esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment
### Experimental Results
#### 4.4.1 Comparison of Various Methods in Semantic Segmentation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**NTUT Sunny** & **Size** & **Avg IOU** & **Avg REC** & **Avg PRE** & **Avg F1-Score** & **FPS** \\ \hline MultiNet & 1.2 GB & 0.971 & 0.962 & 0.975 & 0.979 & 0.91 \\ VGG16-FCN & 1.6 GB & 0.987 & 0.980 & 0.980 & 0.980 & 0.87 \\ BiSeNet & 207 MB & 0.908 & 0.984 & 0.921 & 0.907 & 2.89 \\ BiSeNet V2 & 45.9 MB & 0.956 & 0.942 & 0.925 & 0.955 & 3.09 \\ LWF-VGG FCN tiny & 29 MB & 0.985 & 0.979 & 0.981 & 0.977 & 2.87 \\ LWF-VGG-FCN & 226 MB & 0.987 & 0.980 & 0.980 & 0.981 & 1.77 \\ \hline NTUT cloudy & & & & & \\ \hline MultiNet & 1.2 GB & 0.959 & 0.948 & 0.961 & 0.964 & 0.91 \\ VGG16-FCN & 1.6 GB & 0.967 & 0.976 & 0.976 & 0.976 & 0.87 \\ BiSeNet & 207 MB & 0.890 & 0.906 & 0.904 & 0.912 & 2.89 \\ BiSeNet V2 & 45.9 MB & 0.937 & 0.954 & 0.952 & 0.961 & 3.09 \\ LWF-VGG FCN tiny & 29 MB & 0.970 & 0.974 & 0.977 & 0.975 & 2.87 \\ LWF-VGG-FCN & 226 MB & 0.972 & 0.978 & 0.981 & 0.980 & 1.77 \\ \hline NTUT rainy & & & & & \\ \hline MultiNet & 1.2 GB & 0.795 & 0.778 & 0.802 & 0.811 & 0.91 \\ VGG16-FCN & 1.6 GB & 0.850 & 0.780 & 0.913 & 0.831 & 0.87 \\ BiSeNet & 207 MB & 0.774 & 0.792 & 0.806 & 0.809 & 2.89 \\ BiSeNet V2 & 45.9 MB & 0.815 & 0.804 & 0.811 & 0.852 & 3.09 \\ LWF-VGG FCN tiny & 29 MB & 0.942 & 0.944 & 0.940 & 0.936 & 2.87 \\ LWF-VGG-FCN & 226 MB & 0.945 & 0.945 & 0.941 & 0.939 & 1.77 \\ \hline NTUT night rainy & & & & & \\ \hline MultiNet & 1.2 GB & 0.933 & 0.912 & 0.934 & 0.941 & 0.91 \\ VGG16-FCN & 1.6 GB & 0.940 & 0.959 & 0.959 & 0.965 & 0.87 \\ BiSeNet & 207 MB & 0.904 & 0.914 & 0.907 & 0.913 & 2.89 \\ BiSeNet V2 & 45.9 MB & 0.952 & 0.963 & 0.955 & 0.962 & 3.09 \\ LWF-VGG FCN tiny & 29 MB & 0.970 & 0.974 & 0.980 & 0.976 & 2.87 \\ LWF-VGG-FCN & 226 MB & 0.977 & 0.986 & 0.989 & 0.987 & 1.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of different approaches using the various road datasets. Avg: average; IOU: intersection over union; REC: recall; PRE: precision; FPS: frame per second. road image, LWF-VGG FCN and LWF-VGG FCN tiny used the theorem from Section 3.2.3 which removed the max pooling and changed the convolution strides to maintain the output size. This method could solve the complicated feature in rainy day. It also reduced the computational load and accelerating computation speed. LWF-VGG FCN tiny had approaching inference speed compared to the state of the arts.
## 5 Simulation Results and Analysis
### Simulation 3: Performance of 2D DOA Estimation with Respect to the Number of Snapshots  
The third simulation presents the performance with respect to the number of snapshots. It ranges from 100 to 1000 with an interval of 100, while SNR is fixed at \(-6\) dB and the number of sensors is fixed at 7. As shown in Figures 7 and 8, our method works very well even when the number of snapshots is 100.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Estimator** & **MVATA without KW** & **ML with KW** & **Interferometer with KW** \\ \hline
**Time consumed** & 18.4 ms & 34.5 ms & 0.59 ms \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average time consumed per run of the three estimators.  
Figure 5: RMSE of 2D DOA estimation versus the number of sensors from 5 to 15. (**a**) RMSE of the elevation angle, (**b**) RMSE of the azimuth angle.  
