esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid-Margin Softmax for the Detection of Trademark Image Similarity
## 4 Conclusions  
The detection of TM image similarity (TMISD) is an essential procedure for court judgments on TM infringement cases and TM legal registration, while the training data-building of similar TM pairs and model generalization on fast-growing numbers of new TM designs are huge challenges for the task. To address these issues, similarity detection models based on loss-driven metric learning methods were researched. Compared to data-driven methods, including the triplet network model and fine-tuning method, the optimization of the softmax loss function had a larger gain in performance, with less data preparation and training cost.  
A hybrid-margin softmax (HMS) is proposed based on the peculiarity of TM images. Additive margins are attached to the cosine and angular term of softmax in the angular space to tolerate the slight differences between the similar parts of similar TM image pairs. The weights of the classifier and extracted feature vectors in the softmax are not normalized, aiming to best preserve the information of input images.  
The detection model trained on HMS is further penalized to improve the discrimination ability of TM images. The model can be trained on small numbers of TM training data. Experiments indicate that the model trained on HMS achieves the best performance on the TMISD task with up to 98.97% accuracy and an F1 score of 0.9746, compared to other transformations of softmax. The model can also achieve high performance with fewer SNN layers. Furthermore, the HMS-driven model trained completely on TM image data generalized well on the FR task, with up to 90.57% accuracy and an F1 score of 0.9002.  
Methodology, C.W.; software, C.W.; validation, C.W. and H.S.; data curation, H.S.; writing--original draft preparation, C.W.; writing--review and editing, G.Z. and H.S.; visualization, C.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Natural Science Foundation of China (Grant: 62173222).  
Not applicable.  
The TM dataset used in this research can be obtained from the following link: [[https://pan.baidu.com/s/11](https://pan.baidu.com/s/11) glt9y9j327 xKCyq4v5 TyeQ?pwd=tmid]([https://pan.baidu.com/s/11](https://pan.baidu.com/s/11) glt9y9j327 xKCyq4v5 TyeQ?pwd=tmid), accessed on 26 March 2024. If the link fails, you can contact the corresponding author for a new link.  
The authors declare no conflicts of interest.
# Review of Radiation-Induced Effects in Polyimide
## 3 Results
### Changes in Electrical Properties
There are not many studies addressing the change of PI volume charge transport characteristics after proton and VUV irradiation. [PERSON] et al. [86; 87] reported a decrease in RIC of up to three orders of magnitude for PI material irradiated with 60-110 keV protons to a maximum fluence of 10\({}^{14}\) protons/cm\({}^{2}\), that was attributed to the pyromellitimide groups' (C-N, C=O) degradation. Further, formation of a graphite-like layer on the surface of PI samples that have been proton-irradiated with particle beams of energy ranging from 10 keV to a few MeV up to a maximum fluence of \(2.4\times 10^{16}\) protons/cm\({}^{2}\) has been reported to cause a decrease in surface resistivity [88]. Several research groups have studied the ultraviolet-laser induced conductivity of PI films under ultraviolet laser irradiation [89; 90; 91]. A permanent increase of PI's electrical conductivity up to fifteen orders of magnitude was observed, which is attributed to the radiation-induced creation of surface conductive region. In this region, the chain scission between imide nitrogen atoms and phenyl rings of the ether segments is taking place under laser irradiation, followed by the formation of the network of fused aromatic rings with an effective \(\pi\)-orbital overlap facilitating the electrical conduction.
# Latest Advances in the Global Navigation Satellite System--Reflectometry (GNSS-R) Field
## 4 Land Applications  
Since the first GNSS-R ground-based and aircraft experiments started to appear, the sensitivity of the GNSS-R signals to several ECV and other geophysical parameters have been explored by many researchers. GNSS-R satellite missions provided a new view of the world, and it favored global assessments of soil moisture, vegetation, wetlands, and floods. Next, we summarize some of the advances in the field.
