esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Formulations
### Multi-Objective Function Formulation  
In this paper, two main objective functions were considered subject to network operational constraints. These objective functions included a reduction in real power loss and an enhancement of the voltage profile of the network. It is worth noting that the system power loss at the node can either be reduced or worsened with the integration of DG if not properly sized. The proper size of the DG to be integrated can be determined with the notion to minimize the active total power loss in the system using the exact loss formula given by  
\[P_{L}=\sum_{i=1}^{N}\sum_{j=1}^{N}\left[\varsigma_{ij}\left(P_{i}P_{j}+Q_{i}Q_ {j}\right)+\beta_{ij}\left(Q_{i}P_{j}-P_{i}Q_{j}\right)\right] \tag{1}\]  
\[\varsigma_{ij}=\frac{r_{ij}}{V_{i}V_{j}}\cos\left(\delta_{i}-\delta_{j}\right) ;\ \beta_{ij}=\frac{r_{ij}}{V_{i}V_{j}}sin\left(\delta_{i}-\delta_{j}\right) \tag{2}\]
## 2 Related Work
### Industrial Scene Small-Sample Detection Challenges  
Industrial manufacturing environments present distinct detection challenges due to inherent complexities in object characteristics and data acquisition constraints. In aluminum casting inspection, deep object detection methods face significant obstacles when dealing with simulated defects and complex geometric variations under industrial conditions [8]. Recent research has examined difficulties in processing small-scale components, occlusion patterns, and constrained training datasets within manufacturing facilities [9]. Industrial stamping processes demonstrate similar constraints where deep metric learning approaches must operate effectively with limited sample sizes for tool condition diagnosis [10].  
Ship component detection encounters specific small-sample challenges due to the diverse patterns, complex geometric shapes, and varying size specifications inherent in maritime manufacturing. The acquisition of defective assembly samples requires extended production periods, as assembly errors occur infrequently during normal operations. Traditional few-shot learning approaches face difficulties in ship component recognition due to high visual similarity between correctly and incorrectly assembled components, where subtle assembly differences must be detected reliably [11]. Advanced prototype-based methods show effectiveness in laboratory conditions yet struggle when applied to ship manufacturing environments where lighting variations, component wear, and assembly contexts affect detection accuracy [12]. These limitations highlight the necessity for robust detection mechanisms specifically designed for industrial small-sample scenarios.
## 5 Conclusions  
Throughout the present study we have presented a comprehensive Species Distribution Model (SDM) for eight commercial pelagic fish species in the Mediterranean Sea. Initial datasets were created by merging several external databases (Aquamaps, CMEMS, EMODnet) in order to produce a model capable of predicting the probability of occurrence in every coordinate pair in the Mediterranean. Ten regression algorithms were compared in order to identify the best performing one, namely XGBoost, which had never previously been used for SDM. With feature engineering, we explored several aspects of the time series data resulting in a large number of features (6830), followed by feature selection with the use of the ensemble Reciprocal Ranking method. After ranking the features, it was found that approximately the top 2% of features were constructing the best model with a 34% performance boosting for the eight species, on average. Feature selection, in combination with the evaluation schema of 10-fold cross validation, reduced the danger of over-fitting and created a robust general model with a cross-validated RMSE ranging from 0.22 to 0.07 depending on the species.  
After creating the model, the importance of feature categories and environmental predictors were further explored and discussed. The strongest feature categories are the neighbor-based ones, the features with extreme values (minima, maxima, quantiles), and the monthly and surface features, while the overall strongest predictors are salinity, temperature, distance to coast, dissolved oxygen and nitrate. Finally, using Aquamaps probabilities as target variable, it was improved in both coverage and resolution. High resolution habitat maps were created (\(\times\)8 higher) for the whole basin (67,000 points instead of 902) which can be used by researchers to understand the link between marine fishes and environmental factors.  
