esa-sceva/satcom-chunk-collection huggingface dataset:
# The Role of Data Analytics in the Assessment of Pathological Speech--A Critical Appraisal
## 2 ML Methods in Laryngeal Pathology Assessment
### MD vs. DD Comparison Criteria  
To help in contrasting and adding value to the controversial and collaborative aspects of MD- vs. DD-based ML applications to clinical decision-making, the following is a list of the most relevant criteria to compare both approaches:  
* **Detection and classification performance**: The ability to detect pathological from non-pathological behavior in phonation, given in universally accepted scores, such as sensitivity, specificity, accuracy, area under the ROC curve, and other similar quality standards.
* **Training capability**: The ability to create internal representations from benchmark databases universally accepted as valid standards in reasonable and well-defined termination conditions, granting scalability.
* **Generalization capability**: The possibility of maintaining the classification performance from training when new unseen datasets are presented to the classifier.
* **Explainability**: The capability of associating performance quality scores with pathology syndromes, offering clinically interpretable indications on which syndromes can be associated with specific classification scores.
* **Semantic load**: The capability of associating performance quality scores to a priori established specific study hypotheses, allowing their acceptance or rejection in formal terms, extending to new hypotheses from observed explained results.
* **Clinical acceptance**: The ability to produce performance quality scores that might be adapted to the dashboards used in voice disorder clinical scenarios.
* **Computational costs**: The possibility of supporting the training, testing, and validation phases on reasonable standard computing platforms and servers, limiting computation power and memory requirements to reasonable terms.
* **Portability**: The capability of deploying computing solutions on limited portable devices, for instance, using tiny ML techniques.
* **Robustness**: The ability to achieve reasonable performance facing scarce, noisy, or low-quality audio signals.
* **Development effort**: The efforts demanded system deployment, testing, and evaluation to satisfactory performance standards.  
These criteria will help in building a listing of advantages and disadvantages favoring one approach or the other, as in Table 1. The observations presented in the table respond to general features of behavior, not to specific implementations, which may be subject to other special design criteria.
## 2 Materials and Methods
### MODIS Data and Processing  
We used the 8 day, 500 m surface reflectance product (MOD09G1, collection 5) from the MODIS/TERRA satellite provided by ORNL-DAAC ([[http://daac.ornl.gov/cgi-bin/MODIS/GR_col5_1/mod_viz.html](http://daac.ornl.gov/cgi-bin/MODIS/GR_col5_1/mod_viz.html)]([http://daac.ornl.gov/cgi-bin/MODIS/GR_col5_1/mod_viz.html](http://daac.ornl.gov/cgi-bin/MODIS/GR_col5_1/mod_viz.html))) for the 21 sites to compute MODIS NDVI (NDVI\({}_{MD}\)), from which a time series was calculated, with spatial pixel aggregations comprising:  
1. A single pixel with coordinates matching a flux site (MODIS\({}_{0.5\times 0.5}\): \(0.5\times 0.5\) km);
2. Nine pixels centered around a flux site (MODIS\({}_{1.5\times 1.5}\): \(1.5\times 1.5\) km);
3. Twenty-five pixels centered around a flux site (MODIS\({}_{2.5\times 2.5}\): \(2.5\times 2.5\) km);
4. Forty-nine pixels centered around a flux site (MODIS\({}_{3.5\times 3.5}\): \(3.5\times 3.5\) km).  
The MOD09G1 product includes optimal reflectance values for nine bands in the visible and NIR regions, and the 8-day compositing period uses highest quality pixels (clear sky conditions and no snow cover) selected with optimal viewing angles and minimal cloud or cloud shadow impacts that were retained for further processing. NDVI\({}_{MD}\) for the aggregation of pixels \(\geq\)0.5 km (MODIS\({}_{1.5\times 1.5}\), MODIS\({}_{2.5\times 2.5}\) and MODIS\({}_{3.5\times 3.5}\)) was calculated as:  
\[NDVI_{MD}=(\rho_{NIR}-\rho_{rad})/(\rho_{NIR}+\rho_{rad}) \tag{4}\]  
where \(\rho_{rad}\) and \(\rho_{NIR}\) are the reflectance values for MODIS band 1 (620-670 nm) and band 2 (841-871 nm), respectively.  
