esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium
## 5 Simulation Results
### UC3: Tall Buildings  
Use Case 3 is a particular case for the urban scenario, where CPE nodes are in the middle of taller buildings in urban areas. Simulation results show that, similar to the urban scenario, the lamp post usage is essential to achieve 100% user coverage. If only the facade is used, the coverage is reduced to 35% due to the limited number of buildings in the investigated area, as shown in Figure 12.  
The backhaul connection distances are similar to the urban and rural scenarios, confirming that the backhaul distances are independent of the EN location or the nodes' averaged bit rate since all cases are required to allocate the same bit rate. However, the serving distance is comparable with the rural scenario (\(p_{90}\) is 110 m) since the optimisation method aims to reduce the number of hops and the required bit rate per CPE (250 Mbps) is still under the minimum requested bit rate of 1 Gbps. As a result, the supported bit rate is only 2 Gbps showing a link efficiency of nearly 20%.  
Finally, the most notable difference is the average node utilisation of only 28 ENs/km\({}^{2}\) which is much lower than the 375 of the UC2. However, this is because the number of CPE is eight times lower, leading to a similar ratio of ENs needed per CPE, which is 3.7 for UC2 and 3.5 for UC3.
## 3 The Proposed Method
### Identifying Redundant Failure Modes
#### 3.1.1 Identification of Redundant Failure Mode for a Subsystem with Two Failure Modes  
Consider a subsystem consisting of any two failure modes of the system. Without loss of generality, the performance functions of the subsystem are given as follows:  
\[\left\{\begin{array}{l}G_{1}(\mathbf{\delta})=\mathbf{a}^{\mathsf{T}}\mathbf{ \delta}+a_{0}\\ G_{2}(\mathbf{\delta})=\mathbf{b}^{\mathsf{T}}\mathbf{\delta}+b_{0}\end{array}\right. \tag{7}\]  
where \(\mathbf{a}=\left(a_{1},a_{2},\ldots,a_{n}\right)^{\mathsf{T}}\) and \(\mathbf{b}=\left(b_{1},b_{2},\ldots,b_{n}\right)^{\mathsf{T}}\) denote the coefficient vectors, while \(a_{0}\) and \(b_{0}\) are given constants. The correlation angle \(\gamma_{12}\) between two failure modes can be obtained as follows:  
\[\gamma_{12}=\arccos\left(\frac{\mathbf{\alpha}^{\mathsf{T}}\mathbf{b}}{\|\mathbf{ \alpha}\|\|\mathbf{b}\|}\right) \tag{8}\]  
where \(\|\bullet\|\) denotes the norm of a vector.  
Let \(d_{i}\) denote the minimum distance from the origin to the failure surface in the normalized variable space. In this study, \(d_{i}\) is limited by \(0\leq d_{i}\leq 1\). This is primarily because \(d_{i}<0\) means that the system has an unacceptable reliability for practical problems, and secondly because \(d_{i}>1\) means that the corresponding failure volume is equal to zero and has no effect on system reliability.  
As shown in Figure 3, the identification criterion for a subsystem with two failure modes is that the correlation angle \(\gamma_{12}\) satisfies [30]  
\[0\leq\gamma_{12}\leq\left|\arccos\left(\frac{|a_{0}|}{\mathbf{a}^{\mathsf{T}} \mathbf{a}}\right)-\arccos\left(\frac{|b_{0}|}{\mathbf{b}^{\mathsf{T}} \mathbf{b}}\right)\right| \tag{9}\]  
\(G_{1}\) is the redundant failure mode when \(\frac{|a_{0}|}{\mathbf{a}^{\mathsf{T}}\mathbf{a}}\) is greater than \(\frac{|b_{0}|}{\mathbf{b}^{\mathsf{T}}\mathbf{b}}\); otherwise, \(G_{2}\) is the redundant failure mode, as shown in Figure 3b. This conclusion is also applicable for three- and n-dimensional problems. For the sake of simplicity, those identified redundant failure modes do not participate in the subsequent identification process. Thus, the number of the above subsystems does not exceed \(\frac{m(m-1)}{2}\).