Conceptualization, G.S., A.T.; methodology, D.E., A.A., A.T., G.S.; software, D.E., A.A.; validation, D.E.; formal analysis, A.T.; resources, D.E.; data curation, D.E.; writing--original draft preparation, D.E.; writing--review and editing, A.A., A.T., G.S.; visualization, D.E.; supervision, A.A., A.T., G.S.; project administration, G.S.; funding acquisition, G.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by European Union's Horizon 2020 Research and Innovation Program (H2020-BG-12-2016-2), grant number No. 727277-ODYSSEA (Towards an integrated Mediterranean Sea Observing System). The article reflects only authors' view and that the Commission is not responsible for any use that may be made of the information it contains.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 6 Simulation Results and Discussions
### Random Errors in Trajectory  
The above works are all under the assumption that positioning information is perfect. In practical scenarios, however, the trajectory may have some random errors [24; 25; 27; 28]. Considering that the random error follows a uniform distribution, then the position of the vehicle becomes \(\mathbf{P}_{\text{V}}(t)=[x_{\text{start}}+v\cdot t+\gamma_{\text{traj}}\ \text{m},\ h_{\text{V}}\ \text{m},\ 100+\gamma_{\text{traj}}\ \text{m}]\), where \(\gamma_{\text{traj}}\sim\mathcal{U}[-a_{\text{traj}},a_{\text{traj}}]\). Figure 11 shows the spectral efficiency for one vehicle pass considering that random trajectory errors exist. It is shown that the performances of all random error cases are almost the same. Therefore, the proposed system is robust to positioning information error. Moreover, it is noteworthy that compared with [37; 38], using PI for obtaining CSI is more energy-efficient since conventional pilot-based methods are not necessary.  
Figure 11: Spectral efficiency for one vehicle pass with random errors in trajectory. \(K=8\).  
Figure 10: The highest spectral efficiency for one vehicle pass with different numbers of RISs \(K\). \(K=2,4,6,16\), and \(M\times N=128^{2}\). Note that the upper bound for SE is based on (42).
# Distributed Information Compression for Target Tracking in Cluster-Based Wireless Sensor Networks  
[PERSON] and [PERSON]  
1 Department of Electrical Engineering, Graduate Institute of Communication Engineering,  
National Chung Hsing University, Taichung 402, Taiwan; [EMAIL_ADDRESS] (S.-K.L.); [EMAIL_ADDRESS] (K.-J.L.); [EMAIL_ADDRESS] (H.-P.T.)  
1  
Footnote 1: email: {shi-kuan.liu-wu-  
network topology. Therefore, the Level 1 clustering utilizes a static clustering scheme to build up a basic network architecture, which may eliminate the overheads of dynamic network topology control. In the presence of the target, the Level 2 clustering utilizes a dynamic clustering scheme to adaptively form a sub-cluster for the tracking task. Thus, a hybrid static/dynamic clustering scheme is applied to achieve distributed target detection and tracking.  
Since the sensed data have some uncertainty and redundancy inherently, the second perspective is to investigate the target characteristics, such that the presentation of certain supplementary information can be applied to improve estimation accuracy, as well as reduce the amount of transmitted data. Within the sub-cluster, the sensing nodes provide their local estimates through Bayesian particle filtering [2] and forward the pre-processed results to the leader, considering the Bayesian estimation to extract parameters from the Gaussian noise structure.  
The major contributions and key features of this work are: (1) proposing a novel cooperative positioning approach to adaptively maintain the information of measurement uncertainties; (2) developing an information compression scheme for target tracking in a two-level hierarchical wireless sensor network, which allows the selected active sensors to utilize information compression for energy conservation (e.g., using a small number of samples to roughly reconstruct the estimation distribution); (3) one of the main advantages of the Bayesian framework is that the tasking sensor carries along a complete distribution of the estimates of the target position. Thus, the processed estimation distribution is inherently a measure of the accuracy of the positioning system. With proper settings to reduce the computational burden, a particle filter can be an accurate, practical and flexible location estimation technique. Our previous work [3] proposes a low-complexity indoor tracking system using Bayesian filtering with a few samples (20 samples) in a wireless sensor network. Moreover, [4] also presents a case study of applying particle filters to location estimation for ubiquitous computing and shows that it is practical to run particle filters on devices with limited capabilities.  
The rest of the paper is organized as follows: Section 2 reviews tracking systems and compares and contrasts the related works with the proposed CTCI scheme. Section 3 depicts three data processing schemes. Section 4 estimates the data size of CTCI and presents an analysis to provide a lower bound of tracking accuracy. In Section 5, the performance evaluation is depicted with varying the system parameters. Finally, we summarize the conclusions in Section 6.
## 1 Introduction  
Spatial-temporal variation in landscape patterns can affect many hydrological processes, such as surface run-off and bio-geochemical cycles, which can subsequently result in a large mass of pollutants flowing into water bodies [1; 2; 3; 4]. Rivers are an important landscape composition type. The water quality in rivers can be significantly influenced by the landscape patterns of a river watershed, which indicates that landscape patterns have an intimate relationship with any change in water quality [5].  
