esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Cost and Justification, Limitations and Future Directions  
The costs of third-generation PVAS devices are significantly higher compared to traditional methods such as vertebplasty and balloon kyrophoplasty. This increased cost is primarily attributed to the advanced materials (e.g., titanium components) and the sophisticated engineering of these devices. These factors can be a barrier to widespread adoption, especially in resource-limited settings. Furthermore, their deployment often requires advanced technical expertise, and operator inexperience may increase the risk of complications or diminish the effectiveness of the intervention.  
However, the higher costs of these systems can be justified by their long-term benefits. Third-generation devices have been shown to reduce revision rates, lower the risk of re-fractures and adjacent vertebral fractures, and improve patient outcomes overall. Additionally, their use in complex cases, such as those involving multiple myeloma or older fractures with marrow edema, expands the range of treatable conditions, further supporting their adoption in clinical practice.  
Despite the clear advantages, there remain areas for improvement. One key issue is patient selection, as not all fractures may benefit equally from the same device. Future studies should focus on refining criteria for selecting the most appropriate device based on fracture type, patient age, and comorbidities. Furthermore, long-term studies comparing the efficacy of these devices in preventing adjacent fractures and maintaining height restoration are needed to fully assess their impact on patient outcomes.
# An Open-Source R Package for Building and Using US Environmentally-Extended Input-Output Models
## 2 Materials & Methods
### Model Exporting  
Models can be conveniently exported to.csv files, Excel(r) workbook (.xlsx), or.bin format, in a user-specified directory that suits various applications. Recently exported model files overwrite existing files by default.  
One outlet for the USEEIO model is the USEEIO API [31], which was designed for dynamic access by applications or other uses. This wrapper function exports model components required by the API to a user-specified directory basedir and sub folders.  
writeModelforAPI(model, basedir)  
To comply with format requirements of the API, model matrices, including \(V\), \(U\), \(U_{d}\), \(A\), \(A_{d}\), \(B\), \(C\), \(D\), \(L_{d}\), \(M\), \(M_{d}\), \(N\), \(N_{d}\), _Rho_, and _Phi_, are written to.bin files in the 'basedir/build/data/modelname/' folder, where _modelname_ is the name of the given model. Model demand vectors are written to.json files in 'basedir/build/data/modelname/demands/' folder. Model description and metadata of indicators, demands, sectors, flows, and years are written to.csv files in the 'basedir/build/data/' folder.  
A Python script ([[https://github.com/USEPA/USEEIO/blob/master/olca/u2o.py](https://github.com/USEPA/USEEIO/blob/master/olca/u2o.py)]([https://github.com/USEPA/USEEIO/blob/master/olca/u2o.py](https://github.com/USEPA/USEEIO/blob/master/olca/u2o.py)), (accessed on 14 March 2022)) is available to generate a fully-linked JSON-LD model compatible with the openLCA JSON-LD schema [32], which leverages the output of writeModelforAPI.  
The matrices that are written to.bin files for the API use can also be exported to individual.csv files, by specifying to_format = \"csv\". The files can be saved to any user-specified folder, outputfolder, and do not have to be the same basedir in writeModelforAPI.  
writeModelMatrices(model, to_format = \"csv\", outputfolder)  
A consolidated Excel(r) workbook (.xlsx) may be created to store the model matrices mentioned above, model commodity and industry output (\(q\) and \(x\)), model demand vectors, and model sector crosswalk, plus the metadata of demands, flows, indicators, commodities, or industries (depending on if the model was a commodity or industry model), final demand, and value added..  
writeModeltoXLSX(model, outputfolder)  
A 16-digit hash of the full model object can be created to assign the model object a unique id.  
