esa-sceva/satcom-chunk-collection huggingface dataset:
# Single-Carrier Rotation-Interleaved Space-Time Code for Frequency-Selective Fading Channels +
## 3 Rotation-Interleaved Multi-Stream Space-Time Code
### Symbol-Wise Random Phase Rotation
After proving the case with \(\textbf{B}^{(1)}\) being non-zero, we may repeat the above procedure by considering other super-codewords with \(\textbf{B}^{(1)}\) being strictly zero and \(\textbf{B}^{(2)}\) being non-zero. The matrix **B** can now be permuted such that \(\textbf{B}^{(2)}\) lies in the diagonal and **B** contains only other blocks \(\textbf{B}^{(i)},i=2,\cdots,M\). It can be reduced to a full rank matrix (with probability one) with all leading entries in the form of (13) (the smallest power of \(\beta\) is two). The same procedure then repeats with \(\textbf{B}^{(1)}\) and \(\textbf{B}^{(2)}\) both being zero and \(\textbf{B}^{(3)}\) being non-zero. As such, it can be repeated for all other remaining super-codewords with \(\textbf{B}^{(m)},m=3,\cdots,M-1\) respectively being zero. A major implication of the above proposition is that when the angles in the phase rotations are random, the codeword difference matrix **B** (11) is full-rank irrespective of the type of modulation employed in the STC. Hence, the limitation is relaxed as to what type of STC scheme previously proposed is applicable in our design, making the proposed scheme more flexible in accommodating different modulation schemes due to different system requirements such as data rate. Note that in practice, however, the angles produced are not truly random over \([0,2\pi)\) but they are countable rational numbers produced from a pseudo-random generator. In section 5, simulation results will show that full diversity can be obtained using the computer-generated random number sequence produced in Matlab.
# RS-YOLOX: A High-Precision Detector for Object Detection in Satellite Remote Sensing Images
## 5 Discussion
### Other Interesting Discussions  
Our research is to use deep learning technology to achieve the automatic detection of objects in remote sensing images. In the introduction part, we mentioned that the method of manually extracting features is not enough to fully extract the target features, and using deep learning methods to automatically extract features can greatly improve the efficiency.  
In fact, in the field of remote sensing applications, although automated methods are highly efficient, they may not be the optimal method in certain specific environments. In some cases, manual methods of processing remote sensing data can also achieve good results. For example, [PERSON] et al. [58] collected data by controlling the drone, and then used related software to reconstruct the point clouds and textured 3D models of the survey objects. This study did not use a fully automated process, and many critical steps were performed manually, but according to these methods, photorealistic 3D models can be reconstructed. This shows that in the field of remote sensing, the use of artificial methods can also achieve good enough results.  
Automated methods can process large amounts of data in a short period of time, and they have the advantages of convenience and high efficiency. However, they cannot replace the flexible thinking of humans. In some situations that require complex operations, manual methods are obviously more reliable than automated methods. We pursue technological innovation to facilitate people's lives, but we do not want the automation of machines to replace some activities that require human creativity because the accumulation of human experience and knowledge is the most valuable treasure, which cannot be replaced by machines.
## 3 Signal Model  
A block diagram of the system model for a QPSK-received signal is presented in Figure 3, where a MATLAB simulation model is used to generate a QPSK-modulated signal \(S(t)\) and an interference signal \(J_{i}(t)\) with the Additive White Gaussian Noise (AWGN) \(w(t)\).  
Figure 3: Block diagram of system model for a QPSK receiver.  