We used the MOD13Q1 NDVI dataset with 250 m pixel size and a 16 day compositing period to quantify landscape spatial variability in the area surrounding an eddy covariance tower because this product has an increased spatial resolution of 250 m compared to MOD09 (500 m). The best image at the beginning of the season, which was as close as possible to the starting day of greening, was selected for each site (Table S1). Pixels corresponding to an area of \(7\times 7\) km were extracted, then the extracted area was classified, and the spatial heterogeneity indicator (SHI) was computed (determination methodology for SHI is described in Section 2.4). MODIS data quality was assured by making use of the quality assurance and control (QA/QC) flags available in MOD09G1.
## 3 Review and Application of Dynamic Discrete-Element Method
### Shovel-Force Analysis  
During the shoveling section, EWLs are subjected to shovel force. If this force is not managed properly, there is a risk of tire slippage [19]. Accurately analyzing the shovel force is important. Shovel force can be broadly categorized into three types, including horizontal insertion force, vertical shovel force, and the bucket rotation torque. Due to the complexity of the actual shoveling section, analyzing these forces according to different shoveling scenarios is necessary.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of EWL** & **Measured Shovel** & **Calculated Shovel** & **Error** \\ _/lon_ & **Force/kN** & **Force/kN** & **\%** \\ \hline
3 & 127 & 136 & 7.08 \\
5 & 160 & 163 & 1.88 \\
7 & 207 & 209 & 0.96 \\
9 & 270 & 265 & 1.85 \\
12 & 380 & 344 & 9.4 \\
15 & 420 & 353 & 15.9 \\
35 & 900 & 700 & 28.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons of shovel force.  
During the entire shoveling section, loaders experience five different forces. \(F1\) represents the insertion force. \(F2\) is the shovel force at the bucket bottom. \(F3\) is the gravity of the materials being loaded. \(F4\) accounts for the frictional forces between the loaded material and the inner wall of the bucket (including friction resulting from relative sliding and rolling). \(F5\) corresponds to the sliding force on the slip surface [20], as illustrated in Figure 2a.  
In Figure 2b, the \(\alpha\) represents the angle between the movement direction of the EWL and the horizontal plane, the \(\beta\) is the angle between the horizontal direction and the cutting edge of the bucket, and the \(H\) is the height of the filled material. Based on geometric and kinematic relationships, the relationship between bucket shoveling volume and the trajectory of EWL can be derived [21]. Expressions for other forces can also be derived. The derivation section requires the use of numerical computations, which can introduce significant errors. The DEM can effectively solve these issues.
## 6 Transport Layer in WMSN
### Congestion Control
By exploiting this feature, the congestion control mechanism is based on load repartitioning over the multiple paths, instead of decreasing the transmission rate in case of congestion in order to maintain the quality of video streaming. The load repartition based congestion control (LRCC) uses queue length as congestion detection indicator along with collision rate. LRCC also uses explicit congestion notification by using especial control messages (called congestion notification messages). In reception of these notification messages, the source node will try to balance its traffic on the available paths while reducing the amount of data sent on the current congested path in order to reduce the congestion as well as maintain its sending rate unchanged.
## 3 Data and Methods  
Seismic, logging, thin-section, and core data are involved in this study. There are 305 2D seismic survey lines, 3D seismic data from Lunnan with an area of 1350 km\({}^{2}\), the Middle-Lower Cambrian samples from the Shenairike, Sugaitebulake and Xiaoerbulake section, core samples from well ST 1, ZS 5, Y 10 and YM 36, and logging data from more than 20 drilling wells of the middle-Lower Cambrian in the Tarim Basin.  