Figure 6: Scatter plot of 2D DOA estimates of the proposed method.
## 3 Materials and Methods
### Vulnerability and Storage Efficiency
Compression can be achieved by applying dimensionality transformation to increase the storage efficiency of the data. Suppose we reduce three-dimensional data \((x,y,t)\) to Hough-X space of \((t,x)\), that is, \((U_{x},a_{x})\). Storage space-saving is achieved through the number of available dual points \(D\), being less than the number of points \(M\) in the corresponding linear sub-trajectory; hence, achieving in the whole trajectory \(CR=\frac{MD}{D}\) or \(CR=M:1\), where for example, \(M\) spatial points correspond to one dual point, as shown in Figure 1. This conserves space and achieves more compression, as depicted in Figure 3, and thus it is expected to have a greater impact on large-scale spatio-temporal databases. Potentially, by employing a dual method based on Hough-X, we could generate a trajectory codebook by applying Hough-X transformation to all linear sub-trajectories of a given set of trajectories in a map region. In the training step, dual points that stem from the same linear part are similar and must be grouped into the same cluster; also, each cluster is assigned to a single representative vector, called a dual code-vector. Hence, each trajectory inside the codebook is represented by its dual points. At this point, we should note that the Hough method acts as a clustering one. Actually, \(K\)-means is a popular method for both clustering and codebook design. In the coding step, each input dual points vector is compressed to the nearest dual code-vector referenced by a simple index. The index of the matched code-vector in the codebook is then transmitted to the decoder over a channel and is used by the decoder in order to retrieve the similar trajectory dual points from an identical codebook. The key operation is that it is stored and transmits the index of the dual code-vector, rather than the entire code-vector. As a result, the recommended schema is space-compressed because of the duality, and also more robust in comparison with the suggested methods in previous works [9; 12].
# A Real-Time Water Level and Discharge Monitoring Station: A Case Study of the Sakarya River
## 3 Results
### Evaluation of 2023 Hydrological Year Real-Time Monitoring Station Data
While cumulative precipitation is useful for assessing long-term trends, daily precipitation data, particularly considering the characteristics of the basin and the retention capacity of the dam, allows for a more precise analysis of the system's short-term responses. Therefore, daily precipitation totals were utilized to more accurately reflect short-term variations in water levels. Figure 18 presents the seasonal variation in daily average discharges obtained from the real-time monitoring station between September 2022 and August 2023. The sharp peaks in seasonal variation are explained by the daily total precipitation changes. The annual average discharge value of the real-time monitoring station was calculated to be 50.71 m\({}^{3}\)/s and has been added to the graphs as a comparison axis. During the winter months, discharge values generally range between 20 m\({}^{3}\)/s and 70 m\({}^{3}\)/s, often remaining below the annual average discharge value. However, significant increases in discharge levels are observed in January and February, corresponding with increased precipitation during this period, leading to discharge values occasionally exceeding the average. In the summer months, discharge values fluctuate between 30 m\({}^{3}\)/s and 90 m\({}^{3}\)/s, with a notable decline in July and August. This decrease in discharge levels corresponds to a reduction in precipitation, with values typically falling below the annual average. In spring, discharge values fluctuate widely, ranging from 30 m\({}^{3}\)/s to 130 m\({}^{3}\)/s. In March and April, the discharge levels generally exceed the annual average, driven by increased precipitation. This period marks the highest discharge levels observed throughout the year. Overall, the discharge measurements obtained from the monitoring station generally align with seasonal precipitation changes, either remaining below or exceeding the annual  
Figure 18: Seasonal Discharge Variation In River Monitoring Station. average as dictated by these variations. This indicates the station's capacity to accurately represent the underlying hydrological processes. Furthermore, the consistency of the discharge data with the annual average values reaffirms the accuracy and reliability of the measurements obtained from this station.
