esa-sceva/satcom-chunk-collection huggingface dataset:
A novel sensing technique is proposed in [5], where Fiber Bragg Grating (FBG) sensors in a high-scattering optical fiber doped with MgO nanoparticles are proposed for polarization-dependent temperature sensing. The paper depicts how the inscription of high-reflectivity FBGs onto a fiber core doped with nanoparticles obtains reflectors into a fiber with Rayleigh scattering properties which can be suitably handled. Optical sensing is mainly investigated in the Near-Infrared (NIR) wavelength range, even if the Medium-Infrared (Mid-IR) wavelength range could be more useful in specific applications, since most biomolecules and atmosphere contaminants exhibit their fingerprints and can be detected more efficiently. In [6], a Mid-IR spontaneous emission fiber source, operating from 3.5 to 8 \(\upmu\)m wavelength, is proposed for the first time, providing the output power at the mW level. The Mid-IR spontaneous emission fiber sources are an alternative to broadband Mid-IR supercontinuum fiber sources, both in complexity and cost. The interest towards this emission wavelength range is significant for molecular sensing applications across biology and chemistry, in medicine, agriculture, defense, and environmental monitoring. The need for high-capacity communication networks is strictly related to the progress in optic and photonics to efficiently exploit the information delivered by the optical sensing circuitry. In [7], a dual-polarization quadrature phase-shift-keying (DP-QPSK) non-coherent reception scheme based on a delay-line interferometer (DLI) is proposed to improve the reception performance. A suitable data recovery algorithm is optimized for DLI-based DP-QPSK demodulation. In [8], the main objective of the study is to minimize the optical communication node resources, such as transponders, multiplexers, and wavelength selective switches, to provide and maintain high-quality dense wavelength division multiplexed (DWDM) network services by using a new generation of optical nodes. The data traffic-congestion problems arising in the context of both reconfigurable optical add-drop multiplexer technologies are investigated, showing interesting numerical tests carried out for realistic networks of different dimensions and traffic demand sets. To conclude, the eight papers reported in this Applied Sciences Special Issue provide excellent examples of the current progress in the area of Dielectric Photonic Devices and Systems technology. There is information regarding new materials and components, sensing applications, and optical communication.
In the study of offloading mobile data from cellular network to opportunistic networks, researchers assume that a node will publish the content or data to opportunistic network if someone subscribes it, whereas we think this principle is not suitable for a large environment. For example, node A in city-1 subscribes to content and node B who is in city-2 has this content. City-1 is far away from city-2. If node B publishes the content onto an opportunistic network in city-2, the probability that this content can be delivered to city-1 and received by node A within a certain limited time is scant. Therefore, we think the distance between the subscriber and the provider of the content impacts the opportunistic routing performance and offloading efficiency.  
In this paper we study the opportunistic data offloading from cellular networks through direct wireless communications between smartphones, using WiFi and Bluetooth. The main contributions of this paper are: (1) we study the issue of subscription selection which has not been studied previously; (2) we propose a distance-based mobile data opportunistic offloading model that we have called Distance-based Opportunistic Publish/Subscribe (DOPS).  
When a user wants a specific content, he/she sends a content request to a content subscribing server (CSS) through an Internet connection. When another user having the requested content knows that the content is requested, he/she delivers the content to the requestor through opportunistic forwarding if the distance between the two users is shorter than the longest distance it travelled recently during the time duration before the subscription's deadline. In some cases, many users may want the same content, and the traffic offloading efficiency increases if these subscribers of a common content receive the content through opportunistic networks. DOPS cannot work for time sensitive applications, such as video streaming. The content suitable to be delivered in DOPS is time insensitive data, such as multimedia newspapers, music, and movie trailers. Small video and music files are delivered as bulk files, not streaming files, which means the receiver cannot open the file before receiving it completely.
