esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Detection of Multilevel Communities: Scalable, Selective and Resolution-Limit-Free +
## 4 Discussion
### Multilevel Communities in Real-World Networks  
In previous literature, real-world networks are rarely considered as multilevel networks. Even when studying with multiresolution methods and obtaining various network divisions, people often _artificially_ select their favorite communities to interpret, and ignore all the rest. Our study in this paper reveals that, even filtered by our strictest strategy, real-world networks still exhibit multilevel structures, whose topologies are surprisingly all best and unique. To our viewpoint, communities suggested by stable plateaus should all have their own particular information [5]: some of them have been interpreted as being relevant to the a priori knowledge, or metadata of the network, while the rest still await proper interpretations. We believe that for real-world networks, resolution scales are essential: communities detected within different resolution scales capture different aspects of the network structure, and should be interpretable by different node attributes or metadata. For example, for the karate club network, our detection on the 2-community level captures the communities all in a strong sense, while on the 4-community level it recovers the metadata. For the dolphins' network, our 2-community level corresponds to a \"known division\" during the absence of individual SN100, while our 7-community level is consistent with [PERSON]'s division, which is presumably for the period that SN100 was present. Exploring the relationship between the detected communities and the metadata is a challenging work, yet it is believed to be promising to yield insights of genuine worth [55]. Multiple resolutions apparently provide more information than fixed resolutions, thus investigating real-world networks in a multi-resolution inspection should be worth considering in future studies.
## 3 Types of Underwater Communication Systems
### Underwater Wireless Communication Protocols
The main design objective of the ORIT protocol is to maximise the goodput, the ratio between delivered data per unit of time, tackling the long propagation delay and narrow channel bandwidth constraints [111]. Furthermore, cross-layer protocol design is referenced as a way to improve performance in wireless networks, particularly in rough conditions similar to the underwater medium [60]. [PERSON] et al. [60] make reference to a modular cross-layer communication solution for underwater multimedia applications that merges MAC, routing, and physical functionalities. [PERSON] et al. [60] reported various challenges for implementing protocols in underwater wireless communications, as follows: (i) severely impaired acoustic channel--the underwater acoustic channel is affected by time-varying multipath and fading, which complicates reliable communication [5; 60; 94; 108]; (ii) limited bandwidth--the available acoustic bandwidth is highly dependent on transmission distance, with only a few kHz available at long distances due to high environmental noise and medium absorption [5; 60; 111; 5]; (iii) high propagation delays--the propagation delay in underwater environments is significantly higher than in terrestrial channels, which can lead to increased latency in communication [60; 62]; (iv) high BER--underwater communication can experience high bit error rates and temporary losses of connectivity, often referred to as shadow zones [60; 102; 101]; (v) device failures--underwater devices are prone to failures due to fouling and corrosion,which can affect their operational reliability [60]; (vi) energy constraints--batteries used in underwater devices are energy-constrained and cannot be easily recharged, limiting the operational time of these devices [5; 60]; (vii) localisation challenges--accurate localisation of nodes is difficult due to the lack of Global Positioning System (GPS) functionality underwater, necessitating alternative localisation schemes [5; 21; 60]; and (viii) mobility issues--the mobility of nodes can lead to outdated information, decreasing the packet delivery ratio as mobility increases [5; 60; 108]. These challenges require the development of specialized communication protocols that can effectively address the unique conditions of underwater environments.
## 4 Experiments
### Experiment Setup
#### 4.1.2 Evaluation Criteria  
The evaluation metrics in PASCAL VOC [33] are widely used in object detection. First, we calculated the recall and precision, defined as (10) and (11), respectively. Here, TP, FP, and FN mean true positive predictions, false positive predictions, and false negative predictions, respectively. Recall indicates the proportion of positive instances correctly predicted by the model in all positive instances in the dataset, with higher values indicating fewer misses. Precision is a ratio used to calculate how many instances are judged to be positively predicted correctly, with higher values indicating fewer incorrect predictions.  
