esa-sceva/satcom-chunk-collection huggingface dataset:
# Resource Capacity Requirement for Multi-Terminal Cooperation in Container Ports
###### Abstract  
Capacity sharing among neighboring terminals offer a means to meet increasing or unexpected demand for cargo-handling without additional capital investment. This study proposes a model for capacity requirement planning of major resources, such as quay cranes (QCs), storage space, and gate, in multiterminal port operations where demand is time dependent. A resource profile simulation is run to generate random events across the terminals and estimate the capacity requirement in the form of workload distributions on port resources over time-shifts. The effects on workload requirement, arising from multiterminal cooperation, are subsequently evaluated in consideration of different container flows among terminals. Experimental results suggest that higher transferring rate between terminals will reduce the QC intensity and storage space requirements but increase gate congestion. Variabilities in the QC intensity and storage space requirements also increase due to shorter stays and more movements in container inventory at the yard. The interaction effect between transferring and trans-shipment rates further shows that the average resource requirements for a terminal can be greatly reduced when the interterminal transferring of containers contributes positively to a more even workload redistribution across terminals. The most significant improvements occur when trans-shipment rate is 85% and transferring rate is 75% for QC intensity; trans-shipment rate is 90% and transferring rate is 60% for storage capacity; and trans-shipment rate is 80% and transferring rate is 75% for gate congestion.  
resource profiles; workload distributions; capacity requirement planning; trans-shipment hub; multiterminal operations +
Footnote †: 2021: 11, 9156. [[https://doi.org/10.3390/app11199156](https://doi.org/10.3390/app11199156)]([https://doi.org/10.3390/app11199156](https://doi.org/10.3390/app11199156))
# Interference Alignment in Multi-Hop Cognitive Radio Networks under Interference Leakage
## 1 Introduction
### Motivation and Contributions  
Even though cognitive multi-hop transmission offers numerous advantages to SUs, the primary-secondary interference is one of the most challenging issues to be solved in CR networks. To this end, IA, which can design coordinated signals to eliminate the interference in PU-SU, has become preferable [29]. Motivated by the advantages of multi-hop relaying and IA, herein, we investigate the interplay of the number of hops, relays, interference alignment, and interference leakage. Our main contributions are as follows:  
* A decode-and-forward (DF) multi-hop SN is considered, and end-to-end SNRs are derived for two cases: (1) perfect interference alignment; (2) in the presence of interference leakages.
* Exact outage probability is derived for perfect IA and interference leakages.
* Approximate ergodic capacity expressions are derived for both cases.
## 2 UAV Communication
### Characterizing the UAV Network
#### 2.1.4 Star or Mesh?  
UAV network topology for communication applications is an area that has received little focus yet. A solo UAV driven by a single command and control station is the most fundamental system. Multi star, hierarchical mesh, star and mesh are network architectures that can be realized in a multi UAV system. In a star topology, all UAVs connect directly to ground nodes. Moreover, all types of communication are ensured through ground nodes between UAVs. In this scenario, there is the possibility of latency, intermittency in links and need for high capacity downlinks. Moreover, in view of the mobility aspect, steerable and direction-seeking antennas may be needed to keep a correct point of reference towards the ground node.  
In some of existing literature, the researchers argue that legacy network technologies will not meet the demanding needs of UAV networks. In related studies, employment of mesh networks is advocated for civilian applications [50]. Multiple considerations including alterations in topology, interference, transmission deviations due to power constraints, multiple links on antennas, variation in number of network nodes, topography and weather constraints are all familiar. There are several challenges related to adhoc networks including formations that do not maintain symmetry, network nodes shifting away during mission profiles and intermittent connectivity. However, optimally configured mesh network systems would be capable of addressing some of the known issues [51]. In order to have fully converged networks and address the above stated issues, future networks necessitate self-healing attributes. Moreover, networks must ensure reconfiguration capability over wrecked links and consistent connectivity for successful accomplishment of mission profiles. Figure 6 depicts the star and mesh topology of UAV networks.
