esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
### Experiment 1  
The clay/pebble mixture (condition 1) and fine silt (condition 2), once sufficiently settled, produced very similar mean anchoring strengths of 16.9 lbf and 16.1 lbf, respectively. Beach sand, however, produced a mean anchoring strength of only 4.3 lbf, or approximately 25% of that of the first two conditions. A one-way, single-factor analysis of variance (ANOVA) was conducted comparing the anchor strength data for the three sediment conditions collected in Test 1 with a significance level of 0.05, with a resulting f-value of 162.222 that exceeded the f-critical value of 3.101 and a \(p\)-value of 4.431 \(\times\) 10\({}^{-30}\). Thus, we conclude that the influence of sediment condition on mean anchoring strength is significant, with a 95% confidence level. The variances for conditions 1, 2, and 3 (14.81 lbf, 11.94 lbf, and 1.07 lbf, respectively) demonstrate a likelihood of unequal population variances, however.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Average Anchor Failure Tension (lbf)**} & \\ \hline \hline  & **Clay/Pebbles** & **Fine Silt** & \multicolumn{2}{c}{**Beach Sand**} & \multicolumn{2}{c}{**Configuration**} & \\ \hline Test 1 & 16.9 & 16.1 & 4.3 & 90 & Spike + 8 Wire Root Array (Straight Pull) \\ \hline Test 2 & 19.3 & 30.9 & 3.9 & 90 & Spike + 4 Wire Root Array (Straight Pull) \\ \hline Test 3 & 15.3 & 15.9 & 2.0 & 90 & Spike Only (Straight Pull) \\ \hline Test 4 & 21.9 & 22.5 & 3.2 & 30 & Spike + 8 Wire Root Array (Angled Pull) \\ \hline Test 5 & 15.5 & 12.1 & 1.2 & 30 & Spike Only (Angled Pull) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average anchor failure tensions (lbf) by sediment type and test.  
Thus, three two-sample \(t\)-tests (two-tailed) were conducted, assuming unequal variances at a significance level of 0.05, to confirm which groups had significantly differing means.  
The first \(t\)-test compared the anchoring strength data for conditions 1 and 2, with a resulting t-statistic of 0.851 and a \(p\)-value of 0.398; therefore, we conclude that a significant difference does not exist between the mean anchor strength for condition 1 vs. condition 2. The second \(t\)-test compared the anchoring strength data for conditions 2 and 3, with a resulting t-statistic of 17.985 and a \(p\)-value of 6.076 \(\times\) 10\({}^{-19}\); thus, we conclude that a significant difference does exist between the mean anchor strength for condition 2 vs. condition 3. Finally, the \(t\)-test comparing the anchoring strength data for conditions 1 and 3, with a resulting t-statistic of 17.381 and a \(p\)-value of 3.552 \(\times\) 10\({}^{-18}\), allows us to conclude that a significant difference does exist between the mean anchor strength for condition 1 vs. condition 3.  
As predicted, the Experiment 1 analysis confirmed, at a 95% confidence level, that sediment type does have a significant influence on anchoring strength, but only between condition 3 (sand) and the other two conditions (clay/pebble mixture and silt). No significant difference in mean anchoring strength exists between clay/pebble mixture and silt.
## 1 Introduction
### Design Methods against Obsolescence
#### 1.3.2 Modularity  
Modularity is a concept that breaks down a system into pieces of components with standardized interfaces. Thus, modular systems are open systems. That allows the exchange of components inside the system. Modularized system architectures expose only the interface to the outer world and hide their internal implementation. The coupling of the software to the hardware is loose [13] (p. 11).
## 7 Conclusions  
In this paper, the effectiveness of the MAB-based autonomous decentralized channel selection methods for massive heterogeneous IoT networks is evaluated. Specifically, the FSR can reach 95% when the numbers of channels and IoT devices are 60 and 10,000, respectively, while 12% channels are suffering traffic load by other kinds of IoT devices.  
