esa-sceva/satcom-chunk-collection huggingface dataset:
# Online Identification of Shearer Cutting State Using Infrared Thermal Images of Cutting Unit
## 1 Introduction
Many scholars have devoted a lot of research to the field of image tracking, and have achieved satisfying results [30; 31; 32; 33]. Most of the target-tracking methods adopt a filtering algorithm to find objects with high similarity to targets. However, when the position of the target changes or disappears, the target cannot be tracked accurately, and the tracking effect is also slightly insufficient. In this paper, a tracking method based on a combination of morphology and spatio-temporal context is put forward to track the location of a shearer cutting unit using infrared thermal-imaging video, allowing the characteristics of the temperature field in the tracking area to be obtained. Finally, an intelligent classifier should be established to achieve the efficient recognition of the shearer cutting state. Various artificial intelligence pattern recognition techniques have been widely used for fault detection or condition monitoring, such as artificial neural networks [34], adaptive neuro-fuzzy inference systems [35], support vector machines (SVMs) [36], and so on. SVMs have good generalization abilities and robust performance under small sample conditions. They can effectively avoid the problems of overlearning, local minimum, nonlinearity, and dimension disasters which are found in traditional statistical methods [37]. In this paper, a SVM classifier is constructed based on extracted feature vectors to automatically identify the cutting state of a shearer. Experiments were performed to verify the applicability and effectiveness of the proposed method. The rest of this paper is organized as follows. In Section 2, the location detection of the shearer cutting unit is presented in infrared thermal images. Section 3 introduces the tracking method based on the combination of morphology and spatio-temporal context. In Section 4, the online identification system for a shearer cutting state is provided. In Section 5, the experimental results for the proposed shearer-cutting-state recognition method are discussed. Finally, conclusions are summarized in Section 6.
## 4 Test Results and Discussion
### Distribution of Acceleration Magnification Factors  
In both the free-field system and the soil-caisson system model tests, acceleration responses of the soil and the caisson foundation at different depths were measured. Acceleration magnification factors (AMFs) along the vertical direction are calculated by comparing peak accelerations measured at the different depths with the peak acceleration of the table excitations. The AMF distribution results of the free-field (referred to as \"FF\") system and soil-caisson (referred to as \"SC\") system model tests are illustrated by the dashed and solid lines in Figure 6, respectively.
## 3 Results and Discussion
### Temperature-Dependent Variations of VOC Levels in Adult Formulas During Storage  
In the untargeted stability study using HS-SPME-GC-MS, identification was based on comparing the mass spectrometry (MS) patterns with those in the NIST library. The results are summarized in Table S3 (Supplementary Materials), and heatmaps were created using the chromatographic peak areas obtained from a set of new SAF samples. These samples were stored for 30 days at 4 \({}^{\circ}\)C, room temperature, and 60 \({}^{\circ}\)C to facilitate comparison, as quantification was not possible (Figure 4).  
A total of 40 compounds were observed, including 11 aldehydes, 10 ketones, 9 alcohols, and 2 esters, among others. Notably, hexanal, 2-heptanone, heptanal, 2-heptanal, 2-ocrenal, nonanal, and 2-hydroxybenzaldehyde were present in both new SAF samples. In contrast, pentyl acetate and 1-octen-3-ol were found in only one sample, while benzaldehyde, 2-pentylfuran, and octanal were found in the other sample.  
Hexanal appears stable at 4 \({}^{\circ}\)C, with a slight increase noted in one sample stored at room temperature. However, both samples showed a significant increase in hexanal levels after 30 days at 60 \({}^{\circ}\)C. The other compounds in the fresh samples seem stable at both 4 \({}^{\circ}\)C and room temperature. In contrast, most compounds exhibited some increase when stored at 60 \({}^{\circ}\)C, except for nonanal and 2-hydroxybenzaldehyde, which remained stable at all temperatures. Moreover, compounds such as 1-hexanol, octanal, 1-octanol, 2-methyldecaline, and 17-octadecenoic acid appeared after 30 days of storage at all studied temperatures. Other compounds, including 1-pentanol, 1-heptanol, 2-nonanone, 1-decanol, 1-octanol, 2-decanone, decanal, \(\gamma\)-octadecone, 2-decneal, cyclodecanone, 6-undecanone, hexanoic acid pentyl ester, 2-undecanone, 2-undecanen, and 2-butyl-2,7-octadien-1-ol, were only present after 30 days of storage at 60 \({}^{\circ}\)C.  
