esa-sceva/satcom-chunk-collection huggingface dataset:
# A Deep Learning Framework for Signal Detection and Modulation Classification
## 2 Communication Signal Description and Dataset Generation  
In realistic communication processing, the signal may be distorted by the effect of non-linear amplifier and channel. In actual situation, the received signal in the communication system can be expressed as:  
\[r(t)=\int_{\tau=0}^{\tau_{0}}s(n_{clk}(t-\tau))h(\tau)\mathrm{d}\tau+n_{add}(t) \tag{1}\]  
where \(s(t)\) is the transmission signal, \(n_{clk}(t)\) is timing deviation, \(h(t)\) represents the transmitted wireless channel, \(n_{add}(t)\) is additive white Gaussian noise.  
In this section, we will describe different modulated signals and their sample representation for our DL framework. We will also explain the reason why we use it and the method we enhance it.
## 3 Test and Verification
### Test Error Analysis  
According to Equation (18), there are three sources of error in indicator testing: (1) angle testing error, \(\varepsilon\) (\(\Delta\theta\)) = 0.015\({}^{\prime\prime}\); (2) the temperature sensor thermistor temperature test error limit is \(\varepsilon\) (\(\Delta T\)) = 0.2 \({}^{\circ}\mathrm{C}\), (3) the calculation error of temperature gradient ratio \(N\): \(\varepsilon\) (\(\Delta N\)) = 0.2 \({}^{\circ}\)C/0.1 \({}^{\circ}\)C = 2. The error in the rate of angle change under specified temperature conditions is:  
\[\varepsilon(\Delta v_{set}) =\Big{|}\frac{\partial f}{\partial\Delta\theta}\Big{|}\varepsilon( \Delta\theta)+\Big{|}\frac{\partial f}{\partial\Delta T}\Big{|}\varepsilon( \Delta T)+\Big{|}\frac{\partial f}{\partial N}\Big{|}\varepsilon(N) \tag{26}\] \[\varepsilon(\Delta v_{set}) =0.126^{\prime\prime}/^{\circ}\text{C}\]  
The results of this testing system's indicators are:  
\[v_{set}=0.3247^{\prime\prime}/^{\circ}\text{C}\pm 0.126^{\prime\prime}/^{ \circ}\text{C} \tag{27}\]  
The relative error between the test results and the thermal deformation simulation analysis is:  
\[\varepsilon_{r}=\frac{\|v^{\prime}-v_{0}\|}{\|v_{0}\|}=9.55\% \tag{28}\]  
It can be seen that the relative error between the indicator test results and the thermal deformation simulation analysis results is 9.55%. The two are basically consistent.
# FM-UWB: Towards a Robust, Low-Power Radio for Body Area Networks
## 3 Single-User FM-UWB Communication
In the first, there is a line of sight propagation between the transmit and the receive antenna, while, in the second, an obstacle is placed between the two antennas. The degradation of the link quality can be observed on the oscilloscope screen once the obstacle is in place; however, it is still possible to establish communication. In the first case, BER is below \(10^{-5}\), while, in the second, it increases to \(3.1\times 10^{-4}\). In both cases, 100 kb/s 2-FSK modulation is used. The two examples are shown; however, the BER will vary as the location of the obstacle changes, as well as the surroundings in general. The example in Figure 9 shows performance, in terms of Symbol Error Rate (SER), for different M-FSK modulations. With the implemented receiver baseband bandwidth, up to 8-FSK signal can be received at 100 ksym/s. In general, M-FSK offers better sensitivity for the same data rate; however, it also requires a more complex M-FSK demodulator. The selection of the most efficient modulation then depends on the targeted application. The spectrum at the output of the wideband demodulator is shown in Figure 10 for some of the used modulations. If only a single transmitter is using the given RF band, there is no reason not to use the entire available sub-carrier band; however, if multiple nodes need to transmit, the bandwidth can be reduced to provide enough room for additional channels. This potential for configurability is one of the strong points of FM-UWB. Figure 8: _Cont._  
Figure 6: Test setup used for measuring performance of FM-UWB receiver with different sub-carrier modulations. Figure 7: BER for different data rates. Figure 8: Over the air measurement (**a**) with direct line of sight and (**b**) without the direct line of sight. Signal is transmitted at \(-15\,\mathrm{dBm}\), distance between the transmit and receive antenna is roughly \(2\,\mathrm{m}\), \(100\,\mathrm{kb}/\mathrm{s}\), and \(2\)-FSK modulation is used. Figure 10: Demodulated spectrum for different symbol rates and different sub-carrier modulation complexity: (**a**) \(100\,\mathrm{kb}/\mathrm{s}\), \(2\)-FSK modulation; (**b**) \(400\,\mathrm{kb}/\mathrm{s}\), \(2\)-FSK modulation; (**c**) \(100\,\mathrm{ksym}/\mathrm{s}\), \(4\)-FSK modulation and (**d**) \(100\,\mathrm{ksym}/\mathrm{s}\)\(8\)-FSK modulation. Figure 9: SER for \(2\)-FSK, \(4\)-FSK and \(8\)-FSK sub-carrier modulation.
