esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Group II  
#### 3.2.1 V1--Baseline  
The baseline assessment of the USS score showed an average of 29.80 \(\pm\) 5.57 (95% CI: 27.72). The VAS score of recorded pain was 6.03 \(\pm\) 1.73 (95% CI: 5.39-6.68). The number of lesions and mean size measured in millimeters were 2.87 \(\pm\) 1.31 (95% CI: 2.48-3.35) and 5.7 \(\pm\) 2.3 (95% CI: 4.87-6.60), respectively (Table 6).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **USS Score** & **Number** & **Size** & **Duration [*]** & **Free Period [*]** & **Pain** & **Site** & **Temperature** \\ \hline
**Mean** & 29.80 & 2.867 & 5.733 & 3.567 & 6.433 & 6.033 & 5.167 & 37.39 \\ \hline
**Std. deviation** & 5.567 & 1.306 & 2.318 & 1.040 & 1.888 & 1.732 & 2.230 & 1.736 \\ \hline
**Std. error of mean** & 1.016 & 0.2384 & 0.4233 & 0.1899 & 0.3447 & 0.3162 & 0.4071 & 0.3170 \\ \hline
**Lower 95\% CI of mean** & 27.72 & 2.379 & 4.868 & 3.178 & 5.728 & 5.387 & 4.334 & 36.75 \\ \hline
**Upper 95\% CI of mean** & 31.88 & 3.354 & 6.599 & 3.955 & 7.138 & 6.680 & 5.999 & 38.04 \\ \hline \end{tabular}
\end{table}
Table 6: Summary of the descriptive statistics of the outcome parameters or group II evaluated at the V1 timepoint [* last reported presentation].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **USS Score** & **Number** & **Size** & **Duration [***]** & **Free Period [***]** & **Pain** & **Site** & **Temperature** \\ \hline
**Mean** & 28.17 & 2.464 & 4.333 & 2.000 & 10.00 & 4.833 & 4.533 & 37.12 \\ \hline
**Std. deviation** & 4.488 & 1.279 & 1.807 & 0.000 & 0.000 & 1.341 & 2.193 & 1.157 \\ \hline
**Std. error of mean** & 0.8194 & 0.2336 & 0.3299 & 0.000 & 0.000 & 0.2449 & 0.4004 & 0.2112 \\ \hline
**Lower 95\% CI of mean** & 26.49 & 1.989 & 3.659 & 2.000 & 10.00 & 4.333 & 3.714 & 36.68 \\ \hline
**Upper 95\% CI of mean** & 29.84 & 2.944 & 5.008 & 2.000 & 10.00 & 5.334 & 5.352 & 37.55 \\ \hline \end{tabular}
\end{table}
Table 4: Summary of the descriptive statistics of the outcome parameters or group I evaluated at the V2 timepoint [** from the protocol inclusion].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **USS Score** & **Number** & **Size** & **Duration [***]** & **Free Period [***]** & **Pain** & **Site** & **Temperature** \\ \hline
**Mean** & 19.30 & 0.8333 & 1.300 & 3.783 & 9.783 & 2.000 & 1.600 & 36.70 \\ \hline
**Std. deviation** & 5.820 & 0.8743 & 1.368 & 0.3130 & 0.3130 & 1.762 & 1.868 & 0.7504 \\ \hline
**Std. error of mean** & 1.063 & 0.1596 & 0.2498 & 0.05715 & 0.05715 & 0.3216 & 0.3411 & 0.1370 \\ \hline
**Lower 95\% CI of mean** & 17.13 & 0.5069 & 0.7890 & 3.666 & 9.666 & 1.342 & 0.9025 & 36.42 \\ \hline
**Upper 95\% CI of mean** & 21.47 & 1.160 & 1.811 & 3.900 & 9.900 & 2.658 & 2.298 & 36.98 \\ \hline \end{tabular}
\end{table}
Table 5: Summary of the descriptive statistics of the outcome parameters or group I evaluated at the V3 timepoint. [** from the protocol inclusion].
## 5 Simulations Setup and Results
### Simulation Parameters  
Our simulation parameters were closer to a real cloud environment. In contrast with the methods we have compared our results to, the most developed testbed was the one used by SPS (it consisted of four computers with 2.8 GHz i5-760 Intel processors and 4 GB of RAM). These platforms are considered very limited compared to what the cloud can offer nowadays and to our testing platform. To give an example, we have used 1800 Virtual Machines, 64 servers, 132 hosts (every host: 16 cores, 256 GB). Thus, our testbed is far more resourceful than the one used by the other methods.
