esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area
### Geological and Seismotectonic Setting
The Tsoukalades-Katouna fault block, located to the south of the previous fault block and bounded to the north by the FAFZ, to the south by the Pigadisanoi-Fraxi fault zone (PFFZ) and to the west by the Tsoukalades-Agios Nikitas fault zone (TANFZ). 3. The Agios Nikitas fault block, located in the northwestern part of the island and bounded to the east by the Agios TANFZ and to the west by an almost N-S striking fault zone parallel to the coast. 4. The Drymonas fault block, located east of the Agios Nikitas fault block and bounded to the west by the Agios Nikitas fault zone (ANFZ), to the east by the N-S striking Drymonas fault zone (DFZ) and to the south by the NW-SE striking Kalamitsi-Exantheia fault zone (KEFZ). 5. The Mega Oros-Skaroi fault block, located east of the Drymonas fault block and bounded to the north by the NW-SE striking PFFZ and to the south by the NE-SW striking Sivros-Nidri fault zone (SNFZ). Figure 1: Map of the Hellenic Arc showing the location of Lefkada Island at the northwestern part of the Hellenic Arc along with the prominent morphological features of the Hellenic Arc and the major morphoneetectonic features based on Mariolakos and Papanikolaou [70; 71] and the seismic risk zones of Greece. Lefkada belongs to zone III of the current Greek Building Code with a Peak Ground Acceleration (PGA) value of 0.36 g for a return period of 475 years. The Vlicho-Poros fault block located in the southeastern part of the island and bounded to the northwest by the NE-SW striking SNFZ and Vassiliki fault zone (VFZ) and to the southeast by the NW-SE striking Syvota-Sivros fault zone (SSFZ). * The Sykeros-Achrada fault block, located in the southern-central part of the island and bounded to the northwest by the NE-SW striking VFZ and to the northeast by the NW-SE striking SSFZ. * The Lefkata fault block located, in the southwestern part of the island and bounded to the north by the NW-SE striking KEFZ and the Ionian thrust on to Paxoi unit, to the east by the NE-SW striking VFZ and to the west by the dextral strike-slip Athani fault zone (AFZ). Some of the aforementioned main fault blocks are composed of smaller fault blocks. The most characteristic case is the Dragano-Athani graben within the fault block of the Lefkata peninsula. This graben is bounded to the west and east by NNE-SSW striking faults. Its western margin is defined by the AFZ with a total horizontal displacement of approximately 860 m corresponding to a Quaternary slip rate of about 8 mm/year [81] and similar geometric and kinematic characteristics with the Lefkada segment of the Cephalonia Transform Fault Zone [79]. Figure 2: **(a)** The neetoctonic map of Lefkada Island from [PERSON] et al. [77]. The Egremnoi and Porto Katsiki coastal areas are located in the southwestern part of the fault block of Lefkata peninsula and are composed of Jurassic-Moocene limestones of the Paxoi geotectonic unit. **(b)** Based on the landslide susceptibility map of the Ionian Islands compiled by [PERSON] et al. [82], these slopes are formed in coastal areas highly susceptible to failure.
## 3 Results and Discussion
### Mechanical Properties of GO/AMT-CE Composites  
The mechanical properties of GO/AMT-CE composites were determined by the UTM at room temperature. Figure 6a shows that the addition of GO/AMT can improve the tensile strength of CE from 30 to 62 Mpa, and Figure 6b indicates that the elongation at break increases from 2.4% to 4.8%. In addition, the tensile modulus increased from 1400 to 1600 Mpa (Figure 6c), indicating that the addition of GO/AMT significantly improved the strength and toughness of CE resin.  
Figure 5: Thermal stability of GO/AMT-CE composites with different GO/AMT contents.  
Figure 6: Mechanical properties of GO/AMT-CE with different GO/AMT contents: (**a**) tensile storage (**b**) elongation at break, and (**c**) tensile modulus.  
The mechanical properties of GO/AMT-CE composites under wet-heat conditions were also carried out by putting the resin samples into 23 \({}^{\circ}\)C water for 240 h (Table 2). From this table, it can be seen that GO/AMT-CE composites had higher wet-heat resistant property compared to pure CE. The significant improvement of mechanical properties was mainly ascribed to the excellent mechanical properties of GO and the interface combination between GO and resin. The epoxy group on the surface of GO formed an oxazolidinone ring with the -NCO of CE resin, thereby forming a more obscure network structure and improving the bonding strength. Moreover, a stronger steric effect resulted in improved dispersibility of GO in the resin matrix in the presence of the AMT structure.