## 4 Research on Vibration Reduction of Shafting with Function Control Force Exerted by Smart Spring
### Polynomial Fitting of Function Control Force  
According to the above analysis, the required control force is related to acceleration and time, so that  
\[F_{\text{N}}(t)=\text{a}_{0}+\text{a}_{1}t+\text{a}_{2}t^{2}+\cdots+\text{a}_{ m}t^{m}. \tag{2}\]  
When the shaft system was accelerated with an angular acceleration of 20\(\pi\) rad/s\({}^{2}\), the times to reach 6000 r/min, 6600 r/min, 7200 r/min, 7800 r/min, and 8400 r/min were 10 s, 11 s, 12 s, 13 s, and 14 s, respectively, and the optimal control forces were 1000 N, 650 N, 200 N, 50 N and 25 N, respectively. Equation (2) is used to fit the functional relationship between control force \(F_{N}\) and acceleration and time. The goal of fitting is to minimize the square sum \(D\) of the upper deviation, that is, Equation (3) is the minimum.  
\[D=\sum_{i=1}^{N}{\delta_{i}}^{2}=\sum_{i=1}^{N}\bigl{[}F_{\text{N}}(t_{i})-F_{ \text{OP}_{i}}\bigr{]}^{2}. \tag{3}\]  
In Equation (3), the optimal control force is \(F_{\text{OP}_{i}}\), the fixed control force is \(F_{\text{N}}\left(t_{i}\right)\) and the variance is \(\delta\)  
Figure 19: Relationship between the radius of the axis orbit and the fixed control force under (**a**–**e**) speeds, respectively 6000 r/min, 6000 r/min, 6600 r/min, 7200 r/min, 7800 r/min and 8400 r/min.  
According to the least square theorem, if \(N>m+1\), then there is a unique set of polynomial coefficients \(a_{0}\), \(a_{1}\)...\(a_{m}\), which minimize the value of Equation (3), and  
\[x_{m}=\left(A^{\mathrm{T}}A\right)^{-1}A^{\mathrm{T}}b. \tag{4}\]  
In Equation (4), \(x_{m}=[a_{0}\ a_{1}\ a_{2}\cdots\ a_{m}]^{\mathrm{T}}\), \(b=[F_{\mathrm{OP}_{1}}\ F_{\mathrm{OP}_{2}}\cdots F_{\mathrm{OP}_{N}}]^{ \mathrm{T}}\), the value of matrix \(A\) is as follows:  
\[A=\left[\begin{array}{cccccc}1&t_{1}&t_{1}{}^{2}&\cdots&t_{1}{}^{m}\\ 1&t_{2}&t_{2}{}^{2}&\cdots&t_{2}{}^{m}\\ \vdots&\vdots&\vdots&&\vdots\\ 1&t_{N}&t_{N}{}^{2}&\cdots&t_{N}{}^{m}\end{array}\right].\]  
By substituting the time and fixed control force values corresponding to speeds of 6000 r/min, 6600 r/min, 7200 r/min, 7800 r/min, and 8400 r/min into Equation (4), the results show that \(a_{0}\) = 143,280, \(a_{1}\) = -57,510, \(a_{2}\) = 7687.2, \(a_{3}\) = -342.14, that is, the fitting function of optimal control force and time for 10 s s t t \(\leq\) 14 s is as follows:  
\[F_{\mathrm{N}}(t)=143,280-57,510t+7687.2t^{2}-342.14t^{3}. \tag{5}\]  
According to Equation (5), the curve shown in Figure 20 is obtained.  
According to the simulation results of the shaft center trajectory under constant speed, the optimal control force of the shafting was 1000 N in the speed range of 0-6000 r/min, i.e., within 0-10 s, the function control force expression of the optimal control force and time is as follows:  
\[F_{\mathrm{N}}(t)=\left\{\begin{array}{ll}1000,&0\leq t\leq 10\\ 143,280-57,510t-7687.2t^{2}-342.14t^{3},&10<t\leq 14\end{array}\right.. \tag{6}\]
# Video Streaming Adaptive QoS Routing with Resource Reservation (VQoSRR) Model for SDN Networks
## 4 Experiments and Results
### Results Comparison  
The video quality has been studied in two experiments (testing the DR-RA algorithm and the reservation method) using both objective and subjective metrics. These experiments showed a better perception of video quality when using the VQoSRR method than the default settings for SDN controllers when delivering video based on resolution (SD and HD) QoS thresholds. These thresholds were packet loss and bandwidth. Furthermore, the following conclusions can be drawn: (1) generally, the number of video frames received by VQoSRR is higher than the number of frames received without using it, since the proposed QBR avoids routes that have the highest loss rate; (2) a lower received quality video results in degradation in the video viewing experience. For example, the perceived video quality achieved is highest in the case of VQoSRR for all transmitted videos, even the best effort videos. However, all of them experience more degradation in video viewing in the case of No_VQoSRR. (3) Tests of DR-RA cover only short video lengths; an analysis of long-duration videos may be necessary to determine the most appropriate interval of the algorithm. In addition, changing routes frequently by DR-RA can cause other QoS problems, such as increasing the delay variation experienced by end-users. Therefore, rerouting should not be subject to frequent changes. (4) The subjective MOS metric reflects the differences in results more clearly than the objective SSIM metric; (5) MOS test results indicate that VQoSRR improves video QoE by up to 97.3% and 76% for HD and SD video resolutions, respectively, compared to not using it. (6) We recommend evaluating the effectiveness of VQoSRR's QoS scalability when there is a large amount of high-priority videos.