Figure 12: Implementation results in terms of FSR for the MAB-based channel selection methods.  
\begin{table}
\begin{tabular}{c c} \hline \hline Experiment time [s] & 7200 \\ \hline Transmission power [mW] & 20 \\ \hline Bit rate [kbps] & 50 \\ \hline \multirow{3}{*}{Channel (center frequency [MHz])} & 34 (922.6), \\  & 37 (923.2), \\  & 40 (923.8) \\ \hline Number of IoT node \(M\) & 50 \\ \hline Number of available channels \(K\) & 3 \\ \hline Sleep interval of IoT node [s] & 1.0 \\ \hline Number of load node (LoRa) & 5 \\ \hline Load channel (center frequency [MHz]) & 34 (922.6) \\ \hline Sleep interval of load node [s] & 10.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The experimental settings.  
In addition, the performance in terms of FSR improves by 20% compared to the equality channel allocation. In addition, the MAB algorithm, termed MTOW, can achieve the highest FSR in any setting. Moreover, the performance of the MAB-based channel selection methods is implemented and evaluated on IoT devices. Experimental results show that the MTOW-based channel selection method can achieve the highest FSR and FI, i.e., around 0.95, and 0.998, respectively. In summary, the MTOW-based channel selection method can get high FSR either in simulation or experiments. As described above, due to the high FSR and FI, and easy application concerning the practical IoT devices, the MTOW-based channel selection method may become an efficient technique to support massive IoT applications, such as smart city, personal IoT, Smart grid, industrial assets monitoring, agriculture, and many other applications [35] in the next generation of wireless communication networks. In our further work, we will investigate the impact of the parameters in the MAB algorithm on the performance for massive heterogeneous IoT networks. For instance, the \(e\) value in \(e\)-greedy algorithm, the \(a\) in MTOW algorithm, and so on. Moreover, the time consumption of the evaluated algorithms in this paper will be experimentally analyzed later.  
conceptualization, S.H., A.L., S.-J.K., Y.S. and M.H.; methodology, S.H., A.L., S.-J.K. and M.H.; software, S.H., R.K.; validation, S.H.; formal analysis, S.H., A.L., S.-J.K., M.H.; investigation, S.H., A.L., M.H.; resources, S.H.; data curation, S.H., R.K.; writing--original draft preparation, S.H.; writing--review and editing, S.H., A.L., S.-J.K., Y.W., Y.S. and M.H.; visualization, A.L., M.H.; supervision, A.L., S.-J.K., Y.S. and M.H.; project administration, M.H.; funding acquisition, M.H. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by JSPS KAKENHI Grant Numbers JP22H01493 to M.H. and JP22K14263 to A.L.  
The authors declare no conflict of interest.
## 4 Results
### Contact Angle Measurements  
The mean values of contact angle measurements are summarized in Figures 5 and 6. The water contact angles were \(63.675^{\circ}\pm 0.608^{\circ}\), \(66.275^{\circ}\pm 0.826^{\circ}\), \(70.010^{\circ}\pm 0.667^{\circ}\), and \(105.263^{\circ}\pm 0.823^{\circ}\) at 60 nm, 96 nm, 149 nm, and 368 nm silver particle size, respectively. The water contact angles increased continuously with the size of the silver particles increasing.  
The mean contact angles of 60 nm, 96 nm, and 149 nm were below \(90^{\circ}\), indicating the water behaved as a good wetting liquid on the tested surface, contributing to the hydrophilic nature of the modified titanium surface. With 368 nm sized silver particles, the mean contact angle was higher than the borderline value to demonstrate that the water behaved as a poor wetting liquid on the titanium surface covered by bigger silver particles. Moreover, the statistical analysis showed that the means differed significantly (\(p<0.01\)) with increasing silver layer thickness.