## 5 Discussion
### Subjectivity Issue of Fugl-Meyer Assessment  
In this study, the doctor's manual assessment result served as the ground truth when training the employed machine learning models. Therefore, a high recall score corresponds to good machine learning model performance in terms of matching the doctor's assessment result. However, as mentioned in related research on FMA impairment level recognition, the conventional FMA is prone to subjectivity issues [8; 10; 11; 12; 13]. The interaction between the full and none levels exhibited distinct boundaries, whereas interactions involving the partial level tended to yield uncertain and subjective assessment results. This ambiguity arises from the indistinct boundary at the partial level, which behaves like a transitional phase between the none and full levels, as shown in Table 1.  
Interesting findings arose with respect to the portion of classification across impairment levels. According to Table 6, the proposed method mostly showed an interaction of the misclassification rate between full and partial levels in ME, HG, TA, PG, and CG. Meanwhile, misclassification between the partial and none levels mostly appeared in MF. Misclassification cases were rare between the full and none levels, indicating that the system could easily recognize the clear boundaries between these levels. This finding supports the rule of thumb according to which uncertainty is rare between the full and none levels.  
Furthermore, the result also indicates that the proposed method differs from the doctor's decisions in areas with a high risk of subjectivity. This difference mainly appeared because the machine learning model used muscle information to decide upon the impairment level. In addition, differences appeared in the results involving the partial impairment level, which is associated with a high subjectivity risk. Therefore, the proposed method may assist doctors or physiotherapists in clarifying the patient's impairment level under uncertain assessment conditions, objectifying the FMA assessment.
# Multi-Device Security Application for Unmanned Surface and Aerial Systems
#### 1.1.1 Phase 1  
In the first phase, a virtual environment is used to test MLS between two host computers. This network configuration provided benchmark statistics for an MLS implementation over an 802.11 WiFi network. We used WireShark to capture and analyze packets. Once the implementation was verified, we compared the plaintext packet size to the ciphertext size, corresponding to the various MLS ciphersuites to calculate protocol overhead. The major differences in overhead were generated by the size of the cryptographic hash function, corresponding to authentication tags, in the selected ciphersuite.
# Self-Tuning Algorithm for Tuneable Clamping Table for Chatter Suppression in Blade Recontouring
## 6 Validation of the Concept
### Milling Stability through Semidiscretization Method  
The stability and chatter frequency charts for the standalone (stiff) clamping, the TCT under broad-bandwidth tuning and the self-tuned TCT obtained through semidiscretization [43] are respectively shown in Figure 8a,b. For the self-tuned case, the chatter frequency is computed for every unstable point of the \(\Omega\)--_a_ mesh as in [49], and optimal tuning is iteratively performed according to the algorithm in Figure 7 until the process is stable or a maximum of 6 iterations is reached.  
The results from the semidiscretization calculations confirm the superior performance of the iterative strategy over the fixed-frequency strategy, with the exceptions of the aforementioned spindle speeds at which the two strategies converge. Moreover, it can also be noticed that the introduced strategy also increases the stability limit for spindle speed ranges where a dominant period doubling behavior may be expected, even if the expressions driving their stability limit differ from those of the Hopf-kind instabilities [50].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{4}{c}{**Tool/Material**} & \multicolumn{4}{c}{**Process**} \\ \hline \(D\) (mm) & \(Z\) & \(\mathbf{K_{c}}\) (MPa) & \(\mathbf{K_{e}}\) (N/mm) & immersion (\%) & strategy & \(\beta_{0}\) & \(f_{Z}\) (mm) \\ \hline \(12\) & \(3\) & \([800,\,320]^{\mathsf{T}}\) & \([30,\,40]^{\mathsf{T}}\) & \(25\) & down-milling & \(0.67\) & \(0.1\) \\ \hline \multicolumn{4}{c}{**Part Dynamics**} & \multicolumn{4}{c}{**TCT**} \\ \hline \(\omega_{2}\) (Hz) & \(\zeta_{2}\) (\%) & \(m_{2}\) (kg) & \(\mu\) (\%) & \(m_{1}\) & \(\zeta_{1}=\zeta_{1,\mathrm{o},+}\) (\%) & \([f_{\text{min}},\,f_{\text{max}}]\) & \\ \hline \(300\) & \(0.5\) & \(0.5\) & \(5\) & \(10\) & \(12.2\%\) & \([0.5,\,2]\) & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Definition of the dimensionless dynamic and tuning parameters of the structure.  
