esa-sceva/satcom-chunk-collection huggingface dataset:
# SnoreLab Application in the Assessment of Obstructive Sleep Apnea Syndrome: A Pilot Study
## 3 Results  
Thirty-four patients underwent overnight hospital PSG at the ORL department of Foggia University Hospital during the study period. Out of this cohort, 3 patients were excluded due to an inability to operate a smartphone, and 12 were excluded owing to technical artifacts observed during PSG evaluation. Consequently, 55.9% of the initial sample (\(n\) = 19; 15 men, 4 women), with an average age of 50.9 \(\pm\) 10.5 years, was included for analysis. None of the enrolled patients reported difficulties in adhering to the study protocol or utilizing the SnoreLab application.  
According to the AHI, patients were classified as affected by simple snoring (31.6%; \(n\) = 6), mild OSAS (36.8%; \(n\) = 7), moderate OSAS (10.5%; \(n\) = 2), and severe OSAS (21.1%; \(n\) = 4). The demographic characteristics of the cohort (Table 1), stratified based on the severity of OSAS, are consistent with findings in the current literature. They demonstrate a higher prevalence of the condition among male subjects and a corresponding escalation in comorbidities associated with the severity of the disease [12].  
The AHI and the percentage of sleep time with oxygen saturations lower than 90% (Tc90) obtained from PSG were compared with the app-based Snore Score (Table 2). Spearman correlation analysis revealed a positive correlation in both instances, although statistical significance was not reached (AHI: [PERSON]'s \(p\) = 0.145, \(p\) = 0.554; Tc90: [PERSON]'s \(p\) = 0.437, \(p\) = 0.07).  
The overall cohort exhibited a Snore Score of 24.8 \(\pm\) 22.2 alongside an AHI of 15.1 \(\pm\) 17.0, indicating moderate OSAS (Table 2). Interestingly, elevated Snore Scores were observed in both the simple snoring (30.7 \(\pm\) 19.2) and severe OSAS group (35.2 \(\pm\) 21.4) (Kruskal-Wallis \(p\)-value = 0.176).  
Although the cohort exhibited an overall AHI of 15.1 \(\pm\) 17.0, indicative of moderate OSAS, the average Snore Score reported was 24.8 \(\pm\) 22.2, consistent with the median score observed among SnoreLab users. Consequently, following the instructions provided in the app, this finding does not warrant immediate medical intervention. The
# Dynamic Allocation of SDN Controllers in NFV-Based MEC for the Internet of Vehicles
## 2 Related Work
### Data Center  
The work in [37] aims at minimizing the total cost due to the response time and maintenance of clusters of SDN controllers. To this, a two-phase algorithm is adopted. The goal of the first phase is to model the assignment problem as a stable matching with transfers. The second phase turns the matchings into connections between controllers and switches, which serves as input to a game theoretic strategy to achieve Nash equilibrium.  
The proposals in [42] provide load balancing among SDN controllers. They are divided into centralized and decentralized approaches. However, centralized one is not feasible to be deployed in real environments, since it has limitations inherent to its architecture: single-point of failure and system bottleneck. On the other hand, the distributed approach is scalable, stable and more suitable for real environments. Its execution is divided into two phases: distributed startup and regionally balanced migration. In the initialization phase, switches are randomly assigned to a set of controllers. The second phase uses two variables: threshold and effluence. For this purpose, the number of output flows from the switch determines the weight of a switch, whereas the sum of all the weights of all switches assigned to a controller determines its weight at the time of measurement. In this way, the threshold is calculated using a factor that gives weight to past measurements, which is lesser than the weight of the current measurement multiplied by the average weight of all controllers. The authors adopted the term \"effluence\" to represent the result of the threshold multiplied by a factor greater than 1. If the controller load is less than or equal to the threshold, the controller is defined as being available to receive switches. However,if the load is greater than the effluent then the controller is considered as overloaded and the switches must be assigned to other controllers marked as available. This work also presents how the solution would be implemented using the OpenFlow protocol.  
The authors in [43] propose a heuristic approach to achieve an approximate solution within an acceptable time duration for the positioning of VMs in a balanced way considering the occupied and available servers resources (CPU, memory, and disk), intending to maximize the service rate and the index that determines the balance between VMs. The proposed heuristic uses a packing and genetic algorithm (PGA) based on Tetris and NSGA-II.
