esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimized Polarization Encoder with High Extinction Ratio for Quantum Key Distribution System
###### Abstract  
Polarization encoding is a promising approach for practical quantum key distribution (QKD) systems due to its simple encoding and decoding methodology. In this study, we propose a self-compensating polarization encoder (SCPE) based on a phase modulator, which can be composed of commercial off-the-shelf (COT) devices. We conducted a proof-of-concept experiment to test the SCPE, which demonstrated an in-system quantum bit error rate (QBER) of 0.53% and long-term running stability without any active adjustments. Additionally, we conducted experiments with transmission over commercial fiber spools of lengths up to 100 km and obtained a secure finite key rate of 3 kbps. Our polarization encoder is a promising solution for various polarization encoding protocols, including BB84, MDI, and RFI.  
quantum key distribution; polarization encoder; Sagnac loop +
Footnote †: journal:  
use polarizing beam splitters (PBS) for decoding, meaning they require an additional step to transform the polarization states from \(|L\rangle,|R\rangle,|D\rangle,|A\rangle\) to \(|H\rangle,|V\rangle,|D\rangle,|A\rangle\). An additional polarization converter to transform the polarization states into \(|H\rangle,|V\rangle,|D\rangle,|A\rangle\) will increase the complexity of the QKD system. It is evident that the Sagnac scheme must work together with the corresponding decoder and cannot serve as a substitute for the polarization modulator.  
In this paper, we propose a self-compensating polarization encoder (SCPE) named SCPE, which exhibits high intrinsic stability and is insensitive to the phase differences caused by temperature changes. Furthermore, the SCPE has a high extinction ratio and low bit error rate. Importantly, the SCPE can directly generate the four polarization states of \(|H\rangle,|V\rangle,|D\rangle,|A\rangle\), effectively replacing the need for polarization modulators in QKD systems. In experimental tests, the SCPE exhibited an average quantum bit error rate (QBER) of 0.53% without any active compensation. The QKD system was also tested over commercial optical fibers, where a total of \(N=10^{10}\) pulses were sent out, and we ultimately achieved a key rate of 3 kbps at a distance of 100 km.
# Effect of Different Drying Methods on the Quality of _Oudemansiella raphanipes_
## 3 Results and Analysis
### Effects of Different Drying Methods on the Volatile Substances of O. raphanipes  
In recent years, the HS-SPME method has been widely used for the extraction of trace amounts of volatile and semi-volatile substances. Meanwhile, gas chromatography-mass spectrometry (GC-MS) has the advantages of high sensitivity, strong qualitative ability, stability, and reliability, and is suitable for the qualitative and quantitative analysis of unknown components in multi-component mixtures. In order to evaluate the effect of different drying methods on the flavor of _O. raphanipes_, the volatile substances were determined by HS-SPME-GC-MS. There are multiple pathways for the production of volatile components: chemical or enzymatic oxidation of unsaturated fatty acids, as well as interactions with proteins, peptides, and free amino acids, degradation of long-chain compounds, and the Meladic reaction [41; 42]. Total ion chromatograms of volatile compounds in _O. raphanipes_ dried using different methods are shown in the Supplementary Materials Figure S1. The response intensity of volatiles in HD, ID, and VD in different drying methods is shown in Table 5. Among the three types of dried samples, 75 common volatile substances were detected, including 15 alkanes, 11 alcohols, 8 esters, 7 aldehydes, 7 ketones, 6 olefins, 6 acids, 4 benzenes, and 11 others. Alkanes are the main components of the entire volatile compound, followed by alcohols and esters.
###### Abstract  
Most of the current research work on the Narrowband Internet of Things (NB-IoT) is focused on enhancing its network coverage. Many of the existing NB-IoT channel coding techniques are based on repeating transmission data and control signals as a way of enhancing the network's reliability and therefore, enabling long-distance transmissions. Although most of these efforts are made at the expense of reducing the energy consumption of the NB-IoT network, they do not always consider the channel conditions. Therefore, this work proposes a novel NB-IoT Energy-Efficient Adaptive Channel Coding (EEACC) scheme. The EEACC approach is a two-dimensional (2D) approach which not only selects an appropriate channel coding scheme based on the estimated channel conditions (dynamically classified as bad, medium or good from initial based on a periodically assessed BLER performance outcome) but also minimizes the transmission repetition number under a pre-assessed probability of successful transmission (based on the ratio of previous successful transmissions over the total number of transmissions). This results in creating a single mixed gradient based on which a higher or lower Modulating Coding Scheme (MCS) is selected on each transmission. It is aimed at enhancing the overall energy efficiency of the network by dynamically selecting the appropriate Modulation Coding Scheme (MCS) number and efficiently minimizing the transmission repetition number. Link-level simulations are performed under different channel conditions (good, medium, or bad) considerations to assess the performance of the proposed up-link adaptation technique for NB-IoT. The obtained results demonstrate that the proposed technique outperforms the existing Narrowband Link Adaptation (NBLA) as well as the traditional repetition schemes in terms of the achieved energy efficiency as well as network reliability, latency, and scalability.  
