esa-sceva/satcom-chunk-collection huggingface dataset:
# Drilling Fluid and Cement Slurry Design for Naturally Fractured Reservoirs
## 4 Preventive and Corrective Lost Circulation Measures  
The way that lost circulation measures or methods are applied could be classified based on the time these measures were implemented, whether they were applied before (preventive measures; pretreatment and/or borehole stress treatments to strengthen the wellbore) or after (corrective measures; mitigating lost circulation and, where possible, providing additional wellbore strength) the occurrence of a lost circulation event [4; 8; 38; 53; 54]. The preventive approach to mitigate the loss of circulation is easier than the corrective approach.  
The hierarchy in the application of preventive and corrective (remedial) measures (a) and the mechanism of wellbore strengthening are shown in Figure 1.  
Wellbore strengthening (or creating of stress cage) is a preventive treatment and can be used to efficiently plug and seal fractures while drilling before losses occur [53; 55]. The sealing of fractures during drilling is achieved by maintaining a certain concentration of bridging material (Wellbore Strengthening Material (WSM), e.g., deformable graphite, granular, cellulosic material, blends) in the mud or by using pills to strengthen sections that have already been drilled, but without losses. Wellbore strengthening is a method for increasing the fracture resistance by increasing the hoop stress in the near wellbore area and consequently the ability of the wellbore walls to withstand higher dynamic pressure (higher ECDs) without causing mud loss.
## 3 Methodology
### Evaluation Indicators  
In order to measure and detect the correction effects of the three time series models, the following five evaluation metrics are used in this study:  
(1) Mean absolute error (_MAE_) is used to measure the average absolute error between the predicted value and the real value on the experimental data set. The smaller the value of _MAE_, the better the accuracy of the prediction model. For a test set containing n messages, _MAE_ is defined as:  
\[MAE=\frac{1}{n}\sum_{t=1}^{n}\lvert actual(t)-forecast(t)\rvert \tag{12}\]  
(2) The root means square error (_RMSE_) is used to measure the deviation between the observed flow and the true value, which represents the expected value of the squared error and should also be as small as possible. _RMSE_ is defined as follows:  
\[RMSE=\sqrt{\frac{\sum_{t=1}^{n}\lvert actual(t)-forecast(t)\rvert^{2}}{n}} \tag{13}\]  
(3) Average absolute percentage error (_MAPE_) is used to measure the relative errors between the average test value and the real value on the test set. The smaller the value of MAPE, the better the accuracy of the prediction model. _MAPE_ is defined as:  
\[MAPE=\frac{1}{n}\sum_{t=1}^{n}\left|\frac{actual(t)-forecast(t)}{actual(t)} \right|\times 100\% \tag{14}\]  
(4) Pearson's correlation coefficient \(R\) reflects the degree of linear correlation between two random variables. The closer r is to 0, the more linearly irrelevant the predicted and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter Nature** & **Parameter Name** & **Value** \\ \hline \multirow{4}{*}{Trend parameters} & _growth_ & _linear_ \\  & _changepoints_ & _None_ \\  & _n\_changepoints_ & 25 \\  & _changepoint\_range_ & 0.8 \\  & _changepoint\_prior\_scale_ & 0.5 \\ \hline \multirow{4}{*}{Periodic parameters} & _yearly\_seasonality_ & 10 \\  & _weekly\_seasonality_ & _false_ \\  & _daily\_seasonality_ & _false_ \\  & _seasonality\_mode_ & _additive_ \\  & _seasonality\_prior\_scale_ & 20 \\ \hline \multirow{2}{*}{Flow parameters} & _flow_ & _flow_ \\  & _flow\_prior\_scale_ & 10 \\ \hline \multirow{4}{*}{Other parameters} & _mcmc\_samples_ & 0 \\  & _interval\_width_ & 0.8 \\ \cline{1-1}  & _uncertainty\_samples_ & 1000 \\ \cline{1-1}  & _stan\_backend_ & _cmdstampy_ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example of Prophet model parameter setting.  