# Password Similarity Using Probabilistic Data Structures
## 4 Experimental Analysis
Choosing a low number of functions resulted in collisions by the same function, choosing too many functions resulted in filling the buckets easily, accelerating the occurrence of false positives. Figure 7a represents the performances of the system, in terms of time required to execute a \(QInsert\) of a random string divided in bi-grams or a \(Distance\) operation. The graph shows that the system had a linear response to the length of the input. We analyzed lengths from 1 to 64 chars, which included the most common lengths of password strings [7, 20][44]. Furthermore, the plot shows that the SHA512 hashing method was clearly slower than MD5. This speed gap was due to the intrinsic complexity of the algorithm. That introduced a trade off between the security of the scheme in terms of resistance to collisions and the speed of the overall procedure. The last graphical representations in Figure 7b describe the difference between the three implemented distance measures presented in Section 3.1. The behaviour of the three functions was comparable across the spectrum of passwords lengths employed to generate the plots in Figure 7a. From the plot it is clear that [PERSON]'s method and the Cosine-based one gave more accurate results than the the Jaccard-based solution. The plot also shows the performance difference of our C implementation with a reference implementation of the CTPH scheme: ssdeep. Comparisons with the system presented in Table 1 would be infeasible due to the difference of application field (RAPPOR), or the unavailability of reference implementations and original data set used in the paper (SSDD and Schnell's solution). The data visualized in the plot confirm the fact that ssdeep was not well suited to process small inputs such as passwords. Figure 6: Performance of the filter in terms of False Positive Percentage with different sizing of \(\Gamma\) (number of hash functions employed) and \(\kappa\) (size of the internal bucket). (**a**) Performance of a Bloom Filter based on MD5 hash functions in respect to number of hash functions used. (**b**) Performance of a Bloom Filter based on SHA512 hash functions in respect to number of hash functions used. (**c**) Performance of a Bloom Filter based on MD5 hash functions in respect to size of the internal bucket. (**d**) Performance of a Bloom Filter based on SHA512 hash functions in respect to size of internal bucket.
# Deploying CPU-Intensive Applications on MEC in NFV Systems: The Immersive Video Use Case
## 2 Related Work
### The ETSI MEC Framework and Its Reference Architecture
#### 2.1.1 The ME Host  
The ME host consists of the ME platform and the virtualization infrastructure that provides compute, storage, and network resources to ME apps [8].  
The ME platform offers the functionalities required by ME apps to implement services. It is responsible for the local handling of the Domain Name System (DNS) protocol, and for enforcing the related traffic routing rules in the MEC data plane [10].  
ME apps are instantiated on the virtualization infrastructure upon a validated request originated by ME management blocks. After instantiation, they register and discover services, communicating with the ME platform through the Mp1 reference point [28]. MEC defines two other reference points related to the ME platform, namely, Mp2 and Mp3. Through Mp2, the ME platform controls the MEC data plane, while Mp3 can be used to connect different ME platforms.
## 3 Materials and Methods
### Data Collection  
Undoubtedly, there is a direct relationship between weather conditions and road surface conditions. There are different factors that influence the salting (g/m2) operation to maintain the road during winter. These factors are surface temperature (\({}^{\circ}\)C), air temperature (\({}^{\circ}\)C), dew point temperature (\({}^{\circ}\)C), level of grip, ice layer (mm), precipitation (mm), snow height (mm), freezing temperature (\({}^{\circ}\)C), maximum wind speed (m/s), conductivity (mS/cm) and the concentration (g/L) of salt on the road surface. Historical data regarding these input variables were collected by road weather stations and sensors at test site E18 measured every 10 min in February 2019. This test site is in Northern Europe, Sweden, and is exactly located between Vasteras and Enkoping [29]. In fact, the air temperature, dew point temperature, precipitation, and maximum wind speed were measured by the road weather station. Furthermore, the surface temperature, snow height, freezing temperature, concentration, and conductivity were measured using a sensor mounted on the wheel track. In addition, the level of grip and ice layer was measured using an optical sensor.
