esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This was the first study to investigate the differences between the effect of a combined training program at moderate or at high intensity on paraspinal muscle fiber characteristics. Under the conditions of the present study, a 12-week HIT program consisting of cardiorespiratory interval, general resistance and core muscle training could counteract the muscle fiber shift in the MF that was observed in low back pain. Training at moderate intensity did not induce a muscle fiber shift, which might suggest that the physiological effects of increased intensity could have caused the increased effectiveness. Future studies should focus on the long-term effects of HIT on both muscle fiber characteristics, as well as on clinical outcomes. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] contributed to the conception and methodology of the study. [PERSON], [PERSON] and [PERSON] contributed to data acquisition. [PERSON], [PERSON] and [PERSON] contributed to data analysis. [PERSON] was a major contributor to writing the original draft. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] contributed to reviewing and editing the manuscript. Supervision was performed by [PERSON], [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript. This research received no external funding. **Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethical Committee of Hasselt University and Jessa Hospital Hasselt under protocol name 15.142/REVA15.14 (date of approval: 10 February 2016). **Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author. **Conflicts of Interest:** The authors declare no conflict of interest. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**MIT Group** & **PRE** & \(\pm\)**SE** & **POST** & \(\pm\)**SE** & _p-Value_ \\ \hline
**\% type I** & 69.05 & 3.29 & 69.05 & 2.89 & No interaction * \\
**\% type II** & 30.95 & 3.29 & 30.95 & 2.89 & No interaction * \\ \(\bullet\) **\% type IIa** & 17.01 & 2.50 & 18.02 & 2.15 & \\ \(\bullet\) **\% type IIax** & 8.89 & 1.75 & 6.56 & 1.38 & \\ \(\bullet\) **\% type IIx** & 10.76 & 2.37 & 10.25 & 2.37 & \\ \hline
**HIT Group** & **PRE** & \(\pm\)**SE** & **POST** & \(\pm\)**SE** & _p-Value_ \\ \hline
**\% type I** & 68.04 & 2.53 & 71.80 & 3.63 & No interaction * \\
**\% type II** & 31.96 & 2.53 & 28.20 & 3.63 & No interaction * \\ \(\bullet\) **\% type IIa** & 19.55 & 1.84 & 18.65 & 2.88 & \\ \(\bullet\) **\% type IIax** & 6.27 & 1.26 & 7.99 & 1.92 & \\ \(\bullet\) **\% type IIx** & 9.61 & 1.81 & 3.55 & 3.05 & \\ \hline \hline \end{tabular} Values are displayed as mean (in %) and standard error (SE). PRE: before the start of the 12-week intervention; POST: after the end of the 12-week intervention. * no significant interaction was found, no post-hoc test.  
\end{table} 
Table 1: Results of the mixed model analysis of variances for the cross-sectional area of the erector spinae.
## 3 Proposed Solution
### Cluster Head Rotation
In this case, the notification of the existing CH regarding its role transfer to BFN is directly listened by all CM nodes. These CM nodes set their CH field with the newly designated CH and later on communicate their data to it for aggregation. _Case II_: If some nodes have indirect (multi-hop) access to CH and other have direct (single-hop) access. Since, ECHeART offers both direct and multi-hop access of CM nodes to the CH. for the multi-hop communication, the intermediate node between the transmitting CM and the targeted CH communicate the new CH decision to its linked CMs. This node then sets its CH field to this newly designated CH and communicates with it. The other nodes which have direct access to existing CH listen to the role transfer decision directly and set their CH field to this new one. With this, the existing CH is demoted to a non-CH node role, _i.e._, CM and BFN are promoted to CH. In this new setup, the cluster size and its cluster members remain the same. The only consumption of energy is in the CH role transfer and in communicating this decision to the cluster members.
# Domain-Invariant Few-Shot Contrastive Learning for Hyperspectral Image Classification
## 2 Related Work
### Contrastive Learning  
Contrastive learning is an effective technique in unsupervised learning. It trains models by learning how similar or different the extracted samples from data are.  
Noise Contrastive Estimation (NCE) [22] was proposed by [PERSON] and [PERSON] in 2010. This method estimates model parameters by contrasting with noise data, laying the theoretical foundation for the development of subsequent contrastive learning techniques. Additionally, Exemplar CNN [23] promotes unsupervised visual feature learning effectively by performing data augmentation on each training sample and treating it as a positive sample, while using random samples as negative samples. This method demonstrates the potential of learning from the data itself. SimCLR [24], an important unsupervised contrastive learning framework, was proposed by [PERSON] et al. in 2020. SimCLR enhances visual representation learning by amplifying similarities among positive pairs while reducing them among negative pairs. Although SimCLR is unsupervised, its core ideas and mechanisms provide an important foundation and inspiration for supervised contrastive learning.  