## 2 Thermodynamic Design and Optimization
### Hypothesis and Assumptions  
The main assumptions, that are common to all the considered cases, are collected in Table 1. In detail, the temperature of NG coming from the grid (T\({}_{1}\)) is taken equal to 20 \({}^{\circ}\)C, regardless of value of the supply pressure. The temperature values at the outlet of inter and after-cooler on both the compression lines are set at 30 \({}^{\circ}\)C. Each compressor is characterized by polytropic efficiency of 0.63 and electromechanical efficiency equal to 0.90. These values were imposed following previous evaluations on the performance of volumetric compressors for this kind of applications discussed in a previous Authors' study [24].  
Regarding the cooling section, a temperature equal to \(-\)50 \({}^{\circ}\)C is selected at the outlet of the chiller unit (T\({}_{8}\)), whose Energy Efficiency Ratio (EER) is imposed equal to 1.1. The influence of the chiller outlet temperature on the cycle performance was evaluated by the Authors in [24], highlighting that this unequivocally affects the overall consumption of the circuit, which decreases as the outlet temperature decreases. The effectiveness of both the regenerative heat exchangers is assumed equal to 0.70, while the pressure drops through the various heat exchangers (including inter and after-coolers) are assumed equal to 2% [24]. Finally, the turbo-expander, when included in the layout, works with an isentropic efficiency equal to 0.70. The natural gas composition of the stream coming from the grid is assumed as 100% CH\({}_{4}\). This approximation has negligible effects on the thermodynamic performance, as already underlined in previous Authors' study [24].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Units** & **Values** \\ \hline \(T_{1}\) & [\({}^{\circ}\)C] & 20 \\ \(\dot{m}_{LNG}\) & [kg/s] & 1 \\ \(\eta_{pol}\) (each compressor) & [–] & 0.63 \\ \(\eta_{em}\) (each compressor) & [–] & 0.90 \\ \(\eta_{ls}\) (TE) & [–] & 0.70 \\ Reg. HE effectiveness (\(\epsilon\)) & [–] & 0.70 \\ Chiller EER & [–] & 1.1 \\ Pressure losses (each HE) & [\%] & 2 \\ \(T_{8}\) & [\({}^{\circ}\)C] & \(-\)50 \\ \(T_{out}\) intercooler/aftercooler & [\({}^{\circ}\)C] & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Assumptions for parametric analysis.
## 2 Description of the Proposed Method  
### Emotion Evaluation and Distribution in the Pleasure-Arousal Space  
Acoustic stimuli, such as noise, speech, or music induce specific emotional states in listeners. These emotions may be classified from a discrete or a dimensional perspective [20]. In the discrete model, six basic emotions are usually recognized: joy, sadness, surprise, fear, anger, and disgust [21]. The dimensional model represents all possible emotions on a two-dimensional or three-dimensional scale. The first dimension is Pleasure ranging from negative to positive feelings, the second dimension is Arousal referring to alertness and activity with the range from calm to excited states, and the third dimension is Dominance describing emotional states from being controlled to controlling [22]. For the discrete emotions mapped in the space of first two dimensions, the negative emotions of anger and sadness correspond to low Pleasure, positive emotions such as surprise and joy, have high Pleasure, passive apathetic emotions are characterized by the lowest Arousal, and frantic excitement corresponds to the highest Arousal [23].  
Using these first two dimensions, the 2D diagram in a Pleasure-Arousal (P-A) space [24] is divided into four emotion quadrants (\(EQ_{1}\)-\(EQ_{4}\)) that can be categorized as \(EQ_{1}\) = pleasant with high intensity of feeling, \(EQ_{2}\) = unpleasant with high intensity; \(EQ_{3}\) = unpleasant with low intensity; \(EQ_{4}\) = pleasant with low intensity. In relation to pleasantness and feeling intensity, the basic importance weights for each of the emotion quadrants were defined as documented in Figure 1. This approach is used in further analysis for the determination of the final evaluation decision.
