-t_{k}\right)+O\left(h^{4}\right)\right)+O(h^{2}). \tag{26}\]  
On the other hand, the expression of \(\widetilde{A}\left(t_{i}\right)\) which is the approximate value of \(A\left(t_{i}\right)\) is  
\[\widetilde{A}\left(t_{i}\right)=e^{-\left(\lambda_{1}+\lambda_{2}\right)t_{i} }+\frac{h}{2}[\widetilde{A}\left(t_{0}\right)\left(\tilde{Q}^{\prime}\left(t _{i}-t_{0}\right)\right)+2\sum_{k=1}^{i-1}\widetilde{A}\left(t_{k}\right) \left(\tilde{Q}^{\prime}\left(t_{i}-t_{k}\right)\right)+\widetilde{A}\left(t _{i}\right)\left(\tilde{Q}^{\prime}\left(0\right)\right)]. \tag{27}\]  
Therefore,  
\[\left|A\left(t_{i}\right)-\widetilde{A}\left(t_{i}\right)\right| \leq \frac{h}{2}\left[\left|A\left(t_{0}\right)-\widetilde{A}\left(t _{0}\right)\right|\left|\tilde{Q}^{\prime}\left(t_{i}-t_{0}\right)\right|+2 \sum_{k=1}^{i-1}\left|A\left(t_{k}\right)-\widetilde{A}\left(t_{k}\right) \right|\left|\tilde{Q}^{\prime}\left(t_{i}-t_{k}\right)\right|\right.\] \[\left.+\left|A\left(t_{i}\right)-\widetilde{A}\left(t_{i}\right) \right|\left|\tilde{Q}^{\prime}\left(t_{0}\right)\right|+\left|A\left(t_{0} \right)+2\sum_{k=1}^{i-1}A\left(t_{k}\right)+A\left(t_{i}\right)\right|\left| O(h^{4})\right|\right]+\left|O(h^{2})\right|\] \[\leq hL\sum_{k=0}^{i}\left|A\left(t_{k}\right)-\widetilde{A}\left(t _{k}\right)\right|+T\left|O(h^{4})\right|+\left|O(h^{2})\right|,\]  
where \(L=\underset{0\leq k\leq n}{max}\left|\tilde{Q}^{\prime}(t_{k})\right|\). Then based on Lemma 3, we can get  
\[\underset{0\leq i\leq n}{max}\left|A\left(t_{i}\right)-\widetilde{A}\left(t _{i}\right)\right|\leq H\mathrm{e}^{2 TL}, \tag{28}\]  
where \(H=T\left|O(h^{4})\right|+\left|O(h^{2})\right|\). It is obvious that when \(h\) tends to be zero, the error tends to be zero.
## 2 Materials and Methods  
For this work, two neighbouring dwellings (D1 and D2) have been selected whose hourly demand has been registered for a whole year. In each of them, an SAPV energy system with an ESS made of batteries has been proposed, using an energy analysis, as described in [38]. Subsequently, using the SMC simulation method described in [38] the initial design has been optimized to achieve an LOLP around 2.6% (approximately between 200 and 240 h per year with energy supply from a diesel generator) in each dwelling.  
The SMC consists of a simulation of 100 iterations of the behaviour of the system, each of them consisting of one year (8760 h). For each iteration of the study, an hourly sequence of the demand in each house during one year is used, randomly obtained from the real values measured during 2019.  
The solar radiation (and therefore the power production per unit in the PV panels) is assumed to be the same for both dwellings, since they are close to each other. At each iteration, the daily radiation curve is randomized from the real values recorded during 2019.  
Additionally, in each iteration, the failures of the generation system are also randomized, using a failure rate per year (\(\lambda_{c}\)). As explained in [40], the time to failure (TTF) of the PV plant and the mean time to repair (TTR) are modelled using an exponential and a Rayleigh distribution, respectively. Then, TTF and TTR are randomly generated with the inverse transform method [41].  
From this simulation the mean values of the indices to be estimated are obtained: energy not used (ENU), hours with energy not used (HNU), ENS and LOLP. These indices are obtained every time for each dwelling individually and for the complete system.  
The results obtained with the SMC simulation of each dwelling constitute the S1 scenario described in Section 2.1, which corresponds to the reliability of the system working as isolated dwellings. In this initial scenario, there is no interconnection between both houses. This scenario has been compared with a set of scenarios called S2\({}_{\mathrm{x}}\) (for different values of x), which consist of cooperative scenarios in which both dwellings are considered interconnected in an MG and can share their resources, as explained in Section 2.2.  
The main objective of defining this set of scenarios is to optimize the cooperation strategy so that both houses can benefit in a similar way. Figure 1 shows a diagram of the scenarios considered.  
