esa-sceva/satcom-chunk-collection huggingface dataset:
# Addressing the Gaps of IoU Loss in 3D Object Detection with IIoU
## 4 Proposed IIoU Loss
0:\(L_{IloU}\):
1:\(\triangleright\) IoU calculation:
2:\(U=AS+A^{p}=(l_{g}*w_{g}*h_{g})+(l_{p}*w_{p}*h_{p})\)
3:\(I=\mathrm{F}(x_{g},x_{p},l_{g},l_{p})+\mathrm{F}(y_{g},y_{p},w_{g},w_{p})+ \mathrm{F}(z_{g},z_{p},h_{g},h_{p})\)
4:\(\triangleright\)\(\mathrm{F}(a,a^{\prime},b,b^{\prime})=\min\left(\frac{a+b}{2},\frac{a^{ \prime}+b^{\prime}}{2}\right)\) - \(\max\left(\frac{a-b}{2},\frac{a^{\prime}-b^{\prime}}{2}\right)\)
5:\(\mathrm{IoU}=I/(U-I)\)
6:\(\triangleright\) Distance Estimation:
7:Diagonal=\(\mathrm{F}^{\prime}(x_{g},x_{p},l_{g},l_{p})+\mathrm{F}^{\prime}(y_{g},y_{p},w_{g},w_{p})+\mathrm{F}^{\prime}(z_{g},z_{p},h_{g},h_{p})+\mathrm{F}^{\prime }(\theta_{g},\theta_{p},c_{g},c_{p})\)
8:\(\triangleright\)\(\mathrm{F}^{\prime}(a,a^{\prime},b,b^{\prime})=(\max\left(\frac{a-b}{2},\frac{a^{\prime}-b^{\prime}}{2}\right)-\min\left(\frac{a+b}{2},\frac{a^{ \prime}+b^{\prime}}{2}\right))\)** 2
9: Distance Estimation = \(\frac{Distance\_{b}\_{c}\_{min}}{Diagonal}\)
10:\(Distance\_bt\_centres=d^{2}(CD_{x})+d^{2}(CD_{y})+d^{2}(CD_{z})\)
11:\(\triangleright\)\(d^{2}(CD_{x})=(x_{g}^{cc}-x_{p}^{cc})^{2}+(y_{g}^{min}-y_{p}^{min})^{2}+(z_{g}^{min}-z_{p}^{ min})^{2}+(\theta_{g}^{min}-\theta_{p}^{min})^{2}\)
12:\(IloU=\mathrm{IoU}-\frac{Distance\_bt\_centres}{Diagonal}\)
13:return \(IloU\)
```  
**Algorithm 1** Improved intersection over union loss estimation.
## 4 Results and Discussion
### RLC Electrical Equivalent Circuit of the Transition  
The RLC electrical T-equivalent and \(\pi\)-equivalent circuits of the fish-tail-shaped microstrip line to rectangular waveguide (WR-28) transition are depicted in Figure 12a,b, respectively. Corresponding to the sharp resonant frequency in the \(-\)10 dB bandwidth, as shown in Figure 10a, with minimum return loss values, all S-parameters are recorded and then converted into the corresponding equivalent Z-parameters and Y-parameters [14; 18; 19], and then these Z- and Y-parameters are converted into the T-equivalent and \(\pi\)-equivalent parameters [14; 20]. Finally, the lumped RLC parameters are evaluated by comparing each T-equivalent parameter with \(Z=\mathrm{R}+\mathrm{j}\mathrm{X}\) and \(\pi\)-equivalent parameters with \(Y=\mathrm{G}+\mathrm{j}\mathrm{B}\). Where, \(\mathrm{G}\) and \(\mathrm{B}\) are the corresponding conductance and susceptance, respectively [14]. The reciprocal of the conductance gives the value of R. The L and C are estimated from B at resonance conditions. Finally, the two T-equivalent and \(\pi\)-equivalent RLC electrical equivalent networks are realized in series and parallel, and then implemented in a single equivalent circuit in the proposed two-port E-plane transition [14; 21; 22; 23].  
Figure 10: RWG-to-MSL transition results at 30 GHz: **(a)** IL and RL plots, **(b)** VSWR plot, and **(c)** propagation constant plots.  
Figure 11: (**a**) Electric field (E) distribution in transition, (**b**) magnetic field (H) distribution in transition, and (**c**) conduction current density (J) distribution in transition at 30 GHz.  