generateModelIdentifier(model)
## 5 3 GPP Standardization for URLLC
### Possible 5G Integration Plan by 3 GPP
#### 5.4.2 Non-Standalone (NSA)  
In NSA, the LTE radio cells and NR radio cells are combined using dual-connectivity to provide radio access. The core network could be either 5 GC or EPC based on the operator's choice [52]. On the basis of the operators, they can provide 5 GC for 5G customers or facilitate the existing 4G deployment combining NR radio and LTE resources with current EPC. NSA needs tight integration with the LTE RAN. Three variations of NSA, as defined by 3 GPP, are as follows:  
1. LTE eNB and EPC as master and NR en-gNB as secondary.
2. NR gNB and 5 GC as master and LTE ng-eNB acting as secondary.
3. LTE ng-eNB and 5 GC as master and NR gNB as secondary.  
Options defined by 3 GPP for 5G deployment use either existing EPC [53] or the 5 GC [54]. Both architectures follow different design principles. EPC can be considered as an evolution of earlier generation packet-based core networks. 5 GC is designed based on \"cloud-native\" approach, with virtualization and cloud computing as its core. 5 GC provides improved QoS features and superior network slicing.
## 3 Experimental and Simulation Set-Up Details
### Air Piloted-Jet Flame (Sandia Flame D)  
The burner geometry of the turbulent reacting configuration Sandia Flame-D [62] consists of a central fuel nozzle with diameter d\({}_{\text{fuel}}\) = 7.2 mm (the wall thickness is 0.25 mm) surrounded by a pilot jet burner with d\({}_{\text{pilot}}\) = 18.2 mm (the wall thickness is 0.35 mm). The main jet is constituted by a mixture of 25% methane and 75% air by volume. The pilot jet burns a premixture of acetylene (C\({}_{2}\)H\({}_{2}\)), hydrogen (H\({}_{2}\)), air, carbon dioxide (CO\({}_{2}\)), and nitrogen (N\({}_{2}\)). It is operated lean. This configuration was planted in an air-wind tunnel around the nozzle presenting the coflow stream with diameter d\({}_{\text{coflow}}\) = 165 mm. Based on the central fuel nozzle properties it exhibited a Reynolds number of 22,400. Various scalar data obtained from Raman/Rayleigh/LIF (Laser-Induced fluorescence) measurement techniques and velocity data from two-component LDV measurements are available [62]. Figure 2 features the computational configuration studied where a 3D structured grid with 40 blocks and 1,808,440 control volumes has been found sufficient to accomplish grid-insensitive solutions. The height of the numerical grid applied was 80 d\({}_{\text{fuel}}\). Regarding the numerical set-up, the waveTransmissive condition was used for the outlet with fixed pressure value; P = 1 atm, and zero gradient boundary conditions were set for all other parameters. All the operating conditions are provided in Table 1. To assess the convergence of the number of stochastic fields (SFi), the LES was carried out for different numbers of stochastic fields (4,6,8,16 SFi) for 0.38 s of physical time, respectively, to ensure that the flow reaches the statistically steady state with time step \(\Delta\)t = 3 \(\times\)\(10^{-7}\). The calculations were accomplished on 96 processors.
## 4 Device Characterization
### Sensitivity of the Receiver Module  
A chip-on-board (COB) module including our Ge PDs integrated with polymer ML, commercial transimpedance amplifiers for acquiring voltage signal from the PD, 850 nm vertical-cavity surface-emitting lasers (VCSELs), and drivers for the lasers, are used for the eye diagram and bit error rate measurement. A non-return-zero (NRZ) pseudorandom binary sequence (PRBS) data stream with a length of 231-1 is applied to the driver, and either a digital communication analyzer (Keysight, E8363B) or a signal quality analyzer (Anritsu, Atsugi, Japan, MP1800A) is connected to the COB module for the eye diagram and bit error rate measurement, respectively. In Figure 13a-d, the measured eye diagrams from the output of a four-channel photo-receiver using this COB module with input data rates all set at 25.78 Gbps and device biases all set at \(-\)3.3 V are shown, in which clear eye diagrams can be observed. The temperature of the COB module is controlled to be 25 \({}^{\circ}\)C. Then, the sensitivities are measured to be \(-\)9.2 dBm with a bit error rate (BER) of \(10^{-12}\), is competitive compared to commercial products. Note that because the extinction ratio of the signals is 4.78 dB, the sensitivities defined by optical modulation amplitude (OMA) and average optical power (AOP) are almost the same.  
