esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Key Differentiators in Energy Savings  
1. Dynamic Adjustments: The results of this study highlighted adaptability across varying conditions, including outdoor heat index fluctuations (27-50 \({}^{\circ}\)C) and indoor temperature and humidity ranges (20-40 \({}^{\circ}\)C and 40-100%, respectively). This level of adaptability has not been fully explored in comparable studies.
2. Room-specific Trends: Unlike the generalized energy savings reported in prior research, this work provides detailed, room-specific NES values: \(\circ\) Classrooms achieved a daily NES of 25.62% at 24 \({}^{\circ}\)C and a steady-state NES of 51.45%, demonstrating optimal energy utilization. \(\circ\) Computer rooms had the highest daily NES of 60.05% and a steady-state NES of 72.94%. \(\circ\) Laboratories demonstrated daily NES savings of 24.66%, but showed no significant improvement in steady-state duration, with a decline of 7.41% at 24 \({}^{\circ}\)C. \(\circ\) Shared offices achieved savings, with a daily NES of 41.87% at 25 \({}^{\circ}\)C, but presented significantly shorter steady-state periods (49.03%). \(\circ\) Solo-occupancy offices had moderate savings, with a daily NES of 25.42% at 25 \({}^{\circ}\)C, but achieved significantly longer steady-state periods (51.19%) when compared with other room types.  
Although this study was limited to spaces commonly found in educational buildings (e.g., classrooms, laboratory rooms, computer rooms, and office spaces) being cooled by split-type ACUs, the modeling methods used to predict and classify power consumption and generate decision rules for ACU setpoint automation can be utilized in scenarios with bigger spaces and other types of cooling system technologies. These areas should be considered in future investigations and research.
## 2 Materials and Methods
### Determination of Total Polyphenol  
Total polyphenol yield (\(Y_{\mathit{TP}}\)) was determined using the Folin Ciocalteu reagent and expressed as mg gallic acid equivalents (GAEs) per g of dry mass. An aliquot of 0.78 mL of distilled water, 0.02 mL of sample, and 0.05 mL of Folin-Ciocalteu reagent were mixed. After exactly 1 min, 0.15 mL of aqueous sodium carbonate (20%, _w/v_) was added, and the mixture was allowed to stand at room temperature in the dark, for 60 min. The absorbance was read at 750 nm. The total polyphenol concentration (\(C_{\mathit{TP}}\)) was calculated from a calibration curve, using gallic acid as a standard. Yield in total polyphenols (\(Y_{\mathit{TP}}\)  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Independent** & \multirow{2}{*}{**Code Units**} & \multicolumn{4}{c}{**Coded Variable Level**} \\
**Variables** & & \(-\)**1** & **0** & **+1** \\ \hline \(R_{\text{L/S}}\) (mL g\({}^{-1}\)) & \(X_{1}\) & 50 & 100 & 150 \\ \(C_{\text{DES}}\) (\%, _w/v_) & \(X_{2}\) & 60 & 75 & 90 \\ \(T\) (\({}^{\circ}\)C) & \(X_{3}\) & 40 & 60 & 90 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The values of the independent variables used for the experimental design.  
was determined as mg gallic acid equivalents (GAEs) per g of dry mass (dm), using the following equation:  
\[\gamma_{TP}\;(\text{mg}\;GAE\;\text{g}^{-1}\;\text{dm})=\frac{C_{TP}\times V}{m} \tag{2}\]  
where \(V\) is the volume of the extraction medium (L), and m the dry mass of the material (g) [26].
## 2 Autonomous Driving in Adverse Weather Conditions
### Algorithmic Challenges in Adverse Weather
#### 2.2.2 Lane Detection and Road Boundary Identification  
Lane detection and road boundary identification are critical for safe autonomous driving but become significantly more challenging in adverse weather. Rain, snow, or even strong sunlight can obscure lane markings, while wet roads can create reflections that confuse traditional lane detection algorithms. [PERSON] et al. [44] addressed these challenges by developing 3D-LaneNet+, an anchor-free lane detection method using a semi-local representation. Their approach showed improved robustness in various weather conditions, demonstrating the potential of advanced algorithms to overcome weather-related challenges in lane detection.
