esa-sceva/satcom-chunk-collection huggingface dataset:
# Sales Forecasting with LSTM, Custom Loss Function, and Hyperparameter Optimization: A Case Study
## 2 Materials and Methods
### Custom Loss Function Cost  
We designed a custom loss cost function focused on satisfying the demand of the lumber mill, considering the storage cost and the cost of processing the products. In this way, the function cost consisted of two relevant variables that allowed the calculation of the total cost associated with the model's forecasts. * Cost Per Excess Inventory (CPEI), which represented the storage cost per unit of excess inventory. * Cost Per Inventory Shortage (CPIS), which was associated with the production cost per missing product unit. For the selection of the values for both variables assumed in the custom loss function cost proposed, we considered that the loss of having shortage inventory should be higher than the loss of having excess inventory. We established the above because an excess of inventory represents a cost for the company for the storage of the products, operating, and logistics, while having an insufficient inventory when customers want to purchase products requires that the company produces more products in a short time, which, after multiple occasions, can signify an additional cost to satisfy the demand; however, it is necessary because missing products represents an absence of sales, which could cause an inadequate reputation for the company that could result in a decrease in its sales in the future. In preliminary tests, we set the value of the cost function variables to 2.0 for the cost per unit due to excess inventory and 6.0 for the cost per unit due to a shortage of inventory. This allowed us to clearly differentiate between the impact of inventory shortages and excess inventory when calculating the company's losses. Algorithm 1 shows the designed cost function. The function iterated over the differences between the predictions made by the model and the actual values for a data instance. After that, the function calculated the total cost considering those differences, \begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Value Range** \\ \hline LSTM units & 100–400 \\ Epochs & 10–50 \\ Batch size & 1–Total blocks \\ Learning Rate & 0.0001–0.001 \\ Dropout & 0.2–0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Value range for LSTM network hyperparameters. the inventory stocks, and the defined multipliers. Finally, the function returned the total accumulated cost. Additionally, the function tracked and updated the inventory as forecasts were processed--an inventory update occurred when there was an excess or shortage of products to satisfy the client's demand. Keeping the current inventory level up to date was essential for the custom function to adequately perform the total cost calculation. Through the custom cost function, it is possible to have a more suitable mechanism to evaluate the performance of an LSTM network based on the costs obtained from its forecasts. The function is particularly useful in practical applications where forecasts can have direct economic impacts, such as in the case of the lumber mill considered in this paper.
## 3 Results and Discussion  
### DL Algorithms' Hyperparameters  
The DL network structure as well as its training parameters were determined in terms of what are known as hyperparameters. Since there is no existing straightforward and systematic procedure to optimally tune those hyperparameters, we adopted a trial-and-error strategy. Although it is time-consuming, this strategy remains, to the best of the authors' knowledge, the most efficient method, so far, of time-series modeling and analysis used by researchers and data scientists. The hyperparameters of the RNN, GRU, and LSTM DL networks selected are summarized in Table 3 below.  
The number of units has a significant effect on the prediction accuracy. Increasing the number of units generates better fitting data. However, this number cannot be increased to a very high value since this may increase the training time and cause overfitting. After several trials, the number of units adopted was 200. The batch size hyperparameter was the number of samples handled before updating the DL model. This number is usually set to powers of 2. The higher this number, the faster the algorithm converges. However, the choice of batch size is case-sensitive and strongly linked to the dataset size. We selected 64 as an intermediate value, which provided reasonable results without inducing a longer convergence time. The number of epochs refers to the number of opportunities that each sample in the training dataset has had to update the internal model parameters. After trying different values, the adopted value was determined at 1000. Scaling the dataset before initiating the DL model training is a common practice in data science. Since the majority of activation functions operates in the range [-1, 1], here, we adopted the Min-Max scaler. For activation of the neurons, we selected a rectified linear unit (ReLU), which is a piecewise linear function that outputs a value equal to the input if it is positive and outputs zero otherwise. It has been used frequently in training various DL structures [7,9,21] and has been found to provide better fitting data while being easier to train. In DL, optimizers are the algorithms employed to optimally tune the weights and biases of the ANN while minimizing a loss function. The training process is consequently transferred to an optimization problem where the objective function is the error between the ANN output and the target value of the same output, and the decision variables are the features of the network (weights and biases).