Figure 12: Photocurrent vs. displacement of the focuser center relative to the PD center.  
Figure 13: Eye diagram measured from the COB module for (**a**) the 1 st channel, (**b**) the 2 nd channel, (**c**) the 3 rd channel, and (**d**) the 4 th channel.
# Lifetime Maximization via Hole Alleviation in IoT Enabling Heterogeneous Wireless Sensor Networks
## 4 Performance Evaluation
### End to End delay Comparison  
Figure 7 leads to E2E delay comparison among LiMHA, ODTS, LAEHA, N-SEP and P-SEP. Contrary to energy tax, E2E delay decreases with increase in number of nodes. With number of nodes increases traffic load on lowest region increases thus sending high traffic load consumes lot of time which increases the E2E delay. Moreover packets collision between nodes increases the overall waiting time. ODTS has lower delay as compared to LiMHA. The main reason behind this with less number of nodes the distance between nodes and ants in a region increases thus ants have to wait for the packet to reach to it which increases the overall sending time but still better than LiMHA. Also due to less number of nodes probability of node failure tends to increase rapidly thus nodes have to choose other routing path for packet transmission. In term of achieving better results with minimum E2E delay in sparse area network LAEHA performance is better due to the following reason: LAEHA does not perform number of calculations for finding the forwarder nodes. However in LiMHA normal nodes in a sector have to decide whether to send the data to normal nodes in lower region or to forward the received data to super nodes in respective region. Moreover number of neighbor requests in LAEHA are less as compared to LiMHA and ODTS that add extra E2E delay into the routing technique with high number of neighbor requests. When network size grows up, the performance of ODTS is remarkable as compared to LiMHA in terms of achieving minimum E2E delay level because data is forwarded to best routing path which is most energy efficient path and this level increases on average as compared to LAEHA which has high level of minimum delay among others on an average. In LiMHA, the network area is divided into sectors and in each sector normal node has to choose its data forwarder such that the packet successfully reaches to its destination. Similarly, the E2E delay of P-SEP from all baseline schemes including N-SEP is minimum due to the factor of its greedy approach of selecting cluster head at an optimal distance from sink and member nodes with in the threshold value of communication range. That is the main factor its performance degrades as communication radii increases, however, its E2E delay remains smaller than rest of the counterpart schemes. N-SEP performs well with respect to baseline schemes excluding P-SEP as shown in Figure 7.
## 2 Active HVS System Coupled with NESI
\begin{array}{l}(m_{uf}+b_{f})z_{uf}^{\prime\prime}-b_{f}z_{sf}^{\prime\prime }-c_{f}z_{sf}^{\prime}+c_{f}z_{uf}^{\prime}-k_{f}z_{sf}+(k_{f}+k_{tf})z_{uf}- u_{f}=k_{tf}z_{rf}\end{array}  \tag{3}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(m_{s}\) & 690 kg & \(c_{a}\) & 800 Ns/m & \(l_{a}\) & 1.4 m \\ \(l_{\varphi}\) & 1222 kgm\({}^{2}\) & \(k_{if}\) & \(2\times 10^{5}\) N/m & \(l_{r}\) & 1.5 m \\ \(m_{uf}\) & 45 kg & \(k_{tr}\) & \(2\times 10^{5}\) N/m & \(c_{f}\) & 1000 Ns/m \\ \(m_{ur}\) & 45 kg & \(k_{f}\) & 18,000 N/m & \(c_{r}\) & 1000 Ns/m \\ \(m_{a}\) & 80 kg & \(k_{r}\) & 22,000 N/m & & \\ \(k_{a}\) & 8000 N/m & \(l_{f}\) & 1.3 m & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Structural parameters of the active HVS system coupled with NESI. Figure 1: Active HVS system coupled with NESI. The dynamic equation describing the rear tire is  
