esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine-Learning-Based Model for Hurricane Storm Surge Forecasting in the Lower Laguna Madre
## 5 Results
### Preliminary Modeling Results
#### 5.1.2 LSTM-Only Model  
The LSTM preliminary model only considers past water surface elevations for its prediction and ignores weather information. The month of June 2010 saw the impact of Hurricane Alex in Mexico. The extent of the storm affected the water surface elevation of the Laguna Madre, which can be seen in the peak of the blue line in Figure 6.  
It can be seen from the prediction that when the storm hits, the model tries to replicate what happened but is delayed in its prediction. This is because the model only considers past elevations to construct its prediction, and since storm surges are produced by weather fluctuations, the model has no information to anticipate the surge. Subsequently, the model tries to continue the surge but cannot estimate it accurately.  
Even in storm surge conditions, the LSTM architecture outperforms the CNN model in its overall score but fails to detect the storm as it hits and can only produce a delayed response.
## 1 Introduction
If the material parameters are known, this is certainly beneficial for the simulation of the curing process and the development of the shrinkage. This however is not necessarily applicable when the temperature of the epoxy is rising due to the curing heat if the latter cannot be transported away fast enough. This may be the case, e.g., due to a small surface-to-volume ratio or if a resin is used that releases a high amount of heat during the hardening process. Optical Backscatter Reflectometry (OBR) is, similar to fiber Bragg gratings, a strain measurement method that utilizes optical fibers. OBR does not suffer from the above mentioned disadvantages and was the measurement method of choice for the work presented here. It should be mentioned, however, that OBR has two main disadvantages. Firstly, an OBR measurement takes some seconds. Thus, e.g., high frequency measurements such as fatigue tests cannot be monitored continuously during the test and need to be stopped temporarily to perform an OBR measurement [16]. A second disadvantage of the OBR is that the instrument is significantly more expensive than the instruments used with fiber Bragg gratings. The cost is about 5-10 times higher, but the commercial prices tend to vary with time. The cost disadvantage of the OBR is somewhat compensated by using cheap regular optical fibers as sensors instead of more costly fiber Bragg gratings. In this work, we present for the first time investigations regarding the cure shrinkage of large amounts of pure epoxy utilizing OBR. Purposely, the samples were not temperature controlled since temperature gradients, e.g., at walls, may lead to different cure behaviors (time-wise) and thus lead to strain fields which are the subject of those investigations.
# When 5G Meets Deep Learning: A Systematic Review
## 3 Results
### What are the Main Problems Deep Learning Is Being Used to Solve?
#### 3.1.10 Application Characterization  
In cellular networks, self-organizing networks (SON) is a technology designed to plan, deploy, operate, and optimize mobile radio access networks in a simple, fast, and automated way. SON is a key technology for future cellular networks due to the potential of saving capital expenditure (CAPEX) and operational expenditure (OPEX). However, SON is not only about network performance but also QoS. A better planning of network resources can be translated into a better service quality and increasing revenues.  
The authors in [66; 67] presented a framework for self-optimization in 5G networks called APP-SON. It was designed to optimize some target network key performance indicators (KPIs) based on the mobile applications characteristics, by identifying similar application features and creating clusters using the Hungarian Algorithm Assisted Clustering (HAAC). The homogeneous application characteristics of cells in a cluster are identified to prioritize target network KPIs in order to improve user quality of experience (QoE). This is achieved through cell engineering parameters adjustments. The deep learning model was used to establish cause effect between the cell engineering parameters and the network KPIs. For instance, Video application KPIs can be used to detect that this type of traffic occupies more than 90% of the total traffic, and thus adjust the cell engineering parameters to give priority to video traffic.
# A Microservice-Based Smart Agriculture System to Detect Animal Intrusion at the Edge
## 3 Background
### Fog Computing  
#### 3.3.1 Containerization  
A software deployment process that bundles the application's code with all the files and libraries the application needs to run on any infrastructure [25]. By virtualizing the operating system kernel, this technology enables user-space software instances to be divided into multiple independent units that run in the kernel as opposed to a single instance. This particular software instance is referred to as a **container**, a software package that provides the complete runtime environment for an application. With containerization, people can create individual packages or containers that can run on all types of devices and operating systems. Containerization is lightweight, portable, scalable, fault-tolerant, agile, and saves hardware resources.
