esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The water cycle in the key agricultural and pastoral zones (KAPZs) is an important factor for maintaining the stability of the ecosystem. Groundwater collection and lateral seepage are indispensable parts of the water cycle, and it is difficult to monitor the groundwater situation in each area. The strength of the alternate circulation of groundwater is directly related to the utilization value and development prospects of groundwater; therefore, creating an effective method for the detection of groundwater burial depth has become an issue of increasing concern. In this paper, we attempt to create a method for the detection of groundwater burial depth that combines cokriging interpolation, spatial autocorrelation, geographically weighted regression, and other methods to construct a quantitative relationship between different land cover types and groundwater depth. By calculating the band index of the land cover type, the groundwater depthinformation of the unknown area can be obtained more accurately. Through collaborative kriging interpolation, normalized difference vegetation index (NDVI), precipitation, and hydrogeological conditions were used as covariates. The groundwater burial depth of Wengniute Banner in 2005, 2009, 2013, and 2017 was the response variable, and the groundwater burial depth in the study area was calculated. The groundwater burial depth data after the cokriging interpolation was used to transform the raster data into vector data in space using the improved hydrological response unit (HRU) model to make it more suitable for the actual groundwater confluence. Subsequently, 551 minimum response units (MHRUs) were obtained by division, and the spatial autocorrelation analysis was performed accordingly. The groundwater burial depth in the study area is spatially distinct from east to west, and the groundwater level shows a trend of being high in the west and low in the east, gradually increasing due to precipitation and rivers. The average change of groundwater depth in the time series is not significant, but it does gradually show a trend of accumulation. According to the aggregation characteristics of spatial autocorrelation analysis, a geographically weighted regression model of groundwater depth and NDVI, normalized difference drought index (NDDI), and net relatedness index (NRI) was established. The NDVI representing the forest land and the Adjusted R\({}^{2}\) of the groundwater depth is 0.67. The NRI representing the cultivated land and the Adjusted R\({}^{2}\) of the groundwater depth is 0.8675. The NDDI representing the bare land and the Adjusted R\({}^{2}\) of the groundwater depth is 0.7875. It shows that the band index representing the ground type has a good fitting effect with the groundwater burial depth.  
minimum hydrological response unit; groundwater depth; cooperative kriging interpolation; spatial autocorrelation; geographic weighted regression
# IoT: A Long-Reach Fully Passive Low-Rate Upstream PHY for IoT over Fiber
## 2 Materials and Methods
### FBG-Based IoToF: A Fully Passive Optical Architecture
In the electrical path of Figure 2a, signals are amplified by the operational amplifiers (OP-AMPs) in a transimpedance configuration and then acquired by a multichannel analog-to-digital converter (ADC) module to reach the data receiver. These signals are processed and demodulated in the data receiver to connect with the IoT gateway to share the information on the Internet. The devices in Figure 2a can be also translated into PHY functional sub-layers depicted in Figure 3. First, the information layer converts information-to-electrical (I/E) pulses, which in turn undergo electrical-to-acoustic (E/A) conversion alongside an appropriate coding and modulation format to be sent through an acoustic channel. This represents the mechanical structure used to reach the acoustic-to-optical (A/O) conversion at FBGs, which only allows imprinting mechanical displacement into frequency modulation (FM). The optical channel transports the original information through long distances to finally reach the optical receiver. There, the received signal undergoes O/E conversion, which is performed directly without any intermediate optical-to-acoustic conversion, by photo-detecting amplitude modulated (AM) signals resulting from the edge-filtering FM optical signals. Finally, in the electrical domain, the signal is then digitalized with an ADC performing electrical-to-digital (E/D) followed by digital-to-information (D/I) conversion, i.e., demodulation and decision. Marks from \(M_{1}\) to \(M_{3}\) are inserted in Figure 3 to report piecewise characterization results from the IoToF prototype described in the next section. Figure 3: Flowchart of the physical connectivity (PHY) sublayer communication. I/E: information-to-electrical, E/A: electrical-to-acoustic, A/O: acoustic-to-optical, O/E: optical-to-electrical, E/D: electrical-to-digital, and D/I: digital-to-information conversions. \(M_{1}\), \(M_{2}\), and \(M_{3}\), represent the measurement points used for the system characterization. Figure 2: (**a**) Schematic of the proposed IoToF approach used to interlink several IoT devices to a receiver and (**b**) scheme of the fiber Bragg grating (FBG) operation principle.