## 6 Opinion Event Detection in the Social Web
### Opinion Event Detection Algorithm  
The system consists of three components (see Figure 4). The first component is the _opinion timeline extraction_ which takes as input the raw, time-stamped documents corresponding to an entity of interest, and outputs an opinion timeline. The second component is the _event detection component_ which takes as input the opinion timeline and outputs a list of proto-events. The last component is the _event labeling component_, which transforms the proto-events into events with a full description using the evidence documents of the proto-events as source material.  
The \"opinion timeline extraction\" runs the opinion detection algorithm on the source documents and extracts all opinions about the target entity. They are grouped into bins \(w_{j}\), where each bin contains all opinions in a certain time period. For each bin we can compute a number of different values:  
* the sum and average of the _subjectivity_ as the sum/average of the absolute value of the opinions;
* the number of positive and negative opinions;
* the total number of opinions.  
Each of these values is computed for each bucket, giving one time series per value type. Using several time series instead of only using the average opinion allows us to detect and distinguish different kinds of opinion change, e.g., an increase/decrease in average opinion _vs._ increase/decrease in polarization.  
The \"event detection component\" takes the opinion timeline and detects bursts in any of the contained time series to find opinion change events. As the time series are highly correlated, we will often have parallel bursts in more than one time series. We merge these bursts if they have an overlap larger than \(\omega\). Each of those bursts corresponds to a proto-event, where the time span \(i_{i}\) is equal to the time span of the burst, in the case of merged bursts to the union of the original bursts' time span. The change indicator of the proto-event is the name of the time series where the burst occurs and the sign of the burst (values inside the burst are larger/smaller than outside it). We assign the documents published in \(i_{i}\) containing the target entity to the proto-event as evidence documents.  
Finally, the \"event labeling component\" takes the proto-events and detects a useful label for summarizing it based on the evidence documents. Summarization is a broad area of research [39]. The sort of information contained in a summary differs according to the mechanism used in the summarization process. It may highlight the basic idea (generic summarization), or it may highlight the specific user's individual area of interest (personalized summarization [40]). In this work, we focus on generic summarization and we create the label as the \(k\) terms from the evidence documents that have  
Figure 4: Architecture of the system.  
the highest _tf-idf_ value. However, based on the use case many other possible labeling algorithms such as multi-document summaries are possible [41].
# AI-Based Analysis of Policies and Images for Privacy-Conscious Content Sharing
## 5 Experimental Evaluation
### Information Retrieval from Privacy Policies
#### 5.1.2 Experimental Setup  
We used the python language to implement the techniques described in Section 3. The APP-350 corpus contains files in yaml format. The Python _PyYAML_ library was used to parse and read the files contained in the corpus. The _nltk_ library was used to stem the words contained in text and remove stopwords. The _re_ (regular expression) standard Python library was used to remove dates, numbers, and urls from privacy policies texts. _Pandas_ is a Python library that allows to easily handle a set of data represented in tables. We used Pandas to construct a data structure based on the information contained in the annotated dataset. We built a Pandas table (DataFragment) to store in rows of segments and columns all the annotations present in the APP-350 corpus. This structure indicates which annotations the paragraphs are tagged with. The TfIdfVectorizer from _scikit learn_ library was used to construct the TF-IDF vector on the set of paragraphs. A row in the TF-IDF vector represents a paragraph and the columns are the TF-IDF features.  
For performing machine learning, we used the Weka [33] library for Python, which comes with the implementations of the main classification algorithms. We chose to use random forest with default configuration, since it is known to be effective for text classification. We also chose to wrap the Random Forest with the Weka AttributeSelectedClassifier, which performs attribute selection on the features of the dataset to retain only those features that are useful to increase the classification results. The _liac-arff_ library was used to save the training dataset in an sparse 'arff' file, which is the native file format of Weka.  
We used 10-fold cross validation for evaluating our techniques on the APP-350 dataset. We considered the following metrics:  
* Precision: ratio between the number of instances correctly classified as positive (TP) and the total number of instances classified as positive (TP + FP);
* Recall: ratio between the number of instances correctly classified as positive (TP) and the number of instances that are actually positive (TP + FN);
* F\({}_{1}\): harmonic mean of precision and recall;
* Accuracy: the ratio of correctly classified instances: i.e, \(\dfrac{TP+TN}{TP+TN+FP+FN}\).  
For each technique, we report the individual results for each specific sub-tag, and the aggregated results for generic tags (i.e., data types).