The research methods employed in this paper are as follows: first, 2D and 3D seismic data were used to establish a seismic survey network covering the study area, and Landmark 5000 software was used to carried out fine interpretation of seismic data and detailed analysis of reflection characteristics. Then, the main characteristics and distribution of typical seismic facies were studied. Meanwhile, based on outcrop, core and logging data, the Middle Cambrian lithofacies types and characteristics of the Tarim platform area were studied to define the types and division schemes of sedimentary facies, subfacies and microfacies. Next, we traced the distribution range of different types of seismic facies through seismic facies interpretation, and attributed plane characteristics to clarify the distribution characteristics of corresponding sedimentary facies, studying the thickness of the Middle Cambrian gypsum-salt rocks, gypsum-salt rocks, gypsum-midstone and gypsum-polysiferous dolomite strata. Microscopic thin-section observation, physical parameters, and breakthrough-pressure testing were conducted on 64 core samples from 4 wells. By establishing the relationship between various parameters, a comprehensive evaluation of the sealing ability of different rock types of the Middle Cambrian in the study area was quantitatively conducted. Based on the macroscopic characteristics and microscopic sealing ability of the Middle Cambrian strata, a comprehensive evaluation table for the sealing ability of the caprocks is established to comprehensively evaluate the Middle Cambrian caprocks in the platform area and clarify the distribution of favorable areas for caprock development.
# Lithium-Ion Battery State of Health Estimation Based on CNN-LSTM-Attention-FVIM Health Features
## 5 Verify the Feasibility of the Model and Analyze the Relevant Results
### Description of Results Related to a Single Dataset
The prediction model proposed in this study can accurately estimate and predict the SOH of batteries across varying training sample sizes, owing to its effective extraction and learning of battery degradation characteristics, while maintaining prediction accuracy unaffected by sample quantity constraints.
# Research of Tri-State Optical Signal Detectors for Ternary Optical Computers
## 2 Materials and Methods
### Design of Three-State Optical Signal Detectors  
#### 2.3.1 Design of Decoding Circuit for One LCD Pixel  
The decoding circuit of one pixel is shown in Figure 5, where Rb is the protective resistor of the photosensitive sensor Rg, which senses the light intensity of the liquid crystal. When the liquid crystal pixels show different polarization states, the electric current information from Rg is different, i.e., the current through Rg is positively correlated with the light intensity. First, we connect two fixed circuits R1 and R3 at the negative terminal of Rg and access the adjustable resistor R2 in the middle of the two fixed resistors. We set the voltage divider output points V1 and V2 in the middle of Rg and R1, and R1 and R2. We connect V1 and V2 to inputs 1D and 2D of latch 74 HC573. The addition of the adjustable resistor R2 improves the flexibility and fault tolerance of the circuit.  
The circuit can be described as follows. When the measured pixel is white, V1 \(>\) V2 \(>\) 3.5 V, and the latch output is 11; when the measured pixel is light green, V1 \(>\) 3.5 V, V2 \(<\) 3.5 V, and the latch output is 10; when the measured pixel is blue, V2 \(<\) V1 \(<\) 3.5 V, and the latch output is 00.
## 4 Evaluation and Result  
### 50G QSFP28 PAM4 Optical Transceiver Fabrication  
In this study, a 50G QSFP28 PAM4 optical transceiver was fabricated according to the proposed design scheme. Figure 8 shows the PCB board and parts of the QSFP28 optical transceiver being loaded. The NRZ electrical interface is implemented to meet the CEI-28G-VSR standard for LAUI-2 with two 25 Gbps high-speed current mode logic(CMLs), the PAM4 optical interfaces designed to comply with standard IEEE802.3 cd, and are implemented to transmit up to 40 km at a speed of 50 GBASE-ER (LC) on a single mode fiber. The EML-TOSA, linear PIN-PD ROSA, PAM4 DSP IC, and other parts needed for the operation were outsourced by each supplier. Figure 9 shows the final QSFP28 optical transceiver assembled with a manufactured PCB in the case. The case dimensions are 72.4, 18.35, and 8.5 mm for the width, length, and height, respectively, according to the standard document SFF-8661.