# Automatic Digital Modulation Classification Based on Curriculum Learning
## 3 Curriculum Learning Based Modulation Classification
### Implementation of the StudentNet  
In our design, the StudentNet should be trained twice. The first training is the pre-training process. Firstly, the pre-training was carried out without the supervision of MentorNet to obtain features of sample in training set. Subsequently, the obtained features were transferred to MentorNet for the extraction of curriculum. Herein, we focus on training the StudentNet under the supervision of MentorNet and testing the performance of the proposed classifier.  
Figure 4: Average loss of actual and predictive modulation type at different signal-to-noise ratios (SNRs).  
Figure 5: The data-driven curriculum learned by MentorNet: (**a**) Epoch percentage = 20 and (**b**) epoch percentage = 90.  
The diagram of the second training under the supervision of MentorNet is shown in Figure 6a. Obviously, the StudentNet training can be divided into two steps. The first step is called forward propagation, in which StudentNet obtains the predicted label of training samples by convolution operations and pooling, and then computes the loss between the actual label and predicted label. According to the value of computed losses, the MentorNet assigns corresponding weight to loss. In the second step, named back propagation, the weighted loss is passed back to the upper layer and each layer needs to manipulate own its parameters according to the received loss. After training the StudentNet, the parameters of each layer are saved in a memory.  
During verifying the performance of our classifier, the parameters are loaded into the StudentNet from memory. Afterwards, the samples in test set are transferred into classifier and processed into grayscale images before coming into the StudentNet. Finally, the predicted labels are obtained by forward propagation. The diagram of performance testing is shown in Figure 6b. Unlike the training of StudentNet, the testing process does not require the involvement of MentorNet and back propagation.
# An Attentive Fourier-Augmented Image-Captioning Transformer
## 4 Experiments and Results
### Ablation Study  
We present five variants of our model. First, we implemented a variant that uses only Fourier extracted features (AFCT-F1) for the encoder, while the decoder remains unchanged. AFCT-F1 is used to investigate whether there are any qualitative benefits to adding the Fourier extracted features to the total image feature representation. Second, we implemented a variant with six layers instead of the standard three (AFCT-F2). This was chosen to ascertain whether there were any additional benefits to having more layers. Third, we implemented a variant similar to AFCT-F1 but with only one attention head (AFCT-F3). This variant examines the general impact of having just a single head. Fourth, we implemented a variant without the Fourier or the multi-head attention (AFCT-F4). AFCT-F4 is used to investigate the impact of having neither the Fourier attention, nor the self-attention layer when extracting the image features, with the input sent straight to the position-wise feed-forward network. Fifth, we implemented AFCT-F5, a variant that replaces both the encoder self-attention layer and the decoder masked self-attention layer with a Fourier transform layer and a masked Fourier transform layer, respectively. This was created to demonstrate the difference between Fourier transforms applied to images vs. text in an image-captioning scenario. Assume there were eight attention heads unless explicitly stated otherwise. See Table 2 for the model configuration. The results of these studies are shown in Table 3 and discussed in the Discussion section (Section 5).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **\# Layers** & **\# Heads** & **Model-Size (MB)** & **Encoder** & **Decoder** \\ \hline AFCT-F1 & 3 & 8 & 485 & FFT & CA, masked SA \\ AFCT-F2 & 6 & 8 & 836 & FFT & CA, masked SA \\ AFCT-F3 & 3 & 1 & 485 & FFT & CA, masked SA \\ AFCT-F4 & 3 & 8 & 411 & FC & CA, masked SA \\ AFCT-F5 & 3 & 8 & 522 & FFT & CA, masked FFT \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model configuration for the variants used in the ablation studies. Abbreviations: CA = cross-attention; SA = self-attention; FFT = fast Fourier transform; and FC = fully connected.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **B1** & **B4** & **M** & **R** & **C** & **S** \\ \hline AFCT-F1 & 80.3 & 38.5 & 28.8 & 58.2 & 127.7 & 22.5 \\ AFCT-F2 & 80.2 & 38.0 & 28.5 & 57.7 & 126.7 & 22.2 \\ AFCT-F3 & 80.3 & 38.5 & 28.8 & 58.2 & 127.7 & 22.5 \\ AFCT-F4 & 80.2 & 38.1 & 28.8 & 58.0 & 128.0 & 22.4 \\ AFCT-F5 & 77.2 & 30.2 & 24.8 & 52.3 & 104.9 & 17.7 \\ AFCT & 80.5 & 38.7 & 29.2 & 58.4 & 130.1 & 22.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results from the different variants. All variants’ results reported after CIDEr-D optimization. Abbreviations: B1 = BLEU-1; B4 = BLEU4; M = METEOR; R = ROUGE-L; C = CIDEr; and S = SPICE.
