esa-sceva/satcom-chunk-collection huggingface dataset:
# Wintering Conditions and Heat Loss during Hibernation in the Brown Long-Eared Bat
## 1 Introduction  
The microclimate of a refuge (the refugioclimate) affects the survival of bats during winter [1; 2; 3]. Each species has its own preferences regarding the refugioclimate [4; 5; 6]. In addition, the makeup of a refugioclimate determines both the thermal comfort and frequency of awakenings [2; 7; 8; 9].  
The interior microclimates of underground environments (caves, adits, bunkers, etc.) where bats hibernate can be highly diverse due to the number of entrances, airflow direction and velocity, depth and other properties of each underground system [10; 11]. The field of science that researches and analyses such microclimates is called _micrometeorology_[12; 13; 14; 15], sometimes referred to as _care meteorology_[16].  
Hibernation occurs as a response to unfavourable environmental conditions. In order to survive this period, the hibernators must accumulate fat and find a suitable site, where they can lower their body temperature and maintain it at an appropriate level, usually close to that of the ambient temperature.  
Metabolism slows down considerably during hibernation. Many researchers address the problem of changes in the metabolic rate and the effect of hibernation on the condition of the body [17; 18; 19; 20].  
The emission of heat from a bat's body into the surroundings takes place through convection, conduction, radiation and evaporation [21]. A few studies have attempted to measure the total energy expenditure during hibernation [18; 22; 23; 24; 25; 26; 27]. Furthermore, studies concerning energy expenditure to date have been based on incomplete conditions in the hibernation. The authors have usually only provided selected parameters of the underground microclimate, rather than the parameters of the refugioclimate (primarily the ambient temperature, T\({}_{\mathrm{a}}\)) [28]. The modelling of energy transformation is based on the optimal values of the selected parameters, primarily T\({}_{\mathrm{a}}\), or T\({}_{\mathrm{a}}\) and relative humidity (Rh) [2].  
This leads to discrepancies between the calculated and the observed hibernation time. In contrast, there are many studies concerning the effect of T\({}_{\rm a}\), Rh and airflow velocity (v) on human thermal comfort and human energy expenditure, for instance, in mines [29; 30].  
The aim of this study was to estimate heat loss in the brown long-eared bat _Plecotusauritus_ ([PERSON], 1758) during hibernation depending on the refugioclimate conditions, as determined by T\({}_{\rm a}\), Rh and v. Heat loss was calculated using convective heat transfer equations. The results were compared to the heat loss calculated based on the fat burned during hibernation.
## 3 Results and Discussion
### Removal of TSS, NH\({}_{3}\)-N and COD  
#### 3.2.1 Reduction Rate of TSS  
Phytromediation treatment of sewage wastewater using _Lemma minor_, _Salvinia minima_, _Ipomoea aquatic_ and _Centella asiatica_ showed a decreasing in TSS reading (Figure 3). The average initial reading for TSS was 83.33 mg/L. On the second day of phytoremediation treatment, the TSS value was decreased to 67.33 mg/L, 61.67 mg/L, 68 mg/L and 70 mg/L for the treatment by _Lemma minor_, _Salvinia minima_, _Ipomoea aquatic_ and _Centella asiatica_, respectively. On the final day of treatment, the TSS value in the treatment sample for _Ipomoea aquatic_ recorded the lowest average reading, which was 12 mg/L, followed by _Salvinia minima_ (18.67 mg/L), _Centella asiatica_ (27 mg/L) and _Lemma minor_ (41 mg/L). The reduction in the average reading for TSS was primarily due to the plants' root systems. On that account, plants with fibrous roots were usually able to accumulate more TSS compared to those with taproot [30]. The root capacity increases with the growth of the plant, thus more suspended solids can be filtered [31]. Besides, a lower water flow also caused the increase in suspended solids and solids filtration by plant tissues.