# Maximum Transmit Power for UE in an LTE Small Cell Uplink
## 2 Cell Topology and Related Work
In summary, dynamic small cell base station deployment can be beneficial to reduce the downlink energy consumption. However, with mobile base stations, the issues of base station location, height, and mobility management will be more complex and will have a great impact on the quality of service in future cellular networks, as these networks will have to handle a large number of UE and their frequent handoffs due to the very dense short-footage small cells. Conventionally, a static small cell base station deployment strategy is considered while optimizing the UE energy consumption in the uplink direction. In terms of downlink energy efficiency, a small cell on/off scheme can be adopted along with the static small cell base station deployment. Also, the abovementioned downlink-centric design considerations of dynamic small cell base station deployment can be adopted. As the focus of this work was to minimize the UE power consumption by finding the upper bound for the transmit power of UE in the uplink direction only, we adopted the conventional static small cell deployment strategy to model the network.
# A Synthetic Approach for Biosynthesis of Miquelianin and Scutellarin A in _Escherichia coli_
## 2 Materials and Methods
### Whole-Cell Biotransformation and Flavonoid Extraction  
_E. coli_ strains were pre-cultured in 3 mL of LB liquid medium with antibiotics and incubated at 37 \({}^{\circ}\)C in 200 rpm overnight. Five-hundred microliters of inoculum was transferred into 50 mL of Luria-Bertani (LB) liquid medium with appropriate antibiotics and cultured at 37 \({}^{\circ}\)C until the optical density at OD\({}_{600\mathrm{nm}}\) reached 0.5-0.7. The culture was induced by adding isopropyl-\(\beta\)-D-thiogalactopyranoside (IPTG) with a final concentration of 0.5 mmol/L and incubated at 15 \({}^{\circ}\)C for protein expression, and to increase cell biomass. The following day, each culture was supplemented with specific flavonoids; apigenin in _E. coli_ harboring UGT88D8 to produce scutellarin A, and quercetin in _E. coli_ harboring VvGT5 to produce miquelianin. Then, the flasks were incubated at 15 \({}^{\circ}\)C for 60-72 h for biotransformation. Each culture was extracted with an equal volume of ethyl acetate. The organic layer was collected and concentrated to dryness through the evaporation of excess solvent, followed by the addition of methanol to dissolve remaining products. The extracts were subjected to high performance liquid chromatography (HPLC) and liquid chromatography quadrupole time-of-flight-electrospray ionization mass spectrometry (LC-QTOF-ESI/MS) analyses.  
Following the confirmation of products, we performed the bio-catalysis reaction with the strains containing sugar cassettes, including UGTs. Equal quantities (200 \(\mu\)L) having the same cell density (0.7) inoculums were transferred into 50 mL _E. coli_ flasks containing LB medium. Identical conditions were applied for IPTG induction, incubation temperature, and shaking of the culture. _E. coli_ BL21 (DE3) _Dggi_\(\Delta\)_zwf_\(\Delta\)_ushA_ piBR181-glk.pgm2.galU.ugd.UGT88D8 was fed with apigenin while _E. coli_ BL21 (DE3) _Dggi_\(\Delta\)_zwf_\(\Delta\)_ushA_ piBR181-glk.pgm2.galU.ugd.VvGT5 was fed with quercetin. One milliliter sample was drawn from each culture at 12 h time intervals until 72 h. The samples were centrifuged, and supernatants were extracted with a double volume of ethyl acetate, followed by drying the samples. Then, the samples were dissolved in 100 \(\mu\)L methanol. These reaction mixtures were analyzed by HPLC-PDA and LC-MS for confirmation of glucuronic acid conjugated derivatives of quercetin and apigenin.