\[(m_{ur}+b_{r})z_{ur}^{\prime\prime}-b_{r}z_{br}^{\prime\prime}-c_{r}z_{sr}^{ \prime}+c_{r}z_{ur}^{\prime}-k_{r}z_{sr}+(k_{r}+k_{tr})z_{ur}-u_{r}=k_{tr}z_{rr} \tag{4}\]  
The dynamic equation describing the vehicle seat is  
\[m_{a}z_{a}^{\prime\prime}+c_{a}(z_{a}^{\prime}-z_{sa}^{\prime})+k_{a}(z_{a}-z_{ sa})+k_{ba}(z_{a}-z_{ba})^{3}+c_{ba}(z_{a}^{\prime}-z_{ba}^{\prime})-u_{a}=0 \tag{5}\]  
The dynamic equation describing the NESI installed between the vehicle body and seat is  
\[b_{a}(z_{b}^{\prime\prime}-z_{sa}^{\prime\prime})-k_{b}(z_{a}-z_{b})^{3}-c_{b}( z_{a}^{\prime}-z_{b}^{\prime})=0 \tag{6}\]  
The dynamic equation for the NESI installed between the vehicle body and the front tire is  
\[b_{f}z_{bf}^{\prime\prime}-b_{f}z_{uf}^{\prime\prime}-c_{bf}z_{sf}^{\prime}+c _{bf}z_{bf}^{\prime}+k_{bf}(z_{bf}-z_{sf})^{3}=0 \tag{7}\]  
The dynamic equation for the NESI installed between the vehicle body and the rear tire is  
\[b_{r}z_{br}^{\prime\prime}-b_{r}z_{ur}^{\prime\prime}-c_{br}z_{sr}^{\prime}+c _{br}z_{br}^{\prime}+k_{br}(z_{br}-z_{sr})^{3}=0 \tag{8}\]  
As shown in Figure 1, when the pitch angle of the vehicle body is small, the displacements \(z_{sf},z_{sr}\) and \(z_{sa}\) can be approximated as  
\[z_{sf}=z_{s}-l_{f}\sin\varphi\approx z_{s}-l_{f}\varphi z_{sr}=z_{s}+l_{r} \sin\varphi\approx z_{sr}+l_{r}\varphi z_{sa}=z_{s}-l_{a}\sin\varphi\approx z_ {s}-l_{a}\varphi \tag{9}\]  
Substituting Equation (9) into Equations (1)-(8) yields  
\[\left\{
# Extracting Sentence Embeddings from Pretrained Transformer Models
## 4 Results
### Token Aggregation and Post-Processing Techniques
#### 4.1.3 Isotropy  
Many post-processing methods have previously been proposed to improve the isotropy of representations. It was argued that representations from the BERT model fall into the narrow cone and, therefore, are anisotropic. Thus, by raising isotropy, one can improve regular task performance.  
We calculate the IsoScore isotropy metric on the Wikitext-2 dataset after applying various post-processing and token aggregation methods for multiple models. We exclude whitening post-processing, as it always produces representations with the IsoScore at the maximum of 100%, just due to its working principle. Then, we compare the isotropy score to the STS, clustering, and classification task performance by calculating the Pearson correlation coefficient. For semantic textual similarity tasks, results are shown in Figure 1, and for clustering tasks, results are shown in Figure 2.  
For each method, the baseline IsoScore is very low: always below 5%. After applying weighted token aggregation techniques or post-processing, it is always increased. The smallest improvements of up to 7-8% score are observed for templated models T0 and T4, while the highest, over 80%, are for random embeddings. For other models, the IsoScore reaches around 17%.  
Why is the IsoScore of random embeddings improved so much compared to the other models? The answer may be related to the inner workings of RE. Once we generate the random embeddings, these token-level representations have a maximum possible isotropy. The RE model represents text sequences as averages of these vectors; thus, isotropy going from the token level to the document is reduced. However, token aggregation and post-processing techniques mitigate this isotropy loss, allowing for document embeddings to regain most of it back from the token-level ones. Other models start with already low isotropy for token-level representations and thus have less space to improve it.  
