esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cyanobacteria-Based Biofilm System for Advanced Brewery Wastewater Treatment
## 3 Results & Discussion  
### Attachment Efficiency  
After activating the wastewater recirculation system, the culture started flowing over the inclined biofilm surface initiating adhesion and biofilm formation. A different initial adhesion rate was observed for the two surface materials tested (Figure 2). In the case of glass, which is a mildly hydrophilic material [36], biomass attachment was rapid and efficient, surpassing 90% after two days of cultivation. On the contrary, adhesion on the polyurethane surface did not occur at an equally high rate, as after two days of cultivation 42.6% of the total biomass remained in suspension. Following this initial adhesion phase, the fraction of biomass that remained attached exceeded 98.3% and 97.4% for glass and polyurethane, respectively, nine (9) days into the experiments. At the end of cultivation, both experiments exhibited complete biomass attachment (>99%), while statistically significant differences on biomass attachment were not observed between the two surface materials.  
There have been conflicting reports on whether hydrophobic or hydrophilic surfaces enhance adhesion [28; 29; 36]. Hydrophobic surfaces are generally considered more susceptible to biofilm formation; however, factors such as surface roughness, wettability, hydrodynamics, and the nature of EPSs produced by the microorganisms play equally important roles [29; 36; 37]. In this study, the more hydrophilic surface (glass) favored initial adhesion; thus, it is recommended for attached cultivation of _Leptolyngbya_ sp. This finding could indicate that the _Leptolyngbya_-based consortium utilized in this study produces EPSs of a mostly hydrophilic nature. Various species of cyanobacteria have been reported to se crete hydrocolloidal EPSs which draw extensive research interest due to their applications in the bio-materials field (hydrogels, etc.) [38].  
The predominant role of EPSs and cell-to-cell interactions in biofilm formation is further indicated by the high attachment efficiency after the initial adhesion phase. The microbial community appeared to create an EPS/cell \"coating\" on the substratum surface, negating its impact on adhesion efficiency. Similar amounts of biomass remained attached on both materials after this point as the biofilm matured (irreversible adhesion). [PERSON] et al. reported that microbial accumulation on surfaces can significantly decrease their hydrophobicity and binding characteristics [39].
## 3 A Taxonomy for Planar FSS
### Patch Cell  
A simple squared patch design for 77 GHz band, with a unit cell size of 0.51 wavelengths, was presented in [50]. By incorporating energy-harvesting circuitry into a basic patch unit cell, an FSS with a size of 0.42 wavelengths was developed, offering potential applications for building envelopes where energy harvesting could be of interest [51].  
A more intricate design involves single- and dual-polarized structures with multilayer metallic surfaces. This design, consisting of pairs of rectangular patches surrounded by a square loop, is tuned to operate at 1 GHz. Its complexity allows for a remarkably small electrical size of just 0.02 wavelengths [26]. Another advanced approach features cascaded  
Figure 5: Some examples of cross unit cells: **(a)** Jerusalem cross [35]; **(b)** modified Jerusalem [37]; (c) bow-tie cross [39]; **(d)** fractal cross [45]; **(e)** convoluted cross [42]; **(f)** tapered cross [43].  
layers, with top and bottom layers made of mutually perpendicular metal gratings, and a middle layer that includes metal arms connected by vias to the bottom layer. This triband design operates at closely spaced frequencies (9.9, 10.4, and 10.6 GHz) and has a unit cell size of 0.28 wavelengths [52].  
For more widely separated frequencies (9.3, 11.3, and 13.6 GHz), a multilayer FSS with three metallic layers separated by dielectric materials is employed. This design, measuring 0.62 wavelengths, consists of full circular patches in the center, four quarter-circle patches in the corners, and four half-circle patches along the sides on both the top and bottom layers. The middle layer has circular apertures that mirror this topology, with an overall size of 0.14 wavelengths [53].  
Another notable design is a cross-polarization converter FSS, centered at 5.8 GHz, which is relatively larger, sizing up to 0.35 wavelengths. The top layer of this structure is composed of two vertically and two horizontally oriented monopole-via-monopole modules, while the bottom layer replicates the same structure, but rotated clockwise [54].  
