esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Smart Feasant Farming System and Data-Mining Process
### Cloud-Computing Platform
#### 3.4.4 Machine-Learning Service  
One of the major components of the cloud-computing platform layer is the machine-learning service, which allows for implementing predictive algorithms and works in conjunction with the IoT data analytics service to enable machine-learning-based predictive analytics for the purpose of predicting crop yield.  
In this context, being considered a de facto computational notebook for data science, Jupyter Notebook is supported by many of the most popular cloud platforms as a rapid, iterative and interactive way of implementing machine-learning algorithms for data mining, which mainly includes splitting datasets into training and validation sets as well as training and validating machine-learning models. In this work, we have chosen k-fold cross-validation as the preferred method for training and validating predictive models as it is the most recommended method for machine-learning model evaluation [19].  
Implementing machine-learning algorithms also implies selecting those that are theoretically more appropriate given the nature of the data to be processed and the type of data-mining task to be carried out (in our case, prediction). In addition, it implies analyzing target data to identify those features or variables that are more relevant for use in generating machine-learning models, a task that is known as feature selection.  
Feature selection can be carried out using two main different approaches: wrapper-based approaches and filter-based approaches. On the one hand, with the wrapper-based approaches, multiple machine-learning models are evaluated using procedures that incrementally add or remove features to find the approximately optimal combination that maximizes model performance. These procedures are mostly realized by greedy search algorithms; a greedy algorithm is any algorithm that follows the problem-solving heuristic of making the locally optimal choice at each stage.  
On the other hand, filter-based approaches allow for evaluating the relevance of the features outside of the machine-learning models using statistical calculations, keeping only the features that pass some criterion. Unlike filter-based approaches, wrapper-based approaches completely depend on the underlying machine-learning algorithms and tend to be computationally intensive; they, however, usually provide the best-performing feature set for a particular type of machine-learning model [20].  
This component is finally responsible for making crop yield predictions by exploiting resulting machine-learning models.
# A Formal Analysis of the Mimblewimble Cryptocurrency Protocol +
## 2 The Mimblewimble Protocol
### Verification of Transactions  
If \(v\) is the value of a transaction (either input or output) and \(H\) is a point over an elliptic curve, then \(v.H\) encrypts \(v\) because it is assumed to be computationally hard to get \(v\) from \(v.H\) if we only know \(H\). However, if \(w\) and \(z\) are other values such that \(v+w=z\), then if we only have the result of encrypting each of them with \(H\) we are still able to verify that equation. Indeed:  
\[v+w=z\Leftrightarrow v.H+w.H=z.H,\]  
due to simple properties of scalar multiplication over groups. Therefore, with this simple operations, we can check sums of transactions amounts without knowing the actual amounts.  
Nevertheless, say some time ago we have encrypted \(v\) with \(H\) and now we see \(v.H\), then we know that it is the result of encrypting \(v\). In the context of blockchain transactions this is a problem because once a block holding \(v.H\) is saved in the chain it will reveal all the transactions of \(v\) coins. For such problems, MW encrypts \(v\) as \(r.G+v.H\), where \(r\) is a scalar and \(G\) is another point in \(H\)'s elliptic curve, \(r\) is called _blinding factor_ and \(r.G+v.H\) is called _Pedersen commitment_. By using Pedersen commitments, MW allows the verification of expressions such as \(v+w=z\) providing more privacy than the standard scheme. In effect,if \(v+w=z\) then we choose \(r_{v}\), \(r_{w}\) and \(r_{z}\) such that \(r_{v}.G+r_{w}.G=r_{z}.G\) and so the expression is recorded as:  
\[\overbrace{(r_{v}G+v.H)}^{v}+\overbrace{(r_{w}.G+w.H)}^{w}=\overbrace{r_{z}. G+z.H}^{z},\]  
making it possible for everyone to verify the transaction without knowing the true values.
