esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### ECG-ID Dataset Results
#### 4.4.1 TCN Model Results  
These results show a clear trend with regard to the performance of the temporal convolutional network model with respect to MSE, RMSE, and r, as the number of samples per second varies from 18 to 120. There is a monotonic decrease in MSE and RMSE, thus proving that the accuracy in reconstructing signals improves when the number of samples increases from 18 to 120. In particular, from 0.0214 to 0.0161, MSE is decreasing, and RMSE from 0.1463 to 0.1267, thereby improving the fidelity of the reconstructed ECG signal. Another measure of interest is Pearson's r, which quantifies the linear correlation between the original and reconstructed signals. This measure shows an upward trend from 0.6976 to 0.7866, indicating that with more samples used, the reconstructed signal goes increasingly in line with the original one. This result could, therefore, imply that the TCN model benefits to a large extent from a higher number of samples, which helps in capturing fine details and temporal dependencies within the ECG data. The model converges at about 120 samples, reflecting the optimal balance between sample size and reconstruction accuracy, hence applicable in the effective processing of ECG signals. In Table 2, performance is registered for the TCN model from different lower to higher sampling rates. Figure 13 presents the scatter plot for TCN model results.  
\begin{table}
\begin{tabular}{l l l l} \hline  & \multicolumn{3}{c}{**For Lower Sampling Rates**} \\ \hline
**Samples per Second** & **MSE** & **RMSE** & **Pearson’s r** \\ \hline
18 & 0.0214 & 0.1463 & 0.6976 \\ \hline
19 & 0.0215 & 0.1469 & 0.6942 \\ \hline
20 & 0.0216 & 0.1472 & 0.6940 \\ \hline
21 & 0.0194 &
# An Enhanced Message Distribution Mechanism for
## 3 Motivation
### Problem Description  
A complete packet in-out (sometimes using flow-mod) process starts from sending a message by the switch. We focus on northbound communication, so we divide it into the following five stages:  
1. the southbound interface of the controller receives the data packet; and the core module completes the preliminary analysis;
2. push to all apps that have registered the message type;
3. apps complete the logic processing;
4. encapsulate the strategy and send it to the controller core module; and,
5. the controller calls the southbound interface to issue a policy message.  
We abstract the packet in-out process into a mathematical problem for easy analysis. Firstly, items 1 and 5 depend on the performance of the controller. The values of these two items will not change if the controller has been selected, the hardware equipment is the same, and the data plane environment is always the same. We define it as a constant determined by objective factors, as:  
\[T_{rcv} =C_{1}(c,net) \tag{1}\] \[T_{std} =C_{2}(c,net) \tag{2}\]  
Secondly, item 2 is related to the processing capacity and queue length of each app. Without considering other time consuming, the time that it takes is:  
\[T_{rq}=\frac{l_{r}}{e} \tag{3}\]  
where \(l_{r}\) is the length of the receive queue and \(e\) is the efficiency of app processing.  
Subsequently, item 4 is similar to item 2, but, if multiple apps share a single-process core module of the controller, the sending queue will be shared, which is:  
\[T_{sq}=\frac{\sum_{i}l_{i}}{E} \tag{4}\]  
where \(l_{i}\) is the length of apps' send queue and \(E\) is the average processing efficiency of core modules.  
Finally, the analysis of item 3 is divided into two parts. One part is the time that is consumed by the callback function and the trigger event. We assume that these time consumings of all apps are the same, which is defined as \(t_{complex}\); the other part is the processing consumption of each app. When the strategies of all apps are not conflicting and effective, the processing time should be calculated until the slowest app completes the strategy. Accordingly, the total processing time is:  
\[T_{p}=\max_{n}\tau_{i}+n*t_{complex} \tag{5}\]  
where \(\tau_{i}\) is the processing time of app \(i\) and \(n\) is the number of valid apps.  
In summary, the packet in-out time is:  
\[T=T_{rcv}+T_{rq}+T_{p}+T_{sq}+T_{std} \tag{6}\]  
Additionally, the optimization goal of northbound communication is:  
\[\min T \tag{7}\]  
The next part of the article will analyze Equation (7) in a qualitative way.
