esa-sceva/satcom-chunk-collection huggingface dataset:
# JLMS25 and Jiao-Liao Mandarin Speech Recognition Based on Multi-Dialect Knowledge Transfer
## 5 Experiment
### Main Results  
The experiments were conducted on the KeSpeech and JLMS25 datasets. As shown in Table 2, we first compared MDKT with models from other literature, including SHL-MDNN, JMAA [3], full fine-tuning of the backbone model, and adapters. JMAA exhibited underfitting due to a limited number of parameters. The performance of SHL-MDNN and full fine-tuning, based on wav2 vec 2.0, showed no significant differences, while MDKT demonstrated superior performance on both categories of Jiao-Liao Mandarin speech data.  
We also conducted a comparison with the adapter module. Due to computational resource limitations, we could not pre-train the wav2 vec 2.0 model on the diverse dialect speech data from the KeSpeech dataset, which prevents a direct comparison with the method proposed by [PERSON] et al. [12]. However, to evaluate the effectiveness of the WFA adapter and AttAdapter modules, we compared them with the adapter module under experimental conditions. Both utilize a three-stage training method, with adapter modules added after the ASR encoder. With a similar number of language-dependent parameters, we compared the performance of a three-layer adapter against our method. In terms of parameters, the adapter had 6.8 M language-dependent parameters, while our method had 7.5 M, increasing by only 0.7 M. Table 2 shows that this model architecture significantly enhanced the performance of the backbone model on the low-resource Jiao-Liao Mandarin speech dataset. Our method outperformed the backbone model trained using multi-dialect data. After fine-tuning on the Jiao-Liao Mandarin speech data, CER decreased by 5.4% and 7.7%, while WER reduced by 6.1% and 10.8% on the KeSpeech Jiao-Liao Mandarin and JLMS25 datasets, respectively. The combination of WFA adapter and AttAdapter outperformed the individual adapters, with CER reducing by 0.4% and 5.4%, and WER by 0.1% and 7.9% on the KeSpeech Jiao-Liao Mandarin and JLMS25 datasets, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**KeSpeech**} & \multicolumn{2}{c}{**JLMS25**} & \multicolumn{2}{c}{**Mean**} \\ \cline{2-7}  & **CER** & **WER** & **CER** & **WER** & **CER** & **WER** \\ \hline SHL-MDNN & 0.363 & 0.552 & 0.271 & 0.444 & 0.317 & 0.498 \\ \hline JMAA & 0.682 & 0.895 & 0.619 & 0.852 & 0.651 & 0.879 \\ \hline Full fine-tuning & 0.364 & 0.533 & 0.281 & 0.467 & 0.323 & 0.5 \\ \hline adapters & 0.314 & 0.473 & 0.258 & 0.438 & 0.286 & 0.455 \\ \hline MDKT & **0.310** & **0.472** & **0.204** & **0.359** & **0.257** & **0.411** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of various approaches.  
These results clearly demonstrate that WFA adapter and AttAdapter effectively enhance the model's adaptability to various dialects while mitigating the risk of overfitting.
###### Abstract  
In this study, CuO was synthesized as a microwave absorber in the pyrolysis of a biomass model (sugarcane baggase). CuO was synthesized for 5 min of irradiation using the following techniques: microwave (MW), ultrasound (US), combined mode (MW-US), and conduction heating (CH) as a reference material. The use of these treatments promotes changes in the morphology, as MW and US generate leaves and monolithic faceted morphologies, respectively. Changes were also generated in some textural characteristics such as crystal size, surface area, and volume-pore size. They were produced as a consequence of changes in the conditions during the crystallization stage produced by the different irradiation types. The microwave-assisted pyrolysis was performed aiming for the maximum liquid fraction (bio-oil) in the products. The reaction time, the size of the biomass, and the CuO synthesis method were also analyzed. The following particle size (ps) intervals were studied: ps \(<\) 0.5 mm, 0.5 mm \(<\) ps \(<\) 1.7 mm, 1.7 mm \(<\) ps \(<\) 3.5 mm. The best conditions at 1160 Watts in the microwave were: 4 min of reaction, particle size lower than 0.5 mm, and CuO synthesized by US. The use of CuO in the pyrolysis almost triples the amount of the obtained liquid fraction, when compared with the pyrolysis without the use of a microwave absorbent. The CuO was reduced to Cu\({}_{2}\)O and Cu after the pyrolysis. In this work, a reduction in the reaction times from hours to minutes was achieved during the synthesis of CuO and the pyrolysis biomass. The liquid fraction (bio-oil) can be raw material to obtain value-added chemical products or biofuels.  