Similar VOCs, including aldehydes, ketones, alcohols, and volatile acids, were reported by [PERSON] et al. [30] in their analysis of milk-based infant formulas, as well as by [PERSON] et al., (2009) in their characterization of infant formulas and breast milk [34]. [PERSON], [PERSON], and [PERSON] also noted the formation of aldehydes and ketones in milk powders stored at various temperatures, which aligns with our findings [35]. [PERSON] et al., noticed the presence of aldehydes and ketones that influence the sensory properties of infant formulas [36]. They also found that long-term storage (up to one year) at room temperature leads to significant variations in volatile compounds, similar to our observations, with aldehydes and ketones being the most prevalent, significantly impacting the sensory attributes of infant formulas [36].
# A Designed Calibration Approach for the Measurement -While-Drilling Instrument
## 2 Materials and Methods
### Improvement of the Calibration Methods
#### 2.3.2 Optimization of Plane Fitting Method  
There is a noteworthy phenomenon that the calculation of the angle \(\gamma_{k}\) of deviation in Equation (16) is only related to the dataset \(\mathbf{D}_{k-x}\), and not to \(\mathbf{D}_{k-x}\). Thus, a theoretical hypothesis about the attitude information solution of the MWD instrument was presented, that the calculation of azimuthal angle \(\psi\) and pitch angle \(\theta\) are independent of the angle \(\gamma_{k}\). In this case, the step of gathering dataset \(\mathbf{D}_{k-x}\) can be omitted and the PF method is also simplified.  
This hypothesis can be proved mathematically on the following precondition. Firstly, the output of the tri-axial accelerometer needs to be calibrated to its own mirror coordinate frame in advance by the EF method. The output vector can be expressed as \(\mathbf{u}_{\mathbf{a}}^{\mathbf{so}}=(u_{ax}^{\otimes},u_{ay}^{\otimes},u_{az}^{\otimes}) ^{\text{T}}\). Secondly, the parameters (\(A_{ax}\), \(B_{ax}\), \(C_{ax}\)) related to the misalignment error matrix \(R_{ab}^{\otimes}\) have been solved by the PF method, on the condition that the dataset \(\mathbf{D}_{\mathbf{a-x}}\) is prepared. After calibration with the IPI method, the mirror coordinate frame of the tri-axial magnetometer has the same misalignment as the tri-axial accelerometer to the _car_ frame.  
Then, the irrelevance between the misalignment angle \(\gamma_{k}\) and the calculation of the attitude information will be exhibited by the following equations.  
(1) The solving equation of the pitch angle \(\theta\) is derived with the vector \(\mathbf{u}_{\mathbf{a}}^{\mathbf{so}}=(u_{ax}^{\otimes},u_{ay}^{\otimes},u_{az}^{\otimes}) ^{\text{T}}\) of the gravity vector \(\mathbf{G}\) in the mirror coordinate frame.  
\[\tan\theta=-\frac{u_{ax}^{b}}{\sqrt{u_{ay}^{b}\,{}^{2}+u_{az}^{b} \,{}^{2}}}=-\frac{u_{ax}^{\otimes}A_{ax}+u_{ay}^{\otimes}B_{ax}+u_{az}^{ \otimes}C_{ax}}{\sqrt{\left(u_{ax}^{\otimes}2+u_{ay}^{\otimes}2+u_{az}^{ \otimes}2\right)-\left(u_{ax}^{\otimes}A_{ax}+u_{ay}^{\otimes}B_{ax}+u_{az}^{ \otimes}C_{ax}\right)^{2}}} \tag{22}\]  
where \(\mathbf{u}_{\mathbf{a}}^{\mathbf{b}}=(u_{ax}^{b},u_{ay}^{b},u_{az}^{b})^{\text{T}}\) is the gravity vector \(\mathbf{G}\) in MWD the _car_ frame, and (\(A_{ax}\), \(B_{ax}\), \(C_{ax}\)) can be estimated by the sampling data set \(\mathbf{D}_{\mathbf{a-x}}\) according to Equation (14).  
Figure 2: The flowchart of recursive least squares criterion.