# A Comprehensive Approach to Bias Mitigation for Sentiment Analysis of Social Media Data
## 2 Related Work
### Research Gap  
Although a significant amount of research has been conducted in the field of sentiment analysis, there are still some crucial gaps that our study aims to address. First, most existing research does not consider the potential influence of implicit bias in the sentiment analysis process. Such biases can skew the results and lead to inaccurate or misleading conclusions. Our research seeks to mitigate this by developing a bias-aware sentiment analysis technique. Secondly, the majority of sentiment analysis techniques are binary in nature, considering only positive or negative sentiments. This can overlook the nuances of human emotions and the complexity of language. Our multi-dimensional approach to sentiment analysis allows for a more comprehensive understanding of sentiments expressed in text. Lastly, there is a lack of effective methods for detecting gender-inclusive language in sentiment analysis. With the rise of awareness around gender inclusivity, it is essential to develop techniques that can accurately analyze sentiments in a gender-inclusive language. Ourresearch proposes a novel methodology for this purpose, contributing to the broader efforts to make AI and NLP technologies more inclusive and representative of diverse user populations.  
In light of these gaps, our research presents significant contributions to the field of sentiment analysis. By addressing these issues, we aim to improve the accuracy and inclusivity of sentiment analysis techniques, making them more useful and relevant in today's diverse and dynamic digital landscape. In this research, we propose a novel method for bias-aware sentiment analysis that combines the approaches of data augmentation and adversarial training. We also introduce bias-penalizing terms into the loss function. These terms effectively act as a regularization mechanism that discourages the model from making biased predictions. By optimizing this modified loss function, the trained model aims to be both accurate in its sentiment classification and mindful of potential biases. We evaluated our method on a dataset of movie reviews, and we show that it outperforms baseline methods in terms of accuracy and bias.
## 6 Conclusions  
Based on the results obtained in the present research, the following conclusions have been reached:  
* Machine learning models are a significant alternative for ozone concentration forecasting. However, it is paramount to identify the variables influencing the phenomenon in constructing the feature vector. This enables the models to discern patterns and relationships among the variables correctly.
* Computational complexity can significantly impact the best model selection, as the computational resources required for hyperparameter optimization can be time-consuming and may not be feasible.
* A substantial volume of historical records to train models can enhance their precision. However, certain models do not efficiently handle large datasets, leading to disproportionately increased evaluation times. Therefore, employing a technique that allows for selecting a sufficiently representative sample to achieve results with an acceptable level of reliability without adversely affecting the outcomes of the models is of paramount importance. This approach ensures both the effectiveness and efficiency of the model training and evaluation process.
* Establishing a baseline is a critical step in model evaluation. It helps set realistic expectations and understand the value added by complex models.
* The GB models' lower MAE scores, especially compared to the relatively high baseline MAEs, suggest that these models have successfully captured significant underlying trends and patterns in the hourly ozone concentration profile.
* Elevated errors observed during specific time intervals warrant further investigation as potential focal points for future enhancements in model performance. Delving into the underlying reasons for these heightened error rates during particular hours and exploring what additional data or modifications in feature engineering might mitigate these discrepancies could yield significant advancements in model accuracy and reliability.
* Tree-based models like Random Forests or Gradient Boosting demonstrated better generalization to unseen data, but they tend to be more prone to overfitting than SVR models. This might necessitate more frequent retraining of tree-based models compared to SVR. However, given their shorter training times, they still present a better option in terms of model maintainability.  
All authors have contributed equally to this research. Conceptualization, R.D.-G. and M.A.-V.; methodology, R.D.-G. and M.A.-V.; software, R.D.-G.; validation, R.D.-G. and M.A.-V.; formal analysis, R.D.-G.; investigation, R.D.-G.; resources, R.D.-G. and M.A.-V.; data curation, R.D.-G.; writing--original draft preparation, R.D.-G.; writing--review and editing, R.D.-G. and M.A.-V.; visualization, R.D.-G.; supervision, R.D.-G. and M.A.-V.; project administration, R.D.-G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
# Biosignals Monitoring of First Responders for Cognitive Load Estimation in Real-Time Operation
## 3 Materials and Methods
### Sensors
#### 3.2.2 Wristband  
A wristband device, Empatica E4 ([[https://www.empatica.com/research/e4h](https://www.empatica.com/research/e4h)]([https://www.empatica.com/research/e4h](https://www.empatica.com/research/e4h)), accessed on 12 June 2023), has been selected to extract physiological measurements by using an EDA sensor, a PPG sensor, a temperature sensor, and a 3-axis accelerometer. Its battery lasts for more than 20 hours in streaming mode (i.e., sending data over Bluetooth). The same mobile application used for receiving data from the chest band is used for receiving  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Sensor**} & \multicolumn{2}{c}{**Framework**} \\ \cline{2-5}  & **Measurement** & **Frequency (Hz)** & **Measurement** & **Frequency (Hz)** \\ \hline \multirow{3}{*}{**Chest Band**} & Temperature & 1 & Temperature & 1 \\  & Respiration Rate & 25 & Respiration Rate & 1 \\  & ECG & 250 & Heart Rate & 1 \\ \hline \multirow{3}{*}{**Wristband**} & Accelerometer & 32 & Heart Rate & 0.5 \\  & PPG & 64 & Temperature & 1 \\  & EDA & 4 & GSR & 1 \\ \hline \multirow{2}{*}{**HoloLens**} & Eye Gaze & 30 & Eye Gaze & 30 \\ \hline \multirow{2}{*}{**Facial**} & Temperature & 10 & Temperature & 10 \\  & EMG & 1000 & EMG & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sensors’ data acquisition frequency and data sample rate sent to the framework.  