## 4 Discussion  
In this study, to develop a real-time monitoring application, a stepwise approach was applied to improve usability, starting with an analysis of needs. The initial FGIs for needs analysis identified requests for \"side effects and adverse reaction identification services\", \"concomitant drug identification capabilities\", and \"remote feedback functions\". The primary goal of a clinical trial is to assess the benefit-to-risk ratio of the drug or treatment under consideration [28]. The therapeutic benefits of an agent, which represent its impact, can be determined within a predictable range. Simultaneously, the risks and adverse effects should be investigated in consideration of the causal link between adverse events and clinical trials. In this regard, the collection of reliable data for adverse events is crucial but quite difficult [29; 30]. Thus, the function request for \"side effects and adverse reaction identification services\" appears to be a way to solve the difficulties associated with clinical trials. In many sponsor-initiated trials, predominantly clinical trials, the information and communication technology devices and software used for the clinical trial are usually developed by the sponsors and applied to the clinical trial sites. This system forces the trial site's clinical trial professionals to learn to operate a new device or system and to educate the participants whenever they receive a request [31]. In contrast, the functions of an application can be learned relatively easily if the application design matches the system of the user's institution, emphasizing the importance of a similar structure [32]. Despite the fact that personal health records in the hospital information system (HIS) are now required in clinical trial research, a number of obstacles, such as the lack of interoperability between clinical trial research systems and HIS, make their usage challenging. This may indicate the need for HIS compatibility [33; 34; 35]. Moreover, though the target group of each clinical trial varies depending on the stage and type of the trial, healthy volunteers participating in the phase 1 trial are relatively easy to generalize and constitute the largest number of participants across clinical trials. This indicates the need for application development based on its use in healthy adults. Especially in specialized domains, such as medical systems, the participation of actual users in program development is crucial. In this regard, visualizing the contents of the developed application, information architecture, and wireframes to
# A Comparative Evaluation of Cement and By-Product Petrit T in Soil Stabilization
## 3 Results and Discussion  
### Consistency Limits (Atterberg Limits)  
Figure 1 shows the immediate effect of adding various amounts of binder (cement or Petrit T) on the soil consistency limits. The addition of low amounts of either type of binder (1 to 4%) had an immediate effect on increases in both the liquid limit (LL) and the plastic limit (PL). The liquid limit remained almost constant for further increases from 4 to 7%, although it dropped at higher percentages of binder amount (10% and 15%). After treatment, the plastic limit significantly increased at low binder contents (1 to 7%) compared to a slight increase when the amount of binder was increased between 7 to 15%. Consequently at low amounts of binder (1-4%), the plasticity index (PI) slightly increased and then decreased as the amount of binder was increased. From Figure 1, it can be seen that the trends  
have generally been in the same direction, but with more marked effects for the cement treatment on both liquid and plastic limits. Moreover, both binder types have approximately the same behavior trend on the reduced plasticity index for up to 10% of binder content. In contrast, cement has more effect on reducing the plasticity index at high binder content (15%).  
The immediate increase in the liquid limit and plastic limit was attributed to the flocculation and agglomeration of soil particles caused by the hydration reaction of the binder. A similar trend is found in previous studies. For cement treatment, reference [31] suggested that the presence of entrapped water within the intra-aggregate pores due to flocculation and agglomeration of soil particles was the main reason behind an increase in the liquid limit after treatment. In contrast, increased amounts of binder produced an increase in cementitious materials and this lead to decreased liquid limits. References [32; 33; 34] have reported about increases in the liquid and plastic limit of soils after have been treated with various binder types.  
Flocculation and agglomeration of the soil particles after treatment is the main reason behind the immediate change in the liquid and plastic limit of soil, which leads to a reduction in the plasticity index of soil with the increasing amount of binder. A similar trend is observed for a wide range of soil treated with different binder types [35; 36; 37; 38; 39].  