true values. The closer r is to 1, the more positively correlated the predicted and true values. With \(x\), \(y\) representing \(actual(t)\) and \(forecast(t)\), respectively, \(R\) is defined as:  
\[R=\frac{\sum_{i=1}^{n}(x_{i}-\overline{x})(y_{i}-\overline{y})}{\sqrt{\sum_{i=1 }^{n}(x_{i}-\overline{x})^{2}}\sqrt{\sum_{i=1}^{n}(y_{i}-\overline{y})^{2}}} \tag{15}\]  
(5) The Nash-Sutcliffe efficiency coefficient (\(NSE\)) is generally used to verify the simulation results of the hydrological model, and the value of \(NSE\) range is \(-\infty\)\(\sim\) 1; the closer to 1, the better the simulation effect. \(NSE\) is defined as:  
\[NSE=1-\frac{\sum_{t=1}^{T}(Q_{0}^{t}-Q_{m}^{t})^{2}}{\sum_{t=1}^{T}(Q_{0}^{t}- \overline{Q_{0}})^{2}} \tag{16}\]
# Neural-Network-Assisted Polar Code Decoding Schemes
## 4 Numerical Simulation
### Performance with Different Code Lengths  
This section simulates the KSNNAD, LSNNAD and SC BER performance with a code rate of 0.5 and code lengths of 64, 128, 256, 512, and 1024. The results are shown in Figure 14. The BER curve of KSNNAD with a code length of 64 is marked as KSNNAD \(N=64\), while LSNNAD with a code length of 64 marked as LSNNAD \(N=64\), the FSC decoding scheme with code length 64 marked as FSCD \(N=64\), and so on.  
As shown in Figure 15, the decoding performance of the KSNNAD and LSNNAD are better than that of the SC decoder. When the code length is 1024, compared to the SC decoder, LSNNAD needs 0.049 dB less to reach BER \(10^{-4}\), while KSNNAD needs approximately 0.127 dB less.
## Appendix A
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Categories**} & \multicolumn{6}{c}{**(1)**} & \multicolumn{6}{c}{**(2)**} \\ \cline{2-10} \multicolumn{1}{c}{} & \multicolumn{1}{c}{**Implementation of Some Green Purchasing**} & \multicolumn{6}{c}{**MEASURING Their Own Green Purchasing Rate**} \\ \hline
**Items** & **GPP** & **In(Population)** & **In(SFS)** & **N** & **GPP** & **In(Population)** & **In(SFS)** & **N** \\ \hline Paper Products & 1.74 *** & 0.05 & 1.15 *** & 1602 & 0.58 *** & 0.26 & \(-\)0.11 & 1335 \\  & (0.35) & (0.24) & (0.34) & & (0.15) & (0.21) & (0.26) & 1335 \\ Stationery & 1.70*** & 0.34 & 0.54 & 1598 & 1.00 *** & 0.10 & 0.14 & 1238 \\ Cars & 0.80 *** & 0.20 & 0.61 ** & 1590 & 0.51 *** & \(-\)0.00 & 0.20 & 1020 \\  & (0.18) & (0.19) & (0.26) & & (0.15) & (0.25) & (0.30) & 1020 \\ Lighting Equipment & 0.81 *** & 0.30 & 0.39 & 1595 & 0.92 *** & 0.18 & \(-\)0.15 & 994 \\  & (0.17) & (0.19) & (0.25) & & (0.16) & (0.26) & (0.32) & 993 \\ Office Equipment & 1.05*** & 0.27 & 0.54 ** & 1585 & 1.43 *** & \(-\)0.11 & 0.25 & 993 \\ Office Equipment & (0.17) & (0.20) & (0.26) & & (0.18) & (0.30) & (0.37) & 993 \\ Office Furniture & 0.90 *** & 0.38 * & 0.40 & 1592 & 1.35 *** & \(-\)0.08 & 0.39 & 916 \\  & (0.16) & (0.20) & (0.26) & & (0.18) & (0.32) & (0.39) & 916 \\ Imaging Equipment & 0.92 *** & 0.24 & 0.50 ** & 1583 & 1.24 *** & \(-\)0.15 & 0.35 & 903 \\  & (0.16) & (0.19) & (0.25) & & (0.19) & (0.32) & (0.39) & 903 \\ Electronic Devices & 0.92 *** & 0.24 & 0.52 ** & 1578 & 1.37 *** & \(-\)0.05 & 0.27 & 854 \\  & (0.16) & (0.19) & (0.25) & & (0.19) & (0.33) & (0.40) & 854 \\ Uniforms & 1.01 *** & 0.37 * & 0.53 ** & 1582 & 1.70 *** & \(-\)0.40 & 0.65 & 867 \\  & (0.17) & (0.20) & (0.26) & & (0.22) & (0.39) & (0.47) & 993 \\  & & & & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Differences in the share of green purchasing and measuring of 21 categories with and without a GPP. