esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The study [22] emphasizes that UAV cooperation can perform significantly better than a non-cooperative case with respect to the probability of outage of the OTFS-based LEO-SatCom transmission. Therefore, there is a growing attention in understanding the performance of OTFS in relay systems under high mobility circumstances. Selective DF protocol-based cooperative communication, wherein the relay retransmits the symbol to the destination on the condition that the current SNR at the relay is greater than a threshold, is considered in [12]. The paper expresses the end-to-end performance of a selective DF-based MIMO-OSTBC (Orthogonal Space Time Block Code) collaborative wireless system over time-varying fading channels. In [23], better end-to-end error performance was obtained using MIMO and STBC together with SDF protocol when compared to AF and DF protocols. In [24], it is expressed that the SDF-based collaboration protocol can be used with MIMO techniques; to further improve the PEP performance of the cooperative system, STBC and maxima rate combining (MRC) are used. In study [25], it was stated that, compared to the DF protocol, SDF-supported relays can prevent the transmission of non-correct information to destinations to increase system performance. It has been emphasized that the SDF protocol is advantageous over its classical DF counterparts. As mentioned above, SDF protocol is a recently used method in cooperative communication [15; 23; 24; 25]. In our work, we have also improved the system performance by using OTFS modulation in the SDF protocol or scheme where the receiver part is mobile. There are efforts to obtain customized traditional point-to-point single-input single-output (SISO)-OTFS studies in the literature, but their BER performance seems rather low. It is possible to use cooperative communications in order improve BER performance, but it is noticeable that there are very few OTFS studies in the area of cooperative communications. To the best of our knowledge, we also address for the first time in the literature that the bit error rate (BER) performance of a mobile receiver is improved by using the selective decoding-and-forwarding (SDF) scheme in combination with OTFS transmissions in the scope of cooperative communications.
## 4 Experimental Results and Analysis
### Recognition of Targets at Several Scales and Angles  
To determine the superiority of this approach, its findings were compared with those obtained by the standard unenhanced color segmentation recognition algorithm, template matching algorithm, and the SIFT and SURF feature point matching algorithms.  
The comparison uses the G-component of the RGB model for the purpose of image segmentation, subsequently applying the process of fitting rectangular shapes. The form matching algorithm utilizes the template given in this work, employing an equivalent number of templates. The OpenCV library employs the SIFT and SURF algorithms for feature matching. The results of multi-scale and multi-angle recognition are shown in Figure 12.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Serial No.** & **1** & **2** & **3** & **4** & **5** \\ \hline Angle Ratio & \(-\)30\({}^{\circ}\) & \(-\)15\({}^{\circ}\) & 0\({}^{\circ}\) & 15\({}^{\circ}\) & 30\({}^{\circ}\) \\ Scales Ratio & 0.8 & 0.9 & 1 & 1.1 & 1.2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Modification of template angle and scale.  
Figure 11: Identification of a certain target. (**a**) Template structure, (**b**) Edge data of the image to be compared, (**c**) Recognition outcomes.  
The results obtained from various algorithms are displayed in Table 5.  
The table illustrates that the form matching algorithm successfully matches at different angles and scales, providing evidence that the system can precisely identify the target within a specific range of rotation angles and scales. The color segmentation method is invariant to changes in target scale and is exclusively driven by lighting conditions. However, SIFT and SURF algorithms do not yield a high recognition rate when applied to multi-scale targets.  
For this experiment, we have selected photographs of cucumbers that are developing at different sizes and orientations. These photographs exhibit diverse levels of light intensity, and the subject is somewhat obscured. The results of the recognition are shown in Figure 13.  
Figure 12: Multi-angle and multi-scale cucumber fruit identification: (**a**) \(\omega=-15^{\circ}\); (**b**) \(\omega=-10^{\circ}\); (**c**) \(\omega=0^{\circ}\); (**d**) \(\omega=10^{\circ}\); (**e**) \(\omega=15\); (**f**) S = 0.8; (**g**) S = 0.9; (**h**) S = 0.1; (**i**) S = 1.1; (**j**) S = 1.2.  
Figure 13: Target recognition: (**a**) greenhouse shooting images; (**b**) database images.
