esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Method
### Specific Details of the Fault Diagnosis Method (ISCV-ViT)
#### 3.2.4 Training Strategy  
In the process of model training, it is our target to have the predicted value infinitely approximate the actual value. That is, the difference needs to be minimized, which means that the selection of the loss function is very critical in this process. The process of feeding the input feature \((x)\) into the model to obtain the predicted value \((y)\) is called the forward pass. The process of updating model parameters according to the D-value between the predicted value and the actual value is called the backwards pass. In between, the loss function provides input data for the backwards pass by accepting the predicted value of the model and calculating the difference. Based on the above analysis and the characteristics of classification problems, the loss function of the proposed ISCV-ViT method selects the cross-entropy (CE) function (13).  
\[Loss\big{(}y_{ji},\widehat{y}_{ji}\big{)}=-\frac{1}{k}\sum_{j}^{k}\sum_{i}^{n} \big{[}y_{ji}\,log\,\widehat{y}_{ji}+\big{(}1-y_{ji}\big{)}log\big{(}1-\widehat {y}_{ji}\big{)}\big{]} \tag{13}\]  
where \(y_{ji}\) are the actual values, \(\widehat{y}_{ji}\) are the predicted values, \(k\) is the number of training samples, and \(n\) is the number of categories. When the D-value \(L(y_{ji},\widehat{y_{ji}})\) has been calculated, the Adam optimizer [48] is used to update learnable parameters in the backwards pass. To achieve the best iteration effect, the mean and variance of the gradient are included in the update calculation of the step size. This is exactly what [PERSON] does when he combines the advantages of the SGDM and RMSProp [49] optimization algorithms. The final calculation of the learnable parameter \(\theta_{t}\) is as follows:  
\[\theta_{t}=\theta_{t-1}-lr*\widehat{m_{t}}/\Big{(}\sqrt{\widehat{\theta_{t}}}+ \epsilon\Big{)} \tag{14}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline  & **Value** \\ \hline Input image size & [224, 224, 3] \\ Number of Transformer blocks \(L\) & 12 \\ Embedding dimension \(d_{model}\) & 64 \\ Number of attention heads \(h\) & 12 \\ Position encoding & 1D \\ \hline \hline \end{tabular}
\end{table}
Table 1: The adjustable hyperparameter settings of the ISCV-ViT.  
Figure 5: Flowchart of the ISCV-ViT model.  
where \(\theta_{t-1}\) is the learnable parameter obtained from the \((t-1)\)th update, \(lr\) is the learning rate (stride), \(\widehat{m_{t}}\) and \(\widehat{v_{t}}\) are the first-order and the second-order moment estimates of the gradient after deviation correction, respectively, and \(\varepsilon=10^{-8}\), which is used to avoid dividing by zero.
# Application of Machine Learning to Assess the Quality of Food Products--Case Study: Coffee Bean
## 2 Materials and Methods
### Color Analysis  
In this research, a color analysis was performed by determining the L, a, b component parameters of the L*a*b* color space model [34; 35; 36] using an X-Rite SP-60 spectrophotometer (X-Rite, Grand Rapids, MI, USA). In order to perform the experiment, coffee beans were placed on plastic Petri dishes. This experiment was performed precisely by carrying out 15 repetitions for each test class of coffee. In analytical methods as well as machine learning, it is important to obtain a reliable number of repetitions to obtain reliable results.  
The second step involved performing color analysis with the RGB (Red Green Blue) model, which is more popular in machine learning due to its digital solutions [37; 38]. Data on the R, G, B components for the RGB model were extracted from digital images of each coffee research class. In order to extract the necessary information about the RGB color space model contained in the image, the available Python environment supported by the open source libraries Pandas, numPy and Python Image Library (PIL) [39] was used. The PIL library enabled processing and at the same time influenced image modification by acquiring the necessary image pattern, i.e., the extracted object in the form of coffee beans. In order to extract numerical data from the image, the mentioned numPy library was used  
Figure 1: Examples of samples of selected coffee beans, considering the quality status: (**a**) standard coffee, (**b**) overdeveloped coffee, (**c**) underdeveloped coffee.  
to determine measures and statistics of the RGB model. The Pandas library allowed for the analysis of the aforementioned data extracted from the image showing them in a clear (readable, tabular order) manner, which significantly proper representation of numerical data emerges as a key aspect in data preprocessing.