link adaptation; adaptive; energy efficiency; data rates; throughput; Modulation Coding Scheme (MCS); repetition number; Narrowband IoT (NB-IoT) IEEEexample:BSTcontrol
# 3D Convolution Recurrent Neural Networks for Multi-Label Earthquake Magnitude Classification +
## 2 Our Approach
### Recurrent Neural Network (RNN)  
In sequence-to-sequence learning, RNNs are a subset of neural networks that can untangle irregular input features by utilizing their primary internal memory and process-changing sequences through recurrent layers. RNNs can unfold sequence-to-sequence learning tasks as they retain temporal features across their inputs, making them optimal for time-domain-related tasks. Numerous practical applications [28] propose employing 'gatedrecurrent units', also known as GRUs, since they overcome the issue of vanishing gradients that might occur while training neural networks. Due to the lack of an output gate, GRUs have fewer network parameters and are thus more viable for practical implementation than LSTM-based recurrent networks. GRUs operate based on a single gating unit and can concurrently update the gated units in a recurrent structure. Moreover, GRUs train faster, are computationally more efficient and have comparable performance to LSTMs on less training data [17; 31]. Furthermore, the model is intended to be deployed in real-time in the future, and GRUs having fewer parameters than LSTMs make it an ideal candidate for the magnitude classification task. Following are the equations for updating gated recurrent unit [18] states:  
\[h_{i}^{(t)}\quad=\quad u_{i}^{(t-1)}h_{i}^{(t-1)}\;+\;(1\;-\;u_{i}^{(t-1)}) \sigma\Bigg{(}b_{i}+\sum_{j}U_{i,j}x_{j}^{(t-1)}+\sum_{j}W_{i,j}r_{j}^{(t-1)}h _{j}^{(t-1)}\Bigg{)}, \tag{2}\]  
where \(u\) stands for the update gate and \(r\) for the \"reset\" gate. Their value is separately defined as:  
\[u_{i}^{(t)}=\sigma\Bigg{(}b_{i}^{u}+\sum_{j}U_{i,j}^{u}x_{j}^{(t)}+\sum_{j}W_{ i,j}^{u}h_{j}^{(t)}\Bigg{)}, \tag{3}\]  
\[r_{i}^{(t)}=\sigma\Bigg{(}b_{i}^{r}+\sum_{j}U_{i,j}^{r}x_{j}^{(t)}+\sum_{j}W_{ i,j}^{r}h_{j}^{(t)}\Bigg{)}, \tag{4}\]  
In gated recurrent units, portions of the state vector are \"ignored\" independently using update and reset gates, allowing the temporal information and forget-states of distinct units to be controlled dynamically.
# Optimization Method for Stiffened-Plate Layout in Box Structures Based on Load Paths
## 3 Application on Gantry of Machine Tool  
The main part of the aluminum ingot composite machining unit is composed of gantry, vertical milling shaft parts, crossbeam, and side milling parts, as shown in Figure 3. The machine is equipped with a large 2000 mm cutterhead, which enables rough machining of aluminum ingots with dimensions up to 5000 \(\times\) 2000 \(\times\) 850 mm. Accordingly, the large  
Figure 2: Schematic diagram of load path classification.  
diameter tool with high cutting speed puts high demands on the rigidity and stability of the machine itself in the actual production.  
The gantry, as the primary load-bearing part, not only supports the weight of the crossbeam and milling components but also withstands cutting forces. The gantry, with a height of 4020 mm, length of 8010 mm, and width of 3290 mm, is composed of external skin and internal stiffened plates, constituting a typical box structure. Therefore, in order to achieve lightweight construction of the machine tool while ensuring rigidity and stability, it is necessary to optimize the gantry structure and internal stiffened-plate layout.
