esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Warehouses: Rationale, Challenges and Solution Directions
## 4 Results
### RQ-5: What Are the Current Challenges and Strategies for Transitioning to Smart Warehouses?
#### 4.6.2 Challenges  
Overall, there is a lot of uncertainty due to developments as globalization and supply chain interdependence, which poses some great challenges for the warehouse management [16; 29]. The current research limits itself to configurations with selected parameters on capacity and location, so it is not yet adapted to big data [18; 25] and the reading performance on passive and active RFID-tags in different systems and parts of the system is unclear [13; 14]. Moreover, in a world that is getting smaller and in constant connection due to technological developments in relation to globalization, not all systems are currently able to interact with various systems from different supply chain actors [14; 16]. As programming languages are not standardized, it is a challenging and costly process for companies to link various systems together, whether it is within the warehouse or amongst the actors [22; 28]. Besides the initial investment and development costs, one also has the cost of maintenance and operations in mind [18; 25].  
More research is required on the robustness of the system [21; 29]. Former solutions will reduce potential risks [17]. Furthermore, there is a lack of knowledge on the use of artificial intelligence (AI) and robotics in warehousing in relation to the adoption of IoT [3]. The life expenditure of remote hardware might be improved to ensure the system does not fail [28]; systems must be highly reactive and adaptable to changes on local as well as market/global levels (e.g., supply and demand) or concerning technological changes [25; 29]. Finally, the system must also be resistant to and even eliminate human errors [22; 28], a human-friendly interface could provide a solution [15].  
Another challenge in realizing a smart warehouse would be the security of the system [21; 23; 31]. The need for technological advancements is undisputed, but its acceptance is not. There are major issues when it comes to privacy and confidentiality of the data collected by the system [28]. Another security risk that should be taken into account is any hazardous damage due to incompatibility between products within the warehouse, as the system should also be able to prevent those kinds of risks [23]. Finally, the system must also take human safety into account [15; 23; 27; 28].  
It is challenging to see how current flexible systems are in terms of data processing, storage, adapting to new technologies, security, and combining various systems from different supply chain actors.
## 2 PPO Algorithm Background  
[PERSON] et al. [3] proposed the Proximal Policy Optimization (PPO) algorithm, which presents a novel approach to reinforcement learning that focuses on improving the stability and sample efficiency of policy optimization. PPO belongs to the family of policy gradient methods and aims to address some of the limitations of earlier algorithms like TRPO [35].  
PPO introduces the concept of a clipped surrogate objective function, which plays a central role in its stability. This function limits the extent to which the policy can change during each update, effectively preventing overly aggressive updates that can lead to policy divergence:  
\[L^{CLIP}(\theta)=\hat{E}_{t}[min(r_{t}(\theta)\hat{A}_{t},clip(r_{t}(\theta),1- \epsilon,1+\epsilon)\hat{A}_{t})]. \tag{1}\]  
where \(\hat{E}_{t}\) represents the empirical average across time \(t\). \(\epsilon=0.2\) is a hyperparameter. \(clip(\cdot)\) is the clipping function that restricts \(r_{t}(\theta)\) into the interval \([1-\epsilon,1+\epsilon]\). \(r_{t}(\theta)\) denotes the probability ratio, which is the ratio between the current policy, \(\pi_{\theta}(a_{t}|s_{t})\), and the policy from the previous time step, \(\pi_{\theta_{old}}(a_{t}|s_{t})\):  
\[r_{t}(\theta)=\frac{\pi_{\theta}(a_{t}|s_{t})}{\pi_{\theta_{old}}(a_{t}|s_{t})}, \tag{2}\]  
\(\hat{A}_{t}\) denotes the advantage estimator, which is defined as follows:  
\[\hat{A}_{t}=\delta_{t}+(\gamma\lambda)\delta_{t+1}+\cdots+(\gamma\lambda)^{T- t+1}\delta_{T-1}, \tag{3}\]  
\[\delta_{t}=R_{t+1}+\gamma V_{\mu}(s_{t+1})-V_{\mu}(s_{t}), \tag{4}\]  
where \(\gamma=0.99\) is the discount factor. \(\lambda=2\times 10^{-4}\) is the learning rate. \(t\) is the current time step. \(T\) is the total number of time steps in the end. \(R_{t+1}\) is the reward received in the next time step. \(V_{\mu}(s_{t+1})\) is the value function of the next time step, and \(V_{\mu}(s_{t})\) is the current value function. Both functions are output by the critic network with parameter set \(\mu\). By constraining policy changes, PPO ensures smooth learning and reliable convergence.  