# Challenges of Using Blockchain in the Education Sector: A Literature Review
## 3 Results
### Challenges of Blockchain in Education  
The 32 reviewed articles highlighted different challenges. These challenges should be considered when using blockchain in the education sector. We used the technology, organization, and environment (TOE) model [29] to categorize the challenges. Researchers have utilized this approach widely to analyze the adoption of information technology. The technological, organizational, and environmental aspects are all utilized in this framework to define technological advancement adoption decisions. The technological perspective defines the technological attributes that are related to the adoption of blockchains, such as security, privacy, scalability, immutability, and cost-effectiveness. The organizational perspective identifies the organizational aspects and assets of an organization, such as organizational readiness that are important to the acceptance of technology. The environmental setting represents the environmental aspects in which the organization performs its essential services such as laws and regulations support. Table 1 presents these categories and selected articles that are included in each of these categories. The technological challenges are as follows: poor usability, lack of scalability, limited interoperability and standardization (classified under the immaturity of blockchain challenge), the complexity of integration, security issues, privacy, immutability and lack of flexibility, and data unavailability. In the organizational context, there is a lack of adequate skills, financial barriers, and a lack of management commitment and support. In the environmental context, there are legal issues and a lack of regulatory compliance, market and ecosystem readiness, and sustainability concerns.  
Figure 2: Publication year of included articles.
## 4 Results and Analysis
### Network Parameter Evaluation
#### 4.2.2 Study of the Jitter in SDN  
The jitter is the term used to describe the irregularity in the timing of data packet arrivals and is a key parameter for network performance analysis, especially for maintaining QoS in time-critical applications, such as VoIP and video conference, where predictions in packet delivery are needed to ensure service quality. High jitter levels indicate irregular delays in packet transmission, often caused by network congestion, varying routes, or differing processing times [60]. The calculated jitter for the multiple message packets is shown in Figure 18 and the statistics are presented in Table 5.  
The average jitter of 0.050 s (50 ms) indicates that packets arrive later than their expected times. This delay is caused by network conditions and processing times due to the encryption process. Although most packets arrive close to their intended times, one packet is consistently delayed. This slight delay affects the network's overall performance. For real-time critical services such as video conferencing or VoIP, the acceptable level of jitter is below 30 ms [61]. However, for network control, routing updates, and non-real-time-sensitive data transfers such as message transfer [62], the jitter is manageable around 200 ms [63]. With an average jitter of 50 ms and a maximum jitter of 164.9 ms, the current encryption method employed as a policy may not be suitable for  
Figure 18: Jitter (sec) for 31 message packets transmitted in the SDN environment among different hosts (h1 and h4) connected to different switches.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Statistic** & **Value (ms)** \\ \hline Data Count & 31 \\ Mean Jitter & 50.026 \\ Standard Deviation & 26.851 \\ Range & 17.7–164.9 \\
25 th Percentile (Q1) & 32.800 \\ Median (Q2) & 47.313 \\
75 th Percentile (Q3) & 57.363 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistical overview of jitter in the network.  
applications with strict latency requirements, such as real-time multimedia or IoT control. However, the policy may still be relevant for tasks such as secure data or message transfer, where jitter is not a primary concern. To mitigate the issues related to latency-critical tasks, approaches such as optimization of packet scheduling, minimization of computational overhead [64], or traffic classification-based adaptive encryption techniques shall be employed.
## 4 Theoretical Analysis and Field Distribution  
In this section, the distributions of the electromagnetic fields of typical coils and the proposed coil are simulated and compared. The strength of stimulation and the focality are also analyzed. Moreover, the influence of the key factors, such as the diameter and the intersection angle, are discussed for further optimization.
## Materials and Methods
### Characterization  
Fourier transform infrared (FTIR) spectra of the samples were obtained using an FTS-3000 spectrophotometer (Digilab, Cambridge, MA, USA) within the range of 4000-400 cm\({}^{-1}\) at room temperature. The powder samples were characterized by a KBr pressed disk technique with a weight ratio of the sample to KBr of 1/100.  