# The Influence of Laser Linewidth on the Brillouin Shift Frequency Accuracy of BOTDR
## 4 Experiment Results and Discussions
### BGS Width Evaluation  
To verify the influence of the laser linewidth on the BGS width, multiple BFS measurements were taken by utilizing the five lasers mentioned above as the seed source, respectively, when the temperature of the thermostat was fixed at 50 \({}^{\circ}\)C. The room temperature was kept at a roughly constant temperature of 25 \({}^{\circ}\)C. The width of the probe pulse was orderly adjusted to 12 ns, 22 ns, 32 ns, and 42 ns, with the same peak power of 23.09 dBm. For further analysis, we extracted the measured BGSs at 9880 m of tested fiber, the middle of the heated fiber, and fitted them to the Lorentzian profile based on the Levenberg-Marquart algorithm. These BGSs were finally normalized by peak power and are presented in Figure 11a-e, where the relative frequency of the horizontal axis was obtained by \(f-f_{\text{B}}\). The BGS widths versus the laser linewidth is summarily plotted in Figure 11f, when the width of the probe pulse was increased from 12 ns to 42 ns.  
From Figure 11f, the BGS width narrowed from \(\sim\)108 MHz to \(\sim\)88 MHz as the probe pulse width was increased from 12 ns to 42 ns, which roughly agrees with the theoretical simulations (see Figure 3b, blue line). However, the BGS width showed no distinct tendency as the laser width was broadened from 98 Hz to 1.05 MHz. Hence, it proves that the measured BGS width depended largely on the probe pulse width when the laser linewidth was less than 1 MHz, which conformed to the conclusion reported previously [23].  
Figure 10: BFS distributions at different temperatures when the laser linewidth is: (**a**) 1.05 MHz; (**b**) 101 kHz; (**c**) 10.2 kHz; (**d**) 3.1 kHz; and (**e**) 98 Hz. (**f**) Measured results of BFS versus temperature.  
Figure 11: Normalized BGS extracted at the middle of the heated fiber when the laser linewidth is: (**a**) 1.05 MHz, (**b**) 101 kHz, (**c**) 10.2 kHz, (**d**) 3.1 kHz, and (**e**) 98 Hz; (**f**) BGS widths versus laser linewidth and pulse width.
# Homogeneity and Trend Analysis of Climatic Variables in Cap-Bon Region of Tunisia
###### Abstract  
As a semi-arid Mediterranean country, Tunisia is affected by the impacts of climate change, particularly the coastal regions like the Cap-Bon. Irregular rainfall, rising temperatures and the recurrence of extreme events are all indicators that affect ecosystems and populations and make them more vulnerable to the influence of climatic variables. Therefore, an analysis of the trends of climate variables can contribute to facilitating the development of effective adaptation strategies. In this matter, this study was conducted to assess the homogeneity and trends of minimum and maximum air temperature (Tmin and Tmax) and precipitation (P) in the Cap-Bon region. Daily data were collected from the meteorological station of Nabeul for the period of 1982-2020. Pettitt and SNHT tests for homogeneity were applied to identify the breakpoints in multi-time scales of Tmax, Tmin and P data series. The Mann-Kendall (MK) test was used to detect the change in the time-series trend. A modified Mann-Kendall (mMK) test was used to remove the autocorrelation effect from the data series. Both the MK and mMK tests were used at the 5% significant level. The magnitude of the climatic trend was estimated using the non-parametric Sen's slope estimator. Contrary to Tmin and P, the results of the homogeneity tests revealed the existence of significant breakpoints in the annual, seasonal and monthly Tmax time series. For most cases, the breakpoint occurred around the year 2000. For Tmin, significant breakpoints were recorded in March and April, while a significant shift in the P time series was detected in December. The Mann-Kendall results show a significant warming trend in annual Tmax, with magnitudes equal to 0.065 and 0.045 \({}^{\circ}\)C/year before and after the breakpoint, respectively. Nevertheless, non-significant tendencies were observed in the annual Tmin and P time series. On the monthly time scale, Tmax exhibited a significant upward trend in June and August, before the observed breakpoints, with Sen's slope values equal to 0.065 and 0.045 \({}^{\circ}\)C/year, respectively. Regarding the Tmin data, a significant positive trend was observed in July at a rate of 0.033 \({}^{\circ}\)C/year.  