Another crucial aspect of PPO is its use of importance sampling. This technique enables a balance between exploration and exploitation by adjusting the policy updates based on the ratio of probabilities between the new and old policies. This allows the agent to explore new actions while still staying close to its current policy, preventing excessive deviations. The PPO algorithm also boasts high sample efficiency, making it particularly suitable for applications where data collection is resource-intensive. It efficiently learns from a relatively small amount of data, which is vital for real-world scenarios where collecting extensive data can be costly or time-consuming.
## 2 Coexistence and Interference in WBASNs
### Inter-Domain Coexistence and Interference  
Inter-domain coexistence and interference occur between many technologies that work in the same frequency at the same time and place. For example, WBASN, WiFi, and Zigbee devices may work within the 2.4 GHz band in one room. The performance degradation of WBASNs is investigated by experiment in [16]. The effect of other technology on WBASNs is explored with respect to the mathematical function of BER and symbol error rate. The simulation results show that WiFi negatively impacts WBASNs at the 6 th, 7 th, 8 th, and 9 th channels. The packet loss rate of WBASNs is high when there is a WiFi or Zigbee device within their transmission range.  
Figure 2: Categories of inter-WBASN interference mitigation schemes.
## 1 Introduction  
Sinusoidal oscillators play an essential role in most electronic systems. They find numerous applications in instrumentation, measurement, control and communication systems. New oscillator circuits based on various active elements have been proposed continuously [1; 2; 3; 4]. Most oscillators proposed in the literature have been on an _ad hoc_ basis, _i.e._, a topology is chosen using an arbitrary procedure and is then analyzed. For the design of oscillator circuits, it is useful to follow systematic methodologies to obtain novel circuits. This is beneficial in developing analog tools for circuit design automation. The first systematic synthesis of canonic oscillators using controlled sources was given in [5]. The design of single op-amp single resistance controlled oscillators was reported in [6]. The generation method by using unity gain cells was given in [7]. Generalized synthesis approach for Gm-C oscillators using the signal flow graph approach was given in [8; 9]. A systematic state-variable approach to the synthesis of canonic current feedback op-amps-based oscillators was given in [10]. In recent times, the concepts of pathological elements have been proven to be very useful for circuit analysis, transformation and synthesis. Their applications have been presented in the literature [11; 12; 13; 14]. By the use of nodal admittance matrix expansion method and pathological elements, the systematic synthesis of the current conveyor-based active RC canonic oscillators was reported in [15; 16].  
To conveniently derive oscillator circuits, a systematic synthesis method is presented in this paper. By exploiting circuit transformations to the original biquadratic band pass filter, the oscillator circuit can be obtained. It retains some useful properties of the original band pass filter. Two circuit examples are given to demonstrate the feasibility of the proposed method. The workability of the derived oscillators is verified by PSPICE simulation.
## 3 Numerical Experiments
### Multiple Impacts at Fixed Intervals  
First, we consider an impact load applied as half sine wave with a duration of 0.02 s and an amplitude 1N. Four impacts are applied at 3 s intervals. The application time instants are \(t_{1}=0.1\) s, \(t_{2}=3.1\) s, \(t_{3}=6.1\) s and \(t_{4}=9.1\) s as shown in Figure 5. The measured response is obtained using the finite element model. The response is fed into the algorithm without applying any noise and the accurate mode shapes are considered. Next, the 5% zero-mean Gaussian noise is added to the measured response as well as a 5% deviation is added to all the modal parameters so that the used mode shapes are inaccurate. The four possible combination of the clean and contaminated data are considered for the algorithm input. The contaminated data is used to study the stability of the algorithm. Figure 6a shows the identified results using the proposed algorithm. The figure shows that the peak load value is captured with good accuracy (relative error is 2.91%),which is the most critical value to be identified in the case of impact load. However, the figure also shows some noise in between the peaks which has resulted in inflating the errors to some extent. The PREM and ACM errors for all these cases are listed in Table 1.  
Figure 4: Patran model of the simply supported beam.  
Figure 5: Time history of load for the considered multiple impacts.  
The results suggest that the proposed online-identification method can efficiently reconstruct the dynamic loads. Due to the limited number of mode shapes we can observe some error in identifying the exact dynamic load. If we look into the results in Table 1 we can see that both the PREM and the ACM errors are reasonable. Also, the identified load pattern is well in agreement with the exact load pattern as can be seen in Figure 6. Clearly, when we use polluted data the error increases but this increase in the error remains in line with the inaccuracy in the data i.e., introducing 5% noise into the input leads to a proportional increase in the identification error. This suggests that the algorithm is stable and can produce meaningful results even when the data is contaminated with noise.