\[Recall=\frac{TP}{TP+FN} \tag{10}\]  
\[Precision=\frac{TP}{TP+FP} \tag{11}\]  
AP is the area bounded by the P(R) curve and the X-axis, with the calculation formula shown in (12). P(R) is a curve based on the above Precision and Recall, while \(mAP=\frac{1}{N}\sum_{i=1}^{N}AP_{i}\), where N is the number of categories. IOU is the intersection ratio of two bounding boxes, reflecting the coincidence degree between the bounding boxes. The higher the IOU value is, the higher the coincidence degree of the two bounding boxes and the lower the coincidence degree. We set the IoU threshold to 0.5, and used AP@.5 and mAP@.5 as the model performance evaluation indices.  
\[AP=\int_{0}^{1}P(R)dR \tag{12}\]
# Wireless Sensor Networks for Enabling Smart Production Lines in Industry 4.0
## 2 Methodology
### Telemetry Data  
Having accurate and timely monitoring data is crucial for our closed-loop network framework. On the other hand, the monitoring data should introduce limited overhead. Different methodologies exist to obtain telemetry data in order to know the state of the network. The most frequently used method for link monitoring is extracting received signal strength (RSS) information from the data streams. An implementation of a control system using telemetry via received signal strength indicator (RSSI) readings is provided in [31]. Commercial monitoring systems also exist for monitoring network link quality via specific sensor modules that use the RSSI information from the packet headers.  
In-band network telemetry (INT) is a monitoring system which, compared to network monitoring by traffic probing and equipment polling, uses the data streams in the network for acquiring monitoring information by adding telemetry data on a per-packet basis. INT-based monitoring methods are common for wired networks with programmable data plane technology [32; 33; 34; 35], and research on using INT-based monitoring for wireless networks is ongoing [36; 37]. On the MAC layer, an INT header is added that contains information on the network state, that is, downlink RSSI, throughput, latency, and packet error rate information. This information is used to determine whether QoS requirements are met, and to reconfigure the network if they are not met.
## 4 Antenna Array
### Array with Sequential 90\({}^{\circ}\) Rotation of the Antenna Elements
#### 4.3.1 Uniform Amplitude and Phase Distribution  
The aforementioned design considerations lead to the amplitude and phase values for each antenna feeding port listed in Table 5.  
A comparison between the normal array and the array with the sequential 90\({}^{\circ}\) rotation of the antenna elements, when applying uniform amplitude and phase, is depicted in  
Figure 14: Sequential 90\({}^{\circ}\) rotation of the antenna elements.  
Figure 15. It can be seen that both configurations provide the same co-polar pattern with broadside radiation, with slight differences regarding the side lobe level of the vertical polarization. The measured cross-polarization is again higher than the simulation, as it is experienced in the previous measurements. However, it can be stated that the cross-polarization performance of the array with the sequential 90\({}^{\circ}\) rotation is considerably enhanced within the main beam with the penalty of increasing it in the ranges \(\theta\pm 50^{\circ}\) but far beyond the antenna main beam. The measured improvement at \(\theta=0^{\circ}\) is almost 10 dB and 8 dB for the horizontal and vertical polarization, thus achieving a measured cross-polarization suppression of 32 dB and 36 dB, respectively, which still remain below the simulated values. This slightly difference can be expected, since the measured range of the cross-polarization values are relatively low in the logarithmic scale, leading to an extremely sensitive measurement. It has to be also considered that the antenna elements are not exactly identically constructed due to fabrication tolerances and manufacturing processes. It can be also seen that the measured cross-polarization patterns agree with the predicted simulation data.  
Figure 15. Comparison normal array and array with sequential 90\({}^{\circ}\) rotation @ \(f_{0}\) = 1.325 GHz: normalized co-polar & cross-polar patterns (\(\varphi=~{}90^{\circ}\)). Uniform amplitude and phase distribution.
## 4 Experiment and Results  
### Dataset Pre-Processing  
A self-made dedicated dataset for small target detection took images of the solder joints of the circuit board, which included a total of 8600 pictures of solder joint defects. They were divided into training sets and test sets according to the ratio of 9:1 and marked in PASCAL VOC format using the labellmg tool. There were six types of solder joint defects, which were marked as shot out, oxidation, welding missed joint, extensive solver, solder projection, and inveracious solder. We randomly divided 7735 pictures as the training set and 860 pictures as the test set.  