In the context of constant accelerating urbanization, surface water quality is being threatened by human activities [6; 7]. Issues, such as the deteriorating hydrological environment, have not been previously solved by simply monitoring the water quality or controlling the pollution of water resources. Therefore, exploring the relationship between landscape patterns and surface water quality is beneficial for creating plans and strategies for the management and optimization of water resources [8; 9; 10; 11; 12; 13]. Existing research has mostly focused on how land use change affects surface water quality [14; 15; 16; 17; 18; 19; 20] before discussing the correlation between landscape composition types and surface water quality. Other studies analyzed this correlation by calculating landscape indices [21; 20]. However, research on the effects of landscape patterns on surface water quality is still scarce [22; 23],thus understanding how landscape composition and configuration influence surface water quality is crucial [24].  
We chose the Yi River watershed in China as the study area in this study. Surface water samples were collected and laboratory experiments were conducted to obtain surface water quality data. A geostatistical methodology was applied to analyze the spatial-temporal variation in the surface water quality in the watershed. The landscape indices of the study area were calculated. Comparing the landscape indices with the change in surface water quality revealed the relationship between the indices and the water quality. Ordination analysis was undertaken to explore the impact strengths of landscape composition and landscape configuration. We discussed the law behind the effect of and responding mechanism between the variation in landscape patterns and surface water quality in order to provide a scientific foundation for the spatial development of watersheds and strategy development for improving the sustainability of surface water quality and the surrounding environment.
# Nondestructive Evaluation of Aluminium Foam Panels Subjected to Impact Loading
## 3 Results and Discussion
### Repeated Impact Tests
The ten repeated impact tests at an impact energy \(E_{i}\) equal to 35 J produced a lower value of peak load (\(F_{MAX}\)) and higher value of depth (\(w\)) respective to the values obtained by the single impact test carried out on a Type IV specimen at an impact energy \(E_{i}\) equal to 350 J (Figure 10). The initial slope of the load-displacement curve and the slope after the first peak load were similar for the three tests (repeated impact test at impact energy equal to 35 J and single impact test at impact energies equal to 350 and 400 J), as shown in Figure 10.  
Concerning the load trend during the repeated impact tests carried out on the Type IV specimen, the load value increased up to the 6 th impact, which produced the initial skin perforation, and a peak value of \(1.19\times 10^{4}\) N was achieved (Figure 8). The load variation for each impact test is reported in Table 3.  
The full skin perforation occurred after the 8 th impact, highlighted by the increase in load value in the 9 th impact test, when the skin effect was no longer present. Concerning the depth trend during the repeated impact tests carried out on the Type IV specimen, the depth value decreased after each impact due to core crushing and densification, up to the 6 th impact, which produced the initial skin perforation. Then, the depth value highly increased after the 7 th and 8 th impacts, when full skin perforation occurred. The depth value was almost constant, with a small percentage variation after the 8 th, 9 th and 10 th impacts, where the effect of the skin was missing. The total depth calculated after 10 impacts was 36.45 mm, corresponding to the complete panel perforation. The load and depth variation for each impact test are reported in Table 3.  
Table 4 shows the comparison between the repeated impact tests and the single impact test at 350 J for the Type IV panel in terms of the maximum values of load \(F_{MAX}\) and depth. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Impact Number** & **Load [N]** & **Load Variation [\%]** & **Depth [mm]** & **Depth Variation [\%]** \\ \hline
1 st & \(5.93\times 10^{3}\) & - & 7.37 & - \\ \hline
2 nd & \(7.98\times 10^{3}\) & +34.52 & 5.68 & \(-\)22.86 \\ \hline
3 rd & \(9.40\times 10^{3}\) & +17.74 & 5.36 & \(-\)5.62 \\ \hline
4 th & \(1.08\times 10^{4}\) & +14.57 & 3.74 & \(-\)30.31 \\ \hline
5 th & \(1.11\times 10^{4}\) & +3.53 & 3.84 & +2.65 \\ \hline
6 th & \(1.19\times 10^{4}\) & +6.56 & 3.29 & \(-\)14.31 \\ \hline
7 th & \(1.09\times 10^{4}\) & \(-\)8.62 & 3.84 & +16.73 \\ \hline
8 th & \(1.04\times 10^{4}\) & \(-\)4.31 & 4.41 & +14.96 \\ \hline
9 th & \(1.05\times 10^{4}\) & +1.01 & 4.48 & +1.52 \\ \hline
10 th & \(1.01\times 10^{4}\) & \(-\)3.68 & 4.26 & \(-\)4.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Load and depth percentage variation after the repeated impact tests.