# Machine Learning for Predicting Neurodevelopmental Disorders in Children
###### Abstract
The utilized classification models were k-nearest neighbor (k-NN), support vector machine (SVM), linear discriminant analysis (LDA), and radial basis function neural network (RBFNN), with findings suggesting that the proposed methodology can help speech pathologists in estimating intellectual disabilities at an early age. Further, ML on resting-state electroencephalography recordings attempted to distinguish healthy individuals from those with intellectual and developmental disorders [37]. Their approach achieved a balanced accuracy of 91.67% and identified a lower beta activity in the 19.5-21 Hz range as the most distinguishing characteristic for individuals with these disorders. Furthermore, ML and regression models were compared for early ASD and ID diagnosis [38]. Using logistic regression, SVM, and ensemble learning techniques, 241 children with ASD were diagnosed. Of these children, 40.66% had both ASD and ID, and the researchers' findings suggested that ML models based on socio-demographic and behavioral observation data, like SVM, may better identify autistic children with ID than regression models. More studies reveal the ML efficiency in SLD [10; 21; 39; 40], and CD [41]. A common observation in real-world scenarios is that multiple disorders can be further explored in research, examining their co-occurrence within a person [6; 15; 19; 25; 42; 43]. Consequently, there is a growing need for automated diagnostic tools to help experts accurately and efficiently identify NDs in children. SmartSpeech (Ioannina, Greece) is an innovative system that uses a serious game and a machine learning model to assess a child's developmental profile [19; 43]. The aim of this study is to improve the model's ability to capture complex patterns in the game dataset that are difficult for humans to comprehend, enhancing its accuracy. This study can contribute to advance our understanding of NDs, aid clinicians in early detection, support children with NDs during a crucial period, and improve digital diagnostic tools. This study employs a complete machine learning strategy with logistic regression, using [PERSON]'s factor score estimation on the SmartSpeech game dataset to make predictions about NDs.
## 3 Naming Conventions  
Before looking at stronger versions of the LLL, as well as related results, let us summarize some naming conventions that have become commonplace over the years.  
* **Why \"local?\"** The \"local\" adjective in the name refers to the situation that each event is typically dependent only on a small number of others. This can be visualized by a _dependency graph_, in which each event is represented by a vertex, and the event is mutually independent of all events to which its vertex is not connected. In this graph representation, each event is dependent only on its neighborhood--that is, subject to local dependencies only. This explains the \"local\" in the name.  
The dependency graph is not the same as the graph that we could obtain by connecting two nodes whenever they are dependent. For example, if all the events are pairwise independent, but not mutually independent, then the latter graph would have no edge, wrongly suggesting that all the events are mutually independent.  
* **Naming the lemma after [PERSON]** The first version of the LLL was published in the paper [2], jointly authored by [PERSON] and [PERSON]. Then, why is the lemma not called \"Erdos-Lovasz Local Lemma?\" The reason is that [PERSON] insisted in every lecture he gave about the subject that this result was created by [PERSON] alone, even though they applied it together in their joint paper. This is mentioned in an interview with [PERSON], see [3] (in Hungarian).
* **Symmetric vs. asymmetric versions.** The original version of the LLL (Lemma 3) is _symmetric_ in the sense that each event is treated equally: they all have to satisfy the same probability bound (3). In the _asymmetric_ variant (see Section 4.2), the events may satisfy different probability bounds.
# Research on Temperature Compensation Method in Crankshaft Online Measurement System
## 5 Conclusions  
The method does not require additional hardware circuit compensation, and only needs to perform temperature characteristic compensation experiments on the used displacement sensor in advance to improve the influence of temperature on the measurement performance of the displacement sensor. On this basis, this technical solution can be directly applied to the production line conditions, because the possible influence of the production environment's temperature on the calibration parts and crankshaft has been  
Figure 14: The deviation value measured before and after temperature compensation (* is deviation before compensation, o is deviation after compensation).  
Figure 13: Measured radius after temperature compensation.  
fully considered, and the compensation formulas at different temperatures have been given. This method hardly increases the cost, and the compensation effect is also very obvious.  
Aiming at the problem that the displacement sensor in the crankshaft measurement system is affected by temperature, this paper proposes an improved dragonfly algorithm optimized BP neural network to compensate for LVDT, and combines the advantages of elite decision-making and dragonfly algorithm to solve the BP neural network. The weights and thresholds are optimized globally, which reduces the system error of the BP neural network and improves the accuracy. Experiments verify the effectiveness of IDA-BP for LVDT in temperature compensation.  
According to the ambient temperature of the crankshaft online measurement system, a temperature compensation method is proposed. In the actual measurement, environmental temperature and equipment temperature changes are taken into consideration. During the measurement process, it is not affected by the workshop environment to ensure measurement accuracy. After temperature compensation, the radius of the crankshaft journal is closer to the standard value. This method is also suitable for the measurement of other shaft parts.  
Conceive, methodology, and structure of this paper, T.G., X.Q. and P.L.; Resources, P.L.; Supervision, X.Q. and P.L.; Writing--original draft preparation, T.G.; Software, T.G.; Performance evaluation, T.G.; Writing--review and editing, T.G. and X.Q.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key R&D Project of Jiangsu Province, under the grant number BE20160034.  
Not Applicable.  
The authors declare no conflict of interest.
# Periodic Monitoring and Filtering Suppression of Signal Interference in Mine 5G Communication
## 3 Materials and Methods  
### Proposed Method  
Aiming at the problems of mine 5G communication mentioned above, a fusion anti-interference scheme based on a deep complex network and blind source separation is proposed, which will improve the performance of mine 5G communication through periodic monitoring and filtering suppression of interference, as shown in Figure 1.  
