esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this paper, we investigate an IDS named DOIDS for UWSNs opportunistic routing to prevent common routing attacks and to secure data transfer tasks. DOIDS adopts the local monitoring mechanism. Each node in the network monitors the energy consumption information, forwarding information, and link quality information of each candidate node in its candidate node set. Based on the DBSCAN algorithm and time decay function, the routing attack nodes in candidate nodes are identified. Finally, the simulation analysis shows that the algorithm can effectively improve the average hop count, delivery rate, and energy tax performance of packets transmitted to the sink node when there is a small proportion of malicious nodes in the network. Compared with the existing security mechanism against routing attacks in UWSNs, DOIDS has higher detection accuracy. In the future, there are still practical challenges that need to be addressed as we apply DOIDS to marine applications. First of all, our scheme has a better effect on a small number of malicious nodes. However, we should also consider how to improve the detection rate when there are a large number of malicious nodes in the network. In addition, water flow can cause dynamic motion of underwater sensor nodes in practical applications. We plan to analyze and model the mobility of underwater nodes in the future. Finally, we need to improve the scheme to implement the detection of other attack types, such as collision attacks at the MAC layer.  
Conceptualization, R.Z. and H.Z.; methodology, H.Z.; software, H.Z.; validation, H.Z., J.Z. and R.Z.; formal analysis, Q.W.; investigation, Q.W.; resources, R.Z.; data curation, H.Z.; writing--original draft preparation, H.Z. and R.Z.; writing--review and editing, H.Z. and J.Z.; visualization, R.Z.; supervision, R.Z. and Q.W.; funding acquisition, R.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Tianjin Intelligent Manufacturing Special Fund Project Underwater Heterogeneous Node Communication and Positioning Integrated Ad Hoc Network System Research and Development, Project No. 20201207.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 12: (**a**) Detection accuracy comparison; (**b**) False detection rate comparison.
# Personalized PPG Normalization Based on Subject Heartbeat in Resting State Condition
## 6 Classification Setting
This feature normalization was performed with respect to the subject baseline on the Peak Rate, IBI, and RMSSD features as follows: \[featNorm_{i}=\frac{feature_{i}-\overline{featureBL_{i}}}{\overline{featureBL_ {i}}}\] (4) where \(i\in PeakRate,IBI,RMSSD\); \(feature_{i}\) represents the feature value before the normalization; \(featNorm_{i}\) is the new normalized value; and \(\overline{featureBL_{i}}\) is the mean value of the \(i-th\) feature evaluated on the subject resting state;
* PersFreqN: amplitude normalization followed by the Personalized Normalization based on the resting state heartbeat, described in Section 5. The pre-processing and feature extraction operations applied to each signal and the three considered normalization strategies are summarized in Figure 5.  
For each analysis, four different classification models were tested: a Classification and Regression Tree (CART) with Gini's diversity index as the criterion of splitting and 100 as the max number of decision splits, and three Support Vector Machines (SVMs) with different kernels: Linear (SVM Linear), Gaussian (SVM Gaussian), and polynomial Cubic (SVM Cubic). In particular, for the Gaussian kernel SVM, the kernel scale was set to 3.3 in order to consider a medium Gaussian SVM. A Leave One Subject Out (LOSO) cross-validation [66] was applied to evaluate the performance of the trained classifiers. At each iteration, the data used to train the classifier consisted of the signals collected from all the subjects, except one, whose instances were instead used to test the performance of the model. An overall confusion matrix was finally generated, joining the single confusion matrices resulting from each iteration. From this confusion matrix, several well-known evaluation metrics were extracted. In particular, we selected accuracy to evaluate the general performance of the classifier and the single class F1-score [67] to assess, instead, the goodness of the classification model in recognizing the single classes. The classification settings described above are summarized in Table 3.
