esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Obtaining 2D Poses
#### 3.1.2 Additional Signal Processing and Selection  
Signal processing beyond the translation and scaling of the features described above can be considered in two categories. The first is further improvement of the quality of the pose information extracted from video. Here, smoothing of the scaling using the median to remove small shifts in joint position and removing outliers in cases where the network predicts a distorted pose in some of the frames have proven to be most effective. Note that these steps are applied to the video signal only. With respect to the sensor signal, the only prepossessing we have found to be consistently effective was linear interpolation of the sensor data to 50 Hz to match the video frame rate.  
The second is the removal from the signal of components that are unlikely to be reliably translated from video to sensor signals (see also Section 2). In essence the idea is to entirely remove certain types of information from our classification process, opting to have less, but more reliable information. Examples are: computing the norm of 3D sensor signal. gravity removal (\"linear acceleration\") and low pass filtering. The latter was motivated by the insight (see Section 2 point 3) that phenomena such as high frequency \"ringing\" of the signal caused by sudden, strong impacts that are fundamentally invisible in the video data. This means that the simulated sensor data generated from videos will not contain such \"ringing\".  
These steps need to be applied to both the sensor signals and the video signals and done during: the training of the regression model, the generation of the synthetic training data, and the classification of real sensor data with the model generated using the simulated data. Obviously, in cases where the information is not present in the video signal at all, it is sufficient to remove it from the sensor.
# Volume Loss Assessment with MT-InSAR during Tunnel Construction in the City of Naples (Italy)
## Appendix A Proof of V\({}^{*}\)\({}_{\rm L}\) = V\({}_{\rm L}\).
In Proceedings of the Tunnelling 82, Proceedings of the 3 rd International Symposium, Brighton, UK, 1 January 1982; pp. 173-181. * [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Relating twin-tunnelling-induced settlement to changes in the stiffness of soil. _Acta Geotech._**2023**, _18_, 469-482. [CrossRef]
* [PERSON] and [PERSON] (2007) [PERSON]; [PERSON] Describing settlement troughs over twin tunnels using a superposition technique. _J. Geotech. Geenvironmental Eng._**2007**, _133_, 445-468. [CrossRef]
* [PERSON] and [PERSON] (2008) [PERSON]; [PERSON] Numerical analysis of the interaction between twin-tunnels: Influence of the relative position and construction procedure. _Tunn. Undergr. Space Technol._**2008**, _23_, 210-214. [CrossRef]
* [PERSON] (1978) [PERSON] Ground movements caused by tunnelling in soil. In Proceedings of the International Conference on Large Movements and Structures, Cardiff, UK, July, 1978; pp. 812-948. * [PERSON] and [PERSON] (1975) [PERSON]; [PERSON] Displacements around soft ground tunnels. In Proceedings of the 5 th Pan American Conference on Soil Mechanics and Foundation Engineering, Buenos Aires, Argentina, 1975; pp. 571-632. Available online: [[https://books.google.com.au/books/about/5](https://books.google.com.au/books/about/5) th_Panamerican_Conference_on_Soil_Mecha.html?id=tWHAoAEACAA&redir_esc=y]([https://books.google.com.au/books/about/5](https://books.google.com.au/books/about/5) th_Panamerican_Conference_on_Soil_Mecha.html?id=tWHAoAEACAA&redir_esc=y) (accessed on 14 March 2023). * [PERSON] (1988) [PERSON] Ground movements resulting from urban tunnelling: Predictions and effects. _Eng. Geol. Spec. Publ._**1988**, \(5\), 79-92. [CrossRef]
* [PERSON] et al. (2001) [PERSON]; [PERSON] [PERSON]; [PERSON] Permanent scatterers in SAR interferometry. _IEEE Trans. Geosci. Remote Sens._**2001**, \(3\), 8-20. [CrossRef]
* [PERSON] et al. (2002) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. _IEEE Trans. Geosci. Remote Sens._**2002**, _40_, 2375-2383. [CrossRef]
* [PERSON] et al. (2000) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry. _IEEE Trans. Geosci. Remote Sens._**2000**, _38_, 2202-2212. [CrossRef]
* [PERSON] et al. (2016) [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] Persistent Scatterer Interferometry: A review. _ISPRS J. Photogramm. Remote Sens._**2016**, _115_, 78-89. [CrossRef]  
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## 2 Train Occupancy Time and Graphic Analysis
### Graphic Analysis of One Train  
With the concept of a standard train, this section analyzes the occupancy time of one single train on the train timetable. For one single train \(i\), the occupancy time of train \(i\) on the timetable starts at the departure time at the departure station. However, it is more important to determine the end time when train \(i\) occupies the train timetable. To solve this problem, the concept of virtual (additional) trains in UIC406 was introduced. As shown in Figure 2, an auxiliary virtual standard train is added after train \(i\) with the minimum tracking headway time \(I_{is}\).Then, the departure time of the virtual standard train at the departure station is the time point when the occupancy of train \(i\) on the timetable ends.  
