esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Evaluation  
### Experimental Setup  
Figure 5 shows the experimental setup of the coordinated MISO photonic mmWave communication system in W-band using NNWSC. The arrows represent the direction of signal transmission. The 3-sub-band NM-CAP signals, each with a baud rate of 2.5 GHz, are converted from digital to analog by the AWG with a 60 GSa/s sampling rate. In addition, 16 QAM was selected as a robust and practical modulation scheme that balances spectral efficiency with reliable performance under the achievable SNR conditions in our experimental setup. Figure 5a,b show the spectrum of the output signals from AWG Channel 1 and Channel 2, respectively. The optical link section includes two external cavity lasers (ECLs) and two Mach-Zehnder modulators (MZMs), with a wavelength of 1550 nm. ECL-1 and ECL-2 generate stable optical signals, which are connected to MZM-1 and MZM-2, respectively, for modulating the optical signals. After being amplified by erbium-doped fiber amplifiers (EDFAs), the modulated optical signals are then transmitted in optical fibers. After 5 km or 10 km transmission, each optical signal is detected by a photodetector (PD). The PDs convert the optical signals into electrical signals, providing the input for subsequent mmWave signal generation.  
After the optical signal is converted to an electrical signal by the PD, it enters the mmWave transmitter section. First, the electrical signals are modulated to two 91.9 GHz mmWave local oscillators (LOs) by W-band mixers. We first generated an 11.4875 GHz RF LO signal using a radio frequency signal generator (ROHDE&SCHWARZ SIGNAL GENERATOR SMR 30). This signal was then upconverted to 91.9 GHz using an \(\times\)8 multiplier, providing the required LO signal, which was subsequently fed into the mixer. The mmWave signals are amplified by two power amplifiers (PAs) to ensure sufficient strength for wireless transmission and then transmitted by two horn antennas.  
At the receiver end, another antenna captures the mmWave signal transmitted through the air. The mmWave signal is amplified by a low-noise amplifier (LNA) and then down-converted to a baseband signal by the down-converter RF mixer. The baseband signal is captured by a real-time oscilloscope (OSC) with a 50 GSa/s sampling rate and processed offline by MATLAB R2021b.
# Improvement of Intake Structures in a Two-Way Pumping Station with Experimental Analysis
## 3 Overview of the Two-Way Pumping Station
### Test Schemes  
Three intakes with different bell mouth heights were designed. For the intake, it contains a division pier, bell mouth and flow guide cone. The ellipse and straight lines were used to construct the profile of the bell mouth. A cartesian coordinate system is established at the center point of the impeller, which is the intersection point of the impeller centerline and the rotation axis, as is shown in Figure 3. The impeller centerline is the \(X\)-axis and rotation axis represents the \(Y\)-axis.  
To facilitate the parameterized control of the profile, constraints were imposed on the ellipse, that is, the ellipse is tangent to the straight line at the inlet of the conical tube and the major axis is parallel to the rotation axis. Then, the bell mouth profile can be controlled by the following geometric relationship [14]:  
Ellipse equation:  
\[\frac{(x-D_{1}/2)}{a^{2}}+\frac{[y-(h_{0}+h_{1}-b)]^{2}}{b^{2}}\ =\ 1 \tag{1}\]  
Figure 3: Schematic diagram of the bell mouth.  
Figure 2: Three-dimensional perspective view of the model pump unit.  
where \(a\), \(b\) are the length of semi-minor axis and semi-major axis of the ellipse, respectively, \(h_{0}\) is the distance from the inlet of the conical tube to the impeller centerline and \(h_{1}\) is the distance from the inlet of the conical tube to the lowest point of the bell mouth.  
According to Equation (1), the derivative of \(y\) is:  
\[\frac{dy}{dx}\ =\ \frac{-b^{2}(x-D_{1}/2)}{a^{2}(y+b-h_{0}-h_{1})} \tag{2}\]  
Referring to the proposed constraints:  
\[\frac{-b^{2}(x-D_{1}/2)}{a^{2}(y+b-h_{0}-h_{1})}\ =\ \frac{1}{\tan\theta} \tag{3}\]  
Substituting the coordinates of the tangent point into Equation (1):  
\[\left\{\begin{array}{ll}a\ =\ \frac{(D_{1}-D_{0})h_{1}-2h_{1}^{2}\tan\theta} {2}\sqrt{\frac{D_{1}-D_{0}}{h_{1}^{2}(D_{1}-D_{0})-4h_{1}^{2}\tan\theta}}\\ b\ =\ \frac{(D_{1}-D_{0})h_{1}-2h_{1}^{2}\tan\theta}{D_{1}-D_{0}-4h_{1}\tan \theta}\end{array}\right. \tag{4}\]  
When changing the height \(H\), all other geometric parameters should remain constant, as is shown in Table 2. In this way, the \(a\) and \(b\) in Equation (4) can be solved and the profile of the bell mouth can be uniquely determined.  