# A Multisensor Approach to Satellite Monitoring of Trends in Lake Area, Water Level, and Volume
## 2 Materials and Methods
### Water Level, Mean Depth, and Volume
For each lake, the water levels derived from these two methods were compared to the ICESat-1 GLAS laser altimeter measurements of water level on the corresponding dates, averaging all 70 m-diameter GLAS footprints that fell on each lake surface on each orbit (Figure 7). A total of 2390 laser spots on 54 separate orbit tracks were used. Some orbit tracks crossed more than one lake, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Year** & **Day** & **Date** & **Region** & **Offset Mean (m)** & **Offset Stdev (m)** \\ \hline
2017 & 234 & 2017-08-22 & northeast & \(-\)24.22 & 10.94 \\
2017 & 234 & 2017-08-22 & southeast & \(-\)23.72 & 11.17 \\
2017 & 282 & 2017-10-09 & northwest & \(-\)5.77 & 6.00 \\
2017 & 282 & 2017-10-09 & southwest & \(-\)4.35 & 6.23 \\
2016 & 296 & 2016-10-22 & north-center & \(-\)22.38 & 6.12 \\
2016 & 296 & 2016-10-22 & south-center & \(-\)18.87 & 8.16 \\
2015 & 101 & 2015-04-11 & northwest & \(-\)23.66 & 9.27 \\
2015 & 101 & 2015-04-11 & southwest & \(-\)21.68 & 11.98 \\
2015 & 117 & 2015-04-27 & northwest & \(-\)11.68 & 12.70 \\
2015 & 238 & 2015-08-26 & east & \(-\)13.23 & 7.49 \\
2014 & 322 & 2014-11-18 & north-center & \(-\)10.86 & 9.44 \\
2014 & 322 & 2014-11-18 & south-center & \(-\)17.95 & 11.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEM tiles (AST14) and their measured offsets from the SRTM DEM. resulting in 76 combinations of lake and orbit track. An additional 644 laser spots on 16 orbit tracks were not used due to abnormally high noise levels in the GLAS data; these noisy tracks (also discussed in [7]) had standard deviations of the GLAS measurements for individual lakes from 0.1 to 0.7 m, and were identified and removed based on that basis. Hypsographic curves [63; 64] for each lake were constructed, for use in estimating lake volumes on each date. These curves relate lake area (on the X axis) to depth (Y axis). Using the lake area vs. water level data from Method 2, points representing the measured values of these two variables on each date were plotted, and then a LOESS model was used to interpolate values for water level at 0.1 km\({}^{2}\) increments of lake area, from 0.1 km\({}^{2}\) to the maximum areal extent of each lake. This hypsographic curve was then used as a look-up table, to determine the mean depth and total volume for each lake on each date, based on its surface area, in increments of 0.1 km\({}^{2}\).
# Image Retrieval Method Based on Image Feature Fusion and Discrete Cosine Transform
## 4 Experimental Results and Analyses  
### Evaluation Methods  
#### 4.1.1 Similarity Measure  
Let \(p=(p_{1},p_{2},\ldots,p_{n})\) and \(q=(q_{1},q_{2},\ldots,q_{n})\) are two n-dimensional real vectors. The following function can measure the similarity between them.  
Cosine similarity [57] is a commonly used similarity measure for real-valued vectors. The cosine of two non-zero vectors can be derived by using the Euclidean dot product formula:  
\[p\cdot q=\|p\|\cdot\|q\|\cos\theta \tag{33}\]  
Given two vectors of attributes, the similarity \(\cos(\theta)\) is represented using a dot product and magnitude as:  
\[sim(p,q)=\cos(\theta)=\frac{p\cdot q}{\|p\|\|q\|}=\frac{\sum_{i=1}^{n}p_{i}q_{ i}}{\sqrt{\sum_{i=1}^{n}p_{i}^{2}}\sqrt{\sum_{i=1}^{n}q_{i}^{2}}} \tag{34}\]  
where \(p_{i}\) and \(q_{i}\) are components of vectors \(p\) and \(q\), respectively.  