## 2 Data and Methodology
### Methodology
#### 2.3.3 The Random Forest Classifier  
In this study, we utilized the random forest algorithm for classification. This method reduces the overfitting risk often associated with individual decision trees by constructing multiple trees and aggregating their predictions, resulting in strong robustness [39; 40]. The algorithm effectively handles high-dimensional data, maintaining powerful classification performance, even with a large number of features, thus mitigating the curse of dimensionality [41]. Its random selection of samples and features further enhances its resilience to noise, improving the accuracy when dealing with noisy datasets. Although random forest is typically regarded as a \"black box\" model, it offers insights into feature importance, providing some level of interpretability in the decision-making process.  
Through experimentation, we determined that optimal results were achieved by configuring the RF classifier with 200 trees. The application of RF to crop classification has demonstrated its efficacy, yielding favorable outcomes [42,43].
## 4 Discussion
### Use of Filters  
There is a lack of this description in mobile device studies, and perhaps that is why its use depends on the researcher's choice. In this review, the filters used in the studies were Butterworth and Kalman. The Butterworth filter is a type of electronic filter developed to have a flat frequency magnitude response, as far as possible mathematically, in a defined passband. The Butterworth filter has five frequency orders and its use varies according to the desired response frequency [23]. The effect of noise suppression and signal distortion produced by the Butterworth filter can be investigated and controlled regardless of whether it is a linear operator [24]. The Kalman filter can be used to integrate two sources of information in a fusion of sensors and obtain the best estimate of unknown variables. This is what happens, for example, in the Inertial Measurement Unit (IMU), which is used in recordings with mobile devices through MEMS. In these measurements, two sensors (accelerometer and gyroscope) are fused to estimate an object's motion, speed and orientation throughout accelerations and angular data. The conventional Kalman filter is linear with Gaussian noise and could present problems in modeling nonlinear functions or linear functions with non-Gaussian noise [25]. To improve the applicability of the Kalman filter, scientists have created variants of the calculations, and it is recommended that the modeling of IMU error sources must be performed separately. Through the studies reviewed, the choice of filters used and the choice of the cutoff frequency are not standardized in the literature, since studies with similar objectives have chosen different frequencies and filters. However, while reading the articles, we identified that in studies focused on static balance analyses or specific movements, the cutoff frequencies were lower. On the other hand, in studies about gait or dynamic balance, in which several movements are occurring at the same time, the cutoff frequencies were higher. This may be related to the fact that in dynamic activities the sensor suffers more from noise interference. Therefore, describing the signal filtering system is important for replicating the experiment and standardizing data processing for balance analysis, using mobile devices.  
The studies in this review do not properly describe signal processing, but the majority stated that the data had a good correlation and validation with the gold standard equipment, such as force platform and kinematics. Even so, when authors do not describe the filtering process, the results can be questioned, and the work methodology becomes fragile, interfering with its replication. The literature shows that the impossibility of reproducing some studies has reached high values in the scientific community, and this demonstrates that the published results are less reliable than has been claimed [26]. Many causes contribute to this phenomenon, and we highlight the analysis and reporting of data. Data analysis and reporting needs to be written in clear detail to achieve the principle of scientific reproducibility.
