esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Performance Test
### IP Camera Bandwidth Consumption Comparison  
We have measured a wireless IP camera placed in the rural area transmitting MPEG-4 codec compressed video over http protocol in order to test the number of bytes per second and the number of messages per second in the network. It lets us know the camera bandwidth consumption. The video resolution was \(320\times 240\) at 25 fps. The wireless IP camera also transmitted audio at 24 Kbps from the camera to the web page. We have measured, for 2 minutes and 30 seconds, the following situations:  
* 1 st Situation: The camera is acquiring video from a fixed place where there is not any motion, and the camera is not moving.
* 2 nd Situation: The camera is acquiring video from a fixed place where there is motion, but the camera is not moving.
* 3 rd Situation: The camera is acquiring video while it is moving.
* 4 th Situation: There are 4 cameras acquiring video from fixed places where there is not any motion and the cameras are not moving. We show how much bandwidth just one camera needs.  
Then, we have measured the network for 15 minutes in order to know how the network performs for longer times.  
Figure 12: Octets per second obtained by the four situations.  
Figure 12 shows the number of bytes per second for the 1 st, 2 nd, 3 rd and 4 th situations. We can observe that the worst case is when the video acquired has motion. On the other hand, we think that the moving camera situation is rampant because of the bandwidth used to control the camera. There is no case with more than 140,000 bytes per second (1.12 Mbps). Then, we have divided the number of bytes per second obtained by the number of cameras. The average number of bytes is around 109.2 KBytes (873 Kbits), while the average number of bytes per second when there is just one camera is 63.6 KBytes (509 Kbits). So the system allows 34 cameras in each access point.  
Figure 13 shows the number of packets per second for the 1 st, 2 nd, 3 rd and 4 th situations. We can observe that the worst case is when the video acquired has motion. The moving camera sends messages in a rampant manner without any rule. The average number of packets per second, due to one camera, when there are four cameras is around 167 packets/s, while the average number of packets per second when there is just one camera is 180 packets/s. So the system allows many cameras.  
Figure 14 shows the number of bytes per second when there are four cameras.  
Figure 13: Packets per second obtained by the four situations.  
Figure 15 shows the number of packets per second in the 4 th situation, but showing the measurements of all cameras. We can observe that it is not more than 1000 packets per second in the wireless network, so there are not so many messages through the medium at the same time. As the number of packets sent through the network is proportional to the number of cameras in the wireless network, 34 cameras will send less than 34,000 packets per second through the network.
# A Risk Assessment Framework for Mobile Apps in Mobile Cloud Computing Environments
## 2 Materials and Methods
### Study Sample and Datasets  
To build the study's datasets, over 30,000 Android Package (APK) files were collected from the AndroZoo [29] and RmvDroid [30] repositories. The apps' APK files were screened with the antivirus engines of Virus Total, [[https://virustotal.com](https://virustotal.com)]([https://virustotal.com](https://virustotal.com)) (accessed on 15 January 2022). The results were used to assign a label (benign or malicious) to each app. An app was deemed malicious if at least 15 of the VirusTotal antivirus engines flagged it as malicious, and benign if none of the antivirus engines flagged it as malicious. This resulted in a sample of 28,306 apps comprising 9879 benign and 18,427 malicious apps. The APK files were decompiled to obtain the manifest file of each app by applying the APK Easy tool, [[https://apk-easy-tool.en.lo4d.com/windows](https://apk-easy-tool.en.lo4d.com/windows)]([https://apk-easy-tool.en.lo4d.com/windows](https://apk-easy-tool.en.lo4d.com/windows)) (accessed on 3 February 2022). Next, the permissions and intents of each app were extracted using custom-built software. It was established that across the collection of apps in the sample, a total of 132 unique permissions and 131 unique intents were used. Three study datasets were constructed as follows:  