An interference monitoring and suppression loop is proposed, which consists of a multiplier, digital-signal-processing module, interference-monitoring module, threshold-judgment module, blind source-separation module, and separation matrix register. The overall operation process of the scheme is as follows.  
First, the multi-channel input signal is left-multiplied by a separation matrix to separate the signal from the interference, and the signal component enters the digital signal-processing module, which includes conventional receiver algorithms, such as channel estimation and channel equalization. Then, the processed signal is divided into two channels. The channel other than the output channel enters the interference-monitoring module. Usually, the interference-monitoring module runs periodically. Once the input interference exceeds the configured threshold, a single operation of the blind source-separation module will be triggered.  
Compared with the conventional receiver process (i.e., only the digital signal processing module), only one fixed matrix multiplication \(y=Wx\) is added to the serial step. Although a small, fixed delay is brought, depending on the effective suppression of interference, it will not only reduce the system's demand for subsequent algorithms, but also shorten the convergence time of algorithms, such as equalization. Therefore, this method will not introduce significant delay and ensure the low delay of 5G communication systems.  
Compared with the signal-processing flow of the serial blind source separation (multi-channel input \(\rightarrow\) blind source separation \(\rightarrow\) digital signal processing \(\rightarrow\) symbol decision), although the proposed method sacrifices the responsiveness to fast-changing interference, it avoids the excessive delay and accumulated delay caused by real-time blind source separation. It is more suitable for the actual signal-processing flow, taking into account the interference suppression capability and low delay characteristics.
# A Review and Experimental Evaluation on Split Learning
## 4 Conclusions  
In this paper, we first present a systematic review of existing SL paradigms, classifying them according to their core characteristics. We group these paradigms into three main types: model split-only, weight aggregation-based, and intermediate data aggregation-based approaches. For each type, we evaluate its performance across four key dimensions and provide insights into the scenarios where each approach is most suitable. Through comprehensive experiments on five widely used datasets in both IID and non-IID settings, we compare and analyze the performance of various SL paradigms using the SLPerf framework. Our findings highlight several important conclusions: for improved performance in non-IID scenarios, combining SL with FL is beneficial; to reduce communication costs, selecting the appropriate cut layer and SL paradigm is crucial, and so on. These results provide valuable engineering guidance and provide deeper insight for future SL research. Most importantly, our work contributes to the field by establishing a fair and accessible benchmark to evaluate SL performance.  
Software, Z.H. and T.Z.; conceptualization, C.C.; survey, Z.H. and T.Z.; writing--original draft preparation, Z.H. and T.Z.; writing--visualization, Z.H. and T.Z.; writing--review and editing, C.C., Y.W. and B.W.; funding acquisition, C.C.; validation, Z.H. and T.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under grant numbers 62202170 and 62202169.  
Figure 11: Effect of client number and \(\alpha\) on model accuracy [108].  
Data Availability Statement: Real-world datasets that we used in our experiments are publicly available. The code for our experimental evaluation is available at [[https://github.com/Rainysponge/SLPerf](https://github.com/Rainysponge/SLPerf)]([https://github.com/Rainysponge/SLPerf](https://github.com/Rainysponge/SLPerf)) (accessed on 10 February 2025).  
Conflicts of Interest: Dr. [PERSON] was employed by Tencent AI Lab. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Materials and Methods
### Statistical Analysis  
We applied statistical techniques to ensure the robustness and reliability of the identification of relationships between the \(CBF\) and \(CMRO_{2}\), \(CBF\) and \(rCCO\), and \(CMRO_{2}\) and \(rCCO\). Correlation coefficients (R) and \(p\)-values were calculated using MATLAB's fitnlm function, with statistical significance defined as \(p<0.05\). To evaluate the power law exponent (\(\gamma\)) for each relationship, we conducted a one-sample \(t\)-test to determine if the mean value of \(\gamma\) was statistically different from 1 or 0. This analysis tested whether the observedrelationships deviated from linearity (\(\gamma\) = 1) or if the exponent indicated no significant relationship (\(\gamma\) = 0). This approach provided valuable insight into the nature of coupling between the variables. A similar method was applied to assess whether the intercept term (_a_) and the scaling factor (_k_) were significantly different from 0, further verifying the strength and validity of the fitted models.  
To evaluate the adequacy of the sample size in detecting significant non-linear relationships, we conducted a one-sample \(t\)-test to determine whether the observed power law exponent (\(\gamma\)) values significantly deviated from 1, indicative of a linear relationship [29]. Using a sample size of 10 pigs, a significance level of \(\alpha\) = 0.05, and the observed means and standard deviations of \(\gamma\), we calculated the effect size (Cohen's d) [30,31] for all relationships during the CA and CPR phases. The effect size was computed as the standardized mean difference using the formula  
\[d=\left|\mu-1\right|/\sigma, \tag{8}\]  
where \(\mu\) is the observed mean, and \(\sigma\) is the standard deviation. The statistical power was then calculated using the MATLAB function _sumspizerpwr_, which evaluates the statistical power based on the effect size, sample size, and significance level.
