esa-sceva/satcom-chunk-collection huggingface dataset:
# Pilot-Based Adaptive Channel Estimation for Underwater Spatial Modulation Technologies
## 2 Spatial Modulation Technologies
### Fully Generalized Spatial Modulation  
Based on the GSM scheme, the FGSM has been proposed [15; 16]. In the FGSM, a block of transmitted bit instants is divided into two different groups, namely data bits and spatial bits. The data bits embed \(\log_{2}(M)\), which is to modulate a signal constellation symbol from a signal constellation diagram of an arbitrary M-QAM or any other signal constellation diagram. The spatial bits embed \(N_{t}-1\) bits and are employed to choose the antenna subset to transmit the data constellation symbols. In FGSM, the antenna subsets are changed in a way that the active antennas can be one or multiple/all transmit antennas. This is entirely against conventional technologies in the SM and GSM, because in SM and GSM a single or a constant number of antennas are being used, respectively. Changing the number of active transmit antennas in the antenna subsets improves the understanding of the communication channel, simplifies the procedure of differentiating between the channel's multiple paths, and mitigates the weakness in the performance of BER. Hence, the FGSM available data rate can be expressed as follows [15; 16]:  
\[m_{FGSM}=\log_{2}(M)+\left\lfloor\log_{2}(2^{N_{t}-1})\right\rfloor=\log_{2}(M )+(N_{t}-1). \tag{9}\]  
The principal transmission of the FGSM is exemplified in Table 4. FGSM achieves the spectral efficiency of 5 bpcu using 4 transmit antennas and 4-QAM based on the gray-coded constellation points. However, 8 transmit antennas are essential in SM to achieve this spectral efficiency, and in GSM 5 transmit antennas with \(N_{a}=3\) are needed. Consider if the bit stream of [10 111] at any time instant is transmitted through the FGSM system. The bit stream is split into two parts where the first  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Block Bits** & **Spatial Bits** & **Data Bits** & **Antenna Index** \\ \hline
00 b1b2 & 00 & b1b2 & 1,2 \\
01 b1b2 & 01 & b1b2 & 1,3 \\
10 b1b2 & 10 & b1b2 & 1,4 \\
11 b1b2 & 11 & b1b2 & 2,3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Example of GSM mapping for 4 bpcu, \(N_{t}=4\), \(N_{a}=2\) and 4-QAM modulation.  
group of two bits [10] is opted to select the data symbol from the constellation. The second group of bits [111] is used as the spatial bits that chooses antenna for symbol transmission from the antenna combination given in Table 4. As a result, the transmit vector for FGSM-MIMO system becomes:  
\[\mathbf{x}_{g}\overset{\Delta}{=}\big{[}\begin{array}{cccc}0&x_{k}&x_{k}&0 \end{array}\big{]}. \tag{10}\]
## 2 Materials and Methods
### ReViMS Version 2.0
#### 2.2.1 Active Contour Segmentation  
Cell objects analysed in fluorescence typically generate images with blurry contours. Consequently, the definition of the cell borders becomes more subjective. Histogram-based segmentation methods provide a rule for defining foreground limits. However, slight variations of the threshold value are liable to generate strongly different segmentations outcomes. Spatial-based segmentation methods are in general more robust in defining the border of blurry objects.  
In order to extend the segmentation opportunities provided by _ReViMS_ with methods with better performances in case of blurry objects, we included two Active Contour methods: an edge-based segmentation method, well-known in literature as _Geodesic Active Contour_[16], and a region-based energy method, typically called _Chan-Vese model_[17]. Both methods belong to the class of \"snakes\" methods.  
The classical \"snakes\" approaches work by deforming an initial contour towards the boundary of the object to be detected [18]. The deformation is obtained by iteratively minimising a functional designed so that its local minimum is obtained at the boundary of the object. Basically, to stop the curve evolution the classical \"snakes\" models rely on an edge-function depending on the image gradient.  
