esa-sceva/satcom-chunk-collection huggingface dataset:
# Touch, Texture and Haptic Feedback: A Review on How We Feel the World around Us
## 3 Sensors
### Piezoresistive Sensor  
Piezoresistive tactile sensors monitor the change in pressure by having two films separated by a thin sheet used to detect minute changes in resistance. As force is applied  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensing Modality** & **Design** & **Advantages** & **Disadvantages** \\ \hline \multirow{2}{*}{Strain Gauge} & & Low-cost & Prone to errors from moisture \\  & [42; 43; 44] & Versatile & Requires supplementary devices to amplify data \\  & & Good sensing range & Difficult to assemble \\ \hline \multirow{2}{*}{Accelerometer} & [45; 46] & Good accuracy & \\  & & Versatile & Noise \\  & & High precision & \\ \hline \multirow{2}{*}{Piezoresistive} & [47; 48; 49; 50; 51; 52; 53; 54] & High accuracy & \\  & & High spatial resolution & High power \\  & & Small and light & \\ \hline \multirow{2}{*}{Piezoelectric} & [55; 56; 57; 58; 59] & High sensing range & Poor spatial resolution \\  & & High precision & Limited to dynamic touch scenarios \\ \hline \multirow{2}{*}{Optical} & [60; 61] & High accuracy & \\  & & High precision & \\  & & Good spatial resolution & \\ \hline \multirow{2}{*}{Multimodal} & [62; 63; 64; 65] & Compensates for other sensor limitations & High cost \\  & & Difficult to manufacture & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative study of various actuators used for touch sensing.  
to the sensor, the thin beam sheet is strained which causes a change in the resistance of the piezoresistive element on the beam surface. Piezoresistive sensors have been used in a variety of different studies, due to their small profile and high performance, showing texture discrimination by showing different signal types at different texture types [47; 48; 49; 50]. Other studies have also shown improved accuracy ranges through layering the piezoresistive film on top of each other or by way of inducing specific shapes and patterns onto the film [51; 52; 53; 54]. However, piezoresistive sensors generally require high power consumption.
## 3 Materials and Methods
### In Situ Horizontal Push Shear Test  
In situ horizontal push shear test is an advanced geotechnical testing method aimed at evaluating the shear strength characteristics of rock masses under field conditions. This experimental method involves applying controlled horizontal forces to prepared rock samples while monitoring the resulting displacement and failure modes. The testing device usually consists of a reaction frame, hydraulic loading system, and precision displacement sensor to ensure accurate measurement of the shear stress displacement relationship. Compared with laboratory experiments, this method provides more reliable shear strength parameters, especially for fractured or weathered rock masses, as it considers scale effects and maintains the natural structure of the rock mass. The obtained data is crucial for slope stability analysis and engineering design under challenging geological conditions.
## 5 Analysis and Evaluation
### Offloading Traffic  
In this session, we calculate the name resolution traffic offloaded by ENRS from the global NRS. We set the L3 HMs as the global NRS deployed on AS-level and set the L1 and L2 level HMs as the local NRS. The lengths of the REGISTER message and QUERY message are 240 bits and 248 bits. We conduct the network size as ranging from 100 to 1600, and the number of queries varies from 1000 to 8000. We add up all the traffic offloaded by HMs in each hierarchy if the requested EID is satisfied by ENRS. The results are shown in Figure 8.  
From Figure 8 we can learn that L2 HM has higher offloading traffic and the offloading traffic declines with the growth of node size because the hit ratio decreases, as seen in Figure 7. From Figure 8 we also learn that the offloading rate of L1 tends to be stable at about 2.0%. Though multi-copy of name bindings can help to improve the hit ratio as well as the offloading traffic, ENRS requires a trade-off between the storage cost and the benefit of the offloading rate. The control traffic offloaded by ENRS is significant.  
Figure 7: The comparison of average hit ratio. (**a**) demonstrate the variation tends of average hit ratio with different number of nodes in ENRS, KNN-DNRS and Random-NRS. (**b**) shows the variation of average hit ratio of each hierarchical HMs in ENRS with different number of nodes.