## 4 Joint User Scheduling and Hybrid Beamforming Design for Maximizing EE
### CCCP Algorithm
, \tag{58}\]  
\[\
abla\text{I}_{k,l}\Big{(}\mathbf{W}^{(t-1)},\zeta_{k,l}^{(t-1)}\Big{)}=\left[ \left\{\frac{\left(\overline{\mathbf{H}}_{k,l}\right)^{T}}{1+\zeta_{k,l}^{(t-1)} }\right\}_{l=1}^{L}-\frac{\sigma^{2}+\sum\limits_{l=1}^{L}Tr\left(\overline{ \mathbf{H}}_{k,l}\mathbf{W}_{l}^{(t-1)}\right)}{\left(1+\zeta_{k,l}^{(t-1)}\right)^{2} }\right]^{T}, \tag{59}\]  
\[\
abla\text{I}_{k,l}\Big{(}\mathbf{W}^{(t-1)},\zeta_{k,l}^{(t-1)}\Big{)}=\left[ \left\{\frac{\left(\overline{\mathbf{H}}_{k,l}\right)^{T}}{1+\zeta_{k,l}^{(t-1)} }\right\}_{l=1}^{L}-\frac{\sigma^{2}+\sum\limits_{l=1}^{L}Tr\left(\overline{ \mathbf{H}}_{k,l}\mathbf{W}_{l}^{(t-1)}\right)}{\left(1+\zeta_{k,l}^{(t-1)}\right)^{2} }\right]^{T}, \tag{60}\]  
In the constraint \(C_{8}:4\zeta_{k,l}+\left(\eta_{k,l}-\text{SINR}_{l}^{\min}\right)^{2}\geq \left(\eta_{k,l}+\text{SINR}_{l}^{\min}\right)^{2}\forall k,l\), we replace the concave function \(\left(\eta_{k,l}-\text{SINR}_{l}^{\min}\right)^{2}\) with its first-order Taylor expansion, which is reflected in line 3 of Algorithm 1. The first-order Taylor expansion of \(\left(\eta_{k,l}-\text{SINR}_{l}^{\min}\right)^{2}\) can be expressed as:  
\[\left(\eta_{k,l}-\text{SINR}_{l}^{\min}\right)^{2,te}=\left(\eta_{k,l}^{(t-1)}- \text{SINR}_{l}^{\min,(t-1)}\right)^{2}+ \begin{bmatrix}2\left(\eta_{k,l}^{(t-1 )}-\text{SINR}_{l}^{\min,(t-1)}\right)\\ -2\left(\eta_{k,l}^{(t-1)}-\text{SINR}_{l}^{\min,(t-1)}\right)\end{bmatrix} ^{T} \begin{bmatrix}\eta_{k,l}-\eta_{k,l}^{(t-1)}\\ \text{SINR}_{l}^{\min}-\text{SINR}_{l}^{\min,(t-1)}\end{bmatrix} , \tag{61}\]  
_Optimization_: The optimization step is reflected in line nine of Algorithm 1. According to (56), (58) and (61), the problem \(Q_{6}\) can be equivalently converted to  
\[ \begin{split} Q_{7}:\max\limits_{\mathbf{\eta},\text{SINR}_{l}^{\min}, \zeta}&\text{EE}=& 2q\bigg{(}B\sum\limits_{l=1}^{L}Y_{l}(\text{SINR}_{l}^{\min})\bigg{)}^{\frac{1}{2} }-q^{2}\bigg{(}\sum\limits_{l=1}^{L}Tr(\mathbf{W}_{l})+P_{0}\bigg{)}\\ &+\lambda_{1}\sum\limits_{l=1}^{L}\text{P}(\eta_{k,l})^{te}-\sum\limits_{l}^{L} \lambda_{2}\bigg{(}\sum\limits_{k}^{|\text{I}_{l}|}\eta_{k,l}-U_{s}\bigg{)}^{2} \end{split}  \tag{62}\]\[s.t.\ C_{3},\ C_{5},C_{6},C_{7}\ in\ Q_{6}, \tag{63}\] \[C_{2}:\Gamma_{kJ}(\mathbf{W})-\mathrm{I}_{kJ}(\mathbf{W},\zeta_{kJ})^{ t_{k}}\leq 0,\forall k,l,\] (64) \[C_{8}:4\zeta_{kJ}+\Big{(}\eta_{kJ}-\mathrm{SINR}_{l}^{\min}\Big{)}^{2,t_{k}} \geq\Big{(}\eta_{kJ}+\mathrm{SINR}_{l}^{\min}\Big{)}^{2}\forall k,l, \tag{65}\]  
For the problem \(Q_{7}\), the variables \(\Big{(}\eta_{kJ},\mathrm{SINR}_{l}^{\min},\mathbf{W},\zeta\Big{)}^{(t+1)}\) can be updated by the iterative optimization. _Feasible Initial Point_: It should be noted that the CCCP algorithm needs a feasible initial point to ensure that the algorithm converges to a stationary point, as the selection of the initial point can affect the performance of the CCCP algorithm. To find a better initial point, we adopt the following method, which is reflected in line one of Algorithm 1.
