esa-sceva/satcom-chunk-collection huggingface dataset:
# Last-Mile Optimization Using Neural Networks
## 3 Proposed Method
### Classification Methods Used
#### 3.5.3 Support Vector Machine  
In the specific context of our research, focusing on the optimization of last-mile delivery processes in urban environments, an SVM was developed in the mid-1990s based on statistical learning theory. It has gained popularity as a classifier [38], especially for binary  
\begin{table}
\begin{tabular}{c c} \hline \hline Category 1 & Fashion, beauty, and decoration \\ \hline Category 2 & Books, press, and multimedia \\ \hline Category 3 & Leisure, tourism, and food \\ \hline Category 4 & Services and others \\ \hline \hline \end{tabular}
\end{table}
Table 2: Categories of products.  
classification tasks, due to its simplicity and efficiency. However, since many real-world datasets consist of more than two categories, the SVM classifier has been extended to handle multiclass classification problems. This development has made SVM a more versatile classification method that can be applied to a wider range of datasets [39]. In addressing the unique complexities of urban logistics, our study leverages SVM's evolved capabilities to contribute to the advancement of sustainable and optimized urban delivery systems, thereby enhancing the overall effectiveness of last-mile delivery in e-commerce platforms.
# Unsupervised Detection of Multiple Sleep Stages Using a Single FMCW Radar
## 3 Sleep Stage Detection  
### Physiological Characteristics of the Sleep Stages  
This study performed three-stage sleep detection using physiological characteristics specific to each sleep stage. This method differs from PSG, which monitors sleep stages based on the variability in the subject's EEG measurements during sleep [31].  
First, the entire wake-sleep cycle can be broadly divided into wake and sleep states. The sleep state is further divided into _REM_ and Non-_REM_ sleep stages. In the wake state, the subject's body movements were observed more frequently than in other stages; breathing rate and heart rate were higher than those in the sleep state, and their cycles were irregular. In the _REM_ sleep stage, breathing and heart rate were higher, with irregular changes over time, compared with the intervals adjacent to the non-_REM_ sleep stage. Normal _REM_ sleep rarely involves body motion, but patients with _REM_-sleep behavior disorders experience sudden body movements during _REM_ sleep. Lastly, the non-REM sleep stage, a very stable phase in the sleep state, is characterized by little body movement and relatively low breathing and heart rates compared to the wake and _REM_ sleep stages.
## 3 Extended Kalman Filter  
The main drawbacks of this model include the fact that the model can only consider the measurement noise without taking into consideration uncertainties in the underlying dynamics. Therefore, we develop a stochastic gLV model with Extended Kalman Filter (EKF) algorithm to model COVID-19 spread dynamics.  
The nonlinear dynamic stochastic model that captures the COVID-19 spread rate along with the measurement model is defined as:  
\[\begin{array}{rcl}\frac{\mathrm{d}}{\mathrm{d}t}x_{i}(t)\equiv\hat{\mathbf{x}}&=& \mathbf{f}(\mathbf{x}(t),\mathbf{u}(t))+G(t)\mathbf{w}(t)\\ y_{i}(t)\equiv\mathbf{y}&=&\mathbf{h}(\mathbf{x}(t),t)+\mathbf{v}(t)\\ i&=&1,2,\cdots,N\end{array}, \tag{10}\]  
where \(N\) denotes the number of state variables (i.e., \(N=2\) in the model), \(\mathbf{x}(t)\in\Re^{N}\) is the system state vector, \(\mathbf{y}(t)\in\Re^{N}\) stands for the observation vector. The nonlinear state model is defined by \(\mathbf{f}:\Re^{N}\rightarrow\Re^{N}\), which is the discrete-time form of Equation (9). The nonlinear observation model is defined by \(\mathbf{h}:\Re^{N}\rightarrow\Re^{R}\). \(G(t)\) is a coefficient matrix associated with the process noise. \(\mathbf{w}(t)\sim\mathcal{N}(0,Q(t))\) and \(\mathbf{v}(t)\sim\mathcal{N}(0,R(t))\) represent the zero-mean Gaussian uncorrelated white-noise process and measurements, respectively, with covariance matrices given by  