## 1 Introduction
### Interoperability Specifications and International Initiatives  
Some of the most relevant international initiatives are openEHR [7] and the HL7 CIMI working groups [8], which are focused on defining CIMs. These initiatives are focused on obtaining a consistent semantic definition of CIMs able to be implemented in multiple scenarios. They are working on defining CIMs compatible with Archetype Description Language, each of them on their own specific reference model. CIMI is working on the definition of CIMs able to be the CIMI Reference Model Specification and define HL7 FHIR profiles. On the other hand, the openEHR CIMs are known as archetypes based on the openEHR reference models and they are able to be transferred as EHR extracts [7]. In addition, they are working on supporting mappings to HL7 FHIR resources given the increased acceptance of EHR vendors.  
There are additional specifications that aim to define how clinical information is structured in order to be transferred between EHR systems. Some of the most relevant specifications are the Clinical Information Model [5], HL7 CDA templates [9] and ISO 13606 [10]. There are national and regional Ministries of Health working on each of these specifications. The CDISC consortium developed several specifications to define how information associated with clinical trials should be structured. They have several standards which are able to define how to satisfy regulatory submission to the Food and Drug Administration (FDA) such as the Operational Data Model (ODM) [11]. Some of their standards are harmonizing element names, definitions and metadata to establish a standardized data collection baseline across all submissions through Clinical Data Acquisition Standards Harmonization (CDASH) [12].
## 4 Results
### Prediction Results of BOTA-LSTM Model  
Figure 10 shows the predictive performance of the proposed BOTA-LSTM model on a single ring. The model exhibits high consistency between predicted and monitored values during the training phase, which indicates that the model can capture the characteristics well. In the testing phase, the model shows a better fit for horizontal attitudes than vertical attitudes, with a noticeable decline in vertical attitude prediction accuracy between rings 400 and 453. This decline, as illustrated in Figure 5, is attributed to the shield machine encountering a complex stratum, which complicates the prediction of vertical attitude. Table 5 displays the high R2 values for the training set, indicating high predictive accuracy for HDSH, HDST, VDSH, VDST, and total thrust. Moreover, the RMSE values are also relatively low, which further confirms the good performance of the model on the training set. Table 6 presents the testing set results. For HDSH, HDST, VDST, and total thrust, the R2 values are still high at 0.94, 0.93, 0.85, and 0.83, respectively. However, for VDSH, the R2 value drops significantly to 0.76. The reason is that VDSH exhibits more frequent fluctuations compared to the other three types of attitudes, making it more challenging to predict. However, it is noted that the RMSE for VDSH on the testing set is still relatively low, indicating that the prediction of vertical attitude is still acceptable.  
The multi-ring prediction of shield attitude can provide decision makers with sufficient time to develop a reasonable plan. Figure 11 shows the multi-ring shield attitude prediction of the BOTA-LSTM model. In terms of R2 and RMSE, the prediction accuracy of the model shows a gradual decline in the first five rings, followed by a more rapid decrease. The prediction accuracy of the fifth ring can still be considered to reach a satisfactory level, which can provide some reference for engineering decision making. Therefore, the outputs within five rings are selected as the final prediction results. In Section 5, the multi-ring prediction model is applied to achieve shield attitude correction. To demonstrate the multi-ring prediction accuracy of the proposed BOTA-LSTM model, the Bayesian optimization-based LSTM model (BO-LSTM) is introduced for comparison, with results shown in Tables 7 and 8. The hyperparameter settings of the BO-LSTM model, except for the absence of an attention unit, possess the same domain as the BOTA-LSTM for the other hyperparameters, as shown in Table 3. Specifically, the LSTM unit is 191, the dropout is 0.23, the learning rate is 0.013, and the batch size is 209. The comparison results show that the proposed BOTA-LSTM model outperforms the BO-LSTM model in terms of R\({}^{2}\) across different tasks and rings. This demonstrates that the BOTA-LSTM mode can identify more critical moments in the time series and allocate more weight to these moments, thereby achieving more accurate predictions of multi-ring shield attitudes.  
Figure 10: Prediction results of BOTA-LSTM model. (**a**) HDSH, (**b**) HDST, (**c**) VDSH, (**d**) VDST, and (**e**) total thrust.