## 2 Methods
### Descriptive Analyses
The goal is to find the combination of variables that maximizes the occurrence of a specific outcome, \(Y\), which could be severe accidents, for instance. To define the combination of variables that includes \(x_{j}\), let \(C=\left\{x_{j1},x_{j2},\ldots,x_{jk}\right\}\) be a combination of \(x_{j}\) with \(k\) other variables, where \(x_{j1},x_{j2},\ldots,x_{jk}\) are selected from the full set \(x_{1},x_{2},\ldots,x_{m}\). The frequency \(F(C)\) of each combination \(C\) is defined as the number of entries \(D_{i}\), where all variables in \(C\) take the value \(1\). \[F(C)=\sum_{i=1}^{n}I(C,D_{i}) \tag{3}\]  
where the indicator \(I(C,D_{i})\) is defined as follows:  
\[I(C,D_{i})=\left\{ \begin{array}{ll}1&\text{if $x_{j1}=1,\ x_{j2}=1,\ \ldots,x_{jk}=1$ in $D_{i}$,}\\ 0&\text{otherwise}\end{array} \right. \tag{4}\]The MCV is the combination \(C^{*}\) that includes \(x_{j}\) and maximises the frequency \(F(C)\) in relation to a specific outcome \(Y=1\) :  
\[\text{MCV}=\max_{C\subseteq\{x_{1},x_{2},\ldots,x_{m}\},x_{j}\in C}F(C|Y=1) \tag{5}\]  
The MCV approach involves searching through all possible combinations that include the specific variable \(x_{j}\), calculating the frequency with which these combinations occur when a specific outcome \(Y=1\) is observed, and identifying the combination with the highest frequency. The MCV method analyses how frequently a particular variable occurs in combination with one or more other variables, identifying the most common combination in which the variable appears.
## 3 Methodology
### Performance Measure  
#### 3.4.1 Classification Metrics  
In the field of classification, a key factor in evaluating the performance of any model is the ability to train the model to correctly classify the categories of stadium fire risks. Evaluation indicators are quantitative indicators of model performance. An evaluation metric can only reflect part of the performance of the model. If the selected evaluation indicators are unreasonable, wrong conclusions may be drawn. Therefore, different evaluation indicators should be selected for specific data and models. This study uses several commonly used metrics to evaluate the performance of the learned model, namely accuracy, precision, recall, macro F1 score, AUC score, ROC curve and precision-recall curve, most of which are calculated based on the confusion matrix. In Table 4, a confusion matrix is a representation of the predictions for a classification problem that summarizes the number of correct and incorrect predictions for each class.  
4.2 Accuracy: The ratio of the correct number of samples classified by the classifier to the total number of samples.  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{2}\]  
Precision: The ratio of the total number of positive samples correctly classified by the classifier to the total number of samples identified as positive samples by the classifier.  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{3}\]  
Recall or Sensitivity: The ratio of the total number of correct positive samples classified by the classifier to the total number of real positive samples.  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{4}\]  
F1-score: as the harmonic mean of recall and precision, it is better than independent precision or recall indicators, which is an important indicator for evaluating classification models [20]. Precision and recall have their own shortcomings. If the threshold is high, the precision is high, but there will be a lot of data loss; if the threshold is low, the recall will be high, but the prediction will be very inaccurate. Therefore, the F1-score is used to evaluate the classifier more comprehensively and can balance the effects of precision and recall.  
\[\text{F}_{1}=2\frac{\text{precision}\times\text{recall}}{\text{precision}+ \text{recall}} \tag{5}\]  
In addition, the area under the ROC curve (AUC) and the area under precision-recall curves (Auprc) can be used as scalar metrics to evaluate classification performance.