\[\begin{array}{rcl}E\{\mathbf{w}(t_{2})\mathbf{w}^{T}(t_{1})\}&=&Q(t_{2})\delta(t_{2 }-t_{1})\\ E\{\mathbf{v}(t_{2})\mathbf{v}^{T}(t_{1})\}&=&R(t_{2})\delta(t_{2}-t_{1})\\ E\{\mathbf{w}(t_{2})\mathbf{v}^{T}(t_{1})\}&=&0\\ \delta(t_{i},t_{j})&=&\begin{cases}0&\text{if }\ i\
eq j\\ 1&\text{if }\ i=j\end{cases}\end{array} \tag{11}\]  
where \(E\{.\}\) is the expectation operator. The EKF structure for the state estimate \(\hat{\mathbf{x}}\), in the absence of the control input, is given by  
\[\dot{\mathbf{x}}(t)=\mathbf{f}(\hat{\mathbf{x}}(t),t)+K(t)[\mathbf{y}(t)-\mathbf{h}(t)(\hat{\mathbf{x} }(t))] \tag{12}\]  
where \(K(t)\) represents the Kalman gain. [14] provides detailed derivations of the EKF. The state vector is augmented to estimate both the dynamic states and the unknown parameters. We consider the following augmented state vector \(\mathbf{z}(t)\) to estimate both the model states \(\mathbf{x}(t)\) and parameters \(\mathbf{p}(t)\)  
\[\mathbf{z}(t)=\begin{bmatrix}\mathbf{x}(t)\\ \mathbf{p}\end{bmatrix}. \tag{13}\]  
Therefore, the augmented system becomes  
\[\begin{array}{rcl}\hat{\mathbf{z}}(t)&=&\mathbf{F}(\mathbf{z}(t))+\mathbf{\zeta}(t)\\ \mathbf{y}(k)&=&\mathbf{h}(\mathbf{z}(t))+\mathbf{v}(t)\end{array}, \tag{14}\]  
where \(\mathbf{\zeta}(t)\) denotes the zero-mean Gaussian white-noise for the augmented dynamic, and  
\[\begin{array}{rcl}\mathbf{F}(\mathbf{z}(t),\mathbf{p})&=&\begin{bmatrix}\mathbf{f}(\mathbf{x}(t),\mathbf{p})\\ 0\end{bmatrix}\\ \mathbf{h}(\mathbf{z}(t))&=&\mathbf{h}(\mathbf{x}(t),\mathbf{p})\end{array}. \tag{15}\]  
For our gLV dynamic model, the observation model is defined as  
\[\mathbf{y}=\mathbf{x}(t)+\mathbf{v}(t). \tag{16}\]
## 2 Intercellular Communication: EVs  
Cells can share between them biological information using lipids, proteins, or nucleic acids as mediators, which are carried within small, nano-to-micrometer lipid-membared EVs released by them [16]. EVs are present in biological fluids, including blood, urine, milk, saliva, and cerebrospinal, amniotic, and seminal fluids [17].  
EVs play a key role in intercellular communication in physiological and pathological cellular processes [18; 19] and are considered a valuable source of useful biomarkers [20; 21; 22].  
The International Society for Extracellular Vesicles (ISEV) encourages the use of the term \"extracellular vesicles (EVs)\" as a generic term for all secreted vesicles, considering the lack of consensus for the identification of specific markers to distinguish between the different subtypes of EVs [23]. Formerly, the nomenclature assigned to EVs subgroups was based on differences in the size and formation mechanism [23], and classified them into three main groups: 1. shedding microvesicles, with a size range of 100-1000 nm [24]; 2. apoptotic bodies (1-5 \(\upmu\)m diameter), released into the extracellular environment by dying cells [25]; and 3. exosomes, small vesicles between 30-150 nm in size produced from the endosomal compartment [23].  
However, distinguishing between the various groups is still problematic, so in this review, we refer to EVs, regardless of the classification used in the works cited, which often do not report specific data that would allow a precise identification of EVs subtypes.  
It is known that EVs are involved in numerous physiological and pathological processes, including immune response, signal transduction, tumor progression, and inflammation [4]. Consequently, EVs have potential as carriers of molecular biomarkers for diagnosis and prognosis in a range of conditions, including cardiovascular, renal, and neurodegenerative diseases, and cancer [26], or as transporters of therapeutic agents [27; 28].  
EVs carry proteins, lipids, mRNA, and miRNAs, and their contents depend on the type and function of the cell in which they originate [29].  