## 4 Example Analysis
### The Time-Varying Performance Function of the In-Service Transmission Tower
#### 4.2.3 Resistance Degradation Model of the In-Service Transmission  
On the time-varying performance of the in-service transmission towers, the influence of the degradation of yield strength of steel members is considered in this paper, and the resistance \(R(t)\) in Equation (1) is taken as yield strength \(f_{y}(t)\) here. Therefore, according to the relationship between the \(f_{y}(t)\) of corroded steel and the corrosion damage factor \(w_{s}(t)\) suggested in Ref. [38], the Equation (1) can be rewritten as  
\[f_{y}(t)=f_{y0}\xi(t)=f_{y0}(1-0.9852w_{s}(t)) \tag{22a}\] \[w_{s}(t)=\left\{\begin{array}{l}L_{0}\alpha K(1-p)t/A_{0},t<t_{L}\\ L_{0}\alpha K[(1-p)t_{L}+t-t_{L}]/A_{0},t\geq t_{L}\end{array}\right. \tag{22b}\]  
where \(f_{y0}\) is the initial yield strength of steel members, \(L_{0}\) and \(A_{0}\) denote the initial perimeter and initial area of steel members, respectively, \(t_{L}\) is the anti-corrosion life of different anti-corrosion strategies, and is taken as 10 years here, \(\alpha\) is the distinguishing coefficient between ordinary steel and weathering steel with the value of 0.7, \(K\) is the average corrosion rate of steel members on one side with the value of 0.025 mm/year, and \(p\) is the protection efficiency and is taken as 95% here.
## 2 Materials and Methods
### Procedure  
Each patient's maxilla and mandible incisors, canines and premolars were subjected to the vital tooth whitening procedures. Two types of splint were made: first one was used to position the spectrophotometer used to quantify color modification (Easyshade V, Vita, Bad Sackingen, Germany) in maxilla central incisors and maxilla and mandible canines [16]; the second one was used to carry out home tooth whitening of incisors, canines and premolars in both arches (customized soft scalloped bleaching trays with 1 mm thick reservoir) [17; 18]. The in-office treatment phase began with the removal of plaque and white spots from the tooth surfaces using a nylon brush with a low-speed rotating instrument and non-abrasive cleaning paste. Anterior and premolar teeth were isolated with a double arch rubber dam (OptiDam, Kerr, Bioggio, Switzerland), with the edges of the perforations inverted in the gingival sulcus and silk ligatures on the tooth necks [19]. The product used for bleaching was a chemoactivated bleaching agent whose active ingredient was hydrogen peroxide at a concentration of 40% (Boost, Ultradent, South Jordan, USA); the product was delivered using a double syringe and, when mixed, acquired a (red-colored) gel consistency. Patients in Group 1S underwent a single in-office tooth whitening session. They received two 20-minute applications of bleaching agent on the vestibular and lingual teeth faces. Patients in Group 2S underwent two whitening sessions performed in two consecutive weeks, receiving two applications of the same bleaching agent every session in the same way as Group 1S. After each application, the bleaching agent was eliminated by aspiration, washed with water and air dried. After the second application, the cervical ligatures and dam were removed and the patient was instructed to follow oral hygienic and dietary standards and not to smoke until one week after home treatment ended [20]. The home treatment phase began seven days after the in-office treatment and after verifying that no complications had occurred. The home treatment was explained in detail to patients; patients were given a demonstration of how to load the trays with the bleaching agent and how to fit it into the mouth. Patients were also given written instructions (Table 1), including a description of the procedure (application of the whitening tray loaded with the bleaching agent and left in place for 90 minutes per day) and the schedule of follow-up check-ups. Patients were informed that the home whitening procedure had to be repeated daily until tooth color stabilization. The bleaching used was a transparent 16% carbamide peroxide gel (Opalescence PF 16, Ultradent) delivered with dispensing syringes. Intraoral digital photographs were taken before the start of treatment, one week after the in-office whitening sessions and at the end of the home whitening phase [21; 22]. Color modifications to six teeth were recorded (maxilla central incisors, maxilla and mandible canines) by means of the Easyshade spectrophotometer (Figure 3) used with the previously fabricated customized positioning splints in
## 3 Results
### Users' Comments  
In the questionnaire commentary section, most of the participants mentioned the M condition as \"fast\" and \"convenient\". Participants typically mentioned that during this condition, \"you are not distracted by pronouncing words\" and \"all attention is focused on the screen\".  
