esa-sceva/satcom-chunk-collection huggingface dataset:
# Software Architecture for Mobile Cloud Computing Systems
## 4 Frequency, Classification, and Mapping of the Existing Research
### Taxonomical Classification of Existing Research Themes
For example, the mapping in Figure 5 helps us to answer some important questions like:  
* _What research themes got the most and least attention in the last five years?_ By analyzing the research mapping in Figure 5 and combining the publications years and research (sub-) themes in the last three years (2017-2019), most of the studies focused on (a) computational efficiency of a mobile device with studies including but not limited to [S8, S21, S78, S90, S98] and (b) storage efficiency and data analytics [S33, S83, S97] by offloading its data to cloud servers. There is less research on SaaS for mobile computing and security and privacy of mobile data. * _What years are reflected as the most progressive based on the number of research publications and what was the focus of research during those years?_ By analyzing the temporal distribution facet or publication frequency (x-axis, left), we can identify 2013 to 2018 as the most progressive years in terms of total publications (93 published studies). During these years, the studies focused on all identified themes and their sub-themes. The most focused area of research during these years was offloading mobile device data to cloud-based servers. Most recently, mobile cloud computing has been exploited for internet of things-based systems. We conclude that the taxonomical classification (in Figure 4) and holistic mapping (in Figure 5) helps us to answer RQ-2, which investigates the contributions and progression of the research. Both the classification and mapping guide the results of the mapping study and also help us to present the existing and emerging trends of research later in the paper. **Miscellaneous Research Areas:** As presented in Figure 4, a total of 10 studies focused on miscellaneous architectural issues for MCC systems that cannot be classified under any other themes. The results from these studies address a wide variety of issues that include but are not limited to designing [S20], architecting [S63], developing [S68], and operating scalable MCC systems [S9]. These studies represent the foundation level research on MCC systems without an explicit relation to the applied architectural solutions. It is vital to mention some interesting studies that exploit the mobile cloud technologies to support social devices [S22] that aim to create a social network on the nearby mobile devices. Another interesting study focused on exploiting the MCC infrastructure to provide a context-aware adaptive middleware for mobile devices [S68]. We considered these studies as random solutions (once-off research ideas) that lack the follow-up research efforts. The classification and detailed discussion of solutions later in the paper avoids miscellaneous solutions to exclude once-off solutions to avoid any bias in the results of the SMS. Figure 5: Systematic mapping of the research themes on software architecture for MCC.
## 4 Experiments and Results
### Prediction
#### 4.2.6 Single Model  
Given the higher computational costs of training multiple models than a single model, we investigate using a single model trained at a specific \(\text{CWA}_{U}\) threshold for making predictions at different thresholds. We trained a logistic regression model using features at the 40% \(\text{CWA}_{U}\) threshold, referred to as _SingleModel@40_. We then compared its performance with models trained for specific \(\text{CWA}_{U}\) thresholds, referred to as _IndividualModel@k_, where \(k\) represents the percentage threshold.  
To ensure a fair comparison, we selected the fold with the highest performance from the outer loop in the SingleModel@40's cross-validation, and used this fold to compare models across all the \(\text{CWA}_{U}\) thresholds. Features were computed for each fold at the 20%, 40%, 60%, and 80% thresholds, with predictions made using SingleModel@40. For IndividualModel@k, a separate model was trained at the k% \(\text{CWA}_{U}\) threshold and tested on the same data. Note that in this context, a fold is a subset of the students. By definition, the data itself changes as we change the \(\text{CWA}_{U}\).  
Table 6 summarizes the results. As expected, using a single model across different \(\text{CWA}_{U}\) thresholds leads to a general decrease in F1-scores compared to models explicitly trained for each threshold.
###### Abstract  
The last few decades have witnessed the rapid development of passive backscatter technologies, which envision promising cost-efficient ambient Internet of Things (IoT) for various applications, such as distributed solar sensor networks. However, limited by the harmonic interference caused by the conventional frequency-shifting-based backscatter control methods, existing backscatter communication technologies cannot support the growing scale of the network. To tackle this issue, we propose a harmonic interference resilient frequency-shifting technique to compress the harmonics during backscatter communication. Different from conventional backscatter tags that shift the frequency with square waves with a constant pulse width, we dynamically modify the pulse width of the square wave to compress different parts of the harmonic waves. Furthermore, we propose a lightweight communication coding algorithm to enhance the compatibility of our system with backscatter applications. We implement the system with off-the-shelf components and conduct comprehensive experiments to evaluate the performance. The results demonstrate our harmonic interference resilient backscatter system can compress the harmonic interference and reduce the BER (bit error rate) by 70%.  