Figure 1: Sensor selections related to the physiological variables.  
data from the wristband. The EDA sensor measures the galvanic skin response (GSR) in microsiemens, and data are acquired four times per second. Wrist temperature is measured with an infrared thermometer in degrees Celsius. The accelerometer acquires the data 32 times per second. The PPG has a 64 Hz sampling frequency and measures the difference of light between oxygenated and non-oxygenated peaks. The HR is inferred from the data from the PPG sensor and the accelerometer using the deep learning model provided in [67]. HR is stabilized by the algorithm that provides 4.03 bpm as mean absolute error (MAE). Finally, HR is transmitted to the framework at 0.5 Hz and temperature and GSR at 1 Hz (see Table 1).
# Soft Iterative Decoding Algorithms for Rateless Codes in Satellite Systems
## 3 Efficient Soft Iterative Decoding Algorithms for HARQ with Rateless Codes in Satellite Systems
### HARQ with Rateless LDPC Codes  
#### 3.2.1 Operational Principle  
A raptor code is a concatenated code with an inner LT code and an outer LDPC code [2], and the structures of the inner and outer codes are generally analyzed with Tanner graphs. With a systematic LT code as the inner component, which has a rateless property and can be formulated by its own parity check equation similar to that of the LDPC code, the whole concatenated code can be considered as a rateless LDPC code.  
In Figure 2, a satellite system using an HARQ scheme is shown, where ACM is operated with the conventional LDPC code defined in the existing standards, and incremental redundancies are accomplished by employing rateless LDPC codes [6]. At the encoder in the transmission part, the conventional LDPC encoder takes \(\mathbf{u}\) as the information word, and generates the codeword \(\mathbf{U}\). For the LDPC code specified in the DVB-S2/S2X, the size of \(\mathbf{U}\) is 64,800 and 16,200 bits for normal and short frames, respectively. Then, an LDPC codeword, \(\mathbf{U}\) forms the systematic part of the inner LT code, and parity words, \(\mathbf{p}_{1},\mathbf{p}_{2},...\) are generated infinitely by the LT encoder. In our application, we use the following RSD to generate the parity words [1].  
\[\sigma(i)=\left[\rho(i)+\tau(i)\right]/\beta,\text{for }i=1,...,k, \tag{1}\]  
where  
\[\rho(i)=\begin{cases}1/k,&\text{for }i=1,\\ \frac{1}{i(i-1)},&\text{for }i=2,...,k,\end{cases} \tag{2}\]  
\[\tau(i)=\begin{cases}\Lambda/(ik),&\text{for }i=1,...,k/\Lambda-1,\\ \Lambda\ln(\Lambda/\delta)/k,&\text{for }i=k/\Lambda,\\ 0,&\text{for }i=k/\Lambda+1,...,k,\end{cases} \tag{3}\]and \(\Lambda=c\ln(k/\delta)\sqrt{k}\) for some suitable constant \(c>0\), \(\delta\) is an allowable failure probability, and \(\beta=\sum_{i=1}^{k}[\rho(i)+\tau(i)]\). In this study, \(\delta=0.9\) and \(c=0.32\).  
The codeword \(\mathbf{c}\) from the rateless LDPC encoder consists of \(\mathbf{U}\) and \(\mathbf{p}_{i}\)s, and \(\mathbf{p}_{i}\)s are accumulated in the buffer. A type-II HARQ scheme can be operated by using this rateless LDPC code as follows: \(\mathbf{U}\) is transmitted at the first transmission. If the decoding of \(\mathbf{U}\) fails, a part of the generated parity blocks, say \(\mathbf{p}_{1}\), is transmitted in the second transmission. If the decoding of \(\mathbf{U}\) combined with \(\mathbf{p}_{1}\) fails again, more parity blocks, \(\mathbf{p}_{2},\mathbf{p}_{3},...\) are transmitted until the ACK for successful decoding is received or until the limited number of retransmissions is reached.