Figure 8: Results of the semidiscretization calculations for the milling case in Table 2 with standalone (stiff) clamping, TCT under broad-bandwidth tuning and TCT under self-tuning algorithm. (**a**) Stability lobe limit diagrams. (**b**) Chatter frequency diagrams.
## 3 Test Data Analysis
### Frequency Response Transfer Analysis
#### 3.4.2 Transfer Function Analysis  
In actual engineering prediction, because the force between different subsystems is usually difficult to measure, and the vibration response \(R\), \(R_{2}\). \(R_{n}\) of each subsystem is easy to measure. Therefore, the concept of response transfer ratio needs to be introduced, which is defined as the ratio of the vibration responses of different subsystems under the  
Figure 20: Comparison diagram of frequency-division vibration-level transmission difference of each track component. (**a**) Rail–track bed; (**b**) track bed–tunnel wall.  
excitation of the same vibration source load in the frequency domain [31]. The definition formula is shown in Formula (5):  
\[I_{n}^{m}(w)=\frac{R_{m}(w)}{R_{n}(w)} \tag{5}\]  
where \(R_{m}(w)\) represents the frequency-domain vibration response of the mth subsystem, \(R_{n}(w)\) represents the frequency vibration response of the \(n\)th subsystem, \(I_{n}^{m}(w)\) is the response transfer ratio of the \(m\)-th and \(n\)-th subsystems.  
Therefore, according to the above method, the transmission coefficient analysis is performed on the measured vibration acceleration data, as shown in Figure 22.  
According to Figure 22a, the vibration transmission of the rail-track bed under the four working conditions is mainly concentrated in the frequency band below 1484 Hz. The steel-spring-floating slab track has a high rail-track bed transfer rate in most frequency bands, especially in the frequency band of 39-68 Hz, the steel-spring-floating slab track rail-track bed transfer coefficient curve also has a peak point.  
According to Figure 22b, the vibration transmission between the track bed and the tunnel wall under the four working conditions is mainly concentrated in the middle and high frequency bands above 1806 Hz.
# Prediction of Dry Mouth Condition Using Radiomics Features from Tongue Diagnosis Image
## 2 Materials and Methods
### Data Acquisition  
Tongue image data and saliva secretion measurements were obtained through a research protocol approved by the Institutional Review Board (IRB, DKUH 2022-09-02 and DKUH 2023-09-020) at Dankook University Hospital, Korea. This study enrolled 123 patients who sought medical care at the hospital for symptoms of dry mouth, with an average age of 55.7 years, and 55.3% being female. All participants were prescribed pilocarpine to alleviate the symptoms of dry mouth. The following exclusion criteria were applied to ensure the validity of the study results: patients with uncontrolled severe acute renal or cardiac conditions that could interfere with the study outcomes within six months prior to screening, patients who had taken medications known to induce dry mouth (such as antidepressants, antihistamines, chemotherapeutic agents, or diuretics) in the four weeks before enrollment, patients with psychiatric disorders, patients diagnosed with asthma, patients with acute iritis or narrow-angle glaucoma, and pregnant or breastfeeding women, as well as those planning to conceive during the study period.  
Tongue images of the dorsal surface were captured using an iPhone 14 camera (Apple Inc., Cupertino, CA, USA). The sampling area was determined based on the ease of image acquisition and the appropriate size of the extraction image. Thus, we focused on the tongue, from which three samples were extracted. Saliva secretion was assessed using the Mucus device (ICST corporation, Saitama, Japan) [20; 21]. Measurements were taken after more than 10 min had elapsed since the patients had last consumed food. During the measurement procedure, patients were instructed to protrude their tongue, and saliva secretion was measured for 2 s at the center of the tongue (approximately 10 mm from the tip). Each measurement was repeated three times, and the average value was recorded for multi-linear regression analysis. To classify patients based on their salivary output, we established a threshold value of 27.0 [21]. Patients were categorized as either normal or abnormal based on whether their measured saliva secretion was above or below this critical value.  
Figure 1: Workflow scheme.