For the condition T, 12 participants mentioned, as its main disadvantage, the inconvenience associated with the need to \"constantly repeat\" the same word. Six participants even mentioned that they (at least once) lost count, due to this problem.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **N** & **T** & **F** \\ \hline
**M** & \(0.11\) & \(0.00015\) * & \(0.00026\) * \\
**N** & & \(0.00083\) * & \(0.11\) \\
**T** & & & \(0.29\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(p\)-values obtained in a post-hoc Tukey test for individual convenience scores given to different selection confirmation methods (* \(p<0.05\)). See the caption of Figure 1 for the description of the confirmation methods.  
Figure 2: Participants’ scores for different selection confirmation methods (\(N=23\), M \(\pm\) 95% CI): M—confirmation by pressing the left mouse button, N—confirmation by pronouncing the circle number, T—confirmation by pronouncing the word “ty” (”you” in Russian), F—confirmation by pronouncing the word chosen by the participant himself.  
The condition N was rated as the most convenient among the vocalization conditions by 17 participants. In the comments, some explained that in this condition, \"you concentrate better on the object\" and that the pronunciation of the number \"helps to complete the task and do not lose count\".  
For the condition F, eight participants noted that they liked to choose their \"own\" confirmation sound. However, as in the T condition, quite many (also eight) participants noted that they were \"tired of repeating the same word\".  
Seven participants reported that they felt the conditions with vocalization \"working slower\" than the condition with the manual confirmation. We could not determine if this was related to the actual interface latency, due to the ring buffer used, or the impression arose, due to cognitive factors. Cases when the selection did not trigger immediately after confirmation action caused frustration (especially with the voice confirmation, during these issues, participants had to repeat the same word again), which was pointed in the commentary section. Four participants did not notice any delays in manual confirmation at all.
# Atmospheric Effects on Satellite-Ground Free Space Uplink and Downlink Optical Transmissions
## 2 Uplink and Downlink Channels in Satellite-Ground FSO Communications
### Angle-of-Arrival Fluctuations  
In FSO communications channels, the wavefront of a laser beam arriving at the receiver is distorted because of atmospheric turbulence. This phenomenon causes image motion in the focal plane, which is also known as angle-of-arrival fluctuation. For a plane wave, the angle-of-arrival fluctuation is given as follows [2]:  
\[\sigma_{\text{P}}^{2}=2.914\left[\int_{\text{h}_{0}}^{\text{H}}\text{C}_{n}^{ 2}(\text{h})\text{dh}\right]\text{D}^{-\frac{1}{3}}\sec(\theta) \tag{15}\]  
The above equation is further simplified [16]:  
\[\sigma_{\beta}^{2}=0.182\left(\frac{\lambda}{\text{D}}\right)^{2}\left(\frac {\text{D}}{\text{r}_{0}}\right)^{\frac{5}{3}} \tag{16}\]  
where D is the diameter of the receiver aperture and r\({}_{0}\) is the coherence length. The angle-of-arrival fluctuation leads to a loss of spatial coherence in the optical beam [7]. Because of the loss of spatial coherence, laser beams can only be focused to a limited extent. This results in significant reductions in the power level of optical communication systems. When the size of the receiver aperture is increased, relatively fast fluctuations caused by small-sized eddies are averaged out. This helps reduce channel fading on a downlink FSO channel. Hence, the size of the receiver diameter should be considered when designing FSO channel links.