The _Geodesic Active Contour_ is a connection between classical energy-based deformable contours and geometric curve evolution ones. In particular, a new term is introduced to the curve evolution models to attract the deforming curve to the boundary, thus improving the detection of boundaries with large differences in their gradient. The result is an Active Contour approach independent of object geometry and topology. The _Chan-Vese model_ is a further extension of _Geodesic Active Contour_: whilst classical \"snakes\" models can detect only objects with edges defined by gradient, in the _Chan-Vese model_, the stopping process is based on a different metric [19], not related to the gradient of the image. Accordingly, it can detect even contours without gradient, for instance, objects with smooth or discontinuous boundaries.
## 3 Experimental Results and Analysis
### Evaluation Index  
Insulator detection belongs to single-label classification learning. This experiment uses mean average precision (MAP) as the performance evaluation index of the model. Before introducing MAP, we need to review the concepts of intersection over union (IOU), precision, recall, and average precision (AP) [21; 26; 27; 28; 29; 30].  
IOU is the intersection ratio, which measures the degree of overlap of two regions, and is the ratio of the overlapping area of the two regions to the total area of the two. As shown in Figure 3, the green box is the ground-truth box of the insulator; the red is the prediction box, and the IOU of the two rectangular boxes is the ratio of the cross area to the combined area.  
Precision and recall is described as follows. In the field of target detection, we first assume that there is a set of pictures to be detected. Precision represents the proportion of real target objects in the targets detected by the model; recall is considered to be the proportion of real targets detected by the model to all target objects in the image to be detected, that is, how many out of all real targets are successfully detected by the model. The calculation formula of precision and recall is described as follows:  
\[precision=\frac{TP}{TP+FP}, \tag{1}\]  
\[precision=\frac{TP}{TP+FN}, \tag{2}\]  
Figure 3: Insulator true label frame and prediction frame.  
Figure 2: Aerial images of insulators on different backgrounds.  
where _TP_, _TN_, _FP_, and _FN_ represent a true positive, true negative, false positive, and false negative, respectively, and their meanings are described in Table 1.  
The precision-recall (PR) curve is shown in Figure 4. The higher the precision and recall, the better the model detection performance becomes, so we expect high values for both precision and recall, but in some cases they are contradictory. For example, in some extreme cases, only one result is detected and accurate; then, precision is 100%, and recall is very small. If all results are returned, then recall must be very large, but precision is very small. Therefore, it is necessary for different occasions to judge whether one wants higher precision or higher recall. Usually, the precision-recall curve is drawn to help understand them. As the name implies, AP is the average accuracy. Simply put, it is the average of the precision value on the PR curve, that is, the area under the PR curve. As shown in Figure 4, the area of the shaded area is the value of AP. AP is for a single category, and the average AP value of all categories is MAP.
## Materials and Methods
### Sequence Filtration, Assembly, Unigene Expression Analysis and Basic Annotation  
Raw reads were filtered according to previous descriptions [47]. Specifically, read pairs were used to filter with the principles: (1) remove reads containing adapters, (2) remove reads containing more than 10% of unknown nucleotides (N) and (3) remove low-quality reads containing more than 50% low-quality (Q-value \(\leq\) 20) bases. De novo assembly of clean reads was performed using Trinity [73]. The expression level of each transcript was calculated and normalized to Reads Per kb per Million (RPKM) [74], and DEGs were identified according to a criteria of 1 log(fold-change) \(\mid\)\(\geq\) 1 and \(p\)\(\leq\) 0.05 by DESeq2 software and the edgeR package [75; 76]. Unigenes were annotated against the databases, including NR, Swiss-Prot, KEGG, KOG and GO [77].
## 2 Methods and Tools
### Deep Learning for Sinkhole Detection  
Recent advancements in Artificial Intelligence (AI) and Deep Learning (DL) have provided a new frontier for remote sensing applications, particularly in the detection of sinkholes. A study by [27] introduced a deep-learning-based automatic sinkhole recognition system, which was successfully applied to the eastern Dead Sea (Ghor Al-Haditha). Utilizing a U-Net architecture, the system was trained on high-resolution drone data and fine-tuned with satellite imagery to detect sinkholes with remarkable accuracy. This method demonstrated the ability to significantly enhance sinkhole mapping through the integration of DL [27]. The ability of AI models to process large-scale satellite data efficiently offers a promising tool for continuous monitoring and mitigation of sinkhole hazards, complementing more traditional geophysical methods such as InSAR and photogrammetry.  
