esa-sceva/satcom-chunk-collection huggingface dataset:
# Technology Acceptance in Healthcare: A Systematic Review
## 6 Conclusions  
This study aimed to systematically provide an overview of the studies published on technology acceptance in healthcare. The study provided a classification analysis that includes the studied technology acceptance models, the influential factors, the confirmed relationships among those factors, the types of the studied information technologies, participants, year of publication, and countries (direction of research). Following the PRISMA guidelines, 1768 published studies were reviewed, and 142 studies were found to be valid and included in the statistical analysis. According to the findings, it is clear that TAM and UTAUT are the prevailing technology acceptance models. Additionally, the analysis found that the constructs of TAM and UTAUT were the most utilized factors to understand the acceptance of technology in healthcare. Moreover, other factors were extensively studied including, computer anxiety, computer self-efficacy, innovativeness, and trust. Overall, room is still available to integrate various technology acceptance models or add other factors to the current models to produce more robust and valid acceptance models.  
On the other hand, some technology solutions were found to be dominant, including electronic health records, telemedicine, and mobile applications. In general, the results were scattered in terms of the research directions (technology country). Healthcare workers (i.e., physicians, nurses, and healthcare professionals) were the main focus of the reviewed studies. Patients' technology acceptance was only discussed in around 10% of the reviewed studies. In addition, the reviewed studies were mainly conducted in Taiwan and the USA, with minimum research articles in Arab and African countries.
# Flow Table Saturation Attack against Dynamic Timeout Mechanisms in SDN
## 3 Typical Flow Table Saturation Attack
### Attack Process  
The attacker first infers which header fields in the packet are sensitive to the control logic and can trigger a new rule installation. To achieve this, the attacker sends several probing packets with the same packet header and measures their RTTs. If the first packet experiences a long forwarding delay while the subsequent ones have small RTTs, it indicates that the probing packet triggers a new rule installation. After triggering a new rule, the attacker can change the value of the packet header field to craft a new probing packet. If the new one has a longer delay, it indicates that the changed field is used in the match fields of flow rules. By enumerating all packet header fields, the attacker can acquire the match fields of the control logic.  
After that, the attacker probes the timeout values to make the attack traffic stealthy. To infer the idle timeout value, the attacker generates packets with specified packet intervals. The attacker sends one probing packet and waits for \(T_{0}\) to send another. If the second one receives a quick response, it indicates that its timeout is larger than \(T_{0}\). Otherwise, the timeout value is smaller. In this way, attackers can infer the accurate idle timeout value by gradually increasing the packet interval from the minimum value or applying a binary search.  
Then, the attacker launches saturation attacks based on the probed information. The attacker generates a malicious flow entry by sending a crafted packet to trigger the flow rule installation, and resends packets before the rule times out. As long as the inter-arrival time is less than the idle timeout, the flow entry can be permanently active. By repeating this process, the attacker can generate thousands of flows to overflow the flow table.
# An Efficient Relayed Broadcasting Based on the Duplication Estimation Model for IoT Applications
## 3 Duplication Suppression and Re-Queuing
### Re-Queuing to Compensate for Delivery Failure  
It is clear that all of the frames cannot be successfully delivered to all of the nodes, even with a suitable duplication suppression scheme. We consider three reasons for frame delivery failure: (i) the rebroadcasting of a frame is excessively suppressed, (ii) the error rate of a wireless channel is temporarily high due to fading or shadowing, and (iii) concurrent transmissions by hidden nodes result in a collision and/or interference. To overcome these problems, we propose a compensation scheme called _re-queuing_. This scheme is designed to improve the reliability further by granting an additional chance of transmission to the frame that has been deleted from the transmission buffer due to the duplication suppression scheme or has not been successfully delivered to adjacent nodes. Please note that a frame can be re-queued after it has been transmitted or deleted, thus it can be transmitted up to twice due to the re-queuing scheme. The two main issues in the re-queuing scheme are determining which frames to re-queue and when to do it. Unlike the unicast communication, it is  
Figure 6: The transmission probability obtained from (**a**) duplication ratio with various values of \(\alpha\) and (**b**) approximated duplication ratio with various values of \(\mu\).  
difficult to determine the data frame to be retransmitted because there is no ACK frame in the relayed broadcasting. Moreover, the data frame does not have to be retransmitted immediately after detecting its delivery failure, because it can be recovered by a duplicate transmission from other adjacent nodes. These two issues are discussed in the following subsections.