## 2 Materials and Methods
### Headspace Solid-Phase Micro-Extraction (HS-SPME) of Wine Volatiles  
Experimental conditions for HS-SPME wine aroma sampling were optimized previously (unpublished data). Three different fibers (Supelco, Bellafonte, PA, USA), coated with polydimethylsiloxane 100 mm, Carbowax/divinylbenzene 65 \(\upmu\)m, and divinylbenzene/Carboxen/polydimethylsiloxane (DVB/CAR/PDMS) 50/30 mm, were tested; for each sorbent, a multivariate design of experiments coupled with surface-response methodology was applied to model the mutual influence of the sample temperature, the exposure time of the fiber to the headspace, and the NaCl concentration added to the sample on the global extraction efficiency. The DVB/CAR/PDMS sorbent was finally preferred because it extracted the largest number of volatiles and provided the most intense chromatogram. The optimal HS-SPME conditions are the following: Aliquots of 5 mL of wine samples and NaCl at 30% \(w/\
u\) are introduced in a 10 mL vial tightly capped with a PTFE-silicone septum. The sample, kept under magnetic stirring, is placed inside a cylindrical cavity in an aluminum block heated by an electrical heater at a constant temperature of 30 \(\pm\) 1 \({}^{\circ}\)C. The temperature is controlled by a Vertex probe immersed in a vial filled with water, located inside of a second cavity close to the wine sample. The fiber--previously conditioned at 270 \({}^{\circ}\)C, as recommended by the manufacturer--is exposed to the sample headspace for 30 min, then successively removed and inserted into the GC injection port where desorption takes place at 270 \({}^{\circ}\)C for 5 min.
## 4 Evaluation and Results
### Results
#### 4.2.1 Transit Link Traffic  
Figure 3 depicts the comparison between the unicast and multicast VNF-AS modes in context to the burden on the transit link. The transit link sustains the traffic load from the CDN and towards the RAN. The figure shows that the aggregated data for all the students participating in an online session fetched from the CDN in the case of unicast mode are 6,330,300 megabits--whereas the data fetched from the CDN for the same number of students in the case of multicast transmission are 721,300 megabits. This shows a huge amount of savings for MNO in terms of cost as well as the lowered burden on the transit link for VNF-AS multicast.
# Low-Complexity GSM Detection Based on Maximum Ratio Combining
## 3 MRC-RS Detection Algorithm
### Quantitative Symbols  
This subsection describes modulation symbol detection for the M-QAM and M-PSK constellations.  
For the M-QAM constellation, if the modulation signal set \(S\) with \(|S|=M\) is a square or rectangular lattice constellation, it can be viewed as the Cartesian product of two sets \(S_{1}=N_{1}-PAM\) and \(S_{2}=N_{2}-PAM\). \(N_{1}-PAM=\{-N_{1}+1,-N_{1}+3,\cdots,-1,1,\\ \cdots,N_{1}-3,N_{1}-1\}\), \(N_{2}-PAM=\{-N_{2}+1,-N_{2}+3,\cdots,-1,1,\cdots,N_{2}-3,N_{2}-1\}\), where \(N_{1}\) and \(N_{2}\) are powers of 2.  
We calculate the estimated modulation symbol \(\widetilde{s}_{\alpha}\) corresponding to the \(\alpha\)-th TAC in \(round(L/2)\) TACs.  
\[\widetilde{s}_{\alpha}=\frac{\left(\bar{\mathbf{h}}_{\alpha}\right)^{H}\mathbf{y}}{ \left\|\bar{\mathbf{h}}_{\alpha}\right\|^{2}}, \tag{11}\]  
where \(\alpha\in\{1,2,\cdots,round(L/2)\}\), \(\bar{\mathbf{h}}_{\alpha}\) represents the sum of column vectors \(\mathbf{H}\) corresponding to the \(\alpha\)-th TAC, that is,  
\[\bar{\mathbf{h}}_{\alpha}=\mathbf{h}_{\alpha(1)}+\mathbf{h}_{\alpha(2)}+\ldots+\mathbf{h}_{ \alpha(N_{p})}=\sum_{l=1}^{N_{p}}\mathbf{h}_{\alpha(l)}. \tag{12}\]  
The quantitated modulation symbol \(s_{\alpha}\) corresponding to the \(\alpha\)-th TAC can be expressed as  
\[\Re(s_{\alpha})=min\left(\max\left(2\times round\left(\frac{\Re(\widetilde{s }_{\alpha})+1}{2}\right)-1,-N_{1}+1\right),N_{1}-1\right), \tag{13}\]  
\[\Re(s_{\alpha})=min\left(\max\left(2\times round\left(\frac{\Re(\widetilde{s }_{\alpha})+1}{2}\right)-1,-N_{2}+1\right),N_{2}-1\right), \tag{14}\]  
\[s_{\alpha}=\Re(s_{\alpha})+j\Im(s_{\alpha}). \tag{15}\]  
For the M-PSK constellation, the amplitude of its constellation point is 1. We also calculate the estimated value \(\widetilde{s}_{\alpha}\) of the modulation symbol corresponding to the \(\alpha\)-th TAC among \(round(L/2)\) TACs using Equations (11) and (12), where \(\alpha\in\{1,2,\cdots,round(L/2)\}\).  