_Received Signal Model for The QPSK System_  
For a QPSK system, the received signal, \(r(t)\), is the input signal to the IIR NF, and it consists of a QPSK-modulated signal \(S(t)\) with the interference signal, \(J_{i}(t)\), and the AWGN, \(w(t)\), as an illustration of our method, and is expressed as:  
\[r(t)=S(t)+w(t)+J_{i}(t) \tag{1}\]  
where \(S(t)\) is the transmitted signal (QPSK), and has the following format:  
\[S(t)=S_{1}(t)+jS_{2}(t)\]  
and \(J_{i}(t)\) is the interference signal:  
\[J_{i}(t)=J_{i1}(t)+jJ_{2}(t)\]  
Equation (1), then sampled at the chip rate to convert it into discrete time \(n\), is \(r(n)\), and is expressed as:  
\[r(n)=S(n)+w(n)+J_{i}(n) \tag{2}\]  
where \(J_{i}(t)\) can be modeled as:  
\[J_{i}(n)=\sum_{i=1}^{m}A_{i}cos(\omega_{i}n+\theta_{i}) \tag{3}\]  
* \(A_{i}\) is the unknown amplitude of the \(i\)th interference signal;
* \(\omega_{i}\) is the unknown frequency of the \(i\)th interference signal and \(\omega_{i}=2\pi f_{i}\) ;
* \(\theta_{i}\) is the phase delay of the \(i\)th interference signal, which is considered to be a random variable uniformly distributed in the range \([-\pi,\pi]\);
* \(m\) is the number of CW interferences, and \(n\) is the time index. Substituting interference signal \(J_{i}(n)\) into Equation (2): \[r(n)=S(n)+w(n)+\sum_{i=1}^{m}A_{i}cos(2\pi f_{i}n+\theta_{i})\] (4)
_Article_  
Long Term Stable \(\Delta\)-\(\Sigma\) NDIR Technique Based on Temperature Compensation  
[PERSON] * and [PERSON] *  
Department of Mechatronics Engineering, National Changhua University of Education, NO. 2, Shi-Da Road, Changhua 50074, Taiwan, R.O.C.  
* Correspondence: [EMAIL_ADDRESS] (C.-H.S.); [EMAIL_ADDRESS] (J.-H.Y.); Tel.: +886-922-217-650 (C.-H.S.); +886-938-753-692 (J.-H.Y.)  
Received: 30 November 2018; Accepted: 10 January 2019; Published: 16 January 2019  
**Featured Application: With the development of industrial precision measurement, highly accurate and stable sensing systems usually require temperature compensation to maintain stability. In this study, the effects of ambient temperature are analyzed and a temperature compensation model is established for the NDIR gas sensing system with \(\Delta\)-\(\Sigma\) structure to achieve a long term stable gas measurement.**  
**Abstract: For a fast and long term stable Non Dispersive Infrared (NDIR) technology of gas concentration measurement, the temperature compensation is required. A novel proposed \(\Delta\)-\(\Sigma\) NDIR system was investigated and built with a closed-loop feedback system to stabilize the signal readings without temperature drift. The modulation of the infrared heater gives a corresponding signal of gas concentration based on our proposed \(\Delta\)-\(\Sigma\) conversion algorithm that was affected by the drift of temperatures for the infrared sensor. For our study, a new temperature compensation model was built and verified that formulates the relationship between gas concentration and temperature of sensor. The results show that our proposed \(\Delta\)-\(\Sigma\) can measure efficiently with half of the startup time than our previous design and maintain long term stability.**  
**Keywords: \(\Delta\)-\(\Sigma\); NDIR; temperature compensation**
## 5 Conclusions and Future Work  
In this paper, we proposed a selective and aligned online data augmentation algorithm to improve massively many-to-many machine translation, especially in non-English directions. The algorithm introduces a selective online back-translation method to pick suitable and high-quality training samples with appropriate selection criteria based on the combination of CE loss and QE scores. During training, we fine-tune the baseline MNMT model with contrastive learning to represent similar sentences across languages in a shared space and minimize the representation distance of similar sentences. In addition, we further boost the SOBT algorithm with cross-lingual online substitution, generating code-switched source sentences on-the-fly to strengthen transfer learning between zero-shot language pairs. Based on a better MNMT initialization fine-tuned with the SOBT algorithm, CLOS can further bring continued and significant improvements without any explicit external training resources. We thoroughly evaluated our experiments on two widely used multilingual translation benchmarks: IWSLT2014 and OPUS-100. Experimental results show that our approach substantially improves translation performance of both English-centric and non-English directions, outperforming previous state-of-the-art methods, such as mRASP2 and ROBT.  
In the future, we will extend our methods by leveraging both source- and target-side monolingual data since monolingual data is abundant and easy-to-access. We also intend to investigate the benefits of pre-trained models in massively many-to-many translations. In addition, we will also work on how to reduce the computational costs of multi-step fine-tuning.  