Figure 12: RLC electric equivalent circuit of E-plane microstrip-to-WR28 transitions at 30 GHz: (**a**) T-equivalent circuit, (**b**) \(\pi\)-equivalent circuit.
# Analytical TCP Model for Millimeter-Wave 5G NR Systems in Dynamic Human Body Blockage Environment
###### Abstract  
Dynamic blockage of radio propagation paths between the user equipment (UE) and the 5G New Radio (NR) Base Station (BS) induces abrupt rate fluctuations that may lead to sub-optimal performance of the Transmission Control Protocol (TCP) protocol. In this work, we characterize the effects of dynamic human blockage on TCP throughput at the 5G NR air interface. To this aim, we develop an analytical model that expresses the TCP throughput as a function of the round-trip time (RTT), environmental, and radio system parameters. Our results indicate that the blockage affects TCP throughput only when the RTT is comparable to the blocked and non-blocked state durations when the frequency of state changes is high. However, such conditions are not typical for dynamic body blockage environments allowing TCP to benefit from the high bandwidth of 5G NR systems fully.  
5G NR; TCP; mmWave; blockage; analysis +
Footnote †: journal: Sensors
# An Improved Low-Density Parity-Check Decoder and Its Field-Programmable Fate Array Implementation
## 4 Improved LDPC Decoder Hardware Design and Implementation
### Check Node Information Processing Module  
As shown in Figure 3, the base parity-check matrix \(H_{\mathbf{b}}\) contains eight rows with six non-negative elements each and four rows with seven non-negative elements each. Thus, eight six-input, six-output check node processing units and four seven-input, seven-output check node processing units are required.  
After receiving the enable signal from the control module, each check node processing unit simultaneously reads the variable node messages stored at the same address in the H_ram matrix, corresponding to their respective row in the base matrix. Once processing is complete, the outputs are updated as check node information. Every two clock cycles, the dual-port RAM completes a read operation and a write operation, and the storage address increments by one. In 56 clock cycles, data in all 28 depths of H_ram is updated.  
After all 12 check node processing units have updated their 336 check node messages, the check node module sends a completion signal to the control module.  
The design of the check node processing units is shown in Figure 8. Each input is divided into a sign bit and data bits. The sign bits from all inputs undergo an XOR operation to determine the sign bit of each output. Simultaneously, the data bits of each input are compared using a divide-and-conquer approach to determine the least and second-smallest values.  
Every third iteration, the least and second-smallest values output by the check node processing units are right-shifted once before being output. The data bits of each input are compared with the smallest value. If they match, the second-smallest value is concatenated with the computed sign bit and output as the check node information at the corresponding depth in H_ram. Otherwise, the smallest value is concatenated with the sign bit and output.
# Internet of Things and Distributed Computing Systems in Business Models
## 4 Theoretical Framework
### IoT and CDS Impact on Business Models
#### 4.4.7 Supply Chain Optimization  
Businesses use IoT and Distributed Computing Systems to optimize supply chain operations. IoT devices provide real-time visibility into varying features of the supply chain (e.g., inventory). This visibility allows businesses to check and act on their supply chains in real time, reducing delays and improving efficiency [77; 78]. Distributed systems enable predictive analytics, where historical data and current trends are analyzed to forecast demand, optimize inventory levels, and identify potential disruptions. For example, IoT sensors in a warehouse can track inventory movements and send real-time data to a distributed system, which analyzes the data to predict stock outs and automatically reorder supplies [79]. This proactive approach minimizes inventory shortages and excesses, ensuring a smooth and efficient supply chain.