# Evaluation of VoIP QoS Performance in Wireless Mesh Networks
## 4 Results and Analysis
### Analysis Summary
Figure 18: All scenarios’ packet-loss % comparison for: (**a**) VoIP initiators; (**b**) VoIP receivers. In order to draw a holistic conclusion from our experiments, we have identified the scenarios with best MOS values and the scenarios with the worst MOS values, as presented in Figure 20. The analysis shows that the following scenarios have the best MOS values (above 3.2) (highest first):  
* PL4 = Platoon, waypoint mobility, 802.11g, G.729 codec, AODV
* FV2 = Flooded village, no mobility, 802.11g, 802.11, G.711, AODV
* FV4 = Flooded village, no mobility, 802.11g, 802.11, G.729, AODV
* PL2 = Platoon, waypoint mobility, 802.11g, 802.11, G.711, AODV
* FR2 = First responders, waypoint mobility, 802.11g, 802.11, G.711, AODV
* FR4 = First responders, waypoint mobility, 802.11g, 802.11, G.729, AODV
* FRW4 = First responders, random waypoint mobility, 802.11g, 802.11, G.729, AODV  
Figure 19: MOS values average comparison for VoIP initiators and receivers. Figure 20: Average MOS values in all scenarios. The following scenarios produce the worst MOS values in the listing order (lowest first) below 3.1 (fair), which is falling below the acceptable quality threshold:  
* IV6 = Isolated village, no mobility, 802.11n, G.729 codec, AODV
* IV5 = Isolated village, no mobility, 802.11n, G.711 codec, AODV
* IV1 = Isolated village, no mobility, 802.11g, 802.11s, G.711 codec, HWMP
* PL3 = Platoon, waypoint mobility, 802.11g, 802.11s, G.729 codec, HWMP
* FR3 = First responders, waypoint mobility, 802.11g, 802.11s, G.729, HWMP
* FV3 = Flooded village, no mobility, 802.11g, 802.11s, G.729, HWMP
* IV2 = Isolated village, no mobility, 802.11g, 802.11, G.729 codec, AODV  
Additionally, the analysis of the scenarios in terms of the standards, codecs and protocols integration is presented in Figure 21. The analysis of the results shows that IEEE 802.11g without the IEEE 802.11s integration along with G.711 and G.729 codecs and AODV routing protocol results in achieving better VoIP performance, followed by the IEEE 802.11g integration with IEEE 802.11s using the same codecs and HWMP protocol. While the IEEE 802.11n with the same voice codecs and AODV routing protocol result in poor VoIP performance, the analysis also shows that scenarios with mobility mostly result in producing better MOS scores as compared to scenarios with no mobility.
# Evaluation of the Variability of Vibration Measurement Results in Rolling Bearing Quality Control
## 4 Results and Discussion
### Repeatability and Reproducibility of the Bearing Vibration Measurement Result
In the standard method, the calculation of coefficient R&R expressed as a percentage is done by division of the obtained parameter R&R by the designated process variation related to the standard deviation or by the manufactured product's design tolerance zone. The international standards do not clearly specify the limits obtained in vibration level quality control, for instance depending on the bearing type. Bearing manufacturing plants regulate these limits themselves, also based on customer requirements. For the purpose of this testing, it was decided to adopt the following limits: low frequency band (50-300 Hz)--20 And, medium frequency band (300-1800 Hz)--12.6 And, high frequency band (1800-10,000 Hz)--9.8 And. The R&R percentage values presented in Table 7 were obtained by division of the resulting repeatability and reproducibility by the tolerance range in the given band. The quotient result is multiplied by 100% and the interpretation of the results of value %R&R is presented as follows: lower than 10%--acceptable measurement system; between 10% and 30%--the system can be accepted conditionally; higher than 30%--the measurement system cannot be accepted, improvement required. The results demonstrate that the repeatability, i.e., the variation caused by the clamp, is between 1.36 and 1.75 And, depending on the band. Reproducibility, i.e., the variation caused by the fitting, varies between 1.02 and 1.77 And, depending on the band. Both parameters are higher in higher bands. The resulting R&R value constitute a substantial part of the vibration level tolerance field in the high frequency band (25.42%) and a rather substantial part of tolerance in the medium frequency band (15.7%). The variation caused by the clamp and fitting has the least impact on the low frequency band (8.5% of the total vibration level tolerance field in this band). \begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multicolumn{3}{c}{**Band**} \\ \hline  & Low & Medium & High \\ Repeatability EV, And & 1.36 & 1.63 & 1.75 \\ Reproducibility AV, And & 1.02 & 1.13 & 1.77 \\ R\&R, and & 1.7 & 1.98 & 2.49 \\ R\&R, \% & 8.5 & 15.7 & 25.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Values of main parameters of the repeatability and reproducibility method, obtained for all three tested systems.