Conceptualization, W.Z. and L.D.; data curation, S.W.; formal analysis, W.Z. and L.D.; funding acquisition; investigation, W.Z. and S.W.; methodology, W.Z., L.D. and J.L.; project administration; resources, W.Z. and J.L.; software, W.Z.; supervision, L.D.; validation, W.Z.; visualization, W.Z.; writing--original draft, W.Z.; writing--review and editing, L.D. and J.L. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Key R&D Program of China (2020 AAA0107905).  
Not applicable.  
The dataset used in this paper can be accessed by [[https://wit3.fbk.eu/2014-01](https://wit3.fbk.eu/2014-01)]([https://wit3.fbk.eu/2014-01](https://wit3.fbk.eu/2014-01)) and [[https://opus.nlpl.eu/opus-100.php](https://opus.nlpl.eu/opus-100.php)]([https://opus.nlpl.eu/opus-100.php](https://opus.nlpl.eu/opus-100.php)) (accessed on 1 March 2023).  
The authors declare no conflict of interest.
# Research on the Mechanism and Dynamic Characteristics of Intermitt Failure of Electrical Connectors
## 4 Simulation Case
### Dynamics of Intermittent Failure Under Different Relative Motion Parameters  
In the simulation, the effects of amplitude \(Z_{c}\) and frequency \(f\) on intermittent failures are investigated. The number of intermittent failures \(N_{\text{I}}\) and the duration of intermittent failures \(D_{I}\) during the simulation time interval \((0,t)\) are expressed as  
\[N_{F}=2 tf \tag{18}\]  
\[D_{F}=\frac{\pi-\arccos\frac{m}{A}-\arccos\frac{n}{A}}{\pi f} \tag{19}\]  
According to Equations (18) and (19), \(\overline{D}_{F}\) can be expressed as  
\[\overline{D}_{F}=\frac{D_{F}}{N_{F}}=\frac{\pi-\arccos\frac{m}{A}-\arccos \frac{n}{A}}{2\pi f^{2}t} \tag{20}\]  
According to Equation (20), the three-dimensional correlation surface of amplitude-frequency-duration can be plotted, as shown in Figure 14.  
Additionally, the average duration \(\overline{D}_{I}\) decreases with the increase in \(f\). As shown in Figure 14, \(\overline{D}_{I}\) decreases rapidly with the increase in \(f\) before 0.3 kHz, and then the descending trend is slowed down with the decrease in \(f\). Moreover, \(\overline{D}_{I}\) is found to increase with the amplitude \(Z_{c}\). In the simulation, the amplitude \(Z_{c}\) is fixed, while the frequency \(f\) is taken as the single factor variable for investigating the dynamic characteristics of intermittent failure.  
Figure 13: Intermittent failure dynamic characteristics for different numbers of conductive spots.  
The other parameters are set as shown in Table 1 and frequency \(f\) is set to 100 Hz and 1000 Hz separately. Thus, the contact resistance curves under different movement frequencies are shown in Figure 15.  
The duration of intermittent failure under 100 Hz is found to be longer than the duration of intermittent failure under 1000 Hz. and number of intermittent failures under 100 Hz is less than number of intermittent failures under 1000 Hz.  
To further analyze the dynamic characteristics of intermittent failures, a large number of simulations are implemented under the same condition. In the simulations, the movement frequency \(f\) is increased from 100 Hz to 1000 Hz at the step of 100 Hz. The average number of intermittent failures \(\overline{N}_{I}\) and the average duration of intermittent failure \(\overline{D}_{I}\) as functions of frequency \(f\) are shown in Figures 16a and 16b, respectively.  
From the simulation results, it can be seen that the average number and the average duration of the intermittent failure under the frequency \(f\) are consistent with the theoretical analysis. The effects of the movement amplitude \(Z_{c}\) on intermittent failure dynamic behaviors could be analyzed using the same approach as that for the effects of movement frequency.  
Figure 14: The three-dimensional correlation surface of amplitude-frequency-duration.  
Figure 15: Contact resistance curves under different motion frequencies.
# Experimental Study of the Formation and Evolution of Gas Jets in Supersonic Combustion Chambers
## 3 Results  
The instantaneous images obtained with the proposed imaging system were used to study the formation and evolution of a nitrogen jet in supersonic airflow. The present section describes and discusses the experimental results, and is organized as follows. Section 3.1 presents the typical instantaneous flow field diagram under various working conditions; this includes an analysis of the structural characteristics of the flow field under different jet angles and jet momentum flux ratios. Meanwhile, Section 3.2 describes the spatiotemporal characteristics of the flow field under typical working conditions, using the images' time series. Finally, the temporal statistical characteristics of the flow field are discussed in Section 3.3.