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Categories**} & \multicolumn{6}{c}{**Share of Municipalities Measuring Their Own Green Purchasing Rate**} \\ \hline  & **All** & **with GPP** & **w/o GPP** & **Difference** & **APE** & **All** & **with GPP** & **w/o GPP** & **Difference** & **APE** \\ \hline Paper Products & 82\% & 97\% & 77\% & 20\% & 0.14 *** & 64\% & 75\% & 99\% & 15\% & 0.12 *** \\ obs. & 1629 & 396 & 1229 & & 1602 & 1342 & 386 & 952 & & 1335 \\ Stationery & 76\% & 96\% & 70\% & 26\% & 0.19 *** & 44\% & 63\% & 36\% & 27\% & 0.23 *** \\ obs. & 1625 & 396 & 1225 & & 1598 & 1243 & 380 & 860 & & 1238
# Software-Defined Heterogeneous Vehicular Networking: The Architectural Design and Open Challenges
## 4 Towards Software-Defined Heterogeneous Vehicular Networks
### Architecture Design for SDHVNet
Moreover, the southbound application programming interface (API) facilitates communication between the network infrastructure plane and the control plane. _OpenFlow_ is usually one of the most commonly used southbound APIs. Nevertheless, _OpenFlow_ needs to be considerably enhanced in order to meet the challenges of dynamic vehicular networking environments. The control plane being a software platform is responsible for the management of networking functions virtualized from the network infrastructure plane. It thus collects and maintains the status of all SDN switches, creates and retains an up-to-date networking topology and accommodates an up-to-date frequency manager in order to determine the frequency of a requested vehicular application and service (or any other content) along with the particular duration it has been requested. Subsequently, the cache manager ensures an intelligent edge-based caching by employing dynamic cache management policies and cache eviction strategies. Handover decision manager guarantees that the vehicles remain seamlessly connected to the optimal radio access technology in an _always best connected_ mode for satisfying the bandwidth and stringent latency requirements of safety-critical vehicular applications and concurrently preserves precious network resources by mitigating handover failures and unnecessary handovers. Trajectory prediction is one of the key components of the control plane and primarily forecasts and updates the anticipated trajectories of the SDN switches. Stationery switches can easily be reached via a reliable connection. However, the real challenge is associated with the mobile switches. Hence, vehicles that are associated with the roadside cloudlets could be easily accessed via the control plane. However, vehicles that are temporarily disconnected need considerable attention: (a) trajectories of public transport are generally fixed, and as such, they could be reached via their fixed time schedules; and (b) connected vehicles are anticipated to be equipped with navigational systems, and drivers are generally expected to traverse along the navigational trajectory, so these navigational routes could be considered as the vehicles' trajectory for traversing through the network. Lastly, the applications plane  
Figure 4: A topological architecture of software-defined heterogeneous vehicular network (SDHVNet). offers a set of vehicular applications and services indispensable for formulating a next-generation seamless, ubiquitous and undifferentiated ITS platform.