IsoScore Correlation with Task Performance  
Six out of eight models have a moderate correlation (more than 54%) between Isoscore and the average Spearman correlation of semantic textual similarity tasks (see Figure 1). For clustering (see Figure 2), it is less apparent, with only four models reaching moderate Pearson correlation. However, for both STS and clustering tasks, the best score for each model is always reached by some improvement of IsoScore, compared to the isotropy score of the plain averaging setup.  
In contrast, the classification does not improve with token aggregation and post-processing techniques; therefore, we do not observe correlation (and do not show it here).  
Figure 1: Relation between average Spearman correlation for STS tasks and IsoScore of Wikitext representations for each model. Pearson correlation coefficients are shown.
## 6 Experiments and Results
### Evaluation Measures  
The framework was evaluated using the accuracy, precision, recall, and G-mean measures. The Accuracy (\(ACC\)), Precision of the intrusion class (\(PREC_{I}\)), Precision of the normal class (\(PREC_{N}\)), Recall of the intrusion class (\(REC_{I}\)), Recall of the normal class (\(REC_{N}\)), and G-mean (\(GM\)) measures are described by Equations (3)-(8), respectively:  
\[ACC=\frac{TP+TN}{TP+TN+FP+FN} \tag{3}\]  
\[PREC_{I}=\frac{TP}{TP+FP} \tag{4}\]  
\[PREC_{N}=\frac{TN}{TN+FN} \tag{5}\]  
\[REC_{I}=\frac{TP}{TP+FN} \tag{6}\]  
\[REC_{N}=\frac{TN}{TN+FP} \tag{7}\]  
\[GM=\sqrt{REC_{I}*REC_{N}} \tag{8}\]  
where \(TP\) is the true positive indicating the number of intrusion instances which are predicted as intrusion, \(TN\) is the true negative indicating the number of normal instances which are predicted as normal, \(FP\) is the false positive indicating the number of normal instances which are predicted as intrusion, and \(FN\) is the false negative indicating the number of intrusion instances which are predicted as normal.
## 2 Battery Modeling  
### Battery Model  
For the accurate estimation of SOC, a reliable battery model is required. Considering the model accuracy, the structure complexity, and the computation time, the first order resistor-capacitor (RC) model as shown in Figure 1 is adopted to model the lithium-ion battery.  
The electrical behavior of the model can be written as follows:  
\[U_{t} =U_{oc}-U_{1}-I_{L}R_{0} \tag{1}\] \[\dot{U}_{1} =\frac{I_{L}}{C_{1}}-\frac{U_{1}}{R_{1}C_{1}} \tag{2}\]  
where \(U_{t}\) denotes the terminal voltage of the battery, \(U_{oc}\) is the open-circuit voltage, \(U_{1}\) is the polarization voltage of the RC network, \(I_{L}\) is the load current, \(R_{0}\) represents the ohmic internal resistance, and \(R_{1}\) and \(C_{1}\) represent the polarization resistance and polarization capacitance, respectively.  
The nonlinear relationship between the open-circuit voltage and the SOC is described using the fifth-order polynomial model as:  
\[U_{oc}(SOC)=k_{0}+k_{1}SOC+k_{2}SOC^{2}+k_{3}SOC^{3}+k_{4}SOC^{4}+k_{5}SOC^{5} \tag{3}\]  
where \(k_{0}\), \(k_{1}\), \(k_{2}\), \(k_{3}\), \(k_{4}\), \(k_{5}\) are the parameters to be identified.