X-ray diffraction (XRD) patterns were obtained on a Bruker D8 Advance X-ray Diffractometer (STADI P, Karlsruhe, Germany) with Cu-Ka radiation (\(\lambda\) = 0.1540 nm) at a scan rate of 2\({}^{\circ}\)/min from 1.5\({}^{\circ}\) to 20\({}^{\circ}\) with a step distance of 0.02\({}^{\circ}\). The basal spacing reflections of the samples were calculated from Bragg's Law, and the voltage and current of the X-ray tubes were 40 kV and 30 mA, respectively.  
The molecular weight and molecular weight distribution were characterized by a gel permeation chromatography system (GPC515-2410, Waters, Milford, MA, USA) equipped with a 2410 Refractive Index Detector and a 996 Photodiode Array Detector using tetrahydrofuran (THF) as an eluent at a flow rate of 1.0 mL/min. The molecular weight calibration curve was obtained using polystyrene standards.  
The particle size and size distribution of the samples were determined by dynamic light scattering using a Malvern Mastersizer 2000 (Malvern Instruments, Malvern, UK) at 25 \({}^{\circ}\)C.  
Scanning electron microscopy (SEM, SU8010, HITACHI, Tokyo, Japan) was used to observe the surface morphology of the samples with an accelerating voltage of 15 kV after coating the samples with thin layers of gold.  
The dispersion morphology of the clay layers in the polymer matrix was obtained using transmission electron microscopy (TEM, JEM-2100, Tokyo, Japan) with an accelerating voltage of 200 kV.  
The weight losses of the samples were determined using a NETZSCH thermogravimetric analyzer (TGA, STA409 PC, Bavaria, Germany). The samples were between 2.0 and 5.0 mg and were heated from 25 \({}^{\circ}\)C to 800 \({}^{\circ}\)C at a heating rate of 10 \({}^{\circ}\)C/min under N\({}_{2}\) at a flow rate of 140 cm\({}^{3}\)/min.  
The glass transition temperature of PS/OMMT was obtained by using differential scanning calorimetry (DSC, 6220, Japan) at a heating rate of 10 \({}^{\circ}\)C/min under N\({}_{2}\) at a flow rate of 100 cm\({}^{3}\)/min.  
The tribological properties of the PS/OMMT samples were tested using an MRS-1J four-ball tester. GCr15 steel bearing balls with a 12.7 mm diameter (grade 10) were cleaned ultrasonically with ethanol before the test. All the tests were performed at a rotating speed of 1450 r/min for 60 min under a load of 392 N at room temperature. The coefficient of friction (COF) was recorded three times, and the average COF values were calculated.
## 2 Materials and Methods
### Methods  
The MyoMOTION MR3 motion analysis system (Noraxon Inc., Scottsdale, AZ, USA) was used to collect the kinematic gait variables. It is a set for three-dimensional motion evaluation using an inertial measurement unit (IMU). The system combines the technology of wireless data transmission and IMU sensors, enabling the assessment of any movement in three-dimensional space (e.g., changes in angles between segments, linear acceleration). Each sensor in the set combines an accelerometer, a gyroscope, and a sensor for measuring the Earth's magnetic field. The IMU sensors were affixed to the participant's body following a schema that aligns with the MR3 software's (version 3.18) requirements. This setup facilitated not only the capture of data but also its thorough analysis, employing a sampling rate of 200 Hz throughout the testing phase. For the purpose of acquiring exhaustive gait metrics, a sum of 15 sensors was utilised--allocating three sensors per extremity (both right and left), along with three sensors positioned along the spinal region (specifically on the spinous processes of the 7 th cervical vertebra and 7 th thoracic vertebra, as well as within the sacral region), and a single sensor was positioned on the forehead (as illustrated in Figure 1). Prior to every data gathering session, the IMU sensors underwent calibration to ascertain body orientation, employing a stance where the arms were held parallel to the torso, thus establishing a zero-degree angle benchmark for the reference posture. The participants' task was to cover a 100-m distance thrice with three types of gait (1) free walking (walking without poles), (2) walking with classic NW poles, and (3) walking with mechatronic poles. The distance was determined based on the literature and resulted from the length of a single and double step (gait cycle) of an adult human [22]. The order of the gait types was randomised to avoid any potential order effects. Before the experiment, each participant was allowed to warm up and stretch for 10 min. The experiment was  
Figure 1: Illustration of sensors’ placement on the body and the poles. The image was taken upon receiving written permission from the participant. performed continuously, with no breaks between the different types of gait. The movement with NW poles took place after an NW instructor had given specific instructions. The walk occurred on an artificial surface with participants' habitual (preferred) walking speed. Two passes were made for each gait type, which allowed for an average of 70 complete gait cycles per pass. Following the International Society of Biomechanics recommendations [23; 24; 25], the following parameters were documented for both the right (RT) and left (LT) extremities:  
1.