# Minimizing Energy and Computation in Long-Running Software
###### Abstract  
Long-running software may operate on hardware platforms with limited energy resources such as batteries or photovoltaic, or on high-performance platforms that consume a large amount of energy. Since such systems may be subject to hardware failures, checkpointing is often used to assure the reliability of the application. Since checkpointing introduces additional computation time and energy consumption, we study how checkpoint intervals need to be selected so as to minimize a cost function that includes the execution time and the energy. Expressions for both the program's energy consumption and execution time are derived as a function of the failure probability per instruction. A first principle based analysis yields the checkpoint interval that minimizes a linear combination of the average energy consumption and execution time of the program, in terms of the classical \"Lambert function\". The sensitivity of the checkpoint to the importance attributed to energy consumption is also derived. The results are illustrated with numerical examples regarding programs of various lengths and showing the relation between the checkpoint interval that minimizes energy consumption and execution time, and the one that minimizes a weighted sum of the two. In addition, our results are applied to a popular software benchmark, and posted on a publicly accessible web site, together with the optimization software that we have developed.  
Keywords:optimum energy savings; minimum execution time; checkpoint interval; analytical models +
Footnote †: journal: Applied Sciences
## 4 III-V-on-Silicon Devices for Optical Communication Applications
### III-V-on-Silicon Laser Sources for Optical Communication Applications  
#### 4.1.1 1550 nm Semiconductor Optical Amplifiers Integrated on Silicon Photonic Integrated Circuits  
Optical amplifiers form an essential part of most photonic circuits in order to compensate for losses [16]. Amplifiers can be used in receivers, boosting the signal before being detected which increases sensitivity [17], but are also used in more complex photonic circuits [18]. The III-V-on-silicon platform offers a large design space for semiconductor optical amplifiers (SOAs), as the optical mode distribution over the III-V and silicon device layer can be tailored by design. This allows optimization of the gain and saturation power of the amplifier. Amplifiers are generally optimized either for high output power or for low power consumption and small footprint [19]. A compact and low injection current III-V-on-silicon optical amplifier has been realized in which the optical mode is predominantly confined to the III-V waveguide [20]. 18 dB small signal gain with low power consumption was demonstrated. The output power in the silicon waveguide as a function of the waveguide-coupled input power at several wavelengths is shown in Figure 5a for an injection current of 100 mA. We see that the output power saturates at 10 dBm. The gain saturation is shown in Figure 5b. The noise figure at 1550 nm reaches 7 dB for low optical input power levels at the highest amplifier drive current of 100 mA, as shown in Figure 5c. Gain-clamped optical amplifiers were also demonstrated. In this case a distributed feedback grating is implemented underneath the optical amplifier (see Section 4.1.2) for the device to lase away from the gain peak of the III-V active region. The gain as a function of optical output power is shown in Figure 5d. The gain is substantially lower than for the classical configuration, however it remains flat over a wider range of optical output power compared to the classical SOA.
# Inertial Sensors-Based Assessment of Human Breathing Pattern: A Systematic Literature Review
## 2 Methodology
### Data Extraction and Categorization
#### 2.4.2 Devices and Sensor Setup  
A total of 26 different types of devices were used to analyze BP. The most common manufacturer was STMicroelectronics International N.V., Amsterdam, The Netherlands, with devices used in five studies [50; 51; 52; 59; 66], followed by NXP Semiconductors N.V., Eindhoven, The Netherlands, with devices used in four studies [54; 61; 62; 65]; Analog Devices, Inc., Norwood, MA, USA, with devices used in four studies [49; 58; 62; 65]; InvenSense, Inc., San Jose, CA, USA, with devices used in three studies [62; 63; 68]; and SparkFun Electronics, Niwot, CO, USA, with devices used in two studies [60; 67]. Other manufactures were referenced with devices used only in one study each: a Delsys Trigno(tm) [48], LG Nexus 5 [69], MC10, Inc./Medidata BioStamp nPoint(r) [55], Biocubica Srl Soundi [56], ZurichMOVE 9-axis IMU [57], Honeywell HMC1052 dual-axis magnetometer [65], Xsens/Movella MT9-B IMU [53], and BioPac Systems strain gauge transducer [49]. In 1 study, only the sensor type was reported, omitting the manufacturer or device [64], and in 12 studies, the authors had combined more than one sensor to collect and analyze BP data [49; 53; 55; 56; 59; 60; 62; 63; 64; 65; 67; 69]. The sampling rates varied from 10 Hz to 2000 Hz with the most common frequencies, 50 Hz and 100 Hz, being used in 9 studies [50; 51; 52; 56; 57; 61; 63; 67; 69].  