Within the realm of supervised contrastive learning, [PERSON] et al. [25] extended the SimCLR framework and proposed a supervised contrastive learning method that utilizes label information to further optimize the contrastive learning process. In their work, the model not only learns to cluster similar (positive) samples together but also ensures a significant distance between different categories (negative samples), significantly improving the performance of classification tasks.  
Moreover, supervised contrastive learning has been proven particularly effective for few-shot learning scenarios, as it precisely uses label information to enhance the model's learning ability with limited data [26]. In the task of few-shot HSI classification, [27] first introduced contrastive learning into the few-shot learning method based on prototype networks, effectively solving the classification difficulties caused by scarce sample quantities. However, this method simply applied supervised contrastive loss after obtaining the features, without fully exploring how contrastive learning could optimally function in HSI classification. Inspired by SimCLR, we designed a crop-type method as a data augmentation method, replacing the traditional use of noise addition, and used a projection head to map features into a new dimensional space for computing supervised contrastive loss, thereby addressing the high inter-class similarity and large intra-class variability of hyperspectral images.
## 4 Results and Discussion  
### The Role of Knowledge Distillation  
The results showed that the 1D-CNN had a better performance after KD. For the same network structure, the impact of KD may be reflected in the weight distribution. Therefore, in Figure 6, we show the weight distribution of the 1D-CNN with and without KD. In Figure 6, it can be intuitively seen that KD brings a more regularized weight distribution to the 1D-CNN. Such weights will effectively reduce overfitting. It can be inferred that the label of the teacher model improves the performance of the student model by regularizing the weight distribution.
## 3 Results and Discussion
### Application to Real Samples  
Three real samples from the Bay of Cadiz (southwest Spain) were used to study the applicability of the optimized method to determine and quantify the different nickel species present in the samples following the diagram shown in Figure 3. The analysis of a unique subsample allows the determination of the labile and no-labile fraction.  
The extraction efficacy obtained in the absence of humic acids (30.15) allows the determination of the labile fraction in the samples, since it represents the percentage of labile nickel which has crossed the membrane. A preconcentration factor (\(PF_{labile}\)) for labile fraction can be calculated using Equation (4):  
\[PF_{labile}=\frac{\Delta V\times E(without\ ligands)}{100} \tag{4}\]  
where \(\Delta V\) is the volume ratio between the two aqueous solution, and _Ee(without ligands)_ is the extraction efficacy when ligands are not presented in the sample. In this case, a value of 17.61 was obtained as \(PF_{labile}\), which means a concentration on the receiving solution was 17.61 times higher than in the sample.  
Then, the extraction efficacy calculated from the DOC concentration using Equation (3) allows the quantification of total nickel concentration in a similar way. Finally, the non-labile fraction may be estimated by the subtraction of total and labile fractions.  
The results obtained with this method are shown in Table 3 with those obtained with well-established methods for total and labile fractions. Regardless of the fraction, no significant differences were observed between the two methods for a significant level of 0.05 with an average relative error less than 4%. From these results it is possible to conclude that labile fractions obtained with the liquid membrane system correspond to that determined by AdCSV, including the free nickel ion plus weak inorganic and organic complexes [5]. Then, the non-labile fraction could be related to the stronger organic complexes.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{
\begin{tabular}{c} **DOC** \\ **(mg-L\({}^{-1}\))** \\ \end{tabular} } & \multicolumn{3}{c}{**Total Nickel (\(\mu\)g-L\({}^{-1}\))**} & \multicolumn{3}{c}{**Nickel Labile Fraction (\(\mu\)g-L\({}^{-1}\))**} \\ \cline{2-7}  & & **Reference** & **This** & **\(\varepsilon\) (\%)** & **Reference** & **This** & **Method** & **\(\varepsilon\) (\%)** \\ \hline
1 & 3.11 & \(0.73\pm 0.01\) & \(0.73\pm 0.02\) & \(0.00\) & \(0.65\pm 0.05\) & \(0.66\pm 0.02\) & 1.54 \\
2 & 3.46 & \(1.40\pm 0.05\) & \(1.39\pm 0.05\) & \(-0.71\) & \(1.22\pm 0.08\) & \(1.25\pm 0.04\) & 2.40 \\
3 & 7.04 & \(2.12\pm 0.16\) & \(2.04\pm 0.08\) & \(-3.77\) & \(1.58\pm 0.24\) & \(1.63\pm 0.07\) & 3.16 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Total and labile nickel concentrations in natural water samples.  
Figure 3: Speciation diagram using the liquid membrane system.