In this study, three model intakes with different heights were investigated, namely, \(H\) = 204 mm (original scheme), \(H\) = 164 mm and \(H\) = 142 mm. The model passages are made of welded steel plate with surface coating to meet the requirements of roughness similarity (Figure 4).
## 3 Experiment Results and Analyses
### Dynamic Compressive Strength of Sample  
Figure 9 illustrates the variation in dynamic compressive strength of the prefabricated fractured rock specimens with respect to the mean strain rate.  
Figure 8: The variation in average strain rate of prefabricated fractured rock specimens with impact pressure.  
Figure 7: Variation in loading velocity with impact air pressure.  
Figure 9 shows that the dynamic compressive strength of the prefabricated fractured rock samples increases as the average strain rate increases, with a gradual decrease in the rate of increase. The experimental results establish that the increase in shock pressure leads to an increase in both the mean strain rate and impact velocity. Therefore, the mean strain rate and impact velocity increase, so that micro-cracks inside the sandstone samples are not cracked and expanded in time and the deformation lag phenomenon occurs; therefore, the samples are more compact, and as a result, the compressive strength increases as the mean strain rate increases.  
The relationship between the dynamic compressive strength of the prefabricated fractured rock samples and the average strain rate is expressed as a power function, indicating that the dynamic compressive strength has a clear effect on the strain rate, and the fitting relationship is shown in Formula (2):  
\[\sigma=13.69\dot{\varepsilon}^{0.43}\quad(R^{2}=0.9395) \tag{2}\]
## 3 Results and Discussion
Figure 2: **(a)** Side view space filling model of the host–guest complex, **(b)** Side view space filling-ball stick (hybrid) model of the complex and **(c)** top view hybrid model of the complex. The crystal structure of 4-ATP alone reveals no similar arrangements of the aromatic rings [17]. Figure 3 shows the packing diagram of pure 4-ATP crystal and the 4-ATP as guest molecule in the lattice of 4-ATP/\(\beta\)-CD complex. The cyclodextrin molecules have been eliminated for clarity. The solid-state structural data has been submitted to the Cambridge Crystallographic Data Center. The structure deposition number is CCDC 879687. Crystallographic data table for the complex is provided in Table 1. \begin{table}
\begin{tabular}{|c|c|} \hline
**Parameter** & **Crystal Data** \\ \hline CCDC deposition number & 879687 \\ \hline Empirical formula & C54 H84 N2 O46.75 S2 \\ \hline Formula weight & 1,573.35 \\ \hline Temperature & 100 (\(\pm\)2) K \\ \hline Wavelength & 1.54187 Å \\ \hline Crystal system & Monoclinic \\ \hline Space group & P21 \\ \hline Unit cell dimensions & a = 14.8541 (\(\pm\)3) Å & \(\alpha\) = 90\({}^{\circ}\) \\  & b = 32.3999 (\(\pm\)6) Å & \(\beta\) = 103.750(7)\({}^{\circ}\) \\  & c = 15.7645 (\(\pm\)11) Å & \(\gamma\) = 90\({}^{\circ}\) \\ \hline Volume & 7369.6(\(\pm\)6) Å\({}^{3}\) \\ \hline Z & 4 \\ \hline Density (calculated) & 1.418 Mg/m\({}^{3}\) \\ \hline Absorption coefficient & 1.596 mm\({}^{-1}\) \\ \hline F(000) & 3312 \\ \hline Crystal size & \(0.09\times 0.07\times 0.05\) mm\({}^{3}\) \\ \hline Theta range for data collection & 2.73 to 68.23\({}^{\circ}\) \\ \hline Index ranges & \(-17\leq\) h \(\leq\) 17, \(-\)38 \(\leq\) k \(\leq\) 38, \(-\)18 \(\leq\) l \(\leq\) 14 \\ \hline \end{tabular}
\end{table} 
Table 1: Crystal data and structure refinement for 4-ATP/\(\beta\)-CD. Figure 3: **(a)** The packing diagram of pure 4,4\({}^{\circ}\)-diaminodiphenyl disulfide molecule (view: slightly rotated from along \(b\)-axis); **(b)** The packing diagram of 4,4\({}^{\circ}\)-diaminodiphenyl disulfide inside the cavity of \(\beta\)-CD (with the cyclodextrin molecules removed). In this view, the crystallographic \(b\)-axis is horizontal.