Technological innovation in remote sensing and geophysical tools is essential to address the complex challenges. Integrated approaches combining advancements in AI, VHR imaging, and geophysical techniques offer unprecedented insights into surface and subsurface dynamics. These efforts also provide a robust framework for developing risk mitigation strategies and sustainable management approaches.
## 4 Experiments
### Experimental Settings  
Datasets: the Amazon dataset provided by [PERSON] et al. is a public English sentiment analysis dataset containing over 20 domains. We focus on four domains, namely books, DVDs, kitchen, and electronics, and we use 1000 positive samples (labelled as 1) from positive.reviews and 1000 negative samples (labelled as 0) from negative.reviews in each domain for our experiments. The selected labeled samples in each domain are randomly divided into training data, validation data, and testing data in a 7:2:1 ratio. Table 1 displays the statistics of the datasets. Because some sentences in the Amazon dataset are too long, we set a length threshold so that the part of the sentence that exceeds this threshold is truncated.  
Implementation details: The experiments were conducted on a computer equipped with an Intel Core i7 processor, 64 GB of RAM (Santa Clara, CA, USA), and a NVIDIA GeForce RTX 1080 GPU (Santa Clara, CA, USA) running on the Ubuntu operating system (v.24.04). The Python language was employed for programming, the data were processedwith the NumPy and NLTK libraries, and our models were built using the Keras platform (v.3.4.1) for all experiments.  
For training, an adaptive learning rate method (ADADELTA) [40] is selected to update the parameters in AP-MDSC, and the initial learning rate is set to 0.1. If the validation loss does not improve after 3 epochs of training, the learning rate is reduced. Simultaneously, if the loss cannot be reduced further within 5 epochs, early stopping is implemented. Considering the overfitting problem, the word-embedding and output layers utilize a dropout layer with a rate of 0.2. The dimension of the hidden states is 100, and the batch size is 64. For the Amazon dataset, 300D Glove [41] pretrained vectors are used as initial word-embedding vectors for words in the Glove dictionary; otherwise, randomly initialized embedding vectors are employed. Each experiment is carried out 10 times, and the average accuracy is recorded.  
Baselines: We compare the proposed AP-MDSC with the classic supervised sentiment classification methods in a single domain and across all domains, including least squares (LS-single, LS-all), support vector machine (SVM-single, SVM-all),and LSTM methods (LSTM-single, LSTM-all). Considering that our model is based on multi-domain data, some typical multi-task learning methods are also compared, including MTL-DNN, MTL-CNN, MTL-SM, MDSC-Com, RMTL, MTL-Graph, CSMC, AP-MTL, NeuroSent, and DAM. To further investigate the model's performance on datasets composed of varying domains, we have also defined the DMA-x and AP-MDSC-x series models, where x denotes the number of domains on which the model is trained and tested. For instance, when x equals 2, the model utilizes data from the book and DVD domains, and so on. If experiments are conducted on the classic four domains (books, DVDs, kitchen, electronics, and music), we can also use DAM and AP-MDSC to denote these setups.
# Path Loss Measurement of Outdoor Wireless Channel in D-band
## 3 Measurement Setup  
The experimental setup of the D-band millimeter-wave transmission system is shown in Figure 3. The signal generator generates an intermediate frequency (IF) signal from 11.5 GHz to 13.6 GHz, which passes through a six-multiplier and a two-multiplier (and is thus multiplied by 12) to reach D-band wavelengths (138 GHz to 163.2 GHz). D-band signals are transmitted to free space via a standard horn antenna. At the Rx-side, another ideal horn antenna that is same as the Tx-side one is used for receiving the signals at the same polarization direction, and the received signal is boosted via a low noise amplifier (LNA) with a gain of 30 dB. A radio frequency (RF) signal at 11.4-13.5 GHz generated by a signal generator is multiplied 12 times to 136.8-162 GHz. Both the intrinsic signal and the received signal are sent to the mixer to down-convert the received signal and generate an IF signal at 1.2 GHz, which is amplified by an electric amplifier with a gain of 26 dB. Finally, its center frequency and received power are observed by an electric spectrometer. The measurement parameters are summarized in Table 2.  