# A Synthetic Aperture Radar-Based Robust Satellite Technique (RST) for Timely Mapping of Floods
## 6 Results  
Food mapping was conducted for each investigated flood event, serving as a valuable tool for assessing our approach. In the following subsections we provide regional-scale flood maps, offering both an overview and a detailed perspective of the five study areas. In each map, in addition to the involved river/s and its/their main flow direction (black arrow), the SAR-RST-FLOOD and reference data outputs are plotted in different colors.
## 5 Numerical Model Establishment and Results Analysis
### Ventilation Shift Deformation Analysis
As the excavation face moved away from the analysis section and the support work was completed, the arch top settlement gradually increased to a stable state, with a final settlement of 11.73 mm. The horizontal displacement at the middle point of the side wall at the end of each construction step for the analysis section was extracted to obtain the ventilation tunnel's side wall horizontal displacement curve (Figure 10). The dashed line in Figure 10 has the same meaning as that in Figure 9.  
From Figure 10, the overall horizontal displacement curve exhibits an \"S\" shape. The horizontal displacements at the left and right wall measurement points are approximately equal. The final deformation at the left point is 9.55 mm and at the right point is \(-\)9.71 mm (with the negative indicating the negative direction of the coordinate axis). Both displacements represent compression deformations toward the interior of the ventilation tunnel. Before the completion of the full-section excavation (before S21), the construction of the ventilation tunnel had a minimal impact on the horizontal displacement at the analysis section, with a deformation of only 0.11 mm. As the full-section excavation ended and the TBM face progressed further (S21), the horizontal displacement of the side wall increased,  
Figure 10: Horizontal displacement of the sidewall. but the incremental deformation was small, with a final deformation of approximately 0.5 mm. During the subsequent construction steps (S22-S24, S22 involved the support construction of the full-section excavation, and S23-S24 only involved the TBM pilot tunnel excavation), the horizontal displacement of the side wall increased slowly, with a final deformation of only about 0.7 mm. This was because the area of the TBM pilot tunnel was smaller than that of the ventilation tunnel cross-section, and the unexcavated rock mass in the TBM pilot tunnel constrained the deformation of the surrounding rock at the measurement point, resulting in a small increase in horizontal displacement. As the excavation began (S25), the constraint of the rock mass disappeared, and the horizontal displacement of the side wall increased rapidly, reaching 7.52 mm. As the initial support for the enlargement was gradually completed, the rate of increase in horizontal displacement slowed down, eventually stabilizing. From the ventilation tunnel deformation analysis, it can be seen that the deformations from S21 to S25 were the most intense throughout the entire construction process. Therefore, during excavation, attention should be paid to the blocks of surrounding rock formed by joint combinations, and reinforcement support should be carried out promptly and, if necessary, in advance.
###### Abstract  
Ion selective electrodes (ISE) were evaluated for use in a flue gas desulfurization (FGD) wastewater monitoring system. Calcium, chloride, nitrate, and nitrite ISE's were calibrated in a broad range of concentrations that were designed to model an actual FGD wastewater sample that was obtained from an industrial partner. Ideal Nernst, Modified Nernst, and a multiparameter regression analysis was performed for each electrode and evaluated on general fit and sensitivity at anticipated operating conditions. The Ideal Nernst equation, even with correction for ionic strength, was not able to properly model actual electrode performance. The multiparameter regression was able to model the electrode performance with relative errors of 10-25% when ionic strengths were below 0.1 M. Through the evaluation of real-time sensor usage at real conditions, a methodology of washing and sampling rate is suggested to minimize error in the readings.  