## 2 Evaluation Criteria
### Communication Control Ability  
The communication control ability of rescue robots plays a crucial role in the successful execution of their missions. This ability primarily involves real-time communication between the operator and the rescue robot [60], ensuring precise control by the operator and effective information collection and transmission by the rescue robot to better assist rescue personnel in accomplishing tasks. Key elements of the communication control ability encompass command accuracy and comprehensive signal processing [61]. Therefore, the communication control evaluation criteria are presented in Table 4, providing comprehensive details for analysis.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Main Criteria** & **Corresponding Criteria** & **Literature Sources** \\ \hline \multirow{6}{*}{Mobility} & Mass-to-volume ratio & [29; 30; 31; 38] \\  & Unit pressure exerted by the tracked device on the ground & [2; 25; 39; 40] \\  & Maximum speed of wheel motion & [23; 29; 34; 35; 41] \\  & Minimum turning radius & [42; 43; 44; 45] \\  & Maximum width across trenches & [29; 34; 35; 46; 47] \\  & Maximum height of obstacles to be overcome & [29; 34; 35; 46; 48] \\  & Maximum angle for climbing & [29; 34; 35; 46; 49] \\  & Number of steps climbed per unit time & [29; 34; 35; 46; 50] \\  & Success rate of mechanism transformation & [32; 33; 51] \\  & Success rate of path planning & [36; 37; 52; 35] \\  & Subversive resistance on complex road surfaces & [29; 30; 31; 32; 33; 41] \\  & Smoothness of walking on complex road surfaces & [30; 31; 41; 54] \\ \hline \hline \end{tabular}
\end{table}
Table 2: The mobility evaluation criteria.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Main Criteria** & **Corresponding Criteria** & **Literature Sources** \\ \hline \multirow{3}{*}{Detection perception ability} & Probability of life body identification & [55; 56; 57; 58; 59] \\  & Maximum radius for effective search & [55; 56; 57; 58; 59] \\  & Maximum depth for effective search & [55; 56; 57; 59] \\ \hline \hline \end{tabular}
\end{table}
Table 3: The detection perception evaluation criteria.  
The corresponding hierarchical structure of these criteria is established based on the above comprehensive analysis and quantification of the evaluation criteria for rescue robots, as illustrated in Figure 1. The hierarchical structure allows for a clear evaluation of the rescue robot's performance on each individual criterion and how these criteria impact the overall performance of rescue robots.
# Effect of Post-Activation Potentiation on Weightlifting Performance and Endocrinological Responses
###### Abstract  
_Purpose_: This study examined the acute performance-enhancing effects and endocrinological responses of a supramaximal clean pull performed at 120% of clean and jerk, one repetition maximum, on clean performance. _Methods_: Eight (\(n=8\)) ranked collegiate level weightlifters attended two days of testing in a randomised order. A control session was used to identify a baseline measure of kinetic and kinematic clean performance and endocrinological status following three cleans interspersed with one-minute recovery between repetitions. The experimental condition required participants to perform a single clean pull at 120% of clean and jerk, one repetition maximum, followed by three minutes recovery, prior to executing three cleans with one-minute recovery between repetitions. All cleans were performed on a dual force plate set up, synchronised with a 3D motion capture system to simultaneously record barbell and ground reaction force data. All endocrinological data were measured prior to the participant warming up and also following each testing protocol. _Results_: The results indicated that no significant differences were found between the control and PAP condition (\(p=0.140\)-\(0.902\)); however, effect sizes from group analysis identified moderately negative to trivial effects across kinetic, kinematic and endocrinological variables (\(d=-0.30\)-\(0.14\)). Further analysis on an individual level demonstrated values, both negative and positive, ranging from extremely large (\(d=-4.10\)) to trivial (\(d=0.04\)). _Conclusions_: The findings suggest a potentially negative affect of PAP on kinetic and kinematic measures of clean performance. However, individual responses varied, and thus some weightlifters may find this useful.  
biomechanics; testosterone; kinetics; kinematics; clean Article