# Highly Porous Organic Polymers for Hydrogen Fuel Storage
## 3 Highly Porous Organic Polymers for H\({}_{2}\) Storage
### Porous Aromatic Frameworks (PAFs)
Addition of magnesium alkoxide was also studied by simulation, and is predicted to enhance the interaction between H\({}_{2}\) and polymer pores, yielding a predicted H\({}_{2}\) uptake of 7.12 wt % at 298 K and 100 bar [111]. Note that this is approaching the DOE 2020 targets for room temperature performance. Similar to what was observed for CMPs, a nitrogen-rich porous aromatic framework (N-PAF) was also developed with the \(S_{\text{BET}}\) = 1790 m\({}^{2}\)/g and H\({}_{2}\) uptake up to 1.87 wt % at 77 K/1 bar [112]. Based on this study, the nitrogen substitution does not improve material performance. Figure 11: Basic synthetic route for porous aromatic framework-1 (PAF-1) (cross-coupling is promoted by Ni(0) catalyzed Yamamoto-type Ullman reaction under inert atmosphere.). Recently, [PERSON] et al. copolymerized the porous aromatic framework, PAF-1, with the polymer with intrinsic microporosity, PIM-1 [113]. By varying the relative contents of PAF-1 and PIM-1, they demonstrated that the PAF polymer retains a much larger internal surface area and hence a higher H\({}_{2}\) store capacity. In fact, PAFs are the only polymer to date to show significantly higher H\({}_{2}\) storage capacity than the most MOFs at high pressures. Data for performance at 1 bar is limited to a single study, showing H\({}_{2}\) storage capacity similar to MOFs under these conditions.
## 4 Current Approaches to Measure Resilience in Smart ICI
### Quantitative (Holistic) Approach
#### 4.2.4 Resilience Capacities Metric  
In [46], the authors introduced a metric for management systems as a solution for decision support and design. Similar to other quantification-based approaches, this proposed resilience metric aimed to link the three main resilience capacities (absorption, adaptation, and restoration) with recovery time. Their model [46], expressed in Figure 8, was employed  
Figure 8: Description of the Quantitative Resilience Management Tool [44].  
to evaluate a resilience factor \(\rho_{i}\). Here, \(S_{p}\) represents the speed recovery factor value, \(F_{o}\) denotes the initial infrastructure performance level, \(F_{r}\) corresponds to the total performance after recovery, and \(F_{d}\) refers to the performance level immediately post-disruption.  
\[\begin{split}&\rho_{i}(S_{p},F_{r},F_{d},F_{o})=S_{p}\frac{F_{r}F_{d}}{F_{ o}F_{o}}\\ &\textit{where};S_{p}=\begin{cases}(t_{s}/t_{r}^{*})exp[-a(t_{r}-t_ {r}^{*});for;t_{r}\geq t_{r}^{*}]\\ (t_{s}/t_{r}^{*});otherwise\end{cases}\end{split} \tag{8}\]  
Furthermore, the authors discovered that these measures are generated based on the specific historical knowledge of each affected organization and also by applying the exact disruption event time, denoted by the indices \(t_{s}\)--the slack time (maximum acceptable time post-disaster before recovery occurs) and \(t_{r}\)--the time to achieve the final recovery (attain equilibrium). In Equation (8), \(\alpha\) describes the parameter measuring the \"decay\" in resilience, and \(t_{r}^{*}\) represents the time required to implement the recovery program and plan.  
The time to recovery in this paper is assessed from the failure itself until the infrastructure restores an acceptable level of functionality. The model highlights the significance of the time to restart the infrastructure and taking the \"slack time\" into account. If the recovery process extends beyond the slack time, the resilience of the infrastructure will eventually decrease. Conversely, if the initial recovery is faster than the slack time, the resilience metric declines, and if the first recovery is considered effective but the infrastructure requires an extended period to recover after initial actions, the resilience metric also declines.
# Assessment of Laser Galvanometer Scanning System Accuracy Using Ball-Bar Standard
## 2 Materials and Methods
### Selection of the Reference Standard  
The selection of applicable standards that meet metrological traceability requirements is crucial for carrying out laser projectors' accuracy. The measurement capabilities and operating conditions of the system under test should also be taken into consideration. The standards confirming the metrological accuracy of the systems must have a calibration certificate proving their geometrical characteristics. Different types of standards are presented in [14].  