## 6 Purpose-Based Encryption
### Evaluation
#### 6.6.2 Benefits and Limitations  
Our approach solves the issues identified in the introduction:  
1. _Identity propagation between intermediaries_: Each intermediate service in the call chain (e.g., the shopping and delivery service) uses the SAML response to perform a purpose authorisation request without the user interaction. Therefore, we prevent identity theft in case of users mistakenly decide, which attributes to disclose to a service. According to GDPR, the data controller is accountable for transferring PII to the data processor. That is, the data controller must determine the purposes of PII processing. In other words, when a cloud service registers  
Figure 17: (**a**) Performance of encryption, decryption (**b**) Performance of tokens generation on the y-axis in milliseconds.  
on a cloud provider, it must provide its business information to the cloud provider such as the access purpose of the service. The IdP use this information to authorise a purpose request.
2. _Honest-but-curious IdP or SP_: Here, we assume that SPs receive the SAML response and the tokens and try to learn more information about PII. First, the home IdP issues the SAML response with its private key. Therefore, SPs cannot modify the SAML response to request the visitor IdP for more capabilities than they are allowed. Second, SPs cannot combine different tokens from different transactions or users to gain more capabilities (i.e., the decryption simply fails if they do so). On the other hand, the visitor IdP can only issue a purpose token for an SP but does not have the time token to decrypt PII. However, our concept cannot prevent the collusion attacks between the visitor IdP and the SPs. Here, we assume that adversaries cannot control both systems at the same time.
3. _Dishonest IdP or SP_: A dishonest entity is an entity that does not follow the protocol. For example, an IdP authorises a wrong purpose (with more capabilities) to an SP or an SP forwards its purpose token to another service. In such cases, an honest entity can prove that the other side has violated the protocol and the dishonest entity must pay for the penalty (according to GDPR, an entity may pay a penalty up to 20 million Euro). In the Facebook incident, Facebook would prove that it did not authorise Cambridge Analytica for the purpose \"analysis\", so the dishonest application, which forwarded the user data, would pay for the penalty.
4. _Insider attack on an IdP_: PII is encrypted on the IdP. Therefore, an administrator of one IdP does not have enough tokens to decrypt it. However, our approach cannot prevent the collusion attacks between the home and visitor IdP. Here, we assume that adversaries cannot control both IdPs at the same time.
5. _Malicious host_: A malicious host may change the execution of plaintext code to bypass the disclosure policies and access data. In our case, if the host running the shopping (or delivery service) changes the cryptographic computation in anyways, the decryption simply fails.
# How Deeply to Fine-Tune a Convolutional Neural Network: A Case Study Using a Histopathology Dataset
## 1 Introduction
Transfer learning is a novel domain that has been introduced to help overcome the before-mentioned issues in CNN training, where instead of initializing the network weights from scratch, the weights of a previous network that was trained on a different large dataset can be used. The medical field could benefit a lot from the usage of CNN for image classification, but the main drawback is the number of images available for training, and that is where transfer learning can be used. Using transfer learning for medical images has been successfully applied in References [4; 6; 8]. As pointed out by [PERSON] [15], transfer learning uses two main techniques: fine-tuning, wherein the original weights will be re-tuned to suit the new dataset, or feature extraction, in which the original weights will be fixed and the original layers will serve for feature extraction only. Both techniques can help a lot based on the size of the dataset, as pointed out by [PERSON] et al. [16]. If the dataset is large enough, the original layers can be fine-tuned, while if the dataset is small, the original layers can serve in feature extraction. Also, the similarity between the original dataset and the target dataset can play a very important role. For this study, we opted for fine-tuning instead of feature extraction because of the following reasons: (1) the large number of images contained in the dataset used, and (2) the huge difference (with respect to the domain of the images) between the ImageNet dataset and the histopathology dataset taken into account. Figure 1: Convolutional neural network (CNN) diagram. Fine-tuning an entire CNN architecture can take hours and requires certain hardware, and the effect of each block can play a very important role. Fine-tuning the entire network does not always guarantee to yield the best performance. The main purpose of this study is to determine the effect of fine-tuning a CNN block-wise to assess the performance gained by training each block. Three state-of-the-art CNN architectures with three learning rates are used in this study. The performance measure used is the AUC of the ROC curve. A separate unlabeled test set is used to evaluate the performance of the CNN architectures. The rest of this paper is organized as follows: In Section 2, a brief literature review about using transfer learning in histopathology is given. In Section 3, the proposed methodology is discussed. In Section 4, the results obtained are stated. In Section 5, a brief overview of the findings is discussed. In Section 6, the conclusion is stated.