\(\theta\) is the included angle between \(\widetilde{s}_{a}\) and the positive real axis of the complex plane. The quantization of \(s_{\alpha}\) is shown as follows  
\[\varphi_{\alpha}=\frac{\theta M}{2\pi}, \tag{16}\]  
\[\hat{\varphi}_{\alpha}=\text{mod}(round(\varphi_{\alpha}),M)*\frac{2\pi}{M}, \tag{17}\]  
\[s_{\alpha}=\cos\hat{\varphi}_{\alpha}+j\sin\hat{\varphi}_{\alpha}, \tag{18}\]
## 6 Evaluation
### Performance Evaluation
#### 6.1.1 Vibration Feedback Measurement  
In order to validate the fabricated fingertip actuator for frequency response, a test for vibrations was carried out. To render vibration feedback, we need to make a continuous displacement of the upper air chamber of the actuator. By repeatedly opening and closing the solenoid valves, the air travels inside the air chamber and exhausts from the air chamber. This type of continuous airflow makes the vibration-like frequency of the air chamber. This vibration frequency gives the user vibration feedback. We can control the frequency of the vibration by controlling the on and off cycle with a delay. The air chamber is able to vibrate at different frequencies (from 1 Hz to 100 Hz).  
A three-axis accelerometer module (GY-61 DX1335; Analog Devices, Inc.: Wilmington, MA, USA) was used to measure the vibrations of the air chamber. We obtained the vibration data using a data acquisition device (NI- DAQ USB-6009, National Instrument, Inc.: Austin, TX, USA). Subsequently, the collected data were analyzed using MATLAB in the time and frequency domains. In order to convert the time domain signal into the frequency domain, the fast Fourier transform was used. A dataset of 3000 measurements was collected with a 1000 Hz sampling rate for 3 s. The time and frequency domain analyses for 5 Hz, 25 Hz, and 50 Hz are shown in Figure 8.
## 3 Enhancement of MEMS INS for UAV Navigation
### Measurement Model for Data Fusion
 \tag{28}\]  
where \(\psi_{N},\psi_{E},\psi_{D}\) are the components of \(\psi^{n}\) projected in navigation frame \(n\). Combine Equations (16)-(18) into (15), and the heading error \(\delta H\) can be expressed as follows:  
\[\delta H=\frac{-(c_{11}^{2}+c_{21}^{2})\psi_{D}+c_{11}c_{31}\psi_{E}+c_{21}c_{ 31}\psi_{N}}{c_{11}^{2}+c_{21}^{2}} \tag{29}\]  
Compared with Equation (22), it can be found that the following relationship holds:  
\[\widetilde{z}_{head}=z_{head}^{INS}-z_{head}^{IMG}=\delta H \tag{30}\]  
Consider the definition of state vector \(X\), the following equation can be obtained:  
\[\widetilde{z}_{head}=\delta H= \begin{bmatrix}0_{1\times 3}&0_{1\times 3}&M_{1 \times 3}&0_{1\times 3}&0_{1\times 3}\end{bmatrix} X \tag{31}\]  
where \(M_{1\times 3}\) is defined as follows according to Equation (29):  
\[M_{1\times 3}= \begin{bmatrix}\frac{c_{21}c_{31}}{c_{11}^{2}+c_{21}^{2}}&\frac{ c_{11}c_{31}}{c_{11}^{2}+c_{21}}&-1\end{bmatrix}  \tag{32}\]  
Define \(H_{head}\) as the coefficient matrix of Equation (31), and the attitude error equation can be expressed as follows:  
\[\widetilde{z}_{head}=H_{head}\cdot X \tag{33}\]  
Combine Equations (20) and (31), the measurement model for data fusion can be obtained as follows:  
\[\widetilde{z}=H\cdot X \tag{34}\]  
where the measurement model coefficient matrix \(H\) can be expressed as follows:  