Therefore, the occupancy time of train \(i\) on train timetable \(OT_{i}\) is the period between the departure time of train \(i\) at the departure station and the departure time of the subsequent virtual standard train at the departure station. According to Figure 2, the occupancy time of single train \(i\) can be calculated by Equation (1).  
\[OT_{i}=TT_{i}+I_{is}-TT_{s} \tag{1}\]
###### Abstract  
Background: Temporomandibular joint disc displacement with reduction is one of the most common types of TMJ arthropathy. This single-blinded, randomized clinical study aimed to evaluate the effectiveness of three different therapeutic methods. Methods: Standard splints (Group 1), modified occlusal splint (Group 2), and conventional physical therapy with exercises (Group 3). A total of 48 patients were randomly assigned by a computer-generated allocation sequence to receive rehabilitation. The outcome was defined as improvements in pain and intermittent locking episodes. The follow-up visits were scheduled as one month and a long-term evaluation at one (T1), two (T2), three (T3), and four years (T4). Magnetic resonance images were also taken to evaluate each patient before treatment and at one year. Image analysis involved the evaluation of morphology and the function of intra-articular structures. Variables such as age, gender, and pre- vs. post-treatment values of VAS and TMJ locks between the three intervention categories were compared for statistical evaluations. \(p\) values \(\leq\) 0.05 were taken as being significant. Results: A total of 16 subjects were allocated to each group. At T1, a decrease in pain and TMJ locking episodes was observed, which was maintained throughout the course of the study for four years of follow-ups, with no statistically significant differences. However, there was a tendency for better outcomes in favor of Group 2, with less clicking of the TMJ at opening. Conclusions: The modified mandibular splint seems to be successful as an effective alternative for the management of temporomandibular joint disc displacement with reductions in intermittent locking.  
Keywords:TMJ disorders; temporomandibular joint diseases; temporomandibular joint; temporomandibular joint syndrome; splint therapy +
Footnote †: journal: Applied sciences
# The Effect of Data Transformation on Singular Spectrum Analysis for Forecasting
## 3 Transformation of Time Series  
Data transformation is useful when the variation increases or decreases with the level of the series [1]. Whilst logarithmic transformation and standardisation are the most commonly used data transformation techniques in time series analysis, it is noteworthy that there are other transformations from the family of power transformation such as square root and cube root transformations. However, the interpretability is not as simple and common as that for standardisation and logarithmic transformation.