## 3 MTQADC Model
### Convolutional Feature Learning Module  
The convolutional feature learning module is shown in Figure 5. First, the dimensionality of the problem text relation representation is transformed into the embedding dimension, facilitating subsequent scoring.  
\[\mathbf{R}=\mathbf{Q}_{\mathrm{A}}\cdot\mathbf{W}_{\mathrm{linear}} \tag{8}\]  
\(\mathbf{Q}_{\mathrm{A}}\) is the input question embedding obtained through ALBERT. \(\mathbf{W}_{\mathrm{linear}}\in\mathbf{R}^{\mathrm{G}\times\mathbf{F}}\) is the weight matrix of the linear layer, \(\mathbf{G}\) is the hidden layer with a dimension of 768, and \(\mathbf{F}\) is the embedding with a dimension of 512. To facilitate the use of the convolutional network, the dimensionality is expanded to \(\mathbf{R}\in\mathbf{R}^{\mathrm{G}\times\mathbf{F}\times 1}\). Next, a convolution operation is performed.  
\[R_{\mathrm{CNN1}}(i)=\sum_{j=0}^{\mathrm{P}-1}W_{1}(j)\cdot R(i+j) \tag{9}\]  
\(W_{1}\) is the weight corresponding to the position in the convolutional kernel. \(\mathrm{p}\) represents the size of the convolutional kernel, which is 3. \(i\) is the index of the target position. For each convolutional kernel, \(j\) represents the elements at different positions within the kernel, and \(R(i+j)\) is the value at position \(i+j\) in the input sequence. The convolution operation performs a sliding window operation on the input tensor to extract local features. Then, the convolutional output is passed through the ReLU activation function for a nonlinear transformation.  
\[R^{\prime}_{\mathrm{CNN1}}=\mathrm{ReLU}(R_{\mathrm{CNN1}}) \tag{10}\]  
The first convolutional layer and the second convolutional layer follow similar steps.  
\[R^{\prime}_{\mathrm{CNN2}}=\mathrm{ReLU}\left(\sum_{j=0}^{\mathrm{P}-1} \mathbf{W}_{2}(j)\cdot R^{\prime}_{\mathrm{CNN1}}(i+j)\right) \tag{11}\]  
Finally, batch normalization and regularization are applied to obtain the final relationship representation.  
\[R=\mathrm{Dropout}\big{(}\mathrm{BatchNormNorm}\big{(}R^{\prime}_{\mathrm{CNN2}} \big{)}\big{)} \tag{12}\]  
Through two layers of convolution operations, the model can automatically extract multi-level local features from the relationship embeddings. Each convolution layer uses  
Figure 5: Convolutional feature learning module.  
convolution kernels to perform weighted summation on the input features, extracting information-rich local representations. The use of activation functions introduces non-linearity, enabling the model to learn more complex features. This series of convolution operations not only enhances the model's ability to understand complex relationships but also provides more effective input features for the subsequent scoring function.