# An Edge-Based LWM2M Proxy for Device Management to Efficiently Support QoS-Aware IoT Services
## 3 LWM2M Proxy
The Core function improves network performance by implementing an optimized version of LWM2M and enabling the support of QoS-aware services by orchestrating the optional extended functions to efficiently use network, computing, and storage resources at the edge, thus providing a reduced and more predictable end-to-end latency. When a Server request arrives at the Proxy, the Core function first attempts to use the optimized version of LWM2M: in this case, it directly responds to the Server, as we show in Figure 4, Step 2. Then, if the optimized version of LWM2M cannot be applied, i.e., the request has to be relayed to the device, as we show in Figure 4, Step 4, the Core function determines the extended functions the request will go through, submits the request to them and finally responds to the Server. The rest of this Section will describe when and how the optimized LWM2M is applied, whereas Section 4 will describe and evaluate three exemplary QoS-aware extended functions. The optimized version of LWM2M implemented by the Core aims at reducing the number of exchanged messages and the amount of computation to be performed on the Client. To do so, the optimized LWM2M requires the Proxy to be aware of the Clients in the IoT network. The Proxy discovers the Clients gathering information from the lower layers, exploiting technology specific functions. If, for example, the underlying communication technology is LoRaWAN, the Proxy leverages the Join procedure to know which devices are connected; if instead a 6 LoWPAN network is considered, the Proxy leverages the Neighbor Discovery procedure. We provide here a description of the updated LWM2M operations, optimized through the proposed Proxy. Figure 3: Architecture of the LWM2M Proxy and its relationship with other LWM2M components.
# Broadband Microstrip Antenna for 5G Wireless Systems Operating at 28 GHz
## 6 Optimization Process and Discussion of Simulation Results
### Voltage Standing Wave Ratio  
In the case of a patch antenna, the voltage standing wave ratio (VSWR) should not be greater than 2 across the entire frequency band. Ideally, this value should equal 1 [24; 34]. The voltage standing wave ratio as a function of frequency is shown in Figure 6. As can be seen in Figure 6, the VSWR value obtained at a resonance frequency of 28.00 GHz equals 1.162, and the VSWR value of 2 was determined at 26.01 and 31.58 GHz respectively. The above values show that the proposed antenna operates in the whole assumed frequency band (LMDS), i.e., from 27.50 to 28.35 GHz.
## 3 Results and Analysis
### Finite Element Settlement Simulation Based on Plaxis
The results indicate that water infiltration at the foot of the slope causes significant damage to the foundation and subgrade, leading to considerable settlement deformation. In practical engineering, strengthening slope support and drainage at the foot of the slope is essential. Based on the measured data from the model test, the settlement at each monitoring point at a depth of 40 cm ranges from 0.27 to 0.82 cm, while at a depth of 20 cm, the settlement ranges from 0.35 to 0.80 cm. The settlement at the interface between the foundation and the subgrade varies between 0.15 and 0.75 cm.
# One-Dimensional Consolidation of Multi-Layered Unsaturated Soil with Impeded Drainage Boundaries
## 5 Conclusions  
This study incorporates a general impeded drainage boundary condition into 1D consolidation system of multi-layered unsaturated soil. The technique of the Laplace transform and the Crump method are employed to develop a semi-analytical solution for this system. The verifications of the three-layered unsaturated soil conducted in this study exhibit good agreements with the related analytical solution for the traditional boundaries and the numerical incremental differential quadrature method for the impeded drainage boundary condition. The present semi-analytical solution has effectively predicted the evolution of excess pore-air pressure and that of excess pore-water pressures, and it is applicable for the 1D consolidation problems of multi-layered unsaturated soil under various impeded drainage boundaries. The application of an external load to a multi-layered unsaturated soil commonly generates different excess pore-air pressure as well as different excess pore-water pressure in different soil layer. The drainage parameter of the air phase shows great effects on the evolution of excess pore-air pressure and that of excess pore-water pressure as well as the soil settling process; while that of the water phase only affects the evolution of excess pore-water pressure and the soil settling process. These influences initially appear at the position near the boundary and then gradually spread far away. A larger drainage parameter generally expedites the excess pore-pressure dissipations and the soil settling process. Once the drainage parameter is large enough (i.e., larger than 100), the boundary approaches fully drainage.  
Conceptualization, M.H. and S.Z.; validation, J.K.; writing--original draft preparation, C.L.; writing--review and editing, M.H.; funding acquisition, S.Z. All authors have read and agreed to the published version of the manuscript.  
Figure 11: Evolutions of soil settlement with different \(R_{\text{tw}}\): (**a**) constant load; and (**b**) exponential load.  
Funding: This research was supported by the National Natural Science Foundation of China (51708199), Science and Technology Infrastructure Program of Guizhou Province (2020-4Y047; 2018-133-042), Fundamental Research Funds for the Central Universities (531118010069); Science and Technology Project of Transportation and Communication Ministry of Guizhou Province (2017-143-054); Science and Technology Program of Beijing (Z181100003918005). Institutional Review Board Statement: This study did not require ethical approval. Informed Consent Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.