Textile-based FSS designs have also been proposed, which can be practical for applications like window curtains. One such example is a textile FSS with a 2D periodic array of square resonators, tuned to 70 GHz and 110 GHz, with a unit cell size of 0.45 wavelengths at the lowest frequency [55].  
Table 3 summarizes these results, and Figure 6 illustrates various unit cell designs incorporating patch elements.
# Influences of Temperature and Substrate Particle Content on Granular Sludge Bed Anaerobic Digestion
## 2 Materials and Methods
### Reactor and Experimental Procedure  
The UASB lab-scale reactor dimensions were 85 cm in height and 4.4 cm in internal diameter giving 1.3 L of total volume. A mixture of granular sludge (0.5 L) obtained from various industrial wastewater treatment plants was used after long-term adaptation to manure. The reactor was run using swine manure samples (both raw and centrifuged) for over a year prior to the start of the experiment. The up-flow velocity was set to 1.75 m/h, hydraulic retention time was 3.8 days, and organic loading rate was 6.5 and 4.5 g\(\cdot\)L\({}^{-1}\cdot\)day\({}^{-1}\) for RF and CF samples, respectively. The reactor was equipped with heater temperature controls (\(\pm\)0.5 \({}^{\circ}\)C) and a liquid displacement and buoyancy device for biogas flow measurement working according to the same principle used by [PERSON] et al. [3]. Data were logged online with LabVIEW software (Figure 1). The reactor started at 35 \({}^{\circ}\)C, and it was fed RF and run for 11 days before the temperature was lowered to 25 \({}^{\circ}\)C. After 15 days at 25 \({}^{\circ}\)C, CF was introduced, and the experiment continued at 25 \({}^{\circ}\)C for another 18 days before the temperature was raised back to 35 \({}^{\circ}\)C and run for 15 days (Table 1). The temperature was monitored, showing that the temperature transitions took 4-5 h after each imposed temperature change.
# Effects of Viewing Displays from Different Distances on Human Visual System
## Materials and Methods
### Experimental Design  
In this study, four independent variables were selected: two within-subject variables and two between-subject variables. Thus, a mixed design (i.e., A \(\times\) B (C \(\times\) D \(\times\) S)) was used to represent the experiment. The two independent between-subject variables were (A) viewing distance (3H vs. 6H, where H is the height of the screen) and (B) the display type (2D vs. 3D). Thus, it was a 2 \(\times\) 2 \(\times\) 8 \(\times\) 4 design. Each of the (A) and (B) = 2 \(\times\) 2 = 4 distance-display conditions contained S = 5 participants, and each participant watched all four movies for one hour. Two types of displays were chosen because they have been used in previous studies [6; 8; 11; 37; 38], thus allowing comparison. Moreover, the viewing distance is selected as three times and six times of the display height based on the recommendation made by the International Telecommunication Union (ITU) standards [39]. The two independent variables based on the repeated measures or within the subject variable were (C) viewing time (pre-test, T10, T20, T30, T40, T50, T60, and post-test) and (D) movies (Jurassic World, Avengers, San Andreas, and Godzilla). [PERSON] et al. [5; 40] selected the viewing duration to be 45 min; however, [PERSON] et al. [41] used 10 min, and [PERSON] et al. [38] used 1 h as the viewing period. Hence, in this study, it was decided to perform the experiment for 60 min. The EEG signal was continuously recorded starting at pre-test (3 min prior to the experiment), T10 (first 10-min period), T20 (second 10-min period), T30 (third 10-min period), T40 (fourth 10-min period), T50 (fifth 10-min period), T60 (sixth and final 10-min period), and post-test (3 min period after the experiment). The dependent variables included the relative \(\beta\) power and \(\alpha/\beta\) power of the EEG, and total subjective visual discomfort score. Pairwise comparisons were performed to investigate the source of any significant effect considering the time factor.
## 3 Dataset  
A wide variety of datasets are used to train DNN-based non-intrusive speech quality prediction models. For speech enhancement algorithms, artificially added environmental noise is commonly used. In addition, noise and speech transmitted through cellular networks, or a combination of the two, are used to assess the distortions of telecommunication networks.  