Figure 1: Description of the scenarios considered.
# Research on the A* Algorithm for Automatic Guided Vehicles in Large-Scale Maps
## 2 A* Algorithm and Analysis of Its Problems  
### Overview of the A* Algorithm  
#### 2.1.1 Detailed Steps in Running the A* Algorithm  
The following is the A* algorithm operation flow, which aims to clearly illustrate the core steps of the algorithm:  
1. Initialisation phase: Specify the start node and the target node and initialise two key lists--the open list and the closed list.
2. Selection of expansion nodes: Select the node with the smallest valuation function value \(f(n)\) from the open list and take it as the current node to be expanded.
3. Node expansion process: For the current node to be expanded, generate all its neighbouring nodes and calculate the valuation function value \(f\left(n\right)\) of these neighboring nodes.
4. Update list status: Add the newly generated neighbouring nodes to the open list and update the closed list to ensure that the processed nodes are accurately recorded.
5. Loop execution: Repeat steps 2 through 4 until the target node is successfully found, or the open list is empty (which indicates that a feasible path cannot be found).
# Review of Automatic Estimation of Emotions in Speech
## 8 Discussion  
This paper reviewed modern methods of automatic spoken emotion recognition. Although most current SER does not exploit classical phonetic features directly, we first looked at the acoustic information in speech that assists people to distinguish emotion, as these may help in future SER research. Since listeners focus on specific phonetic and intonational acoustic features when they distinguish emotion in speech, it is reasonable to presume that SER could gain by guiding its automatic analysis and network using trends that are found in human listening.  
As approaches to discern emotion often share much with the related applications of ASR and ASV, we compared these three applications. Many methods for SER derive from general pattern recognition methods that have been developed in other application areas, we observed how speech is very different from other signals to recognize, and how SER objectives differ from other speech applications. We noted that most SER trials have used closed sets of target emotions, and this does not reflect applications in practice.  
Current trends for neural processing have dominated SER (as well as many other applications). These trends will persist, as ANNs have large power as nonlinear processors that can do automatic learning. Research in artificial intelligence does not use earlier expert-system approaches for many practical tasks, instead using systems that automatic machine learning. Nonetheless, there are many cases of ANNs that effectively learn features that humans employ in their pattern recognition.  
Most neural SER networks are huge and rely heavily on resources (i.e., thus unable to be used in devices with few resources), thus using many millions of parameters. Such models are also restrained by their requirements for lots of training data and processing power, and their use of very simple loss functions and gradient training. SER methods derive mostly from algorithms developed for other applications (typically for image processing). It may well be useful to focus ANNs instead on acoustic features that are both specific to speech and helpful to distinguish how emotions differ, which is why we focused on such details, rather than simply describe SER methods.  
The recent trend towards E2E methods exploits a search for a simple, uniform, global model that does not use any human analysis, assuming that any analysis prior to automatic machine learning risks suboptimal decisions. Yet, the typical training method of gradient descent is clearly suboptimal to handle data patterns that are highly complex. Speech is highly encoded, as noted in this paper, with correlations spread non-uniformly and widely across time and frequency. The attention methods of correlation help, but current methods are too simple to exploit efficiently the information that listeners employ for speech perception tasks such as SER.
## 1 Introduction
The filtering was carried out for example by determining local peaks in the OCT data frequency distribution which correlate to the position of the keyhole bottom [52]. Various approaches were used to reduce the spatter formation, especially the use of a local gas flow has proven to be uncomplicated to use. An increase in laser spot size also revealed a spatter reduction because of an elongated melt pool. Furthermore, the previous investigations indicated that there is no direct correlation between keyhole frequency and spatter detachment. Comparative studies for varying laser spot sizes and simultaneous use of shielding gas to examine the effect on spatter formation are pending. Consequently, the effect of the gas flow and a changed Laplace pressure due to a modified keyhole radius are investigated in the following. This publication focuses on the effect of gas flow under varying laser spot sizes d\({}_{\text{spot}}\) and welding speeds v\({}_{\text{w}}\) for partial penetration welding of AISI 304. The effect mechanisms induced by varying gas flow rates V were investigated after an initial description of the reference process using high-speed imaging. This was done by measuring the loss of mass after the welding process, in addition to analyzing high-speed images, weld seam surfaces, and cross sections. Image processing of the lateral high-speed imaging was used to determine spatter quantity. The spatter velocity v\({}_{\text{sp}}\) was also quantified to investigate the energy balance of spatter formation and its defining parameters (see Figure 1). In addition, the cross-sectional areas of the spatters were examined to estimate spatter volume V\({}_{\text{sp}}\) and surface area S\({}_{\text{sp}}\). The keyhole fluctuation was calculated from the measured keyhole length I out of the high-speed images to eliminate it as a defining parameter. At the end of the investigation, only the upward melt flow velocity v\({}_{\text{fl}}\) at the keyhole rear wall was unknown, assuming a constant density \(\rho\) and a higher surface tension \(\sigma\) using gas flow. The upward melt flow velocity was determined by a further evaluation of the lateral high-speed images. Consequently, the procedure described allowed the determination of the defining parameters of the gas flow for spatter detachment at high welding speeds. Figure 1: Approach of the investigation: (**a**) schematic illustration of spatter formation; (**b**) defining parameters of the energy balance for spatter detachment.