The PASCAL VOC dataset [61] is a common public data set used in target detection tasks, including 20 classes of targets such as train, cat, and sofa. The experiment used VOC2012 for training, including 23,080 pictures and 54,900 targets, and used VOC2007 for testing, including 9963 pictures and 24,640 targets.  
Public PCB dataset: The PCB defect dataset has collected a total of 693 pictures. They were divided into training sets and test sets according to the ratio of 9:1.  
In order to expand the training samples, various forms of data augmentation are used for the training set. Firstly, the traditional methods of data augmentation are adopted, for instance, rotation, noise, brightness transformation, random clipping, and amplification, to simulate the differences in shooting time, angle, and definition. So the robustness and generalization of the model improve, and the network can fully learn the detailed characteristics of solder joint defects. In addition, mosaic data enhancement was also introduced.
# Using Wearable Sensors to Estimate Vertical Ground Reaction Force Based on a Transformer
## 3 Methods
### Loss Function  
Using mean squared error (MSE) as a loss function is a standard approach to predicting GRF tasks. MSE loss tends to produce a smoothed or averaged approximation of ground truth. However, in GRF prediction, the peak instantaneous impact of the GRF signal is more important than the average accuracy of the entire sequence. The peak information represents the maximum GRF during motion, which is often associated with knee injury or injury risk. Therefore, the same loss function Gate-MSE as in [21] is used in this paper, which prioritizes the regions with a high impact on the prediction results during the training period and significantly improves the accuracy of the peak estimation for ground reaction forces. The Gate-MSE is described as follows and can be viewed as a linear combination of weighted MSE under the \(T\) threshold.  
\[L_{f}=\sum_{T}w_{t}\frac{1}{|F_{\delta_{t}}|}\left|\left|F_{\delta_{t}}-F_{ \delta_{t}}\right|\right|_{2}^{2} \tag{5}\]  
We define \(F_{\delta_{t}}=\{F_{1},F_{2},F_{3}\ldots F_{f};\forall|F_{f}|<\delta_{t}\}\), which serves as an indexed array for all elements in the ground truth signal \(F\), with an absolute value below the threshold \(\delta_{n}\)  
Figure 3: Model framework. The neural network used in this paper contains a temporal embedding layer, three transformer encoders, and a linear layer. The input is the IMU measurements, and the output is the corresponding vGRF.  
In practice, \(T\) serves as a hyper-parameter where each threshold belongs to the sequence \(\delta=\) [0, 1, 5, 10, 15,... ] in terms of N/kg. The summed loss is equivalent to the MSE when \(T=1\), and we weigh each contribution with \(w_{t}=1/T\), weighted by the number of thresholds.
## 2 System Model
### Problem Formulation  
Sections 2.1 and 2.2 above have described the coexistence, signal, and interference model for the GEO and LEO satellite spectrum-sharing scenario. To track the interference between the GEO communication system and the UD-LSC system, we take the joint beam association problem from LEO satellites and the power allocation problem between multiple LEO beams into consideration. On one hand, the way the LEO satellites and the LBCs are associated determines the off-axis angle between the interfering link and the interfered  
Figure 2: The GEO-LEO interference model and the definitions of different off-axis angles.  
link, which in turn determines the severity of the interference. On the other hand, traffic requests of different LBCs on the ground are uneven, and power allocation of LEO satellites for the LBCs determines the communication satisfaction. In this paper, the communication satisfaction is defined as (6). When \(\sum_{k=1}^{K}C_{k,m}=T_{m}\), LBC \(m\) has a maximum communication satisfaction \(\Gamma_{m}=1\).  
\[\Gamma_{m}=\left\{\begin{array}{ll}1,&\text{if }\sum_{k=1}^{K}C_{k,m}\geq T_{m} \\ \left(\sum_{k=1}^{K}C_{k,m}\right)/T_{m},&\text{if }\sum_{k=1}^{K}C_{k,m}<T_{m} \end{array}\right. \tag{6}\]  
We further propose an optimization problem as (7) to achieve the maximum satisfaction of all LBCs.  