After reviewing the available standards, it was concluded that the system accuracy test would be performed using a ball-bar type standard. The ability of the system to acquisition and recognize the reference spheres images was verified prior to measurements. The image obtained during the ball-bar scan was compared with the image obtained during the scan of the calibration board (Figure 3). For the compared standards, no significant differences were observed. Due to the target range of dimensions tested and the lack of differences in recognizing reference features, the ball-bar standard made of carbon fiber with the near-zero thermal expansion factor (\(\alpha\) = 0.2 \(\times\) 10\({}^{-6}\) K\({}^{-1}\)) and with a nominal length of L = 3000 mm was selected to assess the system accuracy.
## 6 Performance Evaluation
### Evaluating Management Efficiency
#### 6.7.3 Coping with Changes and Disturbances in the Network  
In this part, we evaluate the performance of D-MSR in coping with changes in the network. Figure 32 shows the different behavior of D-MSR and WirelessHART in the case of different numbers of edge failures, which are chosen randomly, thereby implying different hop distances from the gateway. We increase the number of edge failures from 1 to 10 and measure the delay, and the number of required communications for coping with edge failure in D-MSR and WirelessHART.  
In case of edge failure in D-MSR, the connection manager releases all the reserved communication resources that have failed. The routing layer establishes the new routes then the connection manager re-establishes new connections by reserving resources along the new routes. The delay and overhead in re-establishing connections are shown for both D-MSR and WirelessHART in Figure 32.  
In WirelessHART, even though the network may still work through an alternative path when graphs are unreliable, the implemented system management algorithm is set to establish new graph and construct new communication schedule. Moreover, it needs to report the edges failure to the centralized network manager, who subsequently establishes new routes, releases the previous schedules on the old routes and construct new communication schedule (links) on the new routes. In D-MSR, this procedure is done in a distributed manner and this causes the relatively low delay and number of required communications. For example, when the number of broken edges is 10, the number of required  
Figure 31: End-to-end connection establishment delays (**a**) and overhead (**b**) (D-MSR _vs._ WirelessHART).  
Figure 32: Network maintenance delays (**a**) and overhead (**b**) (D-MSR _vs._ WirelessHART).  
communications for network maintenance is on average 48% less for D-MSR, compared to WirelessHART. Furthermore, the network maintenance delay is 79% less.
# A Virtual Network Resource Allocation Framework Based on SR-IOV
## 5 Evaluation
### Dynamic Scheduling
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Time** & \multicolumn{3}{c}{**0 (min)**} & \multicolumn{3}{c}{**10 (min)**} & \multicolumn{3}{c}{**20 (min)**} & \multicolumn{3}{c}{**30 (min)**} \\ \hline state & net & cpu & result & net & cpu & result & net & cpu & result & net & cpu & result \\ VM1 & 55\% & 0 & VF & 55\% & 0 & VF & 55\% & 0 & VF & 55\% & 0 & VF \\ VM2 & 0 & 40\% & & 0 & 40\% & & 0 & 40\% & & 0 & 40\% & \\ VM3 & 30\% & 30\% & & 30\% & VF & 30\% & 30\% & & 30\% & 30\% & 30\% & \\ VM4 & 20\% & 0 & & 20\% & 0 & 30\% & 0 & VF & 30\% & 0 & VF \\ VM5 & 40\% & 0 & & 40\% & 0 & VF & 40\% & 0 & VF & 40\% & 0 & VF \\ VM6 & 60\% & 0 & VF & 60\% & 0 & VF & 60\% & 0 & VF & 60\% & 0 & VF \\ VM7 & 80\% & 0 & VF & 0 & 20\% & & 0 & 20\% & 20\% & & \\ VM8 & 100\% & 0 & VF & 0 & 40\% & & 0 & 40\% & 40\% & 40\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The load of VMs changes over time. VM whose _io_degree_ value is much larger than _VM4_. As can be seen from the results of the scheduling, VF is always dynamically allocated to VMs with high network density.
## 4 Modeling Process and Results
### Construction of the GD-SI Model
#### 4.2.1 GeoDetector Result
The \(q\) values for plan curvature (\(q\) = 0.007) and profile curvature (\(q\) = 0.005) are both less than 0.01, indicating that these two factors are not related to landslide occurrence. In addition, these two factors did not pass the significance test (\(p\) < 0.05). Therefore, plan curvature and profile curvature were eliminated, and the remaining 18 factors were retained for further factor screening.