## 3 Results
### Morphometric and Gravimetric Measurements
#### 3.1.2 The Radicle Length  
Radicle length assessment shows that using gauze as a substrate along with white LED light (4.00 \(\pm\) 0.92 \({}^{\text{a}}\) cm--V3) has a positive effect (or a negative effect because radicles are not meant to be eaten). The lowest values were obtained under red LED light and on biochar (0.87 \(\pm\) 0.36 \({}^{\text{b}}\) cm--V8). While under white and red LEDs and sunlight and on biochar, radicle lengths were visibly shorter than on gauze, under blue LED, radicles on biochar were one cm longer than on gauze. As shown in Figure 2, there were high shifts concerning radicle lengths. The values for samples cultivated on gauze were the following: 3.33 \(\pm\) 2.1 \({}^{\text{a}}\) for V1, 1.57 \(\pm\) 0.85 \({}^{\text{b}}\) for V5, and 2.42 \(\pm\) 1.4 \({}^{\text{ab}}\) for V7. The values for samples cultivated on biochar were: 2.41 \(\pm\) 1.8 \({}^{\text{a}}\) for V2, 1.13 \(\pm\) 0.7 \({}^{\text{b}}\) for V4, and 2.55 \(\pm\) 1.3 \({}^{\text{a}}\) for V6.  
The effect of illumination with different types of LED light on radicle length sprouts cultivated on gauze/biochar is included in the following ANOVA test table (Table 2).  
The ANOVA test results emphasized that a highly significant statistical difference (\(p<0.001\)) existed between the treated sample cultivated on gauze/biochar illuminated with LED light and control (sunlight).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Substrate (Gauze or Biochar)** & \(p\)**-Value** & \(F\) & \(F\) _crit_ \\ \hline Gauze (V1, V3, V5, and V7 & 5.349 \(\times\) 10\({}^{-22}\) & 54.28909083 & 2.682809407 \\ Biochar (V2, V4, V6, and V8 & 4.95 \(\times\) 10\({}^{-30}\) & 89.39846633 & 2.682809407 \\ experimental variants) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: A one-way ANOVA test for _Lepidium sativum_ L. length sprouts.  
Figure 2: The medium values of radicles’ length of _Lepidium sativum_ L. sprouts on gauze (V1 control, illuminated sunlight; V3, illuminated with white LED; V5, illuminated with blue LED; and V7, illuminated with red LED experimental variant) or biochar (V2 control, illuminated sunlight; V4, illuminated with white LED; V6, illuminated with blue LED; and V8, illuminated with red LED experimental variant). Data acquired from the experiment were statistically investigated and were expressed as the mean \(\pm\) SD (standard deviation). Bars are one standard deviation.
## 5 Discussion and Conclusions  
We showed that the BER performance of pre-amplified PAM4 signals can be modeled using simple, fundamental concepts to produce results that are in agreement with experimental results. Our results show that a multi-section SOA could improve the amplification dynamic range by over 3 dB through improved noise performance and through less gain saturation than an equivalent single-section SOA. Even though we have used one specific set of SOA parameters, the results presented would be applicable to SOAs with different parameters for the following reasons. Firstly, the NFs are typical of SOAs and the SOA noise depends mainly on the gain-inversion parameter [22], therefore knowledge of the NF will allow the signal to noise ratio (or/hence BER) at low input signal powers to be calculated. Secondly, for amplification purposes, SOAs with a moderate gain of about 13 dB gain to avoid excessive gain saturation that is limited by the SOA saturation power. Finally, the IPDR is maximized by employing SOAs with moderate gain of 13 dB to 15 dB. In this work, we solely focused on simulating SOAs with a moderate gain of 13 dB. SOAs with high unsaturated gain will have smaller IPDRs because of the smaller upper power limit at which the BER performance will be limited by the gain saturation. For multi-channel amplification, the BER performances for a single wavelength channel and for low-power multi-channels are in agreement with experiments. The true nature of the gain saturation, especially the wavelength dependence of the gain saturation, needs to be investigated further because there may be an optimal spectral placement of the signals with channel spacings of the order of 100 GHz that would allow for improved multi-channel performance when amplifying using SOAs and thus may enable greater routing flexibility in future datacom networks.  
