esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Current Limitations and Future Work  
This work has also some limitations that should be acknowledged. In the first place, flat polyps, specially 0-IIb, according to the Paris classification, are still underrepresented in the dataset, in common with other datasets. This type is less frequently identified, therefore a longer, multicentre acquisition campaign would be necessary to increase their presence in the public datasets. Furthermore, these polyps are also the most difficult to be detected due to their subtle appearance, thus the Computer Assisted Diagnosis (CAD) systems would be more helpful to assist their detection [42]. We consider that increasing the number of images showing these challenging polyps would increase the clinical utility of the CAD systems, although assistance for the rest of types would also remain useful for novice endoscopists with lower Adenoma Detection Rate (ADR). Secondly, images in the dataset are mainly \"polyp centred\". This means that the polyp is clearly visible in the image. This type of image is highly convenient for polyp segmentation as well as classification. Nevertheless, we consider that the inclusion of images where the lumen at or near the screen centre, leaving the polyps peripheral, a typical setup during the clinical exploration, would be of help for detection systems. Besides, the availability of long video sequences, even if not all frames are manually segmented, would also benefit the development of such systems. Lastly, manual segmentation is prone to inter-observer variability in medical images [43; 44], therefore uncertainty should be considered and analysed, both at the dataset creation stage, but it would also be interesting to take it into account the analysis of segmentation results [45].  
As future work, we consider the expansion of the current dataset to include some of the previously mentioned images and sequences. A joint global initiative to gather all efforts would result in a larger and more diverse dataset of polyps, increasing the possibilities of the research community.  
Lastly, it is worth mentioning that, after developing any deep learning method, it is essential to carry out prospective studies and randomized trials to compare their performance with experts clinicians, which is so far not well proven [46], with the final aim of increasing the adenoma detection rate, as indicator of CRC detection. In this regard, a survey is currently undergoing to compare the identification of polyps done by expert gastroenterologists and residents with a deep learning model trained with the PICCOLO dataset.
# Experimental Study on the Condensation Heat Transfer on a Wettability-Interval Grooved Surface
## 3 Results and Discussion
### Droplet Dynamic Behavior
#### 3.1.1 Comparison of Droplet Dynamic Behaviors on Functional Surfaces
Then, part of the droplets could reach the maximum droplet radius on the hydrophobic ridge, as shown in Figure 5b, while some droplets on the ridge near the edge of the grooves were merged or sucked into the hydrophilic groove without growing to the maximum droplet radius. In this context, the maximum radius of the droplet on the hydrophobic ridge depended on the wettability and width of the hydrophobic ridge. The diagram of the maximum droplet radius can be seen in Figure 5c. The droplets in the grooves could not be absorbed by droplets on the ridge, which was different from that on a hydrophobic grooved surface but gradually accumulated to liquid film, and the small droplets at the edge of the ridge would slide into the grooves, accelerating the formation of the liquid film in the hydrophilic grooves. From the above analysis, it can be seen that the droplet shedding on the plain hydrophobic surface mainly depends on gravity, while on the wettability-interval grooved surface, spontaneous suction effect and directional drainage through grooves occurred under the action of wettability gradient and groove drainage, resulting in efficient removal of condensate. To reveal the enhancement of condensation performance on the wettability-interval grooved surface, the spontaneous suction effect and directional drainage behaviors via grooves will be discussed in detail. Figure 4: Condensate dynamic behaviors on the surfaces: (**a**) plain hydrophilic surface; (**b**) plain hydrophobic surface; (**c**) hydrophilic grooved surface; (**d**) hydrophobic grooved surface. The change process of the droplets or liquid is shown in the red circles.
## 3 Results  
### Semantic Segmentation of an Oblique Photography 3D Model  
The data used for the 3D semantic segmentation in this study comprised three sets: a training set, a test set, and experimental segmentation data. Training set data were used to train the classification model, and test set data were used to test the classification results and evaluate the model's classification accuracy. Our experimental segmentation data were used to perform a 3D segmentation experiment according to a semantic segmentation model. Due to a lack of publicly available oblique photography datasets, we independently annotated the training and test data. Their data were divided into packets for small scenes of 140 m \(\times\) 140 m to facilitate data annotation and training tests. The training set included 3D data for 10 scenes, including 3,325,855 triangular mesh surfaces. The test set included the 3D data for two scenes, including 944,858 triangular mesh plates. The experimental segmentation data included the 3D data for two scenes obtained via oblique photography, covering approximately 3 km\({}^{2}\) and 0.1 km\({}^{2}\), respectively. This experimental data were selected from an oblique photography 3D model of Changsha City, China, and the data objects covered buildings, ground, trees, and other elements with strong representation, as shown in Figure 9.