# Electric Stimulation of Astaxanthin Biosynthesis in _Haematococcus pluvialis_
## 4 Conclusions  
The electro-treatment of _H. pluvialis_ cells for astaxanthin induction in the two-chamber bioelectrochemical system was found to be highly dependent on cathodic (reduction) and anodic (oxidation) current conditions. Cathodic current treatment led to significant lysis and breakage of algal cells, even when a low voltage (2 V; current, 3 mA) was applied. In contrast, astaxanthin biosynthesis was induced successfully in _H. pluvialis_ cells using anodic current treatment, with approximately 36.9% improvement achieved compared to that in the control. This was also accompanied by distinct morphological changes in the cells. Cyclic voltammetry analysis showed that the oxidation and reduction environments were significantly different even at the same voltage. This study suggests that electrochemical stimulation can serve as a unique method for promoting astaxanthin accumulation in _H. pluvialis_. However, for practical applications, extensive research, including the characterization of the precise bioelectrochemical mechanisms, as well as the optimization of process components, such as the reactor, electrode material, and electron mediator, is necessary.  
Conceptualization, Y.-K.O. and S.-Y.L.; Methodology, H.-N.F., S.-Y.L., S.-A.C. and J.-Y.L.; Investigation, H.-N.F. and S.-Y.L.; Resources, J.-S.L.; Supervision, Y.-K.O. and J.-S.L.; Writing--original draft preparation, H.-N.F. and S.-Y.L.; Writing--review and editing, Y.-K.O. and B.-L.K.; Funding acquisition, Y.-K.O. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a 2-Year Research Grant of Pusan National University.  
Not applicable.  
The authors declare no conflict of interest.
# Single-Carrier Rotation-Interleaved Space-Time Code for Frequency-Selective Fading Channels +
## 5 Conclusions  
In this paper, a novel space-time coding approach is investigated for single-carrier transmission in a frequency-selective MIMO fading channel. By multiplexing independent streams of constituent space-time codes in a time-interleaved fashion followed by phase rotations, this new space-time code construction method is able to achieve full space-time-multipath diversity. It is also broadly applicable in the sense that, with proper rotations, it is potentially suitable for any number of transmit antennas or any type of modulation. It is shown that, both in theory and in simulations, the new scheme is a full-rate full-diversity scheme, while being flexible enough to incorporate any STC or MIMO scheme originally optimized for a flat fading channel as its constituent codes. Furthermore, iterative frequency domain equalization and decoding can be readily used for the new scheme to yield better performance than some of the best space-time codes designed for the frequency-selective fading channel with comparable receiver complexity.  
Conceptualization, B.K.N.; methodology, B.K.N. and C.-T.L.; validation, writing--original draft preparation, B.K.N.; writing--review and editing, C.-T.L. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the research funding of the Macao Polytechnic University, Macao SAR, China (Project no. RP/ESCA-02/2020).  
Not applicable.  
The authors declare no conflict of interest.
# Deterministic Propagation Modeling for Intelligent Vehicle Communication in Smart Cities
## 5 Measurements Results
### Throughput Analysis in V2V and V2I Links
The best transmission results are obtained when the transceivers (V2V or V2I) are located on the opposite sides (in front). V2V communication provides an average data transmission noticeably better than V2I. However, the use of a greater bandwidth (10 and 20 Mbps) on the transmission threads does not seem to influence too much the number of bytes received. 1808 Kbytes are received on the V2V-Infront case, while 1102 Kbytes are received on the V2I-Infront case. Worst values are obtained on V2I-Sameside (741 Kbytes) and V2V-Sameside (974 Kbytes). The analysis of the effective throughput received (see Figure 17) shows a similar pattern, so we can conclude that V2V-Infront is the best configuration and V2I-Sameside provides the worst results. Figure 15: Disaggregation of the Kbytes transferred by each thread when considering a communication between vehicles (V2V) located on the same side (**top**) or on the opposite side (**bottom**) and different bandwidths (10 Mbps -left-, and 20 Mbps -right-). Figure 16: Average transmission. Number of Kbytes received. At this point, we cannot consider whether it is better or not to choose a lower or higher bandwidth on transmission, but the analysis of PER reveals that it is better to opt for a lower transmission rate, since it guarantees a lower rate of losses, which results in a better functioning of the system.