The training speech dataset was created by combining publicly available datasets and our specific generated dataset to cover a wider range of speech quality degradation. The noisy speech was taken from a noisy speech database for training speech enhancement algorithms and TTS models [28]. The noisy speech dataset does not contain any network-induced speech degradations. Speech quality score labels were calculated using PESQ [5] based on the clean reference speech. Moreover, NISQA Corpus [29], an aggregation of approximately 14,000 speech samples from different datasets, was used. It contains simulated and live phone (mobile phone, Skype, Zoom, Whatsapp) recordings under different noise, networks, and applications such as mobile phone, SKype, Zoom, Whatsapp, and so on. These samples are human-rated and labeled with MOS. NISQA corpus includes VoIP degradations, but the speech files do not contain information on noise and network condition parameters. To understand the prediction performance on different noise types and network conditions, a simulated noise-network-distorted speech dataset containing information on environmental noise conditions and network condition parameters was used. Environmental noise parameters were different noise types at different signal-to-noise ratios, whereas network parameters were packet loss, delay, and jitter.
# Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information
## 4 Sybil Attack Detection System
### Overview of the System  
At this point, we can propose a complete Sybil attack detection system. Figure 3 shows an overview of the Sybil attack detection system. According to the state of the Sybil attackers in the environment, the system can detect attacks both statically and dynamically. The system is divided into four modules:  
1. Denoising: The denoising module eliminates the noise in the CSI stream.
2. Motion detection: The motion detection module detects the presence or absence of motion.
3. Static detection: If all the nodes remain still, the AoA measurement is performed by the self-adaptive MUSIC algorithm. The static detection algorithm, combined with AoA, RSSI and CSI amplitude, determines whether an attack exists; because CSI amplitude and RSSI are positively related to distance.
4. Dynamic detection: However, what if there is movement in the environment? We found that different motions correspond to different amplitude characteristics. Therefore, once there is a client movement in the environment, the dynamic detection algorithm uses the DBSCAN clustering algorithm to determine the attack after feature extraction. Only one central AP is needed to detect attack nodes by the phase and amplitude features.  
When multiple APs receive CSI at the same time, they can improve the accuracy of wireless network Sybil attack detection by applying the self-adopted MUSIC algorithm and DBSCAN algorithm. As shown in Figure 4, when all the clients access the network, the central detection AP is responsible for collecting all the client data and sending the data to the server. Multiple APs can also detect if one of the centre APs is attacked by a Sybil node. Furthermore, all the attacks that we present are from indoors. If the attacker is outdoors, both the RSSI and the amplitude decrease sharply and can  
Figure 2: Different methods of measuring the angle of arrival. (**a**) Traditional MUSIC Algorithm; (**b**) self-Adaptive MUSIC Algorithm.  
be easily detected. In general, experiments show that our detection system can respond to a variety of environments and achieve higher detection rates. Next, we will elaborate on each part in detail. First, the de-noising module and the motion recognition module will be explained.
# A Low Power IoT Sensor Node Architecture for Waste Management Within Smart Cities Context
## 6 Operation Tests  
The testing phase of the IoT node focused on two different tasks: proving the effectiveness of the ultrasound sensor for the measurement of the filling level and proving data transmission through LoRa channel.
## 4 Intelligent Weather-Conscious AMC Scheme for Global Satellite-to-Ground Communications
### DL-Based Estimation Model  
Along with DL development, LSTM is widely used in temporal sequence prediction. Considering that the memorability of LSTM can adequately identify the regular motion of satellites, past SNRs, and weather, we chose the LSTM network as an estimation model to predict the SNR of the next moment.  