## 6 Comparative Interference Analysis
The analysis presented in this section demonstrates the detrimental effects of MVDS side lobe interference on the GEO satellite communication link, with the severity of the impact directly proportional to the transmitted power of the MVDS system. The significant reduction in both \(\left[\frac{\mathbb{C}}{\mathbb{N}}\right]\) and \(\left[\frac{\mathbb{E}_{\mathbb{R}}}{\mathbb{N}_{0}}\right]\) at higher MVDS power levels underscores the need for effective mitigation strategies. In the next section, we will introduce possible simple solutions to address this issue. This outcome suggests that operators can leverage the findings to facilitate a shift towards lower-power transmission strategies. By adopting innovative, high-gain low-noise amplifier (LNA) technologies, it becomes feasible to decrease the transmission power of MVDS systems while maintaining, and potentially improving, service quality. This reduction in transmission power is crucial for mitigating potential interference with geostationary (GEO) satellites, a critical consideration for efficient spectrum utilization and harmonious coexistence of different satellite services. This approach aligns with the growing need for efficient use of the radio frequency spectrum and can contribute to more sustainable telecommunications practices. Operators can effectively leverage high-gain LNA technologies to reduce transmission power requirements. Adjustments at the transmitter side, such as replacing high-power transistors with lower-power alternatives, can further contribute to this reduction. These modifications can typically be made by in-house engineers without incurring significant additional costs. High-gain LNAs are available from MVDS equipment manufacturers at prices ranging from a few hundred dollars, depending on the brand and country of origin [21,22]. Overall, the transition is considered affordable for MVDS operators. A phased transition plan is crucial for the successful adoption of new LNA technologies and low-power transmitters. This approach enables a smooth migration, minimizes service disruption, and allows operators to prioritize critical regions and high-interference zones during the rollout.
## 3 Experiments and Results
### Setup  
Baselines. We compared SBA-RoPE against three methods: Positional Interpolation (PI), Neural Tangent Kernel (NTK), and YaRN. Additionally, to assess performance within the original context window of LLMs, we included baselines of LLMs without fine-tuning.  
Considering computational costs, we selected Pythia-2.8b as our fine-tuning starting point. This model size is sufficient to highlight the performance differences between methods. Pythia [15] includes LLMs ranging from 14M to 12B parameters, utilizing the same architecture as GPT-NeoX [9]. It was pre-trained on the Pile [16] dataset by EleutherAI ([[https://www.eleuther.ai](https://www.eleuther.ai)]([https://www.eleuther.ai](https://www.eleuther.ai)), accessed on 1 March 2024) for research into the interpretability analysis and scaling laws of LLMs, aiming to understand how knowledge develops and evolves during autoregressive transformer training. The model checkpoint used in our experiments is available on Hugging Face ([[https://huggingface.co/EleutherAI/pythia-2.8b](https://huggingface.co/EleutherAI/pythia-2.8b)]([https://huggingface.co/EleutherAI/pythia-2.8b](https://huggingface.co/EleutherAI/pythia-2.8b)) accessed on 1 March 2024). Other than adjusting the implementation of position embeddings using various methods, we also employed Memory-Efficient Attention from xFormers [17], provided by PyTorch [18], to replace the native attention mechanism of GPT-NeoX, thereby accelerating the training and inference processes on NVIDIA V100 GPU. Apart from these modifications, we did not modify the GPT-NeoX model architecture in any way.  
Training. We fine-tuned all model variants using the next-token prediction objective and cross-entropy [19] loss function, combined with various methods. We employed the AdamW [20] optimizer, with \(\beta_{1}\) and \(\beta_{2}\) set to 0.9 and 0.95, respectively. For the scheduler, we used a linear warmup of 60 steps. The maximum learning rate was set to \(2\times 10^{-5}\), with weight decay set to zero. Utilizing eight NVIDIA V100 GPUs, we set the global batch size to 8 and fine-tuned each model variant for 1250 steps. FP16 mixed precision training [21] was enabled. All models were trained using PyTorch and DeepSpeed Zero-3 [22]. For the training dataset, we fine-tuned models using data from PG-19 [23], truncating texts to lengths of 4 k tokens and appending BOS and EOS tokens at the beginning and end, respectively. We fine-tuned two variants of each method for scaling factors \(s=2\) and \(s=4\). Given that the original context window of the Pythia model is 2048, the fine-tuned models have expanded context windows of 4096 and 8192, respectively.