## 3 Materials and Methods
### Single Sand Body Architectural Classification Methodology
#### 3.4.1 Contact Architecture Patterns  
The stacked architecture refers to the contact relationships of different sand bodies formed during the same time period from different river channels or branches of the same river, or from different periods of lobe formation, in either the horizontal or vertical plane. These sand bodies are in direct contact with each other in space.  
(1) Lateral splicing  
In this pattern, lobes from different contemporaneous river channels or different branches of the same river advance and form sand bodies that are laterally spliced. On the plane, this results in large and wide sand bodies where adjacent sand bodies do not cut each other but only make contact at the lobe edges. In cases of subsequent lateral cutting, the laterally formed lobe will cut the earlier lobe.  
(2) Stacking  
In this pattern, lobes formed by two different river channels are stacked vertically. The earlier deposited sand bodies are not eroded or scoured by the later ones. The stacking part exhibits poor connectivity, forming thick layers of sand bodies in the vertical direction.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Contact Relationship Types**} & \multicolumn{2}{c}{**Contact Style Morphology**} & **Connectivity** \\ \hline \multirow{4}{*}{Contact Configuration Pattern} & \multirow{2}{*}{
\begin{tabular}{c} Lateral splicing \\ \end{tabular} } & Butt-jointed type & Lateral truncation type & Disconnected; Well-connected \\ \cline{3-4}  & & Lateral superposition & Vertical superposition & Disconnected; Weakly connected \\ \cline{3-4}  & & & & \\ \cline{3-4}  & & Vertical superposition-cutting & Lateral superposition-cutting & Well-connected \\ \cline{3-4}  & & & & \\ \cline{3-4}  & & & & \\ \hline \multirow{4}{*}{Isolated configuration pattern} & Planar detachment & & & Unconnected \\ \cline{3-4}  & & Vertical detachment & Lateral detachment & \\ \cline{3-4}  & & & & \\ \cline{3-4}  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Contact styles of single sand bodies.  
Overlapping cutting  
Lobe-on-Lobe Overlap: The lobe sand bodies formed by two stages of river channel advancement make vertical contact, where the earlier deposited sand body is cut, eroded, and scoured by the later deposited sand body. This type of contact pattern mainly occurs during the early and middle stages of base level rise and the middle and late stages of base level fall. Based on the differences in the vertical cutting locations of the sand bodies, two types of overlap patterns can be identified: lateral overlap and vertical overlap.
## 2 Materials and Methods
### Dataset and Related Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variable Description** & **Min** & **Max** & **Mean** & **Median** & \(\mathbf{Q_{1}}\) & \(\mathbf{Q_{3}}\) \\ \hline LWAC fixed density (kg/m\({}^{3}\)) & 1700 & 1900 & 1800 & 1800 & 1700 & 1900 \\ LWA particle density (kg/m\({}^{3}\)) & 482 & 1019 & 750.5 & 750.5 & 482 & 1019 \\ Concrete laying time (min) & 15 & 90 & 48.75 & 45.00 & 18.75 & 82.50 \\ Vibration time (s) & 0 & 80 & 30 & 20 & 10 & 40 \\ Experimental dry density (kg/m\({}^{3}\)) & 1069.80 & 2486.84 & 1673.35 & 1677.15 & 1533.35 & 1810.84 \\ \(P\)-wave velocity (m/s) & 3044.25 & 5253.73 & 3778.89 & 3718.49 & 3520.48 & 3945.65 \\ Segregation index & 0.845 & 1.136 & 1 & 0.999 & 0.978 & 1.021 \\ Compressive strength (MPa) & 2.99 & 50.72 & 21.55 & 20.25 & 14.37 & 28.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Variables of the experimental dataset. “Adapted with permission from Ref. [6]. Copyright 2018, Elsevier”. We are interested in predicting the compressive strength of LWAC by applying machine learning to this dataset. This problem has been considered in previous works in which MLR, ANNs and three RT algorithms (CHAID, exhaustive CHAID, and CRT) were used to predict the compressive strength of LWAC [6; 19]. Tables 3 and 4 summarise the best models obtained in these works. However, there are no experiments in the literature that have analysed the performance of other machine learning techniques for predicting the compressive strength of concrete with this dataset.
