esa-sceva/satcom-chunk-collection huggingface dataset:
# Three-Dimensional Reconstruction-Based Vibration Measurement of Bridge Model Using UAVs
## 5 Discussion and Conclusions
### Conclusions  
In this paper, we have described a 3D reconstruction correction method for bridge vibration measurement using a UAV. Through the UAV camera calibration, the projection matrix for each frame of images can be estimated, and the actual displacement of the bridge model can be obtained by recovering the 3D world coordinates using the projection matrices. With the development of computer vision theory and UAV technology in the future, the UAV-based method may play an important role in vibration measurement and damage detection of bridges. The above investigation suggested that:  
The proposed method can estimate the intrinsic and extrinsic parameters (6 DOF motion) of the UAV camera using [PERSON]'s method, and then recover the 3D world coordinates of the target points through the projection matrices. The natural frequencies obtained by this method are consistent with the homography-based method. By using a fixed object on the background as the reference, the proposed method can be used to effectively remove the false displacement caused by the UAV motion.  
To further confirm the applicability of the proposed approach for bridge vibration measurement, different positions of the fixed object are set up. The experimental results demonstrated that the proposed method is more applicable than the homography-based correction method. In practical measurement, it avoids the limitation that the reference points have to be coplanar with the bridge; they can be artificially arranged in any plane parallel to the bridge surface. Regardless of whether the fixed object is close enough to the bridge, the 3D reconstruction can be carried out as long as the distance between them is known, while the measurement results are more accurate for the short distance between the fixed object and the bridge.  
Conceptualization, G.C. and Q.D.; methodology, Z.W., G.C. and X.Y.; software, Z.W.; validation, Z.W.; Data curation, Z.W.; investigation, Z.W. and G.C.; resources, G.C. and Q.D.; writing--original draft preparation, Z.W.; writing--review and editing, G.C., B.Y. and X.Y.; supervision, G.C. and B.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
## 4 Experiments
### Database  
We use the American CMU-ARCTIC database [47] for VC experiments. The multi-speaker TIMIT corpus [48] was used to train the SI-ASR system. The CMU-ARCTIC database is a collection of parallel recordings of seven professional speakers of different genders and accents. Each speaker recorded a set of 1132 sentences in the form of \".wav\" audio files. All the speech signals are sampled at a sampling rate of 16 kHz, windowed by a 25 ms Hamming window, and shifted every 5 ms. In this paper, only the utterances of native US English speakers were taken into account: two females ([PERSON] and [PERSON]) and two males ([PERSON] and [PERSON]). A total of 500 utterances were selected for each speaker to form the non-parallel training set. A further 50 non-overlapping utterances were selected for testing and evaluation. In our experiments, we performed an any-to-one speech conversion, using the female speaker [PERSON] as the target speaker and two male speakers ([PERSON], [PERSON]), and a female speaker ([PERSON]) as the source speakers as follows: [PERSON] to [PERSON], [PERSON] to [PERSON], and [PERSON] to [PERSON].