# Measurement and Analysis of Industrial Forklifts Vibration Levels for Unit Load Testing Purposes
## 2 Materials and Methods
### Measurement System and Setup  
The intensity of vibration caused by the forklifts was measured using a Lansmont SAVER 9X30 data logger (Lansmont Corporation, Monterey, CA, USA) under the different handling scenarios. The data recorder is a battery-powered instrument capable of measuring and recording the vibration environment. Two three-axis accelerometers (DYTRAN 3443C, Chatsworth, CA, USA) were connected to the Saver 9X30 data logger to record data in three different positions on the forklift. All SAVER 9X30s were configured with the following recording setup:  
- Sampling rate: 1000 Hz  
- Recording window for PSD: 15 s  
- Total recording period: 15 min  
- Anti-Aliasing filter frequency: 200 Hz  
- Sample size: 15,000  
- PSD resolution: 0.066 Hz  
Figure 1: Representative pictures of the investigated forklifts: (**a**) gas-powered forklift, (**b**) electric-powered forklift, (**c**) reach truck.  
Figure 2: Investigated road conditions: (**a**) concrete, (**b**) asphalt.  
Vibration profiles were collected from three different positions (Figure 3). Sensor 1 was placed on the carriage of the forklift. Sensor 2 was placed on the back of the heel of the fork tines, and sensor 3 was placed on the tip of the fork tines. The Saver 9X30 was mounted to the carriage using a magnetic mount. The two three-axis accelerometers were connected to the back of the fork tine heel and the fork tine tip using wax and tape to provide extra security. To collect the vibration data, the forklifts were driven on the different road conditions for 15 min, and recording was conducted only when the forklifts were in motion.
## 4 Experimental Results
### Estimation of SoH with Support Vector Machine-Based Approaches  
The results obtained from the SVM-based models developed in light of the parameters explained in detail in the Model Development section are presented in Table 6. The results have indicated that the support vector machines were at an acceptable level. Although it was better than the regression-based models, it was found to be lower than the performance obtained from decision trees. It was observed that the fine Gaussian SVM model performed better results than other models.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Model** & **Model** & **Model** & **Preset** & **RMSE (VAL)** & **MSE (VAL)** & **R-Squared** (VAL)** & **MAE (Test)** & **RMSE (Test)** & **MSE (Test)** & **R-Squared** (Test)** \\ \hline
1 & SVM & Linear & 0.371 & 0.138 & 0.268 & 0.190 & 0.378 & 0.143 & 0.258 & 0.188 \\
**2** & **SVM** & **Fine Gaussian** & **0.228** & **0.052** & **0.723** & **0.120** & **0.229** & **0.053** & **0.727** & **0.117** \\
3 & SVM & Medium Gaussian & 0.243 & 0.059 & 0.685 & 0.123 & 0.251 & 0.063 & 0.673 & 0.124 \\  & SVM & Coarse Gaussian & 0.276 & 0.076 & 0.594 & 0.137 & 0.289 & 0.084 & 0.566 & 0.138 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results obtained from SVM.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Model** & **Model** & **ResNet** & **RMSE (VAL)** & **MSE (VAL)** & **R-Squared** (VAL)** & **MAE (Test)** & **RMSE (Test)** & **MSE (Test)** & **R-Squared** (Test)** \\ \hline
1 & Tree & Fine Tree & 0.164 & 0.027 & 0.858 & 0.102 & 0.096 & 0.009 & 0.952 & 0.056 \\
**2** & **Tree** & **Medium** & **0.152** & **0.023** & **0.877** & **0.101** & **0.115** & **0.013** & **0.931** & **0.079** \\
3 & Tree & Coarse & 0.159 & 0.025 & 0.866 & 0.104 & 0.139 & 0.019 & 0.900 & 0.096 \\
4 & Ensemble & Boosted Trees & 0.197 & 0.039 & 0.792 & 0.148 & 0.190 & 0.036 & 0.812 & 0.147 \\
5 & Ensemble & Bagged Trees & 0.142 & 0.020 & 0.893 & 0.099 & 0.103 & 0.011 & 0.945 & 0.074 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Regression-based ML model results.
