esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Results  
### Simulation Experiment  
To validate the effectiveness of the DBO-Fuzzy PID temperature control algorithm, a simulation model of the DBO-optimized Fuzzy PID controller was developed using the Simulink toolbox in MATLAB 2024a, as illustrated in Figure 14. A step signal was employed as the system's desired input to compare the step response curves of three control algorithms: conventional PID, fuzzy PID, and DBO-Fuzzy PID. An interference signal was introduced during the simulation to evaluate the anti-disturbance performance of each control method. The step change represents one of the most stringent forms of disturbance in control systems and is also the most convenient for computation and implementation. As the most representative signal, satisfactory control performance under step disturbance typically indicates effective handling of other disturbance types.  
We evaluated the dynamic and steady-state performance of the three control methods using three key metrics: settling time, overshoot, and steady-state error. In control theory, settling time is defined as the shortest time required for the controlled variable to enter and remain within \(\pm\)5% of the new steady-state value. Overshoot refers to the ratio of the maximum instantaneous deviation to the steady-state value of the controlled variable. Steady-state error represents the deviation that occurs when the system transitions from one steady state to another.  
The step response curves of DBO-Fuzzy PID, conventional PID, and fuzzy PID controllers were compared, with results shown in Figure 15 and Table 4. The DBO-Fuzzy PID controller demonstrated superior performance among the three methods, achieving a settling time of 35.23 s, which represents reductions of 165.13 s and 123.19 s compared to PID and fuzzy PID, respectively. The overshoot was 2.18%, lower than PID and fuzzy PID by 22.32% and 6.92%, respectively. The steady-state error reached a minimum of  
Figure 14: Simulink model of DBO-Fuzzy PID controller.  
Figure 13: Iterative optimization of dung beetle algorithm.  
0.009 \({}^{\circ}\)C. These results indicate that the DBO-Fuzzy PID control method provides faster and smoother responses to step signals with improved regulation accuracy.  
The simulation results under disturbance signals are shown in Figure 16. When subjected to external disturbances such as changes in nutrient solution volume or ambient temperature, causing deviations from the target temperature, the DBO-Fuzzy PID controller demonstrates faster and more stable disturbance rejection, achieving rapid stabilization. Unlike fuzzy PID control, where quantization and scaling factors are manually adjusted, the DBO-Fuzzy PID controller optimizes these parameters through iterative dung beetle population optimization and fitness function evaluation. Consequently, the designed DBO-Fuzzy PID controller meets the requirements for nutrient solution temperature control, exhibiting enhanced stability and robustness.
# Intelligibility and Listening Effort of Spanish Oesophageal Speech +
## 3 Experiment 1: Preliminary Word Error Rate and Listening Effort Measurements
### Analysis and Results  
Prior to calculating WER, an initial clean-up was performed on the data. This included removing any punctuation or special characters and some typing errors (accented vowels, use of upper and lower case, spelling of proper or foreign names, etc.). The WER was obtained after correcting these transcription errors.  
WER was calculated using the Levenshtein distance [44] between the reference sentence and the hypothesis sentence (the sentence transcribed by the listener). The Levenshtein distance takes into account the insertions, deletions, and substitutions that are observed in the hypothesis sentence. The calculation was performed with the Matlab toolbox [45]. The formula used is shown in Equation (1).  
\[WER=\frac{\mathit{Substitutions}+\mathit{Insertions}+\mathit{Deletions}}{\mathit{ Total number}\mathit{of}\mathit{words}\mathit{in}\mathit{reference}\mathit{sentence}}. \tag{1}\]  
Two different WERs were calculated: One, where all words of the sentence were considered; and two, where only content words were considered. In this case, errors in function words like prepositions, pronouns, conjunctions, etc., were overlooked. This helped us see how much of the content of the conversation was correctly understood. We contrasted these content-words-only WERs with the all-inclusive WERs.  
We performed a 2\(\times\)2 repeated measures ANOVA with the within-subjects factors speaker type (OS vs. HS) and between-subjects factor familiarity (familiar vs. not familiar) to quantify the effects of speaker type and familiarity on WER and LE ratings using the JASP tool [46]. We ran an additional ANOVA to compare the effect of audio device on WER and LE ratings. Sphericity and homogeneity checks were performed on the data with the JASP tool to ensure that assumptions of an ANOVA test are met.  
Finally, we present speakerwise average ASR WERs of the 30 sentences and compare them with the HSR WERs.