## 2 Classifier Design  
### Brief Review on Support Matrix Machine  
#### 2.1.1 Support Matrix Machine  
To effectively preserve the row and column structural information of the original input matrix, [PERSON] et al. [27] proposed an SMM that could retain matrix structure information. Given a matrix sample set \(\{X\}_{i=1}^{n}\), the \(i\)th input matrix sample \(\mathbf{X}_{i}\in\mathbb{R}^{p\times q}\), and corresponding label \(y_{i}\), the SMM's optimization problem can be expressed as (1), whose objective function comprises the hinge loss, kernel norm, and Frobenius norm:  
\[\begin{split}&\min_{\mathbf{W},\xi}\ \frac{1}{2}\text{tr}\Big{(}\mathbf{W}^{T}\mathbf{W}\Big{)}+\tau\parallel \mathbf{W}\parallel_{*}+C\sum_{i=1}^{n}\xi_{i}\\ &\text{s.t.}\ y_{i}\Big{[}\text{tr}\Big{(}\mathbf{W}^{T}\mathbf{ X}_{i}\Big{)}+b\Big{]}\geqslant 1-\xi_{i}\\ &\text{and}\ \tilde{\xi}_{i}\geqslant 0 for\ \forall i\in\{1,2,..,n\},\end{split} \tag{1}\]  
where \(\mathbf{W}\in\mathbb{R}^{p\times q}\) denotes the regression coefficient matrix and \(\text{tr}(\mathbf{W}^{T}\mathbf{W})\) represents the trace of the matrix. To preserve the relevant matrix structural information, we consider the dependence of the regression matrix \(\mathbf{W}\), which can be expressed as \(\text{rank}(\mathbf{W})\). The kernel norm \(\parallel\mathbf{W}\parallel_{*}\) denotes the best convex approximation function of \(\text{rank}(\mathbf{W})\). \(\tau\) is a kernel norm constraint parameter. \(C\) denotes a regularization parameter; finally, \(\xi\) represents a sequence of slack variables utilized for the hinge loss.
# Multi-Objective Service Placement Scheme Based on Fuzzy-AHP System for Distributed Cloud Computing
## 2 Related Work
### SP Approaches
#### 2.2.1 Architecture  
SP approaches have been studied in two different cloud architectures: the architecture of the solution, that uses a (1) centralized architecture or (2) a distributed architecture.  
A centralized architecture usually has a long latency than a distributed architecture, and the performance may degrade as the demand increases. However, the most studied cloud architecture is the centralized CDCs. Thus, a distributed architecture study for SP schemes is definitely required in the future.
# Condition Monitoring of Railway Tracks from Car-Body Vibration Using a Machine Learning Technique
## 7 Conclusions  
In this study, we analysed the effects of railway track irregularities on car-body vibrations through railway vehicle travel simulations conducted by using the SIMPACK software package. We subsequently developed an algorithm based on machine learning to diagnose track conditions by extracting features to detect track irregularities.  
We used the developed algorithm to detect track faults from the car-body vibrations generated by the simulation. Additionally, we examined the possibility of diagnosing track conditions by using real-world data collected by measurements on an actual regional railway line. The results of this study indicated that the developed algorithm could automatically detect faults associated with longitudinal level, alignment, and cross level irregularities by using measured car-body vibrations. In future work, we plan to create higher-precision training data to improve detection performance. We will also attempt to increase the effectiveness of the current approach by increasing the number of training data clusters and developing an algorithm that can detect the magnitude of track irregularities.  
This research was funded by JSPS KAKENHI Grant Number 17K06240.  
**Acknowledgments:** We would like to thank Editage (www.editage.jp) for English language editing.  
**Conflicts of Interest:** The author declares no conflict of interest.  
Figure 14: Detection results for regional railway line. (**a**) Degraded part in longitudinal level. (**b**) Degraded part in alignment. (**c**) Degraded part in cross level.
# Octacalcium Phosphate Bone Substitute (Bontree(r)): From Basic Research to Clinical Case Study
## 3 Results
### Basic Research
#### 3.1.2 In Vitro Study  
The RANKL and ALP activities of the MG63 cells were assessed using ELISA to assess the role of Bontree(r) in bone formation and remodeling (Figure 2A,B). For comparison, a commercial biphasic calcium phosphate ceramic (MBCP+TM) was also assessed as a reference [17]. Prior to in vitro testing, each specimen was compressed into tablets. Bontree(r) refers to both Bontree(r) 0.5-granule and Bontree(r) 1.0-granule samples.  