The Euclidean distance (L2 distance) is the most obvious way of representing the distance between two points. The Euclidean distance between two points \(p\) and \(q\) is given by [58]:  
\[d(p,q)=\sqrt{\left(p_{1}-q_{1}\right)^{2}+\left(p_{2}-q_{2}\right)^{2}+\cdots +\left(p_{n}-q_{n}\right)^{2}} \tag{35}\]  
The Manhattan distance (L1 distance) [59] is a metric in which the distance between two points is the sum of the absolute differences of their Cartesian coordinates. For example, the Manhattan distance \(d_{1}\) between two vectors \(p\), \(q\) is the sum of the lengths of the projections of the line segment between the points onto the coordinate axes.  
\[d_{1}(p,q)=\|p-q\|_{1}=\sum_{i=1}^{n}|p_{i}-q_{i}| \tag{36}\]  
Figure 4: Diagram of proposed image retrieval method.  
The Minkowski distance [60] is a metric in a normed vector space that can be considered a generalization of both the Euclidean distance and the Manhattan distance. The Minkowski distance of order \(k\) between two vectors \(p\) and \(q\) is defined as:  
\[d(p,q)=(\sum_{i=1}^{n}|p_{i}-q_{i}|^{k})^{\frac{1}{k}} \tag{37}\]  
The Canberra distance [61] is a weighted version of the Manhattan distance. The Canberra distance d between vectors \(p\) and \(q\) in an n-dimensional real vector space is given as follows:  
\[d(p,q)=\sum_{i=1}^{n}\frac{|p_{i}-q_{i}|}{|p_{i}|+|q_{i}|} \tag{38}\]
# Explainable Machine Learning for LoRaWAN Link Budget Analysis and Modeling
## 2 AI for Propagation Modeling
### Comparison between DL and ML Approaches
#### 2.3.4 Feature Engineering  
Feature extraction is the main difference between the ML-based and DL-based approaches. While in DL-based models, feature extraction is off-loaded to DL algorithms (CNNs, U-Net), in ML-based models, this is performed prior to addressing the regression problem. The main contribution of DL-based models is the \"black-box\" approach to feature extraction, whereas machine learning techniques extract these features by means of deterministic algorithms such as image processing. ML-based models rely on pre-generated topographical features, which are not standardized. Currently, researchers extract these features using proprietary image processing algorithms applied to Google Maps, OpenStreetMap, or satellite images. Additionally, there is not an established set of extracted features, resulting in differences across the literature. This is partly due to a lack of quantitative understanding of the relevance and impact of these features, which must be addressed with further empirical studies in the future.
# GPU Algorithms for Structured Sparse Matrix Multiplication with Diagonal Storage Schemes +
## 5 GPU Programming  
Owing to their high performance-to-price and performance-to-energy ratios, graphics processing units (GPUs) have been increasingly used in recent years on a broad range of computationally demanding and complex problems. These programmable parallel processors are designed to achieve higher throughput with computing power exceeding multicore CPUs by deploying thousands of relatively light-weight compute threads in parallel [35]. NVIDIA's GPU with the CUDA (Compute Unified Device Architecture) environment supports a standard high-level language (i.e., C language) interface to manipulate the compute cores. It also supports the models of computations that explicitly combine both task-based parallelism and data-based parallelism [36].  
The code running on the GPU is traditionally called the kernel. CUDA threads are grouped into thread blocks. Threads in a thread block reside on the same streaming multi-processor (SM) of a GPU that can share data using shared memory and synchronize. Each thread in a thread block has a unique identification number. Each group of 32 consecutive threads from the same thread block is called a warp. It is the primary unit of execution in an SM. Each SM contains a warp scheduler that is responsible for scheduling the warps to the underlying cores on the SM.