## 4 Results and Discussion
### The Influence of the Back Pressure  
Figure 14 displays the flow structure  
It will be more difficult to form a choking flow which played an important role in affecting the pumping efficiency of the ejector [45]. As can be seen in Figure 15, when the back pressure is less than 4600 Pa, the entrainment ratios are always the same value of 0.546. The boundary layer separation occurs in the mixing chamber section, the normal shock wave disappeared when the back pressure is at 5000 Pa, and the entrainment ratio rapidly drops to 0.27 as shown in Figures 14 and 15. When the back pressure is more than 4600 Pa, the entrainment ratio decreases gradually with the increase of the back pressure until it reaches 0 at 7300 Pa. It shows that the critical back pressure is 4600 Pa. When the back pressure is higher than the critical value, the backpressure is so high that the primary fluid is kept from flowing into the diffuser section, and the choking flow would not be formed due to the largest primary jet core does not move into the mixing chamber. The energy exchange between the primary fluid and the secondary fluid is too short of exchanging momentum and energy with each other enough. The ideal flow passage of the converging-diverging structure of the secondary fluid flow passage is not formed where the secondary fluid cannot be further accelerated in the mixing chamber section to be pumped. Then, the flow rate of the secondary fluid is gradually reduced resulting the entrainment ratio decline (in Figure 14).  
Figure 14: Contours of Mach number for various back pressure.  
In Figures 14 and 16, the length of the shock train is relatively similar, and the flow structure inside the mixing chamber and the throat sections remains the same when the back pressure is less than the critical value (4600 Pa). Also, the entrainment ratio remains constant as the length of shock-mixing layer, and the energy exchange progress is nearly the same. Simultaneously, the energy and momentum transfer process between the primary fluid and the secondary fluid and the velocity distribution in this region are substantially the same. When the back pressure exceeds the critical value, there will be no normal shock exists in the diffuser section. The disturbance caused by the back pressure will propagate to the upstream to destroy the choking flow. Once the choking phenomenon cannot be generated, the mass flow rate of the secondary fluid will sharply drop until zero. When the back pressure is higher than the critical back pressure, the ejector will in the reverse flow mode resulting in mass flow rate of the secondary reduced and the entrainment ratio decreases as shown in Figure 15.  
Figure 16: Comparison of static pressure distribution along the ejector axis by different back pressure.  
Figure 15: Comparison of entrainment ratio and the mass flow rate of two streams under different back pressure.
# Data-Driven Modeling of Low Frequency Noise Using Capture-Emission Energy Maps
## 2 Theoretical Model
It is an iterative algorithm that estimates a maximum likelihood solution of the parameters, based on an expectation (E) step and a maximization (M) step. The GMM is used for clustering Gaussian components, by realizing that two bivariate Gaussian distributions of fitted CEE map can represent clusters. The soft clustering method is applied to estimate cluster membership posterior probabilities [39]. The method assigns a cluster membership score to each data point and ranks the points by their cluster membership score, allowing some data points to belong to multiple clusters.
# Authentication Based on Non-Interactive Zero-Knowledge Proofs for the Internet of Things
## 8 Comparative Analysis  
In the recent literature, it is hard to find novel schemes proposed for the authentication of mobile devices, specifically in the Internet of Things.  
Many existing research works propose lightweight authentication schemes based on challenges and responses, but none of the proposed schemes have been implemented completely. Many authors omit one of the most important pieces of information that can characterize a scheme of this kind: the time required for authentication. Just a few authors have analyzed the transmission bit rate, the percentage of packages that are lost in interactive schemes and/or the total real time that schemes require. In this work, those parameters are studied, but the percentage of lost packages is not analyzed, because the scheme is non-interactive and just one single message is sent, so no loss can happen.