The level of RANKL, an osteoclast differentiation factor [18], was lower in cultures with Bontree(r) than in those with MBCP+TM during both culturing periods; it was 11.3 ng/mL and 19.0 ng/mL for Bontree(r) and 14.1 ng/mL and 28.6 ng/mL for MBCP+TM after 7 and 14 days, respectively. In contrast, the activity of ALP, an osteoblast differentiation marker [19], was significantly elevated in cultures with Bontree(r) (17.9 ng/mL and 22.4 ng/mL at 7 and 14 days, respectively), showing 1.9- and 1.6-fold higher values than in cultures with MBCP+TM (9.2 ng/mL and 13.9 ng/mL at 7 and 14 days, respectively).
# Causal Inference for Modality Debiasing in Multimodal Emotion Recognition
## 5 Experiments
### Main Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Performance**} & \multicolumn{6}{c}{**Performance Drop Gap**} \\ \cline{2-10}  & **[t, a, v]** & **[a, v]** & **[t, v]** & **[t, a]** & **[t]** & **[a]** & **[v]** & **avg** & **std** \\ \hline DCCA [41] & 47.7 & \(-6.2\) & \(-1.1\) & \(-1.0\) & \(-1.0\) & \(-6.6\) & \(-6.4\) & \(-3.7\) & 2.9 \\ DCCAE [42] & 48.2 & \(-6.6\) & \(-1.1\) & \(-0.8\) & \(-1.2\) & \(-7.3\) & \(-8.1\) & \(-4.2\) & 3.5 \\ MCTN [40] & 51.2 & \(-9.1\) & \(-0.8\) & \(-0.5\) & \(-1.0\) & \(-9.8\) & \(-9.6\) & \(-5.1\) & 4.8 \\ MMIN [43] & 52.4 & \(-10.6\) & \(-1.2\) & \(-0.4\) & \(-1.0\) & \(-12\) & \(-11.7\) & \(-6.2\) & 5.8 \\ GCNet [44] & 51.5 & \(-9.5\) & \(-0.4\) & \(-0.2\) & \(-0.3\) & \(-10.4\) & \(-9.8\) & \(-5.1\) & 5.3 \\ DiCMoR [45] & 53.4 & \(-11.0\) & \(-0.4\) & \(-0.7\) & \(-1.0\) & \(-12.0\) & \(-11.4\) & \(-6.1\) & 5.9 \\ IMDer [46] & 53.4 & \(-10.6\) & \(-0.3\) & \(-0.3\) & \(-0.9\) & \(-11.7\) & \(-10.8\) & \(-5.8\) & 5.8 \\ \hline MulT \(\dagger\)[20] & 48.8 & \(\mathbf{-7.4}\) & \(\mathbf{0.3}\) & \(\mathbf{-1.0}\) & \(\mathbf{-1.1}\) & \(\mathbf{-7.4}\) & \(\mathbf{-7.4}\) & \(\mathbf{-4.0}\) & 3.8 \\ + CausalMER & 50.1 & \(-8.0\) & \(-1.0\) & \(-1.2\) & \(-2.8\) & \(-8.7\) & \(-8.5\) & \(-5.0\) & **3.7** \\ \hline PMR \(\dagger\)[23] & 50.3 & \(-11.3\) & \(-2.5\) & \(-2.0\) & \(\mathbf{-3.6}\) & \(-8.9\) & \(-10.7\) & \(-6.5\) & 4.3 \\ + CausalMER & 48.8 & \(\mathbf{-6.7}\) & \(\mathbf{0.1}\) & \(\mathbf{-0.3}\) & \(-7.2\) & \(\mathbf{-7.4}\) & \(\mathbf{-0.8}\) & \(\mathbf{-3.7}\) & **3.7** \\ \hline DMD \(\dagger\)[24] & 50.8 & \(-9.1\) & \(\mathbf{-1.5}\) & \(-2.2\) & \(\mathbf{-3.4}\) & \(-9.5\) & \(-9.3\) & \(-5.8\) & 3.8 \\ + CausalMER & 48.8 & \(\mathbf{-7.6}\) & \(-2.2\) & \(\mathbf{-1.6}\) & \(-4.3\) & \(\mathbf{-7.4}\) & \(\mathbf{-7.4}\) & \(\mathbf{-5.1}\) & **2.8** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The performance drop under six possible missing-modality conditions and the full-modality condition (i.e., “[a]” indicates that only the audio modality is available, with both text and video modalities missing) on the CMU-MOSEI dataset, utilizing the average accuracy metric. Performance drops that are minimal are highlighted in bold. \(\dagger\) indicates our reimplementation. Table 4. This demonstrates that, despite the backbone of CausalMER not being specifically designed to handle situations where certain modality data are missing, it effectively eliminates modality bias, ensuring balanced contributions from each modality.