\[H= \begin{bmatrix}H_{p}\\ H_{head}\end{bmatrix}  \tag{35}\]
## 3 Results and Discussion
### Xrd  
XRD patterns show how both nanoclays' crystal structures changed due to PC and PE adsorption. All the hybrid samples with the M nanoclay significantly decreased 20\({}^{\circ}\), which corresponded to a basal space (d(001) plane) increment. In the L nanoclay, there were samples in which differences were significant and the interlayer space clearly increased, as well as samples in which the basal space was the same as that we found in the original nanoclay, or even less (Figure 15). In these samples, we could not ensure that the PC or PE interactions occurred into the basal space. We deduced that the dye clay interactions took place on the nanoclay surface, and protection was lower than in the other samples. The only way to know the effect of the synthesis factors in the interlayer space increment was by the ANOVA analysis. We used interlaminar space d\({}_{001}\) as a response for the analysis in both hybrid pigments from PC and PE (Table 6). The \(p\)-value from both  
Figure 15: XRD patterns from; PE hybrid pigments with M (**a**), L (**b**) clays, PC hybrid pigments with M (**c**), and L (**d**) clays. analyses was below 0.05 with the general models, and with some interactions and single factors. The model adjustment in the PC hybrid pigments was lower than the adjustment with the PE pigments. Interactions were all significant in the PE samples, but only the AD interaction was significant in the PC samples (Table 7). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**PE** & **20** & **d(001) nm** & **PC** & **20** & **d(001) nm** \\ \hline L & 6.10 & 1.45 & L & 6.10 & 1.45 \\ nPE.3 & 6.00 & 1.47 & nPC.3 & 6.00 & 1.47 \\ nPE.4 & 5.80 & 1.52 & nPC.4 & 6.00 & 1.47 \\ nPE.5 & 5.85 & 1.51 & nPC.5 & 6.00 & 1.47 \\ nPE.6 & 4.80 & 1.84 & nPC.6 & 6.00 & 1.47 \\ nPE.7 & 6.00 & 1.47 & nPC.7 & 6.20 & 1.42 \\ nPE.8 & 5.70 & 1.55 & nPC.8 & 5.10 & 1.73 \\ nPE.10 & 5.40 & 1.64 & nPC.10 & 5.40 & 1.64 \\ nPE.11 & 5.90 & 1.50 & nPC.11 & 5.40 & 1.64 \\ nPE.13 & 5.85 & 1.51 & nPC.13 & 5.75 & 1.54 \\ \hline M & 7.05 & 1.25 & M & 7.05 & 1.25 \\ nPE.1 & 5.75 & 1.54 & nPC.1 & 5.60 & 1.58 \\ nPE.2 & 5.70 & 1.55 & nPC.2 & 6.00 & 1.47 \\ nPE.9 & 5.95 & 1.48 & nPC.9 & 5.95 & 1.48 \\ nPE.12 & 5.75 & 1.54 & nPC.12 & 5.95 & 1.48 \\ nPE.14 & 5.80 & 1.52 & nPC.14 & 5.90 & 1.50 \\ nPE.15 & 5.85 & 1.51 & nPC.15 & 6.45 & 1.37 \\ nPE.16 & 5.95 & 1.48 & nPC.16 & 6.00 & 1.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Interlaminar space d(001)nm from the M and L original clays, and hybrid pigments with PE and PC under different synthesis conditions [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16].
# Robust Visual Localization with Dynamic Uncertainty Management in Omnidirectional SLAM
## 3 Robust Visual Localization  
This robust visual localization technique relies on the epipolarity adaption presented above. As observed in Equation (2), two poses of the robot can be univocally related by feature matching points in two omnidirectional images acquired from such poses; that is, a motion relation that is determined by an angular transformation. Figure 4 expresses such image-to-pose equivalence in terms of motion for comparison purposes. Therefore, this can be also deduced from Equation (1).  