# Risk Elements in Total Suspended Particles in Areas Affected by Opencast Mining of Brown Coal
## 3 Results and Discussion
### Element Contents in the Deposited TSP
Additionally, the Czech Republic has a very dense rail network, resulting in elevated median values of risk element contents (As, Be, Cd, Co, Cr, Cu, Ni, Pb, and Zn) at sites around railway stations in several cities in Central Bohemia [56]. Consequently, the dense traffic in heavily industrial areas, such as the surroundings of the North Bohemian brown coal basin, can contribute to elevated element contents in the deposited TSP. [PERSON] et al. [28] assessed the risk element contents in soils in the area of Litvinov city. They found that the maximum values of As, Cd, Cu, Ni, Pb, and Zn exceeded the preventive values of these elements in soil in the Czech Republic [57] (20 mg/kg for As, 2 mg/kg for Be, 0.5 mg/kg for Cd, 60 mg/kg for Cu, 50 mg/kg for Ni, 60 mg/kg for Pb, and 120 mg/kg for Zn). Moreover, for As, its indicative values, which represent the soil arsenic content that poses a potential risk for crop contamination and a direct threat to human and animal health (i.e., 40 mg/kg of As), were exceeded. The maximum Zn levels exceeded the indicative values, representing a risk to plant growth and soil biological values (i.e., 400 mg/kg of Zn). The presence of elevated soil-borne risk element contents in highly industrial cities is relatively obvious. For example, the contents, sources, and spatial distributions of Zn, Pb, and Cd in the soils of the central, residential, and green areas of the second-largest town in Poland (Krakow) were investigated by [PERSON] et al. [58]. They found elevated contents of Cd and Pb in the Krakow soils, ranging between 2.33 and 4.18 mg/kg of Cd, and between 24.2 and 82.1 mg/kg of Pb. Although it is not possible to determine unambiguously the sources of the pollution, the results of this study indicate the predominant presence of the soil-borne elements in the TSPs as well.
# Toward SDM-Based Submarine Optical Networks: A Review of Their Evolution and Upcoming Trends
## 4 Submarine Amplifiers
### Hybrid Core and Cladding Pump-Sharing EDFA (SDM) Technology  
There are two basic techniques for implementing hybrid-pumped MC-EDFAs [49]. The first is a single-stage amplifier, and the second is a multi-stage amplifier. An important characteristic of cladding-pumped EDFAs is their ability to realize automatic gain control of the individual cores (an important aspect for an SDM MCFs-based network) by using a WDM signal. This happens because optical power for each core in a cladding-pumped MC-EDFA cannot be controlled independently between cores. In others words, the optical gain cannot be controlled by adjusting the cladding-pump power. In its counterpart, the core-pumped MC-EDFA, it is possible to adjust the gain of each core independently, without making power consumption or network elements savings (a key concept for the first generation of SDM technology).  
A new and promising candidate to solve this issue is a hybrid structure which is composed of both elements presented in Figure 24a,b. An MC-EDFA that employs both cladding and core pumping and further introduces the gain control in a multi-core erbium/ytterbium-doped fiber amplifier (MC-EYDFA) was extensively studied in [50]. Moreover, [PERSON] reviewed in [51] the recent development of MC-EDFAs and presented an experimental demonstration of core- and cladding-pumped MC-EFFAs amplifiers. The author concluded that core pumping (besides being more expensive) allows an independent gain control in each core. On the other side, cladding pumping requires fewer optical components by using low-cost, energy-efficient multimode diodes. A small signal gain of \(>\)20 dB could be achieved by employing a side-coupled pumping technique throughout the C-band, and furthermore by extending the doped region beyond the core of few-mode doped fiber.  
Authors in [52] demonstrated a 256-Gb/s transmission by using a 16-QAM modulation format for a distance of 404 km. The hybrid and core-pumping scheme MC-EDFA controlled based on monitored temperature, achieving a reduction in power consumption of up to 38%. Finally, a recent work [49] investigated the static and dynamic gain control characteristics of hybrid-pumped single-stage and double-stage MC-EDFAs. Experiments in [49] studied  
Figure 24: (a) Hybrid + core pump-sharing. (b) Hybrid + variable core pump-sharing methods for MC-EDFA.  
the hybrid-pumped single-stage MC-EDFA with an average flattened gain of 21 dB and the hybrid-pumped double-stage MC-EDFA with a maximum gain of 32 dB.  
Table 8 illustrates all available pumping schemes for MC-EDFA amplification.