# The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review
## 3 Handwritten Chinese Character Recognition
### Deep Learning Based Recognition Methods  
Deep learning is a machine learning method based on traditional neural networks, designed to simulate the human brain's learning process with a multi-level structure and strong abstract learning capability. In contrast to feature extraction using traditional techniques, the deep learning-based approach can usually obtain the specific feature information directly from the original input image. This also enables deep learning-based approaches to provide end-to-end offline handwritten Chinese recognition solutions. The process is shown in Figure 4. From the perspective of network structure, the typical network models currently used for recognizing HCC are deep convolutional neural network (DCNN), ResNet, GoogleNet, deep residual network (DRN), etc. The current research on HCCR focuses on three issues: improving recognition accuracy, creating fast and compact models, and experimenting with other machine learning methods, such as transfer learning. We will also present the applications of deep learning methods in offline HCCR since 2016 from these three aspects and analyze and summarize their performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **MDQFL [30]** & **Combined Sub-Structure [29]** & **Compact MQDF [31]** \\ \hline Year & 2016 & 2016 & 2018 \\ \hline \multirow{4}{*}{Experimental platform} & server with 2 Intel & & a PC with an Intel(R) \\  & 2.6 GHz CPUs and & & Core(TM) i7-2600 \\  & 4 NVIDIA Tesla & & 3.4 GHz CPU and \\  & C2075 GPUs & & 4 GB memory \\ \hline \multirow{4}{*}{Feature} & gradient direction & weighted direction & \\  & histogram (GDH) & code histogram & gradient element \\ \hline \multirow{4}{*}{Feature processing} & quadratic correlation & & normalization- \\  & (ascending dimension), & & cooperated \\  & discriminative feature extraction (descending dimension) & & gradient feature, \\  & & & Gaussian blurring, \\  & dimension) & & sparse coding \\ \hline Classifier & DLQDF & MQDF & MQDF-KSVD \\ \hline Dataset & ICDAR-2013 & CASIA & CASIA-HWDB1.1 \\ \hline Evaluation metric & accuracy & character recognition error rate & accuracy \\ \hline Value & 94.92 & 2.57 & 90.08 \\ \hline \multirow{4}{*}{Advantages} & Improve classification accuracy; Test is faster. &
\begin{tabular}{c} Sub-structure discovery \\ of Chinese characters; \\ Determining the presence \\ of similar characters \\ to improve recognition rate. \\ \end{tabular} & Compact classifiers \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary and comparison of traditional methods for recognizing handwritten Chinese characters since 2016. “n/a” indicates no mention.
## 2 A2G Channel Measurement Campaign
### Measurement Scenarios  
The measurements were taken in a typical rural area in China, which primarily consist of vegetation, farmlands, and residential houses, as depicted in Figure 1a. The receiver was placed approximately 35 m away from the UAV's takeoff point in an open area without any obstructions. The UAV flew along a predefined trajectory, with a constant speed of 27 m/s and a maximum transmitter-receiver (Tx-Rx) distance of 3 km. The maximum flight height was 800 m and the flight duration was approximately 2 h. As noted, a strong LoS component is observed for the most time in our A2G measurement.