Figure 3: Projection conversion from omnidirectional to panoramic view. (**a**) Conversion over the rays’ model side; (**b**) omnidirectional image with angular and radius variables indicated (\(\alpha\),\(r\)); (**c**) same image in its panoramic projection, transferred to (\(u\),\(\varphi\)) coordinates; (**d**) comparison of the number of feature points found on omnidirectional and panoramic images, respectively.  
Thus, localization between poses, as observed in Figure 4a, can be transformed into a visual problem, as shown in Figure 4b.  
Analytically, Equation (1) can be expressed as a linear system with:  
\[D_{i}e_{i}=\left[x_{0}z_{1}\quad y_{0}z_{1}\quad z_{0}x_{1}\quad z_{0}y_{1} \right]\left[e_{1},e_{2},e_{3},e_{4}\right]^{T},\forall i\in[1,\dots,N] \tag{4}\]  
\(\hat{x}=(x_{0},y_{0},z_{0})\) and \(\hat{x}^{\prime}=(x_{1},y_{1},z_{1})\) being the two corresponding points between image poses and \(e_{i}\) represent the composing elements in \(E\), which encode the localization values (\(\beta\), \(\phi\)). Notice that only a reduced amount of matching points is required, \(N_{min}=4\), for retrieving a localization, due the dimensions of \(D_{Nx4}\).  
Finally, [48] describes the basis for a Single Value Decomposition (SVD) of Equation (2), which returns a quaternion set of solutions (\(\beta\), \(\phi\)), as two rotations and translations:  
\[\phi=atan\frac{-e_{1}}{e_{2}};\quad\beta=atan\frac{e_{3}}{e_{4}}+atan\frac{-e_ {1}}{e_{2}} \tag{5}\]  
\[R_{1}=\begin{bmatrix}\cos\beta&-\sin\beta&0\\ \sin\beta&\cos\beta&0\\ 0&0&1\end{bmatrix};R_{2}=R_{\pi}R_{1};\ t_{x1}=[\cos\phi,\sin\phi,0];\ t_{x2}=t_{x1}+\pi; \tag{6}\]
## 3 Methods
### Model for Storing Trajectory Data
#### 3.1.1 Multi-Level Time Encoding Data Storage Model  
The time data format of the gathered trajectory points in this experiment is \"year/month/ day hour:minute: second\", which is not highly versatile for retrieval and computation. This paper introduces a data storage model based on multi-level time encoding to uniformly organize multi-scale time information and enhance the efficiency of multi-scale time calculations. First, within the MapReduce programming model, we compose the Mapper function to parse each cleaned data record and extract time information. Secondly, we divide the time hierarchy into'minutes,' 'hours,' and 'days,' create a customized encoding formula, and utilize the timestamp to convert the extracted time information into multi-level time encoding in integer form. Then, we employ the encoded time as the key and the original data as the value and output this to the Reducer function. Thirdly, in the Reducer function, we aggregate and analyze the multi-level time encoding key-value output by the Mapper. Finally, we write the output from the Reducer function to the Hadoop Distributed File System (HDFS) for shared access within the cluster. The results of multi-level time encoding are continuous integers, which can be directly added or subtracted based on the retrieval time range to improve computational efficiency. Equations (1)-(3), respectively, present the time encoding corresponding to the units of \"minutes\", \"hours\", and \"days\".  
\[MinuteId=[\frac{TS}{MINUTED\_MIL}] \tag{1}\]  
\[HourId=[\frac{TS}{HOUR\_MIL}] \tag{2}\]  
Figure 2: Technological roadmap.  
\[DayId=[\frac{HourId+GMT+8}{24}] \tag{3}\]  
where \(TS\) denotes a timestamp, signifying the count of milliseconds since the commencement of 1 January 1970; \(MINUTED\_MIL\) signifies the milliseconds within a single minute; \(HOUR\_MIL\) signifies the milliseconds within an hour; \(GMT\) denotes Greenwich Mean Time; and \(GMT+8\) represents the time zone of China; square brackets [ ] denote rounding down to the nearest integer. Taking the example of 15 October 2021, at 8 o'clock, the encoding is illustrated as shown in Table 3.  