1. The dataset \"Permissions\" includes all apps in the sample. Each app is represented by a record that maps the use of the 132 unique permissions. The record comprises 132 binary input features (1 indicating that the respective permission is used by the app, and 0 indicating not used), and a label (1--malicious, 0--benign). 2. The dataset \"Intents\" was constructed similarly, with 131 binary input features indicating intent use and a label (benign or malicious). 3. The dataset \"Hybrid\" also includes all apps. Each app is represented by 263 features that map the combined permission and intent use, and a label (benign or malicious). The data indicated that not all permissions and intents were highly used. Tables 1 and 2 show how the 25 most used normal and dangerous permissions and intents are used by the apps in the study sample. While both benign and malicious apps used some of the dangerous permissions, there were dangerous permissions that were used much more by malicious apps rather than by benign apps. One such dangerous permission was READ_PHONE_STATE, mentioned earlier. It was requested by 96.52% of the malicious apps in the sample. Another example is GET_TASK, which was used by 50.17% of the malicious apps. This permission enables access to all apps resident on a device. If obtained by a malicious app, it can lead to
## 3 Test Results
### Hardened Properties of RPC
#### 3.2.5 Effect of WB and Curing Temperature on Drying Shrinkage  
Figure 12 shows the effect of the WB and curing temperature on drying shrinkage for one-day air-cured and seven-day moist-cured specimens. Increasing the WB tends to increase the drying shrinkage in RPC, regardless of the curing regime. This is possibly because at a higher WB, more pores are formed inside the concrete structure. As a result, the evaporation of the water from the pores combined with the formation of microcracks along the interconnected pores leads to higher drying shrinkage. This is confirmed by the porosity results shown in Table 4. For example, while the porosity and density of the normal temperature cured RPC mixture with a WB = 0.18 are 4.14% and 2.23 g/cm\({}^{3}\), those properties of the mixture having a WB = 0.26 are 15.05% and 2.18 g/cm\({}^{3}\), respectively. In other  
Figure 11: Drying shrinkage of plain concrete and RPC: (**a**) one-day air curing; (**b**) seven-day moist curing.  
words, the P/D of the former is 1.85, while the P/D of the latter is 6.89. The lower value of the P/D indicates that the RPC has less the capillary void content and less capillary water, which is associated with drying shrinkage.  
Figure 12b also shows that warm temperature curing tends to decrease the drying shrinkage of RPC significantly. This may be attributed to the formation of low permeable hydration products being built up around the cement particles. As stated previously, low-permeable hydration products are built up around the cement particles rapidly when the RPC is cured at a warm temperature. These low permeable hydration products lead to larger pores, but prevent the evaporation of water through these hydration products. In turn, higher early-age temperature curing results in higher early strength and lower drying shrinkage.
## 2 Wind Speed Modeling
### Component Model  
Component models provide another perspective on wind speed analysis. Wind speed is decomposed into different wind speed components based on the wind speed variety characteristics and time length. Two-component wind speed models and four-component wind speed models are the commonly used. Models of each wind speed component can respectively established based on the probability distribution or time-series data. In a two-component wind speed model, wind speed is the superposition of the average wind speed component and the wind turbulence component. The two-component wind speed model is widely used in time-series wind speed models [27; 28]. In this model, the instantaneous wind speed \(V(t)\) can be expressed as:  
\[V(t)=\overline{V}+V^{\prime}(t) \tag{7}\]  
where \(\overline{V}\) is the average component, and \(V^{\prime}(t)\) is the turbulence component.  
In Equation (7), the average wind speed component can be expressed as:  
\[\overline{V}=\frac{1}{t_{2}-t_{1}}\int_{t_{1}}^{t_{2}}V(t)dt \tag{8}\]  
According to the wind profile, the average wind speed can be described with the following logarithmic distribution or exponential distribution:  
\[\overline{V}(z)=(V^{*}/k)\ln(z/z_{0}) \tag{9}\]  
\[\frac{\overline{V}(z)}{V(Z_{s})}=(z/z_{s})^{\alpha} \tag{10}\]  
where \(z\) is the height from the ground; \(z_{0}\) is the roughness length of the ground surface; \(V^{*}\) is the friction velocity; \(Z_{s}\) is the reference height from the ground; \(k\) is the Karman constant; and \(\alpha\) is the wind profile index.  