## 2 Background
### Wireless Sensor Network Application  
Different types of WSN are used for different applications, as shown in Figure 4, the first use of WSNs was for military applications, where a system of an acoustic sensor on the ocean bottom was developed for monitoring the soviet submarines in the cold war;later on, in 1980, the work started on developing distributed sensor network (DSN). Now, WSNs are used in different areas, such as agriculture, bio-medical, health, traffic, industry, environment, and so on [7,11]. The work of WSN can be divided into two dimensions. The first is monitoring, where it supervises, controls the operation, and analyses the system in real-time. The other is tracking the variations in the behavior of a target, which may be a person, animal, or event [12].  
WSNs plays a significant role when they are deployed in the environment. In the forest, they detect the behavior of wildlife, wildfire outbreaks, and landslides, in addition to monitoring and tracking air pollution, earthquakes, flood detection, and many other disasters [13,14]. In agriculture, WSNs can be used in smart farming, crop management, irrigation management, disease detection, and yield prediction, as well as monitoring temperature, humidity, and soil moisture measurements [15,16]. In the medical field, WSNs can be used for remote patient status monitoring, diagnosis, and emergency response [17].  
Figure 4: Types of WSNs.  
They can also be used in other healthy applications, such as eating habits, fitness issues, and monitoring sportive activities [18]. In transportation, WSNs can be used in parking lot management, railway tracks, and management systems, as well as monitoring traffic and roads [19]. In military and national security applications, WSNs plays a vital role [20]. The widespread application of WSNs has led to the existence of the Internet of Things (IoT), which depends on sensory devices that detect the information and send it through the internet to be processed and make decisions for the ease, safety, and comfort of human life. In the IoT, WSNs are applied in smart homes, health, agriculture, industry, building, city, grids, and much more [21].
# IEEE 802.11 ah: A Technology to Face the IoT Challenge
## 3 Comparative Analysis of IEEE 802.11 ah with Previous IEEE 802.11 Amendments  
In this section, we present a comparison between IEEE 802.11 ah and different IEEE 802.11 amendments. First, we describe the differences between IEEE 802.11 amendments based on MAC features. Later, we provide performance comparison between IEEE 802.11 ah and the previous IEEE 802.11 amendments in terms of throughput versus transmission range characteristics.
# Development of Prediction Method for Dimensional Stability of 3D-Printed Objects
## 2 Experiments  
### Materials and 3D Printing  
In this study, PLA was employed as the material for FDM 3D printing. Filament-type PLA was provided by Polymaker (Shanghai, China) and was used for 3D printing, and the filament diameter was 2.85 mm. The material properties are listed in Table 1.  
The FDM 3D printing equipment was Ultimaker 2+ (Ultimaker, Utrecht, The Netherlands) with the principal specifications detailed in Table 2. The design of three-dimensional models for 3D printing was executed using Solidworks 2021 (SolidWorks Corp., Dassault Systemes, Waltham, MA, USA). Subsequently, the designed 3D model was saved in the standard triangle language (STL) file format. Each model was sliced by Ultimaker Cura 5.0.0 (Ultimaker, Utrecht, The Netherlands), and G-code was generated.  
The base model dimensions encompass 10 mm \(\times\) 10 mm \(\times\) 25 mm along the XYZ axis. Variable printing parameters included the inclination angle, layer thickness, support spacing, and raster angle (Table 3). An illustrative model design is presented in Figure 1. The models featuring inclination angles of 60\({}^{\circ}\) and 75\({}^{\circ}\) were printed without the application of support structures.  
For comparison of the variable parameters, additional printing conditions including printing temperature, nozzle movement speed, and infill density were held constant (Table 4). Subsequent to printing, all 3D objects were subjected to a 3 h cooling period.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Density (g/cm\({}^{3}\)** & **Glass Transition** & **Melting Temp.** & **Young’s** & **Tensile** & **Tensile** \\  & **at 21.5 \({}^{\circ}\)C** & **Temp. (\({}^{\circ}\)C)** & **(\({}^{\circ}\)C)** & **Modulus (MPa)** & **Strength (MPa)** & **Strength (MPa)** \\ \hline \multirow{2}{*}{PLA} & \multirow{2}{*}{1.17–1.24} & \multirow{2}{*}{61} & \multirow{2}{*}{150} & \multirow{2}{*}{2636 \(\pm\) 330} & 46.6 \(\pm\) 0.9 & 43.5 \(\pm\) 3.1 \\  & & & & & (XY axis) & (Z axis) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties.  
Figure 1: Designed 3D object with 45\({}^{\circ}\) inclination angle and support space of (**a**) 5 mm, (**b**) 10 mm, and (**c**) 15 mm.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multirow{2}{*}{**Model**} & **Ultimaker 2+ (Ultimaker,** \\  & **The Netherlands)** \\ \hline Build volume (mm) & 223 \(\times\) 220 \(\times\) 205 \\ Layer resolution (\(\mu\)m) @ 0.4 mm nozzle & 20–200 \\ Accuracy (\(\mu\)m) & 12.5 in XY axis \\ \multicolumn{2}{c}{5.0 in Z axis} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of FDM 3D printing equipment.