# Differentially Private Clustered Federated Load Prediction Based on the Louvain Algorithm
## 8 Conclusions  
Accurate forecasting of residential electricity loads is crucial for integrating demand-side resources and fulfilling demand-side response requirements. By addressing the data heterogeneity and security issues in load forecasting for smart grids, this paper presents a load-forecasting framework for residential energy users that enables collaborative training of personalized forecasting models without the need to exchange load data. Weather and time factors are identified as key influences on user loads, and the analysis of arithmetic examples demonstrates that the proposed algorithm outperforms current mainstream federated learning algorithms in prediction performance. This approach enhances load-prediction accuracy while ensuring the privacy and security of user data, which is essential for smart grid planning, operation, control, and dispatch. Future research may focus on other cyber security issues, such as data poisoning and evasion attacks, the influence of unexpected events on power load forecasting, further optimizing the federated learning algorithm, exploring additional application scenarios, and advancing the development of smart grids. The trade-off between model complexity and interpretability is attracting increasing attention. This is critical for the broader adoption of predictive models in real-world settings. While complex models like deep learning algorithms can provide high accuracy, their \"black-box\" nature makes it difficult to understand how they make predictions. This lack of interpretability can be a significant barrier in practical settings, where understanding the rationale behind a decision is crucial. Therefore, in the future, we will focus on studying the interpretability of the models to enhance user trust in smart grid systems.  
Methodology, T.P., J.H., C.L. and X.C.; Project administration, J.H.; Software, X.J., C.L. and X.C.; Writing--original draft, T.P.; Writing--review and editing, J.H., X.J. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the China Southern Power Grid Corporation Technology Project under grant GDKJXM20222125 (036000 KK52222009).  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest. The funders had no role in the design of the study.  
Figure 5: Comparison of forecasting results. (**a**) Cluster \(1\). (**b**) Cluster \(2\).
# A Binaural MFCC-CNN Sound Quality Model of High-Speed Train
## 4 Results and Discussions
### Performance Comparison with Other Models  
#### 4.3.1 Shallow Neural Network Model  
To compare the performance of the binaural MFCC-CNN model, a shallow neural network sound quality model was built and is shown in Figure 17. Seven commonly used parameters (A-W SPL, loudness, sharpness, roughness, fluctuation strength, articulation index, tonality) were applied as inputs of the model; the output was annoyance level. The input layer, hidden layer, and output layer contain seven nodes, four nodes, and one node, respectively. The number of noise samples in the shallow neural network model is the same as that in the binaural MFCC-CNN sound quality model. The same subjective results were used to compare the performance of this shallow neural network model and the binaural MFCC-CNN model. Figure 18 presents the evaluation result of the shallow neural network model. Although the predicted value of each measuring point is very close to the actual value, the model accuracy (91.1%) is lower than the binaural MFCC-CNN model. One possible reason is that the input parameters in the shallow model do not consider  
Figure 16: Confusion matrix of testing data set of the binaural MFCC-CNN model.  
Figure 15: Training progress of the binaural MFCC-CNN model: (**a**) Training accuracy; (**b**) Training loss.  
the influence of the time-varying characteristics of the sound samples, so the binaural MFCC-CNN model performs better in the evaluation of sound quality.
## 3 Results
A drastic reduction in the activity against the controls was observed in the case of esterase/lipase (C8), leucine arylamidase, \(\beta\)-glucosidase, and in the assimilation of potassium nitrate, D-mannitol, and D-glucose. Live insects receiving the feed with the antibiotic showed high activity of \(\alpha\)-chymotrypsin and assimilation of D-mannitol and adipic acid. Seventeen traits did not change from the control, while about half of the observed traits showed a decline or loss of activity. In the case of dead individuals receiving the feed containing the antibiotic, only an increase in the utilization capacity of phenylacetic acid was observed compared to the control. The activity of the twenty traits did not change. An increase in the L-arginine utilization capacity was observed. Nevertheless, a decrease in enzymatic activity and the ability to utilize substrates was observed for: phosphatase alkaline, esterase/lipase (C8), esterase (C4), valine arylamidase, 