esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiple Object Tracking Using Re-Identification Model with Attention Module
## 2 Related Works  
### Object Detection  
Object detection refers to the localization and classification of specific objects present in an image. Currently, machine-learning- and deep-learning-based approaches are popular for object detection. Machine learning approaches include Viola-Jones object detection based on Haar features [10] and approaches involving the scale-invariant feature transform and the histogram of oriented gradients [11].  
Advances in research on CNNs have significantly influenced the field of object detection [12]. Deep-learning-based methods can be typically separated into two main streams: two-stage detectors and one-stage detectors.  
Two-stage detectors such as the region-based CNN (R-CNN) [7], Fast-R-CNN [13], and Faster-R-CNN [14; 15] combine the region proposal network and classification network. The R-CNN was the first model to use a CNN for object detection, which has proven that CNNs can exhibit high performance in object detection and classification problems. The R-CNN includes two stages: the region proposal stage and the classification stage. The region proposal stage determines the region of an object independent of the class. Subsequently, each candidate region is assigned to a CNN to predict the class of the object location. However, a disadvantage of this method is that applying CNNs to all candidate regions involves considerable computation. To address this shortcoming, researchers have developed improved algorithms such as Fast R-CNN and Faster R-CNN. However, these algorithms are much more time-consuming than the one-stage detector.  
On the other hand, the one-stage detectors include you only look once (YOLO) [16] and the single shot multibox detector (SSD) [17], where the CNN is used with the region proposal in a single shot. A one-stage detector typically includes an algorithm such as YOLO, the SSD, or CenterNet [18]. The YOLO series [19; 20] underwent an upgrade of its own algorithm, resulting in significant performance gains. YOLO divides grid cells into input images and uses a CNN to significantly increasethe computational speed. However, methods involving grid cells pose challenges in the detection of objects smaller than the  
Figure 1: Flowchart of an MOT system.  
grid cells. The SSD overcomes this problem by using a one-stage detector and employing a default bounding box, which shows superior performance over the two-stage detector.  
In this paper, we adopted a single-stage detector YOLOv3 model to locate the objects in each frame of an image, which is capable of high accuracy and high speed.
# Evacuation Management System for Major Disasters
## 3 EMS Platform
Figure 6: Gran Canaria wildfire intervention map. Red and green dots represent assembly points and emergency shelters locations respectively. Blue area depicts the evacuated areas and red area the wildfire boundary.
# Can IMU Provide an Accurate Vertical Jump Height Estimate?
## 1 Introduction
This paper proposes an implementation of a custom-made IMU placed on the distal area of the metatarsal part of the foot for VJ height estimation using the FT method. The proposed solution simplifies the detection of take-off and landing instances, due to the placement of the sensor close to the endpoint of the kinetic chain. A similar approach was adopted by [17; 26] yielding good results. The present paper builds upon the finding of the aforementioned studies in terms of using a new, custom-made sensor device and a sample of specifically trained, elite female athletes. The aim of the paper was to validate the current sensor device and positioning in relation to VJ height estimation. We hypothesized that the present sensor setup will provide valid and reliable results in relation to VJ height calculation in CMJ and SQJ tasks. The paper presents a simple, cost-effective solution to the common problem of in-field VJ height estimation, which can easily be extended to simultaneous testing of multiple athletes.
## 2 State of the Art
The main contributions in this paper are listed below:  
1. A decentralized stochastic optimization algorithm (robust to measurement noise) employing a gradient ascent method that can be applied on the relay node using multi-sensor spatial RSS sampling is proposed to find the optimal position of the relay node between two wireless nodes. 2. The proposed method is validated using simulations and field experiments considering performance metrics, such as the temporal and spatial performance of the relay node, accuracy and success rate in reaching the optimal position. The proposed algorithm is compared with the state-of-the-art methods at relevant places in this paper. The proposed work is a step towards intelligent decentralized relay nodes that uses node mobility to improve connectivity in wireless tethering applications [16; 17; 19; 20; 22].