## 4 Methodology  
Our method comprised four main stages: (1) visual and physiological feature extraction, (2) PRV calculation, (3) feature fusion and dimensionality reduction, and (4) behavioral state classification using a recurrent neural network. This pipeline is illustrated in Figure 1. Video data were acquired from a camera positioned above the neonate, while pulse signals were recorded using Philips IntelliVue MP20/MP50 patient monitors. Scalar intra- and interframe statistics [11] were computed independently within semantically segmented regions, on a frame-by-frame basis, to capture temporal dynamics. These features were subsequently concatenated with derived pulse rate variability (PRV) statistics. The resulting one-dimensional descriptors formed a dynamic sequence of numerical features, which served as input to a recurrent neural network (RNN) for behavioral state classification.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline
**Subject** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline Recording time (hours) & 96.7 & 5.5 & 39.4 & 27.4 & 51 & 105.5 & 50.1 & 36.4 \\ Gender & F & M & M & F & F & M & F & F \\ Gestational age (weeks + days) & 32 & 32 + 3 & 31 + 4 & 35 + 4 & 39 & 32 & 33 & 38 + 6 \\ Birth weight (g) & 2020 & 1840 & 1850 & 1870 & 3150 & 2120 & 2080 & 2840 \\ Postnatal age (days) & 4 & 4 & 10 & 8 & 4 & 7 & 2 & 7 \\ Actual weight (g) & 1900 & 1850 & 1680 & 1820 & 2905 & 2040 & 1960 & 3150 \\ Length (cm) & 46 & 44 & - & 45 & 57 & 45 & 44 & 48 \\ Head circumference (cm) & 32 & 29.5 & - & 32 & 34 & 30 & 32 & 33 \\ Respiratory support & no & no & no & no & no & no & no & yes \\ Any drugs & no & no & no & no & yes & no & no & yes \\ Fitzpatrick scale & 2 & 3 & 2 & 2 & 2 & 2 & 2 & 2 \\ \hline \end{tabular}
\end{table}
Table 1: Properties of the participants.
# Encapsulated Probiotics: Potential Techniques and Coating Materials for Non-Dairy Food Applications
## 3 Probiotic Encapsulation Techniques
### Extrusion  
Extrusion (also known as external ionic gelation, which produces capsules with sizes of 100 \(\upmu\)m to 5 mm) is the oldest and the most common physical technique for encapsulating the probiotic cell. In the extrusion technique, probiotics are first suspended in a biopolymer solution. The suspension is then fed into an extruder (pilot scale) or a syringe needle (laboratory scale) and drips off into a hardening solution (most commonly, calcium chloride) with gentle stirring [40; 45].  
Figure 1: \(Cont\).  
The extrusion technique is relatively simple, direct, straightforward, and gentle (does not involve extreme temperature, pH condition, and organic solvents), thus resulting in relatively high viability (low cell harm) of probiotic microorganisms and requiring a lower operational cost. This technique can be conducted under both aerobic and anaerobic conditions. It is biocompatible and flexible as it does not require any harmful solvents. Using alginate/shellac blend and sunflower oil as wall and core materials, respectively [40; 41; 43; 45], [PERSON] et al. [46] demonstrated that the co-extrusion encapsulation technique increased the probiotic survival of _L. acidophilus_ LA3 by about 80% in simulated gastrointestinal conditions and 83% of the probiotics loaded into dried particles were viable after 60 days storage at room temperature (25 \({}^{\circ}\)C). [PERSON] et al. [47] also demonstrated that encapsulation of probiotic _L. acidophilus_ by ionic gelation between phytic acid and chitosan followed by the addition of calcium carbonate and starch with electrostatic extrusion provided buffering effects and protection against acid injury during simulated gastric conditions and refrigerated storage. The extrusion technique has also been utilized in non-dairy probiotic foods such as _E. ficeum_ in cherry juice [13], _L. lactis_ ABRININW-N19 in orange juice [17], _L. casei_ DSM 20011 in pineapple, raspberry, and orange juices [18], _L. acidophilus_ TISTER 2365 in sweet fermented rice sap beverage [27], _L. acidophilus_ NCFM in mulberry tea [29], _L. casei_ Lc-01 and _L. acidophilus_ La5 in mayonnaise [35], and _L. reuteri_ in chewing gum [36].  