While there are three distinct anatomical regions mentioned--the anterior and posterior regions of the thorax, as well as the abdomen--the precise placement of the devices differs within each of these regions. The anterior region of the thorax was the most reported, being present in 14 studies, of which 6 mentioned the ribs as the fixation site (e.g., 2 nd, 4 th, 5 th, 7 th, 10 th, precordial region [48; 54; 56; 58; 62; 63]), and of which 9 mentioned the sternum (e.g., body of the sternum, suprasternal notch, manubrium, above xiphod process [49; 50; 51; 52; 57; 58; 61; 68; 69]). The abdominal region was reported in 10 studies (e.g., bellow the ribs, linea alba, umbilical region [48; 50; 52; 55; 58; 59; 61; 63; 64; 66]), and the posterior region of the thorax was reported in only 2 studies (e.g., T2, T4, T5, T10 [48; 68]). There were also 5 studies that did not specify an exact location, indicating only the thorax or chest for the fixation of the devices [55; 59; 60; 64; 66]; furthermore, 2 studies indicated a body region not related to respiratory movements or a stationary object [59; 66]. The number of devices used ranged from 1 to 14, with 1 being the most commonly used total of devices reported in 11 studies [49; 51; 53; 56; 57; 60; 62; 65; 67; 69].
## 5 Results and Discussion
### The ResQhot 2.0 Assembly
#### 5.2.3 Stretcher Bed Sliding Module  
The bed's sliding module is designed to allow the stretcher bed to slide down (loading configuration) and slide up (compact transport configuration) during the casualty extraction process (see Figure 6). This module consists of two main mechanisms: The rail mechanism (see Figure 11c) and the lead-screw mechanism (see Figure 11d).  
The sliding rail mechanism holds the stretcher bed on the fixed frame and allows it to slide along one axis. The mechanism bears the maximum load of the bed. Eight pairs of chrome steel metal dual V-wheels are used for the roller mechanism that holds the aluminium V-slot extrusion (i.e., the rail). This mechanism is recommended for accurate linear motion and has a high load capacity [63]. We installed four pairs of V-wheels on each side of the bed (eight pairs in total) to support a maximum load of 120 kg, including the casualty's weight.  
To drive the sliding module, the bed's sliding-rail mechanism is connected to a lead-screw mechanism driven by a motor with a modular belt-pulley transmission system that allows the bed to slide up and down in order to load a casualty. During the design process, we considered several different mechanisms for this module: A ball screw, rack and pinion, lead screw, and winch were considered. Insights from the literature and expert opinions were obtained in order to evaluate the advantages and disadvantages of the different options, particularly with regards to cost-effectiveness, reliability and size, and the lead-screw mechanism was considered to be the most suitable. Even though it is not the most efficient in terms of friction, it allows for precise movement control, easy assembly, and is the most cost-effective solution, given budget constraints. Obviously, precise movement control is a critical parameter in the ResQbot 2.0 design specification since it is directly linked to safety.
## 1 Introduction
Considering the need to develop technology in the area of SDWSNs, which optimizes the delays between the controller and the nodes due to the processing delay in the node, the use of the IEEE 802.15.4 protocol is proposed, without the use of network and transport to transport information that allows the controller to calculate the default route of each node and configure this parameter for the nodes remotely. [PERSON] [13] and [PERSON] [14] algorithms define the route between the sensor node and the gateway and, therefore, the default node to which the sensor node should transmit data. In this context, an algorithmic solution is proposed that allows the transmission of information between the controller and the sensor nodes using the 802.15.4 protocol as a data transport protocol so that the controller finds the optimal tree-type topology and defines the best route between the sensor node and the gateway. In summary, this work has the following features:  
* Use of an SDN-enabled controller that reduces the processing delay in the node. * The best route selection is based on node battery level and distance between nodes. * Use of IEEE 802.15.4 protocol for data transport between the sensor node and the controller
* Proposal of an SDWSN architecture without network-level protocols.