homogeneity tests; trend analysis; Sen's slope estimator; climatic variables; serial correlation +
Footnote †: journal: _Sciences_
## 4 Test Results and Analysis
### Significance Analysis
As can be seen from Figure 7a,c, the _PLR_ first decreases and then increases as the operating speed (\(B\), the corresponding feeding amount) increases, the number of straws entering the threshing drum per unit time is smaller, the rubbing action between straws is weakened, the grain is not sufficiently threshed, and the _PLR_ increases. In contrast, when the operating speed is high, too much straw is fed into the threshing drum per unit of time, the entrainment loss increases, and the _PLR_ also increases. As can be seen from Figure 7b,c, the _PLR_ tends to increase gradually with an increasing spiral angle of the straw guide board (\(C\)), the time of straw threshing in the threshing drum is shortened, and the _PLR_ increases if the grain is not completely threshed. Figure 8 shows the response surface and contour plot of the _PIR_. As can be seen from Figure 8a,b, the _PIR_ tends to increase with increasing drum rotation speed (\(A\)) because the impact force of the threshing teeth on the straw increases with increasing drum rotation speed (\(A\)). The more the straw is crushed, the higher the percentage of impurities rate. As can be seen from Figure 8a,c, as the operating speed (\(B\), the corresponding feeding amount) increases, the _PIR_ first decreases and then increases, and the probability of being beaten and crushed by the threshing teeth is high, resulting in high impurity content. On the other hand, when the operating speed is high, the threshing roller has more straws at the same time, more straws are crushed by the threshing teeth, and the _PIR_ is large. As can be seen from Figure 8b,c, there is a decreasing trend in the _PIR_ with an increase in the spiral angle of the straw guide board (\(C\)). That is because the time of straw threshing in the threshing drum is shortened, the time of straw beating is reduced, the straw is not easily crushed, and the _PIR_ is reduced. Figure 9 shows the response surface and contour plot of the _PBR_. As can be seen from Figure 9a,b, the _PBR_ tends to increase with increasing drum rotation speed (\(A\)) because the impact force of the threshing teeth on grains increases with an increase in the drum rotation speed (\(A\)), and the grain is easily broken, resulting in an increased _PBR_. As can be seen from Figure 9a,c, the _PBR_ first decreases and then increases with increasing operating speed (\(B\), corresponding feeding amount), and the probability of grains being crushed by the threshing teeth is high, resulting in a high _PBR_. On the other hand, when the operating speed is high, the threshing roller has more grains, thus more grains are hit by the threshing teeth, and the _PBR_ is large. As can be seen from Figure 9b,c, there is a decreasing trend in the _PBR_ with an increasing spiral angle of the straw guide board (\(C\)). That is because the time of straw threshing in the threshing drum is shortened, the time of grain beating is reduced, and the _PBR_ is reduced.