# A Blockchain-Assisted Security Protocol for Group Handover of MTC Devices in 5G Wireless Networks
## 7 Performance Evaluation
### Blockchain Operation Cost  
To evaluate the data access latency in the blockchain, we followed the evaluation methodology outlined in [27]. Initially, a blockchain prototype was created. This blockchain comprised numerous blocks, each being 1 MB in size. Every block, stored as a file, contained multiple transaction records, with each transaction being a subscription record of a device. To reduce the storage overhead of the blockchain, this size should be adjusted according to the preferences of the network operators. By default, we followed the Bitcoin model with a block size of 1 MB. All transactions in the blockchain were indexed using Python dictionaries. To compare the performance differences between the blockchain and traditional databases, another centralized database was also constructed using MariaDB v10.4.14 [28]. The read operation \(T_{BC\_read}\) is 0.2914 ms and the write operation \(T_{BC\_write}\) is 0.0434 ms, while for the centralized database, both of the read and write operations \(T_{DB}\) are 0.4956 ms.  
By the proposed scheme, since the writing and querying operations of the blockchain information occur prior to the two phases of handover authentication, specifically during the registration phase and the handover preparation phase, the time taken for blockchain operations has not been included in the computational costs of the first handover authentication and group handover authentication phases.
# Radiation-Hardened 16T SRAM Cell with Improved Read and Write Stability for Space Applications
## 3 Simulation Results and Analysis
### Read Delay Comparison  
Read delay or read access time (RAT) is defined as the time interval from when the word line voltage reaches 50% of VDD, starting from its initial low/high level at the rising/falling edge to when a 50 mV voltage difference occurs between BL and BLB [26]. RAT can be calculated as shown in Figure 4a. Generally, RAT is highly dependent on the read current through the access transistor and pull-down transistor, as well as the bit line capacitance [6]. Certain cells, such as WE-QUATRO, RHBD14T, QUCCE12T, RHPD14T, and RHHS16T, use two access transistors per bit line, resulting in higher bit line capacitance compared to cells that use only a pair of access transistors. Typically, lower bit line capacitance tends to shorten RAT. Cells like RHBD10T, RHD12T, and RSP14T, which have a single access transistor connected to the BL, show shorter RAT than other cells. Cells like RHBD10T, RHD12T, and RSP14T, which have a single access transistor connected to the BL, show shorter RAT than other cells. Among the cells using a single access transistor, RHBD14T shows the shortest RAT due to its highest cell ratio (CR = 3). During the read operation, the voltage at the '0' storage node rises due to the voltage division effect between the access transistor and pull-down transistor, which increases the source and body voltage. Consequently, the drive strength of the access transistor may decrease due to the body effect, but a higher cell ratio can improve RAT [6]. Among the cells using two pairs of access transistors, QUCCE12T and RHPD14T have shorter RATs due to their high CR of 1.8 and 2, respectively. On the other hand, RHHS16T, which has the higher CR (3.3) among the cells with two access transistors per bit line, shows a longer RAT than most cells because it uses only a pair of access transistor connected to the internal node during the read operation, resulting in a single read path. Additionally, RHBD14T, despite having lower bit line capacitance due to using a single access transistor, shows the longest RAT among all the mentioned cells due to its very low CR (1) and the presence of the PMOS transistor in the read discharge path (Figure 4b).
# Estimating Critical Latency Affecting Ship's Collision in Re-Mote Maneuvering of Autonomous Ships
## 1 Introduction
Therefore, this study proposed a method for predicting critical delay that can be helpful in the evaluation of ship domain and collision avoidance behavior to provide clues about the ship collision situation that may occur in remote-maneuvering ships. This study aims to develop a technique of estimating a critical latency that causes a ship's collision when the ship is remotely maneuvered under COLREGs. The key idea of this study is that a collision occurs when the own ship and target ship come across at any one point in the turning circle. In addition, this estimation technique assumes that the turning circle can be implemented using two variables, a certain radius and a certain turning speed. However, the turning circle of actual ships has a nonlinear turning speed due to the ship's hydrodynamic characteristics [11; 31; 32; 33]. To solve this nonlinearity problem, the turning circle was corrected using the ratio of turning time (seconds/degree) in this work. The results of this study demonstrate that the proposed method can identify and quantify the location and time of both the collisions that could be caused by the designed route and the turning maneuver of the own ship. As a result, the method was expected to contribute to providing a clue for avoiding collisions caused by critical latency in remote maneuvering. The rest of this paper consists of the following: the concepts related to critical latency and the definition of terms are discussed in Section 2, and the estimating methods of critical latency are described in Section 3. Section 4 provides the results obtained from the critical latency estimation, which provide clues to the interpretation of the cause of the collisions that occurred due to the critical latency. The results are discussed in Section 5, followed by the conclusion in Section 6.