We acknowledge Science Foundation Ireland for funding this work (12/RC/2276, 14/IFB/2702). [PERSON] acknowledges fruitful discussions with [PERSON], [PERSON] and [PERSON] on quantifying SOA performance.  
This work was conceived by [PERSON] with suggestions from [PERSON] and [PERSON]; [PERSON] wrote the computer code to perform all of the simulations from which the results are presented here. All authors contributed to writing the paper.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.  
Figure 9: BER performance multi-channel PAM4 amplification performance in (**a**) multi-section SOA and (**b**) single-section SOA. Results are presented for one, two, and four co-propagating channels. At low input powers below \(-15\) dBm, the ASE of the SOAs dominates and the performance is independent of the number of channels. The multi-section SOA performs better due to the lower effective NF. At high input powers above \(-10\) dBm, there is a reduction in the useful dynamic range due to the increased crosstalk from the co-propagating channels.
## 4 Case Study
### Simulation Results
#### 4.2.5 The Effect of Communication Delay  
This case is used to cope with the delay of the communication topology proposed under the same operating point change in case 4. All BESSs communicate with each other over a topology structure shown in Figure 3e. Besides, the load change is the same as in case 4. To facilitate grid connection, the reference values of voltage and frequency in the virtual leader are set as 1 \(p.u\). The delay time is set as 100 ms and is far beyond the upper limit, which is defined as 16 ms by the IEEE and international electro-technical commission (IEC) to ensure the stability of the grid [43]. As shown in Figure 9, although communication delay causes some oscillations and a longer time to reach consensus, energy level, voltage, frequency and active/reactive power all can be synchronized. Besides, voltage and frequency can be regulated to 1 \(p.u\), which are the most important conditions for the grid connection of the microgrid.  
Figure 8: Simulation results of step-by-step consensus algorithm with the virtual leader (case 4).  
Figure 9: Simulation results of the consensus algorithm with communication delay (case 5).
# A New Multivariate Approach for Real Time Detection of Routing Security Attacks in VANETs
## 4 The Proposed Detection Scheme
### Step 2: Input Data Rescaling  
The process of constructing the data used in our detection scheme starts with capturing the network traffic and ends with the data rescaling step. At this stage, the three rescaling techniques, namely the Z-Score Normalization (ZSN), the Min-Max Normalization (MMN), and the Normalization by Decimal Scaling (NDS) are applied to compute the new values of each variable contained in the original data. The output data get updated continuously over a certain time interval. The generated data are modeled by multivariate datasets sampled at different times.
## 5 The Use of AI for the Creation of SRE
### Federated Learning  
We are currently in the era of Edge Computing, where applications are executed locally on the end users' devices, while the core service and processing are performed on cloud servers [48]. This offers two main advantages. The first one is privacy because local private data are not sent to central servers; the second one is responsiveness since data are being processed in the device where they reside. This also benefits the networks as less data needs to be transported. Being in the era of Edge Computing affects all types of devices and applications, especially AI. More and more devices are capable of supporting AI applications locally, as advanced CPU and SOC designs come integrated with specific AI accelerators. From mobile phones, sensors, and personal computers to IoT, AI applications can run locally, either partially or completely, with a lot of progress made in this field over the last years [49]. One of the most important relevant learning methods is FL. FL relies on multiple entities called clients to collaborate on the training of a central model while all training data remain decentralized. This leads to individual smaller AI models controlling parts of a communications design while providing and receiving feedback to a global AI model handling the coordination. A high-level depiction of how FL may be implemented in the context of a RIS-aided 6G communication system, is presented in Figure 3. The blue lines, which connect the global large AI model (BS) with the local AI clients (User Equipment-UE), represent the BS-to-RIS and RIS-to-UE links. These links serve the communication rounds needed for the training of the global large AI model at the BS and the update of the local AI client models residing on the UE. This allows the network designer to create smaller models that control only a part of the network that contains a single RIS or a small number of them. Thus, the calculations become less time- and power- consuming and security is improved. FL is not only used in conjunction with RISs. There are also a lot of applications focusing on the creation of edge computing environments; for example, on multiple drones' control, a swarm of drones can be centrally controlled as a single entity, and each drone can also be individually controlled locally. These applications can also benefit from the use of RISs for enhancing the capabilities of the network. The integration of RISs with FL in 6G wireless communications represents a significant advancement in the field. In this paper we focus on the implementation of FL on RISs and RIS-aided SREs. A selection of recent works is provided in Table 5.