# Mutual-Information-Based Incremental Relay Communications for Wireless Biomedical Implant Systems
## 4 The Proposed Protocol
### Transmission Route Selection  
#### 4.2.1 Transmission Route Selection  
The communication flow of the proposed protocol is given in Figure 2. Since the relay nodes and the coordinator are attached to clothes, the implanted sensor is able to transmit the sensed data either to the relay node or the coordinator directly. As demonstrated in Equation (1), longer communication distances suffer higher energy consumption. Thus, if the transmission implant sensor \(S\) is closer to \(R\), it utilizes the cooperative transmission scheme. The transmission scheme selection function \(c(i)\) is given as  
\[d(i)=\left\{\begin{array}{ll}d_{SR_{i}}>d_{R_{i}D},&\text{direct transmission}\\ d_{SR_{i}}\leq d_{R_{i}D},&\text{relay cooperative transmission}\end{array}\right. \tag{11}\]  
where when \(d_{SR_{i}}>d_{R_{i}D}\), the transmission link is established directly between the implanted sensor and the coordinator. The coordinator allocates time division multiple access (TDMA)-based time slots to the in-body sensors to avoid data collision [19]. A series of QoS metrics will be calculated during the data transmission and then a new round will start once the transmission is finished. Otherwise, the communication link is created between the implant sensor and the corresponding relay node. Detailed information on the relaying transmission approach is illustrated in Section 3.2 to Section 3.4.  
Figure 2: The communication flow of the proposed protocol.
## 2 Mmwave 8 \(\times\) 8 AiP Array Platform
### Aip Array Platform Experimental Validation
#### 2.2.2. Aip Beamforming Performance Validation  
The measurement of beamforming patterns of the AiP is also carried out in the CATR, and the block diagram of the AiP beamforming pattern measurement setup is depicted in Figure 6. Similar to the calibration measurement system setup, the operating mode of the AiP is set to Rx and is placed on the CATR turntable. The AiP performs beamforming in the horizontal plane and AiP output power values are measured by the VNA.  
The beamforming patterns measured for the AiP with beam-steering angle of \(0^{\circ}\), before and after calibration are shown in Figure 7. The pattern curves were normalised for a better comparison. As can be seen from the figure, the main beam of the beamforming pattern is oriented to \(0^{\circ}\), and the main beam is about 10 dB above the first side lobes. Also the calibrated AiP beamforming pattern is more symmetrical and the nulls are deeper compared to that without calibration. In addition, the AiP gain is increased by 9.82 dB. Therefore, when the AiP is calibrated, it is able to perform effective beamforming. Additionally, the calibrated AiP beamforming patterns at different angles are presented in Figure 8, where the results are normalized as well. It is shown that the peak directions of the patterns varies with the beam-steering angles, which proves that the AiP beamforming performance.  
Figure 5: Chips average temperature measurements.  
Figure 6: Block diagram of the AiP beamforming pattern measurement setup.
# Combined Investigation of Indoor Climate Parameters and Energy Performance of a Winery
Thus, the scientific interest focuses on different types of buildings, addressing also IAQ improvement interventions, through ventilation, filtering or the limitation of specific activities the burden indoor air; indicatively, investigations on hospitals [19], schools [20], offices [20; 21], athletic centers, namely aquatic [22], but also residences [23], are reported. Relevant, though limited, investigations for agricultural buildings can be detected [24], also addressing biological factors [25]. To the best of our knowledge there has not been published any study for indoor air quality of winery building. The presented work aims to combine an investigation on both indoor climate and energy issues. An extensive measurement campaign was implemented, regarding indoor climate thermal parameters, as well as concentration of substances (CO\({}_{2}\), VOCs, NO\({}_{2}\)) affecting IAQ. The measurements were exploited for the assessment of indoor climate, as well as for the determination of thermal parameters necessary for the energy model developed. It should be noted that most studies investigating wineries energy performance are based on actual consumption or statistical data. The indicated model provides an insight on the respective processes, while it is used in order to formulate and evaluate proposals for reducing the energy consumption of the winery. The proposals include the use of RES and, in particular, the installation of a photovoltaic array on the roof of the premises.