# Automated Assessment of Inferences Using Pre-Trained Language Models
## 3 Results  
### Accuracy of Inference Classification  
Table 4 shows the hyperparameters that resulted in the best models. Different batch sizes yielded the highest F1 scores for different base models. In contrast, the same learning rate (\(10^{-5}\)) and warm-up ratio (0) corresponded to the highest F1 scores. Here, the best warm-up ratio of zero means that the warm-up was not necessary. A non-zero weight decay was useful only for the largest model (RoBERTa-large). This suggests that the regularization via weight decay was effective only for the largest model.  
Table 5 shows the classification accuracies of the best models fine-tuned from the three pre-trained models. Both the precision and recall scores of the RoBERTa-base model were higher than those of the BERT-base model. As a result, the F1 score of the RoBERTa-base model was higher than that of the BERT-base model. The precision score of the RoBERTa-large model was lower than that of the RoBERTa-base model, but the recall scores of the two models were the same. As a result, the F1 score of the RoBERTa-large model was lower than that of the RoBERTa-base model.  
Figure 2 shows the F1 scores of the inference classification by fine-tuning the three pre-trained language models. First, training the inference classifier from the BERT-base model resulted in an average F1 score of 0.87, with a standard error of the mean (SEM) of 0.01. Second, training the inference classifier from the RoBERTa-base model resulted in a higher average F1 score of 0.92, with an SEM of 0.01. The F1 score of the fine-tuned RoBERTa-base model was significantly higher than that of the fine-tuned BERT-base model (paired \(t\)-test, \(p<0.05\)). Third, training the classifier from the RoBERTa-large model resulted in a lower average F1 score of 0.90, with a higher SEM of 0.02. However, the difference in the F1 scores of the fine-tuned RoBERTa-base and RoBERTa-large models were not statistically significantly (paired \(t\)-test, \(p>0.05\)).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Base Model** & **Batch Size** & **Learning Rate** &
\begin{tabular}{c} **Warm-Up** \\ **Ratio** \\ \end{tabular} & **Weight Decay** \\ \hline BERT-base & 16 & \(10^{-5}\) & 0 & 0 \\ RoBERTa-base & 8 & \(10^{-5}\) & 0 & 0 \\ RoBERTa-large & 32 & \(10^{-5}\) & 0 & 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyperparameters for the best models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Base Model** & **Precision** & **Recall** & **F1** \\ \hline BERT-base & 0.89 & 0.86 & 0.87 \\ RoBERTa-base & 0.91 & 0.94 & 0.92 \\ RoBERTa-large & 0.87 & 0.94 & 0.90 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracies of the best models.
## 3 Application of Artificial Neural Networks for the Prediction of the Greenhouse Microclimate
### Other Artificial Neural Networks Models for Prediction of Microclimate in Greenhouses  
Other models using ANNs have also been developed as shown in Table 4. [PERSON] et al. [PERSON] et al. [PERSON] et al. (2018) built a neural network model in a non-linear autoregressive configuration (NNARX). The model was carried out through NNs s using the lagged values of the measurable signals as input vector. The structure of the network consisted of an MLP, concluding that the network for long-range prediction purposes does not guarantee optimal results, with one step or two step prediction being more convenient, but to make a long-range prediction they recommend decreasing the number of autoregressive entries and increasing the number of non-regressive entries. Similarly, [PERSON] et al. [PERSON] et al. (2019) developed intelligent control schemes based on a NNARX to control the internal temperature and humidity of a greenhouse. In their work, they propose two intelligent control schemes, a neural predictive controller (NPC) and a non-linear autoregressive mobile average controller (NARMA-L2).  
The results of the temperature and humidity simulation indicated that the NARMA-L2 controller provides good monitoring of the setpoint and disturbance rejection capabilities. The performance indices showed that the setup time is shorter for the NARMA-L2 controller than for the NPC. They also found that unlike other ANN control schemes, the NARMA-L2 controller uses only a neural network for modeling and control. Models have been made to predict the internal temperature of the greenhouse using an auto-regressive model with external input and neural networks (NNARX) as proposed by [PERSON] et al. [PERSON] et al. (2019), the network structure consisted of an MLP where the entrance to this structure was using a vector containing the regressors of an auto regressive with exogenous input (ARX) model and the training was using the BP algorithm. The models showed good performance (daily average absolute simulation error smaller than 1 \({}^{\circ}\)C) for long periods without the need to readjust the parameters frequently, they also indicate that the number of neurons in the hidden layer of the NNARX system plays an important role in obtaining a good performance.