## 6 Experiments with IDPP
### Simulated Datasets  
To generate a random instance of IDPP, our generator proceeded as follows. We started from a Prufer code of length of \(2n-2\), which defines a unique tree \(\mathcal{T}\) with \(n\) leaves (taxa) and \(n\) internal nodes (characters) [27; 28]. We rooted \(\mathcal{T}\) at a randomly-chosen node and then translated the tree into an \(n\times n\) zero-one matrix \(C\) that encoded the clusters in \(\mathcal{T}\) (each column of \(C\) corresponds to a cluster;  
Figure 8: FCT(0) on profiles of binary trees. (**a**) Running time for 100 trees and varying number of taxa. (**b**) Running time for 100 taxa and varying number of trees.  
a one indicates that a taxon is present in the cluster, a zero that it is absent). We obtained a _seed_ matrix \(B\) by duplicating columns in \(C\). We built different instances of IDPP from a seed matrix by converting a randomly-chosen set of zero- and/or one-entries to question marks.  
The generator can build seed matrices of different densities, where the density of a matrix is the ratio of its number of ones to its total number of entries. Let us call a column of \(C\)_trivial_ if all its entries are one. We built a seed matrix \(B\) by duplicating non-trivial columns where the percentage of ones was above a specified threshold. If the threshold is 25%, we say that the matrix has _medium_ density; if the threshold is 50%, we say that the matrix has _high_ density. We also consider matrices generated without a mandatory threshold, allowing any columns to be duplicated; we refer to such instances as _low-density_ matrices. There is a caveat: The process that we use to generate the starting tree \(\mathcal{T}\) may lead to a matrix \(C\) where no non-trivial column has the mandatory threshold. If this was the case, we reduced the threshold and tried again. For medium-density matrices, if 25% failed, we successively tried 20% and 10%. For high-density matrices, if 50% failed, we tried 40%, 30%, 20%, and 10%.  
Figure 9 shows the execution time of FPP(1) and FPP(0) on medium- and high-density matrices. In both cases, \(n\) and \(k\) are fixed. For each data point reported, we started with a particular (random) seed matrix and then generated multiple IDPP instances by replacing a certain percentage of ones in the seed matrix by question marks. To be consistent, we also converted the same percentage of zeroes to question marks. The percentage ranged from 5%-50% with increments of 5%. The figures also show the time needed to initialize the connectivity data structures and the total time spent on manipulating these structures.  
Figure 9: Running time of FPP(0) and FPP(1) on matrices of order \(300\times 6000\) for (**a**) medium- and (**b**) high-density inputs.  
Without exception, the experiments showed that there was no benefit to enabling edge promotion in HDT; a single level suffices. A more extensive set of results, leading to the same conclusion, is reported in the Supplementary Materials (Figure S13).
## 3 Experiments
### Sea Level Rise and Tidal Evolution
#### 3.3.2 Tidal Evolution  
The water depth changes brought by SLR may, in turn, modulate the response of tides via resonance changes, frictional changes, and other mechanisms [29; 351]. Changes in tidal range, tidal currents, and energy distribution can induce severe adverse effects on human society, since the economy of the estuarine population depends on the stability of the coastal ecosystem, including fisheries, farming, and tourism. Extreme storm events (e.g., hurricanes, typhoons, or tsunamis) may lead to catastrophic consequences to estuarine cities through extensive damage to critical coastal infrastructure and disturbance of the local economy. However, both hurricanes and tsunamis are short-lived, and estuarine and coastal societies have developed knowledge about such extreme events that allow them to respond promptly and quickly recover from natural disasters. Many local populations have implemented estuarine developments, including harbors, roads, and public infrastructures. Storms and other extreme events can have severe short-term impacts on coastal environments, but on longer timescales, the average characteristics of the coastal zones, such as MSL and local tidal range, have historically remained relatively constant. However, under the present-day scenarios of increasing SLR accompanied by the associated changes in tidal range, many coastal communities are now experiencing an alteration of \"stable background\" water levels, and prior assumptions and measures adopted to face worst-case scenarios may no longer be valid, making coastal defense infrastructures insufficient to mitigate upcoming extreme events. In some locations, the changes in MSL and tidal range might occur rapidly, such as that seen in the developed harbor regions of Hong Kong [352]. Thus, it is difficult to predict future coastal flooding events without a careful individual analysis of each coastal location. The combined response of MSL and tidal range can vary significantly by location.  