# A Novel Approach to Enhance Crude Oil Recovery Ratio Using Selected Bacterial Species
## 3 Results
### Recovered Oil from the Core Model  
Dolomite sandstone cores were used to evaluate the potential of isolates in heavy oil recovery. After 12 weeks incubation, 7.9 to 11.38% additional oil-in-place was recovered. The oil initially in place (OIIP) in the core for _Nocardiia cyriacgeorgica_ was 11.4 mL (Soi = 63.6%). After injecting 5-8 PV of formation water, no more oil was produced and residual oil saturation (Soi) was about 20%, which resulted in the recovery of 50.87% of initial oil (OI), corresponding to 5.8 mL of the initial oil present in the core. The column was reinjected with 5-6 PV of brine that resulted in a total recovery of 58.77% (6.7 mL) of initial oil present in the core in which 7.9% (0.9 mL) was produced by the isolate.  
With core treated by _Pseudomonas putida_, the oil initially in place (OIIP) was determined at 12.2 mL (Soi = 82.31%). The water flooding resulted in recovery of 54.09% (6.6 mL) of OIIP. Initial oil saturation (Soi) was calculated to be 63% after being injected with 5-6 PV of oil, which was indicated by the volume of water displaced. A total recovery of 64.75% (7.2 mL) of initial oil present in the core was recorded. An increase in oil recovery of 10.66% (0.6 mL) was detected after 12 week incubation.  
The oil initially in place (OIIP) in the core for _Bacillus_ species was determined, 12.3 mL, Soi = 84.02%. The water flooding resulted in recovery of 52.84% (6.5 mL) of OIIP. Initial oil saturation was calculated to be 63% after being injected with 5-6 PV of oil, which was indicated by the volume of water displaced, that resulted in a total recovery of 64.22% (7.9 mL) of initial oil present in the core. After 12 weeks an increase in oil recovery of 11.38% (1.4 mL) by _Bacillus_ species was detected.
# Voxel-Based Navigation: A Systematic Review of Techniques, Applications, and Challenges
## 4 Key Techniques for Voxel-Based Navigation
### Voxel-Based Modeling
#### 4.1.1 Indoor Environment  
In indoor scene modeling, the primary data sources include BIM and LiDAR. BIM models usually contain highly detailed geometric information, building materials, system components, etc., but these complex details can impose a computational burden during path planning. Converting BIM models to voxel models can simplify data processing, retaining only the necessary geometric and spatial information, thereby optimizing computational efficiency. Ref. [33] proposed a method to discretize BIM models into voxel sets for path planning, significantly reducing the time required for navigable analysis. Ref. [34] proposed a method to automatically extract structures and boundaries from complex building models and convert them into voxel models based on geometric and semantic relationships. This method overcomes the complexity of extracting multi-story indoor spaces, but its accuracy and reliability depend on the quality of the input 3D building models. For buildings lacking CAD floor plans or BIM models, such as historical buildings or newly constructed buildings, LiDAR data become an important data source. Ref. [80] utilized LiDAR data to build voxel models, particularly suitable for 3D point cloud analysis in indoor environments. Ref. [27] presented a 3D indoor point cloud processing method based on octrees for path planning in multistory buildings. The method improves the efficiency of point cloud data processing by applying octree segmentation. Through semantic recognition of architectural elements (such as floors, walls, stairs, and obstacles), it accurately delineates navigable space and computes paths based on user constraints. However, this method relies on the Manhattan world assumption for model construction, which limits its generalizability. Ref. [81] proposed a voxel-modeling method for indoor spaces combining point clouds and scanning trajectories, which is capable of identifying walkable spaces in various rooms without any constraints.
## 3 Transmission Characteristics of Ultra-Bend-Resistant 4-Core SXC  
As shown in Figures 1 and 3, the measured RIP results and geometrical structure of the fabricated 4-core SXC can remain greatly consistent with the expected design. Then, we will test the transmission characteristics of this 4-core SXC and demonstrate its potential optimization of fabrication process in the future.
## 2 Materials and Methods
### NMR Analysis  
NMR analyses were carried out in the laboratories of Metrological Infrastructure for Food Safety (IMPreSA) in Turin. The instrument used was a 600 MHz NMR AVANCE neo 600 from Bruker (Bruker Biospin GmbH, Rheinstetten, Baden-Wurttemberg, Germany), equipped with a 5 mm probe, controlled temperature and autosampler.  