Keywords:backscatter; frequency shift; interference; harmonic suppression
# Proposal for the Evaluation of Eco-Efficient Concrete
## 3 Proposed Assessment Method for the Eco-Efficiency of Concrete
### Definition of Concrete Eco-Efficiency  
There are limitations to assessing the eco-friendliness of concrete according to environmental emissions alone, without consideration of functionality (durability) and economic performance of concrete as an essential construction material.  
Figure 3: Process for the assessment of the eco-efficiency of concrete  
Therefore, the concept of eco-efficiency was applied to assess the eco-friendliness of concrete, as shown in Figure 4.  
The proposed concrete eco-efficiency assessment method is a technique to ensure the production of highly durable and environment-friendly concrete, while minimizing both the environmental load and manufacturing costs on the ecological environment based on the results of the assessment of environmental load, manufacturing costs, and the service life of concrete. Concrete eco-efficiency is improved by increasing the service life (years) of concrete and reducing the costs (KRW, Korea Won) of production. Thus, concrete with a low eco-efficiency value (KRW/year) among other concrete samples of the same strength is regarded as concrete with improved eco-efficiency.  
Equation (2) shows the calculation method for concrete eco-efficiency.  
\[CEI\,=\,(EC\,+\,\mathit{MC})/\mathit{SL} \tag{2}\]  
Here, \(CEI\) is the concrete eco-efficiency index (KRW/year), \(EC\) is the environmental cost (KRW/m\({}^{3}\)), \(\mathit{MC}\) is the manufacturing cost (KRW/m\({}^{3}\)), and \(\mathit{SL}\) is the service life of concrete (year/m\({}^{3}\)).
## 3 Results and Discussion
### Optimization of the HS-SPME Methods
#### 3.1.1 Effect of Sample Dilution  
Because HS-SPME is an equilibrium process, when HS-SPME-GC\(\times\)GC-TOFMS is used to quantify trace compounds, a problem of competitive adsorption exists. Ethanol is the major matrix constituent of Baijiu and has been reported as an important interfering volatile during HS-SPME of trace compounds [27], especially for the hydrophobic analytes, which suffered more strongly from the competition between the aqueous alcoholic solution and the fiber coating [28]. The selection of an appropriate dilution ratio may reduce matrix interferences [29], so we reduced the effect of ethanol in quantitative analysis via sample dilution. Six different diluted alcohol levels of 0.5, 1, 2, 5, 10, and 15% \(v/v\) were carried out. Figure 3A shows that the response areas of all types of trace compounds increased first and then decreased with the change of diluted alcohol content and maximum extraction efficiency at 5% alcohol. Different from the 10% alcohol optimized using HS-SPME-GC-MS [22], this may be due to GC\(\times\)GC-TOFMS being more sensitive in detection and therefore more affected by competitive adsorption. Moreover, lactones are more affected by alcohol, and the peak area decreases rapidly above 5% alcohol. Because ethanol prevented the studied trace analytes from being adsorbed on the saturated fiber, the selection of an appropriate dilution of 5% \(v/v\) may reduce competitive adsorption and make the results more accurate.
## 2 Methodology
### Data Preprocessing  
The dataset used for model training was preprocessed to ensure data integrity and optimize forecasting performance. Power consumption data were collected from the headquarters of Daekyung Engineering Co., Ltd., located in Jeju, Republic of Korea. The data, obtained from the Korea Electric Power Corporation (KEPCO) public data portal, was recorded at 15 min intervals from 1 June 2022 to 31 May 2024. The raw dataset contained timestamps (year, month, day, hour, and minute) and corresponding power consumption values.  
To improve predictive accuracy, additional features influencing power consumption were incorporated. These included meteorological variables (temperature, humidity, wind speed, precipitation, and solar radiation), working day/non-working day indicators, and the COVID-19 social distancing index, alongside the number of employees present in the building. During the COVID-19 period, South Korea implemented a social distancing index, which significantly influenced corporate remote work policies. Given that employee presence directly affects energy usage, this factor was included as a key feature.  
To address missing data, linear interpolation was applied between preceding and succeeding power consumption values. This method preserves data continuity and effectively manages gaps over short time intervals, ensuring the reliability of the dataset for model training. Additionally, to enable the effective processing of the time-series data by the model, a sliding window algorithm was applied. This algorithm restructured the dataset into sequences with a fixed length of 10 time steps, providing sufficient context for the model while maintaining computational efficiency.