# Measurement of Circadian Effectiveness in Lighting for Office Applications
## 3 Results
This decrease in circadian effectiveness complies with the reduced amount of ceiling light that is expected to arrive at the observers' eyes when their gaze is focused slightly downward in order to be in line with the computer monitor's orientation on the office desk. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Workplace** & \(\mathbf{E_{\text{LMK}}^{\text{o}}}\) **in lux** & \(\mathbf{E_{\text{LMK}}^{\text{120*}}}\) **in lux** & \(\mathbf{E_{\text{LMK}}^{\text{CIE}}}\) **in lux** & \(\mathbf{\text{CS}_{\text{LMK}}^{\text{o}}}\) & \(\mathbf{\text{CS}_{\text{LMK}}^{\text{120*}}}\) & \(\mathbf{\text{CS}_{\text{LMK}}^{\text{CIE}}}\) \\ \hline \#1 & 373.9 & 223.1 & 229.9 & 0.351 & 0.267 & 0.271 \\ \#2 & 418.8 & 256.0 & 245.6 & 0.373 & 0.289 & 0.285 \\ \#3 & 362.8 & 218.6 & 203.4 & 0.326 & 0.239 & 0.225 \\ \#4 & 371.1 & 235.5 & 189.2 & 0.331 & 0.256 & 0.221 \\ \#5 & 380.6 & 242.1 & 217.5 & 0.350 & 0.269 & 0.254 \\ \#6 & 394.9 & 244.0 & 240.9 & 0.358 & 0.271 & 0.269 \\ \#7 & 414.4 & 241.5 & 235.0 & 0.364 & 0.268 & 0.264 \\ \#8 & 384.9 & 200.1 & 202.1 & 0.339 & 0.223 & 0.224 \\ \#9 & 268.3 & 155.0 & 158.6 & 0.232 & 0.136 & 0.138 \\ \hline
**average** & **381.6** & **232.1** & **216.4** & **0.340** & **0.253** & **0.241** \\ SD & 36.5 & 27.4 & 23.3 & 0.034 & 0.037 & 0.036 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the illuminance and CS values calculated from the spatially resolved light measurements for the different FOV definitions, i.e., a half-space geometry, an assumed 120\({}^{\circ}\) observer, and the FOV definition of CIE S 026:2018.
# Summertime Aerosol Radiative Effects and Their Dependence on Temperature over the Southeastern USA
## 2 Methodology
### Meteorological Data  
Since we study the temperature-driven changes in AOD, we will also have to consider processes that simultaneously affect both temperature and aerosol causing common cause variation. Changes in temperature can be connected to changes in other meteorological quantities, such as precipitation and wind. In addition, the changes in these quantities may affect aerosol loads. In order to investigate the possible effects of meteorology on the aerosol population, we used precipitation and wind data from the ERA-Interim archive [65]. For the comparisons with the satellite observations, we used the daily forecasts for local noon with \(1^{\circ}\times 1^{\circ}\) degree spatial resolution. We collocated the daily meteorological values of total precipitation, boundary layer height, and the U10 (east-west direction) and V10 (north-south direction) wind speed components at 10 m altitude with the satellite observations for each pixel in the study domain for the years 2005-2011. For the linear fitting, we assumed a 20% uncertainty for the meteorological parameters.