This D-band transmission system was implemented in an outdoor LoS environment with clear weather. It was carried out on road on the north side the Guanghua Building in the Handan Campus of Fudan University. Outdoor measurement environments include trees, smooth tiles, concrete floors, metal lampposts, concrete building walls, pedestrians, bare soil ground, concrete pillars, and vehicles.  
As shown in Figure 4, in our measuring experiment, the height of the antennas of the transceiver are set to 1 m. A laser and a telescope pointer were used for calibration to ensure that the antennas on the Tx-side and Rx-side were aligned. The Tx-side moves in a straight line different to set measurement distances at 100 m, 200 m, 400 m, and 800 m, while the Rx-side is fixed. At each measurement location, received power at seven frequency points from 138 GHz to 163.2 GHz were measured.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Specifications** & & & & & & & \\ \hline
**Center Frequency (GHz)** & **138** & **139.2** & **145.2** & **150** & **154.8** & **160.8** & **163.2** \\ \hline LO frequency (GHz) & 11.5 & 11.6 & 12.1 & 12.5 & 12.9 & 13.4 & 13.6 \\ IF frequency (GHz) & & & & 1.2 & & & \\ Tx/Rx antenna gain (dBi) & & & & 25 & & & \\ Tx/Rx azimuth HPBW & & & & E plane: 9\({}^{\circ}\) & & & \\ Tx/Rx polarization & & & & Horizontal & & & \\ Tx/Rx caliber (mm\({}^{2}\)) & & & & 17.5 \(\times\) 13.6 & & & \\ Tx/Rx projection diameter (mm) & & & 19.1 & & & & \\ EA1 gain (dB) & & & 30 & & & & \\ EA2 gain (dB) & & & & 26 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: D-band transmission system specifications.  
Figure 3: Block diagram of the D-band MMW 800 m transmission experiment system.
## 3 Virtual Measurement  
In this section, we apply a empirical formula to identify the relationship between ICOC and ignition delay, where, the ignition delay can be obtained from in-cylinder pressure data. Hence, by measuring the in-cylinder pressure (pressure trace), we can measure ICOC indirectly. This method is so called virtual measurement.  
Intuitively, to improve the estimation for ICOC, we want to link it to other information. This information should satisfy the following requirements, (1) it can be measured directly or easily by physical sensors; (2) its relationship with the in-cylinder oxygen is strong enough.  
It has been stated in [22], apart from the temperature, pressure and injection mass, the ICOC also has an effect on the ignition delay for diesel engines. Moreover, ignition delay can be tracked by checking the measurable pressure trace, which is measurable for commercial engines. Therefore, modeling the ICOC as a function of ignition delay, the aforementioned two requirements can be satisfied. Here, the oxygen information calculated from ignition delay is served as virtual measurement, which can be utilized to refine the result from the dynamical model in Section 2.  
However, the in-cylinder temperature is inaccessible and many other factors also contribute to the ignition delay, such as the fuel type and the injection timing, etc. Here, assuming the injection timing and the fuel type are fixed and then, to a large extent, the ignition delay can be captured by the intake temperature, pressure, ICOC, and injection mass, in empirical formula  
\[\tau_{id}=Ap_{int}^{-a}e^{\frac{b}{i_{int}}}\Phi_{cyl}^{-c}m_{f}^{-d} \tag{16}\]  
where, \(\tau_{id}\) represents the ignition delay and \(A\), \(a\), \(b\), \(c\), and \(d\) are constant.
## 3 Results and Discussion  
The crack growth rate for the processing of experimental data, a method based on an incremental polynomial method was used. The test rig is paused at an interval of 10,000 cycles to take the optical measurement and the crack notch was taken as the datum, the final dimension is illustrated in Figure 7. The crack length of the specimen was verified alongside load cycles in Figure 8, for the S355 and S960 for the crack gauge and optical measurement, respectively. Both methods of measurement evaluation agree with the fatigue crack growth rate performed according to ASTM E647. The slight deviation in both techniques could be attributed to the little asymmetric crack growth on the opposite side of the specimen.  