# A Novel Magnetic Coupling for Miniaturized Bandpass Filters in Embedded Coaxial SIW
## 2 Materials and Methods  
The layout and the main design parameters of an embedded CSIW resonator are shown in Figure 1. Such a structure consists of a square or circular SIW cavity where several plated via holes have been inserted at the center of the cavity resonator, as was first introduced in [4], and then demonstrated in [5; 6]. By connecting one end of it via holes to ground, the inductive section of a coaxial resonator is created and controlled by adjusting the position and the diameter \(d_{h}\) of the via holes. Conversely, at the other end of the via holes, a circular metal plate with radius \(r_{p}\) is introduced, as shown in Figure 1b. Such a metal plate is located right below the external top metal layer, which is basically a ground plane, creating a small separating gap filled with dielectric material that we named \(g_{p}\) (visible in Figure 1c).  
By doing that, the strong electric field across the gap \(g_{p}\) creates the integrated \(C_{l}\) of a typical combline configuration. This topology enables the implementation of extremely high loading capacitances \(C_{l}\) towards ground, because it is possible to increase the disk plate area considerably while keeping \(g_{p}\) as narrow as possible. The capacitive component of the CSIW can be clearly seen by observing the field line distribution shown in Figure 2. It is worth mentioning that in the selected LTCC fabrication process, \(g_{p}\) depends on the number and thickness of the dielectric tape layers that separate the circular disk plate from the top ground plane.  
In addition, the electrical length of the coaxial transmission line embedded into the substrate depends on the final stack-up thickness, thus becoming an important design parameter since it controls both resonant frequency and unloaded \(Q\) (\(Q_{u}\)) of the CSIW resonator [5]. Finally, the inner via holes with diameter \(d_{h}\) have been symmetrically placed around the cavity center to emulate a wider circular inner conductor that would show a fictitious diameter named \(d_{v}\) (see Figure 1b). The further apart the via holes are, the larger the diameter \(d_{v}\). This enables us to finely set \(d_{v}\) by using only the recommended value for \(d_{h}\) that, in the LTCC manufacturing, should be similar to the dielectric tape thickness to enable a good via filling process.
## 3 Theoretical Background
### Tensor CP (CANDECOMP/PARAFAC) Decomposition  
There are two central tensor decomposition methods: CP decomposition and Tucker decomposition. This paper adopts CP decomposition. Therefore, CP decomposition is introduced as following.  
The CP decomposition is used to decompose the N-order tensor \(\chi\in R^{I_{1}\times I_{2}\times\ldots\times I_{N}}\) into the sum of several rank-1 tensors:  
\[\chi\approx\left[A^{(1)},A^{(2)},....,A^{(N)}\right]=\sum\
olimits_{r=1}^{R} \lambda_{r}a_{r}^{(1)}\otimes a_{r}^{(2)}\otimes\ldots\otimes a_{r}^{(N)} \tag{4}\]  
\(R\) is an integer. The factor matrix of tensor \(\chi\) as follow: \(A^{(1)}=(a_{1}^{(1)},a_{2}^{(1)},\ldots,a_{R}^{(1)})\), \(A^{(2)}=(a_{1}^{(2)},a_{2}^{(2)},\ldots,a_{R}^{(2)})\), \(\ldots\), \(A^{(N)}=(a_{1}^{(N)},a_{2}^{(N)},\ldots,a_{R}^{(N)})\).  