# Through-Life Maintenance Cost of Digital Avionics
## 1 Introduction
Intermittent faults and false alarms directly impact avionics maintenance costs because of the increased downtime, violated regularity of flights, increased number of spare LRU, etc. We should also note that in military avionics the situation with NFF is even worse. According to [8], in the Turkish Air Force, the overall percentage of NFF is 45%, with a distribution of NFF 47.6% for digital and 63% for analog LRU. From the literature review (Section 2), we can conclude that published studies do not examine the combined effect of permanent failures, intermittent faults, and false-positives on onboard electronic systems' maintenance cost. We direct this study on the development of life cycle cost equations that allow airlines to choose the optimal service option from several practically implementable choices. The proposed cost equations consider the impact of permanent failures, intermittent faults, in-flight false-positives, average flight duration, maintainability of LRU, quantity of spare parts in the warehouse, the presence of automated test equipment (ATE), and the fault (or failure) location depth of faulty LRU at I- and D-level. We derive the mathematical equation for the meantime between unscheduled removals (MTBUR) and analyze maintenance options with one, two, and three levels. We determine the quantity of spare LRU that minimizes aircraft delays. We also analyze the dependence of the optimal quantity of LRU in the warehouse regarding the probability of BITE false-positive and rate of intermittent faults. We determine the objective cost functions required to select the optimum variant for servicing avionics singly for the warranty and post-warranty time-intervals since they have varied cost components. The article has the following organization: Section 2 provides a review in the field of modeling avionics maintenance. In Section 3, we describe the proposed mathematical model of avionics maintenance. Sections 4 and 5 consider warranty and post-warranty maintenance models of avionics systems, respectively. Section 6 consider minimizing the expected maintenance cost of avionics systems during service life. In Section 7, we present the results and discussion. In Section 8, we formulate the conclusions. Appendix A, abbreviations, nomenclature, and references are given at the end of the article.
## 4 COIN-LEO Algorithm
### DNN Model Used to Predict Submodel Inference Times  
Traditional time prediction methods simply use the data size divided by the theoretical CPU computing power. However, this method is overly idealized and fails to reflect the complex impact of real-world scenarios and CPU task scheduling on inference time, making it difficult to estimate accurately. Therefore, we trained a deep neural network to obtain more accurate inference time predictions. The DNN model takes into account various factors and features related to the hardware and environment, enabling us to achieve more precise and reliable inference time estimates compared to the simplistic traditional approach.  
We built a deep neural network model to predict the time required for inference tasks on low-Earth-orbit satellites. The model is based on Keras and is a sequential model composed of multiple dense layers. Firstly, the model includes an input layer with a feature dimension of 4, representing the CPU occupation, memory occupation, starting layer for model partitioning and ending layer for model partitioning on the satellite. Next, as shown in Figure 6, we have four hidden layers, each containing 256 neurons with the rectified linear unit (ReLU) activation function, which increases the depth of the model and helps extract higher-level feature representations. Finally, the model outputs the predicted inference time based on the regression problem being solved.  
We used our collected data as the training data for the deep neural network model. We designed an algorithm that can occupy the machine's CPU and memory. We partitioned the model into the smallest units and then combined them into different-sized models to  
Figure 6: DNN model used to predict submodel inference times. (FC Layer: fully connected layer).  
perform inference under different CPU and memory occupation rates, collecting actual execution time data for training purposes.  
We observed that when the CPU occupation increases by more than 20%, there is a significant increase in the inference execution time, which gradually returns to normal afterward. This may be caused by system scheduling. However, when our model is in the pipeline, this one-time increase can be averaged over a large amount of data flow, so it can be considered to have a relatively small impact on the actual inference process. But this kind of anomalous data can affect our model training results, so we remove such data before training.  
During the model training process, we used mean-squared Error (MSE) as the loss function to measure the difference between the model's predicted values and the true values. After training, the model's MSE on the validation set was around 50. This MSE value indicates the average squared error between the model's predicted results and the true inference time, which means the actual error is within \(\pm 50\) milliseconds. Although there is still some error, considering the special environment of the low-Earth-orbit satellite Internet, with limited resources and communication delays, our model has achieved good prediction results, which are sufficient for reinforcement learning training.
## 3 Processing Methods  
The overall processing flow of the interferogram data cube obtained by the SMWIR-IFTS is shown in Figure 2. First, spectrum reconstruction is performed on the interferogram data cube to obtain the absorbance spectrum of the target gas. Next, the absorbance spectra of the target gas are randomly split into training, validation, and test sets in proportion. The training set data is used to train the MDISE model, with the validation set data applied for hyperparameter tuning during the training process. Finally, the test set data is used to validate the trained MDISE model, and the gas concentration inversion accuracy of the MDISE model is obtained.