esa-sceva/satcom-chunk-collection huggingface dataset:
# Synchromodal Supply Chains for Fast-Moving Consumer Goods
## 4 Methodology
### MILP Model
#### 4.3.1 Notation  
As the flow synchronization is the key factor in our study, the input variables include: supply by supply warehouse, demand and standard deviation of demand by port of discharge, lead time (via truck, railroad, and ocean), cost of running the mixing center, target service level, review period by port of discharge, and container fill rate by port of discharge. Both demand and supply are measured in containers. Costs are measured in US dollars. The notation employed for formulating the model is described as follows.  
_SWs:_ set of supply warehouses;  
_MCs:_ set of mixing centers;  
_POLs:_ set of ports of loading;  
_PODs:_ set of ports of discharge.  
Parameters:  
\(tc_{SW,MC}\): truck cost from the supply warehouse to the mixing center;  
\(rc_{SW,MC}\): rail cost from the supply warehouse to the mixing center;  
\(tc_{MC,POL}\): truck cost from the mixing center to the port of loading;  
\(rc_{MC,POL}\): rail cost from the mixing center to the port of loading;  
\(oc_{POL,POD}\): ocean cost from port of loading to the port of discharge;  
_opc:_ operational cost associated with mixing centers;  
_Ce:_ holding cost;  
\(D_{POD}\): demand at port of discharge;  
\(R_{POD}\): review period at port of discharge;\(\sigma_{POD}\): standard deviation of demand at port of discharge;  
\(k\): safety factor that corresponds to the confidence in the data points within a certain standard deviation value (\(k\) = 2.05 based on 98% service level);  
\(L_{POD}\): lead time at port of discharge;  
\(Supply_{SW}\): supply at supply warehouse;  
\(Open_{MC}\): binary to reflect an open mixing center;  
\(M\): an arbitrary large number to ensure the linking constraints.  
Decision variables:  
\(tf_{SW,MC}\): truck flow from the supply warehouse to mixing center;  
\(tf_{MC,POL}\): truck flow from the mixing center to port of loading;  
\(tf_{SW,MC}\): rail flow from the supply warehouse to mixing center;  
\(tf_{MC,POL}\): rail flow from the mixing center to port of loading;  
\(of_{POL,POD}\): ocean flow from port of loading to the port of discharge;  
\(RailOpen_{SW,MC}\): binary to reflect an open rail flow from supply warehouse to mixing center;  
\(TruckOpen_{SW,MC}\): binary to reflect an open truck flow from supply warehouse to mixing center;  
\(RailOpen_{MC,POL}\): binary to reflect an open rail flow from mixing center to port of loading;  
\(TruckOpen_{MC,POL}\): binary to reflect an open truck flow from mixing center to port of loading.
## 3 Results
### Equilibrium Moisture Content and Water Solubility  
Table 3 shows the equilibrium moisture content and water solubility of the different films prepared with TPS and TPSDH. One can observe that the DH treatment of starch slightly promoted water sorption capacity and water solubility of the films. In this sense, [PERSON] et al. [57] reported that DH treatment of starch can damage its granular structure, which culminates in the exposure of its -OH groups, promoting the ability to interact with water molecules and water sorption capacity. [PERSON] et al. [32] also reported an increase in water solubility of rice starch modified with the DH treatment. Furthermore, the CMF incorporation slightly decreased the water sorption capacity of both TPS and TPSDH films, whereas it provided different effects on water solubility for each kind of starch film, reducing solubility for the TPS films but increasing it in the case of the TPSDH films. The small changes in the water sorption capacity provoked by CMFs can be explained in terms of the lower water affinity of the fibers in comparison with TPS [58], reflected on their low equilibrium water content (3%) at 53% RH [35]. In fact, the resulting water content of the microcomposites could be deduced from the mass balance considering the equilibrium water content of both starch matrix and fibers.  
Figure 5: Internal transmittance (\(T_{i}\)) of thermoplastic starch (TPS) and TPS modified by dry heating (TPSDH) films with different contents of cellulose microfibers (CMFs).  