Figure 2 shows the curing time effects on the plasticity index after treatment. It can be seen that the trends are generally in the same direction (a decrease in the plasticity over time), but with more marked effects for the cement treatment. The reduction in the plasticity index is increased as binder content increased. Moreover, Figure 2 shows that a 2% of Petrit T has approximately the same effects as adding 1% of cement. For both binder types, the decrease in the plasticity index is mainly due to increases in plastic limits and decreases in liquid limits over time. A similar trend has been observed in previous studies [22; 31; 32].  
Figure 1: Consistency limits versus binder content of soil after one hour of treatment.
# A New Affinely Adjustable Robust Model for Security Constrained Unit Commitment under Uncertainty
## Appendix A Nomenclature
### Parameters  
\begin{tabular}{l l} \(A_{i}^{s}\) & Generation map for conventional generators \\ \(A_{w}^{s}\) & Generation map for wind generators \\ \(A_{p}^{s}\) & Generation map for PV generators \\ \(FC_{i}\) & Fixed production cost of thermal generator [USD] \\ \(C_{i}\) & Operating variable cost of generator [USD] \\ \(Csu_{i}\) & Startup cost of conventional generator [USD] \\ \(Csd_{i}\) & Shut down cost of thermal generator [USD] \\ \(d_{s,t}\) & Demand in bus \(s\) at time \(t\) [MW] \\ \(g_{up}^{down}\) & Minimum down time of thermal generator \(i\) [h] \\ \(g_{i}^{i}\) & Minimum up time of thermal generator \(i\) [h] \\ \(g_{up,init}^{down,init}\) & Time that thermal generator \(i\) has been down before \(t=0\) [h] \\ \(g_{i}^{max}\) & Rated capacity of thermal generator \(i\) [MW] \\ \(g_{i}^{min}\) & Minimum output of thermal generator \(i\) [MW] \\ \(g_{i}^{on-off}\) & On-Off status of generator \(i\) at \(t=0\) (equal to 1 if \(g_{i}^{up,init}>0\) \\  & and 0 otherwise) \\ \(\hat{g}_{w,t}\) & Power Forecast of wind generator \(w\), at time \(t\) \\ \(\hat{g}_{p,t}\) & Power Forecast of PV generator \(p\), at time \(t\) \\ \(d_{s,t}\) & Load Forecast of demand at bus \(s\), at time \(t\) \\ \(F_{1}^{max}\) & Maximum Capacity of the line \(l\) [MW] \\ \(TCF\) & Transmission capacity factor of the line \(l\) \\ \(L_{i}^{down,min}\) & Length of time the thermal generator \(i\) has to be off at the start time of the planning horizon [h] \\ \(L_{i}^{up,min}\) & Length of time the thermal generator \(i\) has to be on at the start time of the planning horizon [h] \\ \(ramp_{ip}^{down}\) & Ramp-down limit of thermal generator i [MW/h] \\ \(ramp_{ip}^{up}\) & Ramp-up limit of thermal generator i [MW/h] \\ \(PTDF_{l,s}\) & Matrix of Power transfer distribution factors \\ \(LODF_{l,k}\) & Matrix of Line Outage distribution factors \\ \(OTDF_{l,k}\) & Matrix of Outage transfer distribution factors \\ \(\hat{e}_{w,t}\) & Materialized forecast error of the wind generator \(w\), at time \(t\) [MW] \\ \(\hat{e}_{p,t}\) & Materialized forecast error of the PV generator \(p\), at time \(t\) [MW] \\ \(\hat{e}_{s,t}\) & Materialized forecast error of the the demand located at bus \(s\), \\  & at time \(t\) [MW] \\ \(\hat{e}_{w,t}^{max}\) & Maximum forecast error of the wind generator \(w\), at time \(t\) [MW] \\ \(\hat{e}_{p,t}^{max}\) & Maximum forecast error of the PV generator \(p\), at time \(t\) [MW] \\ \(\hat{e}_{s,t}^{max}\) & Maximum forecast error of the demand located at bus \(s\), at time \(t\) [MW] \\ \end{tabular}