## 4 Discussion
Once we have calibrated the time-series imagery from these imagers for the most promising algorithms identified here we will be able to make similar phycocyanin values and CSTB estimates for real satellite imagery. Operational use of this method for estimating phycocyanin values (BGA_PC_RFU) and CSTB will be facilitated by the use of multiple satellite imaging platforms that will improve revisit times and help to alleviate summer cloud cover which limits the utility of single satellite HAB monitoring systems. For example cloud free images of Harsha Lake from Landsat-8 are available for perhaps 1 out of 4 overpasses. We hope to improve our temporal resolution by using the technique described above to approximately every two weeks by using Landsat-8 in combination with Sentinel-2a and -2b at a minimum. We will augment this coverage with WorldView-2 and -3 imagery when it is available to us. Larger water bodies should be able to use Sentinel-3/MERIS/OLCI imagery as well. Figure 13: Comparison of optical sonde BGA values (Bga-PC-RFU) vs. laboratory determined Cyanobacterial Sum of Total Biovolume values measured in Harsha and a nearby reservoir on the same days with Pearson’s \(r^{2}\) (\(r^{2}=0.757\), \(p<0.001\), \(n=39\)). Our results show that several other water quality parameters were also correlated with BGA_PC_RFU values in Harsha reservoir at the time of hyperspectral image acquisition (Table 6). Thirteen water quality parameters had Pearson's r\({}^{2}\) values greater than 0.500. They were, in order of decreasing correlation, Turbidity (NTU) (r\({}^{2}\) = 0.852), total suspended solids (TSS) (r\({}^{2}\) = 0.789), total phosphorous (TP) (r\({}^{2}\) = 0.724), particulate phosphorous (PP) (r\({}^{2}\) = 0.697), volatile suspended solids (VSS) (r\({}^{2}\) = 0.666), dissolved reactive phosphorous (DRP) (r\({}^{2}\) = 0.661), total nitrogen (TN) (r\({}^{2}\) = 0.656), Chlorophyll as concentration and as RFU (r\({}^{2}\) = 0.651 and 0.640, respectively), total dissolved phosphorous (TDP) (r\({}^{2}\) = 0.610), particulate nitrogen (PN) (r\({}^{2}\) = 0.591), bicarbonate (HCO3-) (r\({}^{2}\) = 0.551) and alkalinity as mg of CaCO3 (r\({}^{2}\) = 0.542) (Tables 5 and 6). Of these parameters, only Turbidity (NTU), TSS, VSS, and CHL, are spectrally active. Nonetheless, their strong co-variance with spectrally inactive water constituents explains why local empirical regressions against band reflectance values may be used to locally estimate a number of water quality parameters after local calibration [14; 23; 30] although their portability may be limited. In summary, there are several excellent semi-analytical spectrally based algorithm/imager combinations capable of estimating BGA values and cyanobacterial total volumes in smaller inland water bodies either directly via the phycocyanin signature with CASI, WorldView-2/-3, and
## 2 Second Messengers in Cyanobacteria
### Guanosine-3', 5'-(bis) Pyrophosphate, (p)ppGpp
#### 2.2.1 The Role of (p)ppGpp in Cyanobacterial Cells  
The sources of carbon and energy are usually one and the same in heterotrophic organisms; however, in phototrophic organisms the source of energy, _i.e._, light, differs from the source of carbon. Initial studies of (p)ppGpp in phototrophic organisms compared the effects of variations in light to nutrient starvation. The cyanobacterium _Synechococcus_ sp. PCC 6301 (formerly _Anacystis nidulans_) responds to a reduction of ambient light, which is equivalent to an energy reduction, with reduced growth and an associated decrease in RNA synthesis and a transient increase in GTP levels. The levels of GTP decreased as (p)ppGpp began to accumulate [56]. Notably, amino acid deprivation induced similar responses in _Synechococcus_ sp. PCC 6301 [57]. Nitrogen deprivation also transiently increased intracellular levels of (p)ppGpp in _Anabaena cylindrica_[53] and in _Synechococcus_ sp. PCC 6301 [54]. Under nitrogen starvation in _Synechococcus_ sp. PCC 6301, the regulation of (p)ppGpp is primarily due to (p)ppGpp synthesis rather than decreased degradation of (p)ppGpp [54]. In this organism, light-to-dark shifts and temperature stress led to accumulation of (p)ppGpp with an associated accumulation of heat shock proteins [58]. The effects of these two environmental factors could be separated, as ppGpp levels also increased when this species was exposed to 47 \({}^{\circ}\)C in the dark [58]. Thus, although the source of energy and carbon differ in cyanobacteria, a limitation of either results in a role of (p)ppGpp in transducing the environmental change.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Second messenger** & **Pfam** & **Function** & **Number of genomes** \\ \hline
**(p)ppGpp** & 04607 & (p)ppGpp synthesis and degradation & 83 \\ \hline
**cAMP or cGMP** & 00211 & Adenylate and guanylate cyclase & 65 \\ \hline
**c-di-GMP** & 00990 & Diguanylate cyclase & 61 \\ \hline  & 00563 & Diguanylate phosphodiesteraseterase & 60 \\ \hline  & 00394 & Nitrite reductase & 27 \\ \cline{2-3}  & 07731 & Nitrite reductase & 49 \\ \cline{2-3}
**Nitric oxide** & 07732 & Nitrite reductase & 47 \\ \cline{2-3}  & 13442 & Nitrite reductase & 80 \\ \cline{2-3}  & 00115 & Nitric oxide reductase & 83 \\ \hline
**c-di-AMP** & 02457 & Diadenylyl cyclase & 83 \\ \hline \end{tabular} Note: a IMG, Integrated Microbial Genomes database [51].  