EVs contain substantial amounts of different RNA species, such as miRNA, mRNA, ribosomal RNA, long noncoding RNA, transfer RNA, and small nuclear RNA [30]. Although mRNAs are the most abundant class of RNAs in EVs, many studies have focused on miRNAs because of their apparent role in cancer progression. Vesicular miRNAs were suggested as novel diagnostic, prognostic, and predictive biomarkers in several common cancers [31].  
Exosomes are highly enriched in proteins with various functions, such as proteins associated with cell membrane interaction, invasion, and fusion, e.g., the tetraspanins CD9, CD63, CD81, and CD82. The tetraspanins CD9, CD63, and CD81 are used as specific markers [23]. Furthermore, EVs contain proteins involved in maintenance of cell homeostasis and protection of cells against stress/apoptosis, e.g., molecular chaperones, some of which are called heat shock proteins (Hsp). Hsp60, Hsp70, and Hsp90 are the most commonly present Hsps in EVs and are among the most used vesicular markers [32].
## 3 DCS Scheme Families
### Load Balanced Data-Centric Storage  
_Load Balanced Data-Centric Storage (LB-DCS)_[24] is an organic approach that relies on the home perimeter for data replication and thereby overcomes the unbalanced load constraint in DCS-GHT [1]. LB-DCS functions on top of three mechanisms: (i) A density estimation protocol that is used to estimate the network density \(f\), which is included in _put_ and _get_ protocols; (ii) a modified hashing function that includes \(f\) in its parameter list; and (iii) a storage protocol enforcing QoS in the selection of the number of replicas for data storage. In [24, 25], the authors show that depending on the event type, the number of local replicas should be different. So, when a producer node produces any event it also specifies a value in terms of the parameter \(q\) to specify the number of replicas. The _put_ primitive takes \(q\) along with two other parameters: datum \(d\) and meta-datum \(k\). Depending on the value of \(q\), the home node selects \(q\) neighbor nodes using the _ball_ method to replicate that event. This dispersal method is iterative. A home node, \(H\) (with co-ordinates \(X_{H_{i}}\), \(Y_{H}\)) considers a ball with a radius \(r\) (randomly selected value). The home node sends a request for storage to all sensors within the range of this ball denoted by:  
\[B_{(x_{H},Y_{H})}(r)=\{\textit{sensors-of-coordinate}(x,y)\colon|(x_{H},y_{ H}),(x,y)|<r\} \tag{2}\]  
In turn, when a sensor receives a storage request it acknowledges the request to \(H\). \(H\) calculates the number of acknowledgments (_q'_) received from the nearest sensors. If \(q^{\prime}<q\), then \(H\) sends a storage request to \(B_{(x_{H},Y_{H})}(2*r)\) sensors. This time it considers only the sensors in \(B_{(x_{H},Y_{H})}(2*r)-B_{(x_{H},Y_{H})}(r)\). This process continues until \(H\) receives \(q\) acknowledgements or exhausts sensors within the perimeter. Apart from this QoS, the authors also include non-uniform hashing that can be used to balance the load even in non-uniform distribution such as a Gaussian distribution of sensor nodes in a network. In such a non-uniform WSN, LB-DCS applies two distributed protocols, referred to as _proactive_ (Broadcast) and _reactive_ (Stripes and Fatstripes), to compute the density approximation \(f\) of each zone. The density approximation is used to bias the hash function enabling the distribution of target co-ordinate pairs for storage according to the network distribution. For non-uniform hashing, the _Rejection Method_[26] has been used in LB-DCS.
## 2 Millimeter-Wave Integrated Cellular Network
### 60-GHz Millimeter Wave WLAN  
The IEEE (Institute of Electrical and Electronics Engineers) 802.11 ad WLAN, also known as WiGig (8-Gigabit) [8], handles the unlicensed 60-GHz frequency band, which has at least 5 GHz of continuous bandwidth in several countries. WiGig adopts 2.16 GHz continuous band per channel in transmission, and is capable of transmitting at multiple Gbps rates. WiGig supports three different modulation methods: spread spectrum, single carrier (SC), and orthogonal frequency division multiplexing (OFDM) modulation. Spread spectrum modulation is used exclusively to control message transmission. SC modulation is mandatory and is applicable to low-power devices due to its lower peak-to-average power ratio (PAPR). OFDM modulation is optional, and is applicable to loose power devices. It is more robust to multipath effects than SC modulation. Table 1 gives an example of WiGig's Modulation and Coding Scheme (MCS), in which BOSK represents binary phase shift keying; QPSK represents quadrature phase shift keying; QAM represents quadrature amplitude modulation; and SQPSK represents extended QPSK. Here, the physical (PHY) layer has a maximum data rate of approximately 7 Gbps, while the next-generation IEEE 802.11 ay 60-GHz WLAN is designed to support a maximum throughput of 20 Gbps in the Media Access Control (MAC) layer by space multiplexing or further enhanced MAC (Media Access Control) efficiency.  