\[\mathbf{P1}:\min_{\{P_{k,m}\},\{v_{k,m}\},\forall k,\forall m.}\max\ \Phi_{m} \tag{7}\]  
s.t. \[C_{k,m}=BW\cdot\log_{2}(1+R_{k,m})\] (7a) \[\sum_{k=1}^{K}C_{k,m}\leq T_{m}\] (7b) \[\Phi_{m}=T_{m}-\sum_{k=1}^{K}C_{k,m}\] (7c) \[\sum_{m=1}^{M}v_{k,m}\leq L,\forall k\] (7d) \[\sum_{m=1}^{M}P_{k,m}\leq P_{s,\max},\forall k\] (7e) \[\sum_{k=1}^{K}\sum_{m=1}^{M}v_{k,m}P_{k,m}G_{tx}\left(\gamma_{g}^ {k,m}\right)h_{k,g}^{L}G_{rx}^{G}\left(\delta_{g}^{k,m}\right)P_{rx}^{G}\leq I _{0},\forall g\] (7f)  
In (7), the target of the optimization problem is the sum of \(\Phi_{m}\) and total consumed power \(\sum_{k=1}^{K}\sum_{m=1}^{M}P_{k,m}\). \(\Phi_{m}\) is the gap between traffic requests and provided capacity as described in constraint (6). By minimizing \(\Phi_{m}\), we can obtain a better \(\Gamma_{m}\). (7a) is the Shannon capacity of LBC \(m\) that is provided by LEO satellites \(k\). (7b) indicates that the UD-LSC system works in a cooperative manner in which the sum capacity of LEO satellites needs to be lower than the traffic requests so that no additional capacity is wasted. Not all visible \(K\) LEO satellites are involved in collaboration; if \(v_{k,m}=0\), \(C_{k,m}=0\). (7d) shows that the total number of beams for each LEO satellite cannot be larger than the maximum number of beam \(L\). (7e) is the total power constraint for each LEO satellite. (7f) indicates that the interference from LEO satellites must not exceed the interference threshold \(I_{0}\) of the GEO terminal, which is determined by \(I/N\) parameters of different types of GEO terminals.
## 2 Materials and Methods  
### Overall Flow  
The flow of the proposed multi-constraint feature point tracking is shown in Figure 1. Firstly, we use the optical flow method to match feature point pairs. Then, the BRIEF descriptors of the match feature point pairs are calculated, and a global threshold is used to eliminate matching point pairs with low similarity. RANSAC is then used to eliminate the mismatches feature point pairs that cannot be eliminated by BRIEF descriptor matching. To address potential misjudgments, we evaluate the quality of matching points using optical flow error and descriptor matching error. Matching points of high quality will not be eliminated. Finally, an adaptive mask homogenization method is used to homogenize the feature points, and again extract a sufficient number of feature points.
## 4 Conclusions
Comparing the polycrystalline PMN-PT with LiTaO\({}_{3}\), which is the most used single crystal for EO beam steering, has shown that PMN-PT ceramic performs about seven times better than LiTaO\({}_{3}\). Nevertheless, PMN-PT ceramic is polarization-independent. Therefore, unlike LiTaO\({}_{3}\) single crystal, it does not need orientation adjustment and domain-engineered multi prisms to be used as an electro-optical beam steerer. The PMN-PT ceramic is also easier, cheaper, and faster to produce and is available in larger sizes than most EO single crystals. The following are available online at [[https://www.mdpi.com/article/10.3390/app11083313/s1](https://www.mdpi.com/article/10.3390/app11083313/s1)]([https://www.mdpi.com/article/10.3390/app11083313/s1](https://www.mdpi.com/article/10.3390/app11083313/s1)), Video S1: Electro-optical 633 nm laser beam steering with the 7.5 mm \(\times\) 7.5 mm \(\times\) 40 mm polycrystalline PMN-PT under 1 kV/mm external electric field at 1 Hz. Conceptualization, A.A. and P.M.; methodology, A.A.; software, A.A.; validation, A.A., C.B., M.W., and E.R. ; formal analysis, A.A.; investigation, A.A.; resources, C.B. and M.W. ; data curation, C.B., M.W., and E.R. ; writing--original draft preparation, A.A. and M.W. ; writing--review and editing, P.M. and E.R. ; visualization, A.A. and M.W. ; supervision, P.M.; project administration, P.M.; funding acquisition, A.A. and P.M. All authors have read and agreed to the published version of the manuscript. This research was supported by [PERSON] at AFRL/RY, Dr. [PERSON] at AFRL/RX, DOD SMART program, and by Exciting Technology LLC. Not applicable. The authors would like to thank [PERSON] at AFRL/RY and [PERSON] at AFRL/RX for partially funding this research. The authors declare no conflict of interest.