In Equation (7), the wind turbulence component is mainly described by wind spectrum and correlation functions. A commonly used Davenport spectral density function is:  
\[S(f)=4K\overline{V}(10)^{2}\frac{\left(1200f/\overline{V}(10)\right)^{2}}{f \left[1+\left(1200f/\overline{V}(10)\right)^{2}\right]^{4/3}} \tag{11}\]  
where \(S(f)\) is the spectral density function at frequency \(f\), and \(K\) is the roughness coefficient of the ground. In the Davenport spectral, the reference height is chosen to be 10 meters.  
A four-component wind speed model using basic wind, gust wind, gradient wind, and random wind is used to describe the different characteristics of wind speed. The description of the four-component wind speed model is shown in Table 2, and the comparison between the two-component and the four-component model is shown in Table 3. In [32], a more accurate four-component wind speed model is proposed to simulate the effects of terrain and time delay on wind speed. Two coordinate transformation matrices are used to consider the wind direction factor. The wake effect of wind turbine generators on the wind-speed distribution is considered in the proposed model.
# Accurate Energy Consumption Modeling of IEEE 802.15.4e TSCH Using Dual-Band OpenMote Hardware
## 4 Measurements
### Methodology
#### 4.1.3 Energy Consumption Measurements  
In order to perform the different energy consumption measurements, a setup different from the Gecko setup, described in Section 4.1.2, needed to be used. As the consumption of the OpenMote hardware happened to exceed 50 mA (i.e., the maximum of the Gecko measuring range), we switched to using the Keysight N6705B DC Power Analyzer [24]. Using the two-wire mode, the Voltage Common Collector (VCC) and Ground (GND) pins of the OpenMote-CC2538, in the 2.4 GHz measurement setup, and of the OpenUSB with the OpenMote-CC2538 attached, in the 868 MHz measurement setup, were connected to the power supply output of the N6705B, which was configured to provide an input voltage of 3.0 V. This is the nominal voltage of two serially-connected AA batteries, which can be used to power an OpenMote via an OpenUSB or OpenBattery module. The measurement setups are shown in Figure 5a,b, for the 2.4 GHz and 868 MHz measurements, respectively. For the 868 MHz measurements, the OpenUSB has to be attached to the OpenMote-CC2538 because the former only holds a CC1200 radio transceiver and needs the microprocessor on the latter to control it.  
For most device states, the consumption was averaged over a period of 500 ms. However, some states (e.g., RX and TX states) only last as long as the radio takes to send all bytes. For these states, the average was taken over a period between 3 ms and 4 ms.
## 4 Wireless Body Area Networks (WBANs)
### Requirements for Wireless Medical Sensors in WBANs
#### 4.1.2 Reliable Communication  
Reliable communication in WBANs is of paramount importance for medical applications that rely on WBANs. The communication needs of different medical sensors depending on the need of sampling rates, from less than 1 to 1,000 Hz. One approach to improve reliability is to move beyond telemetry by performing processing of the sensor signal. For example, instead of sending raw electrocardiogram data from sensors, we can perform feature extraction on the sensor, and transfer only information about an event. In addition to reducing the high demands on the communication channel, the reduced communication requirements saves on total energy expenditures, and consequently increases battery life. A careful trade-off between communication and computation is crucial for optimal system design.
## 4 Discussion
### Limitations  
Despite a systematic and thoughtful search strategy, we may have missed some papers worthy of inclusion in this review. As all papers focused on their results, it was often challenging to extract which results were found specifically using 2 PI. Discrepancies in methodologies such as the animal models utilized, seizure-induction methods, seizure durations and magnitudes, and other variables limit the comparability of the included studies. In fact, there are some instances of conflicting observations. Epilepsy is a syndrome referring to a group of distinct neurological disorders wherein epileptic seizures are a common symptom. Therefore, there can be significant variation and heterogeneity in terms clinical manifestation as well as the underlying biological and pathophysiological mechanisms and pathways responsible.  