Tides are evolving at different rates in many coastal zones due to non-astronomical mechanisms [353; 354; 355]. Regionally based investigations have investigated changes in the diurnal and semi-diurnal tides of the Eastern Pacific [27], the Gulf of Maine, [356], the North Atlantic [354; 357], in China [358; 359] and Japan [360], and of different Pacific islands [361]. Tidal evolution might result from harbor modifications [362; 363; 364; 365; 366], often due to land reclamation or channel deepening. MSL may directly influence tidal evolution or be related to tidal variability through secondary mechanisms in several ways and at different scales, from a local to a basin-wide (amphidromic) level. Some possible mechanisms include a \"coupled oscillator\" effect between the open ocean and the continental shelves [367; 368], through warming of the upper ocean [33], which may lead to internal changes in stratification properties and modulation of thermocline depth, and modifications of the surface manifestation of internal tides [369].
## 2 Research Method
### Data Analysis  
#### 2.5.1 Drought  
To the purpose of drought monitoring of the intended area, monthly precipitation data of the synoptic stations of Kermanshah Province were collected from the Iran Meteorological Organization. The gridded data pertaining to TRMM precipitation at 42 points with spatial resolution of \(0.25\times 0.25\) degrees were received from NOAA official site, and were processed via geographic information system, GIS. GIS is a system designed to capture, store, manipulate, analyze, manage, and present spatial or geographic data. Considering the statistical duration of the monthly precipitation from the meteorological stations and the monthly precipitation of TRMM, a joint period from 2000 to 2014 was established. Using statistical tools, the precision of TRMM data against the observational data of the selected synoptic stations was scrutinized. Evaluations were conducted using MATLAB, SPSS (IBM Corporation, NY, USA), and MS-Excel software (Microsoft, WA, USA). After the evaluation of TRMM data and ensuring high spatial and temporal precision, the drought and wet periods of the study area were monitored by the SPI index using monthly, seasonal, and annual scales. Figure 2 shows the TRMM gridded points as well as the selected synoptic stations in the study area.
# Trajectory Optimization with Dynamic Drivable Corridor-Based Collision Avoidance
## 1 Introduction
### Motivations and Contributions  
While constructing a feasible DC for the vehicle decouples the collision avoidance constraints from the environment, significantly reducing the complexity of these constraints and the difficulty of solving the problem, existing DC construction methods remain time-consuming and computationally inefficient due to several limitations. In practice, each DC expansion requires collision detection between the expanded rectangular region and the surrounding environmental obstacles. Because DC construction typically involves multiple expansions, the total number of collision detection operations grows, leading to higher overall computational time. Furthermore, when the environment contains numerous and highly complex obstacles, the time required for DC construction increases accordingly. This not only affects the overall efficiency of the trajectory planning process but also severely compromises the real-time performance of autonomous driving systems.  
Building upon the DC framework established by [PERSON] et al. [27], this work introduces an efficient construction scheme to address computational bottlenecks in the DC modeling process, aiming to improve the efficiency of collision avoidance constraint modeling. The proposed method makes two key contributions: accelerating the DC construction process and reducing the collision detection time involved in DC construction:  
* We propose a grid-based discretization method combined with maneuver merging techniques for efficient obstacle spatial representation, which effectively reduces the number of grids and lowers the computational load required for collision detection.