# Research on Optimal Control of Treadmill Shock Absorption Based on Ground Reaction Force Constraint
## 2 Establishment of the Mathematical Model
### Establishing a System Stability Model  
#### 2.4.1 Establishing the System Dynamics Equation  
This experiment involves a treadmill system, where the airbag pressure can be adjusted to influence the ground reaction force (GRF). The state variable of the system is the airbag pressure P, and the control variable is the speed V. The airbag pressure is influenced by two factors: first, the control exerted by the air pump, and second, the GRF generated during human movement. Considering that the inflation and deflation processes of the airbag are complex nonlinear processes, we can simplify the model by assuming that the inflation rate and deflation rate of the air pump are linearly related, and that the GRF is linearly related to the air pressure. Therefore, a simplified system dynamics equation can be established as follows:  
\[\frac{dp}{dt}=k_{1}\Big{(}v-v_{ref}\Big{)}-k_{2}P \tag{17}\]  
where \(p\) is the airbag pressure; \(t\) is time; \(v\) is the current running speed; \(v_{ref}\) is the set target running speed; and \(k_{1}\) and \(k_{2}\) are system parameters representing the influence coefficient of running speed on air pressure and the rate of air pressure leakage.
# Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning
## 7 Implementation and Real-World Performance Analysis
### Performance Metrics and Evaluation  
The system was evaluated based on detection accuracy, processing speed, and real-time interaction efficiency in the MR environment. The proposed model achieved a mean average precision (mAP) of 98.76 percent for vial classification, demonstrating a significant improvement compared to the baseline YOLOv4-Tiny model. Detection accuracy varied depending on environmental conditions, with the highest performance observed under controlled lighting, where certain vial classes achieved 100 percent accuracy, while accuracy slightly decreased to 89.4 percent when cluttered backgrounds were used. The model performed well when detecting single vials, but exhibited occasional misclassification in  
Figure 7: Mixed Reality (MR) implementation of the proposed real-time vial detection system. The top row shows the real-time detection of individual vials using spatially anchored bounding boxes. The bottom row illustrates multi-vial detection, highlighting cases where the system struggles with misclassification due to visually similar vials (e.g., Epinephrine Pocket and Atropine). The MR system provides an intuitive, interactive interface for medical professionals, enabling real-time decision-making and reducing the risk of vial identification errors.  
multi-vial scenarios, particularly for visually similar vials such as Midazolam and Amiodarone, due to overlapping labels and their high visual similarity. The model achieved real-time inference with an average processing time of 68 milliseconds per frame, resulting in approximately 15 frames per second on the HoloLens 2. Compared to CPU-based inference, the GPU-accelerated execution provided a 3.5 times speed improvement, ensuring the smooth real-time detection in MR environments. The MR implementation allowed users to interact with detected vials seamlessly, with bounding boxes and labels remaining stable even as users moved around, with no noticeable lag or misalignment. Hand-tracking interactions for vial selection were evaluated, and the system demonstrated an average response time of 250 milliseconds, which falls within the acceptable usability limits for MR applications. However, in scenarios where multiple vials were present, the system occasionally misclassified overlapping objects, suggesting that further improvements in occlusion handling and spatial feature extraction are necessary to enhance detection reliability in complex medical settings.