## 4 Design of Dynamic Cache Allocation Mechanism
### Weight Definition
#### 4.1.2 Definition 2: Distance Weight  
The distance from the MTN to the multicast source is essentially the node depth. If the NACK is not hit in the cache of an MTN, it will be forwarded to the upstream until the multicast source. The farther the current node is from the multicast source, the larger the loss recovery delay is. To reduce the loss recovery delay, the larger the distance between an MTN and a multicast source is, the more cache space should be allocated to the multicast group; otherwise, the smaller cache space should be allocated to it. Let \(H_{i,n}\) be the number of hops from the MTN to multicast source \(i\). According to Equation (3), at the same MTN, the distance weight \(D_{i,n}\) of the multicast group \(i\) in the \(n\)-th statistical period can be obtained by  
\[D_{i,n}=\ H_{i,n}\ /\sum_{j}^{M}\ H_{j,n} \tag{3}\]
# Mobility Prediction of Mobile Wireless Nodes
###### Abstract  
Artificial intelligence (AI) is a fundamental part of improving information technology systems. Essential AI techniques have revolutionized communication technology, such as mobility models and machine learning classification. Mobility models use a virtual testing methodology to evaluate new or updated products at a reasonable cost. Classifiers can be used with these models to achieve acceptable predictive accuracy. In this study, we analyzed the behavior of machine learning classification algorithms--more specifically decision tree (DT), logistic regression (LR), k-nearest neighbors (K-NN), latent Dirichlet allocation (LDA), Gaussian naive Bayes (GNB), and support vector machine (SVM)--when using different mobility models, such as random walk, random direction, Gauss-Markov, and recurrent self-similar Gauss-Markov (RSSGM). Subsequently, classifiers were applied in order to detect the most efficient mobility model over wireless nodes. Random mobility models (i.e., random direction and random walk) provided fluctuating accuracy values when machine learning classifiers were applied--resulting values ranged from 39% to 81%. The Gauss-Markov and RSSGM models achieved good prediction accuracy in scenarios using a different number of access points in a defined area. Gauss-Markov reached 89% with the LDA classifier, whereas RSSGM showed the greatest accuracy with all classifiers and through various samples (i.e., 2000, 5000, and 10,000 steps during the whole experiment). Finally, the decision tree classifier obtained better overall results, achieving 98% predictive accuracy for 5000 steps.  
software defined networking; performance analysis; machine learning; wireless nodes; mobility models; artificial intelligence
## 4 Results and Discussions
### Diversity Performance  
The diversity performance is based on the ECC. The ECC performance of the antenna with and without the decoupling structure is shown in Figure 13. With the MEBG structure, the antenna has an ECC value of less than 0.09 for the UWB frequency between 3.1 and 13.5 GHz. Without the decoupling structure, a spike above the 0.09 ECC value was observed between these frequencies. For instance, a significant difference can be observed at 7 GHz and 9 GHz where the ECC has a value above 0.09. This shows that with the introduction of the MEBG structure, the effectiveness of the antenna was not degraded. This makes the antenna a good candidate for MIMO communication systems.  
Figure 11: Surface current distribution at 6.85 GHz (**a**) without decoupling, (**b**) with MEBG decoupling structure.  
Figure 12: Radiation pattern at 4.5 GHz, 6.85 GHz, 7.5 GHz, 8.5 GHz, 9.5 GHz frequencies.
## 4 Results and Analysis
### Evaluation of Hybrid Encryption Algorithm Strength
#### 4.1.3 Comparative Statistical Byte Distribution Analysis of Plain- and Ciphertext  
The comparison focuses on the statistical distribution of byte values between plaintext and ciphertext. First, it shows that the encryption algorithm can hide the natural patterns of the plaintext. This ensures that the ciphertext appears completely unpredictable. As a result, it is resistant to statistical attacks that exploit predictable data structures. Second, by studying the byte distributions, cryptographers can determine whether the algorithm follows key cryptographic principles. One such principle is the avalanche effect, where small changes in the input cause significant changes in the output [54]. This analysis is  
Figure 9: Density plot of byte values in encrypted messages, highlighting that the occurrence of some byte values is more frequent than others in the encrypted messages.  
important in proving the robustness of the algorithm against various types of cryptanalysis, thus establishing it as suitable for secure data protection applications. Figure 10 illustrates the distribution of plaintext and ciphertext byte values.  
The histogram on the left of Figure 10 illustrates the frequency distribution of byte values in the plaintext. The byte values range from 0 to 250, with most being clustered around a specific region. There is a noticeable peak in the histogram corresponding to the value zero. This clearly indicates that this byte value is the most frequently occurring in the plaintext data. The high-frequency density of the zero byte points to data repetition or common zero-padding in the plaintext. More notably, a distribution cluster occurs between byte values 90 and 130, representing certain information patterns or encoding, which are more dominant in the plaintext. Such a non-uniform distribution in plaintext could become a vulnerability if the encryption algorithm fails to effectively diffuse these byte values throughout the ciphertext.  