# Autonomous Navigation Framework for Intelligent Robots Based on a Semantic Environment Modeling
## 2 Related Work
### Environment Modeling for Robots
#### 2.2.1 Map Representation  
In the Simultaneous Localization and Mapping (SLAM) field, filter-based state estimation techniques and graph-based optimization methods are well studied for building environment maps. [PERSON] et al. [20] presented a follow-up research work for an offline SLAM problem by introducing GraphSLAM, which represented the log-likelihood of the data and generated the maps with more than \(10^{8}\) features in urban environments. Ref. [21], used selective resampling operations to reduce the number of particles in Rao-Blackwellized [22] particle filter for building accurate map of environment. Ref. [23], addressed the motion estimation and distortion problem in lidar cloud by proposing a real-time approach divided into two algorithms that run in parallel. The lidar odometry algorithm estimated transform between consecutive frames at a higher frequency, while the lidar mapping algorithm performed fine matching and registration of the point clouds at a lower frequency. Subsequently, Parallel Tracking and Mapping (PTAM) [24] was considered the most popular method to implement visual based SLAM because of its ability to handle large number of 3D points. Later, [PERSON] et al. [25] extended the adaptability of PTAM to intractable environments by designing a new feature-based monocular ORB-SLAM system from scratch.  
The semantic mapping problem has attracted the researchers' attention in recent years. [PERSON] et al. [26] proposed a probabilistic system for extracting semantic information from different heterogeneous modalities and combined it with common-sense conceptual knowledge. It made the semantic maps more descriptive based on the concept of spatial properties that were represented by the chain graph. [PERSON] et al. [27] gave a qualitative description to enhance the representation of the environment and enabled the robot to perceive its surroundings similar to the human. [PERSON] et al. [28] used CNNs and dense SLAM system ElasticFusion for providing dense correspondence from the 2D frame into the 3D map. The semantic predictions from CNN were obtained from multiple viewpoints and then fused into a dense map. [PERSON] et al. [29] proposed an incremental semantic 3D mapping system with a scrolling occupancy grid map while CNN was used for segmentation and computation of pixel label distribution. [PERSON] et al. [30] presented an approach for incrementally building dense 3D maps that were semantically annotated. The class probabilities were assigned to each region of the 3D map and segmented using geometric-based techniques, while CNN enhanced frame-wise semantic segmentation. [PERSON] et al. [31] presented a Dynamic-SLAM by taking advantage of deep learning-based recognition technique, known as SSD, to recognize the dynamic objects at semantic level while recall rate was improved by proposing a missed detection compensation algorithm.
# Recent Progress on Underwater Wireless Communication Methods and Applications
## 4 Future Trends
### Expansion into Emerging Applications  
Underwater wireless communication technologies are breaking free from traditional marine exploration roles, enabling transformative applications across diverse fields. Distributed sensor networks with hybrid acoustic-optical capabilities now support real-time monitoring of ocean acidification and methane hydrate stability, critical for climate modeling and policy interventions. In disaster scenarios, low-latency optical-AUV systems enhance rapid response to submarine earthquakes or oil spills, leveraging high-speed data links for emergency coordination. Biomimetic robotics is revolutionizing ecosystem restoration, with bio-inspired electric field communication systems mimicking electric fish to coordinate artificial swarms for coral reef repair or infrastructure inspection. Military and resource security benefit from quantum-secured optical channels and covert acoustic protocols, safeguarding underwater mining and strategic operations in contested zones. Meanwhile, satellite-integrated networks bridge oceanic data gaps, enabling global applications from anti-illegal fishing to biodiversity mapping. These advancements not only redefine humanity's interaction with the oceans but also drive innovations at the intersection of robotics, environmental science, and cybersecurity.
## 4 Remote Base Unit
### Operating Principle
#### 4.1.3 Source Power Module of the Base Terminal Unit  
This module is really important for the long range telemetry systems in adverse geographic conditions because most of the times there is no electricity available to power measurement equipment and satellite links. Two 120-W solar panels connected in series generate the input voltage for a charge controller which has two main functions; first, to charge three 12-V deep cycle batteries until its optimum level and second, supply 12 V to the DC-AC inverter which powers the satellite modem. The connection diagram is shown in Figure 9.  