# Effect of Local Cyclic Loading on Direct Shear Strength Characteristics of Shear-Zone Soil
## 4 Results
### Studying the Effect of Number of Cycles  
The shear-stress response of the shear-zone soil to a local stress-controlled cyclic direct shear test is illustrated in Figure 10. In this test, the shear-zone soil was applied to 0, 10, 20, 100, and 1000 cycles with the vertical pressure of 100 kPa and cyclic period of 10 s, 60 s, and 90 s. Figure 10a exhibits shear stress for steady loading and local cyclic loading as a function of shear displacement and illustrates the fact that increasing the number of cycles decreased the shear strength of the shear-zone soil under different numbers of cycles. With a cyclic loading application, the shear-zone soil hardened gradually, and then smaller displacements were produced by the same load amplitude. It is especially evident in Figure 10b that the maximum data of deformation were only 0.1 mm for the first cycle. This increased to 3.31 mm after 1000 cycles and was kept approximately constant.  
Figure 11 exhibits that the shear strength ratio decreased as the number of cycles increased because the plastic deformation and fatigue damage of the specimen increased with the number of cycles. In other words, the plastic deformation did not disappear until a great number of cycles had been completed.  
Figure 9: Shear strength ratio against normal stress for shear-zone soil with different cyclic periods.  
Figure 10: Shear stress versus shear displacement curves with different number of cycles (\(\sigma_{n}\) = 100 kPa). (**a**) \(T\) = 10 s; (**b**) \(T\) = 60 s; and (**c**) \(T\) = 90 s.
## 3 Adaptive Data Transmission Algorithm
\begin{table}
\begin{tabular}{c c} \hline \hline
**Condition** & **Frequency of Sending Frames \(f_{T}\)** \\ \hline \(d_{i}\leq TH1\) & 5 Hz \\ \(TH1\leq d_{i}\leq TH2\) & 15 Hz \\ \(TH2\leq d_{i}\leq TH3\) & 30 Hz \\ \(d_{i}\geq TH3\) & 60 Hz \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Frequency of sending frames depending on the parameter \(d_{i}\). additionally 15 Hz. On the other hand, when the value of the parameter \(d_{i}\) for at least one sensor exceeds the \(TH2\) threshold, then the data transmission for these sensors is carried out at frequencies \(f_{\rm T}\) of 5 Hz, 15 Hz and additionally 30 Hz, which is presented graphically in Figure 8a. The situation is similar in the case of exceeding the \(TH3\) value, when the frames are additionally transmitted with a frequency of 60 Hz (Figure 8b). Figure 8c presents an example of a frame transmission structure in which different frequencies \(f_{\rm T}\) (5 Hz, 15 Hz, 30 Hz, and 60 Hz) are used to transmit data from different groups of sensors. Figure 8: Structure of data transmission from individual MEMS sensors.
# Research on Prediction Method of Hydraulic Pump Remaining Useful Life Based on KPCA and JITL
## 4 Life-Cycle Test and Signal Acquisition of Gear Pump
### Extraction of Gear Pump Performance Degradation Features
The number and _CPVs_ of the principal components that meet the requirements (\(\mathit{CPV}\geq\) 85%) are listed in Table 5.  