The input of the LSTM network state \(s(t)\) is divided into two parts: location information and weather information. We classify the global weather into three types: sunny, cloudy, and rainy, and represent them with one-hot encoding \((sunny,\ cloudy,\ rainy)\), denote as \(w_{t}\). We denote rainfall and snowfall weather uniformly as rainy because they are both precipitations. The UE on the ground can acquire the weather conditions \(w_{t}\) and its real-time position \((lat_{t},lon_{t})\) and the satellite position for the next moment by storing the satellite orbit information. We use latitude and longitude to describe the location of the UE on the ground. In a practical scenario, we can use GeoHash [38] to compress the latitude and longitude information to 6 bits to reduce the bandwidth consumption while preserving the location information. As satellites have altitude, any 3D coordinate system can, theoretically, describe their position. We use the geographic coordinate system [39] to describe the satellite position, where \((x_{t},y_{t},z_{t})\) denotes the longitude, latitude, and altitude, respectively, of the satellite at the moment \(t\).  
After introducing the input for the LSTM network, we introduce its architecture. As shown in Figure 3, the input parameters need to go through the embedding layer first. The embedding layer has two blocks, whereby the first block aims to process weather information and the other block aims to process the location information. Two embedding blocks converge together into an LSTM layer. The LSTM layer, differently from conventional RNNs, controls the flow of information through three gates: the forget, memory, and output gates.
## 4 Conflict Resolution in the Actual Scheduling Process
The aim of one scenario was to simulate the network model for the expression of processes that created a series of messages in which data were transmitted from the vehicles to the hybrid VANET cloud server, by storing appropriate information in adequate structures of the repositories of the data warehouse (DW). The network was related with the interoperable server and the DW. A number of 802.11 pRSU units were implemented in the simulation laboratory and, during this scenario, we simulated from one to ten 802.11p OBUs. For this scenario, we obtained the statistical data relating to the parameters of transmission, the technical parameters of messages (Table 4), and other statistical data [45]. To complete the algorithms (Section 5) used to assign priorities for the different types of context and provided messages, the opinions of specialist experts from the transport sector were analysed. To assign concrete priority values in the provision of contextual information and services, we used multi-objective decision support methods, such as additive waiting and evaluation of concordance coefficients, for the assignment of the priorities of the experts. Another simulation scenario was used to analyze the data flows by transferring them in two directions: from the vehicle to the DW server and from the DW server to the vehicle. The types of sensors applied in this experimental scenario are presented in Table 5.  
The duration of the data transfer cycles started at 60 s. The data transfer speed was chosen to be 27 Mb per second and the size of the packet was approximately 1000 MB. The experiment included the algorithm for defining the priority assignments of service providers described in Section 5. The prototype system was simulated with the functionality of context-awareness of the surrounding environment. Another group of experiments was undertaken with the aim of identifying the potential for application of the algorithms provided in Section 5. Statistical data and collected data were used during short intervals. For illustrative purposes only, the trends over time (in seconds) of parameters, namely, the accumulated values, the dynamics of the values of predicted utility \(Exc_{j}\), and the dynamics of the obtained values of normalized function \(Z_{j}\), are shown in Figure 3. The dynamics of the reduced importance of the \(Ty_{j}\) values are shown in the same figure for comparison purposes. These results are not accurate and only have demonstrative ability. Nonetheless, they show the possibility of simulating the performance of changes in the service provision process by assessing the accumulated values over the short experimental period. The changes in the values depend on the parameters of the channel quality, among others. To include the estimation of situations involving collisions, and the required bandwidth of the channels, it is necessary to carry out additional experiments with a number of dropped packets.
# Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network
## 1 Introduction
Through this research, it is hoped to provide new insights and methods for the development of UAV navigation algorithms, thereby promoting the further application and development of UAV technology.