Keywords:pyrolysis; biomass; CuO microwave absorber; sugarcane baggase; synthesis by microwave; synthesis by ultrasound +
Footnote †: journal: Applied Soft 2019, 9, 5525; doi:10.3390/appr9245525
## 3 Data and Methods
### Auxiliary Data Preparation  
The high accuracy of the input auxiliary data such as sea surface temperature, wind speed and significant wave height is required as these input data can significantly affect the accuracy of _SSS_ retrieval. It is also necessary to obtain the brightness temperature data and auxiliary data simultaneously. In this study, together with the radiometer measurements, most of the auxiliary data were obtained by in-situ measurements with a high accuracy. The descriptions of the data processing for the auxiliary data are given below:  
(1) The _SSS_ and _SST_ were measured by the SeaBird CTD (SBE 37-SI MicroCAT) and RBR XR-620, respectively. These two measurements were compared to ensure that the surface water properties were measured instead of the subsurface water properties. When the two measurements were not the consistent (mostly the differences less than 0.2 \({}^{\circ}\)C and 0.1 psu), an average value was then used.  
(2) The wind speed and direction were measured by a wind monitor (RM Young anemometer, Model 05106) with the accuracy of 0.3 m/s and 3\({}^{\circ}\). The anemometer was installed at a height of 8 m above the sea surface, thus the wind speed at 10 m above the sea surface was used as an input for the  
Figure 4: A flowchart describing the vector radiative transfer model with the input and output data.  
sea surface emission model (i.e., \(U_{10}\)), as approximated by Equation (1). The wind speed given at any elevation can be calculated from the friction velocity \(U\), by:  
\[U(z)=\frac{U_{*}}{0.4}\log\left(\frac{z}{Z_{0}}\right), \tag{1}\]  
where \(U_{*}\) is related to \(Z_{0}\) by:  
\[Z_{0}=0.0000684/U_{*}+0.00428U_{*}{}^{2}-0.000443, \tag{2}\]  
and the azimuth angle is defined as \(\phi_{w}=0\) with the wind blowing towards the radiometer.  
(3) Significant wave height (_SWH_) was obtained from the ERA5 hourly reanalysis data. Although the remote sensed _SWH_ could achieve better accuracy than the numerical model results, only few stations could be matched to the remote sensed _SWH_ data due to the lack of satellite _SWH_ data in coastal regions. Thus, the _SWH_ data from ERA5 hourly reanalysis data were used instead of the remote sensed _SWH_ products. In fact, the ERA5 is the fifth generation ECMWF atmospheric and oceanic reanalysis production with the improved temporal-spatial resolution of 0.5 degrees in space and 1 h in time.
## 2 Materials and Methods
### Platelet Preparation and Aggregation Study  
This study was approved by the Institutional Review Board of Taipei Medical University (TMU-JIRB-N201812024), and it conformed to the directives of the Helsinki Declaration. In accordance with our previous study, the platelet suspensions were prepared from 30 healthy human blood samples [18] and mixed with an acid-citrate-dextrose solution (9:1, _v/v/v_). After centrifugation at 120\(\times\)\(g\) for 10 min, the supernatant (platelet-rich plasma) was supplemented with EDTA (2 mM) and heparin (6.4 U/mL), incubated for 5 min, and again centrifuged at 500\(\times\)\(g\) for 10 min. The platelet pellet was suspended in 5 mL of Tyrode's solution, and the mixture was incubated for 10 min at 37 \({}^{\circ}\)C. After centrifugation of the suspension at 500\(\times\)\(g\) for 10 min, the washing procedure was repeated. The washed platelets were finally suspended in Tyrode's solution containing BSA (3.5 mg/mL). The platelet count was monitored using a Coulter counter (Beckman Coulter, Miami, FL, USA). The final concentration of Ca2\({}^{+}\) was 1 mM. Washed human platelets (3.6 \(\times\)\(10^{8}\) cells/mL) were untreated or pretreated with a solvent control (0.1% DMSO) or PTE, Ro106-992, and BAY11-7082 at the same concentrations (3 and 7 uM) for 3 min before being stimulated with collagen (1 ug/mL) for 6 min. Platelet aggregation was measured using a Lumi-Aggregometer (Payton, Scarborough, Ontario, Canada), and the turbidimetric method was adopted for measurement [18]. The level of platelet aggregation was measured as a percentage of the control (the group treated with Tyrode's solution) in light transmission units.