# Touch, Texture and Haptic Feedback: A Review on How We Feel the World around Us
## 4 Haptic Feedback
### Cutaneous Feedback
#### 4.2.1 Vibrotactile Stimulation  
The most common type of cutaneous feedback applied is vibrotactile stimulation. Vibrotactile stimulation is both low-cost, low-power, and low-profile, allowing for it to be attached in a variety of locations within the hand. Many electronic devices including phones and computers make use of vibrations for alerts and most commercially available virtual reality systems such as the Oculus Quest and HTC Vive make use of vibrotactile stimulation as their primary form of cutaneous feedback [120]. Vibrotactile phantom stimulation is used to provide the illusion of slippage and weight onto objects by way of a series of magnetic pins that vibrate in response to interactions in the virtual system [121; 122]. Since human texture recognition is associated with micro vibrations in the range of 1 Hz to around 500 Hz, there is a possibility in mimicking these micro vibrations through small actuators to replicate finger movements and haptic feedback [123]. The previously mentioned Dextres, aside from generating kinesthetic feedback, also incorporates cutaneous feedback in the form of vibrations from a piezovibe, which is described as a series of resonating piezoelectric beams [93]. Different research has also combined visual and haptic feedback by showing different textures through a vibration/friction highlighting its possible use in mimickingtexture [124, 125]. Vibrotactile stimulation, while simple and easy to implement, is, however, very limited in the types of stimulation that it can achieve, hence why it is generally paired with other feedback systems instead [90, 93].
## 2 Materials and Methods  
### Sample Preparation  
Raw materials of OF and commercial FW fertilizer (Daewon-Nongsan, Chungbuk, Republic of Korea) were purchased for NaCl concentration analysis and hyperspectral image acquisition. Six OF raw materials were selected: castor oil cake, rapeseed oil cake, soybean oil cake, bone dust, rice bran, and fish meal; these items are widely consumed in Korea. The spectral data of the six raw materials and FW fertilizer were then obtained. The FW fertilizer was uniformly mixed with the raw materials, for FWC concentrationsranging from 0 to 100%, i.e., 0, 5, 10, 15, 20, 30, 40, 50, and 100%, and the spectral data of the various samples within the Vis/NIR region were obtained. A total of 275 mixture samples with different FWC concentrations were fabricated on the laboratory scale. FW fertilizer and OF particle sizes were measured as ranging from 0.1 to 5 mm.  
The ICP-OES method, one of the most widely used physicochemical analysis techniques in agricultural studies [25; 26], was used to analyze the NaCl concentrations in the six major OF components and FW fertilizer. Table 1 lists the ICP-OES measurement results (ICP-OES 5100 series, Agilent Technologies, Santa Clara, CA, USA).  
In Table 1, FW fertilizer, bone dust, and fish meal have relatively high NaCl concentrations compared to the plant-based OF materials. Only the FW fertilizer has a very high level of capsaicin. As expected, although bone dust and fish meal have higher NaCl levels than the other components, the FW fertilizer has the highest concentrations of both NaCl and capsaicin.
## 3 Price Predictions Using Deep Learning Models
### Time-Series Forecasting Models
#### 3.2.4 Gated Recurrent Units (GRUs)  
While drawing inspiration from the powerful Long Short-Term Memory (LSTM) networks, GRUs offer a streamlined approach by streamlining the internal gating mechanisms, resulting in a more parsimonious architecture with fewer parameters. This translates to significant advantages in terms of training speed and computational efficiency, making them particularly well suited for large-scale time-series forecasting tasks [42]. They consist of three key components: the update gate, the reset gate, and current memory.  
* Update Gate: This controls the extent to which the candidate hidden state, incorporating new information, replaces the previous hidden state. It determines which information to retain from the past.
* Reset Gate: This decides how much of the past hidden state to forget before incorporating new information. It effectively resets the memory when necessary.
* Current Memory: This captures the current understanding of the sequence, reflecting the combined influence of previous inputs and the update gate's selection.
# From Brown-Field to Future Industrial Networks, a Case Study
## 4 Brown-Field Traffic Analysis  
In this section, we first present a comparative analysis between common assumptions in the literature and data from a brown-field to demonstrate the differing outcomes. We then suggest a characterization approach that can potentially reduce the complexity of the configuration and performance managements of converged IT-OT networks.  