## 2 Methods/Experimental Approach
### Power Consumption Model
#### 2.1.3 The Transceiver Energy Consumption Model
The energy consumption during the sleep period can be modelled as  
\[E_{Sleep}=P_{Sleep}Expected[T_{sleep}] \tag{17}\]where \(P_{Sleep}\) is the transceiver's power consumption during sleep mode which can be computed from the current consumption obtained from the transceiver's manual, and the nominal battery voltage and \(Expected[T_{sleep}]\) is the expected (mean) sleep time duration which can be modelled as  
\[E_{Transitions}=\sum_{i=1}^{n}N_{st}(j)E_{st}(j) \tag{9}\]0  
where \(pdf(T_{sleep})\) is the probability density function of the sleep time which can be modelled as  
\[E_{Transitions}=\sum_{i=1}^{n}N_{st}(j)E_{st}(j) \tag{9}\]1  
It is also important to highlight that a significant quantity of energy is consumed in the idle state of the NB-IoT node's radio operation. This energy cost for a node in idle mode is modeled as approximately equivalent to the energy cost in the receive mode. Therefore, protocols that assume that the receiving and idle power are of little consequence are not efficient for NB-IoT networks. The energy cost during idle listening due to the time spent listening while waiting to receive packets is quite significant. During the idle state, the transceiver can be considered to be just listening to the channel noise (no actual information being received or transmitted), and therefore, consumes \(P_{Idle}\) amount of power for a normally distributed random time when there is no significant activity of the radio \(T_{Idle}\). The energy consumption of the transceiver during idle state can then be modeled as  
\[E_{Transitions}=\sum_{i=1}^{n}N_{st}(j)E_{st}(j) \tag{9}\]2
# Efficient Transmit Antenna Subset Selection for Multiuser Space-Time Line Code Systems
## 4 Complexity Analysis
### Complexity of Proposed MMSE-Based Decremental TAS Selection Algorithm  
From Algorithm 4 with (46) in line 3, the numbers of RMs and RSs for the proposed MMSE-based decremental TAS selection algorithm can be obtained as Line 3: RMs and RSs can be computed in a similar manner used in line 9 of Section 4.1. Line 7:  
* RM in \(\hat{\mathbf{\Theta}}_{k,q}=\hat{\mathbf{V}}_{N_{T}-k+1}\hat{\mathbf{H}}_{k,q} \Rightarrow 32K^{2}\),
* RS in \(\hat{\mathbf{\Theta}}_{k,q}=\hat{\mathbf{V}}_{N_{T}-k+1}\hat{\mathbf{H}}_{k,q} \Rightarrow 32K^{2}-8K\),
* Line 8: RMs and RSs are the same as those, except for RS in \(\hat{\mathbf{V}}_{k-1}-\psi\), in line 7 of Section 4.2. Line 11: RSs are evaluated as in line 7 of Section 4.1. Thus, the overall complexities of the proposed MMSE-based decremental TAS selection algorithm (called proposed MMSE incremental) in terms of RMs and RSs, respectively, are given as  
\[N_{\text{proposed}-\text{MMSE}-decremental}^{RM}=16K^{3}+K^{2}\big{(}24N_{T}^{2}+40 N_{T}+24-24N_{S}^{2}-24N_{S}\big{)} \tag{55}\] \[+K\big{(}36N_{T}^{2}+44N_{T}-36N_{S}^{2}-36N_{S}\big{)}+20\big{(}N _{T}^{2}+N_{T}-N_{S}^{2}-N_{S}\big{)}\] (56) \[N_{\text{proposed}-\text{MMSE}-decremental}^{RS}=16K^{3}+K^{2}\big{(}3 0N_{T}^{2}+46N_{T}+4-30N_{S}^{2}-30N_{S}\big{)}\] \[+K\big{(}27N_{T}^{2}+39N_{T}-27N_{S}^{2}-31N_{S}-2\big{)}+10\big{(} N_{T}^{2}+N_{T}-N_{S}^{2}-N_{S}\big{)}\]
## 5 Experiments
### CPU Usage and CPU Power Consumption  
A set of tests was carried out to compare the CPU usage and the energy consumption for PoW and PoA. For such a purpose, a blockchain network was deployed with 24 nodes, three of them acting as validators/sealers. Such an amount of nodes was selected to recreate a realistic scenario for the developed DApp.  
These tests considered two scenarios. On one hand, the first one was aimed at evaluating the network resource consumption at idle state (i.e., with no transaction load, just block mining/sealing). On the other hand, the second scenario put under stress the network by launching different amounts of transactions (between 100 to 1000). CPU usage was measured using the Geth built-in metrics. Note that Geth measures CPU usage as the percentage of use of one processing core (i.e., 100% means that one processing core is fully dedicated). The electrical consumption was measured using HWiNFO [110], a well-known software that provides real-time CPU information. More precisely, the metric recorded by this tool was \"CPU Package Power\", which provides the total energy consumed by a CPU.  