For example, the CP decomposition of a 3-order tensor \(\chi\in R^{I\times J\times K}\) as Figure 1, it is as follow:  
\[\chi\approx\sum\
olimits_{r=1}^{R}\lambda_{r}a_{r}\otimes b_{r}\otimes c_{r} \tag{5}\]  
where \(a_{r}\in R^{I}\), \(b_{r}\in R^{J}\), \(c_{r}\in R^{K}\), \(r=1\), \(2\), \(\ldots\), \(R\), \(\lambda_{r}\) is the coefficient. Set  
\[\hat{\chi}=\sum\
olimits_{r=1}^{R}\lambda_{r}a_{r}\otimes b_{r}\otimes c_{r}= \left\|\lambda;A,B,C\right\| \tag{6}\]  
where corresponding element of tensor is \(\hat{x}_{ijk}=\sum\limits_{r=1}^{R}\lambda_{r}a_{ir}\otimes b_{jr}\otimes c_{ kr}\), \(i=1,2,\ldots,I\); \(i=1,2,\ldots,J\); \(i=1,2,\ldots,K\); The objective function of CP decomposition is as follow.  
\[\min\|\chi-\hat{\chi}\| \tag{7}\]  
Firstly, we must determine the number of rank-1 tensors, which is expressed as R. However, it is an NP hard problem. In general, we traverse R starting at 1 until we find a suitable solution. When the number of rank-1 tensors is determined, CP decomposition can be performed by Alternating Least Square (ALS).
# A Rotating Object Detector with Convolutional Dynamic Adaptive Matching
## 5 Conclusions  
We systematically analyzed the problem of feature misalignment and imbalanced sample training in the current model for RS detection. To begin, in the backbone network, we devised an adaptive rotation convolution that aligns with the spatial direction of the target. The convolution kernels dynamically rotate based on the orientation of the aerial target. Different branches were designed to enable the network to efficiently capture the directional features of multiple targets in the image. The designed DCM module can be plug-and-play ported to any backbone network with convolution layers. Secondly, an adaptive equalization loss function was designed during the training to improve the contribution of high-quality samples and to ensure the stability of sample training. The effectiveness of the method has been proven on three common datasets: DOTA, HRSC-2016, and UCAS-AOD.  
Conceptualization, L.Y. and D.J.; Methodology, L.Y.; Software, Y.Z.; Validation, D.J. and Y.Z.; Formal analysis, D.J.; Investigation, X.L.; Resources, S.H.; Data curation, Y.Z.; Writing--original draft preparation, L.Y.; Writing--review and editing, D.J.; Visualization, L.Y.; Supervision, D.J.; Project administration, L.Y.; Funding acquisition, D.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The HRSC-2016, DOTA, and UCAS-AOD datasets are available at following sites: [[https://aistudio.baidu.com/datasetdetail/54106](https://aistudio.baidu.com/datasetdetail/54106)]([https://aistudio.baidu.com/datasetdetail/54106](https://aistudio.baidu.com/datasetdetail/54106)), [[https://captain-whu.github.io/DOTA/dataset.html](https://captain-whu.github.io/DOTA/dataset.html)]([https://captain-whu.github.io/DOTA/dataset.html](https://captain-whu.github.io/DOTA/dataset.html)), and [[https://github.com/Lbx2020/UCAS-AOD-dataset](https://github.com/Lbx2020/UCAS-AOD-dataset)]([https://github.com/Lbx2020/UCAS-AOD-dataset](https://github.com/Lbx2020/UCAS-AOD-dataset)), accessed on 6 January 2024, respectively.  
The authors declare no conflicts of interest.
However, when more than one inverter is connected to an inverter in parallel, even if each inverter has an anti-islanding detection function, the detection of islanding might fail due to the interaction of one inverter with the other. When all the energy produced by a PV system is only charging an EESS, and no utility power source is involved, the inverters might be operating as an isolated island. The islanding detection method used by an inverter of a distributed system determines the state of the utility power lines by injection of a disturbance signal into the inverter to change the frequency or voltage. When one inverter changes the frequency or voltage signal to detect an isolated island, the other inverter might fail to detect islanding if its signal is added to the signal of another inverter and they offset each other. In this paper, experimental devices capable of simulating a PV power source and energy storage system were constructed. Islanding detection of individual inverters (PV inverter or EESS inverter) was tested, and when the two inverters (PV inverter and EESS inverter) were connected in parallel, islanding detection was tested. In particular, the EESS was tested for an islanding operation situation in the charging mode, which is operated in connection with the PV system and the power grid. Especially, islanding detection failed depending on the charging capacity of the EESS. Because EESS has charge and discharge modes, it is different from situations in which PV inverters are parallel connected to the power grid. In other words, the EESS not only releases electricity energy but also acts as an R-load. Therefore, in this paper, this study was carried on islanding detection when the PV inverter and EESS inverter (charge/discharge mode) operate under islanding in parallel connection to the power grid.