## 5 System Architecture for the Generation and Utilization of the 3D Indoor Routing Service
### Generating the Routing Graph  
For the computation of meaningful shortest routes within the building, different building elements such as rooms, corridors and doors have to be modeled. For this purpose, a comprehensive route graph has been automatically created. Thereby, the methodology of the graph is based on the _Weighted Indoor Routing Graph_ (WIRG) [40], thus all doors are represented as nodes in the graph and edges represent connections between adjacent doors. The doors of the building can be automatically retrieved from OSM, because they are mapped as OSM-nodes. Vertical connections, such as stairways or elevators, are represented as edges in the graph and can be automatically retrieved from different OSM keys, such as _connector:ids_ or _buildingpart:verticalpassage_, _etc_. (cf. Section 4). By computing the centerline of a corridor and adding vertical edges from the doors to this centerline, it is possible to model corridors (cf. [40]). The complete algorithm for the automated routing graph generation is briefly described in Figure 3 as pseudo-code.  
Figure 2: System architecture and processing workflow of the 3D indoor routing service.  
After the extraction and generation of the routing graph, it is transferred to the PostgreSQL/PostGIS database. The network is stored in one physical database table. It consists of link IDs, source and target of the corresponding edges, the 3D coordinates (both combined and separated into x, y, z) and the 3D length of the edge which is used as the cost parameter for Dijkstra. An excerpt of the route graph database table is provided in Figure 4(a) and the complete 3D route graph is visualized in Figure 4(b).  
Figure 3: Pseudo-code algorithm for the automated generation of a WIRG from _IndoorOSM_.
## 2 Analysis of the Reverse Torque and Vibration in the Mud Pump
### Relationship between Mud Pump Vibration and Input Parameters
#### 2.4.2 Relationship between Different Input Parameters and Mud Pump Vibration  
The mud pump exhibited different mechanical characteristics when operated at different voltages. Figure 11 shows the mechanical characteristic curves of the mud pump at rated voltage values of 60%, 80%, 90%, 100%, and 110%.  
When the voltage is Un, the relationship between the rotor amplitude and rotational speed is analyzed based on the data of the speed and torque. When the rotational speed is 0 rpm, the displacement of the rotor over time is shown in Figure 12. From the figure, it can be observed that the rotor amplitude is 2.2411 mm. By conducting the same analysis, the amplitude corresponding to each rotational speed on the mechanical characteristic curve can be obtained. The analysis results are shown in Figure 13, indicating that the rotor amplitude increases with an increase in the rotational speed initially, followed by a decrease. For a motor speed between 0 and 1440 rpm, the amplitude increases slowly with an increase in the speed. When the motor speed is between 1440 and 1500 rpm, the amplitude decreases rapidly with an increase in the motor speed. The maximum amplitude of 2.8179 mm was observed at a rotational speed of 1440 rpm. According to the relationship between mud pump torque and rotational speed on the mechanical characteristic curve, the mud pump torque also exhibited an initial increase followed by a decrease with an increase in the rotational speed. Furthermore, both the maximum torque and vibration occurred at a rotational speed of 1440 rpm.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Property** & **Value** \\ \hline Density (kg/m\({}^{3}\)) & 7800 \\ Tensile strength (MPa) & 620 \\ Yield strength (MPa) & 420 \\ Thermal conductivity (W/(m\(\cdot\)K)) & 10 \\ Elastic modulus (GPa) & 210 \\ Poisson’s ratio & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance parameters of stainless-steel 022 Cr22 Ni5 Mo3N.  
Figure 10: Three-dimensional model of the mud pump rotor.  
Figure 11: Mechanical characteristic curves of the mud pump at different voltages.  
Figure 12: Relationship between rotor displacement and time when the rotational speed is 0 rpm.