# RL-Based Sim2 Real Enhancements for Autonomous Beach-Cleaning Agents
## 6 Experiments and Results
### Simulated Robot Training Results  
Two distinct RL models, PPO and DDPG, were trained over a span of 2 million timesteps, translating to a total training duration of approximately 27.3 h for PPO and 31.3 h for DDPG, respectively. For the DDPG agent, training was conducted using default parameters from the Stable Baselines3 library, with a learning rate of 0.001, buffer size of 1,000,000, learning initiation at the 100 th iteration, batch size of 256, \(\tau\) set to 0.005, and a \(\gamma\) value of 0.99. These parameters were optimized to balance exploration and exploitation, ensuring stable and efficient learning. Similarly, the PPO agent was configured with a learning rate of 0.0003, 2048 steps per update, batch size of 64, and training across 10 epochs. Additional settings included a \(\gamma\) of 0.99, Generalized Advantage Estimation (GAE) \(\lambda\) of 0.95, and a policy clipping range of 0.2, also tailored to foster both stability and efficiency in learning. The performance of each agent is graphically depicted in Figure 10, where the training graph illustrates the mean episode reward over the course of the training period. The primary objective of the training in simulation is to facilitate transfer learning (Sim2 Real) for seamless deployment and adaptation in the real-world platform.  
From the graph, it is evident that both agents were capable of effectively learning from the environment, as demonstrated by the gradual increase in the reward over time. Notably, the PPO agent exhibited a particular enhancement in performance, achieving higher rewards in the initial stages of the training process. This suggests that the PPO agent was able to learn more quickly and efficiently compared to the DDPG agent, as can be inferred from the steeper slope of improvement in the corresponding curve.  
The graphical analysis indicates a robust learning curve for both models, with the PPO agent demonstrating a consistent and superior performance throughout the training period. The DDPG agent, while showing a steady increase in rewards, lagged slightly behind the PPO, particularly in the early phases. However, both agents ultimately converged adequately, showcasing the efficacy of the RL models in navigating the simulated environment and addressing the complex task of autonomous movement and obstacle negotiation. It is noteworthy that the weights of the models used for the subsequent experiments correspond to those achieving the best rewards and embodying the desired action policy.
# Reversible Data Hiding for DICOM Image Using Lifting and Companding
## 1 Introduction
In other words, the data embedding technique modulates all the high-High (_HH_) sub-band co-efficient. So the proposed scheme does not require any location information but use the particular sub-band i.e., _HH_ sub-band to decode the watermark bits. Hence the scheme does not require transmitting location map with the host data. This proposed location map free technique gives better payload capacity with good visual quality. The significant contributions are performance study in various lifting based DWT, Rayleigh fading wireless channel, and collusion attacks. The scheme can also unambiguously distinguish parties involved in collusion operation and innocent users. The simulation results demonstrated the performance efficiency of the proposed scheme. Side by side, the results are compared with related data hiding methods. The rest of the paper is structured as follows: Section 2 describes the key features of the DICOM file format. Section 3 focuses on lifting based DWT transform. Section 4 discusses the basic of companding technique. Section 5 explores the algorithm for embedding and extracting the watermark. Section 6 presents the experimental results to show the effectiveness of the proposed data hiding method, and Section 7 describes conclusions and the scope of future work.
## 4 Discussion
### Normalized Difference Red-Edge Index Best Predicts Cover Crop Biomass
InSAR coherence layers generated using Gamma software are now also present in ASF's more generalized VERTEX platform (in addition to the HyP3 tool used in this study) [71]. ASF's cloud-based SAR processing tools were critical to this study, as InSAR processing would likely be prohibitively cost-, time-, and computer resource-intensive for a local machine. ASF capabilities will likely be critical to the processing of imagery from the upcoming NASA-ISRO SAR (NISAR) mission operating at L-band and S-band wavelengths which is slated to acquire an unprecedented volume of satellite imagery on a daily basis. With relevance to the NISAR mission, the suitability of L-band SAR data to estimate cover crop biomass could be evaluated, as previous work demonstrated their efficacy in estimating crop biomass and LAI [40; 43]. Further the dual-frequency and greater polarimetric capabilities of NISAR will likely provide opportunities for cover crop biomass characterization with SAR imagery that are well beyond what was possible in this study.