* We propose a dynamic DC expansion strategy based on spatial margins for efficient construction of drivable areas. This method avoids redundant expansion steps, ensures appropriate DC sizing for vehicle navigation, and improves computational efficiency.
## 2 Health Condition Monitoring
### Condition Monitoring
#### 2.3.2 Monitoring System for Ship Application  
Among the existing condition monitoring system used onboard, Kongsberg, Transas, and Siemens occupy absolute advantages. According to the development process of monitoring technology, the marine condition monitoring system has experienced four stages: conventional instrument monitoring, centralized system, distributed system, and Fieldbus distributed system [66; 67; 68]. The typical feature of a centralized monitoring system (Direct digital control system) is to use the computer with a strong function to centrally monitor and control the power devices and systems in the engine room. The typical representative used on-board is the data chief-III system of Norcontrol company. The distributed monitoring system (distributed Multi-level microcomputer monitoring system) is a centralized and decentralized system composed of a microcomputer, digital regulator, programmable controller, and other units. Its typical representatives include the data chief 1000 system of Norcontrol and the SimOS ima32c system of Siemens. Fieldbus distributed system (Fully distributed monitoring system) uses Fieldbus as the internal control network of each subsystem and realizes the configuration of the control system at the field level. At present, the representative products of such systems include the chief data C20 developed by Norcontrol company.  
Recent literature shows that CBM is an advanced maintenance strategy in many industrial fields, as well as in the maritime field [20; 69]. Through the real-time monitoring of the MSAE, a large number of condition monitoring processing can be transformed into CBM to avoid the occurrence of severe faults and reduce the risk of casualties and maintenance costs.
# Characteristics of Collimators Based on the Large-Mode-Area CMCF for Coupling Laser Beam
###### Abstract  
A new collimator based on a homemade concentric multilayer-core fiber (CMCF) is proposed and experimentally demonstrated. This collimator was fabricated using a tail fiber with large mode area and single-mode operation. By exploiting the optical transmission matrix, the propagation characteristic and coupling mechanism of this CMCF-based collimator was introduced meticulously. The coupling losses of the laser beam using this collimator in the off-axis, angular, and axial deviations were analyzed separately. In order to determine the relationship between the geometric redundancy of this collimator and the effective mode field area of the tail fiber, the corresponding mathematical model was established. Through model calculation and experiment measurement, the coupling properties of the collimator were improved effectively. Compared with the common SMF-based collimator, the declination redundancy of the CMCF-based one improved by 20%, which could make the coupling of the optical fiber collimator easier. Therefore, this collimator has potential application value in the laser diode coupling unit and high-speed optical communication system.  
fiber collimator; large-mode-area fiber; coupling loss; geometric redundancy; laser beam pacs: 26
## 4 Experimental Results and Analysis
### The Experiment of Domain Adaptation  
The domain adaptation component of the model in this study is replaced with transfer learning methods used by other scholars, including maximum mean discrepancy (MMD) and multicore maximum mean discrepancy (MK-MMD). The other components remain unchanged, meaning the model differs only in the domain adaptation part, while the fault recognition structure is the same. The methods used in this study were compared with those employed by other scholars across 12 transfer learning tasks, with the results shown in Figure 19. As shown in the figure, the accuracy of the proposed method outperforms that of the comparison method.