# Sparsity-Inducing Super-Resolution Passive Radar Imaging with Illuminators of Opportunity
## 3 Super-Resolution Imaging for Passive Radar  
The ESPRIT algorithm for passive radar imaging has been investigated in [14], which employs the rotation invariance technique and eigenvalue decomposition to estimate the amplitude and spatial frequency of each scatterer. In this section, two super-resolution algorithms including RELAX and CS are presented in detail.  
After discretization, Equation (14) can be rewritten as  
\[s\left(m\right)=\sum_{k=0}^{K-1}h\left(k\right)\exp\left[jm\Delta\Theta\cdot \omega_{k}\right], \tag{15}\]  
where \(\Theta=m\Delta\Theta\), \(m=0,1,...,M-1\), with \(m\) being the sampling number. Then, Equation (15) can be reformulated as the vector-matrix form  
\[\mathbf{s}=\mathbf{Q}\mathbf{h}, \tag{16}\]  
where \(\mathbf{s}=\left[s\left(0\right),s\left(1\right),\cdots,s\left(M-1\right)\right]^{T}\), \(\mathbf{Q}=\left[f\left(\omega_{0}\right)\), \(f\left(\omega_{1}\right),\cdots,f\left(\omega_{K-1}\right)\right]\), \(f\left(\omega_{k}\right)=\left[1,e^{j\Delta\Theta\omega_{k}}\), \(e^{j2\Delta\Theta\omega_{k}},\cdots,e^{j\left(M-1\right)\Delta\Theta\omega_{k} }\right]^{T}\), \(\left(k=0,1,...,K-1\right)\), \(\mathbf{h}=\left[h\left(0\right),h\left(1\right),\cdots,h\left(K-1\right)\right]^{T}\), with \(\left[\cdot\right]^{T}\) being the transpose operator.
# Design of Infinite Impulse Response Filters Based on Multi-Objective Particle Swarm Optimization
###### Abstract  
The goal of this study is to explore the effectiveness of applying multi-objective particle swarm optimization (MOPSO) algorithms in the design of infinite impulse response (IIR) filters. Given the widespread application of IIR filters in digital signal processing, the precision of their design plays a significant role in the system's performance. Traditional design methods often encounter the problem of local optima, which limits further enhancement of the filter's performance. This research proposes a method based on multi-objective particle swarm optimization algorithms, aiming not just to find the local optima but to identify the optimal global design parameters for the filters. The design methodology section will provide a detailed introduction to the application of multi-objective particle swarm optimization algorithms in the IIR filter design process, including particle initialization, velocity and position updates, and the definition of objective functions. Through multiple experiments using Butterworth and Chebyshev Type I filters as prototypes, as well as examining the differences in the performance among these filters in low-pass, high-pass, and band-pass configurations, this study compares their efficiencies. The minimum mean square error (MMSE) of this study reached 1.83, the mean error (ME) reached 2.34, and the standard deviation (SD) reached 0.03, which is better than the references. In summary, this research demonstrates that multi-objective particle swarm optimization algorithms are an effective and practical approach in the design of IIR filters.  
infinite impulse response filters; Butterworth filters; Type I Chebyshev filters; multi-objective particle swarm optimization +
Footnote †: journal: _signals_
# On Applicability of Network Coding Technique for 6 LoWPAN-based Sensor Networks
## 3 Network Coding for 6 LoWPAN-Based Wireless Sensor Networks
### IPv6 over Low-Power Wireless Personal Area Networks (6 LoWPAN)  
In [36], the 6 LoWPAN RFC4944 standard was introduced to support IPv6 packets transmission in the IEEE802.15.4-based sensor networks. The Maximum Transmission Unit (MTU) of the IPv6 streams cannot be smaller than 1280B, while the IEEE 802.15.4 standard offers a maximum frame payload of 127B [37]. The 6 LoWPAN provides an adaptation layer that is located between the MAC layer and the IPv6 protocols stack. Additionally, it ensures IPv6 packets fragmentation to cope with small payloads. Besides, it allows for deep headers compression, in order to minimise the upper layers overhead. Since the IEEE802.15.4 standard offers a maximum 250 kbps data rate, the IPv6 protocols stack overhead would significantly reduce the network goodput. To increase sensor nodes resources utilization, the 6 LoWPAN proposes--as an option--the so-called route-over routing mechanism. Fragments of IPv6 packets are transmitted to the destination nodes without reassembling at intermediate nodes, and without forwarding the full IPv6 packets at the network layer. This overcomes the problem of a larger memory size that is required in the nodes to collect fragments before reassembling. Unfortunately, in lossy sensor network, the route-over routing increases the packet loss ratio. Therefore, the traditional forwarding method (mesh-under routing) is often applied in the software used in sensor modes (i.e., in the Contiki Operating System).