The latest semiconductor technology applies 60-GHz communications to small handheld devices.
# An Overview on Deep Learning Techniques for Video Compressive Sensing
## 3 Image Compressive Sensing  
Recently, deep learning is used in various computer vision tasks and it shows high performance results in several applications such as CS reconstruction algorithms. Since many computer vision algorithms applied on 2D signals (e.g., [32] in which ISTA-Net is applied in a video CS context) are extended to be applied on 3D signals (e.g., videos), we introduce in this section recent image CS algorithms.  
Among the reconstruction methods, various block-by-block methods are already proposed such as stacked denoising autoencoder (SDA) [33], non iterative reconstruction using CNN (ReconNet) [34] and DR2-Net [35] which are deep learning based end to end reconstruction networks. However, the outputs of these algorithms suffer generally from blocky artifacts. Therefore, the use of a BM3D algorithm, as a post processed procedure, is compulsory to eliminate the blocky artifacts in reconstructions. Among the well mentioned algorithms in image reconstruction, we have the iterative shrinkage thresholding algorithm based network (ISTA-Net) [36] that integrates the traditional ISTA into a neural network to achieve superior reconstructed quality, its enhanced version ISTA-NET+, trainable ISTA for sparse signal recovery (TISTA) [37] and ADMM-Net [38] which is proposed by adapting ADMM method for CS magnetic resonance imaging (CS-MRI) using neural networks. Experimental results in various research works prove that deep learning networks can successfully solve the two main issues of compressive sensing: the design of proper sampling matrices and the reconstruction process. The performances are significantly increased and lower computation complexity is obtained than traditional methods. [PERSON] et al. [39] and [PERSON] et al. [40] proposed CNN-based methods for 2D image reconstruction that split the reconstruction process into two stages. Firstly, the initial reconstruction which aims to recover the images from the patches. Secondly, a better quality reconstruction is obtainedfrom the enhancement of the initial reconstruction. In [39], deep networks are used in the reconstruction phase by imitating the traditional CS image recovery and the training of the sampling matrix through a CNN network. These two theoretically separated networks are considered as an encoder-decoder approach to generate the CS measurements and to reconstruct the 2D images.  
Deep compressive sensing was extended to multi-scale schemes [40; 41; 42] utilizing image decomposition. In [41], a multiphase reconstruction process is proposed. The first phase is dedicated to a multi-scale sampling and an initial reconstruction that are jointly trained. Then, the quality of the initial image is enhanced with convolution layers and ReLU activation function. The third phase, used in the experimental comparison because of its better performances, is enhanced with Multilevel Wavelet Convolution (MWCNN).
## 3 Results and Discussion
### HS-SPME-GC-MS Detection and Analysis of the Volatile Components and Contents of Instant Black Tea  
Analysis of Volatile Compounds Characteristics of Different Instant Teas  
Through the use of headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS), a total of 65 volatile compounds were detected, including 17 aldehydes, 8 alcohols, 15 esters, 12 alkanes, 4 ketones, 4 acids, 2 phenols, and 3 other compounds. The analysis showed that the primary volatile compounds of instant black tea produced by various methods are aldehydes, followed by esters, alkanes, and alcohols. Instant black tea retained the same sweet, floral, and fruity aromas as original black tea. Aldehydes typically exhibit floral notes, alcohols have unique floral and fruity aromas, esters contribute a fruity scent, and ketones provide a floral, sweet, and fruity odor, and these odor types are present in higher concentrations in each group of instant tea [36; 37; 38; 39; 40]. As shown in Table 6, HS-SPME-GC-MS detected 65 volatile compounds, with 11 key odor substances--Benzyl alcohol, Phytol, Phenylethyl alcohol 1,6,10-Dodecatrien-3-ol,3,7,11-trimethyl-,(E)-, Benzeneacetaldehyde, Undecanoic acid, ethyl ester, Dodecanoic acid, ethyl ester, Tetradecane, 2,4-Di-tert-butylphenol, 2-Pentadecanone, 6,10,14-trimethyl-, and indole--having concentrations generally above 500 ug/kg. These compounds play a crucial role in the formation of the odor of instant black tea. Among most of the volatile compounds in instant black tea, alcohol compounds had the highest relative concentration, followed by esters and alkanes (Figure 6), both of which significantly contribute to the tea's odor, consistent with the loading analysis. In Group 7, ketone compounds showed a relatively higher concentration, while alkanes were lower in this group. Additionally, aldehydes were most prominent in Group 1, where alcohols were present in the lowest relative concentration. These findings demonstrate that the odor components vary depending on the processing conditions. The results suggest that using Wuyi Mountain summer and autumn tea leaves and incorporating \(\beta\)-cyclodextrintrim in the production of instant black tea powder help retain the odor of the tea soup. Figure 5: Loading analysis of instant black tea soup.