## 8 Discussion  
From the literature, there is a need for an automated and zero-touch system, where the user needs to provide its intention for resources, and in return, the system will deploy the resources dynamically. The automatic policy generation, resource activation, resource assurance, and resource monitoring are important features for a zero-touch system. However, there is a lack of automation in the previous studies and none of them uses IBN-based approach for performing E2E network slicing. There are various methods with RL for slicing the RAN and core domains separately, but only few perform slicing for both domains. Such methods lack the existence of any domain orchestrator for controlling the slicing mechanism for both domains. However, in our case, we have domain orchestrators, such as OSM MANO and FlexRAN controller for handling core and RAN domains slicing. Moreover, our IBN system automates the process of policy generation for RAN and core domains. As aforementioned, the IBN system has three major components, i.e., translation, activation, and assurance. Similar to the standard IBN system, our proposed system also has an intent translation module, which can translate user requirements into policies with the help of policy configurators. The activation module deploys these policies over the infrastructure for the creation of slice. In addition, our system facilitates monitoring of virtual and physical resource with the help of OpenStack Neutron and FlexRAN controller monitoring application. The DL module forecasts resources for efficient resource management. Moreover, unlike previously developed systems, our proposed system automates policy generation and slicing creation process through IBN-based approach.
## 3 Results
### Fourier-Transform Infrared (FTIR)  
FTIR spectra and band assignments of sesame stalk feedstock (SF) and its different biochar types produced by the solar parabolic dish collector method (SB-1 and SB-2) and conventional method (SB-3, muffle production) are presented in Figure 6. It was observed that the FTIR spectra of feedstock were less dissimilar from those of sesame biochar types regardless of the peak intensity due to the low temperature of the pyrolysis process. Moreover, there was a slight change in peak intensity and width between SB-2 and SB-3, while SB-1 was more related to feedstock (SF), especially in the fingerprint region. This remark indicates that the pyrolysis condition of SB-2 was near to that pyrolyzed under control conditions (SB-3). The alcohol and hydroxyl compounds in SF and SB-1 were detected by the broad peak of the H-bond at 3338 cm\({}^{-1}\), indicating O-H stretch vibration and affirming the presence of water and hydroxyl ascribed to cellulose and hemicellulose [11,20].  
Figure 5: Zeta potential of sesame stalk feedstock (SF) and its different biochar types: SB-1 (formed around the inner sidewalls of the internal chamber), SB-2, (formed on the upper part of the internal chamber), and SB-3 (conventional production).  
Figure 6: FTIR spectra of sesame stalk feedstock (SF) and its different biochar types: SB-1 (around the inner sidewalls of the internal chamber); SB-2 (formed on the upper part of the internal chamber); and SB-3 (conventional production).  
Additionally, the narrow band at 2915 cm\({}^{-1}\) shows aliphatic compounds assigned to saturated C-H stretching vibrations of the methylene group that are related to hemicellulose and cellulose. These peaks could have disappeared in the SB-2 and SB-3 cases due to the dehydration of cellulosic components and methylene groups and the increase in the aromatic structure [4]. A similar observation was observed at the peak around 1036 cm\({}^{-1}\) of alcohol, C-O stretch, and aliphatic amines C-N that decreased in different biochar types. The peak around 1240 cm\({}^{-1}\) is related to the phenol and C-O stretch group. The band around 1730 cm\({}^{-1}\) demonstrates the presence of hemicellulose, specified by carbonylI compounds (C=O stretching vibration) of the carboxylic acid, ketones, and ester groups. The NHI bend, N-H bend, C=C stretching vibrations, and C=C-C aromatic ring stretch around 1600 cm\({}^{-1}\) were attributed to primary amines, secondary amines, alkenes, and aromatic functional groups.  
Moreover, C-H, N-O, and O-H vibrations dominated the fingerprint region of the spectrum from 1508 to 610 cm\({}^{-1}\). However, the strong peaks around 1510 and 1450 cm\({}^{-1}\) are attributed to the aromatic ring (C=C-C). The peaks between 1500 cm\({}^{-1}\) and 1030 cm\({}^{-1}\) were higher in the feedstocks than those in the three biochar types, and that group became weaker in SB-2 and SB-3 [21].