## 2 Materials and Methods
### Chemicals and Materials  
Sodium chloride (purity > 99.5%) and Retention Index Standard (aliphatic C7-C24 hydrocarbons dissolved in hexane) were purchased from Sigma-Aldrich (Saint Louis, MO, USA). A divinylbenzene/Carboxen/polydimethylsiloxane (DVB/CAR/PDMS) 50/30 \(\upmu\)m (Supelco, Bellafonte, PA, USA) was used in SPME-HS/GC-MS analysis. Standards of the following organic acids and phenolic compounds (purity greater than 97%) were obtained from Sigma-Aldrich: caffeic acid, citric acid, coumaric acid, ellagic acid, ferulic acid, gallic acid, kaempfer, malic acid, protocatechuic acid, quercetin, succinic acid, syringic acid, tartaric acid, tyrosol, and vanillic acid. HPLC-grade acetonitrile ChromasolvTM (Riedel-de HaenTM, Seelze, Germany) and double-deionized water, obtained from a milli-Q water filtration/purification system (Millipore, Bedford, MA, USA), were used for the preparation of the UHPLC mobile phase.
# Discrimination of Smoke-Exposed Pinot Noir Wines by Volatile Phenols and Volatile Phenol-Glycosides
## 2 Results and Discussions
### VP-Glycoside Analysis in Wines by HPLC-HR-MS/MS  
Glycosides in non-smoke-exposed and smoke-exposed wine samples were characterized by HPLC-HRMS/MS. As reported, level 2 annotations (putatively annotated metabolites) were produced by querying and matching MS/MS spectral data with open-access databases [32]. Additionally, glycosides reported in the literature for smoked taint wine were included in the study by matching exact mass and MS/MS data [33]. Despite the high-throughput analysis provided by HPLC-HRMS/MS, including elemental composition, MS/MS data, and isotopic patterns, it is challenging to assign the order of sugars and their absolute configuration when analyzing disaccharides. To avoid the possibility of false positives, when isomers were present, the compounds were indicated as such, and all the annotations were considered putative (level 2, L2). From this search, 35 different VP-glycosides were tentatively annotated according to the criteria established by the Metabolomics Standards Initiative [34; 35]. Detailed information about VP-glycosides, including accepted compound identification, adducts detected, exact mass, mass error, and formula, were listed in Table S3. The top eight most discriminant glycosides identified in this study were hexose (H)-pentose (P)-4-methylguiaacol-Iso, deoxyhexose-P-4-ethylguiaacol, deoxyhexose-H-cresol-Iso, H-H-P-4-ethylguiaacol, syringyl-\(\beta\)-D-glucopyranoside-Iso, H-4-methylguiaacol, H-4-methylguiaacol-Iso2, and H-guiaacol (Figure 1b). These results were in agreement with literature that disaccharides are the dominant VP-glycosides in smoke-exposed grapes and wines [18]. H-P-glycosides are consistently identified as the most abundant VP-glycosides in smoke-exposed _V. vinifera_[24], among others, including syringyl-\(\beta\)-D-glucopyranoside, guiaacyl-\(\beta\)-D-gentiobioside, and syringyl-\(\beta\)-D-gentiobioside. Deoxyhexose-H-cresol, deoxyhexose-H-P-phenol, deoxyhexose-P-4-ethylguiaacol, H-P-4-ethylguiaacol, H-P-4-methylguiaacol, H-P-P-4-methylguiaacol, P-H-cresol, and P-P-H-cresol have been identified in smoke-exposed grapes by HPLC-qTOF-MS [33].
# Stream Data Load Prediction for Resource Scaling Using Online Support Vector Regression
## 2 Related Work
[PERSON] et al. [24] presented an exact formulation and developed a metaheuristic to solve a multi-period, multi-product Inventory-Routing Problem. A Memetic Algorithm was developed to minimize the total vessel service cost for the vessel diversion decision making [25]. [PERSON] et al. [26] proposed a hybrid method based on a Lagrangian relaxation technique through the volume algorithm. Using information from the Lagrangian multipliers, constructive heuristics with local search procedures were utilized to reach the objective of minimizing the makespan in cross-docking. [PERSON] et al. [27] developed a metaheuristic hybrid based on adaptive large neighborhood search and tabu search, called ALNS/TS, which assume a higher number of customer orders and higher capacities of the picking device. [PERSON] et al. [28] proposed a multi-objective mixed integer nonlinear optimization model for the vessel scheduling problem. A few Evolutionary Algorithms had been developed to solve the stream processing and resource allocation problem. Aiming at the characteristics of real-time streaming data and higher value of new data, this paper proposes a new online support vector regression algorithm, which gives different values to different time periods and improves the accuracy of data load prediction.