Figure 8: Bandwidth required in a satellite link for transmitting meteorological data.
## 5 Conclusions  
This study proposes SE-WiGR, a WiFi gesture recognition method that fuses the squeeze-and-excitation (SE) module with VGG16. The process relies on acquired CSI signals, which are filtered and analyzed using principal component analysis and other preprocessing operations to generate Doppler spectrograms. These spectrograms are subsequently fed into the SE-VGG16 network for gesture recognition. The SE module effectively enhances gesture features by combining external excitation signals with squeezing operations while suppressing ambient noise, thereby improving recognition accuracy. Simultaneously, the powerful feature extraction capability of the VGG network further enhances recognition performance. Quantitative results demonstrate that across office and conference room scenarios, SE-WiGR achieves an average accuracy of 94.12% for six gestures (Enlarge, Narrow, Up, Down, Left, and Right), outperforming the standalone VGG16 network by 4.3%, surpassing traditional methods like WiGest (67.5%) and HMM (73.3%) by 26.62% and 20.82%, respectively, and outperforming deep learning baselines such as LSTM (81.3%) and GRU (80.5%). However, despite the SE-WiGR method's successes in gesture recognition, it still faces challenges such as multipath effects, device differences, and gesture diversity in complex environments--issues that will be the focus of future research.  
Conceptualization, F.L. and C.W.; methodology, F.L.; software, Y.L.; validation, F.L.; formal analysis, F.L.; investigation, C.W.; resources, F.L.; data curation, C.W.; writing--original draft preparation, F.L.; writing--review and editing, C.W.; visualization, Y.L.; supervision, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant nos. 62162056 and 62262061) and the Natural Science Foundation of Gansu Province (24 JRRA127).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **F1** & **Parameters** \\ \hline VGG-16 & 89.82\% & 141.3 M \\ NoSqueeze & 89.82\% & 145.3 M \\ SE-VGG-16 & **94.12\%** & **145.3 M** \\ \hline \hline \end{tabular}
\end{table}
Table 12: F1 score (%) and parameters of squeeze operation.  
**Institutional Review Board Statement:** The Ethics Review Committee of Gansu Minzu Normal University confirms that the project involving the use of WiFi sensing technology to recognize human gestures does not require further ethical review. This project does not involve invasive procedures on participants, nor does it involve the collection or processing of sensitive data. Therefore, in accordance with the Declaration of Helsinki and other applicable ethical guidelines, the design of this research project meets ethical standards.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy reasons.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Results and Discussion
### Hydration Process  
The hydration process of the single OPC and OPC-CSA binary blended cement with only NC or with NC and AS together is shown in Figures 4 and 5. It was found that for OPC without the addition of AS (Figure 4a), a reduced induction time and slightly promoted occurrence of the main hydration peak were found with the addition of NC. Besides, the height of the peak was also increased, which indicates that the addition of NC can accelerate the hydration process by promoting the nucleation process at the initial stage of cement hydration. Compared to the sample without the presence of NC, the addition of NC also resulted in an obvious decrease in the released heat after hydration for 72 h (Figure 5a), which matches well with the measured strength shown in Figure 3. Regarding the condition with the addition of NC and AS (Figure 4b), a promoted hydration process with a shortened induction period and an increased height of the main hydration peak was found with the increased dosages of NC. Moreover, compared to the samples with only NC, the addition of AS enhanced the reaction of the aluminate-contained phase as the height of the secondary peak significantly increased. Besides, the synergistic addition of NC and AS increased the total heat released after hydration of 72 h (Figure 5b).  
Figure 4: Heat flow of the OPC and OPC-CSA binary blended cement paste with or without the addition of NC and AS; **(a,b)** single OPC; (c,d) OPC-CSA.  
Regarding the OPC-CSA binary blended cement, the partial replacement of OPC by CSA significantly altered the original hydration process. The height of the main hydration peak was significantly reduced. It indicates the hydration of silicate phases was severely suppressed, which could be caused by the poison of C-S-H nuclei by aluminate ions [35] or the formation of Si-O-Al bonds at the C\({}_{3}\)S surface [36]. Consistent with the OPC system, the addition of NC can also promote the hydration process of the OPC-CSA binary blended cement (Figure 4c). With the combined addition of NC and AS (Figure 4d), a strongly retarded occurrence of the main hydration peak was observed. Furthermore, the aluminate-contained phases in OPC-CSA binary blended cement reacted massively, as indicated by the high amount of heat released at the beginning. Moreover, an increased NC dosage resulted in improved cumulative heat and a left-moving main hydration peak, which again indicates the acceleration effect of NC on the OPC-CSA binary blended cement.