# Representing Deep Neural Networks Latent Space Geometries with Graphs
## 3 Methodology
### Proposed Methodology
#### 3.3.2 Dimensionality and LGGs  
A key asset of the proposed methodology is that the number of vertices in the graph is independent of the dimension of the intermediate representations it was built from. As such, it is possible to compare graphs built from latent spaces with various dimensions, as illustrated in Figure 2. Being agnostic to dimension will be a key ingredient in the applications described in the following section. It is important to note that, while the number of vertices is independent of the dimension of intermediate representations, edge weights are a function of a similarity in the considered latent space, which can have very different meanings depending on the underlying dimensions.  
In the context of DL architectures, a common choice of similarity measure is that of cosine. Interestingly, cosine similarity is well defined only for nonnegative data (as typically processed by a ReLU function) and bounded between 0 and 1. When data can be negative, we use a Gaussian kernel applied to the Euclidean distance instead. The problem remains that cosine or Euclidean similarities suffers from the curse of dimensionality. In an effort to reduce the influence of dimension when comparing LGGs obtained from latent spaces with distinct dimensions, in our experiments, we make use of graph normalization, as defined in step 4 of LGG construction. We also provide a discussion on the complexity of graph similarity computation in Appendix D. Note that a more in-depth analysis and understanding of the influence of dimension on graph construction is a promising direction for future work, as improving the graph construction could benefit all applications covered in this work.
# Cattle Weight Estimation Using Fully and Weakly Supervised Segmentation from 2D Images
## 2 Materials and Methods
### Devices and Settings  
The computer used in this study was equipped with an Intel Xeon processor (NVIDIA TITAN RTX 24 GB). The programming environment consisted of a Windows 10 operating system, CUDA 11.0, Keras 2.8.0, TensorFlow 2.5.0, PyTorch 1.10.0, and Torchvision 0.11.0. The camera used for data collection was a GB-CDX (GB-CDX04, GASI). The maximum resolution of the camera was 1920 \(\times\) 1080 (FHD) at a speed of 60 FPS; however, we used a 1280 \(\times\) 720 (HD) at a speed of 30 FPS. In the training of deep learning networks, large-resolution input requires many parameters and calculations. Max pooling, which is generally used in the pooling layer of convolutional neural networks (CNNs), leaves only large features and connects to the next layer. Therefore, if a resolution that is sufficient to clearly capture cattle is used, the analysis performance will not improve even if a larger resolution is used. Therefore, to improve the efficiency of the storage space, the camera was set to HD resolution.
## 3 Quantification, Assessment, and 3D Visualisation of UES in Naples
### Classification of UES
* _Waterway_ includes streams, drains, docks, and canals and was obtained by computing the values of the distance between the cell and the nearest waterway. The following nine tiers belong to the Carrier functions category:  
* _Railway_ shows the network of transportation by computing the values of the distance between the cell and railway tracks. * _Roads_ contains the network of roads by computing the values of the distance between the cell and roads. * _Airport_ shows the surfaces on which airports are allocated and the buffer of influence for the surrounding areas. Although airports are crucial for long-distance connections, they have a negative impact, in terms of noise and environmental disturbance, on ecosystems. * _Port_ shows the surface of the coast addressed to port functions and the buffer of influence for the surrounding areas, in terms of noise, pollution, transportation of people and wares, and proximity to boarding points. * _Bus/underground stop_ identifies the location of bus or metro stops, visualising the most accessible zones of the focus area. * _Mineral extraction site_ shows the areas in which raw materials are extracted for the construction sector. * _Habitation density_ shows an institutional data set provided by the EEA with information about housing density. * _Waste disposal_ localises the waste disposals that gather waste from the study area. * _Tourism facility_ identifies the highest concentration of the touristic facility points (e.g., hotels, B&Bs, and guesthouses). Finally, the last six tiers belong to the Information functions category:  
* _Cultural site_ highlights cultural heritage by identifying the number of cultural sites. * _Place of worship_ shows the location of worship places, which are related to landscape spiritual values. * _Sport and leisure_ contains sport and leisure surfaces, which are very important as they contribute to the regulation and cultural functions of the landscape. * _Green urban area_ refers to green areas, which are very important in contributing to regulation and cultural functions. * _Attraction place_ represents the places of attraction that polarise the flows of tourists and citizens (e.g., theatres, cinema, and observatories). * _Attractive landscape feature_ represents an excerpt of a point pattern, based on a code that determines the places most photographed by citizens and tourists in the focus area. It simulates landscape attractiveness, as perceived by citizens or tourists. A perceptual investigation about the relationship between aesthetic value and landscape features would require surveys, which the authors have not provided in this contribution. In the same manner as the data resolution and the MMU, the choice of DT influences the final results of the spatial evaluation. It must follow criteria matching the main objectives of the decision-making process. Indeed, environmental studies are affected by the urban configuration [98], which includes the distances and the interactions among elements in terms of effect decay, mostly when dealing with ES approaches.