## 3 Method Implementation
The model first uses cosine similarity to match the node feature vectors inferred by GraphSAGE with entity vectors in the knowledge graph, determining their semantic relevance to find the most similar entity. Next, reasoning is performed based on the matched entity and its associated relationships within the knowledge graph. During this process, semantic matching templates are also used to enhance inference accuracy, ultimately identifying the cause of the fault and providing corresponding recommendations for resolution. The primary knowledge base within the multimodal knowledge graph consists of entities such as devices, fault types, and diagnostic solutions, capturing relationships between faults and devices, as well as between devices and diagnostic pathways. By leveraging knowledge graph reasoning, the model efficiently identifies faults and offers users actionable diagnostic solutions. Based on the results of multimodal knowledge graph reasoning, a fine-tuned CPT text generation model is used to generate a fault diagnosis report, providing users with specific fault descriptions and handling recommendations. The model takes the reasoning results as input for the fine-tuned CPT model. Based on the input fault type and related information, the CPT model generates diagnostic text, which includes two main components: a fault description detailing the current fault status of the device, and handling recommendations to guide the user on effectively resolving the issue. The overall structure of the model is illustrated in Figure 2.
# Portable Optical Fiber Bragg Grating Sensor for Monitoring Traffic Density
## 1 Introduction  
Traffic sensors are devices that detect input data and information for other transport infrastructure systems. Today, traffic sensors are primarily placed next to, above, into or onto a road's surface. The usual basic parameters measured are detecting the presence of a vehicle, vehicle speed, vehicle classification, wheelbase and number of axles, total vehicle weight, road axle load and occupancy, all of which indicate the traffic flow quality.
# YOLOv5-Sewer: Lightweight Sewer Defect Detection Model
## 2 YOLOv5-Sewer Network Design
### YOLOv5-Sewer Network Architecture
#### 2.2.2 C3-Faster  
[PERSON] et al. [24] introduced the FasterNet block, a neural network module that incorporates the PConv convolution technique. This integration is designed to enhance the model's computation speed without compromising its accuracy. The structure of this module is illustrated in Figure 3. The utilization of PConv convolution within the FasterNet block contributes to efficient and rapid computation, making it a valuable component in the proposed model's architecture.  
The Point Convolution (PConv) operation is designed to perform convolution only on select input channels for spatial feature extraction while preserving the remaining channels. This approach aims to reduce memory access and minimize redundant computations. To fully and efficiently utilize all channel information, the proposed model introduces Pointwise Convolution (PWConv) after PConv. PWConv involves a 1 \(\times\) 1 convolution operation along the channel dimension.  
PWConv exhibits a T-shaped convolutional receptive field on the input feature map, focusing more on the central position compared to a regular convolution. This central position is distinguished by a higher Frobenius norm, indicating the presence of more crucial information in the feature map. By incorporating PWConv after PConv, the model strives to capture and emphasize significant spatial features, enhancing the overall effectiveness of the sewer defect detection process.  
Figure 3: The structure of the FasterNet block.  
Figure 2: The structure of the MobileNetV3 block.  
To enhance the network performance and reduce the number of parameters, the FasterNet block module is integrated into the original YOLOv5's C3 module, resulting in the C3-Faster module. The network structure is illustrated in Figure 4. The incorporation of the C3-Faster module into YOLOv5s significantly improves the operational speed of the network. Simultaneously, it successfully reduces the number of parameters, contributing to a more efficient and accurate model.  
The combination of PConv and PWConv, along with the introduction of the FasterNet block module, plays a pivotal role in enhancing the performance of the sewer defect detection model. This integration not only effectively boosts the network speed but also yields satisfactory results in terms of model accuracy, making it a valuable improvement to the YOLOv5 architecture for sewer defect detection tasks.
## 2 Methodology
### Effects of Rain Heights on Rain-Induced Attenuation  
Rain-induced attenuations were computed based on the ITU-R-recommended rain height, the measured seasonal rain height, and the estimated seasonal rain height at all nine stations. The 1 min mean annual rain rate exceeded at 0.01% of time for the nine stations were obtained from the work of [PERSON] [32]. The satellite used as case study is the IS20 which beam internet and DSTV cable television services to South Africa at an elevation angle of 68.5\({}^{\circ}\) and Ku-Band downlink frequency of 12 GHz. The latitudes of the earth stations used for this study are provided in Table 1. The procedure for the computation of rain attenuation can be found in the ITU-R P.618-13 recommendation [6]. The results obtained are presented and discussed compared with other stations' results in the subsequent section.