The histogram on the right of Figure 10 illustrates the frequency distribution of byte values in the encrypted messages. The byte values are evenly distributed across the entire range from 0 to 250. Unlike the plaintext, the distribution in the encrypted messages appears much more uniform. There is an absence of peaks; the frequencies of byte values are more evenly distributed across the whole range, indicating general uniformity. This equal distribution means that each byte value occurs nearly the same number of times, demonstrating a good level of diffusion. This kind of uniform distribution is a desirable property in cryptographic algorithms, indicating that the encryption process uniformly disperses the plaintext byte values across the entire range, thereby resisting cryptanalysis tests and attacks [55]. This uniformity results directly from the avalanche effect, where a slight change in the plaintext leads to a big, unpredictable change in the ciphertext, resulting in high diffusion [56]. This also makes it more difficult for an attacker to find a correlation between the two.
# Confidence Measures for Deep Learning in Domain Adaptation
## 4 Experimental Setup
### Network Training  
Each experiment was carried out following the same setup, based on the network architecture reported in Figure 1. Firstly, \(E_{S}\) and \(C\) were trained on the source dataset, then \(E_{S}\) was used in Step 1 to train the feature generator \(S\). The validation set of the source domain was used to compute the scale factor \(T\) needed for both the confidence measures \(e\) and \(s\) (see Equations (3) and (4)). Features extracted by \(E_{S}\) from the validation set were then used to train an auto-encoder for each class, to compute \(d_{feat}\) and \(CBP\) (see Equations (8) and (9)). Each measure was used to evaluate the reliability of the classifier, predicting whether the network \(C\) would correctly classify the sample or not. For this purpose, a threshold for each measure needed to be set to discriminate certain and uncertain samples. Threshold values were selected to maximize the accuracy on the validation set of the source domain, based on the Receiver Operating Characteristic (ROC) (the ROC is a graphical plot that displays, at different thresholds, the true positive rate against the false positive rate achieved by a binary classifier) obtained using a balanced subset of 5000 classified/misclassified samples (see Figures 2 and 3).  
The obtained thresholds were also used at the end of the adaptation procedure to evaluate the reliability of the classifier on the target domain. Finally, \(E_{I}\) was trained on the target domain using the GAN of Step 2. In this phase, every 1000 iterations, features extracted from the target domain by \(E_{I}\) were fed into the classifier \(C\). The reliability estimator \(M\), a regressors that computes one of the proposed confidence measures, was used to decide when stopping the training.  
Figure 3: _Cont._  
Figure 2: ROC curves obtained using each measures to predict the accuracy on the SVHN validation set. Measures \(e\), \(s\), \(d_{img}\), \(g\), \(d_{feat}\), \(mix_{1}\) and \(mix_{2}\) are, respectively, plotted in (**a**–**g**).
## 4 Evaluation  
The evaluation concept is based on the DRM and differentiates support evaluation, application evaluation and success evaluation [46]. Support evaluation investigates the fulfillment of the user needs on a conceptual and functional level parallel to development. Application evaluation is focused on applicability and usability of the method in the intended application context: the proactive management of requirement changes. Success evaluation aims to assess the usefulness of the method.
## 3 Methodology
### Automatic Dialect Identification
#### 3.2.4 Data Augmentation  
Data augmentation is a practice used in machine learning to significantly increase the size and variety of a training dataset. It enhances model generalization and helps avoid overfitting, which is especially helpful when working with small datasets. Without altering the underlying class labels, augmentation techniques, including rotating, flipping, cropping, or adding noise, add variations to the existing data. Figure 9 shows the class distribution before and after augmentation.  
Figure 8: SHAP diagram for all features.  
Figure 7: SHAP diagram for selected features.  
Figure 9: Class distribution.  
* ADASYN: By creating artificial samples close to the decision boundary, this technique aimed to oversample the minority class (GLF). It increased the number of GLF samples from 298 to 3762 while maintaining the initial number of MSA samples at 3762.
* Random oversampling: This technique matches the number of samples in the majority class (MSA) by randomly duplicating samples from the minority class (GLF). The method resulted in a total of 3762 GLF samples.
* Synthetic minority oversampling technique (SMOTE): SMOTE interpolates between pre-existing minority class samples to generate copied trials for the smaller class (GLF), much like ADASYN. This method increased the total number of GLF samples to 3762.
## 3 Prediction Results of Mining Surface Subsidence of Northern Pei County for Reusing Land Resources
### The Influence of Geological and Mining Conditions on Prediction Parameters
#### 3.1.3 Mining Depth  
The influence of mining depth on subsidence coefficient and horizontal movement coefficient is consistent. As the mining depth increases, the subsidence coefficient and the horizontal coefficient will slightly decrease.  
The mining depth has an effect on the tangent of the major influence angle. When the lithology of overlying strata is the same, the tangent of the major influence angle will increase with the increase of the mining depth. The relationship is given in Equation (7) [24].  
\[tan\beta=exp(0.00681H)\times 1.713 \tag{7}\]  
where:  
\(tan\ \beta\) is the tangent of the major influence angle;  
\(H\) is the mining depth, m.  