The different effect of the fibers on water solubility of TPS and TPSDH films can be explained by the different interactions of CMFs with both starch matrices. The -OH groups present in CMFs could interact with the hydrophilic groups in starch, making it unavailable to interact with the water molecules [59]. This effect was evident for the water solubility of the TPS films, which decreased significantly (\(p<0.05\)) after the CMF incorporation, as previously reported by other authors [56, 60, 61]. Furthermore, the lower water solubility of these microcomposites due to the CMF presence could make starch less susceptible to hydrolysis, though this property mainly depends on its structural characteristics and modifications [62]. However, in the TPSDH films, the CMF incorporation promoted film water solubility, except for the samples containing 5 wt% CMFs. This different effect could be ascribed to a poorer integration and dispersion of the fibers in the starch matrix, having lower adhesion forces at the interface and, thus, favoring water penetration through the junction zones. The higher presence of non-gelatinized starch granules could also contribute to the higher solubility in the TPSDH films since this dispersed phase can reduce the cohesion forces of the matrix, which are related to the interchain bonds that depend on their interfacial adhesion in the continuous matrix.
## 3 Finite Element Analyses
### Tooth Width Analysis  
Trapezoidal Slot  
In this analysis, the slot area, the filling factor and the pole shoes are kept constant and the \(MMF\) is the same for each simulation. The colour plots in Figures 14 and 15 show the influence of the tooth width, as claimed in the previous paragraph: a wide tooth reduces the \(\bar{B}\) field induced inside the stator teeth. Furthermore, the ferromagnetic material does not even saturate with a wide tooth geometry, see Figure 15. The maximum value reached by the flux density is lower than 1.7 \(T\) if the tooth width, \(w_{t}\) is greater than 3 mm, see Figure 16.  
Figure 14: Trapezoidal slot with narrow teeth.  
Figure 13: Trapezoidal slot—impedance comparison.  
Figure 15: Trapezoidal slot with wide teeth.  
Considering the slot geometry of Figure 14, it is important to maintain a narrow slot-opening, such as 1.6 mm, since it leads to a percentage total power losses reduction of 15.69%, see Figure 17. This value is reduced by 0.31% when the tooth width is equal to 4 mm. On the other hand, the difference between \(\Delta\)Z% values for different slot-opening widths, is still significant and independent of the tooth width. According to the FEA results, the lowest percentage difference between impedance can be obtained with a geometry with a slot-opening of 1.6 m and a tooth width of 2 mm: \(\Delta\)Z% = 56.49%, see Figure 18.  
Figure 16: B-field comparison for a trapezoidal slot.  
Figure 17: Slot opening total losses comparison, wt = 2 mm.  
It is possible to replicate this analysis for the rectangular shape, following the same considerations.
# Performance Analysis of the Pilot and Data Component of a CSS Signal for LEO-PNT +
## 2 Signal Model
### Multi-Satellite Access (MSA)  
Multiple satellite access with CSS can be based on two main parameters given in (1): the slope, \(\mu\), and the initial frequency, \(f_{0}\). Both parameters can be used to identify the different users. Unfortunately, there are some consequences on the selection of the MSA scheme that have to be considered when dealing with PNT. On the one hand, the use of multiple slopes may be limited when the number of satellites increases due to interference between satellites. On the other hand, when considering different frequencies for different satellites, we are intrinsically discarding an FSK-CSS modulation for the data transmission. Different schemes for MSA in PNT were proposed and analyzed in [4]. The conclusion is that the so-called multi-dual-slope (MDS) scheme provides the shortest chirp period possible among the analyzed MSA schemes in [4]. As a consequence, the MDS scheme allows us to reduce the processing complexity and to target high data-rate configurations for the data component. For the above reasons, in this paper we use the MDS scheme for the pilot component of the proposed CSS-based PNT signal. The philosophy of the MDS scheme is to use the two following chirp rates: \(\mu_{i}^{(1)}=i\frac{2\mu}{N_{\text{start}}}\) and \(\mu_{i}^{(2)}=2\mu-\mu_{i}^{(1)}\). The first one is used for \(t\leq T_{\text{c}}/2\), and the other one for \(T_{\text{c}}/2<t\leq T_{\text{c}}\).