Additionally, animal models cannot fully replicate human epilepsy. While pharmacological agents such as 4-AP, kainic acid, and pilocarpine are capable of generating seizures, these seizure models are not representative of human epilepsy as a whole. Human epilepsy is characterized by several complex processes, including epileptogenesis and spontaneous interictal activity, which cannot be suitably replicated experimentally. It is also noteworthy that the use of anesthesia in in vivo 2 PI experiments of live animals may represent a confounding variable by exerting unintended effects. These factors affect the translatability of findings presented in such studies. Hence, more research using human tissue is warranted.  
With regard to the application of 2 PI in epilepsy research, the surgical implantation of cranial windows for in vivo 2 PI experiments can present some challenges. It has been reported that image quality may be diminished by craniotomy closure due to wound healing and inflammatory responses [95]. However, this is not a major limitation as these effects can be nullified by employing sterile surgical techniques and by minimizing trauma [95]. Although 2 PI allows for deeper tissue penetration compared to single-photon microscopy, it is still limited to a depth of 2 mm into the brain. Beyond this depth, the scattering effects reduce image quality significantly. However, advances in the development of fiber-optic-based two-photon endomicroscopes potentially allow for in vivo probing of deep brain structures in experimental contexts [96]. Another limitation of the application of 2 PI in epilepsy research relates to the use of calcium-sensitive dyes to stain and subsequently visualize cells. Overexpression of such calcium indicators may cause unintended perturbations of baseline calcium levels. In fact, it has been reported that transgenic mice expressing the calcium indicator GCaMP6 can undergo modulations in electrical activity over large regions of cortex. These effects have been documented in mice of either sex across multiple laboratories [97].
# Fast Reaction to Sudden Concept Drift in the Absence of Class Labels
## 3 The Proposed Approach  
In nonstationary environments and in the presence of concept drift, existing drift detectors analyse the prediction results of the base learner and apply a certain decision model to signal a drift when changes are detected. Generally, most of the existing drift detectors, including the aforementioned methods, evaluate prediction results by analysing the error rate (accuracy) and its corresponding standard deviation, and find the difference between the means of the sub-windows or compare the accuracy of a model with different time windows, etc. [7]. This evaluation method is a measure of the classification performance to evaluate learning algorithms. Conversely, the drift detection method proposed in this paper for a nonstationary environment is quite different.  
This paper proposes a data stream model that reacts quickly to sudden drift, consuming minimal time and memory compared to the state-of-the-art. In a novel way, we combine a diversity measure called a disagreement measure known from static learning in streaming scenarios with a modified PH test. Instead of monitoring the error estimates, DMDDM-S monitors the diversity of a pair of classifiers, where the true label of an example is not necessary to determine whether components disagree. In addition, we adopt k-prototype clustering as a solution to label the unlabelled data and use the newly labelled data with the labelled ones to retrain the model to be consistent with the current concept. In this paper, DMDDM-S is proposed to judge diversity based on an examination of classifier responses to changes in incoming data.
# Transcriptional Analysis of Masson Pine
## 3 Results
### Biosynthesis of Sucrose, Starch and Cell Wall Components under Elevated CO\({}_{2}\) Stress  
The expression levels of the genes in the sucrose and starch synthesis pathways generally showed a trend of up- or slightly down-regulation under CO\({}_{2}\) stress conditions compared with CK, except for _AGP_ (Adenosine Diphosphoglucose Pyrophosphorylase) and _SBE_ (Starch Branching Enzyme) (Figure 3B). AGP catalyzes adenosine diphosphate glucose (ADP-Glc) from glucose-1-phosphate (G-1-P), and its expression continued to decline with increasing stress time. On the other hand, among the genes in starch metabolic pathway, the expression of _GBSS_ (Granule-bound Starch Synthase) was higher than _SSS_ (Soluble Starch Synthase) and _SBE_.  