# A Goal-Driven Evaluation Method Based On Process Mining for Healthcare Processes
## 5 Case Study
### Data Preprocessing  
This stage consists of formatting and abstraction of sub-processes. Event data should be formatted in event logs to carry out PM techniques. For this reason, the surgery process data was converted automatically and modeled with the software that we developed according to the event object model [8].  
Typically, a surgery process comprises the steps of admission to hospital, preoperative examination, surgery, postoperative examination, service transaction, department transaction, intensive care transaction, and discharge activities. A patient can perform any of these activities any number of times. A case object is a patient in this process model, and an event object relates to each activity undertaken for each patient. Age, gender and diagnosis of patient are held at the instance level, and the performed activity, timestamp of activity, department and service of activity, and doctor are held at event level with the event object and its attributes. Age, gender, diagnosis, and surgery name are data attributes of the event; doctor, department, service, and operation room are resource attributes; and the timestamp of activities are the performance attributes. All these attributes can be used to enrich the discovered process model from different perspectives; i.e., data, resource and performance.  
Log mining may result in a complicated, spaghetti-like model with too much detail, which makes it difficult to understand and manage data. The surgery log of this hospital contained a huge number of distinct surgical and laboratory activities. To avoid complications, all the low-level laboratory tests were considered as a single test and all distinct operations as a single surgical activity. An example from an event log of a single patient is given in Table 5.
# Hyperspectral Imaging as a Rapid Quality Control Method for Herbal Tea Blends
## Materials and Methods
### SWIR Hyperspectral Imaging Analysis
#### 2.2.3 Partial Least Squares Discriminant Analysis  
Partial least squares discriminant analysis (PLS-DA) is a supervised algorithm that aims to establish a relationship between the **X** data matrix and known class membership (**Y**). The model was constructed by assigning a class to each species in the PCA score image and each class was assigned a unique score color for differentiation. Cross-validation of the model was performed using the random exclusion method and the validation parameters were used to assess model performance. The PLS-DA model was used to predict the composition of raw material constituents in the tea bag images (external dataset). It was important to ensure that the number of pixels in the prediction image was very close to that in the calibration image so that the PLS-DA model would perform an accurate prediction of the external dataset [13; 22]. Class prediction was achieved by matching the chemical information for each pixel in the tea bag to the raw materials in the calibration image. Where a match was found, the pixels in the tea bag would be assigned a color corresponding to the raw material in the PLS-DA calibration image.
# The Role of Correlation in the Performance of Massive MIMO Systems
## 5 Physical Channel Model under Arbitrary Array Configurations
The mean angle of arrival of the \(u\)-th UE in the vertical direction is given as  
\[\theta_{u,\text{E}}=\arctan\left(\frac{\sqrt{D_{u}+h^{2}}}{h^{2}}\right) \tag{24}\]  
The spatial covariance matrix of the \(u\)-th UE in the URA configuration is given in the Kronecker model as  
\[\mathbf{G}_{u}=\mathbf{G}_{u,\text{A}}\otimes\mathbf{G}_{u,\text{E}}, \tag{25}\]  
where the channel covariance matrix \(\mathbf{G}_{u}\) is a Hermitian Toeplitz positive semi-definite matrix form. Clearly, the correlation coefficients between the adjacent antennas at the base station side depend on angle of departure, antenna spacing (\(d\)), azimuth standard deviation (\(\alpha_{A}\)), and elevation standard deviation (\(\alpha_{E}\)). The channel models described above provide rank-deficient covariance matrices and the distributions of the eigenvalues are different. Note that the narrow angular spread produces a low-rank structure of the base station covariance matrix. This implies a high spatial correlation between the distinct paths that control the communication environment between the base station and UEs. In the following section, we present the numerical results in terms of MSE and SE.
# A Review on Upper Limb Rehabilitation Robots
## 4 Examples of Rehabilitation Robots
### ARMin  
ARMin is an upper limb rehabilitation robot and is characterized by providing seven degrees of freedom (DOF), allowing shoulder rotations in three dimensions, flexion/extension of the elbow, supination/ pronation of the forearm and flexion/ extension of the wrist, in addition to supporting the closing and opening of the hand [42]. ARMin consists of a chair and robotic arm, where the seated patient attaches his/her hand to the robotic arm and adjusts its length to its optimum, as shown in Figure 5[43; 44]. ARMin has three levels of safety, where the device is equipped by many sensors that work as a system to detect any malfunction that could arise. Secondly, the ARMin has no sharp edges, and no joint will move out of the range of a human limb [42]. Thirdly, a unique algorithm was developed to monitor the repeated motions, and immediate shutdown happens in case of malware being found [39].  