# Design of an Autonomous Cooperative Drone Swarm for Inspections of Safety Critical Infrastructure
## 8 Validation
### Automated Fault Detection with Deep Learning  
We validate the performance of our proposed fault detection algorithm. For running deep learning models in a real-time environment, different single-board devices such as the Raspberry Pi 4, Nvidia Jetson Nano, Nvidia Jetson TX2, and Nvidia Jetson AGX Xavier were evaluated [56]. For running the onboard vision-based fault detection algorithm, we used the Nvidia AGX NX Xavier board. AGX NX board is built with Volta architecture-based Graphics Processing Unit (GPU) having 384 Cuda cores and 48 tensor cores, and Carmel CPU. AGX NX can run deep learning model on GPU with 800 MHz frequency on 10 W and 1100 MHz frequency on 15 W.  
The training of deep learning models requires more powerful GPUs with more resources. For the training of the YOLOv4-based fault detection algorithm, we use Nvidia Tesla V100 with 32 gigabytes, which consists of multiprocessors and is partitioned into four equal processing blocks to perform parallel computations. For the training of the proposed YOLOv4-based fault detection model, we use the configurations from [57]. The momentum of the stochastic gradient descent is set to 0.9, the learning rate to 0.001, and the weight decay to 0.005. The images are scaled at 608 by 608 and the batch size is kept to 64. To keep the standardized learning rate of the neural network, the anchors that perform below the specified threshold value are dropped. For onboard real-time fault detection, we use Nvidia AGX NX, which performs detection at 11-17 FPS on different image scales [57].  
Figure 17: Measurements of RSSI for LoRa operating at 868 MHz with a bandwidth of 125 kHz using SF = 7 and a coding rate of 4/5. The red open circles show the RSSI calculated from the path loss model using a transmission power of 10 dBm and antenna gains of 2 dB at both ends. The green curve shows the measured packet error rate in the experiments.  
Figures 18 and 19 show the real-time onboard fault detection performance. The algorithm detects insulator objects with an accuracy in the interval from 0.75 to 0.99. Figure 19 shows normal detected objects with yellow frames and detected faults with gray boxes. The broken powerline insulator is detected with an accuracy score of 0.99 and 0.98, respectively.
# Time-Sensitive Networking Technologies for Industrial Automation in Wireless Communication Systems
## 5 Integration WLAN with 5G TSN
### WLAN Access between UE and TSN Node  
Most industrial IoT devices are easily connected by wired line or WLAN for TSN service, and Figure 9 shows an example where TSN nodes are each connected to an AP through a Wi-Fi interface. The existing WLAN technology does not guarantee deterministic communication because of unstable communication environment and random backoff-based channel access. If WLAN access is used as intermediate node in wireless TSN, hard real-time service cannot satisfy the requirements. To support real-time service, TSN is configured based on 5 GS, and the UE and the TSN node are connected through WLAN. As shown in Figure 9, the 5 GS works as a logical bridge by connecting the TSN backbone. NW-TT within 5 GS connects the TSN backbone and DS-TT connects TSN nodes, and the traffic between NW-TT and DS-TT is transmitted according to the deterministic QoS of 5 GS. WLAN provides connectivity between DS-TT and TSN nodes so that traffic forwarded by DS-TT can be forwarded to and from TSN nodes and vice versa.
## 5 Experiment and Analysis
### Comparative Analysis of Different Datasets in Different Models
#### 5.4.2 Classification Performance of Imbalanced and Balanced Datasets on CBA Models  
As shown in Figure 12, the method proposed in this paper shows good classification results in the balanced dataset based on the SMOTE + Tomek Link mixed sampling method, with a classification accuracy of 95.86% and an accuracy of 86.43% in the imbalanced dataset. The results show that using the SMOTE + Tomek Link method to construct generated data similar to the original data can significantly improve the accuracy of fault diagnosis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Evaluation Indicators** & **Recall** & **Precision** & **\(F1\)-Score** \\ \hline Average improvement rate & 9.2\% & 5.1\% & 7.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Average improvement rate of evaluation indicators.  
Figure 11: A comparison of the CBA and ST-CBA models’ multi-classification \(F1\)-scores.  
Figure 12: The classification performance of the CBA model. (**a**) The imbalanced dataset, (**b**) the balanced dataset.  
Figure 10: A comparison of multiple classification precision values between the CBA and ST-CBA models.