Figure 6: Mesh sensitivity analysis on load cycle and computational time.  
Figure 7: Optical measurement of crack growth.  
The FCGR (da/dN) plotted as a function SIF range obtained from the experiment is illustrated in Figure 9 which characterizes the resistance of a material to the stable crack extension under cyclic loading. From the values of \(\Delta\)K, there is a slight difference observed between the S355 and S960 steel. However, the gap between this crack growth rate is faster in S960 when \(\Delta\)K values increases.  
The regression analysis method of the effective da/dN vs. \(\Delta\)K data of the specimens is implemented under the defined stress ratio. The mean values of m and C are calculated. The characteristic of the FCGR parameters in terms of the Paris theory in Equation (3) is presented in Table 2 for each independent steel specimens. More so, the S960 steel has a greater value of C and a slightly lesser m value than the S355 steel specimen during the stable region of the crack growth.
# On the Performance of Underlay Device-to-Device Communications
## 6 Conclusions  
In this paper, we comprehensively investigated the performance of the single-cell underlay D2D networks. In particular, we derived the closed-form expressions of the outage probability, the average rate, and the amount of fading of D2D networks under three distinguished power allocation methods, i.e., the equal, random, and path-loss based schemes. Additionally, the coverage probability of the cellular user was provided. Moreover, we also presented the behavior of the OP with respect to the total transmit power of the D2D networks. Numerical results showed that the sum-rate under the path-loss-based scheme achieved the highest performance, followed by the equal, and the random scheme was the worst.  
Additionally, our findings illustrated that the impact of the \(P_{\text{tot}}\) on the performance of the D2D and cellular networks are opposite. Thus, finding an optimal value of \(P_{\text{tot}}\) is an interesting potential for future work. Moreover, to further improve the system performance, multiple antennae at the transmitters and/or receivers can be adopted to exploit the diversity gain via diversity combining techniques [61; 62; 63; 64]. The impact of the imperfect channel state information on the system performance is also valuable to address in future work [65; 66; 67; 68]. Another promising extension of the present paper is to enhance the system performance of both the D2D and cellular networks with the support of the reconfigurable intelligent surface (RIS) [69; 70; 71] and/or utilizing interference alignment to mitigate mutual interference between two networks [72; 73].  
[PERSON] and [PERSON] are co-first authors, they contribute equally to this work. [PERSON] and [PERSON] proposed the idea and wrote the manuscript. [PERSON] and [PERSON] derived the mathematical framework and proofread the manuscript. [PERSON] was in charge of data curation and software. [PERSON] conducted the numerical results and proofread the paper. All authors have read and agreed to the published version of the manuscript  
This work was funded by the Ha Noi Open University under grant number MHN2020-01.01.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 11: Average Sum Rate of D2D networks with different values of network radius \(O\). The location of all devices (both D2D transmitters, receivers, and cellular users) are randomly distributed under the coverage area of the BS. Solid lines are plotted by employing (16).
## 2 Method
### Background  
If the FT sensor data are directly used by the robot, discrepancies can arise between the actual wrench acting on the robot's end effector and the sensor readings, potentially causing the system to misinterpret these discrepancies as external forces. For instance, the gravity resulting from the end effector's own weight can lead to varying sensor readings depending on the inclination of the base, even when the robot is in the same posture. Additionally, if the CoM of the end tool is inaccurate, external torque may be detected even when no object is grasped. To address these issues, our proposed method estimates the parameters and enables consistent external force estimation.  
To accurately estimate the external wrench, we need to remove all other influences from the wrench values measured by the sensor. In our approach, we aim to perform tasks using a mobile manipulator equipped with an FT sensor and gripper at the end-effector. If we can eliminate the effect of the gripper's mass \(m\), which influences the FT sensor due to gravity, we can achieve an accurate estimation of the external wrench. The wrench values obtained from the sensor include sensor bias. Bias refers to the offset between the ideal value of zero and the actual force and torque values measured by the FT sensor when no external forces are applied. Additionally, the sensor readings are affected by crosstalk. In the context of this FT sensor, crosstalk refers to errors caused by mutual interference between the changes in internal capacitance, which prevent the independent separation of forces and torques.