## 6 Conclusions  
This study contributes to the advancement of evaluation methodologies for assessing EXO effectiveness in real-world settings. We developed a fatigue level prediction model tailored specifically for BSIEs employing wearable sensors. Our approach involved an experiment to generate a dataset representative of real-world conditions, encompassing intermittent task cycles, awkward postures, and a diverse array of activities involving static, dynamic, and sustained trunk flexion. The results demonstrate that the XGBoost (XGB) classification algorithm can effectively predict fatigue levels in both the back and legs of BSIE wearers utilizing wearable sensors. The selected models exhibited accuracies of up to 95% in the binary classification of leg fatigue using both EMG and IMU sensors and 82%for back fatigue prediction employing a single inertial sensor. This sensor could potentially be substituted with a smartphone or smartwatch for more accessible evaluations. However, tertiary grouping yielded comparatively lower accuracies (legs: 79%, back: 67%) and necessitated four-sensor setups. Overall, further improvements in the outcomes could be achieved by collecting real-world data, refining model parameters, incorporating additional statistical features, and employing a larger, more diverse, and inclusive sample size for dataset generation. Subsequent studies could focus on testing the performance of these models' using data collected in industrial environments. The presented outcomes in this article can be instrumental in designing guidelines for the implementation of BSIEs in real-world scenarios, ultimately enhancing workplace safety and performance.  
Conceptualization, P.M.K. and E.R.; methodology, P.M.K., A.R.K. and E.R.; software, P.M.K. and A.R.K.; validation, P.M.K. and A.R.K.; formal analysis, P.M.K. and A.R.K.; investigation, P.M.K. and A.R.K.; resources, E.R.; data curation, P.M.K.; writing--original draft preparation, P.M.K.; writing--review and editing, P.M.K. and A.R.K.; visualization, P.M.K. and A.R.K.; supervision, E.R.; project administration, P.M.K. All authors have read and agreed to the published version of the manuscript. This study did not receive any external funding.  
The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Rochester Institute of Technology (HSRO #01113021 approved on 1 April 2022). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the corresponding authors on reasonable request.  
The authors declare no conflicts of interest.
## 3 Results
### EOs Rich in Alpha-Phellandrene and Their Main Biological Activities
#### 3.4.4 Food Preservative  
Another potential application of EOs rich in alpha-phellandrene is their use as natural preservative ingredients in food. For instance, a study from Tunisia, [49], found optimal results focusing on the properties of the EO from _S. molle_, with a concentration of alpha-phellandrene of 35.9%, in the inhibition of _Salmonella_ genus growth in raw beef. In another direction of food preservation, [PERSON] et al. [103] proved that the EO from _S. molle_ with alpha-phellandrene within the main components improved the stability of fried salted peanuts by preventing lipid oxidation and development of rancid flavors. In a recent study, [PERSON] et al. [20], focused on biological preservation of mayonnaise and found interesting results with a combination of the probiotic _Lactobacillus plantarum_, basil, and dill EOs (22.7% alpha-phellandrene in the latter) significantly decreasing the number of undesired microflora, making this combination an effective biological preservative for mayonnaise. [PERSON] et al. [104] incorporated thyme EO (1.3% alpha-phellandrene) to extra virgin olive oil proving its ability to preserves its nutritional quality, and sensory attributes, and, most of all, to improve its oxidative stability during long-term storage. [PERSON] et al. [34] evaluated the effects of tomato residuum extract (TRE) dipping and Arabic gum (AG) coating enriched with dill EO (DEO), with alpha-phellandrene as a main component, on the shelf-life extension of refrigerated trout fillets, and found that treatments TRE 3%-AG-DEO 2% and TRE 6%-AG-DEO 2% had significantly effects.
## 5 Experiment Results and Discussion  
The proposed power cable joints recognition, based on IHHT with fractal feature extraction, was performed based on the electrical signal measured in the experiment models. A total of 240 sets of measurement data were associated with the six types of experiment models. Subsequently, 20 tests are randomly selected for the identification training purpose, while the rest is used for the test purpose. Here, a detected partial discharge signal is analyzed by HHT and IHHT respectively, and various features are extracted out of a time-frequency-energy 3D spectrum, following which the intended defect mode identifications are made using an extension method. A random noise between \(\pm 10\%\) to \(\pm 30\%\) is introduced into such features to test the ability against noise interference.