However, the size of beads produced through the extrusion technique is relatively big (up to 5 mm), and the process of bead solidification is also relatively slow. Hence, this technique is not suitable to be used in large-scale production [40; 43; 45]. Over the last decades, an evolving extrusion technique (vibrating nozzle method) has been focused on and studied. This new extrusion technique uses vibrating technology (mechanical principle), in which, when a defined amplitude is enforced, the vibrational frequency will break the extruded fluid into pre-defined-sized droplets. The size of the droplets generated using this technique can be controlled through the diameter of the jet, the velocity of the extrusion process, the viscosity and the surface tension of the fluid, and the frequency of disturbance [41].
# A Comparative Analysis of Arabic Text Steganography
## 2 Steganography
### Steganography Scenario  
We illustrate a typical steganography scenario in Figure 1. The entity responsible for sending the secret information (called the sender) applies a hiding technique to protect the secret message travelling through a public channel such as the Internet. The item type that contains the secret message could be as text, image, video, or audio. Similar to the secret message object, the cover could be an image, video, etc.  
The embedding process needs a secret key (stego key) that protects the concealed message from being extracted by an attacker. After the embedding process, the stego object which represents the hidden message within the cover object is generated. Thereafter, the stego object is sent through the public channel to the receiver. If the stego key is designed as a private key, it will be sent to the receiver as a hidden key within the stego file.  
Otherwise, a stego key will be produced as a public key, encrypted, and sent separately to the receiver via the public channel. The receiver can extract the secret message by exploiting the stego key and applying the extraction algorithm that corresponds to the embedding algorithm.  
The risk appears when the hidden message is transmitted through the public channel where many attackers/eavesdroppers are ready to attack the stego object. The attacker extracts the hidden message by tracing the embedding algorithm and breaking the stego key. If an attacker is incapable of extracting the hidden message, he could tamper with the stego object to produce a tampered object by destroying the hidden message.  
Therefore, it is imperative to build a steganography method that achieves a tradeoff between four evaluation criteria: Invisibility, robustness, capacity, and security. The proposed steganography techniques seek to hide as much information into a cover object as possible without affecting its invisibility. The essence of invisibility is to prevent distortion of the cover object's appearance to ensure it remains unnoticed by the eavesdropper. Robustness prevents the attacker from either extracting or destroying the hidden message. If an attacker notices altered cover object, then he could break into the first shield of the defense. Then, he could break the robustness, which is considered the second shield of the defense. A steganography technique with a high level of perceptual and robustness will achieve a high stage of protection [4; 27]. Hence, it is important to study and analyze steganography techniques and assess their performance using the four evaluation criteria mentioned earlier.
## 1 Introduction
### The NOMA Mathematical Characterization, When Compared with OMA  
NOMA, which has higher spectral efficiency than traditional orthogonal multiple access (OMA) [40; 41], has recently attracted much research interest. For instance, NOMA has been suggested for downlink circumstances in systems utilizing the long-term development of the 3 GPP [42; 43]. NOMA has been suggested as a potential multiple-access method for future cellular communication networks [44; 45]. However, the current multiple-access methods used in cellular communication networks, such as Orthogonal frequency division multiple access (OFDMA) for 4G, FDMA for 1G, time division multiple access (TDMA) for 2G, and code-division multiple access (CDMA) for 3G, are all examples of OMA methods. These methods have a limitation where spectrum resources assigned to users with poor channel conditions are not utilized effectively, which makes them less than ideal.  
The idea of NOMA is put up as a solution to this problem and to further increase spectrum efficiency. NOMA is used in conjunction with superposition coding (SC) at the base station (BS) and successive interference cancellation (SIC) at the consumers to obtain the best possible reception for compromised broadcast stations [46]. In a two-user, single-input, single-output (SISO) NOMA structure, for illustration, the base station supports clients on the same channel simultaneously using the time, software, and bandwidth while overlapping transmissions with a distinct distribution of energy factors. The proximal client, who encounters robust circuit circumstances, unravels its shared communication before deriving it by deducting the other person's statement from its assessment. The far user, who has weak channel conditions, decodes its message by considering the partner's message to be noise. This approach provides both parties with complete accessibility to all resource blocks (RBs), and the nearby person can decode its data without interruption from the distant user. Hence, compared to traditional OMA techniques, the overall performance is improved.