# Mask-Wearing Detection in Complex Environments Based on Improved YOLOv7
## 5 Conclusions  
This paper proposes an improved mask-wearing detection model based on YOLOv7, aiming to address the issues of poor detection performance in complex environments, such as complex backgrounds, small target sizes, information loss due to target occlusion, false positives, and false negatives. We introduced the C2f_SCConv module as a feature extraction module, which enlarges the network's receptive field, allowing for the extraction of richer facial mask features and improving the detection performance of small targets. Additionally, we proposed the SPPFCSPCA module, which integrates a hybrid attention mechanism and optimizes the spatial pyramid pooling structure, resulting in faster model convergence. Finally, we introduced the HAM_Detect decoupled head, which incorporates a hybrid attention mechanism and optimizes the loss function, further accelerating model convergence, mitigating issues caused by target occlusion, false positives, and false negatives, and improving the detection performance of the model in complex environments.  
Building upon these improvements, our model trained on the enhanced dataset from the AIZOO public dataset exhibited excellent performance. Significant improvements were observed in the model's bounding box loss function compared with the baseline YOLOv7 model. The detection accuracy reached 90.1%, showing notable improvement compared with Faster R-CNN, YOLOv5s, YOLOX, YOLOv7, YOLOv7-tiny, and YOLOv8s. In practical detection scenarios, our proposed model performed well in complex environments, such as those with complex backgrounds, small target sizes, partial scene occlusion, and dim lighting conditions. In the future, we will focus on improving the model's lightweight direction, aiming to reduce the model parameters and computational complexity and optimize the model complexity while maintaining good detection accuracy. This will facilitate deployment of the mask-wearing detection model on mobile platforms with limited computational resources.  
Conceptualization, G.F. and W.W.; Methodology, Q.Y. and C.T.; Software, Q.Y. and Y.L.; Validation, Q.Y.; Formal analysis, Q.Y., C.T., Y.L. and X.W.; Investigation, Q.Y. and C.T.; Resources, G.F., Q.Y. and W.W.; Data curation, Q.Y. and X.W.; Writing--original draft, Q.Y.; Writing--review & editing, G.F. and W.W.; Visualization, X.W.; Project administration, G.F. All authors have read and agreed to the published version of the manuscript.  
This research and the APC was funded by Guangdong Provincial Philosophy and Social Science Planning Project, Project Name: Personalized Guidance Research Based on Cross modal High Order Reasoning Model, grant number GD23 YY08.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to contain portrait privacy information.  
The authors declare no conflict of interest.
# Remote Sensing Multimodal Image Matching Based on Structure Feature and Learnable Matching Network
## 4 Experiment
### Results and Analysis
However, the structure orientation of the proposed method is enhanced by the PCA, which has considerably better noise adaptiveness than the log-Gabor and gradient orientations. Therefore, compared with the CFOG and HOPC, the PCA-LMN shows a significant advantage in tests \(T_{5-16}\). In these tests, the average CMR of PCA-LMN is 80.27%, which is 6.54% and 11.29% higher than that of the CFOG and HOPC, respectively.
## 2 Background and Related Work  
### MPEG Video Sequence  
An MPEG video sequence has three types of frames: (a) an I-frame that represents the most important information of the sequence, (b) a P-frame that represents a predictive frame, and (c) a B-frame that is a bi-directional frame, as shown in Figure 1.  
A typical MPEG video sequence looks like the following: \(IBBPPBBPPBBIIBBPPBBPBB\)\(BB\dots\). Since the I-frame contains the most important information of the sequence, encrypting this frame results in the encryption of the whole video sequence [33]. In this paper, the extracted I-frames are treated as JPEG images, and singular value decomposition is used to extract the most relevant parts of the frame to be encrypted using PRESENT ultra-lightweight block cipher and chaotic maps.
## 4 Results and Experiments
### Evaluation Metrics  
Confusion matrices are commonly used for evaluating machine learning models, comparing actual and predicted class information in a structured table format, as described in reference [58]. A confusion matrix simplifies the computation of various performance metrics by providing the following information:  
* True Positives (TPs): Correctly predicted positive instances.