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 4 Recent Advancement on UAV Tracking Antenna
### UAV Tracking Positioning Parameters  
The essence of the UAV tracking antenna is to align the signals from the GCS according to the orientation of UAV yaw, pitch, and roll during flight mission. Taking into consideration the position of the celestial bodies. These parameters are important in the absence of or loss of GPS signal. The three notable UAV tracking parameters are: (i) azimuth angle; (ii) elevation angle; and (iii) range, which are all briefly discussed.  
The azimuth is the angle between North, measured clockwise referenced by any of the celestial bodies in most cases, and the sun. In the absence of GPS, the azimuth can be obtained using the compass bearing system. The azimuth is deployed to measure the angle ranging from 0-360\({}^{\circ}\)[16,85]. Figure 11 illustrates the azimuth angle. The azimuth generally depicts the antenna directivity and, by extension, the antenna gain. An antenna with a uniform azimuth exhibits omnidirectional property while an antenna with partial azimuth is considered as highly directional.  
The elevation angle herein refers to the vertical angle distance between the UAV tracking antenna local horizon or, also called, the observer's local plane and the UAV. The angle of elevation can be calculated by knowing the UAV altitude and the horizontal distance. The range is simply the distance along a great circle between the UAV and the tracking system. Great circle is the shortest distance along the earth equator. In a clear line of sight scenario, the range can be obtained by using a Fri  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Antenna Type** & **Gain (dBi)** & **Beamwidth (Degrees)** \\ \hline Vertical half-wave dipole & 0 & 360 \\ \hline Vertical quarter-wave with ground plane & 0 & 360 \\ \hline Four-element Yagi & 6 & 43 \\ \hline UHF corner reflector & 9 & 27 \\ \hline Two stacked vertical half- wave dipoles & 3 & 360 \\ \hline Small horn antenna for use at 10 GHz & 10 & 20 \\ \hline
3 m diameter parabolic antenna for & & 40 \\ tracking space vehicles at UHF & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparing Antenna type gain and beamwidth relationship [54].  
Figure 11: Azimuth angle.  
ignoring the earth bulge factor [83]. Alternatively, spherical law of cosines equations can be used [31].
## 3 Methodology
### Data Preprocessing  
Prior to the development of the target model, a series of processes are applied to the historical dataset with the objective of producing an efficient model with high performance. This phase is referred to as preprocessing and is comprised of four distinct steps: tokenization, normalization, stop word removal, and stemming [36].  
Figure 2: The flowchart of DL-based failure clustering model development.  
The significance of the aforementioned process lies in the fact that the original text data are characterized by noise, lack of structure, and the presence of superfluous features, including characters, words, symbols, and digits. Consequently, the utilization of the data in its original form for the construction of the model, without preliminary processing, may have a negative impact on the training process. The initial step in the preprocessing phase is tokenization, which is employed to transform the text data into a format that can be analyzed by DL methods. In this phase, the text data are divided into significant units, such as words, terms, and symbols, to create so-called tokens, which are then constructed from the original natural language text. The transformed data can be devoid of superfluous features and noise through the implementation of a normalization process on the target data.  
In addition to data cleaning through tokenization and normalization, the removal of stop words represents an effective step to improve the quality of the dataset. For example, the presence of \"this\", \"is\", \"a\", and \"an\" can have a negative impact on the training process, occupying space and increasing the time required to ascertain the intended meaning. The removal of stop words from short texts can play a pivotal role in enhancing model performance and reducing computational time. By doing so, the intended meaning and useful information can be directly identified.  
The final step in this phase is stemming, which involves applying the prepossessing technique to obtain the root word. The utilization of this technique provides the benefit of concentrating on a particular lexicon rather than employing all possible prefixes and suffixes. In the final step, the preprocessed data are organized and divided into three distinct categories: training, validation, and testing.