The results showed an improvement in the limb's motion, where the user was able to extend his/her limb to further distances. In addition, the strength of the support was decreased gradually as the patient recovered their motor sensory [3; 44].  
Figure 4: Assisted Rehabilitation and Measurement (ARM) guide robotic system. Source: [41].  
Figure 5: ARMin rehabilitation robot. Source: [44].  
Cable actuated dexterous exoskeleton for neuro-rehabilitation (CADEN-7) provides seven degrees of freedom [45]. CADEN-7 is considered an active-assisted robot that uses surface electromyography to control the upper limb [46]. CADEN-7 is also equipped with three levels of safety, taking into consideration the electrical aspects, mechanical aspects and software aspects [45]. The unique design of CADEN-7 makes it suitable to avoid hyperextension and erratic motions. CADEN-7, however, has to be fixed on external forms and cannot be portable due its size and weight [3].
# A Virtual Reality Soldier Simulator with Body Area Networks for Team Training
## 2 System Description
### Communication and Node Authentication Procedures  
To tackle the problem of interference and to reduce the bit error rate (BER) of the wireless data transmission between the sensor nodes and the sink node, communication mechanisms can be applied to build a robust wireless communication system [30; 31]. The communication technique employed for communication between the sensor nodes and the sink node is frequency hopping spread spectrum (FHSS). The standard FHSS modulation technique is able to avoid interference between different sensor nodes in the world-wide ISM frequency band, because there are six BANs in total, all of which are performing orientation updates in a small room. Moreover, the sensor nodes' transmission of data to the sink node is based on packet communication.  
In the beginning, sensor nodes send packets, which include orientation and rotation data. When the sink node receives a packet from a sensor node, an acknowledgement packet will be transmitted immediately (Figure 10). If the acknowledgement fails to arrive, the sensor node will retransmit the packet, unless the number of retries exceeds the retransmission limit. When the sink node receives packets in operation mode, it will check the payload of the packets. As shown in Figure 11, if the payload is equal to the sensor node's unique identification information, the sink node will accept the packet and scan the next sensor node's channel during the next time slot. The ten wireless sensor node channels operate at a frequency range of 2400-2525 GHz.  
Figure 10: Operation mode during each time slot. (**a**) Step 1: automatic packet synchronization; (**b**) Step 2: identification check on valid packets by the sink node.
# Covert Channel Communication as an Emerging Security Threat in 2.5D/3D Integrated Systems
## 3 Types of Physical Covert Channels
### Power-Based Channels
#### 3.2.2 C\({}^{3}\)ApSULe  
Users renting FPGAs from cloud providers assume that their designs are securely separated from users using other FPGAs within the same data center. However, C\({}^{3}\)APSULe shows that this assumption does not hold, due to the leakage of shared power supply units (PSUs) [38]. A physical covert channel attack is introduced between FPGAs that are powered by the same PSU [38]. Furthermore, if the PSU also powers the host computer, CPU-to-FPGA and GPU-to-FPGA covert channels can also be created. This work used ring oscillators to sense and stress the source and sink FPGAs. Voltage and temperature monitors are inaccessible to end-users of cloud FPGAs, which means non-invasive detecting of voltage fluctuations is nontrivial. However, ring oscillators can be implemented to detect voltage changes, because the reconfigurability of FPGAs is still available to the attacker. The varying supply voltage changes the RO frequencies, allowing the attacker to correlate processor workload with RO frequency. Thus, no invasive measurement setups or probes were required for this methodology because of the designed ring oscillators. Similarly,reference [54] used Time-to-Digital Converter-based (TDC-based) voltage sensors instead of ring oscillators to detect the supply voltage fluctuations.  