## 3 Key Technologies of UAV-Assisted SAGINs
### Resource Management
#### 3.4.2 Channel Optimization  
Channel resources are critical yet highly susceptible to interference. Unlike traditional static channel estimation methods, the high mobility of UAVs and satellites continuously alters the line-of-sight (LoS) conditions for UAV-to-ground and UAV-to-satellite links. LoS communication refers to direct E2E communication under unobstructed conditions, and changes in these conditions significantly impact channel impulse response, channel transfer function, path loss, shadow fading, and root mean square delay spread, among other characteristics. [PERSON] et al. [110] considered the impact of the LoS on communication in the joint optimization of offloading and resource allocation in a hybrid cloud and multi-access mobile edge computing (MEC) scenario within an SAGIN. [PERSON] et al. [111] proposed a probability-based service allocation strategy for LoS links. When the LoS probability is high, services are provided by UAVs; otherwise, they are provided by RSUs. Additionally, a mechanism based on the signal-to-interference-plus-noise ratio threshold is employed to reduce interference and improve communication reliability. Moreover, the vibration and pitch angle changes in UAVs during hovering can cause Doppler effects, leading to signal shifts and intensified channel state variations. Therefore, monitoring and sensing channel congestion is crucial for effective channel allocation. Channel tracking refers to the process of the real-time monitoring and updating of the dynamic characteristics of channel state information as it changes over time in a wireless communication system. This is essential for adapting to the time-varying nature of wireless channels and improving the performance of communication systems. In [33], a dynamic channel model and a three-dimensional dynamic turbo approximate message-passing (3D-DTAMP) algorithm are proposed. These are designed to track dynamic channels in Ka-band UAV-satellite communication systems, achieving better channel tracking performance while reducing pilot overhead and main taining comparable complexity. In addition, UAV communication faces the challenge of backhaul link attenuation [112]. Research by [PERSON] et al. [113] investigated the clustering and tracking of multi-path components in time-varying air-to-ground (A2G) channels and proposed a cluster-based tracking (CBT) method. By jointly considering the coupled effects of UAV vertical mobility, A2G communication, and dynamic computational requirements, [PERSON] et al. [114] formulated a non-convex constrained optimal control problem to optimize the overall energy efficiency of UAVs.
# Nucleotides as Bacterial Second Messengers
## 2 The c-di-GMP Nucleotide
### Types of c-di-GMP Receptors  
The c-di-GMP receptors/effectors may have the GGDEF, EAL, and HD-GYP domains. However, some domains are inactive, e.g., c-di-GMP receptors have inactive EAL domains.  
Other receptor/effector proteins with different domains including PiIZ and I-site receptors. Among the receptor proteins, several transcriptional regulators are known, as well as several types of proteins with diverse functions. Non-proteins, such as riboswitches, also function as c-di-GMP receptors.  
The _Pseudomonas aeruginosa_ PiIZ protein, involved in pili formation, is a c-di-GMP receptor in which the PiIZ domain has a high affinity for c-di-nucleotide under in vitro conditions. Other proteins possessing this domain are the BcsA protein of _G. xylinus_, the YcgR of _Escherichia coli_[14], the DgrA of _Caulobacter crescentus_, and the PlzC and PlzD of _Vibrio cholerae_[15].  
The _C. crescentus_ response regulator PopA protein is a GGDEF-domain c-di-GMP receptor, which is involved in the cell cycle [16]. The _Myxococcus xanthus_ hybrid histidine kinase SgmT and the CdgA receptor are also GGDEF-domain c-di-GMP receptors and the CdgA receptor participates in entry of predatory bacterium _Bdellovirio bacteriovorus_ to precells [17].  
Similarly, proteins with EAL domains but without PDE activity can bind c-di-GMP. Examples are _P. aeruginosa_ FimX, associated with motility by type IV pilus, and LapD from _Pseudomonas fluorescens_ with a high affinity for binding cyclic di-nucleotide [18].  