###### Abstract
In considering these premises, the objective of this study is to gain a deeper understanding of the potential of spaceborne very-high-resolution (VHR) optical data to enhance the Land Parcel Identification System (LPIIS). Specifically, the study investigates the potential applications of the high temporal resolution of PlanetScope, a commercial VHR optical-imaging constellation, to develop a workflow for extracting land-parcel boundaries from scenes. To this end, the study used an agricultural region of Umbria, Italy, as a case study. To achieve the research objectives, a time series of twenty-five PlanetScope scenes acquired between March and October 2023 over the test area was segmented. Additionally, auxiliary data for the same region were utilized to assess the accuracy of the results, including the 20 cm RGBNIR orthophoto acquired by the Italian Paying Agency (AGEA--Agenzia per le Erogazioni in Agricolutra) in June 2023 to update the national LPIIS, along with its derived land-cover map and Italian cadastral data. The investigation compared the results of applying different segmentation thresholds to the PlanetScope image time series. The spectral resolution of PlanetScope (eight bands from coastal blue to near-infrared ranges) provided a suitable foundation for the segmentation of the NDVI multitemporal stack, which required only the red and NIR bands. Additionally, the incorporation of PlanetScope's temporal resolution into the analysis demonstrated significant potential for extracting land-parcel boundaries, despite its geometric resolution being lower than that of the orthophoto used by AGEA for the same purpose (3 m vs. 20 cm). This approach addresses the limitations of the current static paradigm of LPIIS, which relies on monop temporal data. This static approach may yield parcel delineations that do not accurately reflect their definitive state, potentially resulting in a partial and less precise representation of the agricultural landscape. In contrast, the method proposed in this study introduces a dynamic LPIS paradigm by incorporating seasonal variations in land cover, thereby improving the accuracy of parcel-boundary delineation. This enables the derivation of spectral characteristics averaged over the entire phenological cycle, ensuring the establishment of more accurate and reliable land-parcel boundaries.
## 2 Materials and Methods
### Spectra Extraction and Preprocessing  
Spectral information was extracted from the hyperspectral images after image correction. Due to limitation of the hyperspectral imaging systems, the reflectance values of the first several bands and the last several bands are noisy and cannot be trusted. Thus, only trusted range of spectrum was used for further analysis. The spectral range of 441-949 nm was selected for visible/near-infrared hyperspectral images and the spectral range of 975-1646 nm was selected for near-infrared hyperspectral images. Wavelet transform (WT) is an efficient method for spectra preprocessing [17]. In this study, WT using Daubechies 8 as basis function and a decomposition level 3 was applied as a preprocessing step to obtain smoothed spectra. After preprocessing of the spectral dimension, image segmentation was applied on the spatial dimensions. Generally, reflectance values of the black background were close to zero, so pixels of rice seeds can be easily segmented by thresholding. Then, each group of connected foreground pixels is regarded as a rice seed. At last, for each rice seed, all pixel-wise spectra were averaged to one spectrum for further analysis.
# On the Evaluation of Complex Networks Designs for an Energy-Efficient IP/WDM Core Network
## 5 IP over WDM Core Network Architecture  
The IP over WDM backbone network as described in [1; 13] and illustrated in Figure 8 is composed of two layers: an IP layer and circuit-switched optical layer. The IP layer includes the IP routers, and the transparent optical layer consists of the optical switches (OXC), multiplexers, transponders, erbium-doped amplifiers (EDFAs), and regenerators. The two layers are interconnected through high speed short-reach optical interfaces to facilitate communication between low-end routers. A high-speed optical cable with multiple fibers is employed for interconnecting optical switches, with each fiber associated with two wavelengths for multiplexing and demultiplexing. The OXC is a preconfigured cross-connect system that is capable of inter nodes switching functionality. A pair of transponders  
Figure 6: Average shortest path length \(<\)\(L\)\(>\).  
Figure 7: Average clustering coefficient \(<\)C\(>\).  
are also employed for converting an O-E-O (optical-electrical-optical) signal at the switch node during transmission. For long distance transmission, erbium-doped fiber amplifiers (EDFAs) are used to facilitate transmission for 80 km and provide 30-dB amplification gain to the signal [blackout resilient network]. Regenerators are an essential part of the IP over WDM network and are responsible for restoring the signal strength. A regenerator receives the optical signal, converts it to an electrical signal, removes unwanted parts, and converts it back to an optical signal for forward transmission. Finally, the electronic IP router connects the low-end routers with the OXC switches and is mainly responsible for traffic aggregation between the metro and core networks [13].  
In the following section, we introduce the energy minimization MILP model to benchmark the IP over WDM network energy consumption between the developed small-world, SFN, and random NSF topologies' interconnections presented in Section 5 with the current NSF design.