# Vitamin D and Rehabilitation after Stroke: Status of Art
## 5 Vitamin D and Post Stroke Recovery: Focus on Rehabilitation
### Supplementation of Vitamin D and Post Stroke Recovery  
A randomized controlled open-label trial from [PERSON] et al. [72] analyzed ischemic stroke survivors for serum 25(OH)D levels in relation with outcome measured with Modified Rankin Scale for neurological disability. The study screened 73 stroke survivors, with a final number of 53 patients with insufficient baseline 25(OH)D levels. Patients were then divided randomly into two arms: the intervention arm (n = 25) received vitamin D and calcium supplementation together with usual care and the control arm (n = 28) received only usual care. In the intervention arm, the proportion of patients achieving a good outcome was higher and the survival probability was greater compared with the control arm. These results point towards a potential benefit of calcium and vitamin D supplementation.  
[PERSON] et al. [67] analyzed vitamin D levels in 23 patients from 1 to 4 weeks after stroke onset (defined subacute group) and 28 patients 4 weeks from stroke insult (defined chronic group).  
They further subdivided patients based on ambulating capacity (ambulating vs. non-deambulating, by means of modified ranking scale) and feeding methods (total parenteral nutrition, enteral nutrition-oral or tube feeding). In the chronic group, 25(OH)D levels were lower than in the subacute group and below normal reference range. Moreover, in subacute group 25(OH)D levels were lower in patients with parenteral nutrition with respect to patients with enteral nutrition, while among chronic patients, 25(OH)D levels were higher in patients capable of walking independently. These results suggest supplementation of vitamin D, especially in non-deambulating patients and total parenteral nutrition.  
More recently, a randomized, double-blind, placebo-controlled trial was conducted by [PERSON] et al. [73] on 100 post-stroke patients (primary outcome: a gain in the Barthel index score at week 8 from onset). Authors did not report any differences between the group receiving vitamin D3 (2000 IU/day) and placebo group. This study showed an important limitation: serum 25(OH)D levels were not measured at admission, nor after 8 weeks (they consider that all elderly patients undergoing rehabilitation probably have vitamin D deficiency). This seems quite unusual considering that this analysis is unique in individuating actual vitamin D deficiency. In our opinion, this study could be influenced by patients with a normal value of vitamin D levels.
## 5 Empirical Analysis
### Robustness Test  
In order to ensure robustness of the results, this paper tested, respectively, with nearest neighbor matching with return (1:1), kernel matching, replacing core variables, as well as changing matching methods on the basis of replacing core variables. The regressions indicate that results from these four methods all support the above conclusions, demonstrating robustness in the empirical results. Table 8 displays results of the robustness test.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **(1)** & **(2)** & **(3)** \\ \hline pc1 & 0.091 *** & & \\  & (\(-\)0.019) & & \\ pc1 \(\times\) D & \(-\)0.079 *** & & \\  & (\(-\)0.013) & & \\ pro & \(-\)0.104 * & \(-\)0.098 * & \(-\)0.107 * \\  & (\(-\)0.055) & (\(-\)0.055) & (\(-\)0.055) \\ sex & 0.014 & 0.008 & 0.011 \\  & (\(-\)0.025) & (\(-\)0.025) & (\(-\)0.025) \\ age & 0.012 & \(-\)0.003 & 0.009 \\  & (\(-\)0.036) & (\(-\)0.037) & (\(-\)0.036) \\ edu & \(-\)0.020 & \(-\)0.023 & \(-\)0.022 \\  & (\(-\)0.021) & (\(-\)0.021) & (\(-\)0.021) \\ freq & \(-\)0.021 & \(-\)0.023 & \(-\)0.022 \\  & (\(-\)0.025) & (\(-\)0.025) & (\(-\)0.025) \\ friends & 0.002 & 0.003 & 0.002 \\  & (\(-\)0.007) & (\(-\)0.007) & (\(-\)0.007) \\ pc2 & & 0.113 *** & \\  & & (\(-\)0.019) & \\ pc2 \(\times\) D & & \(-\)0.077 *** & \\  & & (\(-\)0.014) & \\ pc3 & & & 0.112 *** \\  & & & (\(-\)0.021) \\ pc3 \(\times\) D & & & \(-\)0.074 *** \\  & & & (\(-\)0.014) \\ Constant & 2.327 *** & 2.267 *** & 2.263 *** \\  & (\(-\)0.197) & (\(-\)0.196) & (\(-\)0.199) \\ \hline Observations & 1181 & 1181 & 1181 \\ R-squared & 0.042 & 0.048 & 0.043 \\ \hline \hline \end{tabular}  
* Notes: *, *** respectively denote significance on the 10%, 1% levels, and standard error is in parentheses.  
\end{table}
Table 7: The results of regression.