## 4 Experiment
### Efficiency Comparison  
This section describes the training efficiency of the deep learning models, including GCN, and all the LSTM-based neural networks from the perspective of training epoch and training time. Figure 5a shows the validation loss along with training epoch and Figure 5b illustrates the total training time of each model. Figure 5 reflects the general pattern of how the validation loss changes during the training process and the time consumed for each model. LSTM performs the poorest among these four models. The convergence of the model is slow though it seems that it can be trained without overfitting with many training epochs. This may be because the maximum training epoch of 200 in this study makes the model unable to deliver its best possible performance. However, when the number of epochs increases, the time consumption will also be increased. The total training time consumed by GCN, GC-LSTM, and STGA-LSTM is similar, all around 10 s. These three models converge quickly at around 25 epochs. This shows that the three models learn pretty well on this dataset. However, GCN cannot extract the temporal information from the data, which makes it perform worse than GC-LSTM and STGA-LSTM on both the validation and test dataset. The total training time of GC-LSTM is shorter than the STGA-LSTM models because it does not use the attention mechanism to consider the hidden temporal information and spatial relationships among stations. STGA-LSTM converges faster than GC-LSTM, which proves the attention mechanism improves the model learning ability on  
Figure 4: Performance of the rental and return demand prediction. (**a**) Real rental demand and predicted rental demand at station No. 125; (**b**) Real return demand and predicted return demand at station No. 125.  
the spatial information. The testing loss of STGA-LSTM is lower than GC-LSTM, which indicates that the attention mechanism helps the model find the hidden information from data and improve the accuracy.
# A Channel Allocation Mechanism for Cellular Networks
## 5 Simulation Analyses
### Simulation Results and Analyses
#### 5.2.2 Case 2: Traffic Congestion  
In the traffic accident simulation, this study analyzed the relation of the variability of traffic information and the number of Handover In (\(I_{i}\)). Figure 20 shows the traffic information in the simulation. At the simulation time 16 th min, the traffic accident occurs at the mileage of 5.05 km in simulation, and the traffic jam occurs in Cell\({}_{5}\). The cars move slowly and line up in Cell\({}_{5}\), and they can't go into the Cell\({}_{6}\). For Cell\({}_{6}\), the amount of traffic flow will drop substantially and the average vehicle speed also drops substantially after traffic accident occurrence. When the traffic accident is removed at the simulation time 45 th min, the traffic condition will return to a free flow. Therefore, this study can discover that the amount of traffic flow increases rapidly when the traffic accident is removed. This study analyzed the number of Handover In (\(I_{i}\)) before and after the traffic accident occurrence.  
However, the traffic accidents impact the number of Call Arrival, Handover In, Handover Out, and Call Departures in cellular systems. If this study applies the traditional SCA mechanism to support the handover events in cellular system, it will waste the number in the dynamic environment and cause the higher CBP with the amount of traffic flow to increase rapidly. Figure 21 shows that the number of Handover In (\(I_{6}\)) drops substantially during the traffic accident in Cell\({}_{6}\) between the simulation time 16 th min and the 45 th min. The number of Handover In (\(I_{6}\)) increases substantially in Cell\({}_{6}\) when the traffic accident is removed at the simulation time 46 th min.  
Figure 19: The number of Handover In (\(I_{6}\)) in Cell\({}_{6}\) in a whole day.  
Figure 18: The traffic flow in Cell\({}_{6}\) in a whole day.
# A New Approach to Privacy-Preserving Multiple Independent Data Publishing
## 4 Merging Method
### Algorithms
#### 4.2.2 Algorithm for Privacy Checking  
Our privacy checking algorithm assures the privacy requirement \(R\) in each equivalence class. In the anonymization, column values are permuted randomly to break cross-column associations. There is a possibility of creating some invalid records [22] or incompatible tuples in the process. In line 2, tuple incompatibility is checked as in [22]. If there are incompatible tuples, we generalize the particular cell values to satisfy _k-anonymity_. In line 5 we check the _l-diversity_ privacy requirement as in slicing [25].  
Input: Microdata Table \(T\)  
Output: TRUE, if the equivalence class satisfies privacy requirement \(R\)  
```
1:for each equivalence class \(E\) in \(T^{*}\)do
2: check-out the tuple compatibility as in [22] incompatible tuple chek;
3:if Incompatible tuple exists then
4: generalize the cell values to satisfy _k-anonymity_;
5: ensure the _l-diversity_ of all equivalence classes to satisfy privacy requirement \(R\) as executed in [25];
6:return TRUE.
```  
**Algorithm 1**Anonymization