Figure 14: Transactions per second of the network with PoW and PoA.  
Figure 15: Transactions per second for four different gas limits using PoA.  
Figure 16 shows the CPU usage for the first scenario (i.e., under no load). As it can be observed, even in idle PoW clearly requires much more CPU usage than PoA. However, such a difference between PoW and PoA is narrower in terms of power consumption, as it is shown in Figure 17, although, overall, PoA has a lower CPU power consumption.  
When the network is put under stress, the obtained results differ, as it is shown in Figures 18 and 19. As it can be observed, PoA CPU usage is clearly lower than for PoW (specifically, PoW average CPU usage is 66% higher than for PoA). However, during very specific high-transaction load peaks, power consumption is higher for PoA than for PoW. In fact, PoW average power consumption is only 23% higher than for PoA due to such peaks. In any case, it is important to take such a power consumption percentage with caution, since CPU power measurements are not performed in an isolated way, so other processes can be executed in parallel by the CPU (e.g., by the operating system) and therefore can impact the obtained results.
# Enhanced QoS Routing Protocol for an Unmanned Ground Vehicle, Based on the ACO Approach
## 3 Result and Discussion
### The Experimental Setting  
In these simulation studies, we use the VANET simulator (firefighting UGV), as indicated in Table 2. The simulation area is 5000 m \(\times\) 5000 m, with 57 crossroads and 91 road segments, and the cars drive at speeds ranging from 10 to 20 m/s. The vehicle spacing density is 0.01-0.04 automobiles per meter, and the vehicle safety time is 2 s (s). As the mobility model, the intelligent unmanned ground vehicles model with intersection management is employed, and the vehicles approaching the junctions are set to obey the strategy that says \"first arrive, first go through, and turn right\". Furthermore, the starting points also the tracking routes of the automobiles set at random; Omnt++ has been utilized as the simulator to produce 250 throughput (TH) sessions stochastically. To reduce the random effects for the UGV, and we perform each simulation 50 times to obtain the adequate confidence intervals (CI).
# Survivable Deployments of Optical Sensor Networks against Multiple Failures and Disasters: A Survey
## 2 Existing Survivability Technologies in OSNs
### Pre-Configured Protection Schemes
#### 2.3.2 The Photonic Millimeter-Wave Bridge Scheme  
Being similar to the \(1+1\) protection scheme, the photonic millimeter-wave bridge scheme uses millimeter-wave technology to recover the interrupted traffic, especially in passive optical networks (PONs). Since the topological type of PON is a star structure, it is impossible to recover the interrupted traffic merely by relying on the PON itself. A millimeter-wave wireless link transmits data at high speed from one point to another point. It serves as a bridge and represents the seamless and transparent integration of the wireless link into optical networks. In Figure 4, when a failure occurs at the fiber link, a millimeter-wave wireless link is established to recover the interrupted traffic.  
The photonic millimeter-wave bridge scheme has been widely studied. In [73], the authors proposed millimeter-wave bridge-based RF frequency doubling to overcome accidental fiber cuts in optical access networks with lower high-frequency devices less sensitive to fiber chromatic dispersion. In [74], record-speed wireless data bridging at 220 GHz was demonstrated with a 20 Gbit/s non-return-to-zero (OOK) or a 9 Gbit/s orthogonal frequency division multiplexing (OFDM) signal. In [75], the authors focused on a resilient optical and MMW wireless transmission system for a novel span protection scheme in disaster recovery; the key enabling technologies of optical and wireless sub-systems and devices were shown. In [76], the authors presented an end-to-end intermediate frequency-over-fiber-based fiber-wireless bridge system, which can be applied to applications that require high agility and mobility. In [77], the authors demonstrated novel digital radio-over-fiber architecture able to transport multiple compressed digitized RF services using both optical fiber and wireless millimeter-wave links and which shows 3-times higher efficiency than a common public radio interfaces. In [78], the author showed that transparent waveform transfer can make seamless integration of wireless and wired links, which can provide resilient and low-latency networks.