# Revisiting Low-Resolution Images Retrieval with Attention Mechanism and Contrastive Learning
## 3 Results
### Quantitative Results
#### 3.3.3 The Effectiveness of the Number of Embedding Dimensions  
A typical experiment in previous studies about information retrieval aimed to analyze the impact of the number of dimensions of embeddings. After a series of experiments, it is found that the dimensions of 256 produce the best performance with the mAP of 0.955 and the first rank recall of 0.9453, as shown in Table 4. These findings are consistent with previous research on contrastive learning [16; 26; 37], which shows that the embeddings' dimensions should be either 128 or 256. It is also consistent with studies about image  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{5}{c}{**Recall@k**} & \multirow{2}{*}{**mAP**} \\ \cline{2-2} \cline{4-6}  & **1** & **2** & **3** & **4** & **5** \\ \hline CAR196 & 0.8541 & 0.9218 & 0.9661 & 0.9739 & 0.9817 & 0.8804 \\ CUB200-2011 & 0.9414 & 0.9687 & 0.9765 & 0.9765 & 0.9765 & 0.9379 \\ SOP super-class & 0.9806 & 0.9891 & 0.9932 & 0.9952 & 0.9966 & 0.9141 \\  & 1 & 10 & 100 & 500 & 1000 & \\ SOP class & 0.9470 & 0.9867 & 0.9962 & 0.9986 & 0.9986 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Recall@k and mAP results from CUB200-2011, CARS196, and SOP datasets.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model Name**} & \multicolumn{5}{c}{**Recall@k**} & \multirow{2}{*}{**mAP**} \\ \cline{2-2} \cline{4-6}  & **1** & **2** & **3** & **4** & **5** \\ \hline ViT-B-16 & 0.9414 & 0.9687 & 0.9765 & 0.9765 & 0.9765 & 0.9379 \\ BiT-M-R152x4 & 0.9296 & 0.9648 & 0.9726 & 0.9765 & 0.9804 & 0.9040 \\ Efficient-B7 & 0.7226 & 0.8242 & 0.8828 & 0.9062 & 0.9257 & 0.7906 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Recall@k and mAP comparison between different encoding models.  
retrieval using the embeddings search strategy [3; 6; 8]. We also note that the size of a hidden layer in the projection head is equal to the dimensions of the embedded vector.
# Enhancing Task-Oriented Dialogue Modeling through Coreference-Enhanced Contrastive Pre-Training
## 6 Conclusions and Future Work  
In this paper, we introduce CECPT, a contrastive pre-training framework for dialogue-oriented downstream tasks, to utilize the rich dialogue knowledge in the graph structure. Our experimental results demonstrate that our pre-trained dialogue model, CECPT, surpasses strong baseline models across three critical dialogue applications: intent recognition, dialogue act prediction, and dialogue state tracking. In future work, we intend to explore more with the combination of coreference and graph contrastive learning on the basis of pre-training task designing, trying to explore more in the internal mechanism. Additionally, given the limitations in Section 5, it is crucial to ensure the quality and appropriateness of the dialogue datasets used. Future work could focus on improving the coreference resolution in noisy and domain-specific contexts, as well as developing methods to better handle interruptions and informal language within dialogues. And incorporating domain-specific knowledge bases and ontologies could enhance the model's ability to understand and process specialized dialogues.  
For broader applicability in industrial contexts, such as customer service scenarios, significant challenges persist with numerous colloquial dialogue datasets. These datasets contain many instances of speech pauses and frequent topic shifts. This inherent characteristic significantly affects the performance of coreference resolution tasks, consequently impacting the accuracy of the coreference features used as input in our method. Therefore, for industrial-scale customer service environments, our method is more suitable for text dialogue datasets with clear contextual semantics rather than speech-to-text colloquial dialogue datasets. And in the future, we will provide insights into how our method can be adapted and implemented in such challenging contexts.  
Methodology, Y.H., S.C. and Y.C.; software, S.C. and Y.C.; validation, Y.H., J.F. and C.D.; formal analysis, S.C. and Y.C.; investigation, Y.H., S.C. and Y.C.; data curation, S.C. and Y.C.; writing--original draft preparation, Y.H., S.C. and Y.C.; writing--review and editing, Y.H., J.F. and C.D.; visualization, S.C.; supervision, J.F. and C.D.; project administration, Y.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Beijing Natural Science Foundation (L222006), National Key R&D Program of China (2021 ZD0140408) and China Mobile Holistic Artificial Intelligence Major Project Funding (R22105 ZS, R22105 ZSC01).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
All authors were employed by JIUTIAN Team, China Mobile Research Institute. Authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 5 Performance Analysis of Proposed Schemes: MRS-ST-BL and MRS-SD-BL
### Results
#### 5.3.4 Impact of Battery Threshold on Throughput of Proposed Scheme MRS-ST-BL  
This section presents the impact of change in battery threshold (\(B_{\textit{thres}}\)) on average throughput of the proposed scheme MRS-ST-BL. Figure 9 illustrates the change in average throughput of MRS-ST-BL as the search radius increases for network widths of 100 m and 1000 m. The key observation from the figure is that the maximum throughput attained by our proposed scheme MRS-ST-BL reduces with the increase in battery threshold.  