# Privacy-Preserving Handover Optimization Using Federated Learning and LSTM Networks
## 2 Related
### Deep Learning Approaches and Federated Learning for Handover Management  
Deep learning techniques, particularly Long Short-Term Memory (LSTM) networks, have been extensively explored for improving handover management in wireless communication systems. LSTM networks are well-suited for capturing temporal dependencies in signal strength data, making them ideal for predicting handover events. For instance, ref. [13] proposed an LSTM-based handover prediction model that significantly improved handover prediction accuracy in mobile networks. Their study demonstrated that LSTM networks could effectively learn from historical signal data to predict future handover events, thereby reducing the likelihood of missed or unnecessary handovers. Similarly, ref. [14] demonstrated the application of deep learning for 5G handover prediction, highlighting its effectiveness in reducing the ping-pong effect and increasing handover success rates. Their research emphasized the ability of deep learning models to adapt to the complex and dynamic nature of 5G networks, where traditional handover algorithms often fall short. Ref. [15] demonstrated that LSTM networks significantly enhance handover prediction accuracy. Their LSTM-based handover prediction model effectively learns from historical signal data to forecast future handover events, reducing the likelihood of missed or unnecessary handovers. Ref. [16] discusses the implementation of a novel handover management system leveraging Software-Defined Networking (SDN) and Multiplicative Long Short-Term Memory (mLSTM) networks within 5G environments. Despite these advancements, deep learning models often rely on centralized data collection, which raises concerns about data privacy and security. The centralization of data can lead to potential breaches of sensitive user information, making it crucial to explore decentralized approaches.  
However, challenges remain, such as the need for high-quality data and the difficulty in generalizing models to unseen environments. To address these, federated learning ensures privacy, lightweight models reduce computational demands, and continuous training improves adaptability across diverse network conditions.
## 4 Test Process and Data Analysis
### Variations in the Relationship between the SET Voltage and Laser Energy  
In the test, the oscilloscope was used to record the change in the SET voltage of the sensitive region Q9. The change in the transient voltage with the pulse laser energy is shown in Figure 6, below.  
As can be seen from Figure 6, the first transient voltage was observed when the pulse laser energy gradually increased to 2.84 nJ, and it was negative. Then, with the increasing pulse laser energy, the amplitude of the transient voltage also increased, and the maximum value of the transient voltage occurred when the laser energy was 20 nJ.
## 2 Configuration and Kinematics Modeling  
### General Structure of CHCM  
The proposed CHCM adopts a structure of separating the driving subsystem and motion subsystem, as shown in Figure 1. The manipulator, namely the motion subsystem, contains multiple cable-driven joint modules serially connected with adjacent ones by a universal joint. Each joint module comprises a tubular structure and two fixed endplates used to link the tubular structure with universal joints, as shown in Figure 2b. The three driving cables of each joint are equally spaced at 120\({}^{\circ}\) and attached to the rear endplate, through holes in the circumferential directions pierced in tubular structures and endplates. With the cables, the rear endplate of every joint module is connected to the driving subsystem, in which the driving cables are controlled by the actuators. The rotation of actuators is converted to the horizontal linear motion of connecting plates via reducers, leading screws, sliders etc., which are fixed to screw nut seats and the cables are attached to, as shown in Figure 2a. To measure length changes of these driving cables, the linear magnetic encoders are adopted, ignoring the deformation of the cables. As a result, the joint module will present 2-DOF rotational motions with the contraction of the driving cables, namely the rotation of the actuators.
## 3 Challenges for Designing MAC Protocols in UWSNs
### Restricted Bandwidth  
UWSNs communication between nodes, depends on the medium (optical, acoustical, radio frequencies) that is opted to establish the communication link. Which are directly dependent on the available bandwidth of the propagating signal [25]. As the available bandwidth are limited in UWSNs, which directly casts off the transmitting data. UWSNs limited bandwidth can be a reason of network congestion, thereby data loss rates increase, which directly increase latency of the transmitted data packets between nodes. In addition, a delay in data transmission means that a system will keep running for long time to receive acknowledgements which in turn will increase power consumption [52; 54].
## 2 Concept of Time Frequency Analysis and Processing
### Explicit Time-Varying Filter Implementations  
The time-varying filter could be applied by exploiting the energy concentration feature of the signal in two-dimensions instead of only one. In what follows, the principle of the explicit time varying filter is described. The explicit time-varying filter is a linear time varying (LTV) system, and the input-output relation of a discrete-time LTV system \(H\) is [13]  
\[z(n)=H[y(n)]=\sum_{n^{\prime}=-\infty}^{+\infty}h(n,n^{\prime})y(n^{\prime}) \tag{7}\]  
where \(h(n,n^{\prime})\) is the impulse response of \(H\), and \(y(n)\) and \(z(n)\) are the input and output of the time-varying filter, respectively.  