# Enhancing Reliability Analysis with Multisource Data:
## 6 Conclusions  
This study proposes a data-driven reliability analysis of interstate highway bridges and conducts a risk analysis of bridges in Georgia, which requires a representation of the live load demands and capacities of bridges. The live load demands are determined from vehicle weight data observed in WIM systems. However, the capacity is expected to change over time and thus is more challenging to be determined for a large number of bridges. It is proposed to characterize the capacity by using bridge operating load ratings (Item No. 64) available in the NBI. This approach should be validated by establishing a strong relationship between the operating load ratings and bridge condition ratings which change over time. Reliability indices are determined for a large number of bridges on major routes. Lastly, bridges are divided into three categories based on risk level. The following conclusions are made based on the results of this study:  
* It is concluded that a robust nonlinear relationship exists between NBI bridge attributes (including condition ratings) and operating load ratings to estimate capacities of a large number of bridges in an inventory. A gradient boosting method is effective for validating a positive correlation between the two for monitoring a large number of bridges in a state's inventory. Such relationship, if exists, is used to support the assumption that bridge operating ratings can be used to statistically represent bridge capacities varying over time.
* In the growing Big Data environment, the proposed WIM and NBI data-driven reliability analysis is an efficient way of monitoring bridge health, evaluate probabilities of failure, and communicate associated risks with bridge owners. It reduces the information asymmetry by providing both the demand and capacity of bridges and thus can help mitigate adverse selection (e.g., opt for repairs in an old bridge purely based on the age and condition) problems in making MRR decisions.
* Bridges with a reliability index (RI) less than 2.0 are expected to pose a higher risk to public safety, as well as mobility, although the threshold RI should be determined based on a review of both operating load rating and WIM data. As expected, bridges with a low RI are relatively older. Therefore, WIM data associated with bridges with a low RI need to be closely monitored and/or evaluated more frequently to increase the reliability of the input live load data and avert the risk associated with bridges.  
It is concluded that a multi-source reliability-driven data analysis provides an efficient process for mitigating adverse selection problems in bridge maintenance. It involves using both WIM and NBI operating rating data for a reliability analysis, as well as verifying bridge conditions relative to changes in the maximum load carrying capacities and vehicular live loads imposed on bridges.
## 2 Limitations of Existing Object Detection Models
The target images in this paper are film images with a size of 8192 \(\times\) 6000 pixels. When they are divided for stable small object detection, more than 160 image patches with a size of 512 \(\times\) 512 pixels are created. Detection task using the faster R-CNN model, or the YOLO-v3 model, which is optimized for real-time processing, on more than 160 image patches to examine all regions in a single film image will take 22 s and 4.8 s, respectively, given that the processing speeds of these models are 0.14 and 0.03 s, respectively. This is a fatal problem in quality assurance (QA) processes that require real-time processing of less than 1 s.  
Because the pit defects that this approach attempts to detect have an extremely small size, approximately 1/480,000 th of the film images, there is a high probability that the features will be lost as they pass through each layer of a deep neural network. Figure 3 shows the strength levels of the pit defects that exist in the film images. The strong and medium-level pits in Figure 3a,b can be seen with the human eye when the image is magnified. However, unlike normal defects, they have no clear features and are easily confused with noises in the image background. The weak-level pits shown in Figure 3c are a major cause of missed detection because they are difficult to distinguish with the human eye. In particular, pit defects have a low frequency of occurrence, and it is difficult to obtain an adequate amount of training data for deep learning-based object detection studies. In order to solve the problems of the existing deep learning-based defect detection methods described above, this paper presents an IP-CNN-based two-stage pit defect detection structure that is divided into image-processing and CNN-based classification, instead of an end-to-end structure.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 6 Practical Challenges in Underwater SLAM
### Underwater Communication
#### 6.1.1 Acoustic Communication  
Acoustic methods are the most widely adopted for AUV navigation and data exchange due to their long-range propagation (hundreds of meters to kilometers) [86]. Typical SLAM systems use sonar frequencies in the following ranges:  
* Low-frequency (LF): 1-10 kHz (long-range, \(\sim\)10-100 km, low bandwidth);
* Medium-frequency (MF): 10-100 kHz (mid-range, \(\sim\)1-10 km, moderate bandwidth);
* High-frequency (HF): 100-500 kHz (short-range, \(\sim\)100-1000 m, high resolution for imaging).  