## 4 Agro-Food Substrates for Anaerobic Digestion
### Livestock Manure
#### 4.1.3 Poultry Manure  
Poultry manure has a high potential organic substrate (20% or more of DM) for treatment; however, the higher nitrogen content compared to manure from other farm animals (Table 4) makes it problematic for the AD process [76]. Ammonia inhibition is a common problem for the AD process using substrates such as poultry litter [77], and swine manure [78]. The inhibitory effects of free ammonia on the metabolism of methanogens were observed by several authors [79]. Commonly, to avoid ammonia inhibition, poultry manure is diluted with water [80] to decrease the high percentage of total solids (Table 4); however, this technique will decrease the biogas production per unit of fermenter volume, and increase the consumption of water and the processing cost for the slurry discharge. In other studies, the co-digestion of poultry manure with other types of livestock dung, such as cattle manure [81], swine manure [82] or with other substrates, like rotten potatoes [83] has been evaluated. Alternatively, in order to use chicken manure as a single substrate, the technical stripping of ammonia can be applied to evaporate ammonia into the gas phase [84].  
In addition, the use of diets integrated with exogenous enzymes and probiotics for broilers reared in cages produced a high biogas yield, with possible benefits for the AD process, which may cause an inducted positive artificial environment for the process [85]. It may be considered as a pre-treatment stage, promoting the hydrolysis of substances present on the excreta and allowing higher performances of the microbial population in the subsequent steps.
## 3 Developing Models: NLP and Word Embeddings  
### Data Collection and Preprocessing  
The two primary datasets are collected from Twitter, sourced from Kaggle, and YouTube, comprising comments from the UCI DL Repository. Figure 4 includes consolidating segmented files in the datasets, lowering dataset sizes by decreasing the number of rows, and removing extraneous columns. The data are subjected to a purification procedure that involves eliminating stop words and punctuation, as well as standardizing label types. The script is designed for a comparative analysis of various NLP models, with a focus on text classification tasks. It utilizes LSTM, BERT, and ELMo models to classify text data into categories (e.g., \"Spam\" vs. \"Quality\").  
Figure 4 shows the script encompasses data loading, preprocessing, model training, and evaluation. Table 1 shows the loaded dataset that contains various features like \"Tweet\", \"following\", \"followers\", \"actions\", \"is_retweet\", \"location\", and \"Type\". The \"Type\" column categorizes tweets into \"Quality\" or \"Spam\", which could be the target variable for our classification task. Table 2 shows the loaded dataset that contains various features like comment_id, content, date, and class.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**ID** & **Tweet** & **Following** & **Followers** & **Actions** & **Is Retweet** & **Location** & **Type** \\ \hline
10091 & It’s the everything else that’s complicated. & 0.0 & 11,500.0 & NaN & 0.0 & Chicago & Quality \\
10172 & [PERSON] sent a glare towards [PERSON] then nodded & 0.0 & 0.0 & NaN & 0.0 & NaN & Quality \\
4.0 & I posted a new photo to Facebook http://fb. & 0.0 & 0.0 & NaN & 0.0 & Scotland, UK & Quality \\
3697 & \#jan Idiot [PERSON] Diagnoses [PERSON] & 3319.0 & 611.0 & 294.0 & 0.0 & Atlanta, Ga & Spam \\
10740 & Pedophile [PERSON] is TERRIFIED Getti... & 4840.0 & 1724.0 & 1522.0 & 0.0 & [PERSON] & Spam \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sample of Twitter Dataset.  
Figure 4: Research methodology.  
Tables 1 and 2 involve meticulously preparing and preprocessing two essential datasets, as referenced in the literature [30]. The initial datasets were specifically designed for analyzing spam on Twitter. The preprocessing is performed with great attention to detail to ensure the quality and reliability of the data, starting by removing unnecessary columns and fixing any missing values. Following that, we proceed with the process of standardizing the labels within the datasets, ensuring consistency across all data points to ensure balanced data are a critical part of this stage, as they play a vital role in achieving accurate and consistent results across various models. The cleaning step is involved as a part of the preparation to remove unnecessary elements like URLs, stop words, and punctuation marks, which have no impact on the model's learning phase, as shown in Algorithms 1 and 2. It involves dividing the data into two distinct sets: 80% is allocated for training purposes, while the remaining 20% is reserved for evaluating the model's performance.