Figure 1: The time frequency distribution of a 4-QAM pulse shaping signal. (**a**) WVD; (**b**) STFT; (**c**) CWD; (**d**) SPWVD.  
For the underspread system, the linear time-varying filter could be expressed based on the general Weyl symbol (GWS), where the GWS of the LTV system \(H\) in continuous time is defined as  
\[L_{H}^{(\beta)}(t,f)=\int_{-\infty}^{+\infty}h\left(t+\left(\frac{1}{2}-\beta \right)\tau,t-\left(\frac{1}{2}+\beta\right)\tau\right)e^{-j2\pi f\tau}d\tau \tag{8}\]  
where \(t\) and \(\tau\) are time variables, \(\beta\) is the real valued parameter controlling the time shifting of \(\tau\), and \(L_{H}^{(\beta)}(t,f)\) is the GWS of system \(H\). The special cases \(\beta=0\) and \(\beta=1/2\) give the Weyl symbol and Zadeh's time-varying transfer function, respectively. The GWS with arbitrary \(\beta\) is not easily reformulated in a discrete-time setting. However, for \(\beta=1/2\), the discrete-time formulation is given by  
\[L_{H}^{(1/2)}(n,\theta)=\sum_{m=-\infty}^{+\infty}h(n,n-m)e^{-j2\pi\theta m} \tag{9}\]  
where \(m\) and \(n\) are discrete time variables, and \(\theta\) denotes the normalized frequency. The discrete-time Zadeh filter is defined by setting \(L_{H}^{(1/2)}(n,\theta)=M(n,\theta)\), where \(M(n,\theta)\) is a time frequency weight function and can be obtained from the time frequency representation of the desired signal \(P_{cw}(n,\theta)\). The impulse response of the Zadeh filter is obtained as  
\[h(n,n^{\prime})=\int_{-1/2}^{1/2}M(n,\theta)e^{j2\pi\theta(n-n^{\prime})}d\theta \tag{10}\]  
Therefore, the linear time-varying filter design problem can be simplified into constructing the indicator function \(M(n,\theta)\) of the pass region \(R\).  
\[M(n,\theta)=\left\{\begin{array}{ll}1&(n,\theta)\in R\\ 0&(n,\theta)\
otin R\end{array}\right. \tag{11}\]  
where the pass region \(R\) is the time-frequency area in which the desired signal's energy is not less than \(\gamma\) percent of the maximum value. It is defined according to the masking threshold \(\gamma\), i.e., \(R:\{n,\theta\}\in\{|P_{cw}(n,\theta)|\geq\gamma P_{cmax}\}\). \(P_{cw}(n,\theta)\) is the desired signal's time-frequency distribution, which is Choi-Williams distribution in this paper. \(P_{cmax}\) is the maximum value of the desired signal's time-frequency distribution, i.e., \(P_{cmax}=\max\{|P_{cw}(n,\theta)|\}\).
## 2 Materials and Methods
### Low-Cost and Geodetic GNSS Instruments: A Comparison in Terms of Observations  
To use low-cost GNSS instruments for displacement detection purposes, they must be able to perform with a similar level of quality and reliability to geodetic GNSS instruments. In the test described in this sub-section, comparisons were made between two low-cost (simpleRTK2B V1 receiver and Survey Calibrated antenna) and two geodetic (Leica GS18 receiver and LEIGS18 antenna) GNSS instruments. Data used for comparison comprise almost 48 h of static observations that were acquired at 1 Hz (Figure 4).  
The performance of all instruments used was analyzed with the help of several linear combinations of raw GNSS observations, where the geometry-free linear combination (_L4_) is of particular interest. All of the previously mentioned linear combinations can also be used for cycle slip detection and repair [39; 40; 41]. One of the parameters that indicate the reliability of the GNSS instrument is the number of cycle slips (i.e., ambiguities) that occur during the survey, where the lowest possible number of cycle slips is desirable.  