Figure 9: Frequency-domain characteristic parameters: (**a**) Gravity frequency; (**b**) Mean square frequency; (**c**) Frequency variance; and (**d**) Average power. As can be seen from Table 5, as the selected kernel parameters are increased, the _CPVs_ of the principal components satisfying \(\mathit{CPV}\geq 85\%\) initially increase and subsequently decrease. To reduce the information loss of the original data and ensure the maximum _CPVs_ for the first and second principal components, the size of the kernel parameter is selected as 450 in this study. Taking gear pump 1 as an example, after the selected time- and frequency-domain feature indices are analyzed and processed by the KPCA method, the first and second principal components are extracted, which are shown in Figure 10.  
The first principal component obtained by the KPCA method shows a clear upward trend with the gear pump performance degradation. At the beginning of the failure of the gear pump (approximately 700 h), the first and second principal components present clear changes. At the full failure of the gear pump (approximately 1033 h), both first and second principal components reach their maximum values, as shown in Figure 10. Therefore, the first and second principal components can be extracted by the KPCA method to reflect the performance degradation trend of the gear pump. This method has good sensitivity \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Series Number of** **Gear Pump**} & \multirow{2}{*}{**Kernel Parameter**} & \multicolumn{2}{c}{_CPV_ **of First and**} & **Number of Principal** \\  & & **Second Principal** & **Components that** \\  & & **Components (\%)** & **Meet Requirements** \\ \hline \multirow{6}{*}{\(1\)} & 250 & 92.8786 & 2 \\  & 350 & 93.3271 & 2 \\  & 450 & **93.5788** & 2 \\  & 550 & 85.7281 & 1 \\  & 650 & 86.5944 & 1 \\ \hline \multirow{6}{*}{\(2\)} & 250 & 90.7480 & 2 \\  & 350 & 91.2361 & 2 \\  & 450 & **91.5465** & 2 \\  & 550 & 91.8763 & 2 \\  & 650 & 85.0722 & 1 \\ \hline \multirow{6}{*}{\(4\)} & 250 & 91.3048 & 2 \\  & 350 & 91.7811 & 2 \\  & 450 & **92.0776** & 2 \\  & 550 & 92.3472 & 2 \\  & 650 & 85.5821 & 1 \\ \hline \multirow{6}{*}{\(4\)} & 250 & 90.0859 & 2 \\  & 350 & 90.6843 & 2 \\ \cline{1-1}  & 450 & **91.0544** & 2 \\ \cline{1-1}  & 550 & 91.4361 & 2 \\ \cline{1-1}  & 650 & 92.0065 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _CPV_ and number of kernel principal components corresponding to different kernel parameters. Figure 10: First and second principal components obtained by KPCA method. to the beginning of the failure and presents a stable change trend with the performance degradation of the gear pump.
## 4 Numerical Analysis
### Data and Parameters  
In this paper, the competitive hinterland of Dalian Port and Vladivostok Port is considered to be the three northeastern provinces of China (Liaoning Province, Jilin Province, and Heilongjiang Province), with a focus on the competition of the two ports in the import and export container market in Northeast China.  
In land transportation, \(m=1\) represents highway transportation and \(m=2\) represents railway transportation. The freight sharing rate is determined based on the average freight turnover ratio of highways and railways in the three northeastern provinces of China. The container transportation cost of highway transportation is set based on the data released by the Price Monitoring Center of the China Development and Reform Commission ([[https://www.cfpci.org.cn/Index](https://www.cfpci.org.cn/Index)]([https://www.cfpci.org.cn/Index](https://www.cfpci.org.cn/Index)) (accessed on 10 May 2023)), while the container transportation cost of railways is set based on the container transportation price released by China Railway Transport Corporation ([[http://www.chinainlandport.org.cn/index.php?m=content&c=index&a=show&catid=34&id=169](http://www.chinainlandport.org.cn/index.php?m=content&c=index&a=show&catid=34&id=169)]([http://www.chinainlandport.org.cn/index.php?m=content&c=index&a=show&catid=34&id=169](http://www.chinainlandport.org.cn/index.php?m=content&c=index&a=show&catid=34&id=169)) (accessed on 13 August 2023)). The average operating speeds of highways and railways are set according to the People's Republic of China Industry Standard (JTGB01-2014): Technical Standard for Highway Engineering and the website data of the China Railway Service Center. Due to the cross-border transportation involving a series of customs clearance operations, such as cargo reloading and customs loading and unloading at border ports, we have set up the cross-border transportation uniformly based on the port charge standards announced by the Suifenhe Railway Port and refer to the railway transportation charges standards for the Russian section of the China Europe Express ([[http://www.suifenhe.gov.cn/channels/4416.html](http://www.suifenhe.gov.cn/channels/4416.html)]([http://www.suifenhe.gov.cn/channels/4416.html](http://www.suifenhe.gov.cn/channels/4416.html)) (accessed on 5 December 2023)).  