# Traffic Signal Control with State-Optimizing Deep Reinforcement Learning and Fuzzy Logic
## 6 Experimental Results
### All-4  
The experimental results were obtained in the original environment with four outgoing lanes on each arm.  
Figure 10: (**a**) Training result graph based on negative reward. (**b**) Training result graph based on average total waiting time. (**c**) Training result graph based on average total waiting time for vehicles to make a left turn. (**d**) Training result graph based on average total waiting time per car.  
In Table 9, for the proposed method, the values of WT\({}_{\text{AVG}}\), WTL\({}_{\text{AVG}}\), and WTC\({}_{\text{AVG}}\) can be reduced by 69.80%, 23.24%, and 60.78% when compared with the traditional method, and 18.46%, 40.36%, and 21.23% when compared with the original DQN with the fuzzy logic method, respectively.  
It is noted that the original DQN with the fuzzy logic method yielded worse results than the traditional method compared to vehicles turning left due to the inappropriate reward, in which the traditional method uses a fixed 10 s green signal in a sequence in every phase.  
However, the proposed method, in which we modified the reward calculation, yielded better results than all the other methods when compared with all metrics.  
The experimental results are presented in Section 6.3. The following value was used to compare the results instead of Equation (10):  
1. The average total waiting time for vehicles to make a right turn (WTR\({}_{\text{AVG}}\)) is the average accumulated waiting time for all vehicles in the outgoing lane to make a right turn per step. \[\text{WTR}_{\text{Total}}=\sum_{\text{i}=1}^{\text{N}}\text{WTR}[\text{i}]\] (12) where N is the number of vehicles at the intersection of the right-turn lane at time step t, and WTR is the accumulated waiting time of vehicles in the right-turn lane. \[\text{WTR}_{\text{AVG}}=\frac{1}{\text{T}}\times\sum_{\text{i}=1}^{\text{T}} \text{WTR}_{\text{Total}}[\text{i}]\] (13) where T is the maximum step, and WTR\({}_{\text{Total}}\) in Equation (12) is the total waiting time of the vehicle in the right-turn lane at time step t.
# A Novel Disturbance Rejection Control of Roll Channel for Small Air-to-Surface Missiles
## 3 Controller Design Base on Extended State Observer
### Controller Design  
The attitude control of the roll channel is to use the output roll angle speed to design an extended attitude observer, estimate the system uncertainty and internal and external disturbances, and design a controller to control and compensate the disturbances, so that the missile roll angle speed can quickly track the roll angle speed command, and at the same time, the system has strong robustness and anti-disturbance ability. The system structure is shown in Figure 1.  
The obtained ESO-based state observer expression is:  
\[\left\{\begin{array}{l}e_{1}=z_{1}-y,\\ \dot{z}_{1}=z_{2}-\beta_{1}e_{1}+b_{x}u_{x},\\ \dot{z}_{2}=-\beta_{2}\text{fal}(e_{1},\alpha_{x},\delta_{x}),\end{array}\right. \tag{23}\]  
where \(e_{1}\) represents the deviation between the estimated roll angle rate \(z_{1}\) and the actual output \(y\). \(z_{2}\) represents the estimation of disturbance in the system and model uncertainty.  
For the above model of the rolling channel, the designed control quantity is the linear error feedback of rolling angle rate, namely:  
\[u_{x}=k(\omega_{c}-y), \tag{24}\]  
where \(y\) represents the output roll angle speed of the system, and \(\omega_{c}\) represents the control instruction of the roll angle speed.  
In order to eliminate the influence of uncertain factors in the loop of the rolling channel, the unknown disturbance observed is compensated, and the control quantity of the inner loop after compensation is:  
\[u=u_{x}-\frac{z_{2}}{b}. \tag{25}\]  
Thus, the tracking design of roll angle rate in roll channel is realized.
## 5 Experimental Results and Analysis
### Ablation Experiment
Visual examples comparing FCOS-O and our method on the DIOR-R dataset are presented in Figure 12, demonstrating that our method detects more instances compared to the baseline.