###### Abstract  
Recently, quadrotor-based drones have attracted a lot of attention because of their versatility, which makes them an ideal medium for a variety of applications, e.g., personal photography, surveillance, and the delivery of lightweight packages. The flight duration of a drone is limited by its battery capacity. Increasing the payload capacity of a drone requires more current to be supplied by the battery onboard a drone. Elevated currents through a Li-ion battery can increase the battery temperature, thus posing a significant risk of fire or explosion. Li-ion batteries are suited for drone applications, due to their high energy density. There have been attempts to use hydrogen fuel cells onboard drones. Fuel cell stacks and fuel tank assemblies can have a high energy to weight ratio. So, they may be able to power long duration drone flights, but such fuel cell stacks and associated systems, are usually extremely expensive. Hence, this work proposes the novel use of a less expensive, low capacity, metal hydride fuel stick-powered fuel cell stack as an auxiliary power supply onboard a drone. A primary advantage of this is that the fuel sticks can be used to cool the batteries, and a side effect is that this slightly reduces the burden on the onboard Li-ion battery and provides a small increment in flight time. This work presents the results of an experimental study which shows the primary effect (i.e., decrease in battery temperature) and the secondary side effect (i.e., a small increment in flight time) obtained by using a fuel cell stack. In this work, a metal hydride fuel stick powered hydrogen fuel cell is used along with a Li-ion battery onboard a drone.  
d rone; battery; cooling; fuel cell +
Footnote †: journal: Applied Soft
## 2 Materials and Methods  
### RNA Isolation, cDNA Library Construction, and Illumina Sequencing  
The total RNA from the leaf, flower and root parts of _C. keiskei_ was extracted using the protocol outlined by [PERSON] et al. [40]. To perform RNA-sequencing, cDNA libraries were constructed, and the sequencing was carried out using the Illumina HiSeq 2500 platform at Phyzen Ltd., South Korea. Each sample yielded \(\sim\)15-16 GB of raw data, consisting of 44.65 million, 41.33 million and 42.86 million paired-end (2 \(\times\) 150 bp) reads for the flower, leaf, and root of _C. keiskei_, respectively. The average insert size of the generated reads was \(\sim\)550 bp.
# Derivation and Validation of Bandgap Equation Using Serepentine Resonator
###### Abstract  
Bandgap refers to a frequency band where free waves do not propagate. One of the characteristics of a bandgap is its ability to block the propagation of bending waves in a specific frequency band with a periodic structure. Additionally, it has been reported in previous studies that the vibration-reduction performance of a bandgap is superior to that of other reduction methods. A bandgap can be generated in various frequency bands through a simple parameter change in the unit structure. However, the bandgap for a desired frequency band can be determined accurately only with intensive simulations. To overcome this limitation, we have mathematically derived the bandgap using a serpentine spring as a unit structure. The bandgap equation is derived from the general mass-spring system and the final bandgap is derived by substituting the system into the serpentine resonator. The error map for the major design parameter is confirmed by comparing the derived bandgap with the simulation result. In addition, the theoretical bandgap is compared to the experiment value and the vibration-reduction performance of the serpentine resonator is also confirmed. Based on the theoretical and experimental result, the proposed serpentine resonator verifies that the bandgap can be derived mathematically without numerical analysis. Therefore, serpentine resonator is expected to have various applications since it dramatically reduces the time and cost for forming the bandgap of the desired frequency band.  
bandgap; serpentine resonator; vibration; frequency band +
Footnote †: journal: Applied Physics Letters
## 2 Attitude Control System Model  
### Reference Coordinate System  
Satellite body coordinate system \(OX_{\text{b}}Y_{\text{b}}Z_{\text{b}}\): The origin of the satellite body coordinate system is located on the center of mass of the satellite, and the three axes are consistent with the main axis of inertia of the satellite, firmly connected to the satellite.  