# Precise Channel Estimation Approach for a mmWave MIMO System
## 7 Simulation Results and Discussion
### Results and Discussions
#### 7.4.1 Comparison of ASE  
In Figure 2a-c, the ASE (in bits/sec/Hz) at the SNR points for OMP, TSSR, VAMP, ADMM and the proposed Ex-ADMM algorithm with perfect CSI is shown. The following expression is used to calculate the ASE [49; 50],  
\[\rm ASE=\ E\Big{(}log_{2}\det(K_{N_{R}}+\Big{(}N_{R}N_{T}\Big{(}\rm\sigma_{q}^ {2}+NMSE\Big{)}^{-1}HH^{H}\Big{)}\Big{)}\]  
The performance of the OMP in terms of ASE at low-to-mid SNR points is moderate, and it gets worse at mid-to-high SNR points as the training symbols' length is increased, i.e., T \(\rm<400\). As is clear from Figure 2a, for all SNR points, VAMP performs very poorly for smaller training symbol lengths; i.e., T \(\rm<400\). When the training symbol length is increased, i.e., T \(\rm\geq 800\), the performance of VAMP increases significantly, as depicted in Figure 2b,c. It was found that, for different T values, the performance of TSSR is very bad at almost every SNR point relative to OMP, TSSR, VAMP, ADMM and Ex-ADMM. ADMM, at all SNR points, performed better than OMP, TSSR and VAMP for all training symbol lengths. In the case of Ex-ADMM, for T = 400, Ex-ADMM outperformed the OMP, TSSR, VAMP and ADMM algorithms at all SNR points. For T = 800 and 1200, at low-to-mid SNRpoints, ADMM and Ex-ADMM performed similarly, but, as the SNR range increased from mid to high, Ex-ADMM outperformed ADMM. Moreover, for high training symbol lengths, Ex-ADMM was very close to achieving the perfect CSI.  
Figure 2: (**a-c**) ASE for different transmit SNRs for a \(64\times 64\) mmWave MIMO channel at T = 400, T = 800 and T = 1200.
# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 3 Issues Faced by RPL during Mobility
### Delay Caused by ETX Probing  
In RPL, when a node discovers a new neighbor it schedules PING request messages to get the neighbor's ETX value. The node then decides whether to change its parent by taking into account the ETX values of both the new neighbor and current parent [45]. This scheduling of ETX probing delays the selection of preferred parents and causes issues in highly mobile environments (e.g., VANETS) where nodes frequently change their ranks.
## 4 Methodology
### Ensemble Predictions  
After parameter tuning, the best fitted models for each of the nine ML models were chosen and are referred to as M1-M9 (cf. Tables S8 and S9 for the best fitted models and the parameter space). Table S8 lists the best selected pre-processing pipelines. In Table S9, all parameters denoted with * represent the best parameters selected in at least one outer cross-validation fold. We used the F1 score as a performance measure and an inner cross-validation technique to decide on the best fit for each classifier. Eventually, we had nine different TLF pipelines, each with a unique setting of pre-processing steps and hyper-parameters for each classification algorithm predicting TLF.  
Using a complete outer cross-validation on the entire patient cohort, a decision matrix \(M\) was formed (Figure 2), where  
\[M[i,j]=P_{\theta_{j}}(Y_{i}=1|X_{i})\]  
Here, \(X_{i}\) corresponds to the input data for the \(i\)-th test patient, \(Y_{i}\) is the predicted class label, and \(\theta_{j}\) are the trained model parameters for the best fitted \(M_{j}\).  
From the decision matrix, we derived three ensemble predictions via: (1) unweighted majority voting \(M_{mw}\), (2) mean probability \(M_{mp}\), and (3) a trained super-learner (SL) \(M_{sl}\)\[M_{mw}[i]=\frac{1}{9}\sum_{j=1}^{9}\mathbb{1}\left(M[i,j]>0.5\right)\]  
\[M_{mp}[i]=\frac{1}{9}\sum_{j=1}^{9}M[i,j]\]  
\[M_{sl}[i]=\sum_{j=1}^{9}S_{b}(a_{j}*M[i,j])\]  
where \(\mathbb{1}(.)\) is the identity function. The SL combines the individual model predictions via weights \(a_{j}\) learned with a logistic regression to predict the class label, where \(S_{b}\) is the sigmoid function with base \(b\).
## 3 The ARCOMEM Approach
### Advanced, Scalable Multimodal Multimedia Content Analysis  
Once the data has been harvested, it must be cleaned, processed and analysed in order to allow the end users to explore and query the data. The ARCOMEM system is designed such that most processing occurs as scalable, distributed map-reduce tasks using Hadoop [28] performed over the HBase database. The final output of these processes is stored as Resource Description Framework (RDF) triples in a knowledge base, which is queryable through the SPARQL query language. Interfaces are built on top to allow the end users to explore the data set without the need to know SPARQL.  