## 6 Performance Evaluation  
In this section, we evaluate the performance of the QDAR algorithm. Firstly, the performance of the QDAR algorithm with different parameters is shown. Then, we compare QDAR with the Q-learning-based lifetime-aware (QELAR) routing protocol and the vector-based forwarding (VBF) routing protocol. Finally, the performances of QDAR and QELAR are compared with different underlying MAC protocols.
# Prototype System for Supporting Medical Diagnosis Based on Voice Interviewing
## 4 Discussion of the Difficulties Encountered  
The project set out to achieve a specific level of technological development, i.e., a diagnostic sensitivity of 75%. During the course of the work, a number of problems were encountered that impacted the difficulty of achieving the target during the experimental phase of the solution. These problems were resolved following additional remedial work carried out after the formal completion of the project.  
The main problems that reduced the effectiveness of the system included the inability to ask precise questions about the patient's condition. These are situations where we measure ourselves against the human factor, and the solution would have been to ask a very precise question that requires an equally precise answer from the patient. In the end, the problem was largely solved by appropriately queuing the questions and breaking down the questions into a series of smaller ones according to the principle of \"general to specific\".  
We have grouped the problems encountered during the work planned in the project schedule and present them in the list below:  
* Irregularities involving an inappropriate sequence of questions asked, which included questions relating to areas of the body unrelated to the reported health problems.
* The practice of asking questions referring to symptoms already mentioned at the time of the first statement, i.e., the so-called verbosity, limited the possibility of obtaining a detailed medical history.
* Lack of precision in identifying the symptoms reported by the patient.
* The interviews collected did not provide detailed information on the medical condition, which hindered an effective diagnostic analysis.
* The need to adapt the questioning process to obtain more balanced and precise data for medical analysis.
* Automated tests of the system showed that the diagnostic sensitivity of the solution under development was too low.  
* Errors in symptom determination due to misclassification of the word 'pain'.
* Generation of questions that were too general. The questions did not detail the interview but added other information that was not very relevant to the diagnosis.  
The problems presented illustrate the spectrum of actions that the project needed to take in order to mitigate their effects. In the next section, we will present the project's conclusions and future plans to develop the technology in the context of global telemedicine developments.
Quantitative Geochemical Prediction from Spectral Measurements and Its Application to Spatially Dispersed Spectral Data  
[PERSON]  
1 Maneral Resources, Commonwealth Scientific and Industrial Research Organisation (CSIRO),  
Kensington, WA 6151, Australia; [PERSON].  
such data for more sophisticated qualitative and quantitative methodologies to aid in the exploration task.  
Due to the ease with which spectral data can be collected, and the fast turnaround of higher order products generated from said reflectance data, it can be highly beneficial in providing a means of early confirmation and assessment of a variety of results that may aid in the exploration decision making process [11; 12].  
While questions pertaining to mineral identification in various mineral systems can be addressed with indices-based methods, the approaches used to relate the bulk, or volumetric, properties of a geological sample to its spectrum require more sophisticated methodologies.  
Volume based assessment of geological samples is generally derived from a lab-based analysis of the sample which comes with an inherent cost and turnaround time. While the cost and turnaround time of accessing results from laboratory samples are generally accounted for it does not mean that earlier access to that knowledge would not aid an explorer or decision maker. An earlier preliminary result may, for example, aid in the earlier definition of an existing economic ore body and allow preplanning prior to laboratory based confirmation. Alternatively, it may assist in the process of apportioning which samples are best suited for a deeper laboratory analysis and/or the actual sampling frequency best suited to answering the question at hand.  
The use of a partial least squares [13] models for regression analysis relating to geochemical properties is well established [14; 15; 16; 17]. These models are generally singular output models and require as many models as the number of parameters that are to be predicted. This use of random forest models [18] have been gaining popularity and applied successfully to both classification [19; 20; 21] and regression [22; 23] problems. As well as proving to be easily implemented and robust they can also make multioutput predictions and therefore reduce the need for multiple models.  
In this study we will investigate a methodology that uses a 2-step process to ascertain if wt.% estimates from whole-rock geochemistry are reliably predictable from spectral measurements of drillcore samples prepared as pulps. The 2-step process makes use of a dimensionality reduction step followed by a multioutput decision tree approach to predict the wt.% of 8 different whole-rock parameters, the majors and LOI, and its predictive effectiveness when applied to a testing set that encompasses a spatial extent extending beyond the initial training and test dataset.