## 3 Passively Augmenting Mobile Devices
### Leveraging Built-In Sensors and Elements
#### 3.2.1 Microphone  
While the built-in microphone of a mobile device can simply serve as a source for any kind of sound process (e.g., audio effect, physical model, etc.), it can also be used as a versatile, high rate sensor [52]. In this section, we demonstrate how it can be augmented for different kinds of uses.  
One of the first concrete uses of the built-in microphone of a mobile device to control some sound synthesis process was done with [PERSON]'s Ocarina [22]. There, the microphone serves as a blow sensor by measuring the gain of the signal created when blowing on it to control the gain of an ocarina sound synthesizer.  
Mobile3D contains an object that can be used to leverage this principle when placed in front of the microphone (see Figure 5). It essentially allows for the performer to blow into a mouthpiece mounted on the device. The air-flow is directed through a small aperture inside the pipe, creating a sound that can be recorded by the microphone and analyzed in the app using standard amplitude tracking techniques. The air-flow is then sent outside of the pipe, preventing it from ever being in direct contact with the microphone.  
The acquired signal is much cleaner than when the performer blows directly onto the mic, allowing us to generate precise control data. Additionally, condensation never accumulates on the mic which can help extend the duration of its life, etc.  
The built-in microphone of mobile devices has already been used as a data acquisition system to implement various kinds of sensors using frequency analysis techniques [53]. Mobile3D contains an object using similar principles that can be used to control some of the parameters of a synthesizer running on a mobile device. It is based on a conical tube (see Figure 5) where dozens of small tines of different length and diameter are placed inside it. These tines get thicker towards the end of the tube and their length varies linearly around it. When the performer blows inside the tube, the resulting airflow hits the nails, creating sounds with varying harmonic content. By directing the airflow towards different locations inside the tube, the performer can generate various kind of sounds that can be  
Figure 5: Mouthpiece for mobile device built-in mic (**on the left**) and frequency-based blow sensor for mobile device built-in microphone (**on the right**).  
recognized in the app using frequency analysis techniques. The intensity and the position of the airflow around the tube can be measured by keeping track of the spectral centroid of the generated sound, and used to control synthesis parameters.  
The same approach can be used with an infinite number of augmentations with different shapes. While our basic spectral-centroid-based analysis technique only allows us to extract two continuous parameters from the generated signal, it should be possible to get more of them using more advanced techniques.
## 4 The SEME-TDHM Algorithm  
This section introduces the SEME-TDHM algorithm based on the previously discussed 2D hyperchaotic map, providing a comprehensive explanation of the encryption process. The process consists of several stages, including preprocessing, single-channel random scrambling, full-channel random scrambling, local and global diffusion, and bit-level diffusion. The decryption process, being the reverse of encryption, ensures the precise recovery of the original image.
## 3 Results  
We constructed a data set based on orbit data simulated using Fengyun-3E satellite orbit data and the operation data of the two-dimensional turntable of the Solar X-ray and Extreme Ultraviolet Imager (X-EUVI).  
The main steps to build the CNN-LSTM model were as follows:  
1. We set the time window size K and transformed the data set according to the time window size to transform the time series into a supervised sequence; that is, we used the past K values to predict the value of the next time and the original value of the next time as the supervised value.
2. We divided the data set used into the training set and test set and converted the data format into the format required in the CNN-LSTM model, namely (samples, time steps, features).
3. The parameters used in the model, including the number of iterations, the amount of data for each iteration, and the number of neurons, were determined through continuous attempts.
4. We established a CNN-LSTM model. After the model for predicting the data in the data flow was built, the data could be predicted.  
The specific process is shown in Figure 8.