All reagents and the deuterated solvent were purchased from Merck KgaA (Darmstadt, Germany), and the ultrapure water was obtained with a Milli-Q gradient IQ 7000 instrument (Millipore Corporation, Billerica, MA, USA).  
Sample preparation: A volume of 300 \(\upmu\)L of the sample was filtered using 0.45 \(\upmu\)m PTFE filters, and 300 \(\upmu\)L of buffer solution was added. The buffer comprised a solution of 190 \(\upmu\)L of D2O (containing 0.05% wt of TMS), 60 \(\upmu\)L of phosphate buffer (pH 7) and 50 \(\upmu\)L of pure ethanol, and the preparation was then filtered using 0.45 \(\upmu\)Nylon filters. D2O was used for the lock on deuterium resonance, and ethanol was used for stabilizing the samples and buffer.  
\({}^{1}\)D \({}^{1}\)H-NMR spectra were acquired via ICON-NMR automation (Bruker Biospin GmbH). Lock, tune and shimming were performed automatically.  
A modified standard Bruker pulse program was used for the multi-suppression of water and ethanol signals. Spectra were obtained at the \({}^{1}\)H frequency of 600.529 MHz applying a standard zgpr pulse sequence for O1 (frequency of water peak) identification and a standard noesypps1d pulse sequence for the multi-suppression of water and ethanol peaks. The experimental parameters were as follows: temperature of 298 K, sweep width of 9615.38 Hz, recycle delay (d1) of 4 s and acquisition time of 1.7 s. For peak suppression, the width of narrow, off-resonance suppression was 2.5 Hz, and the width of broad, on-resonance suppression was 25 Hz. The spectra were acquired with 4 prior dummy scans, and 64 scans were recorded. After acquisition, spectra were processed with Top-spin 4.1.3 (Bruker Biospin GmbH). Phase correction was performed automatically. The chemical shifts (8) were referenced to the TMS resonance.  
The spectral region from 11 to 5.5 ppm of the \({}^{1}\)H-NMR spectra was chosen as the input data for statistical analyses, thus focusing on the region of the spectra where main structural differences related to important aromatic compounds of the samples should have been visible (phenols, aldehydes, aromatic groups). AssureNMR software was used to segment the NMR spectra into rectangular buckets. The width of the buckets was user-defined and equal to 0.05 ppm for \({}^{1}\)H-NMR data [18]. Integration was achieved using the \"sum of total intensities\" mode, and the spectra were scaled to the peak of TMS, using the region between 0.07 ppm and \(-\)0.07 ppm as a reference region. The datasets were scaled with the Pareto scaling method [19] and used for principal component analysis (PCA).
## 6 MIMO Antenna Performance Enhancement Techniques
### Neutralization Lines  
Having capability to pass electromagnetic waves between antenna elements to completely decouple or reduce the effect of mutual coupling by using lumped elements or metallic slits. It improves the antenna bandwidth and reduces the occupying space of antennas. The improvement in bandwidth is realized
# Multi-formalism Models for Performance Engineering
## 4 Multi-Formalism QN/PN Techniques
### Hybrid Cloud
#### 4.1.1 Description of the Problem  
This case study concerns a model of an IT infrastructure based on a hybrid cloud. More precisely, it focuses on modeling the process for dynamic resource provisioning. This method is able to acquire VMs on-demand from the public cloud when requests exceed the capacity set as a threshold on the private cloud. The multi-formalism model implemented consists of elements of Petri nets and queueing networks. The hybrid cloud scenario considered is represented in Figure 13.  
Figure 12: Determining the optimal number of VMs to obtain an average response time \(R\) below a given threshold (\(\tau=200\)s in this example).  
The incoming requests are processed by the user interface of the application and, after some formal and security checks, are sent to the load controller module. To satisfy the performance requirements, the software architecture of the app has been designed assuming that a dedicated VM of the local cloud is assigned to each request in execution. Since several highly fluctuating workloads share the resources of the private cloud, a limit is set on the maximum number of VMs dedicated to this application. When this threshold is reached, the new VMs are acquired on-demand from a public cloud.  