4-nitrophenyl-\(\beta\)D-galactopyranoside, \(\beta\)-galactosidase, acid phosphatase, D-mannose, D-mannitol, malic acid, potassium gluconate, N-acetyl-glucosamine, L-arabinose, \(\beta\)-glucosidase, D-maltose, and the reduction of nitrate to nitrite (Figure 6). The last group consisted of all representatives of _R. dominica_ included in this analysis. Regardless of the variants, all samples were characterized by the high activity of trypsin, cystine arylamidase, valine arylamidase, and protease, and the low activity of \(\alpha\)-fucosidase, \(\beta\)-galactosidase, and \(\beta\)-glucuronidase. Guts prepared from control insects were only characterized by the lack of urease activity, while they had a high activity of traits common to this group mentioned above and \(\alpha\)-glucosidase, \(\alpha\)-chymotrypsin, esterase/lipase (C8), alkaline phosphatase, naphth-AS-BI-phosphohydrolase, and adipic acid utilization. The activity of the remaining traits was at an average level. In the background individuals, only lipase activity (C14) increased compared to the controls. Twenty traits did not change,  
Figure 5: Heatmap based on cumulative correlation matrix (Pearson) of lethal test and bacteria CFU values (**(a)**: _S. oryzae_, **(b)**: _R. dominica_). while suppressed activity was observed for 17 traits. The greatest decrease was observed in chymotrypsin, esculin hydrolysis, acid phosphatase, and \(\alpha\)-glucosidase activity, D-glucose fermentation, and adip acid, phenylacetic acid, D-mannose, malic acid, potassium gluconate, N-acetyl-glucosamine, L-arabinose, and D-maltose utilization. In the variant with food containing the antibiotic, an increase in the activity against the control was observed in the case of leucine arylamidase, 4-nitrophenyl-\(\beta\)D-galactopyranoside, urease, and in the utilization of D-mannose, malic acid, L-arginine, and a reduction of nitrate to nitrite. No activity changes were observed for 25 traits, while suppressed activity was observed for naphthol-AS-BI-phosphohydrolase, alkaline phosphatase, esterase/lipase (C8), esterase (C4), \(\alpha\)-glucosidase, and adip acid utilization (Figure 6).
## 4 Hardware Integration  
The microcontroller on the instrument main board controls the movement of the stepper motors by sending direction and step signals to four DRV8825 drivers, which are also installed on the main board. These signals then drive the four Nema 17 motors, enabling movement on the \(X\) (horizontal) and \(Y\) (vertical) axes. Two motors are controlled by a step and direction signal for movement on the \(X\) axis, and the other two motors are controlled by a separate step and direction signal for movement on the \(Y\) axis, all while adhering to the parameters received via Bluetooth from the GUI running on a Raspberry Pi 4.  
Data acquisition is primarily performed by the receiving antenna, which directs the signal through a Thorlabs RF cable model KMM36 to the LTC5596 power detector board. This board converts the RF signal into a DC signal, which is then sent to the OP07 operational amplifier on the main board's shield. The analog input of the Arduino Mega microcontroller reads the DC signal and stores it on an SD card in matrix form. The antenna begins the measurement in the upper right corner of the instrument and moves from right to left, collecting data as configured in the GUI. After reaching the end of the course, it moves down and back from left to right, collecting more data until it reaches the bottom of the instrument. Finally, the antenna returns to the starting point in the upper left corner.  
Upon completion of the matrix data collection, the Arduino Mega sends a control signal to the Arduino Uno on the secondary shield, which controls two Nema 17 motors to perform the \(Z\) axis translation and rotation movement of the metasurface being characterized. After the \(Z\) axis translation movement or rotation is complete, the Arduino Uno sends a control signal back to the Arduino Mega, and the process repeats. Figure 5a shows the front of the main board cover case, with the fans to cool down the motor drivers and potentiometers to adjust the operational amplifier offset and gain; (b) shows the back of main board cover case, with the HC-05 Bluetooth module fixed; (c) shows the main board with the mounted Arduino Mega 2560, \(X\) and \(Y\) axis DRV8825 motor drivers, ICL7660 voltage converter, OP07 operational amplifier, and the HW-203 SD card module; (d) shows the front of the secondary board cover case, with a 3D-printed Bluetooth symbol that fixes the HC-05 Bluetooth module in the back of cover case (as well as for the main board case); (e) shows the secondary board with the mounted Arduino Uno and \(Z\) axis DRV8825 motor driver; (f) shows the back of the secondary cover case, with the HC-05 Bluetooth module fixed; and (g) shows the angle board with the DRV8825 motor driver responsible for supplying and controlling the metasurface rotation stepper motor.  
Figure 8: \(Cont.\)