## 3 Results
### The Parameter Analysis for Each Cementitious Material in CCSDOT Model
#### 3.3.2 Application in the Suofengying Hydropower Station  
Figure 12 shows the comparison of the concrete's compressive strength with different doses of supplementary cementitious materials (slag and fly ash) at different ages. The content of supplementary cementitious materials varies from 20% to 60% by weight. Figure 12a-e demonstrate the maximum dosage of supplementary cementitious materials is 20%, 30%, 40%, 50%, and 60% by weight respectively. The green, blue, red, and black thick curves indicate the concrete at 180, 90, 28, and 7 days respectively. As the maximum amount of supplementary cementitious materials increases, the compressive strength of concrete with a 0% content of slag decreases; therefore, the compressive strength decreases while the amount of fly ash increases. Relatively, as the amount of supplementary cementitious materials increases, the 180-day compressive strength of concrete with a 0% content of fly ash increases. Therefore, slag is beneficial to the compressive strength of concrete in later periods.  
Figure 12: Comparison of concrete compressive strength with different doses of supplementary cementitious materials (slag and fly ash) at different ages day by day. The content of supplementary cementitious materials is 20% in (**a**), 30% in (**b**), 40% in (**c**), 50% in (**d**) and 60% in (**e**). Black cross marks are tested value.  
For each single figure, the compressive strength of concrete at a 28-day age, 90-day age, and 180-day age increases as the content of slag increases, visually. Moreover, the compressive strength increases as the curing time goes on, which is conformed to the objective pattern. We could select the optimal concrete mix proportion to meet a certain demand through the proposed CCSDOT model and these figures. The compressive strength of concrete at a certain age could also be predicted with the given concrete mix proportion easily.
# SLLB-DEVS: An Approach for DEVS Based Modeling of Semiconductor Lithography Load Balance
## 2 Methodology
### Load Balancer Policy  
Scale-out and scale-up are methodological approaches to improve the processing power of the server. Scale-out is a method of increasing the absolute number of servers, and scale-up is a method of improving each computing power through component upgrades. In a scale-out distributed environment, the load balancer always emphasizes scalability and the overspecification problem. The E-beam cluster architecture should then also consider a new load balancer design to find a compromise between s/w cost and h/w cost charged per computing node.  
* The scalability problem Most of the computation workload performs parallel processing on multiple computing nodes for fast processing. However, due to the saturation core point, performance does not increase linearly according to the number of computing nodes. Then saturation core point is easily reached. An E-beam job has the same limitations as a computation workload, and the allocation of computation nodes above the saturation core point causes an unnecessary waste of computing resources [16].
* The overspecification problem If the load balancer operates ineffectively, the architecture designer favors the system hardware power over necessary performance. In this case, there is an advantage of providing a quick response, compared to the expected response time of the user. However, as a result, the actual usage time of the system is very low compared to the total time, which incurs unnecessary costs.  
The following section analyzes each problem in the E-beam architecture and proposes a solution that can effectively utilize resources in a scaled-out environment.
# Study of Mixed-Mode Cracking of Dovetail Root of an Aero-Engine Blade Like Structure
## 5 Results and Discussion
### Effects of the Flank Angle: A Comparison  
In this section, a comparison of the results of different flank angles is given. The comparison is always performed at the 95% of the crack front. This is to avoid the solution singularity at the free surface of the crack that results from the Franc3D algorithm. The results of the short flank length are shown in Figure 22.  
Figure 21: The 45\({}^{\circ}\) angle, long flank. Stress intensity along the three crack paths selected: (**a**) KI; (**b**) KII; (**c**) KIII.  
At small crack sizes, the K\({}_{\rm I}\) solution of the 45\({}^{\circ}\) angle flank blade had an intermediate trend between the other two angle flank designs. However, the crack changed its growth direction and K\({}_{\rm I}\) trend became lower than the other flank angle designs, at a crack size bigger than 3 mm. This is thought to be related to the selected blade geometric parameters. i.e., this behavior could be specific to blade design. This, however, requires more simulations or testing to validate this assumption.  
The analogous results for the long flank design options are given in Figure 23. The trend is analogous to the one observed for the short flank option with the only difference 45\({}^{\circ}\) angle starts to be beneficial for a longer crack length (c.a. 6 mm).  
Figure 23: Comparison of the different flank angles at 95% crack front for the long flank design option: K\({}_{\rm I}\) stress intensity factor.  
Figure 22: Comparison of the different flank angles at 95% crack front for the short flank design option: KI stress intensity factor.  
The main conclusion of this comparison is that: the 45\({}^{\circ}\) angle is beneficial because it helps reduce the stress intensity factor KI when the crack approaches critical values closer to the fracture toughness.