# A Community-Based Construct Method for an Inter-Satellite Communication Network of Satellite Swarm
## 4 Optimized Constructions of ICNS
### Dynamic-Connected and Dynamic Paths
However, if \(k>\Gamma-\log_{2}|V|\), certain dynamic paths will not finish before the state period ends, which means that the ICNS will never be dynamically connected from slot \(k\) to the last slot of the state period.
## 2 Project Background and Calculation Method
### Determination of disturbance coefficient, \(D\)  
Based on the disturbance situation of the field tunnel [36], referring to the rock disturbance coefficient value chart (Table 1), it is determined that the disturbance coefficient of the area affected by blasting and excavation of the cross-tunnel project is 0.5, that of the area affected by static excavation is 0, that of the area affected by the train dynamic load is 0.2, and that of the area overlapping the blasting load and the train dynamic load is 0.7.
# Total Polypphenols and Antioxidant Properties of Selected Fresh and Dried Herbs and Spices
## 4 Discussion
### Total Polyphenols Content

Table 3: Antioxidant activity of fresh and freeze-dried herbs and spices measured by the DPPH method (% RSA). nols) and the antioxidant activity of fresh and freeze-dried herbs were compared. Additionally, in freeze-dried spice plants, the above components were taken into account, distinguishing between the stems and leaves of these spices. Moreover, the content of polyphenols and the antioxidant activity of retail, commercial food spices of two leading companies on the Polish market were compared. The total content of polyphenols, determined in the analyzed fresh and powdered spices, is presented in Table 1. The highest content of polyphenols in fresh herbs and spices were found in lemon balm, followed by oregano and rosmearny. Differences in the obtained results may be due to the origin of the herbs, climatic conditions during cultivation or the fertilizers used, which significantly affect the content of polyphenic compounds. Additionally, according to [PERSON] et al. [43], in fresh herbal spices, phenolic compounds may be degraded as a result of enzymatic activity. In the present study, in the majority of cases, in the freeze-dried herbs and spices, a significantly (\(p<0.05\)) higher content of total polyphenols in the dried leaves was found in relation to the stems of these herbs (r = 0.8102). There are no comparative data for this type of research in the available literature. Among the analyzed commercial spices of two companies, a comparable content of these compounds was found for six spices, such as ginger, cumin, parsley, oregano, rosmearny, and cayenne pepper. The commercial spices showed that Company 1's thyme, tarragon, basil, bay leaf, and lovage were characterized by a higher average total polyphenol content than the same herbs and spices from Company 2 (\(p<0.05\)). On the other hand, in spices such as black pepper, ginger, or dill from Company 2, higher amounts of the discussed compounds were recorded than in the analogues of Company 1 (\(p<0.05\)). According to the data of other authors [44], the total content of polyphenols in dried herbs or spices was higher (rosemary, sage), comparable (thyme), or lower (ginger, basil) than those determined in this study. Completely different results were obtained by [PERSON] et al. [45]. The total content of polyphenols in the analyzed products may be affected by the extraction efficiency (type of solvent, temperature, and extraction time) [46; 47]. High content of polyphenic compounds, shown in dried herbal spices, may result from the activation of masked polyphenols as a result of high temperature [48].
# A Practical Neighbor Discovery Framework for Wireless Sensor Networks
## 6 Methods of Reducing Collisions  
In real communication scenarios, two neighboring nodes can communicate successfully only when they are not interfered by other nodes. If existing deterministic algorithms are extended to handle multiple nodes directly, communication collisions happen and most nodes cannot find the neighbors. In this section, we analyze the discovery probability and propose two methods to reduce the collisions.