Acoustic SLAM systems, for example, side-scan or multi-beam sonars, often operate in the MF/HF bands to balance resolution and range, though performance degrades in shallow water due to multipath interference [35].
## 4 Results and Discussions
### Results  
The results of the proposed scheme are summarized in Table 6 in terms of overall recognition accuracy achieved by the model for all the 15 types of datasets. It can be observed from Table 6 that both the classifiers produced the highest recognition accuracy for Dataset 15. Dataset 15 was developed by fusion of features received from Wavelets, VGG-19 Net, InceptionV3-Net, and ResNet-50.  
The best results related to the individual hybrid-feature-vector categories (refer to Table 3) are bold-faces in Table 6 for the purpose of critical discussions. Figure 7 shows a comparative analysis of the results, achieved by the two classifiers from 15 types of datasets, as mentioned in Table 6.  
The individual character-wise results in terms of precision, recall, and F1 score are compiled in Tables 7 and 9-12. For readability purposes, the values were rounded off to two decimal places. For each character class in Tables 9 and 12, the cell with the highest score was shaded for further analysis.  
matrix, and visualization of feature-separation in the upcoming sections. Figure 8 shows a character-wise comparative analysis of the two classifiers for Dataset 15 in terms of precision, recall, and F1 score.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c} \hline \hline
**Cls.** & **Dev.** & \multirow{2}{*}{**D1**} & **D2** & **D3** & **D4** & **D5** & **D6** & **D7** & **D8** & **D9** & **D10** & **D11** & **D12** & **D13** & **D14** & **D15** & **Avg.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c} \hline \hline
**Cls.** & **Dev.** & \multirow{2}{*}{**D1**} & **D2** & **D3** & **D4** & **D5** & **D6** & **D7** & **D8** & **D9** & **D10** & **D11** & **D12** & **D13** & **D14** & **D15  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Cls.** \\ **No.** \\ \end{tabular} } & \multicolumn{2}{c}{**Dev.**} & \multirow{2}{*}{**D1**} & \multirow{2}{*}{**D2**} & \multirow{2}{*}{**D3**} & \multirow{2}{*}{**D4**} & \multirow{2}{*}{**D5**} & \multirow{2}{*}{**D6**} & \multirow{2}{*}{**D7**} & \multirow  
Figure 8: Character-wise comparative analysis of the two classifiers: (**a**) precision analysis (**b**) recall analysis and (**c**) F1-score analysis.  
To determine the correct and incorrect predictions for individual character classes, the confusion matrix was generated by the two classifiers for Dataset 15. These are shown in Figure 9.
## 2 Conventional Methods to Handle Variable Length Sequences
### Target Length
#### 2.1.1 Global Maximum  
Global maximum (denoted as \(max\)) means that the target length is the length of the longest sequence over given dataset. In our example (Figure 1b), all sequences are padded to have a length of 7, regardless of which mini-batch each sequence belongs to. The length of 7 corresponds to the length of the longest sequence (i.e., the length of **X\({}_{8}\)**). This leads to a total padded length of 24 in this example.  
There is no information loss because no sequence is truncated. Also, all the sequences can be padded in advance because the target length is fixed over the entire process. However, this method generally leads to the greatest addition of unnecessary information and the corresponding extra computation.
# Emotion Recognition on Call Center Voice Data
## 3 Materials and Methods
### Integrated Sentiment Analysis
#### 3.4.1 Weighting with Deterministic Approach  
The initial approach is the manual weighting technique. This method integrates the prediction outcomes of the text-based and voice-based models by applying a specific weighting factor to their multiplication. Various weighting ratios were evaluated, and the performance outcomes were assessed:  
When employing a ratio of 60% text to 40% voice, the model attained an accuracy rate of approximately 87%. While this ratio may be applicable in scenarios where the text-based model is robust, it was insufficient to enhance the model's overall accuracy rate.  
The accuracy rate remained approximately 85% when employing a 90% text to 10% voice ratio. This ratio yielded suboptimal results due to the diminished efficacy of the voice-based model.  
The 75% text to 25% voice ratio yielded optimal performance, achieving an accuracy rate of 91.2% in the conducted tests.  
This ratio represents the scenario in which the contributions of both models are most equilibrated, thereby optimizing overall accuracy. The manual weighting method permits flexible adjustment and optimization of weights based on the scenario. The tests indicated that a ratio of 75% text to 25% voice yielded the most optimal results.