Useful data on the web is often mixed with lots of irrelevant content, such as adverts. In terms of the archiving of a web page, it is often the case that the archivists want the full content, including adverts, to be preserved to retain the original context surrounding the information on the page. However for automatic analysis and indexing of the archive material, this irrelevant information can get in the way. Within the ARCOMEM system, there are modules (based on BoilerPipe [29] and Readability4J [30] that can attempt to automatically clean the content of a web page to remove the extraneous noisy information before the content analysis modules are invoked.  
The content analysis modules, provided by GATE (the General Architecture for Text Engineering) [31; 32] and OpenIMAJ (the Open toolkit for Intelligent Multimedia Analysis in Java) [33; 34], are primarily based around the detection of entities, topics, opinions and events (ETOEs) in both textual and visual media. Additional information, such as textual terms (words with high term frequency-inverse document frequency values) and near duplicates of images and videos are also recorded. In visual media, specifically, face (and object) detection and recognition techniques are used to detect entities. Visual opinion analysis takes two forms: facial expression analysis and global sentiment/attractiveness/privacy classification [12]. High-level semantic enrichment is used to disambiguate entities and semantically link them to concepts in standard external knowledge bases, such as DBpedia (DBPedia is a machine readable database of knowledge extracted from structured information within the Wikipedia encyclopedia).  
Data on the web is in constant flux, with many pages and items often changing or being updated. To deal with this, the ARCOMEM system architecture is specifically designed to allow resources to be crawled multiple times if required. This allows changes over time to be detected and analysed.
# An Analysis of ML-Based Outlier Detection from Mobile Phone Trajectories
## 1 Introduction
The initial framework described in this paper considers that trajectory data can be obtained from the city _Internet of Things (IoT)_ infrastructure and also from citizens' personal devices, such as smartphones, upon consent. Such a framework would, therefore, infer some form of human behaviour and map such behaviour in time and space to potential Pols which can then be deployed on a Smart City dashboard, or on Smart City user applications. To reach such a level of inference, it is necessary to design a framework that can assist a continuous PoI detection, by comparing different ML approaches, and this aspect is currently a major gap in the literature. This work contributes to overcoming such a gap by providing the following contributions:  
1. Provides the initial functional design of a novel framework for continuous detection of Pols based on smart and anonymised trajectory data collected from personal devices and IoT Smart City infrastructure. 2. Addresses the issue of outlier detection and provides a validation of outlier detection based on two ML algorithms, _density-based spatial clustering of applications with noise (DBSCAN)_ and _local outlier factor (LOF)_. The first reason for selecting these two specific algorithms, DBSCAN and LOF, lies in the fact that related literature states that these two algorithms are within the ones most relevant in the identification of outliers as shall be debated in Section 2. Some authors, such as [PERSON] et al., prefer LOF [5]. Other authors, such as [PERSON] et al., prefer DBSCAN to analyse outliers [6]. While both algorithms exhibit interesting properties, there is no study comparing both of them in terms of capability to support outlier detection, assuming a framework that relies on trajectory data captured by IoT and personal devices. A second reason for considering these two specific algorithms lies in the simplicity of both algorithms, a key aspect to consider in a continuous PoI detection framework, which is further addressed in Section 3. The remainder of this paper is organised as follows. Section 2 provides a description of related literature and of our contributions in comparison to prior work. Section 3 defines our proposal for continuous PoI detection, and its functional blocks, debating PoI detection and inference aspects and introducing also challenges with outlier detection. Section 4 is dedicated to an explanation of which ML algorithms are used to detect outliers giving preference to the most reputable ones and explaining in detail DBSCAN and LOF. Section 5 provides a performance evaluation of the two algorithms based on datasets with distinct features. The paper is summarised in Section 6, where the next steps are also debated.