###### Abstract  
As reconfigurable intelligent surfaces (RISs) have been gradually brought to reality, a large amount of research has been conducted to investigate the immense benefits of RISs. That is because RISs enable us to artificially direct the radio wave propagating through the environment at a relatively low cost. This paper investigates the trade-off between spectral efficiency (SE) and energy efficiency (EE) in the RIS-aided multi-user multiple-input single-output downlink. We develop an optimization framework for designing the transmitting precoding at the base station and the phase shift values at the RIS to balance the EE-SE trade-off. The proposed iterative optimization framework for the design includes quadratic transform, alternating optimization, and weighted minimization mean-square error conversion. Simulation results illustrate our optimization framework algorithm exhibits effectiveness and a fast convergence rate.  
energy efficiency; intelligent reflecting surface (IRS); multi-user downlink; reconfigurable intelligent surface (RIS); spectral efficiency
## 1 Introduction
### Background
#### 1.1.4 Topic Modelling  
As mentioned above, LDA [4] is one of the most popular Topic Modelling approaches. Topic Modelling is an approach to analyse a corpus of unlabelled documents and define a set of topics related to these documents, in order to define a set of clusters of similar documents.  
LDA (Latent Dirichlet Allocation) [4] is a Topic Modelling approach that takes a collection of unlabelled documents and attempts to find the topics in this collection.  
In Figure 1 it is shown the LDA Model architecture.  
In LDA each document is represented as a bag-of-word, i.e., a fixed-length vector with length equal to the vocabulary size. Each dimension of this vector corresponds to the count or occurrence of a word in a document. In order to enhance the performance of LDA some preprocessing is needed, such as stemming, lemmatization and stop-words identification; in this way the bag-of-words will be composed only by relevant terms.  
The adoption of a bag-of-words model makes LDA prone to the same shortcomings of the other unsupervised models, i.e., not taking care of structure of the document or local interaction between words. In LDA it is assumed that word usage is correlated with topic occurrence, as documents on similar topics tend to use a similar sub-vocabulary; the resulting clusters of documents can be interpreted as discussing different 'topics'. The final result of a Topic Modelling performed by the LDA approach is a set of topics and related documents; for each document there is a vector of length equal to number of topics, containing the probability of relationship with respect to the topic. In the model settings it is possible to define the number of desired topics; in addition, it is necessary to manually define a human-readable label for each topic vector.
# A Power-Gated 8-Transistor Physically Unclonable Function Accelerates Evaluation Speeds
## 2 Power-Gated PUF Architecture and Design Methods
### Two-Phase Power Gating Method and Design  
Originally, the power gating technique was introduced to reduce leakage, which is independent of the transistor switching activity and thus lessens power dissipation. This design eliminates the leakage current from unchosen PUF rows, so the energy consumption is roughly proportional to the chosen PUF percentage. In the two-phase power gating method, the power gating cells switch the chosen rows of PUF cells on and off in three stages: a reset stage, phase I power-up and phase II power-up. First and foremost, this enables fast statistical evaluations, which improve PUF performance. Secondly, this method facilitates different combinations of power gating parameters in hardware implementation, so that the in-rush current can be curbed by adjusting these parameters. The flattened currents provide a method against side channel attacks. Moreover, PUF rows without power supply cannot be read out and are protected from security attacks.  
Conventional power gating is conducted via a _SLEEP_ transistor to enable a power or ground connection [47]. Figure 1b shows the single-phase power gating cell is operated via a PMOS transistor _PM2_ as a _SUPPLY_ transistor to switch power on/off and an NMOS transistor _NM8_ for fast resetting. As illustrated in Figure 1c, the two-phase power gating cell employs two PMOS transistors as _SUPPLY_ transistors for two-stage power switching and an NMOS transistor _NM6_ for resetting. In the test circuits, each power gating cell controls the power supply of a row of 128 PUF cells. The reset transistor _NM6_ quickly drains the remaining current and drops _vddv_ to 0 V. Since a smaller transistor curbs the drain current and the _vddv_ output, the two-phase power gating exploits this to generate a gentle voltage incline with a smaller _SUPPLY_ transistor _PM4_ in phase I and create a steep slope of _vddv_ using a larger _SUPPLY_ transistor _PM5_ in phase II. Consequently, the metastability resolving time in phase I is lengthened in the hope of minimum mutual disturbance amongst PUF cells. Meanwhile, if all PUF cells start metastability in a very short period of time, the in-rush current will be significant due to all the cross-coupled transistors in 8T PUF cells being in saturation mode. The prolonged phase I is able to flatten the current peak as well. Afterwards, phase II takes control and increases _vddv_ swiftly. The sizes of two _SUPPLY_ transistors and the time duration of the two phases need to be evaluated to obtain the best trade-off. This will be discussed in the evaluation part in Section 3.1.