## 3 Case Study
### Validation  
To verify the validity of this model on other ship types, CELSIUSBRICKE, a container ship from the Marshall Islands, was selected for a voyage of 24 days in the Maritime Silk Sea (Hainan-Indian Ocean). The ship is 246.87 m long, 32.2 m wide, and 19.3 m deep, with a displacement of 61,521 tons, a draft of 9.8 m, and a maximum speed of 23.0 knots. The route starts in Mombasa (4\({}^{\circ}\)4.490 S/39\({}^{\circ}\)41.172 E) and ends in Shanghai, China (31\({}^{\circ}\)14.442 N/122\({}^{\circ}\)2.208 E). The voyage lasted 24 days, from 2 May 2023 to 25 May 2023, covering a total distance of 6476 nautical miles at an average speed of 13.32 knots. On May 13, 14, 21, and 24, ships slowly circled or stopped sailing in place in the sea area, so the simulated ships did not sail on these four days during the planning process. All information about the voyage comes from the Hilleet website ([[https://www.hifleet.com/](https://www.hifleet.com/)]([https://www.hifleet.com/](https://www.hifleet.com/)), accessed on 30 May 2023). The actual route and planned route are shown in Figure 13.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Realroute** & **TMrouteB** & **IBA*routeB** \\ \hline Average risk & 0.4262 & 0.4424 & 0.3929 \\ Maximum risk & 0.8373 & 0.7121 & 0.6592 \\ Minimum risk & 0.1180 & 0.1252 & 0.1252 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The comparison of risk between Realroute, TMroute, and IBA*route.  
Figure 12: Box plots for Realroute, TMrouteB, and IBA*routeB.  
The planned route is a red line segment, while the actual route is a yellow line segment. It can be seen from the figure that the distance between the planned route result and the actual result is relatively small. The calculation results of the three comparison models show that the average relative error \(R_{E}\) is 1.62%, the goodness of fit \(R_{NL}\) is 98.71%, and the risk is 4.27% lower than the actual route. According to the actual sailing situation, this voyage is safer, and the route planned by the track planning model is thus safer.
# On Developing Techniques for Sharing Satellite Spectrum with Indoor Small Cells in 5G +
###### Abstract  
In this paper, we present two spectrum sharing techniques for a multisystem, incorporating an integrated satellite-mobile system and an autonomous terrestrial-mobile system (iSMS/aTMS), namely orthogonal spectrum sharing (OSS) and non-orthogonal spectrum sharing (nOSS) techniques. aTMS consists of numerous small cells deployed in several buildings, and iSMS consists of a satellite station integrated with complementary ground component (CGC) stations deployed within buildings. By exploiting the high external wall penetration loss of a building, the iSMS spectrum is shared with small cells per building in OSS, and small cells per 3-dimensional (3D) cluster per building in nOSS. An interference management scheme, to avoid interference in apartments with collocated CGC stations and small cells, was developed and an optimal number of almost blank subframes (ABSs) per ABS pattern period (APP) was defined. System-level capacity, spectral efficiency, and energy efficiency performance metrics were derived. Furthermore, we present an algorithm for both OSS and nOSS techniques. With extensive simulation and numerical analysis, it is shown that the proposed nOSS significantly outperforms OSS in terms of spectral efficiency and energy efficiency, and both techniques can meet the expected spectral efficiency and energy efficiency requirements for the fifth-generation (5G) mobile networks.  
s +
Footnote †: journal: Accepted: 6 February 2020; Published: 8 February 2020  
_Article_  
On Developing Techniques for Sharing Satellite Spectrum with Indoor Small Cells in 5G +
Footnote †: journal: Accepted: 6 February 2020; Published: 8 February 2020
# Error Sources of Interferometric Synthetic Aperture Radar Satellites
## 2 Intrinsic Height Errors and Location-Induced Errors
### Location-Induced Errors
#### 2.2.4 Overall  
It is impossible to prevent geometric distortion from occurring, thus combining radar images from both ascending and descending orbits is one solution to acquire complete surface information over a given terrain [50]. It is important to be aware of which geometric distortions are present in the study area. By knowing this information beforehand, one could adjust the region of interest or apply other means to prevent or mitigate interference from geometric distortion.
# Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection
## 2 Principle of the Algorithm  
Target detection is carried out in range-Doppler domain. If the ship moves with certain line-of-sight velocity \(v_{r0}\) it will be shifted to a different Doppler frequency \(f_{\text{DC}}\). The relation between \(v_{r0}\) and \(f_{\text{DC}}\) is given as  
\[f_{\text{DC}}=-\frac{2}{\lambda}v_{r0}, \tag{1}\]  
where \(\lambda\) is the radar wavelength. When this shift is larger than half the clutter bandwidth, i.e., \(\left|f_{\text{DC}}\right|>B_{\text{c}}/2\) where \(B_{\text{c}}\) is the clutter bandwidth, then the target is shifted to the exo-clutter region. In this region generally a detection is possible.  