Regarding port charges, the unit container port charges of Dalian Port and Vladivostok Port shall be set according to the container charge standards published by Dalian Port Container Development Company and FESCO, respectively. The specific model parameters are shown in Table 2.  
Figure 4: International land–sea transport routes from cities in Northeast China to overseas destination ports (Harbin to Busan Port as an example).
## 3 Results and Discussion
### Coagulation Behaviors under Different Rapid Mixing Speed
#### 3.1.2 Removal Efficiency and Zeta Potential  
As shown in Figure 3, the K-GN removal efficiency after coagulation remained stable at about 89% in three different rapid mixing conditions. Although Removal efficiency is not significantly influenced by rapid mixing speed, zeta potential increased with increasing rapid mixing speed. Changes in floc characteristics also lead to changes in zeta potential. Rapid stirring speed may change the zeta potential of the colloid, because the rapid mixing could change the floc size and their surface properties. Positive magnesium hydroxide acts as a charge neutralization species. Similar results were also found in the magnesium hydroxide coagulation process for the removal of reactive dyes [18; 19]. Zeta potential is important in terms of the impact on steady state floc size and response to increased levels of shear. Under different shear conditions, the break-up flocs may have different physical properties. Floc properties impacted significantly on the overall process efficiency [20]. Electrical charge or colloidal properties of the magnesium hydroxide-reactive orange flocs would be greatly affected. Based on this observation, it can be reasoned that charge-neutralization is one of the mechanisms for destabilization and removal of reactive orange [21]. Normally, slow mixing conditions also affects the flocs break-up and growth. However, in the magnesium hydroxide coagulation process, magnesium hydroxide formed and flocs grew fast, then the larger flocs broke into relatively small particles in the slow mixing period. As previously found [13], floc growth is not significantly influenced by a slow mixing period.  
Figure 2: Floc size distribution with different rapid mixing for three stages. (**a**) rapid mixer; (**b**) flocculation basin; (**c**) sedimentation tank.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Rapid Mixing (rpm)**} & \multicolumn{3}{c}{**Average Floc Size (\(\mu\)m)**} \\ \cline{2-4}  & **Rapid Mixer** & **Flocculation Basin** & **Sedimentation Tank** \\ \hline
250 & 8.39 & 7.96 & 14.41 \\
300 & 8.06 & 7.89 & 11.21 \\
350 & 8.04 & 7.95 & 10.58 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average floc size in different process units.  
In this research, the effects of slow mixing conditions on floc properties were not considered. As has been mentioned, large flocs breakage and re-growth will happen simultaneously and, consequently, there is less opportunity for the break flocs to re-grow.
# Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning Applications
## 2 Dimensionality Reduction  
Dimensionality reduction methods seek to improve data quality by lowering their complexity, therefore solving the phenomenon commonly referred to as the \"curse of dimensionality\". (The term \"curse of dimensionality\" refers to the \"blind spots\" created by contiguous areas of feature space devoid of observations, which are found when working in datasets with high-dimensional data without expanding the sample size. This means the data to solve the topical problem become sparse, which makes extracting meaningful results difficult. This problem is commonly observed in real contexts [13]). They fall mostly into two categories: feature selection and feature extraction [14,15]. A diagram summarizing the core concept hierarchy for this knowledge field is presented in Figure 1. In this section, a short overview of the state of the art for both categories is provided and some of the included techniques are presented. Special attention is given to feature selection methods, as they are used for testing purposes later in this document.