flue gas desulfurization; real-time pollution monitoring; ion selective electrode applications; direct potentiometry methodology +
Footnote †: journal: Applied Sci. 2019, 9, 3085; doi:10.3390/app9153085
# Intelligent Healthcare: Integration of Emerging Technologies and Internet of Things for Humanity
## 5 Proposed Architecture for IoT Healthcare Application
As a result, computation is performed closest to the end-users, minimizing transmission delays, reducing data load on backbone connections, and improving computing performance. Depending on the processing results, the results can be sent directly to doctors, families, and emergency services (notification module) or sent to higher calculation layers for further processing. The FC layer is operated by computing servers that are deployed at cloud gateways or computing service centers, also known as cloudlets. The data sent up from the lower layers are analyzed, processed, and aggregated via FC. Although FC's transmission delay is higher than EC's, FC's storage and computing capacity are better than EC's. As a result, FC can handle more complex problems than EC. Like EC, FC's results can be sent directly to the message module or sent to the CC for further processing. Instead of performing computations like traditional solutions, the CC layer in this architecture will receive the results from the FC layer and analyze and store the data to perform big data computation tasks, such as statistics and disease diagnosis, in the medical domain. Figure 9: The all-in-one computing architecture for IoT-based smart healthcare applications. A comparison with existing computing architectures is presented in Table 6. Our architecture has many outstanding advantages. The parameters were considered to compare the different architectures of the layers, the architectures' complexity, the data's reliability in the fog layer, the real-time application support, and the security. The values for the selected parameters can be any of the following: _low_, _moderate_, or _high_. The value for complexity was chosen based on the classes and the function or module implemented in each class. The value for the reliability of the data in the fog layer was chosen based on the availability of clusters or distributed computers in the fog layer. No existing architecture emphasizes data reliability in the fog layer, which is a major concern for urgent care applications. The value for the real-time support application was selected based on the existence of the fog layer and the amount of work performed in this layer. Finally, security issues in the layers were taken care of. Most of the previous jobs should have emphasized the security of the architecture. The use of the IoT as a technology in healthcare is still in its infancy. Therefore, there are still many challenges that need to be addressed by the research community and industry. Some of the existing challenges and solutions are discussed in the next section.
# A Novel Method for Ocean Wind Speed Detection Based on Energy Distribution of Beidou Reflections
## 4 Results, Analysis and Discussion
### Energy Observables Result
#### 4.2.2 High Wind Scenario  
In this section, the proposed observables were measured during Typhoon UTOR. UTOR started as a tropical depression and evolved into a typhoon on August 11 in the western north Pacific. As an example, EMR and EDR were computed with the _Threshold2_ set to 0.25. A detailed discussion on threshold will be presented in Section 4.3. The evolution of EMR and EDR from August 13 to August  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Observable** & \(\mathbf{R}^{2}\) & **Root Mean Square Error (RMSE) (m/s)** \\ \hline Relative amplitude (RA) & 0.744 & 0.94 \\ EDR & 0.765 & 0.90 \\ EMR & 0.769 & 0.89 \\ Coherent time & 0.713 & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Retrieval performances by different observables in gentle wind scenario.  
Figure 7: Scatter plots between in situ wind speed and observables in the gentle wind scenario. (**a**) RA. (**b**) Coherent time. (**c**) EMR. (**d**) EDR. EMR and EDR are computed with _Threshold2_\(=0.3\). The red lines are fitting curves.  
14 are presented in Figure 8b,c. Comparing with the in situ wind speed in Figure 8a, both EDR and EMR present the same trends as illustrated in Figure 8b,c.  
The scatter plots between the wind speed and EMR and EDR during Typhoon UTOR are presented in Figure 9c,d, respectively. Both EMR and EDR show an obvious linear dependence on wind speed. A simple linear polynomial is employed to describe the relationship between wind speed and EMR.  
\[WS=p\cdot EMR+q \tag{13}\]  
where \(p\) and \(q\) are fitting coefficients. As shown in Table 4, the correlation between EMR and wind speed is 0.822, with a RMSE of 3.10 m/s. A similar linear polynomial was employed to establish the relationship between wind speed and EDR.  
\[WS=k\cdot EDR+c \tag{14}\]  
where \(k\) and \(c\) are fitting coefficients. The fitting result with a correlation of 0.866 and a RMSE of 2.69 m/s was obtained.  
As shown in Figure 9a,b, both coherent time and the RA method were also used for wind speed retrieval. Comparing the results for the coherent time and RA methods, the coherent time shows a slightly better retrieval performance when employing a non-linear fitting, with a correlation coefficient of 0.875 and a RMSE of 2.61 m/s.  
Figure 8: Comparison between in situ wind speed measurement and observables during the Typhoon UTOR. (**a**) In situ wind speed. (**b**) EMR. (**c**) EDR. EMR and EDR are computed with \(Threshold2=0.25\).
## 2 Fatigue Strength Assessment of Real Components
### Fatigue Assessment of the Motorcycle Transport Device
#### 2.2.2 Simplification of the PSD Function  
Several methods for simplifying the PSD function were applied to assess their impact on fatigue life predictions. These methods included:  
* Normalization: The PSD function was normalized with respect to its maximum value, ensuring that all cases could be compared on a common scale.
* Reduction of Characteristic Points: The number of characteristic points in the PSD function was reduced to decrease the data complexity while retaining essential features of the load.