\end{table}
Table 1: Total number of genomes with specific pfam domains present in the finished genomes (n = 83) represented in the IMG database a.
## 5 Enhancements to ShinyAnonymizer Data Encryption Techniques
### Technical Details of Encryption Techniques  
#### 5.1.1 Des  
Description: DES is a symmetric block cipher that encrypts data in 64-bit blocks with a 56-bit key. Being one of the most often used encryption algorithms, it has nowadays grown insecure because of the vulnerability to brute-force attacks.  
Application: However, the DES previously working in the encryption of sensitive data, due to the inappropriate minimal length of keys and sensitivity to current computing power, has turned out to be useless.
# Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends
## 5 Data Handling in ML Applications  
In smart buildings, the efficacy of ML applications pivots on the ability to handle data meticulously, spanning from acquisition to processing. Data handling in ML applications involves a sequence of processes designed to prepare and refine data, ensuring its optimal use for generating accurate and actionable insights. This sequence includes data collection, preprocessing, feature extraction, and hyperparameter optimization, each playing a pivotal role in the lifecycle of ML projects. Table 4 provides a comprehensive overview of the data handling techniques critical in ML applications, with explanations tailored to smart building management contexts. These techniques are fundamental for ensuring that the data fed into ML models is of high quality and relevance, leading to more accurate and efficient outcomes. Together, these data-handling processes establish a robust foundation for ML applications in smart buildings, facilitating precise energy prediction, efficient resource management, and improved sustainability. This section delves into each of these aspects, elucidating their importance and detailing the methodologies that underpin effective data handling in the context of machine learning applications for smart buildings.  
\begin{table}
\begin{tabular}{c c c} \hline
**Technique** & **Description** & **Application in Smart Buildings** \\ \hline \multirow{3}{*}{Data Collection} & Gathering raw data from various sources such as sensors, IoT devices, and user inputs & Collecting data on energy usage, temperature, occupancy, etc. \\ \hline \multirow{3}{*}{Data Preprocessing} & Cleaning and preparing data for analysis, including handling missing values, noise reduction, and normalization & Ensuring the quality and consistency of data used for energy prediction and management \\ \hline \multirow{3}{*}{Feature Extraction} & Identifying and selecting relevant features from the dataset that contribute most to the prediction outcome & Features like time of day, weather conditions, and occupancy levels are crucial for energy management algorithms \\ \hline \multirow{3}{*}{Feature Engineering} & Creating new features from existing data to improve the predictive power of the ML model & Deriving patterns of energy consumption or creating aggregated metrics from sensor data \\ \hline \multirow{3}{*}{Data Augmentation} & Enhancing the training dataset by creating new data points from existing data to improve model performance & Better learning patterns of energy usage under varied conditions \\ \cline{1-1} \cline{2-4}  & Reducing the number of variables under consideration to focus on the most important information & Using techniques like PCA to reduce data complexity and improve model efficiency \\ \hline \end{tabular}
\end{table}
Table 4: Data handling techniques in ML applications.