Figure 8(a) shows the impact analysis of battery threshold on performance of our proposed scheme MRS-ST-BL as the search radius increases in a network having width of 100 m. The black crossed line represents the battery threshold of 10%, blue crossed line represents that of 20%, the red crossed line represents that of 30% and the green crossed line represents that of 40%. The main result from this figure is that the maximum throughput attained by our proposed scheme MRS-ST-BL reduces with the increase in battery threshold. The throughput initially increases with the increase in search radius for all battery threshold (\(B_{\textit{thres}}\)) analyzed. With the further increase in search radius, the throughput gradually decreases. Interestingly, the maximum throughput attained by the proposed MRS-ST-BL for each \(B_{\textit{thres}}\) is different. For \(B_{\textit{thres}}=10\), the black crossed line, the throughput attained is maximum among the others. However, for large values of search radius, its throughput is lower than others. When the value of \(B_{\textit{thres}}\) is high, the maximum throughput attained by MRS-ST-BL is reduced. However, for large search radius, its throughput is greater compared to the scheme when \(B_{\textit{thres}}\) has less value.  
Figure 8(b) shows the impact analysis of battery power threshold on performance of our proposed scheme MRS-ST-BL as the search radius increases in a network having width of 1000 m. The main result from this graph is the difference in maximum throughput attained at various \(B_{\textit{thres}}\) is larger compared to that in Figure 8(a). In addition to that, the throughput of MRS-ST-BL at various \(B_{\textit{thres}}\) are indifferent for large values of search radius as compared to that in Figure 8(a).  
This shows that the performance of proposed MRS-ST-BL also depends upon the battery threshold. The proper adjustment of the battery threshold in our proposed scheme MRS-ST-BL results in increased throughput of D2D communications.  
Figure 9: Average throughput of our proposed scheme MRS-ST-BL at different battery threshold when Shape = 1.01 and Scale = 0.01.
# Human Activity Recognition for Assisted Living Based on Scene Understanding
## 6 Design and Implementation  
In this section, we will discuss the following: class diagram, communication between classes, used threads and finally communication with the Gateway-MQTT server.  
As presented in the diagram from Figure 9, the application is modular; if needed, different types of cameras or object detectors can be used.  
* CameraAcquisition and ZEDAcquisition implement the functionalities of IAcquisition interface: we considered the option of using a generic camera or the ZED camera (default option);
* JetsonInferenceObjectDetector and DarknetOD implement the functionalities of IObjectDetector interface: two main options were integrated for object detection-YOLO [41,43] (Darknet implementation [51]) and Mobilenet/Inception [38, 39, 40] (official Nvidia implementation for Jetson [42]).  
For reasons of optimization, threads are used for independent tasks and the communication between them is carried out using priority queues, as follows:  
* AcquisitionAndObjectDetect thread is used for image acquisition and object detection;
* queueImageToHOI stores the object detection results;  
Figure 8: Human–object interaction pipeline architecture.  
* HOIDetector thread reads from queueImageToHOI, performs human-object interaction detection;
* queueHOToMain stores the detected HOIs;
* MQTTConnection thread initiates the connection with the MQTT agent to which the Gateway is connected.  
The diagram presented in Figure 10 illustrates the communication between modules and threads for one frame, from acquisition to activity detection.  
Figure 9: Class diagram.  
HOI detector inference time is considerably longer than the time required to detect objects. That is why we decided to test using multiple threads for HOI detections. Figure 11 describes the communication between threads. Priority queues are used for thread communication.  
There is only one thread for image acquisition and there are object detection, and multiple threads for HOI detection. ImageToHOI priority queue is used between the AcquisitionAndObjectDetect thread and HOIDetector thread(s), while HOIToMain priority queue is used to return the HOI detections to the MQTTConnection thread which updates the current activity and communicates with the MQTT agent.