# A Modified Residual-Based RAIM Algorithm for Multiple Outliers Based on a Robust MM Estimation
## 5 Algorithm Verification and Simulation Results
### Double Outlier Mode with the Largest Characteristic Slope  
For Case 2, a fixed bias of 10 m was added into the double outliers combination with the largest characteristic slope with \(\beta=1\) at each epoch during simulation. The positioning errors (ENU) of the four detectors are shown in Figure 10.  
In Figure 10, the M estimator in detector 2 did not produce an effective robust effect compared with the MM estimator in detectors 3 and 4. The accuracy of detector 4 was slightly poorer than that of detector 3 but the positioning result obtained was robust. The distribution statistics of 3D positioning errors of the four detectors are shown in Figure 11. The numerical results extracted from the detectors in Case 2 are shown in Table 4.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **RMSE (m)** & **95\% (m)** & **Max (m)** & **Time (s)** \\ \hline Detector 1 & 3.6112 & 5.8041 & 10.5030 & 10.7667 \\ Detector 2 & 3.2587 & 5.1707 & 7.5235 & 11.5730 \\ Detector 3 & 1.8418 & 2.6215 & 3.6394 & 173.8353 \\ Detector 4 & 1.8418 & 2.6215 & 3.6394 & 33.5331 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The numerical results of the four detectors in Case 1.  
Figure 10: Positioning errors of four detectors for the worst combination in Case 2: (**a**) detector 1, (**b**) detector 2, (**c**) detector 3 and (**d**) detector 4.  
The simulation time of detector 4 was reduced by 82.33% compared with detector 3 by using the proposed fast subset selection method. Compared with the simulation time in Table 2, the proposed fast subset selection based on the characteristic slope was effective. Considering the considerably improved efficiency of estimation, sacrificing some robustness within a reasonable limit was acceptable.
# Vehicle Distance Measurement Method of Two-Way Two-Lane Roads Based on Monocular Vision
## 3 Experiments and Discussions
### Distance Measurement Experiment and Analysis of Results
Within 0-60 m, that is, when measuring the distance between car A and car B, the average error of Method 3 was 3.79%, and the corresponding distance was 1.2 m. The average error of method 2 was 10.86%, and the corresponding distance was 4.2 m. The average error of method 1 was 3.15%, and the corresponding distance was 0.9 m. The ranging accuracy of method 1 was 0.64% higher than that of method 3 and 7.71% higher than that of method 2. Within 60-120 m, that is, when measuring the distance between car A and car C, the average error of method 3 was 15.29%, and the corresponding distance was 14.5 m. The average error of method 2 is 24.43%, and the corresponding distance is 22.3 m. The average error of method 1 was 7.36%, and the corresponding distance was 7.3 m. The ranging accuracy of method 1 was 7.93% higher than that of method 3 and 17.07% higher than that of method 2. The results showed that when measuring the longitudinal distance between the vehicle and forward vehicle, as well as the longitudinal distance between the vehicle and the opposite vehicle on two-way two-lane roads, the ranging accuracy of method 1 was higher than that of the other two methods. In summary, the vehicle distance measurement method of two-way two-lane roads proposed in this paper was effective. Compared with the traditional geometric ranging method and inverse perspective mapping method, the improved geometric ranging method had higher accuracy in measuring the longitudinal distance between the vehicle and the forward vehicle and the longitudinal distance between the vehicle and the opposite vehicle. In addition, the greater the actual distance, the more obvious the improvement of ranging accuracy, which also corresponded to the above analysis of how the roll angle of the camera affects the ranging results using the traditional geometric ranging method.