* Interpolated Envelope: An interpolated envelope was applied around the PSD curve, smoothing out minor fluctuations and focusing on the primary frequency components  
One of the key aspects of this study was to examine how additional smoothing of the PSD function, following the Welch method, would influence fatigue life predictions. The Welch method is widely used due to its ability to reduce the variance of PSD estimates through overlapping windows and averaging, stabilizing the estimation. However, as demonstrated by [PERSON] et al., the accuracy of PSD-based fatigue life predictions is sensitive to the settings of the Welch method, such as window size, weighting, and overlap [19] Incorrect parameter settings can lead to the distortion of critical vibration frequencies, which are essential for accurate fatigue life prediction.  
To explore this further, an additional smoothing technique was introduced, applying an interpolated envelope to further filter minor fluctuations in the PSD curve. Excessive smoothing can obscure significant frequency components, particularly those related to large stress amplitudes, which are critical for fatigue damage assessment [19]. As a result, the impact of this additional smoothing on fatigue life prediction was analyzed in comparison to the original PSD estimated using the Welch method. In mechanical systems, such as vehicle suspensions, [PERSON] et al. emphasized that precision in PSD estimation is crucial  
Figure 19: Determination of the PSD response reading location.  
for accurate fatigue life predictions, as over-smoothing may lead to the underestimation of fatigue in critical areas [23]. Similarly, in the case of the motorcycle transport device analyzed here, over-smoothing of the PSD data could obscure important vibration frequencies, particularly under high-stress conditions, potentially leading to an underestimation of fatigue damage.
# A Novel Cloud Approach for Connected Vehicles
## 4 Analytic Model
### Message Dissemination
#### 4.2.2 Vehicles in Transmission Range of an RSU  
We denote \(P_{RX}\) as the probability for a vehicle to be in the range of an RSU for the configuration \(X\). Furthermore, we call \(P_{0 RX}\) the probability for a vehicle to be out of range of an RSU for the configuration \(X\). For Configuration 3 (with two RSUs), the probability for a vehicle to be in the range of an RSU is twice \(P_{RSU}\), the range of an RSU, divided by the length of segment \(L\). The probability of not being in the range of an RSU in this same configuration is given by subtracting twice the range of an RSU from the length of a segment, which we divide by this same length as detailed in Table 6:
# Comparison of Amsler-Krumeich and Sandali Classifications for Staging Eyes with Keratoconus
## 3 Results
In patients undergoing keratoplasty for severe KC with the presence of stromal scars, the AS-OCT analysis is crucial for the choice of the proper surgical planning [12; 13; 14]. [PERSON] and co-workers analyzed predictive factors for the formation and type of a big bubble (BB) during DALK and found that type 1 BB formation was significantly associated with the absence of scars in the posterior stroma (stages 1-3 of the Sandali classification); conversely, the presence of posterior scars represents a significant risk factor for type 2 BB formation [15]. In fact, it has been hypothesized that deep scarring may fuse the PDL to the posterior stroma or degrade the PDL itself, preventing type 1 BB formation owing to the direct air dispersion at the level of DM. This poor prognostic value of posterior scars was found to be even stronger in patients with KC compared to patients with other corneal diseases [16; 17]. Since the floor of a type 2 BB consists only of DM, it carries a high risk of perforation, which can mandate a conversion to PK. Furthermore, even in the absence of a frank perforation, the occurrence of a type 2 BB has been associated with an increased risk of postoperative double anterior chamber formation requiring further surgical interventions. As such, the formation of type 2 BB increases the intraoperative challenges of DALK surgery. For this reason, a detailed preoperative assessment of KC by means of AS-OCT and its staging, according to the Sandali classification, may provide useful information for the surgical planning of DALK in terms of both the timing of surgery and the technique employed. Surgical intervention before the development of deep stromal scarring associated in BB-DALK with subsequent type 2 bubble formation and increased risk of conversion to PK is desirable. On the other hand, in severe KC cases, when posterior/pant stromal scars are present, the use of manual techniques should be taken into account in order to avoid intraoperative complications and reducing the risk of PK conversion. We recognized that, despite the two classifications are the most used ones, they suffer from some limitations, as stated by the Global Consensus on Keratoconus [18]. Indeed, the panel agreed that a suitable classification system using this additional information currently does not exist and that further studies that correlate clinical findings, such as visual performance with corneal topometric and tomographic parameters, are needed. Furthermore, the number of stages of the two classifications differs (4 stages for Amsler-Krumeich and 5 stages for Sandali) and they were readapted for statistical purposes.