# Sensor-Data-Driven Prognosis Approach of Liquefied Natural Gas Satellite Plant
## 4 Methodology
### Sensor-Data-Driven Prognosis Methods
#### 4.2.2 [PERSON]'s Double-Exponential-Smoothing Method  
[PERSON]'s double exponential smoothing, also known as [PERSON]'s linear exponential smoothing, is a type of double exponential smoothing that uses two different smoothed series that are centered at different points in time [41]. The method is based on the extrapolation of a line through two centers, as presented below.  
\[y_{1}(k)=\alpha y(k)+(1-\alpha)y_{1}(k-1), \tag{2}\] \[y_{2}(k)=\alpha y_{1}(k)+(1-\alpha)y_{2}(k-1), \tag{3}\]  
where \(y_{1}\) denotes the single-smoothed series obtained by applying simple exponential smoothing to series \(y\), \(y_{2}\) denotes the double-smoothed series obtained by applying simple exponential smoothing to series \(y_{1}\), and \(\alpha\) is the smoothing parameter.  
Then, predicted \(\hat{y}\) on an \(h>1\) horizon is computed by  
\[\hat{y}(k+h|k)=\ell(k)-hb(k), \tag{4}\]  
where \(\ell\) denotes an estimate of the level of the series at time \(k\), and \(b\) denotes an estimation of the slope (or growth) of the series at time \(k\),  
\[\ell(k)=2y_{1}(k)-y_{2}(k-1), \tag{5}\] \[b(k)=\frac{\alpha}{1-\alpha}(y_{1}(k)-y_{2}(k-1)). \tag{6}\]  
In practice, due to noise in the measurements, the predicted values have uncertainty. For considering this uncertainty in \(\hat{y}\), a confidence interval of 5% was included in Equation (4).  
From Equation (4), we can also derive the estimated time for reaching a particular position. For instance, considering value \(y_{p}\), estimated time \(h_{p}\) to reach this value is computed by:  
\[h_{p}=\frac{y_{p}-\ell(k)}{b(k)}. \tag{7}\]
# Assessing the Compaction Quality of Dolomitic Asphalt Pavements Using Ground Penetrating Radar
## 4 Data Processing  
The collected GPR data were processed using GSSI's RADAN 7 software [28]. The raw data were processed to remove noise and to improve the signals. First, metal plate calibration was performed on the data files, and then inline and crossline values were edited manually to prepare the GPR data files for further analysis and to determine the true track of the measurements, respectively. Next, the program automatically performed reflection picking after manually selecting the reflection picking function to reduce undesired signal noise and improve reflections from the pavement layers. The processing was continued by filtering the data, including (1) a triangular finite impulse response (FIR) filter, applying a weighted moving average procedure emphasizing the filter's center rather than the ends of it; (2) a horizontal filter (stacking), merging neighboring radar scans and delivering a single scan through a simple running average; and (3) vertical filters, i.e., a low pass filter to remove high-frequency noise (2 GHz and 4 GHz thresholds for the 1 GHz and 2 GHz antennas, respectively) and a high pass filter to remove low-frequency noise (250 MHz and 500 MHz thresholds for the 1 GHz and 2 GHz antennas, respectively). Finally, the gain properties were set by applying an exponential gain function. The gain was set at 5-7 points. A horizontal background removal filter was applied in certain profiles, particularly those measured along both sections where the data exhibited significant interference or noise from unwanted reflectors--potentially obscuring critical measurements. However, this filter was not required for the GPR profiles collected after each roller pass and following the placement of each asphalt layer. In this case, the filter's length must always exceed the length of the data's longest horizontal \"real\" reflector [28].