## 5 Case Study: Smart Energy Use Case  
To illustrate the applicability and performance of the developed multi-level simulator, we modeled and simulated a smart energy use case with applied homomorphic encryption. This use case is an integration of the use cases of [PERSON] et al. (2016) and [PERSON] et al. (2017). This particular use case describes a smart energy scenario within a city. The city has a photovoltaic system and a windmill as power suppliers and a parking lot and a couple of houses as consumers. Electric vehicles can move inside the city and the parking lot. By using a smart parking service through a mobile APP, users of the system can request to reserve their parking slot of choice within the participating parking facilities. The availability of the parking slots is then displayed through the mobile APP as well as through the optical indicators located on the respective parking slots for random people who do not participate in the smart parking service.  
The described scenario was modeled and simulated using the proposed approach at three distinct levels of abstraction: The first two higher levels were implemented only using classes provided by the implemented core simulator. The third (lowest) level was implemented with OMNeT++ 5.4.1 ([[https://www.omnetpp.org/21-articles/3752-omnet-5-4-1-released](https://www.omnetpp.org/21-articles/3752-omnet-5-4-1-released)]([https://www.omnetpp.org/21-articles/3752-omnet-5-4-1-released](https://www.omnetpp.org/21-articles/3752-omnet-5-4-1-released))) and its INET extension 4.0 ([[https://inet.omnetpp.org/2018-06-28-INET-4.0.0-released.html](https://inet.omnetpp.org/2018-06-28-INET-4.0.0-released.html)]([https://inet.omnetpp.org/2018-06-28-INET-4.0.0-released.html](https://inet.omnetpp.org/2018-06-28-INET-4.0.0-released.html))).
# Fast and Reliable Burst Data Transmission for Backscatter Communications
## 5 Burst Data Transmission Scheme
### Design
The ACK includes the same random number as in the RN16 and the frame length \(l_{p}\) used by the CRFID for subsequent transmission and the number of redundant frames \(M\). The initial value of \(l_{p}\) is 16 bits and the initial value of \(M\) is \(M_{\min}\). Step 2: The number of buffered data \(V\), the number of correctly received frames \(R\), and the number of incorrect frames \(F\) are all set to zero. Step 3: If the data frame of the CRFID is received, let \(V=V+l_{p}\). If the data frame is correct, let \(R=R+1\); otherwise, let \(F=F+1\). Further, judge that, if \(R=N\), \(N\) source data is restored and run Step 4. If \(F=M+1\), the round transmission fails and run Step 4; otherwise, run Step 3. Step 4: Calculate the current goodput \(G=V/\Delta t\), and \(\Delta t\) is the transmission time of the current round measured by the reader. If \(G>G^{\prime}(1+\theta)\) and \(l_{p}<l_{p}^{\max}\), let \(l_{p}=l_{p}+8\), and if \(G<G^{\prime}(1+\theta)\) and \(l_{p}>l_{p}^{\min}\), let \(l_{p}=l_{p}-8\); otherwise do not change the data frame length, where \(G^{\prime}\) is the goodput of the last round. Calculate the number of redundant frames \(M\) for the next round according to Equation (7). Step 5: The reader sends a QueryRep command to request data and returns an ACK frame if receiving the RN16 frame of the CRFID. The ACK contains the same random number as in the RN16 and the transmitting information such as the frame length and the number of redundant frames calculated in Step 4. Then, run Step 2. Pseudo codes of the CRFID and the reader operation procedure are shown in Algorithm 1 and Algorithm 2, respectively. ```
1:while the buffer id not empty do
2:if receive Query or QueryRep command then
3: return RN16
4:end
5:if receive the ACK containing the same RN16 then
6: extract \(N\) data frames of length \(l_{p}\) and encode into \(N+M\) (\(l_{p}\) and \(M\) are carried in the ACK)
7:end
8:repeat
9: track the capacitance voltage and obtain \(U_{\max}\) and \(\tau\), and sleep for a period of \(t_{o}\)
10: compute the optimal number of transmission frames \(n_{k}\) via Equation (6); then, transmit it to the reader
11:until all \(N+M\) frames have been transmitted
12:end
```  
**Algorithm 1** The computational radio frequency identification (CRFID) operation procedure.