###### Abstract  
The lithium-ion (Li-ion) battery is a promising energy storage technology for electronics, automobiles, and smart grids. Extensive research was conducted in the past to improve the prediction of the remaining capacity of the Li-ion battery. A robust prediction model would improve the battery performance and reliability for forthcoming usage. In the development of a data-driven capacity prediction model of Li-ion batteries, most past studies employed capacity degradation data; however, very few tried using other performance monitoring variables, such as temperature, voltage, and current data, to estimate and predict the battery capacity. In this study, we aimed to develop a data-driven model for predicting the capacity of Li-ion batteries adopting functional principal component analysis (fPCA) applied to functional monitoring data of temperature, voltage, and current observations. The proposed method is demonstrated using the battery monitoring data available in the NASA Ames Prognostics Center of Excellence repository. The main contribution of the study the development of an empirical data-driven model to diagnose the state-of-health (SOH) of Li-ion batteries based on the health monitoring data utilizing fPCA and LASSO regression. The study obtained encouraging battery capacity prediction performance by explaining overall variation through eigenfunctions of available monitored discharge parameters of Li-ion batteries. The result of capacity prediction obtained a root mean square error (RMSE) of 0.009. The proposed data-driven approach performs well for predicting the capacity by employing functional performance measures over the life span of a Li-ion battery.  
Keywords:lithium-ion (Li-ion) battery; functional principal component analysis (fPCA); battery monitoring data; state-of-health (SOH) diagnosis; LASSO regression +
Footnote †: journal: Applied
# Privacy Preservation and Analytical Utility of E-Learning Data Mashups in the Web of Data
## 5 Evaluation
### Analytical Evaluation of the De-Identification of Sensitive Data  
We evaluate whether our protocol is capable of de-identifying the sensitive data collected during the anonymization and integration process. We evaluate this feature by analyzing the probability of re-identification of the sensitive data collected. The evaluation is conducted in the worst-case scenario, that is, when the passive adversary is the mashup coordinator since it follows from Section 5.1 that the mashup coordinator is the only party that knows the original values (non-masked) of the aggregate quasi-identifier.  
When the passive adversary is the mashup coordinator participating in the protocol:  
1. Because the mashup coordinator handles the original quasi-identifiers during the execution of the protocol, the mashup coordinator may associate them with the connectors \(Qppc\).
2. Because a connector \(Qppc\) results from a one-way hash function on a nonce and the individual's identifier attribute (both unknown to the mashup coordinator), the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Protocol Step** & **Receive** & **Integrate** & **\(k\)-Anonymize** \\ \hline Step 4 & \((Qppc_{i},Q_{i}^{a})_{i=1}^{n}\) & & \\  & \((Qppc_{i},Q_{i}^{b})_{i=1}^{n}\) & & \\ Step 5 & & & \((Qppc_{i},Q_{i}^{a},Q_{i}^{b})_{i=1}^{n}\) & \\ Step 6 & & & \((Qppc_{i},Q_{i}^{a*},Q_{i}^{b*})_{i=1}^{n}\) \\ Step 9 & \((Cppc_{i},Q_{i}^{a*},Q_{i}^{b*},C_{i}^{b})_{i=1}^{n}\) & & \\ Step 10 & & \((Cppc_{i},Q_{i}^{a*},Q_{i}^{b*},C_{i}^{a},C_{i}^{b})_{i=1}^{n}\) & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Data handled by the mashup coordinator during the execution of the anonymization and integration protocol.  
mashup coordinator will not be able to derive the value of the identifier. Moreover, if the nonce is large enough, the connector will be protected against dictionary attacks and other precomputation attacks, making such attacks infeasible.  
Based on (i) and (ii), we conclude that a malicious mashup coordinator cannot associate the original quasi-identifier attributes of a given individual with their identifier attribute, even if the mashup coordinator carried out a dictionary attack, or similar. Despite not being able to re-identify a record using information learned from the protocol, the mashup coordinator could attempt re-identification through data linkage attacks or re-identification attacks [15]. If re-identification was successful, the mashup coordinator could not unambiguously link the individual's identity with their sensitive information because the proposed protocol satisfies the \(k\)-unlinkability property, being the probability of success of this link less than or equal to \(1/k\).  
Therefore, the proposed anonymization and integration protocol is capable of de-identifying the sensitive data collected, such that the probability that the mashup coordinator re-identifies the sensitive data of an individual is at most \(1/k\).