# Context-Based Energy Disaggregation in Smart Homes
## 5 Experimental Results
### Discussion  
Table 8 compares average results of four tests using the basic [PERSON] [9]'s algorithm, the _User Presence Single Interval Conditioning_, the _User Presence Double Interval Conditioning_, the _Usage Statistics Conditioning_ and a combination of the last two conditioning mechanisms executed together. In almost all cases, a disaggregation precision average improvement is observed with respect to the basic algorithm. Even if the combination of the usage statistics conditioning with the double interval conditioning is better in most cases, percentage-wise, the most effective is the _User Presence Double Interval Conditioning_. As regards the _Recall_ parameter, the average results are a little worse than the _precision_ ones. This is caused by the nature of this parameter that, by definition, also considers the wrongly assigned or unassigned samples of a given device. However, the _recall_ improvement over the basic algorithm tightly depends on the analyzed test case, as, for example, a greater quantity of not assigned power samples can worsen this value.  
The experimentation campaign, carried out with the complete test set over the basic algorithm for each conditioning, has highlighted the average improvements that have been reported in Table 9.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **AFAMAP [9]** & **UP Single IC** & **UP Double IC** \\ \hline Refrigerator & 70.73\% & 78.50\% & 94.78\% \\ LCD-TV & 93.86\% & 90.54\% & 94.04\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Precision percentages comparison between [PERSON] and [PERSON] [9]’s AFAMAP algorithm and the User Presence Single and Double Interval Conditioning.  
Figure 12: Power consumption disaggregation obtained with [PERSON] and [PERSON] [9]’s AFAMAP algorithm—test case 2.  
As can be observed, even though the _recall_ parameter apparently worsened the disaggregation results at test level, the _F-Measure_ evaluation parameter, through the whole test set, reports a significant improvement as well as the _precision_ parameter.
## 2 Materials and Methods
### Proposed Methodology  
This section presents the proposed methodology for a preliminary identification and ranking of the critical constructions in urban areas, based on the joint exploitation of satellite DInSAR measurements and DBSCAN-based techniques.  
The flowchart of the methodology, shown in Figure 2, is composed of three elements:  
* The first one (green box) regards the acquisition and the processing of the SAR images relevant to the analyzed area, in the period of interest (in this case, CSK images in  
Figure 1: Example representing how DBSCAN works. In this figure, the _minPoints_ hyper-parameter is 4, and the _eps_ radius is indicated by the circles. N is a noise point, A is a core point, and points B and C are density-reachable from A.  
the period 2011-2019). The images are processed through a multi-temporal DInSAR technique (in this case, the well-known full resolution SBAS-DInSAR algorithm), in order to obtain spatially dense maps of coherent measurement points (referred to as PSs);
* The second one (red box) regards the clustering operation performed by using the DBSCAN algorithm. The identified clusters represent the different buildings of the investigated area;
* The third one (blue box) regards the analysis of the deformation evolution of each building in the observation period, by analyzing the velocity trends and statistics of the PSs belonging to the cluster-identified buildings. This allows, through the retrieval of synthetic deformation maps of the investigated area (with focus on the buildings), to carry on a preliminary identification and ranking of critical buildings to be further investigated.  
The main characteristics of the satellite input data relevant to the first step of the proposed methodology, i.e., the DInSAR products obtained through the exploited full resolution SBAS technique, have been already explained in detail in Section 2.1. The other two main blocks of the procedure sketched in Figure 2 are described in detail hereafter.
## 2 Materials and Methods
### Accuracy Analysis  
The proof-of-concept database (EMA and EMG data) was processed to analyze the differences between simultaneous and individual recordings employing Matlab (R2019a, MathWorks, Natick, MA, USA). The following parameters were calculated:  
* Standard deviation of instantaneous measurements of Euclidean distance between pairs of sensors for each articulographic recording.
* Average of the EMGrms (40 ms window) signal of each electromyographic recording.  
The variation of both of the parameters between the different registration modalities (simultaneous and individual) were then analyzed.
# The Effects of Topology on Throughput Capacity of Large Scale Wireless Networks
###### Abstract  
In this paper, we jointly consider the inhomogeneity and spatial dimension in large scale wireless networks. We study the effects of topology on the throughput capacity. This problem is inherently difficult since it is complex to handle the interference caused by simultaneous transmission. To solve this problem, we, according to the inhomogeneity of topology, divide the transmission into intra-cluster transmission and inter-cluster transmission. For the intra-cluster transmission, a spheroidal percolation model guarantees a constant rate when a power control strategy is adopted. We also propose a cube percolation mode for the inter-cluster transmission. Different from the spheroidal percolation model, a constant transmission rate can be achieved without power control. For both transmissions, we propose a routing scheme with five phases. By comparing the achievable rate of each phase, we get the rate bottleneck, which is the throughput capacity of the network.  
Keywords:throughput capacity; percolation; heterogenous topology; wireless networks +
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