On the other hand, riboswitches are noncoding mRNA with specific secondary structures that can recognize molecular ligands. Upon the binding of a ligand to riboswitches, the secondary structure of the mRNA changes, leading to changes in transcription, mRNA splicing, or downstream gene translation [19]. GEMMS are riboswitches that specifically recognize c-di-GMP; the c-di-GMP-induced RNA splicing is another type of riboswitch [20].
## 5 Results & Discussion
### Simulation Results of the ZigBee WSN
#### 5.2.3 Data Traffic Received  
Data traffic describes the movement of data received per unit of time. Figure 18 shows that the received data traffic is 0.650, 0.540 and 0.3805 Mbp/s for the star, mesh and tree topology, respectively. This discovery means that the received traffic reaches its maximum in the star topology since all devices communicate via the FFD or PAN coordinator and are responsible for traffic generation and routing. Fewer collisions and reduced packet loss cause the maximum data traffic in the star topology. The received data traffic is the smallest in the tree topology, especially considering that the use of the device (RFD) must be communicated through the coordinator. The communication topology increases the data communication between the device and the PAN coordinator. Moreover, the cause and effect of increased data packet loss can reduce the data traffic received.
# Real-Time Terrain-Following of an Autonomous Quadrotor by Multi-Sensor Fusion and Control
## 2 Quadrotor System Models
### Quadrotor State Estimations  
When the laser-range-finder is used for distance measurement in terrain-following flight, the large vertical drop of the terrain will cause the delay of altitude control and the decrease of the stability of the quadrotor. Therefore, it is necessary to ameliorate the altitude control channel of the quadrotor to improve the stability and real-time performance of the quadrotor in a large maneuvering and large overload terrain-following flight.  
Combined with the linearized dynamics model of the quadrotor in the hovering state in Section 2.2, the state equation of the height channel control model can be written as:  
\[\left\{\begin{array}{c}\dot{\mathbf{x}}=A\cdot\mathbf{x}+B\cdot\mathbf{u} \\ w=C\cdot\mathbf{x}\end{array}\right., \tag{24}\]  
where \(A\), \(B\), \(\mathbf{x}\) and \(\mathbf{u}\) are the same as those defined in section A, \(C=\left[\begin{array}{cccccccc}0&0&0&0&0&0&0&0&1&0&0&0\end{array}\right]\). Using a Laplace transform on both sides of Equation (24), the transfer function of the vertical speed control can be obtained as:  
\[G_{I}(s)=\frac{1}{sm}. \tag{25}\]  
According to the voltage balance equation of the motor armature circuit, the motor model can be approximated as a first-order inertia link and the transfer function of the motor torque is:  
\[G_{\Pi}(s)=\frac{K}{T_{m}s+1}, \tag{26}\]where \(T_{m}\) is the inertia time constant and \(K\) is the open loop gain. Multiplying Equation (25) by Equation (26), the final transfer function of vertical velocity is:  
\[\mathrm{G_{v}(s)}=\frac{K/m}{s(T_{m}s+1)}. \tag{27}\]  
Adding the integral link to Equation (27) to obtain the transfer function of the UAVs' height control channel gives:  
\[\mathrm{G_{h}(s)}=\frac{K/m}{s^{2}(T_{m}s+1)}. \tag{28}\]  
Using the parameter measurement method proposed in the literature [45], the inertial time constant \(T_{m}=0.153\) and the open-loop gain \(K=1542\) of the 2212 KV910 brushless motor were measured by experiment. Substituting \(T_{m}\) and \(K\)\(K\) into Equations (27) and (28), Equation (29) can be obtained:  
\[\left\{\begin{array}{l}\mathrm{G_{v}(s)}=\frac{8398.7}{s(s+6.5)}\\ \mathrm{G_{h}(s)}=\frac{8398.7}{s^{2}(s+6.5)}\end{array}\right.. \tag{29}\]