# Development and Evaluation of Light-Weight Active Noise Cancellation Earphones
## 3 Real-Time Experiments
### Experimental Setups  
At first, we attached an error microphone each to our in-ear, earbud and clip earphones. Figure 4a shows the example of the clip earphone with the attached microphone. For the testing setup shown in Figure 4b, a loudspeaker is used as a noise source to generate the primary noise. The speaker is located 20 cm away from the KEMAR's right ear. The loudspeaker inside the light-weight earphone is used as the secondary loudspeaker to generate the anti-noise, and a MEMS microphone serving as the error microphone is attached on a suitable location of the earphone that is close to the ear canal. A digital signal processing (DSP) development platform (Texas Instrument, TMS320C6713 DSK) is used for implementing the adaptive feedback ANC algorithm. The ADC and DAC card (Heg Co. Ltd., 6713 IFB, Mandideep, India) is used to do the signal conversion. The sampling rate is 8 kHz, and the cutoff frequency of both the lowpass filters (the reconstruction and anti-aliasing filters) is 3.2 kHz. The experimental results are measured by the built-in microphone inside the KEMAR's ear, which has input impedance closely resembling that of an average human ear.  
Figure 4: _Cont._  
The pinna of [PERSON]'s ear is a soft rubber like a real human ear, as shown Figure 4. In the experiments, the clip phone covers the [PERSON]'s pinna, and the error microphone is located inside the clip phone ear pad. The earbud earphone is placed on the outer ear, and the error microphone is attached on the earphone close to the ear canal. The in-ear earphone's plastic cap is inserted inside the [PERSON]'s ear canal. For comparison, we also place the commercial headsets--type A to cover the [PERSON]'s ear, type B to enclose the [PERSON]'s ear, and the plastic cap of the type C earphone is inserted inside the [PERSON]'s ear for measuring their performance. All the experiments are performed in a quiet room, and the primary noise is played at the same level for all experiments.
# SDR Proof-of-Concept of Full-Duplex Jamming for Enhanced Physical Layer Security
###### Abstract  
In order to secure wireless communications, we consider the usage of physical-layer security (PLS) mechanisms (i.e., coding for secrecy mechanisms) combined with self-interference generation. We present a prototype implementation of a scrambled coding for secrecy mechanisms with interference generation by the legitimate receiver and the cancellation of the effect of self-interference (SI). Regarding the SI cancellation, four state-of-the-art algorithms were considered: Least mean square (LMS), normalized least mean square (NLMS), recursive least squares (RLS) and QR decomposition recursive least squares (QDRDRLS). The prototype implementation is performed in real-world software-defined radio (SDR) devices using GNU-Radio, showing that the LMS outperforms all other algorithms considered (NLMS, RLS and QDRDRLS), being the best choice to use in this situation (SI cancellation). It was also shown that it is possible to secure communication using only noise generation by the legitimate receiver, though a variation of the packet loss rate (PLR) and the bit error rate (BER) gaps is observed when moving from the fairest to an advantageous or a disadvantageous scenario. Finally, when noise generation was combined with the adapted scrambled coding for secrecy with a hidden key scheme, a noteworthy security improvement was observed resulting in an increased BER for [PERSON] with minor interference to [PERSON].  
physical-layer security; jamming; full duplex; self-interference cancellation; software-defined radio +
Footnote †: journal: Sensors
## 3 Theoretical Formulation of Turbulent Gas Flow Modeling
### Mesh Independence  
For a CFD model, it is necessary to gain an insight into the sensitivity of the model with respect to various changes to the model parameter values used. For this purpose, a mesh independence test was carried out on the developed model. The element sizes of the walls inside the cut kerf and inner wall of the nozzles were altered to the value range of 0.50 mm to 0.20 mm. The element size of the edges of the kerf on the top surface was changed from 0.20 mm to 0.05 mm to obtain a mesh model with various levels of refinement. Each refinement level was maintained at a ratio of 1.6. Altogether, four mesh models with cell numbers of 304,558, 501,755, 866,070, and 1,393,309 were prepared to carry out the mesh independence test using the \(k\)-\(\omega\) turbulent model. Maximum velocity along the flow direction was used as a parameter to evaluate the dependency of the simulation results on mesh size. Figure 6 gives the dependency of maximum velocity with respect to the cell numbers in the mesh model. When using a finer grid, the basic flow structure  
Figure 4: CFD model. (**a**) CAD model enclosed within the computational domain, (**b**) computational domain, and (**c**) mesh generation.  
Figure 5: Computational domain showing boundary layers. (**a**) Inlet, (**b**) walls, and (**c**) outlets.  
changes little, but it increases the computational cost greatly. Therefore, the grid chosen represents a compromise between the accuracy and computational time.