## 3 Test Results and Discussion
### Load-Bearing Capacity and Ductility  
Table 3 exhibits the load-bearing capacity and ductility of HPLW and LW walls. Figure 11 exhibits the skeleton curves of HPLW and LW walls. As we can see in Table 3 and Figure 11, using high-strength longitudinal rebars and high-strength concrete obviously enhanced the load-bearing capacity. Compared with LW walls, the average yield lateral force and maximum lateral force of the HPLW specimens increased by 58.5% and 107.9%, respectively. The use of a high-strength stirrup also obviously improved the ultimate deformation capacity. All walls exhibited good deformation capacity. The average ultimate drift ratios of the HPLW and LW walls were, respectively, \(1/37.6\) and \(1/68.0\), which greatly exceeded the allowable interstory drift ratio value (\(1/120\)) of RC shear walls according to the design provisions of the Chinese GB50011-2010 code [21].  
Figure 11: Skeleton curves of HPLW and LW walls.  
Figure 10: Lateral load–top displacement hysteresis curves of HPLW and LW wall.
# Halloysite and Laponite Hybrid Pigments Synthesis with Copper Chlorophyll
## 2 Materials and Methods
### Characterization  
The determination of the amount of dye adsorbed by the nano-clay system allowed us to define the synthesis performance of the process. For this purpose, a UV-Vis transmission spectrophotometer (JASCO V650, Easton, MD, USA) was utilized to measure the dye absorbance (%) in separate supernatants. Then the amount of dye adsorbed by nanoclays was calculated as a percentage of the initial concentration in the exchange step. This parameter was employed as a response to minimize in the DoE analysis.  
XRD Bruker D8-Advance equipment (Bruker, Billerica, MA, USA), with a Goebel mirror (power: 3000 W, voltage: 20-60 kV and current: 5-80 mA), was used. Measurements were taken in an oxidant atmosphere at an angular speed of 1\({}^{\circ}\)/min, STEP 0.05\({}^{\circ}\), and an angular scan of 2.7-70\({}^{\circ}\). XRD patterns were obtained to observe variations in the basal space on the layers from the different nanoclays due to the interactions with the natural dye and modifiers.  
For TSR (%), a double UV-Vis/NIR Jasco V-670 spectrometer was used, which works within the wavelength range from 190 to 2700 nm. The equipment has a double-grating monocromator, one for the UV-Vis region (1200 grids/mm) and the other for the NIR region (300 grids/mm). Detectors are a photomultiplier tube for the UV-Vis region and a PbS detector for the NIR region. Both the detector and grating changes are automated at a user-fixed wavelength between 750 and 900 nm. The light sources are a deuterium lamp (190 to 350 nm) and a halogen lamp (330 to 2700 nm). To calculate optical properties, reflectance factors \(\rho(\lambda)\) were applied for the hybrid pigments within the 370-740 nm range with the D65 illuminant and the CIE-1964 standard [11] observer.  
A thermogravimetric analyzer TGA/SDTA 851 (Mettler-Toledo Inc., Columbus, OH, USA) was utilized to ensure that chlorophyll was successfully loaded on both nanoclays. The experimental conditions were a temperature ramp of 5 \({}^{\circ}\)C/min within the 20-900 \({}^{\circ}\)C range with oxidant medium N\({}_{2}\)O\({}_{2}\) (4:1).  
Finally, a SOLARBOX 1500e RH climatic chamber (ERICHSEN, Hemer, Germany) was used to measure the color UV-Vis fastness. All samples were measured using a Konica Minolta sphere integrated spectrophotometer (CM-2600d) to obtain the reflectance factors \(\rho(\lambda)\)(370-740) nm range with the D65 illuminant and the CIE-1964 standard observer. The measurements were made at several exposure time intervals, and color differences were calculated by measuring samples before and after radiation exposure. Color differences \(\Delta\mathrm{E_{ab}}^{*}\) were calculated with the colorimetric attributes of the CIELAB color space.  
Figure 2: Biocomposite preparation after epoxy 55 curing step with GHA and GLAP hybrid pigment addition at different synthesis conditions.