For smaller line-of-sight velocities a detection may be prevented by the presence of strong clutter. Previous studies have shown that effective clutter suppression generally is only possible when multiple receiving channels are available [17; 18]. For instance, techniques like post-Doppler space-time adaptive processing (STAP) [19] effectively suppress the clutter and normalize or whiten the Doppler spectrum so that directly a single CFAR threshold can be applied once the clutter statistics are estimated.  
For normalizing or whitening single-channel data over Doppler, first the average Doppler spectrum has to be estimated. Once the Doppler spectrum is known, the data can be normalized to 0 dB before estimating the sea clutter statistics and computing the CFAR threshold.  
The major processing steps of the proposed algorithm for ship detection in range-Doppler domain using RC data can be summarized as:  
1. Extraction of a small data block from the RC radar data in time domain.
2. Transformation of the data block to range-Doppler domain via azimuth fast Fourier transform (FFT).
3. Normalization over Doppler for achieving a \"flat\" spectrum.
4. Estimation of the ocean clutter statistics.
5. Computation of a CFAR detection threshold based on the ocean clutter statistics.
6. Clustering of multiple detections to a single \"physical object\".
7. Tracking of the clusters, i.e., of the cluster centroid positions.  
Figure 1 illustrates the principle of the algorithm proposed in the paper. In this example, the images are generated using 128 azimuth and 512 range samples from F-SAR [20] X-band data. For visualization purposes, the images are normalized to a noise level of 0 dB.  
Figure 1: (**a**) Range-Doppler image of real X-band RC F-SAR data. The clutter with a bandwidth of around 800 Hz and a ship appearing at \(-500\) Hz are clearly visible. (**b**) Normalized image. (**c**) Clustered detections with their centroids and bounding boxes at azimuth times t = 15 s, t = 20 s and t = 30 s, respectively. (**d**) Target trajectory obtained from a tracking algorithm (orange) and cluster centroids (blue).
## 4 Proposed Work
### Authentication and Secure Key Exchange Protocol  
A secure communication protocol is required to transfer the data between the ZED and the coordinator via a registered ZR. This safe communication protocol obtains the shared secret key from communication that is both authenticated and encrypted between the coordinator and edge devices in the untrusted field. In this secure communication protocol, we performed mutual authentication with less non-volatile memory (ROM). Before performing the mutual authentication, the blockchain system must enroll the blockchain validator and ZED. This proposed blockchain system solves the problem of less non-volatile memory usage and improves key management. The blockchain validator will access the shared ledger that stores digital key credentials of all the enrolled ZED and ZR, including the Zigbee coordinators. The following key exchange and authentication take place whenever communication is needed between the ZED and the coordinator via a registered ZR in a secure and authenticated manner.  
1. Once the ZED and ZRs are registered/enrolled in the blockchain system.
2. The ZED begins communication with the coordinator via a router by sending a session request message MAC Association Request \((AR)||\gamma\) Where \(\gamma\leftarrow[DPr]_{k}eyG\).
3. The coordinator begins communication with the ZED via the router by sending a session to rely on the message MAC Association Reply \((ARE)||\xi\) Where \(\xi\leftarrow[CPr]_{k}eyG\); \(G\) is a Group Generator.
4. The ZED sends a Signed certificate MAC Association Request (AR): \(Signed(ED-ID||PANID||([DPr]_{k}ey,[DPu]_{k}ey)||N1||[CPu]_{k}ey||\gamma)\) where \(\gamma\leftarrow[DPr]_{k}eyG\) stands for coordinator verification.
5. Blockchain's smart contract will perform the end device verification at the coordinator side using the ZED Key.
6. The coordinator sends a signed certificate MAC Association Reply (ARE): \(Signed(ED-ID||PANID||N1||N2)\mid\mid\xi)\) Where \(\xi\leftarrow[CPr]_{k}eyG\); G is a Group Generator for end device verification.
7. The ZED will perform the signature verification using the coordinator device Public Key.
8. The Shared Key Generation can be calculated between both the coordinator and the ZED to have the necessary secure, shared key.
9. ZED secures shared Key: \(K\leftarrow[^{\textit{\tiny{{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{ \it{\it{\it{\it{\it{\it{\it{\it{\it{ \it{ \it{ \it{                                         \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \  
proper validations, the same contract sends the updated confirmation back to the ZED. Otherwise, it will discard the transaction (request).