# Enhancing Chatter Stability for Milling Thin-Walled Blades by Designing Non-Uniform Allowance
## 2 Description of the Methodology
### Results and Discussion
This method calculates the relative deformation of the tool contact point based on a steady-state cutting force numerical simulation model, increases the thickness of the node material in reverse to improve stiffness, and finally obtains the distribution surface of finishing machining allowance through smooth fitting face. Reference [37], compared to reference [36], is also based on the idea of static deformation. A mathematical model for predicting the deformation of thin-walled parts during milling based on the Rayleigh Ritz method was established. The thin-walled parts were discretized to solve the unit allowance volume, and the non-uniform allowance design of the workpiece was completed through smoothing. This method has improved computational efficiency and is relatively simple compared to the former. However, due to the influence of deformation prediction accuracy, and secondly, the topological structure characteristics of the part structure have not been taken into account, there are still certain limitations in the application of thin-walled complex structural components. Reference [38] proposes to measure the static deformation of parts on the machine, then calculate the allowance compensation value, and achieve stable milling of thin-walled parts by adaptively adjusting the cutting depth during precision machining. This method is intuitive, simple, and effective, but it is limited by in-machine measurement and first part trial cutting and is currently only suitable for conventional thin-walled components such as partition frames and wall panels. This paper is based on the finite element method and proposes a stiffness sensitivity theory to enhance the process stiffness through the subdivision of finite element mesh. From the experimental verification analysis and comparison in this section, it can be seen that, on the one hand, it can fit complex structural components well, and, on the other hand, numerical calculations can accurately design the allowance. Compared with previous work, this method has a certain degree of generalizability and adaptability.
# Energy Efficient IoT Data Collection in Smart Cities Exploiting D2D Communications
## 4 The Novel D2D-Based Energy Efficient IoT Data Collection
### The Proposed D2D-EE Solution Step by Step
This maximization guarantees that the data is sent with the minimum TBS is needed, and an energy saving can be obtained thanks to a power decrease related to a lower MCS level;
* from all the resulting MCS values, select the one minimizing the power transmission according to Equation (6): \[MCS^{*}=\underset{MCS_{r^{d}}^{*}}{\text{arg}\,\text{min}}(\delta_{mcs}(MCS_{ r^{d}}^{*})\cdot r^{d})\] (6) where \(\delta_{mcs}(MCS_{r^{d}}^{*})\) is the power offset of each optimal MCS to the basic MCS. This minimization allows to find the MCS with the smallest transmit power as this is equal to: \(p^{d}=\textit{Pbasic}^{d}\cdot\delta_{mcs}\cdot r^{d}\), where \(\textit{Pbasic}^{d}\) is the power per RB for the basic MCS on the D2D link, \(\delta_{mcs}\) is the power offset between MCS and the basic MCS. **Aggregators uplink configuration**: Once the data within a cluster have reached the aggregator, the uplink radio resources are used in cell-mode transmissions towards the eNodeB. Under \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Device 1** & **Device 2** & **Device 3** & **...** & **Device \(j\)** \\ \hline device 1 & 0 & \(c_{1,2}\) & \(c_{1,3}\) &... & \(c_{1,j}\) \\ device 2 & \(c_{2,1}\) & 0 & \(c_{2,3}\) &... & \(c_{2,j}\) \\... &... &... &... &... &... \\ device \(k\) & \(c_{k,1}\) & \(c_{k,2}\) & \(c_{k,3}\) &... & \(c_{k,j}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: LTE-D2D CQI Matrix. the assumption of Round Robin scheduling of the radio resources, each aggregator will have no more than: \(r_{a}=\lceil R/|\mathcal{S}|\rceil\) RBs. In the uplink transmission, each aggregator will then implement the energy efficient data uploading presented in Section 3.2, to find the optimal MCS and number of RBs to adopt, where the maximum number of RBs the aggregator can use in the algorithm is exactly the \(r_{a}\) value.