After defining the data table structure, data is stored in the database encoded according to different time units. Each time level's data forms an individual data table. As per the structure of HBase, the data storage model comprises the Rowkey, TimeStamp, and Column Family. Given the distinctive nature of the data in this paper, records of multiple taxis' trajectories at the same moment are present in the same table. Consequently, as the row key serves as a unique index for the data table, it cannot be utilized for storing time encoding. However, the column family, TAXIDATA, can dynamically expand as required and thus can accommodate the storage of time encoding. The timestamp, which records the time, serves to distinguish data versions. Hence, the initial storage model of encoded taxi trajectory point data is depicted in Table 4. Here, TIME_Code denotes the time encoding of trajectory points, while LAT and LNG represent the geographical coordinates of the trajectory points.
# Dark Spot Detection in SAR Images of Oil Spill Using Segnet
## 4 Analysis of Segmentation Experiments  
### Efficiency Analysis  
We compared the performance of FCN8s and Segnet from the following four aspects: Pixel-classification accuracy (PA), mean accuracy (MA), mean intersection over union (MIoU), and frequency weighted intersection over union (FWIoU). The comparison of the four standard values for FCN8s and Segnet with five boundary statuses (see Table 3) is shown in Figure 13. It can be observed that the performance of Segnet and FCN8s for the first four boundary statuses was almost the same, and the PA was above 95%. However, for Status-e (Weak boundary strength), Segnet was superior to FCN8s in the segmentation effect, and the PA of Segnet reached 93.92%. FCN8s performed slightly worse for weak boundary segmentation, achieving 87.53% of PA. Thus, that Segnet can effectively detect dark spots (oil slicks or lookalikes) in SAR images.  
Figure 12: ROC curves of Segnet and FCN8s.
## 3 Gamma-Ray Detectors  
Similarly to X-ray detectors, for gamma ray detectors, a crucial feature is ER, normally defined for gamma rays as the ratio of full width at half maximum (FWHM) to the peak energy, typically at 662 keV line of the \({}^{137}\)Cs gamma source. Additionally, intrinsic detection efficiency (DE) (which has the same definition used for X-rays, i.e., the ratio of the number of interactions registered by the detector to the number of photons incident on the detector) is normally of crucial importance for detector characterization, but, for CH studies, it is most often not as critical as in other fields, due to the typically limited energies of the gamma rays of interest (a few MeV), and thus it will only be touched upon below. In addition to these two parameters, there are many other features, such as timing resolution, which are important for detector characterization, but they are normally not critical for our topic and will not be discussed here.  
For gamma ray detection [153] in CH, scintillators are typically exploited for depth profiling studies, thanks to their higher efficiency and because energy resolution in these applications is not critical. Solid-state detectors are instead preferred for bulk analysis, where high-energy resolution is a plus.  
ER in contemporary scintillation detectors may vary from 2% to approximately 4% [154]. To the best of our knowledge, the best ever recorded energy resolution with a scintillation detector has been obtained with a small Sr\({}^{2+}\) co-doped LaBr3:5%Ce\({}^{3+}\) scintillator, which showed a 2.04% energy resolution [155].  
ER in solid-state detectors is typically much better than in scintillators and is reported even down to 0.2%. Among solid-state detectors, HPGe normally display the best ER values  
Figure 10: X-ray spectra acquired at different energies of the muon beam in order to scan several depths inside the sample. The sample consisted of several layers of materials (Teflon, Al, Si, Cu, Zn, Ag, Ta, Au) with different thickness placed one after the other in sequence.  
(\(\sim\)0.2%@662 keV) [156] but can only be used at cryogenic temperature (liquid nitrogen). On the contrary, Cadmium Zinc Telluride (CZT), Cadmium Manganese Telluride (CMT), Cadmium Telluride (CdTe) and Thallium Bromide (TlBr) are all solid-state detectors which can be used at room temperature, although normally they are scarcely used in CH. They are characterized by good ER [157, 158], although not as good as HPGe. CdTe detectors, in particular, have shown interesting ER, down to 0.42% [159]. CdTe present appealing features for CH applications, especially for mobile instrumentation, where HPGe detectors are normally not feasible, due to cost, fragility, dimensions, cooling, and weight.