# Geographical Information System Enhancement Using Active Learning-Enhanced Semantic Segmentation
## 4 Conclusions  
This study aimed to inspect the building registration ledger, proposing and testing a procedure for its verification. Human-labeled training data were leveraged to evaluate multiple semantic segmentation methods, with the aim of identifying the most effective technique. Our findings indicated that ResNet50, due to its superior accuracy, emerged as the best candidate. To further refine and expand our GIS-labeled training dataset, the active learning approach was incorporated. This approach pinpointed training data with low similarity, enabling their refinement and, consequently, the generation of a more robust training set. After training the network with this optimized dataset, the model was employed to scrutinize the registered buildings on maps. This procedure was designed  
Figure 11: The result is from verifying the registered buildings through the proposed system: (**a**) a photo taken from a drone, (**b**) a photo predicting the roof using ResNet50, where the blue square in the image represents the building area, (**c**) a map showing building information from the geographic information system, and (**d**) results of marking them on the actual geographic information system with a pink dot indicating buildings confirmed in the ledger.  
to confirm the presence or absence of buildings, aligning with the records of the building registration ledger. Through active learning and the meticulous selection of semantic segmentation methods, our methodology offers a promising avenue for maintaining accurate and up-to-date building information systems and maps. Furthermore, the Contain function of PostGIS was utilized to ascertain that the recognized area was accurately incorporated into the database.  
Conceptualization, S.Y. and S.G.; methodology, S.Y.; software, S.Y.; validation, S.G.; formal analysis, S.Y.; investigation, S.Y.; resources, S.Y.; data curation, S.Y.; writing--original draft preparation, S.Y.; writing--review and editing, S.Y.; visualization, S.Y.; supervision, S.Y.; project administration, S.Y.; funding acquisition, S.G. All authors have read and agreed to the published version of the manuscript.  
This paper was supported the Wonkwang University in 2021.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
The authors thank the anonymous reviewers and editors for their insightful comments and suggestions.  
The authors declare no conflict of interest.
# CONE: A Connected Dominating Set-Based Flooding Protocol for Wireless Sensor Networks
## 2 Related Work
### Clustering  
A similar approach to CDS is called \"clustering\". Clustering partitions a network into clusters. A cluster contains several nodes. One particular node in each cluster has the task to manage its cluster--it is called a \"cluster head\". Figure 6 illustrates a network that uses clustering. If a node separates two cluster heads, it can assist in the communication between these two cluster heads. Therefore, the assisting node is referred to as \"gateway\". Clusters might overlap (see Figure 6a), but non-overlapping clusters (see Figure 6b) are also possible. It can also be the case that two nodes separate a cluster head from the next cluster head. In this case, these two nodes form a so-called \"distributed gateway\" (see Figure 6c).  
Two cluster heads can be neighbors, but it is often desirable that cluster heads are separated [8]. Therefore, the set of cluster heads ideally forms an IS. However, in terms of reducing the set of nodes, it is more useful to keep the number of cluster heads small. Hence, the set of cluster heads should not form a MIS, since this results in a larger number of cluster heads. Therefore, the cluster configuration without the MIS is more beneficial for reducing the set of nodes. The advantages of clustering are similar to the ones of using a backbone. In fact, CDSs are sometimes used in order to find the set of cluster heads and the set of gateways [8]. However, clustering puts more emphasis on enhancing scalability of higher-layer protocols and local resource allocation [8]. Hence, using clustering in network flooding does not provide any added value compared to using a CDS.