In analyzing the network traffic, we first set a framework for the characterization process. Network modeling and characterization play an important role in various network management functions, including configuration and performance management. It is a prerequisite to estimate traffic demands on the network capacity for bandwidth allocation and to identify potential congestion and delays caused by exceeding allocated bandwidth [6]. Accordingly, we focus on the transmitted data in the network and throughput. Network traffic dynamics is an important aspect to consider for configuration and performance evaluation. Therefore, we apply a mixture measurement and study the throughput dynamics. The scope of measurement for the comparative analysis covers connectivity structure and topological structure (VLANs). The reason for this choice is to find points of connections with the body of related works and common practice. The comparisons are motivated regarding the complexity and flexibility required for the realization of the converged IT-OT networks.
# An Algorithm for Making Regime-Changing Markov Decisions
## 4 Approximate Algorithmic Solutions
Proposition 1: _Given a regime modulated Markov decision problem whose dynamics are defined by the stochastic kernel (18) with control costs given by (19) and (20), consider the value functions \((v_{t})_{t=0}^{T}\) returned by the corresponding backward induction_  
\[v_{T}(p,\widehat{s}) = r_{T}(p,\widehat{s}), \tag{38}\] \[v_{t}(p,\widehat{s}) = \max_{a\in A}(r_{t}(p,\widehat{s},a)+\mathcal{K}_{t}^{a}v_{t+1}(p,\widehat{s})),\quad p\in P,\widehat{s}\in\widehat{S},\,\,\,\,t=T-1,\ldots,0.\]  
_Moreover, consider functions \((\hat{v}_{t})_{t=0}^{T}\) returned by_  
\[\tilde{v}_{T}(p,z) = \tilde{r}_{T}(p,z), \tag{39}\] \[\tilde{v}_{t}(p,z) = \max_{a\in A}(\tilde{r}_{t}(p,z,a)+\tilde{\mathcal{K}}_{t}^{a} \tilde{v}_{t+1}(p,z)),\quad p\in P,z\in\mathbb{R}_{+}^{S},\,\,\,t=T-1,\ldots,0.\]  
_where \(\tilde{r}_{t}\), \(\tilde{r}_{T}\) are positively homogeneous extensions of \(r_{t}\), \(r_{T}\) and \(\tilde{\mathcal{K}}_{t}^{a}\) is from (36). Then for \(t=0,\ldots,T\), it holds that_  
\[\tilde{v}_{t}\text{ is positively homogeneous extension of }v_{t} \tag{40}\]  
Proof: Let us prove (40) inductively. Starting at \(t=T\), assertion (40) holds since \(\tilde{r}_{T}\) is a positively homogeneous extension of \(r_{T}\). Having assumed that (40) holds for \(t+1\) with a positively homogeneous \(\tilde{v}_{t+1}\), we apply observation (37) to conclude that  
\[\tilde{\mathcal{K}}_{t}^{a}\tilde{v}_{t+1}\text{ is a positively homogeneous extension of }\mathcal{K}_{t}^{a}v_{t+1}\]  
thus adding \(\tilde{r}_{t}(\cdot\cdot,a)\), which is a positively homogeneous extension of \(r_{t}(\cdot,\cdot,a)\) and maximizing over \(a\in A\) yields (40). To finish the proof, we verify (37). Given \(v\), with a positively homogeneous extension \(\tilde{v}\), for \(p\in P\) and \(z\in\mathbb{R}^{S}_{+}\) it holds that  
\[\tilde{K}^{a}_{t}\tilde{v}(p,z) = \sum_{p^{\prime}\in P}\alpha^{a}_{p,p^{\prime}}\big{(}\frac{z}{ \|z\|}\big{)}\mathbb{E}(\tilde{v}(p^{\prime},\Gamma^{\top}\mathcal{V}(Y_{t+1}) z)) \tag{41}\] \[= \sum_{p^{\prime}\in P}\alpha^{a}_{p,p^{\prime}}\big{(}\frac{z}{ \|z\|}\big{)}\int_{Y}\tilde{v}\Big{(}p^{\prime},\Gamma^{\top}\mathcal{V}(y^{ \prime})z\Big{)}\|\mu(\mathrm{d}y^{\prime})\] \[= \sum_{p^{\prime}\in P}\alpha^{a}_{p,p^{\prime}}\big{(}\frac{z}{ \|z\|}\big{)}\int_{Y}v\Bigg{(}p^{\prime},\frac{\Gamma^{\top}\mathcal{V}(y^{ \prime})z}{\|\mathcal{V}(y^{\prime})z\|}\Bigg{)}\|\mathcal{V}(y^{\prime})z\| \mu(\mathrm{d}y^{\prime}). \tag{42}\]  
From the expression (41) we conclude that \(\tilde{K}^{a}_{t}\tilde{v}\) is indeed positively homogeneous. Setting \(z=\tilde{s}\in\widehat{S}\), we observe \(\tilde{K}^{a}_{t}\tilde{v}(p,\widehat{s})=\mathcal{K}^{a}_{t}v(p,\widehat{s})\), meaning that \(\tilde{K}^{a}_{t}\tilde{v}\) is a function extension of \(\mathcal{K}^{a}_{t}v\).