After a series of reactions, triose phosphate was transformed into UDP-Glc in the cytoplasm (Figure 3A), and then it was catalyzed to sucrose by sucrose-phosphate synthase (SPS). After being subjected to stress treatment, the expression levels of _SPS_ at 6 h, 12 h and 24 h were no significant difference (Figure 3B). Meanwhile, sucrose can be transformed to UDP-Glc and hexose by sucrose  
Figure 2: Influence of high CO\({}_{2}\) concentration on energy metabolism. (**A**) The main energy pathways in plants. Black, orange, blue and green arrows represent photosynthesis, the Embden-Meyerhof-Parnas (EMP) pathway, the pentose phosphate (PPP) pathway and the tricarboxylic acid (TCA) cycle, respectively. The different enzymes are shown in red font. (**B**) Expression changes in the genes involved in metabolic pathways in response to stress. White indicates no change, red up-regulation, and blue down-regulation in each treatment, as shown in the color bar for a log\({}_{2}\) fold change scale. The abbreviations in the figure are shown in Table 2 from Appendix A.  
synthesize (SUS) and cell wall invertase (cwINV), and hexose can then be catalyzed by cytosolic invertase (INV) to form fructose-6-phosphate (F-6-P) and glucose-6-phosphate (G-6-P), which are the synthetic precursors of UDP-GlcNAc (Figure 3A). In the above series of reactions, the expression of _SUS_, _cwINV_ and _INV_ were up-regulated in different degrees under CO2 stress compared with CK. In addition, UDP-GlcNAc is the precursor for cellulose synthesis, which is catalyzed by cellulose synthase complex (CSC), including cellulose synthase subunit (CesA), cellulose synthase (Csl) and its isoenzymes (Figure 3A). The experimental results showed that the expression levels of _CesA_, _Csl A_, _Csl C_ and _Csl D_ were up-regulated with increasing stress time. However, _Csl B_ and _Csl E_ decreased at the same time (Figure 3B). In general, the omics data revealed that gene expression patterns in sugar and cell wall component metabolic pathways were enhanced under elevated CO2 stress in Masson pine.
## 4 The Deployment of WDM-PON Protection Schemes
### Network Availability Considerations for WDM-PON Protection Schemes
The TDM branch network involves \(DF_{A}\) representing the length of distribution fibers (DF + DDF), the optical power splitter/combiner, and the \(ONU\) units connected to the ring through the appropriate AWG element [27]. A calculation of the network availability for RING protection scenarios includes the FF redundancy and thus a parameter of the fiber restoration time is also involved. Moreover, this calculation is affected also by a number of particular optical components considered at the ring architecture design.
# Multi-Grained Similarity Preserving and Updating for Unsupervised Cross-Modal Hashing
## 1 Introduction
To face the second challenge, we capture the multi-grained similarity structure information from global and local view to preserve similarity consistency better. For global view, we use graph convolutional network (GCN for short) to aggregate the similarity structure information from the neighbors of each instance to enrich the coarse-grained similarity information. For local view, cross-modal attention mechanisms are used to perform cross-modal interaction to enhance similarity learning between instance pairs. Furthermore, we use a similarity consistency reconstruction method to ensure the similarity consistency of hash codes. Figure 1: The framework of MGSPU. It comprises four main modules: feature extraction, semantic similarity matrix updating, hash code learning and hash function learning module. **Contributions. ** In summary, the contributions of this paper can be summarized as follows:  
* We propose an effective unsupervised learning framework, called Multi-Grained Similarity Preserving and Updating, which learns high-quality hash codes by comprehensively improving cross-modal similarity learning. * We propose a novel semantic similarity matrix updating strategy to effectively remove noises in the original similarity matrix, which produces high-confidence supervisory signal for cross-modal hashing learning. * We propose a novel multi-grained similarity preserving method to enhance similarity consistency preserving of the cross-modal hash codes. * We conducted extensive experiments on the widely used datasets MIRFLICKR-25K and NUS-WIDE to validate the superiority of the proposed approach and evaluate the effectiveness of each component. **Roadmap. ** The paper is structured as follows: Section 2 provides a summary of related works. Section 3 outlines the problem definition and details the MGSPU approach. Section 4 presents the experimental results and analysis. Lastly, Section 5 concludes the paper.