## 4 Results and Discussion  
### Acoustic Signals Received and Particle Displacement  
At site S1, after an explosion source was released, the cross-ice acoustic signals were received by an ice-mounted geophone, and the particle displacements normalized in three directions are shown in Figure 3a, with some translation in the vertical coordinate direction for a better view. In Figure 3a, it can be seen that the signals generated by the explosion source have significant impact characteristics in the time domain. A detailed explanation of the explosion source can be found in reference [30]. To study the bearing estimation on cross-ice acoustic signals in different frequency bands, the signals were filtered in the frequency domain according to the 1/3 octave. Fifteen signals in the 1/3 octave frequency band centered at frequencies from 40 Hz to 1000 Hz were obtained. The particle displacement caused by the signal wave in each frequency band received by the geophone in the \(x\)-, \(y\)- or \(z\)-directions can be obtained, and the results are shown in Figure 3b-d, with some translation in the vertical coordinate direction. The unit of particle displacement is meters. In order to better understand the signal characteristics of each frequency band, the same translation was performed in the vertical coordinate direction.  
It can be seen from Figure 3b-d that the particle displacement varies with the frequency.  
Figure 3: \(Cont.\)
Peak displacement (the maximum displacement during the sampling time) is an important characteristic point of energy distribution and plays an important role in research. The peak displacements at each center frequency of the 1/3 octave in the three directions of received signals at site S1 are shown in Table 2.  
Figure 3: Three-directional signals received by geophone: (**a**) three-directional displacement, (**b**) \(x\)-direction displacement, (**c**) \(y\)-direction displacement and (**d**) \(z\)-direction displacement.  
It can be seen in Table 2 that the particle peak displacements in each frequency band are different.  
As shown in Figure 3b-d and Table 2, the particle peak displacements in each frequency band are different at site S1. When the frequency band is below 630 Hz (i.e., \(f\) < 630 Hz), the value of the \(x\)-direction peak displacement is higher and greater than 10 \(\times\) 10\({}^{-7}\) m. When the frequency is greater than or equal to 630 Hz (i.e.,\(f\) \(\geq\) 630 Hz), the value of the \(x\)-direction peak displacement is lower and less than 10 \(\times\) 10\({}^{-7}\) m, as shown in Figure 3b. The \(y\)- or \(z\)-direction peak displacement in each frequency band also has similar characteristics, as shown in Figure 3c,d and Table 2. Therefore, the value of the peak displacement in the \(x\)-, \(y\)- or \(z\)-direction is related to the frequency band of the received signals. Similarly, this feature can also be found at sites S2 and S3.
# rStaple: A Robust Complementary Learning Method for Real-Time Object Tracking
## 4 Proposed rStaple Tracking Method
### Scale Estimation  
As mentioned above, the target position can be estimated by the combination of translation filter response map and color histogram score map. In addition, a scale pyramid is formulated on the estimated target position to train a scale filter \(F_{S}\). Given the target size \(W\times H\) and scale factor \(\sigma\), a scale pool \(S\) containing \(N\) scales can be expressed as:  
\[S\in\left\{\sigma^{n}|n=[-\frac{N-1}{2}],[-\frac{N-3}{2}],\ldots,[\frac{N-1}{2 }]\right\} \tag{18}\]  
For each scale \(s\) in the scale pool \(S\), we crop an image patch of size \(sW\times sH\) at the center of the estimated target position and interpolate to \(W\times H\). HLOG is then extracted and extended into a 1-D vector containing the multi-scale representation of the target. A one-dimensional scale correlation filter can be trained using the features of \(N\) scales. The response score of each scale can be calculated by \(g(x_{s})=\exp(-\frac{(s-N/2)^{2}}{2\sigma^{2}})\), which represents the similarity between the target in the scale \(s\) and the learned model. Eventually, the best scale \(s^{\prime}\) can be solved from the following optimization problem:  
\[s^{\prime}=\operatorname*{argmax}_{s}\{g(x_{s})|s\in S\} \tag{19}\]  
Figure 2: Tracking result of trackers with and without histogram enhancement.  
Notice that here only fHOG is adopted to train the scale filter. In order to reduce the errors by inaccurate scale estimation, the result of scale estimation is merely used to update the scale filter, instead of updating both scale filter and translation filter.