## 3 Materials and Methods
### Performance Evaluation  
To evaluate the performances of the method, three sets of calibration samples and CQI were freshly prepared and analyzed every day for six days to control the interday precision, whereas for the intraday precision, six sets were prepared and analyzed in a single day. Calibration curves were obtained by plotting the peak area ratios (PAR) between the analyte and IS quantitation ions versus the nominal concentration of the calibration solution. A linear regression analysis was applied to obtain the best-fitting calibration curve. The limits of detection and quantitation (LOD and LOQ) were calculated according to ICH guidelines using the approach based on the standard deviation of the intercept and slope of the regression [36].  
The method precision was evaluated through the relative standard deviation (RSD%) of the replicate analysis of CQIs. The accuracy was determined by the recovery ratio percentage (RE%) computed between the determined and theoretical amounts of the replicate analysis of the CQIs.  
Three sets of four replicate samples were prepared to evaluate each analyte's matrix effect (ME) and RE%. Set1 was prepared by adding 30 \(\mu\)L of STD work solution to a 1.5 mL blank urine sample to obtain a concentration of 100 pg/mL and then proceeding to extraction; Set2 was prepared by extracting blank pool urine samples and adding 5 \(\mu\)L of ME mix solution to 195 \(\mu\)L of extract; Set3 was prepared by diluting 5 \(\mu\)L of ME mix solution with 0.1% formic acid MeOH:H\({}_{2}\)O 90:10 solution up to a concentration of 500 pg/mL [37].  
ME and RE% figures were calculated for each analyte by comparing the mean results of Set1, Set2, and Set3 according to the formula:  
\[\text{ME}(\%)=\frac{\text{Set2}}{\text{Set3}}\times 100 \tag{1}\]  
\[\text{RE}(\%)=\frac{\text{Set1}}{\text{Set2}}\times 100 \tag{2}\]
## 6 V2X Testbed Setup and Algorithm Implementation
### Algorithm Implementation
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Timestamp** & \begin{tabular}{c} **Log Station** \\ **Id** \\ \end{tabular} & **App Id** & **Action** & \begin{tabular}{c} **Generated** \\ **Station Id** \\ \end{tabular} & \begin{tabular}{c} **V2X** \\ **Technology** \\ \end{tabular} & \begin{tabular}{c} **Message** \\ **Type** \\ \end{tabular} & \
\begin{tabular}{c} **TAI** \\ **Time** \\ \end{tabular} &
\begin{tabular}{c} **UTC** \\ **sns1 data** \\ \end{tabular} \\ \hline
1703071463323 & 101 & 1 & SENT & 101 & DSRC & ETSICAM &.53218 &.16218 & 0102... \\
1703071463323 & 101 & 1 & SENT & 101 & LTE-V2X & ETSICAM &.53218 &.16218 & 0102... \\
1703071463323 & 101 & 1 & SENT & 101 & CELLULAR & ETSICAM &.53218 &.16218 & 0102... \\
1703071464288 & 101 & 1 & RECEIVED & 102 & DSRC & ETSICAM &.54180 &.7180 & 0102... \\
1703071464298 & 101 & 1 & RECEIVED & 102 & LTE-V2X & ETSICAM &.54180 &.17180 & 0102... \\
1703071464304 & 101 & 1 & RECEIVED & 102 & CELLULAR & ETSICAM &.54180 &.17180 & 0102... \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Snapshot of a log file showing Cooperative Awareness Message (CAM) transmission and reception via Dedicated Short Range Communication (DSRC), LTE-V2X (PCS) and CELLULAR (5G).