Note that the voltage regulator within the printed circuit board of the receiver should be overloaded in order to detect transmissions by the source (transmitter) FPGA. This requirement is achieved by introducing \"stressor\" ROs within the receiver. In C\({}^{3}\)APSULe, the ROs are implemented using lookup tables, consisting of 1 inverter and 3 buffer stages. From the sink side, there are ROs that make up the receivers and there are additional ROs that stress the voltage regulator of the sink FPGA. Once the stressors are turned on, the transmitters are enabled for measurement periods dependent on the data being encoded. This causes fluctuations in the PSU, which the receiver measures by counting the RO signal transitions in a fixed measurement interval. The RO counts are averaged over repeated measurements and Manchester encoding is used to minimize the impact of noise in the system. The methodology was implemented with 2 different FPGA boards. Cross-FPGA communication was shown to have \(\sim\)4% BER when there were 10 sets of transmitters, where each transmitter had 800 ROs [38]. Depending on the FPGA board, the amount of LUT resources used varied from 3.4% to 16.6% just for the source side implementation (500-2500 ROs total) [38]. The channel capacity of this methodology was shown to be 3 to 6 bps [38]. Similar bandwidths and BER are achieved when using a CPU and GPU to transmit data with stress tests. While this is a novel and remote covert channel attack, the bandwidth is relatively low. Furthermore, C\({}^{3}\)APSULe relies on the assumption that cloud FPGA providers do not recognize that attackers implement designs with up to 10,000 ROs with the intent to sense voltage drops.
## 3 Experiments and Results
### Optimization of Parameters and Flatness through Optical Ring Cavity  
First of all, by varying pump power and \(R_{1}\), the outputs of MWEDFL are given in Figure 4 without electrical feedback. From Figure 4a, the mean output power is linearly increased with the raise of pump power, and when \(R_{1}\) = 50/50 the maximum always occurs in the pumping range of 0-180 mW, obviously. Additionally, according to the results in Figure 4b-d, the smallest intensity difference (\(\sim\)2.87 dB) also occurs when \(R_{1}\) = 50/50. Comparatively, the similar but larger intensity  
Figure 3: Scheme of dual-channel fuzzy controller, D1/D2: detector, A: amplifier, _diff_-1/_diff_-2: differential amplifiers, MCU: microcontroller unit.  
differences (>4 dB) are presented when \(R_{1}\) = 10/90, 20/80. In result the optimal output power and spectral flatness are obtained when \(R_{1}\) = 50/50, although the number of oscillations is smaller.  
Under the condition of \(R_{1}\) = 50/50, the output characteristics of MWEDFL are tested by varying pump power. From Figure 5a, the oscillation number is quickly increased in the range of \(P\) = 0-70 mW, but it is slowly and nonlinearly raised when \(P\) > 90 mW. Figure 5b shows the similar results also occur in term of mean intensity. Moreover, the stimulated green light with the central wavelength of 546 nm (may due to the excited state absorption in EDF [26]) is found and linearly increased with the addition of pump power (see Figure 5b). We then select \(P\) = 113 mW to get a trade-off among the oscillation number (>10), intensity (\(--\)7.5 dBm) and power of green light (<0.05 mW). Furthermore, the output spectrum of MWEDFL is flattened through accurately adjusting PCs position and pump power. As shown in Figure 6a, in the range of 1556.2-1560.7 nm there are 11 stable oscillations generated with an interval of 0.434 nm and the intensity difference reaches \(\pm\)1.54 dB within 3-dB bandwidth. Comparatively, we gain the \(\pm\)0.42-dB flatness through a fine adjustment and the improvement of flatness is 72.7% (\(=\frac{|\pm 0.42-\pm 1.54|}{|\pm 1.54|}\)) by calculation. Additionally from Figure 6b the side mode suppression ratio reaches over 30 dB.  
Figure 4: The outputs of MWEDFL with different ratios of C1, (**a**) mean output power by varying pump power, and the intensity of each oscillation at \(P\) = 180 mW when (**b**) \(R_{1}\) = 50/50, (**c**) \(R_{1}\) = 20/80, (**d**) \(R_{1}\) = 10/ 90.  