## 4 Experiments and Discussion
### Validation of Proposed Noise Reduction Approach
#### 4.1.2 Experimental Results and Discussion
As observed in these two results, the proposed approach achieved a significant performance improvement compared to the conventional MMSE in overall noise conditions, and the results support our expectation that the proposed hard-decision parameter based on spectral energy variation more correctly determines speech-present and speech-absent bins than the posterior SNR-based decision approach. Finally, we investigated the performance of the proposed approach according to the size of the analysis window. The result and discussion for this experiment is described in Appendix B. We also examined noise reduction performance in the context of speech recognition task. With the proposed speech recognition architecture, the user device performs the voice trigger process. Thus, we investigated the performance improvement of voice trigger detection for noisy speech for each noise reduction method. In this experiment, we simulated noisy speech data by adding different types of noise data to clean speech recorded in a silent environment while adjusting noise conditions (0 dB to 15 dB in SNR). Here, the speech data comprised general conversation speech of five participants recorded for approximately two hours, including 500 utterances corresponding to a predefined trigger word (\"alpha\"). The experimental setup for trigger word detection (including the acoustic models) is described in Section 4.2. Figure 12 shows the trigger word detection performance in terms of equal error rate (EER). The EER of the original noisy speech (\"No processing\") is compared to the performance of de-noised speech processed by the conventional and proposed noise reduction methods. As can be seen, noise reduction processing significantly improved trigger word detection accuracy for overall noise conditions. Note that more improvement was observed under severe noise conditions. Among noise reduction methods, the proposed MMSE method outperformed the other method, yielding approximately 7%, 12%, and 3% relative improvement over spectral subtraction, adaptive comb filtering and the conventional MMSE methods, respectively. We found that adaptive filtering and spectral subtraction provided poor accuracy, which indicates that spectral filtering approaches may induce signal distortions in speech while reducing noise components. This tendency was particularly notable in severely noisy speech, which was also observed in the previous results based on spectral measures. Figure 11: Comparison of noise reduction performance based on SNR.
# Semantic Distributed Data for Vehicular Networks Using the Inter-Planetary File System
## 3 IPFS (Inter-Planetary File System)
### Advantages of Content-Addressing  
Although IPFS works over standard IPv4 or IPv6 networks, the resultant P2P network shares many similarities with ICN. The usage of the ICN paradigm has been thoroughly explored by several researchers [25; 35]. The key advantages shared by both IPFS and ICN are:  
1. Content-addressing. Applications built over IPFS do content requests by CID and do not need to know the location of the content or its uniform resource identifier (URI).
2. Decentralization. As the content is not tied to a specific host, there is no need for permanent servers and the same piece of content can be retrieved from multiple nodes. When one node is disconnected from the network, the network topology changes automatically, and the content published by the detached node is still available from other nodes.
3. Scalability. Network load is naturally balanced avoiding bottlenecks. If the content published by a node is highly demanded, multiple nodes will have a copy and can provide it to others, effectively distributing the network usage.
4. End-to-end data integrity. IPFS hash-addressing method guarantees that the data are never tampered with.
5. Reduced latency and fault tolerance. As every piece of content is transparently retrieved from the best available location, latency is improved, and if the source unexpectedly becomes unavailable or congested, the content is automatically retrieved from another source.  
Nevertheless, IPFS is not as technically complex as ICN. IPFS is open source software that works as a user application over the standard network services provided by any operating system. Compared to a full hardware and software ICN implementation, IPFS incurs in a modest performance hit in the host system and it is burdened by the IP protocol noticeable overhead. The experiment results of Section 7 demonstrate this effect. However, it has the advantage of being multi-platform (Windows, Linux,  
Figure 4: The peer A is the creator of the file. After sharing the hash, D can download the file from A. Immediately, D notifies E that it can download the file from it without connecting to A.  
macOS, and Android) and compatible with existing IPv4 or IPv6 network infrastructure. IPFS software is still in development, but the codebase is stable and mature after years of uninterrupted testing on global public networks with thousands of live nodes across the world.