Inertial coordinate system \(O_{\text{e}}X_{\text{i}}Y_{\text{i}}Z_{\text{i}}\): The origin \(O_{\text{e}}\) is located at the center of the earth, the \(O_{\text{e}}X_{\text{i}}\) axis is in the equatorial plane, and the \(O_{\text{e}}Y_{\text{i}}\) axis points towards the infinite point on the intersection line between the equatorial plane and the ecliptic plane, which is also known as the direction of the vernal equinox. The direction of the \(O_{\text{e}}Z_{\text{i}}\) axis is consistent with the Earth's rotation axis; the \(O_{\text{e}}X_{\text{i}}\), \(O_{\text{e}}Y_{\text{i}}\), and \(O_{\text{e}}Z_{\text{i}}\) axes form a right-handed orthogonal coordinate system.
# Clustering-Based Channel Allocation Method for Mitigating Inter-WBAN Interference
## 2 Related Works
### Channel Allocation Methods
Our work is the most relevant to [26] in that both methods group the WBANs into a set of clusters before allocating the channels. However, in [26], a channel is not allocated directly to a WBAN. They assign a channel to a cluster, and the WBANs in the same cluster share the bandwidth of the channel allocated to the cluster in an FDMA manner. In contrast, we allocate a channel to a WBAN directly to increase the data rate provided to a WBAN. In addition, the authors in [26] recommend the number of clusters needed to partition an interference relationship graph through the simulation studies in a specific network topology. However, our method self-configures the number of optimal clusters by adopting the modularity-based clustering method. We also propose an adaptive module to quickly adjust to the gradual topology changes.
# An Improved Multi-Scale Feature Fusion for Skin Lesion Segmentation
## 3 Proposed Methods
### Tversky Loss Function  
Currently, image segmentation algorithms generally use binary cross-entropy as the loss function. However, when dealing with the problem of extremely irregular and unbalanced foreground-background lesion areas in dermoscopy images, using cross-entropy may lead to poor network training results. Therefore, to solve the data imbalance issue in medical segmentation, the Dice loss function is often introduced. However, this function treats false positives and false negatives equally in terms of detection weight, which may result in high accuracy but low recall in practical application. To address the problem of small and highly imbalanced ROIs in skin lesion segmentation, \(F_{N}\) needs to be weighted higher than \(F_{P}\) for better recall results. Thus, this paper introduces the Tversky loss function [35] to help improve recall rates of results, as shown in Equation (8).  
\[Loss=1-\frac{T_{p}}{T_{p}+\alpha F_{P}+\beta F_{N}} \tag{8}\]  
The Tversky coefficient is a generalization of the Dice coefficient and the Jaccard coefficient, and sets \(\alpha\) and \(\beta\) to 0.3 and 0.7, respectively, to adjust the weights between \(F_{P}\) and \(F_{N}\) and improve the recall when the class is out of balance.
# The Use of Autogenous Teeth for Alveolar Ridge Preservation: A Literature Review
## 2 Alveolar Ridge Preservation in Extraction Sockets  
### Biological Effect of a Tooth Extraction  
Tooth extraction sets off a series of biological events, with a local inflammatory response and an irreversible structural transformation of the periodonotin [3]. In terms of hard tissues, as previously stated, it can be expected a bone resorption, mainly in the first three months, causing both vertical and horizontal changes in the alveolar process [5; 10].  
These anatomical changes are more buckally and horizontally pronounced [3; 4; 5; 6; 7], with an average horizontal reduction of 3.79-3.87 mm and an average vertical reduction on the buccal side of 0.64-1.24 mm [5; 7].  
Concerning the soft tissues, the socket defect will determine the healing process through secondary intention resulting in cell proliferation, whereas the gingival form mostly depends on the external shape of the alveolar bone [5].  
In order to diminish the biological effect of a tooth extraction, an appropriate treatment plan and technique are central [3]. As for the surgical technique, a flapless approach is  
Figure 1: Schematic diagram explaining different dentin processing approaches. UDD—undemineralized dentin matrix; PDDM—partially demineralized dentin matrix; DDM—demineralized dentin matrix).  
considered a simple, atraumatic and conservative method, being the usual choice to reduce post-surgery healing period, discomfort and inflammation [4]. This surgical approach is characterized by the nondetachment of the periosteum, preserving the blood supply to the buccal bone, which, as mentioned before, suffers a more pronounced resorption [18].