The most important quantity for precise position determination in the case of GNSS is the quality of phase observations with both carriers. The noise of phase observations can be determined with the geometry-free linear combination _L4_. However, since _L4_ slowly changes over time due to temporal changes in ionospheric bias, this linear combination must be filtered to remove the low-frequency trend [41]. For each satellite tracked by four GNSS devices, the _L4_ linear combination was filtered with a simple 3-point high-pass filter as follows:  
\[HP=L4_{i-1}-2\;L4_{i}+L4_{i+1} \tag{13}\]  
where \(i\) represents the index of an epoch. The variable \(HP\) contains only amplified (by \(\sqrt{6}\)) noise of linear combination _L4_.
## 4 Corrosion Protection Measures
### Cathodic Protection  
#### 4.2.1. Sacrificial Anode Method of Cathodic Protection  
Cathodic protection [104; 105; 106] is one of the most widely used methods for pipeline anticorrosion protection. In the field of petroleum and petrochemical industry, cathodic protection is widely used and has obvious anti-corrosion effects. It is often used in combination with anti-corrosion coatings to achieve the best anti-corrosion effect. Cathodic protection is generally divided into three categories, as shown in Table 3.
## 2 Related Works
### Sampling Methods for Learning from Imbalanced Data
#### 2.1.3 Oversampling versus Undersampling  
At first glance, the oversampling and undersampling methods appear to be functionally equivalent since they both alter the size of the original data set and can actually provide the same proportion of class balance. However, this commonality is only superficial; each method introduces its own set of problematic consequences that can potentially hinder learning [44]. In the case of undersampling, the problem is relatively obvious: removing examples from the majority class may cause the classifier to miss important concepts pertaining to the majority class. In regards to oversampling, the problem is a little more opaque: the computational complexity is increased rapidly with the production of more positive samples, especially in dealing with large data such as remote sensing data. In addition, oversampling has the risk of over-fitting [41]. For example, since random oversampling simply appends replicated data to the original data set, multiple instances of certain examples become _tied_ leading to overfitting [41]. In particular, overfitting in oversampling occurs when classifiers produce multiple clauses in a rule for multiple copies of the same example which causes the rule to become too specific; although the training accuracy will be high in this scenario, the classification performance on the unseen testing data is generally far worse. Despite some limitations, oversampling and undersampling schemes have their own strengths. For example, one of the main advantages of undersampling techniques lies in the reduction of the training time, which is especially significant in the case of highly imbalanced large data sets [45]. Oversampling can provide a balanced distribution without losing information on majority class.
###### Contents
This is particularly concerning by-products intended for use as raw materials for the extraction of bioactive compounds, as even low levels of contaminants may compromise product safety as well as regulatory compliance. To our knowledge, such data are not available for TPW in Croatia. Fresh TPW typically contains high moisture content, up to 70-80%, depending on the source and processing conditions, making it highly perishable and unsuitable for direct use or storage. Drying is necessary to reduce moisture, prevent microbial spoilage, and stabilize TPW, and it is also crucial for the preservation of heat-labile nutrients (such as lycopene), prevention of color changes, and retention of reconstitution, sensorial, or antioxidative properties. Grinding of plant-based raw materials is another crucial pretreatment step for optimizing TPW utilization. It reduces particle size and disrupts the cell structures, so it can positively influence the extractability of bioactive compounds by facilitating solvent penetration and enzymatic or chemical action. On the other hand, it can also increase oxidation and degradation of unstable compounds [19]. The impact of drying or milling on the extraction yields of bioactive compounds from TPW is still scarce [20; 21]. Further research is required to develop tailored pre-processing protocols that optimize the extraction yields and quality of specific categories of bioactive compounds from TPW [10]. This study was designed to provide novel insights into the chemical composition and food safety characteristics of tomato processing waste (TPW) as a whole, as well as its key components--tomato peels and seeds--derived from a blend of eight distinct indigenous Croatian tomato varieties. In addition, the research systematically evaluated how varying drying conditions and milling intensities influence the yield of valuable bioactive compounds and organoleptic properties of TPW. By addressing these critical processing parameters, the investigation offers a unique foundation for optimizing pre-processing techniques, thereby enhancing the efficient and sustainable utilization of TPW-derived bioactive resources.