The impact of this threshold on global response time and throughputs of both clouds need to be investigated. The objective of the study is the identification of the computational capacity, in terms of number of cores and power, of the servers of the private cloud that are required to satisfy the performance target with cost savings. In fact, VMs with the same computational power as a private provided by the public cloud are much more expensive. Therefore, to save costs, the VMs provided by the public cloud are much less powerful than the private ones. Thus, there is a tradeoff between the VMs provided by the private and public clouds, of performance and infrastructure costs.
###### Abstract  
Visible light communications (VLC) have received significant attention as a way of moving part of the saturated indoor wireless traffic to the wide and unregulated visible optical spectrum. Nowadays, VLC are considered as a suitable technology, for several applications such as high-rate data transmission, supporting internet of things communications or positioning. The signal processing originally derived from radio-frequency (RF) systems such as cooperative or precoding schemes can be applied to VLC. However, its implementation is not straightforward. Furthermore, unlike RF transmission, VLC present a predominant line-of-sight link, although a weak non-LoS component may appear due to the reflection of the light on walls, floor, ceiling and nearby objects. Blocking effects may compromise the performance of the aforementioned transmission schemes. There exist several surveys in the literature focused on VLC and its applications, but the management of the shadowing and interference in VLC requires a comprehensive study. To fill this gap, this work introduces the implementation of cooperative and precoding schemes to VLC, while remarking their benefits and drawbacks for overcoming the shadowing effects. After that, the combination of both cooperative and precoding schemes is analyzed as a way of providing resilient VLC networks. Finally, we propose several open issues that the cooperative and precoding schemes must face in order to provide satisfactory VLC performance in indoor scenarios.  
VLC; indoor; cooperative schemes; precoding; LoS blockage Article  
and Precoding Schemes  
[PERSON], [PERSON]; [PERSON], [PERSON]; [PERSON], [PERSON].  
and Precoding Schemes  
to Overcome LOS Blockage and Interference in Indoor VLC. _Sensors_**2021**, _21_, 861. [[https://doi.org/10.3390/s21030861](https://doi.org/10.3390/s21030861)]([https://doi.org/10.3390/s21030861](https://doi.org/10.3390/s21030861))
# Synthesis and Application in Cell Imaging of
## 3 Results  
### Subsection  
#### 3.1.1 Molecular Design  
The synthetic procedure for MeAcd12C incorporating two 4-vinylpiperidines and a 1-bromododecane was depicted in Figure 1a. 1-bromododecane was incorporated into the N10 position of the acridone, and was then boronated by NBS reagent. The Heck cross coupling reaction and methylation were followed to acquire the final product. All compounds were identified by routine spectral and elemental analyses. The synthetic procedure of the dodecyl-piperidine-substituted acridone (MeAcd12P) was similar to that of MeAcd12C. 1,12-dibromododecane was incorporated into the N10 position of acridone, where the twelve-carbon chain exhibited lipophilic properties (Figure 1b). The bromo-substituted end of dodecyl chain was replaced with piperidine to form dodecyl-piperidine acridone intermediate. This dodecyl-piperidine acridone scaffold was brominated at positions 2 and 7 by N-bromosuccinimide (NBS). Here, bromination of the acridone scaffold was facilitated by NBS because of the incorporated electron-donating group at the N10-position of acridone. Consequently, the 2,7-dibromo-N-(dodecyl) acridone scaffold was incorporated with the two 4-vinylpyridines via a Heck cross-coupling reaction, and methylated with iodomethane to obtain the final product, MeAcd12P.  
Figure 1: Schemes representing synthesis procedures and the corresponding yields from **(a)** MeAcd12C and **(b)** MeAcd12P conditions; **(a)** i, 1-bromododecane, KI, KOH, THF, 40 \({}^{\circ}\)C, 62%; ii, NBS, DMF, 80 \({}^{\circ}\)C, 83.7%; iii, 4-vinylpyridine, Pd(OAc)2, (o-tol)3P, MeCN, Et3N, N2, 80 \({}^{\circ}\)C, 30%; iv, CH3I, DMF; Acetone, 80%. **(b)** i, 1,12-dibromododecane, KI, KOH, THF, 40 oC, 54%; ii, NBS, MeCN, reflux, 97%; iii, piperidine, K2 CO3, THF/H2O, reflux, 91.6%; iv, 4-vinylpyridine, Pd(OAc)2, (o-tol)3P, MeCN, Et3N, N2, 80 \({}^{\circ}\)C, 63%; v, CH3I, DMF, Acetone, 80%.