# Fivadmi: A Framework for In-Vehicle Anomaly Detection by Monitoring and Isolation +
## 2 Related Work
### In-Vehicle Threats
#### 2.2.1 Physical Threats  
**In-vehicle spoofing:**_In-vehicle spoofing_ refers to attacks where an attacker pretends to have another identity and influences the vehicle's operation by providing false or modified data [2; 6; 15; 31].  
**Denial of Service**: In _Denial of Service (DoS)_ attacks, an attacker manages to compromise a component (e.g., ECU, Sensor etc.) and inhibits the regular operation of the IVN by various means such as the intentional flooding of the network with messages [1; 2; 15; 18].  
**Side-Channels**: In _side-channel attacks_, the adversary collects information from a hidden channel that leaks physical or logical parameters of a system and analyses the collected information to establish patterns that can extract private information. The hidden channel may already be available in hardware (such as in a processor's cache implementation), or is created using hardware and software techniques [32; 33; 34; 35]. A wide range of side-channel attacks have been studied. Some of these, related to the automotive domain, are mentioned here. The variation of CAN bus signals can be analysed to break encrypted and authenticated in-vehicle communication protocols (e.g., poor key provisioning to explore additional vulnerabilities). CAN bus signal variation can be measured in different ways, for example, (a) access the wires directly using a high-precision oscilloscope, (b) a compromised ECU connected to the CAN bus at the input and a modified CAN controller capable of sampling the bus at a high frequency [34; 35]. Even stronger key provisioning protocols for CAN bus (e.g., Pns-CAN [36]) can be exploited following this approach [34]. _Cache-based side-channel attacks_ exploit the key-dependent cache access patterns of cryptographic applications. Typically, this approach uses a spy process to intentionally create cache contentions with the cryptographic application. The contentions are then analysed to infer cache access patterns, which are in turn associated with likely key values to extract the secret key processed by the cryptographic application [33; 37]. Side channel attacks are even applicable to Trusted Execution Environment (TEE) [38; 39; 40; 41]. For example, Intel SGX (version 1 and 2) suffers from _Page Fault Attacks_ due to the sharing of memory, managed by the OS, by multiple  
Figure 3: In-Vehicle Single Bus E/E Architecture [13].  
Figure 2: In-Vehicle Domain-based E/E Architecture [28].  
enclaves [39; 42]. Intel SGX is also vulnerable to _interface-based side-channel attacks_, which can be exploited to infer the information of enclave input data, i.e., the observable enclave interface (ECALL/OCALL) invocation patterns (e.g., interface parameter sizes and invocation delay etc.) determined by different enclave input data [43].
# Post-Quantum Biometric Authentication Based on Homomorphic Encryption and Classic McEliece
## 1 Introduction
### Biometric Data Protection  
According to the ISO/IEC 24745 standard [11], the requirements of biometric protection schemes are irreversibility (or non-invertibility), unlinkability and revocability (or renewability). Irreversibility ensures that no sensitive information about biometric data is leaked from protected biometric features. Therefore, protected biometric features can be communicated and stored on an external server without revealing sensitive information. Revocability refers to obtaining different protected biometric features from the same biometric sample when the same biometric sample has been employed to enroll the user in different biometric systems with different databases. Unlinkability refers to obtaining different protected biometric features from different biometric samples from the same  
Figure 1: Biometric authentication architectures: (**a**) device-centric, (**b**) protected and non-device-centric.  
instance. If protected features are compromised, an adversary cannot recover any sensitive information or know the owner of the protected features. Consequently, the compromised protected features can be destroyed without affecting other parallel or new enrollments. Another issue to consider is that the distances between protected biometric features should not preserve the distances between unprotected features in order to avoid the success of similarity-based attacks based on the use of machine learning techniques [12].  
In addition to security requirements, the recognition performance, the size of the protected features as well as the execution times of the authentication steps have to be evaluated [13]. The recognition performance of the unprotected scheme should be preserved or even improved, the verification phase should be carried out at real time and the size of the protected features should be minimized to reduce the storage and transfer operations. Storage and transfer of enrollment data are only performed once to enroll the user. In contrast, since the verification phase is performed many times, the transfer of protected features between the database, computation and authentication servers is higher. Nowadays, this issue is a challenge in reported non-device-centric architectures because, in general, the size of protected features is bigger than unprotected ones.