Figure 5: The outputs with varying pumping power when \(R_{1}\) = 50/50, (**a**) oscillation number within 3-dB bandwidth; (**b**) mean intensity of oscillations (where the subfigure is stimulated green light with central wavelength of 546 nm).
## 4 Experiments and Analysis
### Experimental Results and Analysis
#### 4.4.3 Model Selection Behavior Analysis  
To further understand the behavior of the proposed model selection system, we analyzed the distribution of selected forecasting models at different CPU availability levels. In Figure 9, at both low (20%) and high (100%) CPU levels, the majority of the selected models were TFT, reflecting its high forecasting accuracy in many sensor cases. Interestingly, the meta-learning approach consistently favored TFT, even with low-resource settings, indicating its strong bias toward accuracy over computational cost, which is consistent with our design intent. In contrast, the exhaustive search occasionally selected alternative models such as LSTM and RNN, which may have yielded marginal improvements in latency at the cost of a slight accuracy loss.  
This distribution supports the observation that the meta-learning framework, by incorporating performance-based meta-features, tends to select models with historically better results. Moreover, it highlights the need for future refinement to better integrate resource sensitivity into the selection logic.  
Table 4 shows that skewness and variance carry the largest positive SHAP (Shapely Additive exPlanations) contributions for the TFT class; in practical terms, time series exhibiting heavy-tailed or highly dispersed distributions raise the model selection probability of the Transformer by more than 20 percentage points, whereas lower values of these features favor recurrent architectures such as GRU.
# Multi-Spacecraft Tracking and Data Association Based on Uncertainty Propagation
## 2 Problem Formulation
### Measurement Model  
As illustrated in Figure 1, the inter-spacecraft inertial LOS measurement can be represented by two angular measurements, i.e., the declination \(\alpha\) and the right ascension \(\beta\). The declination \(\alpha\) is the angle relative to the inertial \(x\)-axis and between the inertial \(x\)-\(y\) plane. The right ascension \(\beta\) is the angle between the inertial \(x\)-axis and the inter-spacecraft relative position vector \(\Delta\mathbf{r}_{Ti}=[\Delta\mathbf{x}_{Ti},\Delta\mathbf{y}_{Ti};\Delta\mathbf{z}_{Ti}]^{T} =\mathbf{r}_{O}-\mathbf{r}_{Ti}\). The measurement equation of the \(i\)-th target is given as [5; 41]:  
\[\mathbf{z}_{Ti}=\mathbf{h}(\mathbf{x}_{Ti})+\mathbf{\varepsilon}=\left[\begin{array}{c}\alpha +\varepsilon_{\rho}\\ \beta+\varepsilon_{\rho}\end{array}\right]=\left[\begin{array}{c}\tan^{-1} \frac{\Delta\mathbf{z}_{Ti}}{\sqrt{\Delta\mathbf{x}_{Ti}^{2}+\Delta\mathbf{y}_{Ti}^{2}}}+ \varepsilon_{\alpha}\\ \tan^{-1}\frac{\Delta\mathbf{y}_{Ti}}{\Delta\mathbf{x}_{Ti}}+\varepsilon_{\beta} \end{array}\right] \tag{4}\]  
where \(\mathbf{\varepsilon}=[\varepsilon_{\alpha},\varepsilon_{\beta}]^{T}\) is the Gaussian-distributed measurement noise with the covariance matrix given by \(\mathbf{R}\in\mathbb{R}^{2\times 2}\).  
The goal of the multi-spacecraft tracking is to estimate the states of the targets. The tracking system is given as:  
\[\left\{\begin{array}{l}\mathbf{x}_{Ti}(t_{k+1})=\mathbf{F}(\mathbf{x}_{Ti}(t_{k}), \Delta t_{k})\\ \mathbf{z}_{Ti}(t_{k+1})=\mathbf{h}(\mathbf{x}_{Ti}(t_{k+1}))+\mathbf{\varepsilon}\end{array}\right. \tag{5}\]  
Note that this paper aims at proposing a general method that does not rely on particular dynamic and measurement equations. The Earth-centered dynamic and inertial LOS measurement models are taken as an example for simulation, and the proposed method can be applied for other scenarios.