Last, the depth has a certain influence on the inflection point offset. Generally speaking, with the increase of mining depth, the inflection point offset will increase linearly. In addition, the mining depth does not have much impact on the mining influence propagation angle.
# Far Off-Resonance Laser Frequency Stabilization Technology
## 5 Conclusions and Future Perspectives  
This review introduces several different techniques for far off-resonance frequency stabilization, each with different characteristics and applications. The PDH frequency stabilization has a flexible frequency stabilization range but strict requirements on optical reference cavities in special geometry shapes, including precise temperature control, vacuum condition, and vibration isolation. The Faraday optical rotation frequency stabilization can realize frequency stabilization far from the resonance line. However, it requires the long-period adjustment of the temperature and encounters the problems of large hysteresis and poor repeatability. The frequency stabilization with a transfer cavity requires at least two lasers, one of which is stabilized as the frequency standard. It could realize off-resonance frequency stabilization at any frequency, but the optical path is complicated. The method of optical frequency comb is insensitive towards external temperature or magnetic field changes because the comb is strictly locked to the frequency standard. Besides, it can achieve large frequency detuning above 10 GHz. However, the large volume and the high cost limit its miniaturization and portability.  
These techniques can be optimized from the optical and circuit aspects. In terms of the optical aspects, devices can be miniaturized and integrated. In terms of circuits, by introducing modern intelligent control systems, optimizing PID controllers, and employing digital signal processing technology and LabVIEW processing platforms to optimize the feedback system, a better long-term stability and accuracy can be successfully realized. Moreover, the frequency stabilization systems in this paper are placed in a laboratory environment. If the application is gradually expanded from the laboratory environment to outdoor, even underwater and outer space, environments, it is necessary to develop a portable frequency stabilization system. Through miniaturization, integration, and automation, far off-resonance frequency stabilization technology may have broader application prospects.  
[PERSON] initiated the manuscript. [PERSON], [PERSON], [PERSON], M.D., and [PERSON] participated in the revision of the manuscript. [PERSON] provided overall supervision. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 61703025 and Zhejiang Lab under Grant 2019 MB0 AE03.  
The authors declare no conflict of interest.
## 6 Conclusions  
Sentiment analysis is an important area in natural language processing (NLP) due to its broad range of applications. The initial works on sentiment analysis employed traditional machine learning techniques, where the text was preprocessed to remove stop words, normalize the text, and represent it using frequency-based features such as TF-IDF or bag of words. The cleaned text was then fed into machine learning algorithms such as naive Bayes, SVM, etc., for classification. However, with the advancements in NLP, researchers have shifted their focus toward deep learning techniques. In deep learning, text is first encoded into pretrained word embeddings such as GloVe, word2 vec, or fastText. These embeddings capture the patterns in the text, which are then fed into deep learning models such as CNN, LSTM, GRU, etc. Some sentiment analysis works also use an ensemble approach by combining predictions from multiple machine learning or deep learning models to improve performance. Some commonly used sentiment analysis datasets include the Internet Movie Database (IMDb), Twitter US Airline Sentiment, Sentiment140, and SemEval-2017 Task 4 datasets.  
Despite the progress made in sentiment analysis, existing methods are still vulnerable to poorly structured and sarcastic texts, which highlights the need for more reliable language models. Additionally, most current sentiment analysis works classify sentiments into coarse categories, such as positive, negative, and neutral, but future research should focus on fine-grained sentiment analysis that includes classes with varying emotional intensities, such as strongly positive, positive, neutral, negative, and strongly negative. Sentiment quantification, where the polarity distribution of a topic is calculated, is another area of interest that could be used for strategic decision making.  
Conceptualization, K.L.T. and C.P.L.; methodology, K.L.T., C.P.L. and K.M.L.; software, K.L.T. and C.P.L.; validation, K.L.T., C.P.L. and K.M.L.; formal analysis, K.L.T.; investigation, K.L.T.; resources, K.L.T.; data curation, K.L.T. and C.P.L.; writing--original draft preparation, K.L.T.; writing--review and editing, C.P.L. and K.M.L.; visualization, K.L.T. and C.P.L.; supervision, C.P.L. and K.M.L.; project administration, C.P.L.; funding acquisition, C.P.L. All authors have read and agreed to the published version of the manuscript.  
The research in this work was supported by the Fundamental Research Grant Scheme of the Ministry of Higher Education under award number FRGS/1/2021/ICT02/MMU/02/4; the Deanship of Scientific Research, King Khalid University, Saudi Arabia, under grant number (RGP1/357/43); and the Multimedia University Internal Research Grant under award number MMUI/220021.  
Not applicable.  
The authors declare no conflict of interest.