* False Negatives (FNs): Instances wrongly predicted as negative.
* True Negatives (TNs): Correctly predicted negative instances.
* False Positives (FPs): Instances wrongly predicted as positive.  
Equation (40) [59] is used to calculate accuracy, the simplest and most fundamental metric that can be derived from the confusion matrix.  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{40}\]In addition to accuracy, it is crucial to assess any model using a range of performance metrics, particularly when working with imbalanced datasets. Therefore, the current models were also evaluated using additional metrics such as recall, precision, and F1 score. Precision was calculated by dividing the number of correctly identified positive instances by the total number of instances predicted as positive, including both true positives and false positives. These metrics provided a more comprehensive understanding of the models' performance across all classes. Precision is also referred to as positive predictive value and is calculated using Equation (41) [59]. Recall, calculated using Equation (42) [59], measures the proportion of actual positive samples correctly identified by the model among all samples that should have been identified as positive. Equation (43) [60] computes the F-score, which represents the harmonic mean of accuracy and recall.  
\[Precision=\frac{TP}{TP+FP} \tag{41}\]  
\[Recall=\frac{TP}{TP+FN} \tag{42}\]  
\[Fscore=\frac{2*precision*recall}{precision+recall} \tag{43}\]  
In this case, the objective was to optimize the metrics accuracy, precision, recall, and F-score as specified by the evaluation criteria.
## 3 Methodology and Proposed Model
### Data Pre-Processing
#### 3.2.1 Data Cleaning  
The Kaggle dataset contains four attributes with missing values: Last-Evaluation (1532 missing values), Satisfaction (180 missing values), Department (706 missing values), and Tenure (180 missing values). We removed the instances with missing values for any of these attributes. Notably, the missing values for Satisfaction, Department, and Tenure occurred in records where Last-Evaluation was also missing. As a result, 1532 instances were removed, leaving 12,717 instances in the dataset.
# An Improved Arc Fault Location Method of DC Distribution System Based on EMD-SVD Decomposition
###### Abstract  
The influence of the control strategy of the power electronic converter obscures the fault characteristics of DC distribution networks. The existence of arc faults over an extended period of time poses a grave threat to the security of power grids and may result in electric shock, fire, and other catastrophes. In recent years, the method of fault localization based on the traveling wave method has been a popular topic of research in the field of DC distribution system protection. In this paper, the fault localization principle of the traveling wave method is described in depth, and the propagation characteristics of the traveling wave of fault current in the online mode network are deduced. We present a method for wave head calibration that combines empirical mode decomposition (EMD) and singular value decomposition (VMD). After the fault-traveling current signal has been subjected to EMD, the first eigenmode function is extracted and subjected to singular value decomposition (SVD). After SVD, the detail component can reflect the singularity of the signal. The point of the maximum value of the detail component signal corresponds to the moment when the faulty traveling wave head reaches the monitoring point. Finally, the DC distribution system is modeled based on the PSCAD/EMTDC simulation environment, and the fault location method is verified. The simulation results show that the method can effectively realize fault localization.  
fault location; traveling wave method; EMD; SVD; PSCAD/EMTDC
## Appendix D Instability Detection Using Other \(k\) Lags  
The instability detector based on the BBRBM-ANN model can use different numbers of lags or images of volatility strips and binarized returns. The number of lags \(k\) determines the number of neurons in the BBRBM input layer. We tested different values of \(k\), and in particular, we showed some results of the BBRBM-ANN model trained with \(k=3,8\) and \(k=16\). The BBRBM input layer has 6, 16, and 32 visible units, respectively. In each case, we have considered a hidden layer of 9, 24, and 48 units, respectively, which, in turn, are the ANN's input layer. The ANN is trained as a classifier with synthetic samples that were not used in the BBRBM training. After ANN training, the binarized volatility and return strips from the S&P500 are used and presented to the Boltzmann machine. The values of the hidden layer units are transmitted to the ANN to generate an instability probability computation.  
Figure A3 shows some examples of the evolution of the instability probability, particularly for the case of \(k=3\) and \(k=8\). The one for \(k=16\) is in Figure 7. Observing the plots allows one to visualize that the instability probabilities generated by the ANN appear to have less noise with higher lags. With \(k=3\), the probabilities appear to have discrete values, while with \(k=8\), there appears to be a better resolution in the instability indication because there is not as much variation in probability from day to day. For example, with \(k=8\), it can be better seen how the instability probabilities increase significantly amid the 2000 crisis downturn and the 1987 Black Monday crisis.