## 3 Performance Analysis and Simulations
### Irradiance Uniformity vs. Optical Loss
#### 3.1.2 Optical Loss  
An optical loss factor \(l\) was used to analyze and evaluate the optical efficiency of the proposed CPV with the SOE compared with that of a conventional CPV.  
The optical loss factor \(l\) is determined by:  
\[l=\frac{\mathrm{n}_{\text{opt\_CPV}}^{\text{C}}-\mathrm{n}_{\text{opt\_CPV}}^ {\text{D}}}{\mathrm{n}_{\text{opt\_CPV}}^{\text{C}}}, \tag{3}\]  
Figure 6: Layout of CPV units under ray-tracing simulation: (**a**) a conventional CPV unit; and (**b**) the proposed CPV unit.  
Figure 7: Irradiance distribution on the solar cell: (**a**) the conventional CPV unit; and (**b**) the proposed CPV unit.  
where \(\mathrm{n}_{\mathrm{opt\_CPV}}^{\mathrm{C}}\) and \(\mathrm{n}_{\mathrm{opt\_CPV}}^{\mathrm{D}}\) are the optical efficiency of the conventional and proposed CPV units, respectively:  
\[\mathrm{n}_{\mathrm{opt\_CPV}}^{\mathrm{C}}=\frac{P_{\mathrm{in\_cell}}^{ \mathrm{C}}}{P_{\mathrm{in\_FL}}}, \tag{4}\]  
\[\mathrm{n}_{\mathrm{opt\_CPV}}^{\mathrm{D}}=\frac{P_{\mathrm{in\_cell}}^{ \mathrm{D}}}{P_{\mathrm{in\_FL}}}, \tag{5}\]  
where \(P_{\mathrm{in\_cell}}^{\mathrm{C}}\) and \(P_{\mathrm{in\_cell}}^{\mathrm{D}}\) is the incident solar power received at the MJ solar cell of the conventional and proposed CPV units, respectively, and \(P_{\mathrm{in\_FL}}\) is the incident solar radiation hit on the Fresnel lens's surface.  
Equation (3) is then re-written as follows:  
\[l=\frac{P_{\mathrm{in\_cell}}^{\mathrm{C}}-P_{\mathrm{in\_cell}}^{\mathrm{D}} }{P_{\mathrm{in\_cell}}^{\mathrm{C}}}. \tag{6}\]  
Several simulations were conducted with various sunlight vertical incident luminous flux values to evaluate the loss factor of the proposed CPV unit. The incident power received at the solar cell was recorded in the simulations. Figure 8a shows the incident power received at the solar cell of both conventional and proposed CPV units, and Figure 8b shows the optical loss of the proposed CPV unit compared to the conventional CPV unit. The simulation results showed that the loss factor of the proposed CPV unit was 5.86%. In other words, the proposed CPV unit with the SOE resulted in optical loss of 5.86% compared to the conventional CPV unit without the SOE.
# Research on Distributed Fault Diagnosis Model of Elevator Based on PCA-LSTM
## 2 PCA-LSTM Based Fault Prediction Model
### PCA-LSTM Fault Diagnosis Modeling  
This paper proposes a fault diagnosis model that integrates Principal Component Analysis (PCA) and Long Short-Term Memory (LSTM) neural networks. The input signal consists of various operating data from the elevator collected under different conditions, and the output is the predicted fault type. To ensure accurate fault diagnosis, it is necessary to screen the elevator sampling data, which are then divided into a training set and a test set, as depicted in Figure 3. The specific steps are as follows.  
Step 1: We began by categorizing the initial sample set based on elevator fault types, such as mechanical, electrical, and operational faults. Each category was then subjected to a linear transformation using PCA. The transformed cumulative contribution ratio for each fault category was used to select the most informative principal components, capturing the unique variance within each fault type. This approach enabled us to reduce the sample set size without losing the distinctive fault signatures associated with each category, yielding a new, condensed sample set that encapsulated diverse fault characteristics for enhanced diagnostic analysis.  
Step 2: The new sample set data signals were transformed into a two-dimensional feature map. The samples were randomly selected proportionally and divided into two parts: the training set and the test set.  
Step 3: The LSTM neural network was initialized, and the training set was input to train the LSTM network model.  
Step 4: The test set was input into the trained network to classify the elevator fault types, and the diagnostic results were output.